@Article{info:doi/10.2196/65183,
author="Cai, Ruixue
and Chao, Jianqian
and Gao, Chenlu
and Gao, Lei
and Hu, Kun
and Li, Peng",
title="Association Between Sleep Duration and Cognitive Frailty in Older Chinese Adults: Prospective Cohort Study",
journal="JMIR Aging",
year="2025",
month="Apr",
day="23",
volume="8",
pages="e65183",
keywords="aging",
keywords="frailty",
keywords="cognition",
keywords="cohort study",
keywords="sleep duration",
keywords="sleep quality",
keywords="longitudinal study",
abstract="Background: Disturbed sleep patterns are common among older adults and may contribute to cognitive and physical declines. However, evidence for the relationship between sleep duration and cognitive frailty, a concept combining physical frailty and cognitive impairment in older adults, is lacking. Objective: This study aimed to examine the associations of sleep duration and its changes with cognitive frailty. Methods: We analyzed data from the 2008?2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive frailty was rendered based on the modified Fried frailty phenotype and Mini-Mental State Examination. Sleep duration was categorized as short (<6 h), moderate (6?9 h), and long (>9 h). We examined the association of sleep duration with cognitive frailty status at baseline using logistic regressions and with the future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were used to explore potential nonlinear associations. Results: Among 11,303 participants, 1298 (11.5\%) had cognitive frailty at baseline. Compared to participants who had moderate sleep duration, the odds of having cognitive frailty were higher in those with long sleep duration (odds ratio 1.71, 95\% CI 1.48?1.97; P<.001). A J-shaped association between sleep duration and cognitive frailty was also observed (P<.001). Additionally, during a mean follow-up of 6.7 (SD 2.6) years among 5201 participants who were not cognitively frail at baseline, 521 (10\%) participants developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio 1.32, 95\% CI 1.07?1.62; P=.008). Conclusions: Long sleep duration was associated with cognitive frailly in older Chinese adults. These findings provide insights into the relationship between sleep duration and cognitive frailty, with potential implications for public health policies and clinical practice. ",
doi="10.2196/65183",
url="https://aging.jmir.org/2025/1/e65183"
}


@Article{info:doi/10.2196/68665,
author="Staiano, Walter
and Callahan, Christine
and Davis, Michelle
and Tanner, Leah
and Coe, Chelsea
and Kunkle, Sarah
and Kirk, Ulrich",
title="Assessment of an App-Based Sleep Program to Improve Sleep Outcomes in a Clinical Insomnia Population: Randomized Controlled Trial",
journal="JMIR Mhealth Uhealth",
year="2025",
month="Apr",
day="23",
volume="13",
pages="e68665",
keywords="cognitive behavioral therapy for insomnia",
keywords="mindfulness",
keywords="randomized controlled trial",
keywords="RCT",
keywords="therapy",
keywords="insomnia",
keywords="behavioral",
keywords="app based",
keywords="app",
abstract="Background: Insomnia is the most commonly reported sleep disturbance and significantly impacts mental health and quality of life. Traditional treatments for insomnia include pharmacological interventions or cognitive behavioral therapy for insomnia (CBT-I), but these options may not be accessible to everyone who needs treatment. Objective: This study aims to assess the effectiveness of the app-based Headspace Sleep Program in adults with clinical insomnia on sleep disturbance and mental health outcomes, compared with a waitlist control group. Methods: This randomized controlled trial included 132 adults with clinical insomnia who were assigned to either the Headspace Sleep Program (an 18-session self-guided, in-app program utilizing CBT-I techniques augmented by mindfulness) or a waitlist control group. Sleep disturbance outcomes were assessed by changes in insomnia symptoms (measured using the Insomnia Severity Index) and sleep efficiency (measured via sleep diary and actigraphy). Mental health outcomes included perceived stress (measured by the 10-item Perceived Stress Scale), depressive symptoms (measured by the 8-item Patient Health Questionnaire), sleep quality (measured by the Pittsburgh Sleep Quality Index), anxiety symptoms (measured by the 7-item Generalized Anxiety Disorder Scale), and mindfulness (measured by the Mindful Attention Awareness Scale). Changes from baseline to postintervention and follow-up were assessed for each outcome. Results: Participants had a mean (SD) age of 37.2 (10.6) years, with 69 out of 132 (52.3\%) identifying as female. Those randomized to the Headspace Sleep Program group experienced significantly greater improvements in insomnia symptoms from baseline to postintervention and follow-up compared with participants in the waitlist control group (P<.001, $\eta${\texttwosuperior}p=0.107). Improvements from baseline to postintervention and follow-up were also observed in the Headspace Sleep Program group for sleep efficiency, as measured by both sleep diary (P=.01, $\eta${\texttwosuperior}p=.03) and actigraphy outcomes (P=.01, $\eta${\texttwosuperior}p=.03). Participants in the Headspace Sleep Program group showed greater improvements in insomnia remission rates (8/66, 12\%, at postintervention and 9/66, 14\%, at follow-up) and treatment response (11/66, 17\%, at postintervention and 15/66, 23\%, at follow-up) compared with the control group (remission rate 2/66, 3\%, at postintervention and 0/66, 0\%, at follow-up; treatment response 3/66, 5\%, at postintervention and 1/66, 2\%, at follow-up). The results suggest significant improvements in depressive symptoms (P=.01, $\eta${\texttwosuperior}p=.04), anxiety symptoms (P=.02, $\eta${\texttwosuperior}p=.02), and mindfulness (P=.01, $\eta${\texttwosuperior}p=.03) in the Headspace Sleep Program group. Conclusions: The Headspace Sleep Program is an effective intervention for improving sleep disturbances in adults with clinical insomnia. Trial Registration: ClinicalTrials.gov NCT05872672; https://clinicaltrials.gov/ct2/show/NCT05872672 ",
doi="10.2196/68665",
url="https://mhealth.jmir.org/2025/1/e68665"
}


@Article{info:doi/10.2196/71030,
author="Li, Xueqin
and Liu, Jin
and Huang, Ning
and Zhao, Wanyu
and He, Hongbo",
title="Association Between Internet Use and Sleep Health Among Middle-Aged and Older Chinese Individuals: Nationwide Longitudinal Study",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="16",
volume="27",
pages="e71030",
keywords="internet use",
keywords="sleep",
keywords="Chinese middle-aged and older adults",
keywords="internet frequency",
keywords="cohort study",
abstract="Background: Sleep disorders are common among older adults and have a bidirectional impact on their emotional well-being. While some studies suggest that internet use may offer mental health benefits to this population, the relationship between internet use and sleep outcomes remains underexplored. Objective: This study investigates the association between internet use (including use frequency) and sleep quality and duration in middle-aged and older Chinese adults. Methods: A longitudinal analysis was conducted using the China Health and Retirement Longitudinal Study data from 2015 to 2018. Sleep quality was assessed using the sleep item from the Centre for Epidemiologic Studies Depression Scale, categorized as ``good'' (<1 day; reference), ``fair'' (1-4 days), or ``poor'' (5-7 days). Sleep duration was classified as short (<6 hours), medium (6-9 hours; reference), or long (>9 hours). Adjusted multinomial logistic regressions were used to examine the associations between internet use or frequency in 2015 and sleep quality or duration in 2018, controlling for age, sex, residence, diseases, smoking, drinking, and napping time and further exploring sex and age group variations. Results: The baseline analysis included 18,460 participants aged 45 years and older, with 1272 (6.9\%) internet users, 8825 (48.1\%) participants had fair or poor sleep, and 6750 (37.2\%) participants had abnormal sleep duration. Internet users, particularly those who used it almost daily, were less likely to report poor sleep quality (relative risk [RR] 0.71, 95\% CI 0.54-0.94) and longer sleep duration (RR 0.22, 95\% CI 0.11-0.44) than nonusers. In the longitudinal analysis, baseline internet users had a significantly reduced risk of fair (RR 0.66, 95\% CI 0.51-0.86) and poor sleep quality (RR 0.60, 95\% CI 0.44-0.81), as well as short (RR 0.73, 95\% CI 0.53-1.00) and long sleep duration (RR 0.39, 95\% CI 0.21-0.72) during the follow-up period than nonusers. These associations remained significant for almost daily internet use (RR 0.32, 95\% CI 0.15-0.69). Subgroup analyses by sex revealed a positive relationship between internet use and sleep quality, with a stronger effect in female (poor sleep: RR 0.57, 95\% CI 0.36-0.89) than male (poor sleep: RR 0.61, 95\% CI 0.40-0.92) participants. The effect on sleep duration was significant only in daily male users, showing a reduced risk of long sleep duration (RR 0.30, 95\% CI 0.11-0.78). In the age subgroup analysis, most internet users were in the 45- to 59-year age group, with results consistent with the overall findings. Conclusions: This study suggests that internet use is associated with a reduced risk of sleep problems in middle-aged and older adults. The findings indicate that moderate, regular internet engagement---such as daily use---may promote better sleep health in this population. ",
doi="10.2196/71030",
url="https://www.jmir.org/2025/1/e71030"
}


@Article{info:doi/10.2196/64023,
author="Ban, Yuki
and Waki, Kayo
and Nakada, Ryohei
and Isogawa, Akihiro
and Miyoshi, Kengo
and Waki, Hironori
and Kato, Shunsuke
and Sawaki, Hideaki
and Murata, Takashi
and Hirota, Yushi
and Saito, Shuichiro
and Nishikage, Seiji
and Tone, Atsuhito
and Seno, Mayumi
and Toyoda, Masao
and Kajino, Shinichi
and Yokota, Kazuki
and Tsurutani, Yuya
and Yamauchi, Toshimasa
and Nangaku, Masaomi
and Ohe, Kazuhiko",
title="Efficacy of a Personalized Mobile Health Intervention (BedTime) to Increase Sleep Duration Among Short-Sleeping Patients With Type 2 Diabetes: Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2025",
month="Apr",
day="14",
volume="14",
pages="e64023",
keywords="digital therapeutics",
keywords="behavior change",
keywords="Theory of Planned Behavior",
keywords="sleep duration",
keywords="type 2 diabetes",
keywords="randomized controlled trial",
abstract="Background: A strong association exists between sleep duration and glycemic control in patients with type 2 diabetes (T2D), yet convincing evidence of a causal link remains lacking. Improving sleep is increasingly emphasized in clinical T2D treatment guidance, highlighting the need for effective, scalable sleep interventions that can affordably serve large populations through mobile health (mHealth). Objective: This study aims to pilot an intervention that extends sleep duration by modifying bedtime behavior, assessing its efficacy among short-sleeping (?6 hours per night) patients with T2D, and establishing robust evidence that extending sleep improves glycemic control. Methods: This randomized, single-blinded, multicenter study targets 70 patients with T2D from 9 institutions in Japan over a 12-week intervention period. The sleep extension intervention, BedTime, is developed using the Theory of Planned Behavior (TPB) and focuses on TPB's constructs of perceived and actual behavioral control (ABC). The pilot intervention combines wearable actigraphy devices with SMS text messaging managed by human operators. Both the intervention and control groups will use an actigraphy device to record bedtime, sleep duration, and step count, while time in bed (TIB) will be assessed via sleep diaries. In addition, the intervention group will receive weekly bedtime goals, daily feedback on their bedtime performance relative to those goals, identify personal barriers to an earlier bedtime, and select strategies to overcome these barriers. The 12-week intervention period will be followed by a 12-week observational period to assess the sustainability of the intervention's effects. The primary outcome is the between-group difference in the change in hemoglobin A1c (HbA1c) at 12 weeks. Secondary outcomes include other health measures, sleep metrics (bedtime, TIB, sleep duration, total sleep time, and sleep quality), behavioral changes, and assessments of the intervention's usability. The trial commenced on February 8, 2024, and is expected to conclude in February 2025. Results: Patient recruitment ended on August 29, 2024, with 70 participants enrolled. The intervention period concluded on December 6, 2024, and the observation period ended on February 26, 2025, with 70 participants completing the observation period. The data analysis is currently underway, and results are expected to be published in July 2025. Conclusions: This trial will provide important evidence on the causal link between increased sleep duration and improved glycemic control in short-sleeping patients with T2D. It will also evaluate the efficacy of our bedtime behavior change intervention in extending sleep duration, initially piloted with human operators, with the goal of future implementation via an mHealth smartphone app. If proven effective, this intervention could be a key step toward integrating sleep-focused mHealth into the standard treatment for patients with T2D in Japan. Trial Registration: Japan Registry of Clinical Trials jRCT1030230650; https://jrct.niph.go.jp/latest-detail/jRCT1030230650 International Registered Report Identifier (IRRID): DERR1-10.2196/64023 ",
doi="10.2196/64023",
url="https://www.researchprotocols.org/2025/1/e64023"
}


@Article{info:doi/10.2196/67294,
author="Jones, D. Caleb
and Wasilko, Rachel
and Zhang, Gehui
and Stone, L. Katie
and Gujral, Swathi
and Rodakowski, Juleen
and Smagula, F. Stephen",
title="Detecting Sleep/Wake Rhythm Disruption Related to Cognition in Older Adults With and Without Mild Cognitive Impairment Using the myRhythmWatch Platform: Feasibility and Correlation Study",
journal="JMIR Aging",
year="2025",
month="Apr",
day="7",
volume="8",
pages="e67294",
keywords="sleep",
keywords="sleep/wake",
keywords="circadian",
keywords="activity pattern",
keywords="dementia",
keywords="cognition",
keywords="mobile sensing",
keywords="actigraphy",
keywords="accelerometer",
abstract="Background: Consumer wearable devices could, in theory, provide sufficient accelerometer data for measuring the 24-hour sleep/wake risk factors for dementia that have been identified in prior research. To our knowledge, no prior study in older adults has demonstrated the feasibility and acceptability of accessing sufficient consumer wearable accelerometer data to compute 24-hour sleep/wake rhythm measures. Objective: We aimed to establish the feasibility of characterizing 24-hour sleep/wake rhythm measures using accelerometer data gathered from the Apple Watch in older adults with and without mild cognitive impairment (MCI), and to examine correlations of these sleep/wake rhythm measures with neuropsychological test performance. Methods: Of the 40 adults enrolled (mean [SD] age 67.2 [8.4] years; 72.5\% female), 19 had MCI and 21 had no cognitive disorder (NCD). Participants were provided devices, oriented to the study software (myRhythmWatch or myRW), and asked to use the system for a week. The primary feasibility outcome was whether participants collected enough data to assess 24-hour sleep/wake rhythm measures (ie, ?3 valid continuous days). We extracted standard nonparametric and extended-cosine based sleep/wake rhythm metrics. Neuropsychological tests gauged immediate and delayed memory (Hopkins Verbal Learning Test) as well as processing speed and set-shifting (Oral Trails Parts A and B). Results: All participants meet the primary feasibility outcome of providing sufficient data (?3 valid days) for sleep/wake rhythm measures. The mean (SD) recording length was somewhat shorter in the MCI group at 6.6 (1.2) days compared with the NCD group at 7.2 (0.6) days. Later activity onset times were associated with worse delayed memory performance ($\beta$=?.28). More fragmented rhythms were associated with worse processing speed ($\beta$=.40). Conclusions: Using the Apple Watch-based myRW system to gather raw accelerometer data is feasible in older adults with and without MCI. Sleep/wake rhythms variables generated from this system correlated with cognitive function, suggesting future studies can use this approach to evaluate novel, scalable, risk factor characterization and targeted therapy approaches. ",
doi="10.2196/67294",
url="https://aging.jmir.org/2025/1/e67294"
}


@Article{info:doi/10.2196/68199,
author="Wyatt, Bastien
and Forstmann, Nicolas
and Badier, Nolwenn
and Hamy, Anne-Sophie
and De Larochelambert, Quentin
and Antero, Juliana
and Danino, Arthur
and Vercamer, Vincent
and De Villele, Paul
and Vittrant, Benjamin
and Lanz, Thomas
and Reyal, Fabien
and Toussaint, Jean-Fran{\c{c}}ois
and Delrieu, Lidia",
title="Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="4",
volume="27",
pages="e68199",
keywords="Covid-19",
keywords="pandemic",
keywords="physical activity",
keywords="step",
keywords="activity tracker",
keywords="public health",
keywords="Withings",
keywords="heart rate",
keywords="wearable sensors",
keywords="sleep duration",
keywords="sleep quality",
keywords="pre-pandemic",
keywords="sedentary behavior",
abstract="Background: The COVID-19 pandemic disrupted behavior within populations, affecting physical activity (PA), heart rate (HR), and sleep characteristics in particular. Activity trackers provide unique insights into these changes, enabling large-scale, real-time monitoring. Objective: This study aims to analyze the associations between the features of the COVID-19 pandemic worldwide and PA, HR, and sleep parameters, using data collected from activity trackers over a 3-year period. Methods: We performed a retrospective analysis using anonymized data collected from the 208,818 users of Withings Steel HR activity trackers, spanning 34 countries, over a 3-year period from January 2019 to March 2022. Key metrics analyzed included daily step counts, average heart rate, and sleep duration. The statistical methods used included descriptive analyses, time-trend analysis, and mixed models to evaluate the impact of restriction measures, controlling for potential confounders such as sex, age, and seasonal variations. Results: We detected a significant decrease in PA, with a 12.3\% reduction in daily step count (from 5802 to 5082 steps/d) over the 3 years. The proportion of sedentary individuals increased from 38\% (n=14,177) in 2019 to 52\% (n=19,510) in 2020 and remained elevated at 51\% (n=18,972) in 2022, while the proportion of active individuals dropped from 8\% (n=2857) to 6\% (n=2352) in 2020 before returning to 8\% (n=2877) in 2022. In 2022, the global population had not returned to prepandemic PA levels, with a noticeable persistence of inactivity. During lockdowns, HR decreased by 1.5\%, which was associated with lower activity levels. Sleep duration increased during restrictions, particularly in the countries with the most severe lockdowns (eg, an increase of 15 min in countries with stringent measures compared to 5 min in less restricted regions). Conclusions: The sustained decrease in PA and its physiological consequences highlight the need for public health strategies to mitigate the long-term effects of the measures taken during the pandemic. Despite the gradual lifting of restrictions, PA levels have not fully recovered, with lasting implications for global health. If similar circumstances arise in the future, priority should be given to measures for effectively increasing PA to counter the increase in sedentary behavior, mitigate health risks, and prevent the rise of chronic diseases. ",
doi="10.2196/68199",
url="https://www.jmir.org/2025/1/e68199",
url="http://www.ncbi.nlm.nih.gov/pubmed/40184182"
}


@Article{info:doi/10.2196/65412,
author="Blomenkamp, Maja
and Kiesel, Andrea
and Baumeister, Harald
and Lehr, Dirk
and Unterrainer, Josef
and Sander, B. Lasse
and Spanhel, Kerstin",
title="Assessing the Cultural Fit of a Digital Sleep Intervention for Refugees in Germany: Qualitative Study",
journal="JMIR Form Res",
year="2025",
month="Apr",
day="3",
volume="9",
pages="e65412",
keywords="Ukraine",
keywords="eHealth",
keywords="sleep disturbances",
keywords="low-threshold treatment",
keywords="culturally sensitive treatment",
keywords="refugee",
keywords="digital sleep",
keywords="Germany",
keywords="digital intervention",
keywords="interview",
keywords="content analysis",
keywords="qualitative study",
keywords="mental burden",
keywords="mental health care",
keywords="electronic health",
keywords="digital health",
abstract="Background: Digital interventions have been suggested to facilitate access to mental health care for refugees, who experience structural, linguistic, and cultural barriers to mental health care. Sleep-e, a digital sleep intervention originally developed for German teachers, has been culturally adapted for refugees in Germany mainly coming from African and Middle East countries. With the increasing number of refugees from Ukraine and the associated diversity of cultural backgrounds among refugees in Germany, it is essential to assess whether existing digital interventions are culturally appropriate for this target group as well. Objective: The study aimed to investigate the perceived cultural appropriateness of Sleep-e in both its original and culturally adapted versions among refugees in Germany, hereby exploring and possibly contrasting the needs of refugees coming from Ukraine and other countries of origin. Methods: Overall, 13 refugees (6 from Ukraine, 23-66 years old; and 7 from other countries, 26-41 years old) participated in the study. Each participant went through parts of the original or culturally adapted version of the digital sleep intervention, with 5 participants going through both versions. A total of 17 semistructured interviews (11 for the adapted, 6 for the nonadapted intervention version) and 9 think-aloud sessions (6 for the adapted, 3 for the nonadapted intervention version) were conducted to assess cultural appropriateness, suggestions for adaptations, and perceived relevance. Data were transcribed, categorized, and analyzed using structured qualitative content analysis. Results: The findings showed key differences in the perceived appropriateness and identification between the 2 refugee groups and the 2 intervention versions. Ukrainian participants expressed positive (n=70) and negative (n=56) feedback on the adapted intervention version, which revealed identity conflicts, as the adapted intervention version was targeted at a refugee population with whom they could not fully identify (18 negative feedback quotes concerning the refugee example characters). Whereas they identified with the European context in the original version, they found the problems described less relevant to their experiences. In contrast, participating refugees from other countries found the culturally adapted version more comprehensible and culturally appropriate (55 positive and 5 negative feedback quotes). No significant usability issues were reported, but several participants highlighted the need for an individualization of the intervention content. Conclusions: Neither the original nor culturally adapted version of the digital sleep intervention fully met the needs of all refugee groups, highlighting the complexity of culturally adapting digital interventions for this population. Particularly, the identity conflict of participating Ukrainian refugees regarding the refugee context suggests that adaptation should go beyond regional considerations and consider the dynamics of social identity. These findings emphasize the relevance of including co-design processes with different refugee populations to ensure broad identification and, herewith, cultural appropriateness of digital interventions. Trial Registration: German Clinical Trials Register DRKS00036484; https://drks.de/search/de/trial/DRKS00036484 ",
doi="10.2196/65412",
url="https://formative.jmir.org/2025/1/e65412"
}


@Article{info:doi/10.2196/65000,
author="Shen, Yufei
and Choto Olivier, Alicia
and Yu, Han
and Ito-Masui, Asami
and Sakamoto, Ryota
and Shimaoka, Motomu
and Sano, Akane",
title="Personalized Physician-Assisted Sleep Advice for Shift Workers: Algorithm Development and Validation Study",
journal="JMIR Form Res",
year="2025",
month="Apr",
day="1",
volume="9",
pages="e65000",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="health care workers",
keywords="machine learning",
keywords="medical safety",
keywords="web-based intervention",
keywords="app-based intervention",
keywords="shift work",
keywords="shift work sleep disorders",
keywords="shift workers",
keywords="sleep disorder",
keywords="wearable sensors",
keywords="well-being",
abstract="Background: In the modern economy, shift work is prevalent in numerous occupations. However, it often disrupts workers' circadian rhythms and can result in shift work sleep disorder. Proper management of shift work sleep disorder involves comprehensive and patient-specific strategies, some of which are similar to cognitive behavioral therapy for insomnia. Objective: Our goal was to develop and evaluate machine learning algorithms that predict physicians' sleep advice using wearable and survey data. We developed a web- and app-based system to provide individualized sleep and behavior advice based on cognitive behavioral therapy for insomnia for shift workers. Methods: Data were collected for 5 weeks from shift workers (N=61) in the intensive care unit at 2 hospitals in Japan. The data comprised 3 modalities: Fitbit data, survey data, and sleep advice. After the first week of enrollment, physicians reviewed Fitbit and survey data to provide sleep advice and selected 1 to 5 messages from a list of 23 options. We handcrafted physiological and behavioral features from the raw data and identified clusters of participants with similar characteristics using hierarchical clustering. We explored 3 models (random forest, light gradient-boosting machine, and CatBoost) and 3 data-balancing approaches (no balancing, random oversampling, and synthetic minority oversampling technique) to predict selections for the 7 most frequent advice messages related to bedroom brightness, smartphone use, and nap and sleep duration. We tested our predictions under participant-dependent and participant-independent settings and analyzed the most important features for prediction using permutation importance and Shapley additive explanations. Results: We found that the clusters were distinguished by work shifts and behavioral patterns. For example, one cluster had days with low sleep duration and the lowest sleep quality when there was a day shift on the day before and a midnight shift on the current day. Our advice prediction models achieved a higher area under the precision-recall curve than the baseline in all settings. The performance differences were statistically significant (P<.001 for 13 tests and P=.003 for 1 test). Sensitivity ranged from 0.50 to 1.00, and specificity varied between 0.44 and 0.93 across all advice messages and dataset split settings. Feature importance analysis of our models found several important features that matched the corresponding advice messages sent. For instance, for message 7 (darken the bedroom when you go to bed), the models primarily examined the average brightness of the sleep environment to make predictions. Conclusions: Although our current system requires physician input, an accurate machine learning algorithm shows promise for automatic advice without compromising the trustworthiness of the selected recommendations. Despite its decent performance, the algorithm is currently limited to the 7 most popular messages. Further studies are needed to enable predictions for less frequent advice labels. ",
doi="10.2196/65000",
url="https://formative.jmir.org/2025/1/e65000"
}


@Article{info:doi/10.2196/67861,
author="Brown, Jeffrey
and Mitchell, Zachary
and Jiang, Albert Yu
and Archdeacon, Ryan",
title="Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="28",
volume="9",
pages="e67861",
keywords="snore detection",
keywords="snore tracking",
keywords="machine learning",
keywords="SleepWatch",
keywords="Bodymatter",
keywords="neural net",
keywords="mobile device",
keywords="smartphone",
keywords="smartphone application",
keywords="mobile health",
keywords="sleep monitoring",
keywords="sleep tracking",
keywords="sleep apnea",
abstract="Background: High-quality sleep is essential for both physical and mental well-being. Insufficient or poor-quality sleep is linked to numerous health issues, including cardiometabolic diseases, mental health disorders, and increased mortality. Snoring---a prevalent condition---can disrupt sleep and is associated with disease states, including coronary artery disease and obstructive sleep apnea. Objective: The SleepWatch smartphone app (Bodymatter, Inc) aims to monitor and improve sleep quality and has snore detection capabilities that were built through a machine-learning process trained on over 60,000 acoustic events. This study evaluated the accuracy of the SleepWatch snore detection algorithm in a simulated real-world setting. Methods: The snore detection algorithm was tested by using 36 simulated snoring audio files derived from 18 participants. Each file simulated a snoring index between 30 and 600 snores per hour. Additionally, 9 files with nonsnoring sounds were tested to evaluate the algorithm's capacity to avoid false positives. Sensitivity, specificity, and accuracy were calculated for each test, and results were compared by using Bland-Altman plots and Spearman correlation to assess the statistical association between detected and actual snores. Results: The SleepWatch algorithm showed an average sensitivity of 86.3\% (SD 16.6\%), an average specificity of 99.5\% (SD 10.8\%), and an average accuracy of 95.2\% (SD 5.6\%) across the snoring tests. The positive predictive value and negative predictive value were 98.9\% (SD 2.6\%) and 93.8\% (SD 14.4\%) respectively. The algorithm performed exceptionally well in avoiding false positives, with a specificity of 97.1\% (SD 3.5\%) for nonsnoring files. Inclusive of all snoring and nonsnore tests, the aggregated accuracy for all trials in this bench study was 95.6\% (SD 5.3\%). The Bland-Altman analysis indicated a mean bias of ?29.8 (SD 41.7) snores per hour, and the Spearman correlation analysis revealed a strong positive correlation (rs=0.974; P<.001) between detected and actual snore rates. Conclusions: The SleepWatch snore detection algorithm demonstrates high accuracy and compares favorably with other snore detection apps. Aside from its broader use in sleep monitoring, SleepWatch demonstrates potential as a tool for identifying individuals at risk for sleep-disordered breathing, including obstructive sleep apnea, on the basis of the snoring index. ",
doi="10.2196/67861",
url="https://formative.jmir.org/2025/1/e67861"
}


@Article{info:doi/10.2196/63230,
author="Egmose, Julie
and Kronborg, Thomas
and Hejlesen, Ole
and Hangaard, Stine",
title="Contactless Sleep Monitoring for the Detection of Exacerbations in People With Chronic Obstructive Pulmonary Disease: Protocol for a Longitudinal Observational Study",
journal="JMIR Res Protoc",
year="2025",
month="Mar",
day="14",
volume="14",
pages="e63230",
keywords="disease exacerbation",
keywords="chronic obstructive pulmonary disease",
keywords="contactless measurements",
keywords="sleep monitoring systems",
keywords="heart rate measurement",
keywords="respiration rate measurement",
keywords="radar technology",
keywords="health literacy",
keywords="patient remote monitoring",
abstract="Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are one of the main causes of mortality, and early detection of exacerbations is thus essential. Telemedicine solutions have shown promising results for the detection of exacerbations in COPD and have increasingly been used. However, the effect of telemedicine is divergent. According to several studies, respiration rate (RR) increases before, during, and after an exacerbation and the change is measurable with several contactless devices. Despite this, RR is rarely measured, and telemedicine solutions only use wearable devices for measuring RR, even though wearable respiratory monitoring devices have been associated with certain drawbacks. Contactless devices are often used during sleep, as measurements conducted during sleep minimize the risk of disturbance from physical activities. However, the potential of measuring RR and heart rate (HR) during sleep for the detection of exacerbations in COPD remains unclear. Objective: The aim of this observational study is to investigate whether contactless measurement of RR, HR, and sleep stages can be used to detect exacerbations in people with COPD. Methods: An observational study including 50 participants with COPD will be conducted. The participants reside in Aalborg municipality, located in the North Denmark Region. Participants will use a contactless monitor (Sleepiz One+) near their bed during sleep for a period of 4 months. After data collection, descriptive statistics will be used to identify any extremes or variations in RR, HR, or sleep stages in the nights preceding an exacerbation. Correlation analysis will be performed to evaluate the relationship between the number of exacerbations and extremes or variations in RR, HR, or sleep stages. Finally, qualitative interviews will be conducted with 12 participants to explore their experiences of sleeping with the monitor nearby. Results: Recruitment started at the end of April 2024. A total of 12 participants have been recruited, and the remaining participants are expected to be recruited during March and April 2025. Six out of 12 participants have completed the data collection and qualitative interview stages. Overall data collection is expected to be completed by September 2025. The results are expected to provide insight into the potential for identifying extremes or variations in RR, HR, or sleep stages in the days preceding an exacerbation. Additionally, the results are expected to assess the correlation between the number of exacerbations and extremes or variations in RR, HR, and sleep stages. Conclusions: The findings from this study may clarify the possibility of using a contactless monitor to detect exacerbations in COPD. Furthermore, the results may have the potential to improve the ability to predict exacerbations in the future. International Registered Report Identifier (IRRID): DERR1-10.2196/63230 ",
doi="10.2196/63230",
url="https://www.researchprotocols.org/2025/1/e63230"
}


@Article{info:doi/10.2196/64869,
author="Groninger, Hunter
and Arem, Hannah
and Ayangma, Lylian
and Gong, Lisa
and Zhou, Eric
and Greenberg, Daniel",
title="Development of a Voice-Activated Virtual Assistant to Improve Insomnia Among Young Adult Cancer Survivors: Mixed Methods Feasibility and Acceptability Study",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="10",
volume="9",
pages="e64869",
keywords="cancer",
keywords="survivor",
keywords="insomnia",
keywords="cognitive behavioral therapy",
keywords="technology",
keywords="app",
keywords="oncology",
keywords="mobile health",
keywords="artificial intelligence",
keywords="young adults",
keywords="sleep",
keywords="mHealth",
keywords="CBT",
keywords="voice-activated virtual assistant",
keywords="virtual assistants",
keywords="focus group",
keywords="qualitative research",
abstract="Background: Up to 75\% of young adult cancer survivors (YACS) experience chronic insomnia, negatively affecting physical and emotional health and overall quality of life. Cognitive behavioral therapy for insomnia (CBT-I) is a gold-standard intervention to address insomnia. To improve CBT-I access and treatment adherence, screen-based digital CBT-I platforms have been developed. However, even with these digital products, widespread uptake of CBT-I remains limited, and new strategies for CBT-I delivery are warranted. Objective: The objective of this study is to understand how YACS experience insomnia and how they might incorporate technology-delivered CBT-I into a daily routine and test the feasibility and acceptability of a novel screen-free voice-activated virtual assistant--delivered CBT-I prototype. Methods: Eligible participants---ages 18-39, living with a history of cancer (any type, any stage), self-reporting on average less sleep than National Sleep Foundation recommendations, and English-speaking---were recruited from a major urban cancer center, 2 regional oncology clinics, and 2 cancer survivorship support groups. We conducted 4 focus groups to understand the YACS experience of insomnia, their routine use of technology at home, particularly voice-activated virtual assistants such as Amazon Alexa, and input on how CBT-I might be delivered at home through a smart speaker system. We developed a prototype device to deliver key elements of CBT-I at home along with circadian lighting and monitoring of post-bedtime device use, collected YACS user perspectives on this prototype, and then conducted a single-arm feasibility and acceptability study. Results: In total, 26 YACS (6-7 participants per group) experiencing insomnia participated in focus groups to share experiences of insomnia during cancer survivorship and to provide input regarding a CBT-I prototype. Common triggers of insomnia included worry about disease management and progression, disease-related pain and other symptoms, choices regarding personal device use, and worry about the impact of poor sleep on daily functioning. In total, 12 participants completed device prototype testing, engaging with the prototype 94\% of the assigned times (twice daily for 14 days; meeting predetermined feasibility cutoff of engagement ?70\% of assigned times) and rating the prototype with an overall mean score of 5.43 on the Satisfaction subscale of the Usability, Satisfaction, and Ease of Use scale (range 4.42-7; exceeding the predetermined cutoff score for acceptability of 5.0). All participants completing the study reported they would be interested in using the prototype again and would recommend it to someone else with insomnia. Conclusions: YACS were highly engaged with our voice-activated virtual assistant--delivered CBT-I prototype and found it acceptable to use. Following final device development, future studies should evaluate the efficacy of this intervention among YACS. Trial Registration: ClinicalTrials.gov NCT05875129; https://clinicaltrials.gov/study/NCT05875129 ",
doi="10.2196/64869",
url="https://formative.jmir.org/2025/1/e64869"
}


@Article{info:doi/10.2196/67188,
author="Han, Chuanliang
and Zhang, Zhizhen
and Lin, Yuchen
and Huang, Shaojia
and Mao, Jidong
and Xiang, Weiwen
and Wang, Fang
and Liang, Yuping
and Chen, Wufang
and Zhao, Xixi",
title="Monitoring Sleep Quality Through Low $\alpha$-Band Activity in the Prefrontal Cortex Using a Portable Electroencephalogram Device: Longitudinal Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="10",
volume="27",
pages="e67188",
keywords="EEG",
keywords="electroencephalogram",
keywords="alpha oscillation",
keywords="prefrontal cortex",
keywords="sleep",
keywords="portable device",
abstract="Background: The pursuit of sleep quality has become an important aspect of people's global quest for overall health. However, the objective neurobiological features corresponding to subjective perceptions of sleep quality remain poorly understood. Although previous studies have investigated the relationship between electroencephalogram (EEG) and sleep, the lack of longitudinal follow-up studies raises doubts about the reproducibility of their findings. Objective: Currently, there is a gap in research regarding the stable associations between EEG data and sleep quality assessed through multiple data collection sessions, which could help identify potential neurobiological targets related to sleep quality. Methods: In this study, we used a portable EEG device to collect resting-state prefrontal cortex EEG data over a 3-month follow-up period from 42 participants (27 in the first month, 25 in the second month, and 40 in the third month). Each month, participants' sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) to estimate their recent sleep quality. Results: We found that there is a significant and consistent positive correlation between low $\alpha$ band activity in the prefrontal cortex and PSQI scores (r=0.45, P<.001). More importantly, this correlation remained consistent across all 3-month follow-up recordings (P<.05), regardless of whether we considered the same cohort or expanded the sample size. Furthermore, we discovered that the periodic component of the low $\alpha$ band primarily contributed to this significant association with PSQI. Conclusions: These findings represent the first identification of a stable and reliable neurobiological target related to sleep quality through multiple follow-up sessions. Our results provide a solid foundation for future applications of portable EEG devices in monitoring sleep quality and screening for sleep disorders in a broad population. ",
doi="10.2196/67188",
url="https://www.jmir.org/2025/1/e67188"
}


@Article{info:doi/10.2196/67223,
author="Almalki, Manal",
title="Predictors of the Intention to Stop Using Smart Devices at Bedtime Among University Students in Saudi Arabia: Cross-Sectional Survey",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="10",
volume="9",
pages="e67223",
keywords="smart devices",
keywords="smartphone",
keywords="digital health",
keywords="digital technology",
keywords="sleep quality",
keywords="university student",
keywords="bedtime habits",
keywords="Saudi Arabia",
keywords="path analysis",
keywords="sleep disturbances",
keywords="well-being",
keywords="usage",
keywords="intention",
keywords="behavior",
keywords="mobile phone",
abstract="Background: The widespread use of smart devices, particularly among university students, has raised concerns about their impact on sleep quality. Bedtime usage of smart devices is associated with sleep disruptions and poor sleep quality. Objective: This study aimed to explore the behavioral and perceptual factors influencing university students' intention to stop using smart devices at bedtime in Saudi Arabia. Methods: A cross-sectional survey was conducted in June 2024 and distributed via social media platforms to university students (aged ?18 years). The questionnaire collected data on demographics, smart device usage habits, perceived negative effects on sleep, and physical sleep disturbances. The Pittsburgh Sleep Quality Index was used to assess sleep quality. Path analysis was performed to evaluate relationships between the outcome variables, intended to stop using smart device usage, and 3 latent variables: sleep quality smartphone usage, sleep quality perceived negative effects, and sleep quality during the past month. Model fit was assessed using chi-square, comparative fit index, and root mean square error of approximation. Results: Of the 774 participants, 90.43\% (700/774) reported using their smart devices every night and 72.48\% (561/774) believed bedtime device use negatively affected them the next morning. The most frequently reported next-morning symptoms were fatigue or drowsiness (480/774, 62.01\%). Common purposes for bedtime device use were staying in touch with friends or family (432/774, 55.81\%), entertainment (355/774, 45.86\%), and filling up spare time (345/774, 44.57\%). Overall, 58.26\% (451/774) expressed an intention to stop bedtime device use within the next 3 months. Path analysis demonstrated that frequent nightly use (path coefficient=0.36) and after-lights-off usage (0.49) were positively associated with the intention to stop, whereas spending ?3 hours on devices (--0.35) and engaging in multiple activities (--0.18) had negative associations. The strongest predictors of the intention to stop were perceived negative effects on next-morning well-being (0.71) and difficulty breathing comfortably during sleep (0.64). Model fit was excellent (comparative fit index=0.845 and root mean square error of approximation=0.039). Conclusions: Perceived negative effects on sleep quality and physical sleep disturbances are strong predictors of the intention to stop using smart devices at bedtime among university students in Saudi Arabia. Interventions aimed at improving sleep hygiene should focus on raising awareness about the impact of smart device use on well-being and addressing behaviors such as late-night usage and heavy screen time. Public health strategies should target both psychological and physiological aspects of bedtime smart device use to improve sleep quality in this population. ",
doi="10.2196/67223",
url="https://formative.jmir.org/2025/1/e67223",
url="http://www.ncbi.nlm.nih.gov/pubmed/40063070"
}


@Article{info:doi/10.2196/65075,
author="Fan, Huimin
and Yu, Weijie
and Rong, Hongguo
and Geng, Xiaokun",
title="Associations Between Sleep Duration and Activity of Daily Living Disability Among Older Adults in China: Cross-Sectional Study",
journal="Interact J Med Res",
year="2025",
month="Mar",
day="5",
volume="14",
pages="e65075",
keywords="sleep",
keywords="sleep duration",
keywords="activities of daily living",
keywords="CHARLS",
keywords="survey",
keywords="questionnaire",
keywords="self-reported",
keywords="gerontology",
keywords="geriatric",
keywords="older adult",
keywords="elder",
keywords="elderly",
keywords="aging",
keywords="ADL",
keywords="physical function",
keywords="physical functioning",
keywords="well-being",
keywords="association",
keywords="correlation",
keywords="China Health and Retirement Longitudinal Study",
abstract="Background: China has the largest elderly population globally; the growth rate of the aged tendency of the population was higher than that of Western countries. Given the distinctions in historical, ethnic, and economic status as well as socio-cultural background, Chinese adults had different sleep patterns compared with adults in other countries. Considering the heavy disease burden caused by activities of daily living (ADL) disability, we conducted a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) to test the hypothesis that individuals with short and longer sleep duration are more likely to have ADL disability. Objective: ADL disability is a common condition affecting the quality of life among older people. This study aimed to explore the associations between sleep duration and ADL disability among middle-aged and older adults in China. Methods: This cross-sectional study used data from 17,607 participants from the 2018 CHARLS (from 2018 to 2020), an ongoing representative survey of adults aged 45 years or older and their spouses. Self-reported sleep duration per night was obtained from face-to-face interviews. The ADL was measured using a 6-item summary assessed with an ADL scale that included eating, dressing, getting into or out of bed, bathing, using the toilet, and continence. Multiple generalized linear regression models---adjusted for age, sex, education, marital status, tobacco and alcohol use, depression, place of residence, sensory impairment, self-reported health status, life satisfaction, daytime napping, chronic disease condition, and sample weights---were used. Results: Data were analyzed from 17,607 participants, of whom 8375 (47.6\%) were men. The mean (SD) age was 62.7 (10.0) years. Individuals with 4 hours or less (odds ratio [OR] 1.91, 95\% CI 1.60?2.27; P<.001), 5 hours (OR 1.33, 95\% CI 1.09?1.62; P=.006), 9 hours (OR 1.48, 95\% CI 1.13?1.93; P<.001), and 10 hours or more (OR 1.88, 95\% CI 1.47?2.14; P<.001) of sleep per night had a higher risk of ADL disability than those in the reference group (7 hours per night) after adjusting for several covariates. Restricted cubic splines analysis suggested a U-shaped association between sleep duration and ADL disability. When sleep duration fell below 7 hours, an increased sleep duration was associated with a significantly low risk of ADL disability, which was negatively correlated with sleep duration until it fell below 7 hours (OR 0.83, 95\% CI 0.79?0.87; P<.001). When sleep duration exceeded 7 hours, the risk of ADL disability would increase facing prolonged sleep duration (OR 1.19, 95\% CI 1.12?1.27; P<.001). ADL disability should be monitored in individuals with insufficient (?4 or 5 hours per night) or excessive (9 or ?10 hours per night) sleep duration. Conclusions:: In this study, a U-shaped association between sleep duration and ADL disability was found. Future longitudinal studies are needed to establish temporality and examine the mechanisms of the associations between sleep duration and ADL disability. ",
doi="10.2196/65075",
url="https://www.i-jmr.org/2025/1/e65075"
}


@Article{info:doi/10.2196/67000,
author="Lai, Yi-Jen
and Chiu, Hsiao-Yean
and Wu, Ko-Chiu
and Chang, Chun-Wei",
title="Diaphragmatic Breathing Interfaces to Promote Relaxation for Mitigating Insomnia: Pilot Study",
journal="JMIR Serious Games",
year="2025",
month="Mar",
day="4",
volume="13",
pages="e67000",
keywords="brief behavioral treatment for insomnia",
keywords="sleep self-efficacy",
keywords="mobile health",
keywords="mHealth",
keywords="breathing training cognitive load",
keywords="attention",
keywords="gamification",
keywords="diaphragmatic breathing",
keywords="insomnia",
keywords="sleep",
keywords="games",
keywords="relaxation",
keywords="breathing",
keywords="breathing guidance",
keywords="questionnaire",
keywords="mental",
keywords="cognition",
abstract="Background: Brief behavioral treatment for insomnia is an effective short-term therapy focusing on stimulus control and sleep restriction to enhance sleep quality. As a crucial part of this therapy, diaphragmatic breathing is often recommended when patients fail to fall asleep within 30 minutes. With the rise of health apps and gamification, these tools are increasingly seen as effective ways to boost self-efficacy and user engagement; however, traditional games tend to increase attention, which can negatively impact sleep and contradicts the aim of sleep therapy. This study thus explored the potential for gamification techniques to promote relaxation without disrupting sleep processes. Objective: The study developed 4 breathing guidance mechanisms, ranging from concrete to abstract: number countdown, zoom-in/out, up/down, and color gradients. The objective was to explore the relationship between game mechanics, cognitive load, relaxation effects, and attention as well as to understand how different designs impact users with varying levels of insomnia. Methods: The study was conducted in 2 phases. The first phase involved a questionnaire on the 4 guidance mechanisms. In the second phase, 33 participants classified by insomnia severity completed a Sleep Self-Efficacy Scale. They then engaged in 5 minutes of diaphragmatic breathing using each of the 4 interfaces. Relaxation effects were measured using heart rate variability via a smartwatch, attention and relaxation levels via an electroencephalogram device, and respiratory rate via a smartphone. Participants also completed the Game Experience Questionnaire and NASA Task Load Index, followed by user interviews. Results: The results indicated that competence, immersion, and challenge significantly influenced cognitive load. Specifically, competence and immersion reduced cognitive load, while challenge, negative affect, and positive affect were correlated with relaxation. Negative affect showed a positive correlation with the mean root mean square of successive differences, while positive affect exhibited a negative correlation with the mean root mean square of successive differences. Cognitive load was found to affect both relaxation and attention, with a negative correlation between mental demand and attention and a positive correlation between temporal demand and respiratory rate. Sleep self-efficacy was negatively correlated with temporal demand and negative affect and positively correlated with competence and immersion. Conclusions: Interfaces offering moderate variability and neither overly abstract nor too concrete guidance are preferable. The up/down interface was most effective, showing the best overall relaxation effect. Conversely, the number countdown interface was stress-inducing, while the zoom-in/out interface had a significant impact on insomnia-related issues, making them less suitable for insomnia-related breathing exercises. Participants showed considerable variability in their response to the color gradient interface. These findings underscore the importance of carefully considering game design elements in relaxation training. It is essential that breathing guidance designs account for the impact of the game experience to effectively promote relaxation in users. ",
doi="10.2196/67000",
url="https://games.jmir.org/2025/1/e67000",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053714"
}


@Article{info:doi/10.2196/60630,
author="Izquierdo-Condoy, S. Juan
and Paz, Clara
and Nati-Castillo, A. H.
and Gollini-Mihalopoulos, Ricardo
and Aveiro-R{\'o}balo, Raul Telmo
and Valeriano Paucar, Renson Jhino
and Laura Mamami, Erika Sandra
and Caicedo, Felipe Juan
and Loaiza-Guevara, Valentina
and Mej{\'i}a, Camila Diana
and Salazar-Santoliva, Camila
and Villavicencio-Gomezjurado, Melissa
and Hall, Cougar
and Ortiz-Prado, Esteban",
title="Impact of Mobile Phone Usage on Sleep Quality Among Medical Students Across Latin America: Multicenter Cross-Sectional Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="10",
volume="27",
pages="e60630",
keywords="mobile phone",
keywords="addiction behavior",
keywords="sleep quality",
keywords="medical students",
keywords="Latin America",
abstract="Background: The ubiquitous use of mobile phones among medical students has been linked to potential health consequences, including poor sleep quality. Objective: This study investigates the prevalence of mobile phone addiction and its association with sleep quality among medical students across 6 Latin American countries. Methods: A descriptive, cross-sectional, multicenter study was conducted between December 2023 and March 2024 using a self-administered online survey. The survey incorporated the Mobile Phone Addiction Scale and the Pittsburgh Sleep Quality Index to evaluate mobile phone addiction and sleep quality among 1677 medical students. A multiple regression model was applied to analyze the relationship between mobile phone addiction and poor sleep quality, adjusting for sex, age, and educational level to ensure robust results. Results: Mobile phone addiction was identified in 32.5\% (545/1677) of participants, with significant differences across countries. The overall mean Pittsburgh Sleep Quality Index score was 7.26, indicating poor sleep quality. Multiple regression analysis revealed a strong association between mobile phone addiction and poor sleep, controlled for demographic variables ($\beta$=1.4, 95\% CI 1.05-1.74). Conclusions: This study underscores a significant prevalence of mobile phone addiction among medical students and its detrimental association with sleep quality in Latin America. The findings advocate for the need to address mobile phone usage to mitigate its negative implications on student health and academic performance. Strategies to enhance digital literacy and promote healthier usage habits could benefit medical education and student well-being. ",
doi="10.2196/60630",
url="https://www.jmir.org/2025/1/e60630"
}


@Article{info:doi/10.2196/63897,
author="Chin, Wei-Chih
and Chu, Pao-Hsien
and Wu, Lung-Sheng
and Lee, Kuang-Tso
and Lin, Chen
and Ho, Chien-Te
and Yang, Wei-Sheng
and Chung, I-Hang
and Huang, Yu-Shu",
title="The Prognostic Significance of Sleep and Circadian Rhythm for Myocardial Infarction Outcomes: Case-Control Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="4",
volume="27",
pages="e63897",
keywords="myocardial infarction",
keywords="circadian rhythm",
keywords="actigraphy",
keywords="nonparametric analysis",
keywords="prognosis",
keywords="sleep",
keywords="heart rate variability",
keywords="activity",
abstract="Background: Myocardial infarction (MI) is a medical emergency resulting from coronary artery occlusion. Patients with acute MI often experience disturbed sleep and circadian rhythm. Most previous studies assessed the premorbid sleep and circadian rhythm of patients with MI and their correlations with cardiovascular disease. However, little is known about post-MI sleep and circadian rhythm and their impacts on prognosis. The use of actigraphy with different algorithms to evaluate sleep and circadian rhythm after acute MI has the potential for predicting outcomes and preventing future disease progression. Objective: We aimed to evaluate how sleep patterns and disrupted circadian rhythm affect the prognosis of MI, using actigraphy and heart rate variability (HRV). Nonparametric analysis of actigraphy data was performed to examine the circadian rhythm of patients. Methods: Patients with MI in the intensive care unit (ICU) were enrolled alongside age- and gender-matched healthy controls. Actigraphy was used to evaluate sleep and circadian rhythm, while HRV was monitored for 24 hours to assess autonomic nerve function. Nonparametric indicators were calculated to quantify the active-rest patterns, including interdaily stability, intradaily variability, the most active 10 consecutive hours (M10), the least active 5 consecutive hours (L5), the relative amplitude, and the actigraphic dichotomy index. Follow-ups were conducted at 3 and 6 months after discharge to evaluate prognosis, including the duration of current admission, the number and duration of readmission and ICU admission, and catheterization. Independent sample t tests and analysis of covariance were used to compare group differences. Pearson correlation tests were used to explore the correlations of the parameters of actigraphy and HRV with prognosis. Results: The study included 34 patients with MI (mean age 57.65, SD 9.03 years) and 17 age- and gender-matched controls. MI patients had significantly more wake after sleep onset, an increased number of awakenings, and a lower sleep efficiency than controls. Circadian rhythm analysis revealed significantly lower daytime activity in MI patients. Moreover, these patients had a lower relative amplitude and dichotomy index and a higher intradaily variability and midpoint of M10, suggesting less sleep and wake activity changes, more fragmentation of the rest-activity patterns, and a more delayed circadian rhythm. Furthermore, significant correlations were found between the parameters of circadian rhythm analysis, including nighttime activity, time of M10 and L5, and daytime and nighttime activitySD, and patient prognosis. Conclusions: Patients with acute MI experienced significantly worse sleep and disturbed circadian rhythm compared with healthy controls. Our actigraphy-based analysis revealed a disturbed circadian rhythm, including reduced daytime activities, greater fluctuation in hourly activities, and a weak rest-activity rhythm, which were correlated with prognosis. The evaluation of sleep and circadian rhythm in patients with acute MI can serve as a valuable indicator for prognosis and should be further studied. ",
doi="10.2196/63897",
url="https://www.jmir.org/2025/1/e63897"
}


@Article{info:doi/10.2196/70168,
author="Shang, Chen
and Yang, Ya
and He, Chengcheng
and Feng, Junqi
and Li, Yan
and Tian, Meimei
and Zhao, Zhanqi
and Gao, Yuan
and Li, Zhe",
title="Authors' Reply: Advancing Insights Into Postoperative Sleep Quality and Influencing Factors",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="3",
volume="27",
pages="e70168",
keywords="sleep quality",
keywords="wearable sleep monitoring wristband",
keywords="intensive care unit",
keywords="minimally invasive surgery",
keywords="traditional open surgery",
doi="10.2196/70168",
url="https://www.jmir.org/2025/1/e70168"
}


@Article{info:doi/10.2196/69193,
author="Zhao, Yining
and Hu, Xin",
title="Advancing Insights Into Postoperative Sleep Quality and Influencing Factors",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="3",
volume="27",
pages="e69193",
keywords="sleep quality",
keywords="wearable sleep monitoring wristband",
keywords="intensive care unit",
keywords="minimally invasive surgery",
keywords="traditional open surgery",
doi="10.2196/69193",
url="https://www.jmir.org/2025/1/e69193"
}


@Article{info:doi/10.2196/64749,
author="Seol, Jaehoon
and Iwagami, Masao
and Kayamare, Tawylum Megane Christiane
and Yanagisawa, Masashi",
title="Relationship Among Macronutrients, Dietary Components, and Objective Sleep Variables Measured by Smartphone Apps: Real-World Cross-Sectional Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="30",
volume="27",
pages="e64749",
keywords="sleep quality",
keywords="dietary health",
keywords="unsaturated fatty acids",
keywords="dietary fiber intake",
keywords="sodium-to-potassium ratio",
keywords="compositional data analysis",
keywords="sleep",
keywords="smartphone",
keywords="application",
abstract="Background: Few studies have explored the relationship between macronutrient intake and sleep outcomes using daily data from mobile apps. Objective: This cross-sectional study aimed to examine the associations between macronutrients, dietary components, and sleep parameters, considering their interdependencies. Methods: We analyzed data from 4825 users of the Pok{\'e}mon Sleep and Asken smartphone apps, each used for at least 7 days to record objective sleep parameters and dietary components, respectively. Multivariable regression explored the associations between quartiles of macronutrients (protein; carbohydrate; and total fat, including saturated, monounsaturated, and polyunsaturated fats), dietary components (sodium, potassium, dietary fiber, and sodium-to-potassium ratio), and sleep variables (total sleep time [TST], sleep latency [SL], and percentage of wakefulness after sleep onset [\%WASO]). The lowest intake group was the reference. Compositional data analysis accounted for macronutrient interdependencies. Models were adjusted for age, sex, and BMI. Results: Greater protein intake was associated with longer TST in the third (+0.17, 95\% CI 0.09-0.26 h) and fourth (+0.18, 95\% CI 0.09-0.27 h) quartiles. In contrast, greater fat intake was linked to shorter TST in the third (--0.11, 95\% CI --0.20 to --0.27 h) and fourth (--0.16, 95\% CI --0.25 to --0.07 h) quartiles. Greater carbohydrate intake was associated with shorter \%WASO in the third (--0.82\%, 95\% CI --1.37\% to --0.26\%) and fourth (--0.57\%, 95\% CI --1.13\% to --0.01\%) quartiles, while greater fat intake was linked to longer \%WASO in the fourth quartile (+0.62\%, 95\% CI 0.06\%-1.18\%). Dietary fiber intake correlated with longer TST and shorter SL. A greater sodium-to-potassium ratio was associated with shorter TST in the third (--0.11, 95\% CI --0.20 to --0.02 h) and fourth (--0.19, 95\% CI --0.28 to --0.10 h) quartiles; longer SL in the second (+1.03, 95\% CI 0.08-1.98 min) and fourth (+1.50, 95\% CI 0.53-2.47 min) quartiles; and longer \%WASO in the fourth quartile (0.71\%, 95\% CI 0.15\%-1.28\%). Compositional data analysis, involving 6\% changes in macronutrient proportions, showed that greater protein intake was associated with an elevated TST (+0.27, 95\% CI 0.18-0.35 h), while greater monounsaturated fat intake was associated with a longer SL (+4.6, 95\% CI 1.93-7.34 min) and a larger \%WASO (+2.2\%, 95\% CI 0.63\%-3.78\%). In contrast, greater polyunsaturated fat intake was associated with a reduced TST (--0.22, 95\% CI --0.39 to --0.05 h), a shorter SL (--4.7, 95\% CI to 6.58 to --2.86 min), and a shorter \%WASO (+2.0\%, 95\% CI --3.08\% to --0.92\%). Conclusions: Greater protein and fiber intake were associated with longer TST, while greater fat intake and sodium-to-potassium ratios were linked to shorter TST and longer WASO. Increasing protein intake in place of other nutrients was associated with longer TST, while higher polyunsaturated fat intake improved SL and reduced WASO. ",
doi="10.2196/64749",
url="https://www.jmir.org/2025/1/e64749"
}


@Article{info:doi/10.2196/56667,
author="Khaksar, Sajjad
and Jafari-Oori, Mehdi
and Sarhangi, Forogh
and Moayed, Sadat Malihe",
title="Pediatric Sleep Quality and Parental Stress in Neuromuscular Disorders: Descriptive Analytical Study",
journal="Asian Pac Isl Nurs J",
year="2025",
month="Jan",
day="28",
volume="9",
pages="e56667",
keywords="spinal muscular atrophy",
keywords="neuromuscular disorders",
keywords="sleep quality",
keywords="pediatrics",
keywords="parental stress",
keywords="children",
keywords="parents",
keywords="muscular atrophy",
keywords="muscular disorders",
abstract="Background: Neuromuscular disorders (NMDs) constitute a heterogeneous group of disorders that affect motor neurons, neuromuscular junctions, and muscle fibers, resulting in symptoms such as muscle weakness, fatigue, and reduced mobility. These conditions significantly affect patients' quality of life and impose a substantial burden on caregivers. Spinal muscular atrophy (SMA) is a relatively common NMD in children that presents in various types with varying degrees of severity. Objective: This study aimed to evaluate the sleep quality of children with NMDs, particularly SMA types 1, 2, and 3 and assess the stress levels experienced by their parents. Methods: A descriptive analytical study was conducted from February to October 2023, in selected hospitals and dystrophy associations in Tehran and Isfahan, Iran. A total of 207 children aged 1?14 years with various NMDs were included in the study. Data were collected using a web-based questionnaire with 3 parts: demographic information, the Children's Sleep Habits Questionnaire to assess children's sleep, and the Stress Response Inventory to measure parental stress. Statistical analyses were performed using SPSS version 22, with an $\alpha$ level of .05. Results: Significant differences in sleep quality were found among SMA types, with mean scores of 74.76 (SD 7.48) for SMA type 1, 76.4 (SD 7.29) for SMA type 2, 72.88 (SD 6.73) for SMA type 3, and 75.87 (SD 5.74) for other NMDs (P=.02). A correlation was found between sleep and length of hospital stay (r=0.234, P<.001)and between sleep and the child's sex (r=?0.140, P=.04). Parental stress scores averaged 95.73 (SD 32.12). There was not a statistically significant difference in parental stress scores among the 4 groups (P=.78). This suggests that parental stress levels were similar across different NMD groups. Conclusions: Sleep disorders are prevalent among children with NMDs, especially SMA. Parents experience high levels of stress that can affect the care they provide. Therefore, interventions to improve children's sleep and address parental stress are crucial. Regular screening, counseling, and tailored support are recommended to enhance the well-being of children with NMDs and their families. ",
doi="10.2196/56667",
url="https://apinj.jmir.org/2025/1/e56667"
}


@Article{info:doi/10.2196/67478,
author="Yeom, Won Ji
and Kim, Hyungju
and Pack, Pil Seung
and Lee, Heon-Jeong
and Cheong, Taesu
and Cho, Chul-Hyun",
title="Exploring the Psychological and Physiological Insights Through Digital Phenotyping by Analyzing the Discrepancies Between Subjective Insomnia Severity and Activity-Based Objective Sleep Measures: Observational Cohort Study",
journal="JMIR Ment Health",
year="2025",
month="Jan",
day="27",
volume="12",
pages="e67478",
keywords="insomnia",
keywords="wearable devices",
keywords="sleep quality",
keywords="subjective assessment",
keywords="digital phenotyping",
keywords="psychological factors",
keywords="mobile phone",
abstract="Background: Insomnia is a prevalent sleep disorder affecting millions worldwide, with significant impacts on daily functioning and quality of life. While traditionally assessed through subjective measures such as the Insomnia Severity Index (ISI), the advent of wearable technology has enabled continuous, objective sleep monitoring in natural environments. However, the relationship between subjective insomnia severity and objective sleep parameters remains unclear. Objective: This study aims to (1) explore the relationship between subjective insomnia severity, as measured by ISI scores, and activity-based objective sleep parameters obtained through wearable devices; (2) determine whether subjective perceptions of insomnia align with objective measures of sleep; and (3) identify key psychological and physiological factors contributing to the severity of subjective insomnia complaints. Methods: A total of 250 participants, including both individuals with and without insomnia aged 19-70 years, were recruited from March 2023 to November 2023. Participants were grouped based on ISI scores: no insomnia, mild, moderate, and severe insomnia. Data collection involved subjective assessments through self-reported questionnaires and objective measurements using wearable devices (Fitbit Inspire 3) that monitored sleep parameters, physical activity, and heart rate. The participants also used a smartphone app for ecological momentary assessment, recording daily alcohol consumption, caffeine intake, exercise, and stress. Statistical analyses were used to compare groups on subjective and objective measures. Results: Results indicated no significant differences in general sleep structure (eg, total sleep time, rapid eye movement sleep time, and light sleep time) among the insomnia groups (mild, moderate, and severe) as classified by ISI scores (all P>.05). Interestingly, the no insomnia group had longer total awake times and lower sleep quality compared with the insomnia groups. Among the insomnia groups, no significant differences were observed regarding sleep structure (all P>.05), suggesting similar sleep patterns regardless of subjective insomnia severity. There were significant differences among the insomnia groups in stress levels, dysfunctional beliefs about sleep, and symptoms of restless leg syndrome (all P?.001), with higher severity associated with higher scores in these factors. Contrary to expectations, no significant differences were observed in caffeine intake (P=.42) and alcohol consumption (P=.07) between the groups. Conclusions: The findings demonstrate a discrepancy between subjective perceptions of insomnia severity and activity-based objective sleep parameters, suggesting that factors beyond sleep duration and quality may contribute to subjective sleep complaints. Psychological factors, such as stress, dysfunctional sleep beliefs, and symptoms of restless legs syndrome, appear to play significant roles in the perception of insomnia severity. These results highlight the importance of considering both subjective and objective assessments in the evaluation and treatment of insomnia and suggest potential avenues for personalized treatment strategies that address both psychological and physiological aspects of sleep disturbances. Trial Registration: Clinical Research Information Service KCT0009175; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=26133 ",
doi="10.2196/67478",
url="https://mental.jmir.org/2025/1/e67478"
}


@Article{info:doi/10.2196/53549,
author="Wu, Chanchan
and Chau, Hing Pui
and Choi, Hang Edmond Pui",
title="Exploring Social-Ecological Pathways From Sexual Identity to Sleep Among Chinese Women: Structural Equation Modeling Analysis",
journal="JMIR Public Health Surveill",
year="2025",
month="Jan",
day="21",
volume="11",
pages="e53549",
keywords="sleep",
keywords="social support",
keywords="sexual minority women",
keywords="social-ecological model",
keywords="quality of life",
keywords="structural equation model",
keywords="Chinese women",
keywords="China",
keywords="women",
keywords="structural equation modeling analysis",
keywords="sleep quality",
keywords="sexual identity",
keywords="survey",
keywords="heterosexual",
keywords="cisgender",
abstract="Background: Women and sexual minority individuals have been found to be at higher risk for experiencing poor sleep health compared to their counterparts. However, research on the sleep health of sexual minority women (SMW) is lacking in China. Objective: This study aimed to examine sleep quality and social support for Chinese women with varied sexual identities, and then investigate the in-depth relationships between sexual identity and sleep. Methods: This was a cross-sectional web-based survey. All participants completed a structured questionnaire containing a set of sociodemographic items referring to the social-ecological model of sleep health, the Pittsburgh Sleep Quality Index, the Social Support Rating Scale, and social relationships and environment domains of the World Health Organization Quality of Life-abbreviated short version. Pearson correlation coefficients were used to examine the relationship between sleep quality and social support as well as the two domains of quality of life. Structural equation modeling analysis was used to explore the social-ecological relationships. Results: A total of 250 cisgender heterosexual women (CHW) and 259 SMW were recruited from July to September 2021. A total of 241 (47.3\%) women experienced poor sleep quality and the rate was significantly higher in SMW than in CHW (55.2\% vs 39.2\%, P<.001). Around one-fifth of SMW reported low levels of social support, which was significantly higher than that of CHW (21.6\% vs 5.6\%, P<.001). Pearson correlations showed that overall sleep quality was significantly negatively associated with social support with weak correlations (r=?0.26, P<.001). The final structural equation modeling analysis with satisfactory fit indices identified 6 social-ecological pathways, showing that alcohol use, objective support, utilization of support, and perceived social relationship and environment quality of life played important roles in the sleep quality of individuals from their sexual identity. Conclusions: SMW experienced poorer sleep quality compared to CHW. Further research is recommended to address the modifiable factors affecting sleep and then implement tailored sleep improvement programs. ",
doi="10.2196/53549",
url="https://publichealth.jmir.org/2025/1/e53549"
}


@Article{info:doi/10.2196/53850,
author="Fuller, Chlo{\"e}
and Marin-Dragu, Silvia
and Iyer, Subramani Ravishankar
and Meier, Melanie Sandra",
title="A Mobile App--Based Gratitude Intervention's Effect on Mental Well-Being in University Students: Randomized Controlled Trial",
journal="JMIR Mhealth Uhealth",
year="2025",
month="Jan",
day="14",
volume="13",
pages="e53850",
keywords="gratitude intervention",
keywords="smartphone app",
keywords="gratitude exercises",
keywords="psychological well-being",
keywords="mobile phone",
abstract="Background: Gratitude interventions are used to cultivate a sense of gratitude for life and others. There have been mixed results of the efficacy of gratitude interventions' effect on psychological well-being with a variety of populations and methodologies. Objectives: The objective of our study was to test the effectiveness of a gratitude intervention smartphone app on university students' psychological well-being. Methods: We used a randomized experimental design to test our objective. Participants were recruited undergraduate students from a web-based university study recruitment system. Participants completed 90 web-based survey questions on their emotional well-being and personality traits at the beginning and end of the 3-week research period. Their depression, anxiety, and stress levels were measured with the Depression, Anxiety, and Stress Scale (DASS-21). After the baseline survey, participants were randomly assigned to either the control or the intervention. Participants in the intervention group used both a fully automated mobile sensing app and a gratitude intervention mobile iOS smartphone app designed for youth users and based on previous gratitude interventions and exercises. The gratitude intervention app prompted users to complete daily gratitude exercises on the app including a gratitude journal, a gratitude photo book, an imagine exercise, a speech exercise, and meditation. Participants in the control group used only the mobile sensing app, which passively collected smartphone sensory data on mobility, screen time, sleep, and social interactions. Results: A total of 120 participants met the inclusion criteria, and 27 were lost to follow-up for a total of 41 participants in the intervention group and 52 in the control group providing complete data. Based on clinical cutoffs from the baseline assessment, 56 out of 120 participants were identified as being in a subsample with at least moderate baseline symptomatology. Participants in the subsample with at least moderate baseline symptomatology reported significantly lower symptoms of depression, anxiety, and stress postintervention (Cohen d=?0.68; P=.04) but not in the full sample with low baseline symptomatology (Cohen d=0.16; P=.46). The number of times the app was accessed was not correlated with changes in either the subsample (r=0.01; P=.98) or the full sample (r=?0.04; P=.79). Conclusions: University students experiencing moderate to severe distress can benefit from a gratitude intervention smartphone app to improve symptoms of depression, anxiety, and stress. The number of times the gratitude intervention app was used is not related to well-being outcomes. Clinicians could look at incorporating gratitude apps with other mental health treatments or for those waitlisted as a cost-effective and minimally guided option for university students experiencing psychological distress. Trial Registration: Clinicaltrials.gov NCT06621745; http://clinicaltrials.gov/ct2/show/NCT06621745 ",
doi="10.2196/53850",
url="https://mhealth.jmir.org/2025/1/e53850"
}


@Article{info:doi/10.2196/62943,
author="Kakon, Hafiz Shahria
and Soron, Rashid Tanjir
and Hossain, Sharif Mohammad
and Haque, Rashidul
and Tofail, Fahmida",
title="Supervised and Unsupervised Screen Time and Its Association With Physical, Mental, and Social Health of School-Going Children in Dhaka, Bangladesh: Cross-Sectional Study",
journal="JMIR Pediatr Parent",
year="2025",
month="Jan",
day="14",
volume="8",
pages="e62943",
keywords="screen time",
keywords="parental supervision",
keywords="Strength and Difficulties Questionnaire",
keywords="Spencer Children Anxiety Scale",
keywords="Pittsburgh Sleep Quality Scale",
keywords="children",
keywords="sleep quality",
keywords="headache",
keywords="behavioral problems",
abstract="Background: Children's screen time has substantially increased worldwide, including in Bangladesh, especially since the pandemic, which is raising concern about its potential adverse effects on their physical, mental, and social health. Parental supervision may play a crucial role in mitigating these negative impacts. However, there is a lack of empirical evidence assessing the relationship between parental screen time supervision and health outcomes among school children in Dhaka, Bangladesh. Objective: We aimed to explore the association between supervised and unsupervised screen time on the physical, mental, and social health of school-going children in Dhaka, Bangladesh. Methods: We conducted a cross-sectional descriptive study between July 2022 and June 2024. A total of 420 children, aged 6?14 years, were enrolled via the stratified random sampling method across three English medium and three Bangla medium schools in Dhaka. Data were collected through a semistructured questionnaire; anthropometry measurements; and the Bangla-validated Strength and Difficulties Questionnaire (SDQ), Pittsburgh Sleep Quality Index (PSQI) Scale, and Spencer Children Anxiety Scale (SCAS). Results: A total of 234 out of 420 students (56\%) used digital screen devices without parental supervision. We did not find a substantial difference in the duration of the daily mean use of digital devices among the supervised students (4.5 hours, SD 2.2 hours) and the unsupervised students (4.6 hours, SD 2.4 hours). According to the type of school, English medium school children had a mean higher screen time (5.46 hours, SD 2.32 hours) compared to Bangla medium school children (3.67 hours, SD 2.00 hours). Headache was significantly higher among the unsupervised digital screen users compared to those who used digital screens with parental supervision (175/336 students, 52.1\% versus 161/336 students, 47.9\%; P<.003). Moreover, students who used digital screens without parental supervision had poor quality of sleep. Behavioral problems such as conduct issues (119/420 students, 28.3\%) and peer difficulties (121/420 students, 28.8\%) were observed among the participants. However, when comparing supervised and unsupervised students, we found no statistically significant differences in the prevalence of these issues. Conclusions: The findings of the study showed that the lack of screen time supervision is associated with negative health effects in children. The roles of various stakeholders, including schools, parents, policy makers, and students themselves, are crucial in developing effective guidelines for managing screen use among students. Further research is needed to demonstrate causal mechanisms; identify the best interventions; and determine the role of mediators and moderators in households, surroundings, and schools. ",
doi="10.2196/62943",
url="https://pediatrics.jmir.org/2025/1/e62943"
}


@Article{info:doi/10.2196/65247,
author="Krishnamurti, Tamar
and Moon, Rachel
and Richichi, Rudolph
and Berger, Rachel",
title="Integrating Infant Safe Sleep and Breastfeeding Education Into an App in a Novel Approach to Reaching High-Risk Populations: Prospective Observational Study",
journal="JMIR Pediatr Parent",
year="2025",
month="Jan",
day="14",
volume="8",
pages="e65247",
keywords="SIDS",
keywords="infant death",
keywords="sleep",
keywords="sudden infant death",
keywords="US",
keywords="United States",
keywords="infant",
keywords="infancy",
keywords="baby",
keywords="prenatal",
keywords="safe sleep",
keywords="breastfeeding",
keywords="infant care",
keywords="pregnancy",
keywords="app",
keywords="randomized controlled study",
keywords="TodaysBaby",
keywords="mobile health",
keywords="mHealth",
keywords="smartphone",
abstract="Background: Sudden unexpected infant death (SUID) is a leading cause of death for US infants, and nonrecommended sleep practices are reported in most of these deaths. SUID rates have not declined over the past 20 years despite significant educational efforts. Integration of prenatal safe sleep and breastfeeding education into a pregnancy app may be one approach to engaging pregnant individuals in education about infant care practices prior to childbirth. Objective: This study aims to assess whether pregnant individuals would engage with prenatal safe sleep and breastfeeding education provided within a pre-existing pregnancy app. Secondary objectives were to compare engagement among those at high and low risk of losing an infant to SUID and to assess the importance of end user push notifications for engagement. Methods: This prospective observational study was conducted from September 23, 2019 to March, 22 2022; push notifications were removed on October 26, 2021. TodaysBaby (University of Virginia, Boston University, and Washington University), a mobile health program in which safe sleep and breastfeeding video education was originally provided via texts, was embedded into the MyHealthyPregnancy app (Naima Health LLC). Pregnant mothers who received prenatal care within the University of Pittsburgh Medical Center hospital system were randomized to receive either safe sleep or breastfeeding education beginning at the start of the third trimester of pregnancy and ending 6 weeks post partum. Pregnant persons were designated as high risk if they lived in the 5\% of zip codes in Allegheny County, Pennsylvania with the highest rates of SUID in the county. The primary outcome was engagement, defined as watching at least 1 video either in response to a push notification or directly from the app's learning center. Results: A total of 7572 pregnant persons were enrolled in the TodaysBaby Program---3308 with push notifications and 4264 without. The TodaysBaby engagement rate was 18.8\% with push notifications and 3.0\% without. Engagement was highest in the initial weeks after enrollment, with a steady decline through pregnancy and very little postpartum engagement. There was no difference in engagement between pregnant persons who were low and high risk. The most viewed videos were ones addressing the use of pacifiers, concerns about infant choking, and the response of the body to the start of breastfeeding. Conclusions: Integrating safe sleep and breastfeeding education within a pregnancy app may allow for rapid dissemination of infant care information to pregnant individuals. Birthing parents at high risk of losing an infant to SUID---a leading cause of infant death after 1 month of age---appear to engage with the app at the same rates as birth parents who are at low risk. Our data demonstrate that push notifications increase engagement, overall and for those in high-risk zip codes where the SUID education is likely to have the most impact. ",
doi="10.2196/65247",
url="https://pediatrics.jmir.org/2025/1/e65247"
}


@Article{info:doi/10.2196/58902,
author="Cummins, A. Jack
and Gottlieb, J. Daniel
and Sofer, Tamar
and Wallace, A. Danielle",
title="Applying Natural Language Processing Techniques to Map Trends in Insomnia Treatment Terms on the r/Insomnia Subreddit: Infodemiology Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="9",
volume="27",
pages="e58902",
keywords="insomnia",
keywords="natural language processing",
keywords="NLP",
keywords="social media",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="sleep initiation",
keywords="sleep disorder",
keywords="easly awakening",
keywords="sleep aids",
keywords="benzodiazepines",
keywords="trazodone",
keywords="antidepressants",
keywords="melatonin",
keywords="treatment",
abstract="Background: People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treatments. Objective: The aim of this study is to summarize and chart trends of insomnia treatment terms on a digital insomnia message board. Methods: We performed a natural language processing analysis of the r/insomnia subreddit. Using Pushshift, we obtained all r/insomnia subreddit comments from 2008 to 2022. A bag of words model was used to identify the top 1000 most frequently used terms, which were manually reduced to 35 terms related to treatment and medication use. Regular expression analysis was used to identify and count comments containing specific words, followed by sentiment analysis to estimate the tonality (positive or negative) of comments. Data from 2013 to 2022 were visually examined for trends. Results: There were 340,130 comments on r/insomnia from 2008, the beginning of the subreddit, to 2022. Of the 35 top treatment and medication terms that were identified, melatonin, cognitive behavioral therapy for insomnia (CBT-I), and Ambien were the most frequently used (n=15,005, n=13,461, and n=11,256 comments, respectively). When the frequency of individual terms was compared over time, terms related to CBT-I increased over time (doubling from approximately 2\% in 2013-2014 to a peak of over 5\% of comments in 2018); in contrast, terms related to nonprescription over-the-counter (OTC) sleep aids (such as Benadryl or melatonin) decreased over time. CBT-I--related terms also had the highest positive sentiment and showed a spike in frequency in 2017. Terms with the most positive sentiment included ``hygiene'' (median sentiment 0.47, IQR 0.31-0.88), ``valerian'' (median sentiment 0.47, IQR 0-0.85), and ``CBT'' (median sentiment 0.42, IQR 0.14-0.82). Conclusions: The Reddit r/insomnia discussion board provides an alternative way to capture trends in both prescription and nonprescription sleep aids among people experiencing sleeplessness and using social media. This analysis suggests that language related to CBT-I (with a spike in 2017, perhaps following the 2016 recommendations by the American College of Physicians for CBT-I as a treatment for insomnia), benzodiazepines, trazodone, and antidepressant medication use has increased from 2013 to 2022. The findings also suggest that the use of OTC or other alternative therapies, such as melatonin and cannabis, among r/insomnia Reddit contributors is common and has also exhibited fluctuations over time. Future studies could consider incorporating alternative data sources in addition to prescription medication to track trends in prescription and nonprescription sleep aid use. Additionally, future prospective studies of insomnia should consider collecting data on the use of OTC or other alternative therapies, such as cannabis. More broadly, digital communities such as r/insomnia may be useful in understanding how social and societal factors influence sleep health. ",
doi="10.2196/58902",
url="https://www.jmir.org/2025/1/e58902"
}


@Article{info:doi/10.2196/53957,
author="Scharf, Tamara
and Huber, A. Carola
and N{\"a}pflin, Markus
and Zhang, Zhongxing
and Khatami, Ramin",
title="Trends in Prescription of Stimulants and Narcoleptic Drugs in Switzerland: Longitudinal Health Insurance Claims Analysis for the Years 2014-2021",
journal="JMIR Public Health Surveill",
year="2025",
month="Jan",
day="7",
volume="11",
pages="e53957",
keywords="prescription trends",
keywords="claims data",
keywords="cross-sectional data",
keywords="narcolepsy",
keywords="prescribers",
keywords="prescribing practices",
keywords="medical care",
keywords="stimulants",
keywords="stimulant medication",
abstract="Background: Stimulants are potent treatments for central hypersomnolence disorders or attention-deficit/hyperactivity disorders/attention deficit disorders but concerns have been raised about their potential negative consequences and their increasing prescription rates. Objective: We aimed to describe stimulant prescription trends in Switzerland from 2014 to 2021. Second, we aimed to analyze the characteristics of individuals who received stimulant prescriptions in 2021 and investigate the link between stimulant prescriptions and hospitalization rates in 2021, using hospitalization as a potential indicator of adverse health outcomes. Methods: Longitudinal and cross-sectional data from a large Swiss health care insurance were analyzed from all insureds older than 6 years. The results were extrapolated to the Swiss general population. We identified prescriptions for methylphenidate, lisdexamfetamine, modafinil, and sodium oxybate and calculated prevalences of each drug prescription over the period from 2014 to 2021. For 2021 we provide detailed information on the prescribers and evaluate the association of stimulant prescription and the number and duration of hospitalization using logistic regression models. Results: We observed increasing prescription rates of all stimulants in all age groups from 2014 to 2021 (0.55\% to 0.81\%, 43,848 to 66,113 insureds with a prescription). In 2021, 37.1\% (28,057 prescriptions) of the medications were prescribed by psychiatrists, followed by 36.1\% (n=27,323) prescribed by general practitioners and 1\% (n=748) by neurologists. Only sodium oxybate, which is highly specific for narcolepsy treatment, was most frequently prescribed by neurologists (27.8\%, 37 prescriptions). Comorbid psychiatric disorders were common in patients receiving stimulants. Patients hospitalized in a psychiatric institution were 5.3 times (odds ratio 5.3, 95\% CI 4.63?6.08, P<.001) more likely to have a stimulant prescription than those without hospitalization. There were no significant associations between stimulant prescription and the total length of inpatient stay (odds ratio 1, 95\% CI 1?1, P=.13). Conclusions: The prescription of stimulant medication in Switzerland increased slightly but continuously over years, but at lower rates compared to the estimated prevalence of central hypersomnolence disorders and attention-deficit/hyperactivity disorders/attention deficit disorders. Most stimulants are prescribed by psychiatrists, closely followed by general practitioners. The increased odds for hospitalization to psychiatric institutions for stimulant receivers reflects the severity of disease and the higher psychiatric comorbidities in these patients. ",
doi="10.2196/53957",
url="https://publichealth.jmir.org/2025/1/e53957"
}


@Article{info:doi/10.2196/63529,
author="Waki, Mari
and Nakada, Ryohei
and Waki, Kayo
and Ban, Yuki
and Suzuki, Ryo
and Yamauchi, Toshimasa
and Nangaku, Masaomi
and Ohe, Kazuhiko",
title="Validation of Sleep Measurements of an Actigraphy Watch: Instrument Validation Study",
journal="JMIR Form Res",
year="2025",
month="Jan",
day="6",
volume="9",
pages="e63529",
keywords="actigraphy",
keywords="sleep",
keywords="Motion Watch 8",
keywords="iAide2",
keywords="total sleep time",
abstract="Background: The iAide2 (Tokai) physical activity monitoring system includes diverse measurements and wireless features useful to researchers. The iAide2's sleep measurement capabilities have not been compared to validated sleep measurement standards in any published work. Objective: We aimed to assess the iAide2's sleep duration and total sleep time (TST) measurement performance and perform calibration if needed. Methods: We performed free-living sleep monitoring in 6 convenience-sampled participants without known sleep disorders recruited from within the Waki DTx Laboratory at the Graduate School of Medicine, University of Tokyo. To assess free-living sleep, we validated the iAide2 against a second actigraph that was previously validated against polysomnography, the MotionWatch 8 (MW8; CamNtech Ltd). The participants wore both devices on the nondominant arm, with the MW8 closest to the hand, all day except when bathing. The MW8 and iAide2 assessments both used the MW8 EVENT-marker button to record bedtime and risetime. For the MW8, MotionWare Software (version 1.4.20; CamNtech Ltd) provided TST, and we calculated sleep duration from the sleep onset and sleep offset provided by the software. We used a similar process with the iAide2, using iAide2 software (version 7.0). We analyzed 64 nights and evaluated the agreement between the iAide2 and the MW8 for sleep duration and TST based on intraclass correlation coefficients (ICCs). Results: The absolute ICCs (2-way mixed effects, absolute agreement, single measurement) for sleep duration (0.69, 95\% CI --0.07 to 0.91) and TST (0.56, 95\% CI --0.07 to 0.82) were moderate. The consistency ICC (2-way mixed effects, consistency, single measurement) was excellent for sleep duration (0.91, 95\% CI 0.86-0.95) and moderate for TST (0.78, 95\% CI 0.67-0.86). We determined a simple calibration approach. After calibration, the ICCs improved to 0.96 (95\% CI 0.94-0.98) for sleep duration and 0.82 (95\% CI 0.71-0.88) for TST. The results were not sensitive to the specific participants included, with an ICC range of 0.96-0.97 for sleep duration and 0.79-0.87 for TST when applying our calibration equation to data removing one participant at a time and 0.96-0.97 for sleep duration and 0.79-0.86 for TST when recalibrating while removing one participant at a time. Conclusions: The measurement errors of the uncalibrated iAide2 for both sleep duration and TST seem too large for them to be useful as absolute measurements, though they could be useful as relative measurements. The measurement errors after calibration are low, and the calibration approach is general and robust, validating the use of iAide2's sleep measurement functions alongside its other features in physical activity research. ",
doi="10.2196/63529",
url="https://formative.jmir.org/2025/1/e63529"
}


@Article{info:doi/10.2196/58461,
author="Danoff-Burg, Sharon
and Gottlieb, Elie
and Weaver, A. Morgan
and Carmon, C. Kiara
and Lara Ledesma, Duvia
and Rus, M. Holly",
title="Effects of Smart Goggles Used at Bedtime on Objectively Measured Sleep and Self-Reported Anxiety, Stress, and Relaxation: Pre-Post Pilot Study",
journal="JMIR Form Res",
year="2025",
month="Jan",
day="3",
volume="9",
pages="e58461",
keywords="relaxation",
keywords="stress",
keywords="anxiety",
keywords="sleep",
keywords="health technology",
keywords="intervention",
abstract="Background: Insufficient sleep is a problem affecting millions. Poor sleep can trigger or worsen anxiety; conversely, anxiety can lead to or exacerbate poor sleep. Advances in innovative consumer products designed to promote relaxation and support healthy sleep are emerging, and their effectiveness can be evaluated accurately using sleep measurement technologies in the home environment. Objective: This pilot study examined the effects of smart goggles used at bedtime to deliver gentle, slow vibration to the eyes and temples. The study hypothesized that objective sleep, perceived sleep, self-reported stress, anxiety, relaxation, and sleepiness would improve after using the smart goggles. Methods: A within-participants, pre-post study design was implemented. Healthy adults with subclinical threshold sleep problems (N=20) tracked their sleep nightly using a polysomnography-validated noncontact biomotion device and completed daily questionnaires over two phases: a 3-week baseline period and a 3-week intervention period. During the baseline period, participants followed their usual sleep routines at home. During the intervention period, participants used Therabody SmartGoggles in ``Sleep'' mode at bedtime. This mode, designed for relaxation, delivers a gentle eye and temple massage through the inflation of internal compartments to create a kneading sensation combined with vibrating motors. Each night, the participants completed questionnaires assessing relaxation, stress, anxiety, and sleepiness immediately before and after using the goggles. Daily morning questionnaires assessed perceived sleep, complementing the objective sleep data measured every night. Results: Multilevel regression analysis of 676 nights of objective sleep parameters showed improvements during nights when the goggles were used compared to the baseline period. Key findings include sleep duration (increased by 12 minutes, P=.01); duration of deep sleep (increased by 6 minutes, P=.002); proportion of deep sleep (7\% relative increase, P=.02); BodyScore, an age- and gender-normalized measure of deep sleep (4\% increase, P=.002); number of nighttime awakenings (7\% decrease, P=.02); total time awake after sleep onset (reduced by 6 minutes, P=.047); and SleepScore, a measure of overall sleep quality (3\% increase, P=.02). Questionnaire responses showed that compared to baseline, participants felt they had better sleep quality (P<.001) and woke feeling more well-rested (P<.001). Additionally, participants reported feeling sleepier, less stressed, less anxious, and more relaxed (all P values <.05) immediately after using the goggles each night, compared to immediately before use. A standardized inventory administered before and after the 3-week intervention period indicated reduced anxiety (P=.03), confirming the nightly analysis. Conclusions: The use of smart goggles at bedtime significantly improved objectively measured sleep metrics and perceived sleep quality. Further, participants reported increased feelings of relaxation along with reduced stress and anxiety. Future research expanding on this pilot study is warranted to confirm and expand on the preliminary evidence presented in this brief report. ",
doi="10.2196/58461",
url="https://formative.jmir.org/2025/1/e58461"
}


@Article{info:doi/10.2196/64578,
author="Hartnagel, Lisa-Marie
and Emden, Daniel
and Foo, C. Jerome
and Streit, Fabian
and Witt, H. Stephanie
and Frank, Josef
and Limberger, F. Matthias
and Schmitz, E. Sara
and Gilles, Maria
and Rietschel, Marcella
and Hahn, Tim
and Ebner-Priemer, W. Ulrich
and Sirignano, Lea",
title="Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach",
journal="JMIR Ment Health",
year="2024",
month="Dec",
day="23",
volume="11",
pages="e64578",
keywords="ambulatory assessment",
keywords="depression",
keywords="speech features",
keywords="openSMILE",
keywords="machine learning",
keywords="sleep deprivation therapy",
keywords="remote monitoring",
keywords="depressive disorder",
keywords="mobile phone",
keywords="digital health",
keywords="mobile health",
keywords="mHealth",
keywords="mental health",
abstract="Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time. Using an ambulatory assessment approach, we captured speech samples and assessed concomitant depression severity via self-report questionnaire over the course of 3 weeks (before, during, and after therapy). We extracted 89 speech features from the speech samples using the Extended Geneva Minimalistic Acoustic Parameter Set from the Open-Source Speech and Music Interpretation by Large-Space Extraction (audEERING) toolkit and the additional parameter speech rate. Objective: We aimed to understand if a multiparameter ML approach would significantly improve the prediction compared to previous statistical analyses, and, in addition, which mechanism for splitting training and test data was most successful, especially focusing on the idea of personalized prediction. Methods: To do so, we trained and evaluated a set of >500 ML pipelines including random forest, linear regression, support vector regression, and Extreme Gradient Boosting regression models and tested them on 5 different train-test split scenarios: a group 5-fold nested cross-validation at the subject level, a leave-one-subject-out approach, a chronological split, an odd-even split, and a random split. Results: In the 5-fold cross-validation, the leave-one-subject-out, and the chronological split approaches, none of the models were statistically different from random chance. The other two approaches produced significant results for at least one of the models tested, with similar performance. In total, the superior model was an Extreme Gradient Boosting in the odd-even split approach (R{\texttwosuperior}=0.339, mean absolute error=0.38; both P<.001), indicating that 33.9\% of the variance in depression severity could be predicted by the speech features. Conclusions: Overall, our analyses highlight that ML fails to predict depression scores of unseen patients, but prediction performance increased strongly compared to our previous analyses with multilevel models. We conclude that future personalized ML models might improve prediction performance even more, leading to better patient management and care. ",
doi="10.2196/64578",
url="https://mental.jmir.org/2024/1/e64578"
}


@Article{info:doi/10.2196/59521,
author="Mei, Longfei
and He, Ziwei
and Hu, Liang",
title="Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study",
journal="JMIR Form Res",
year="2024",
month="Dec",
day="20",
volume="8",
pages="e59521",
keywords="smartwatch",
keywords="accelerometry",
keywords="free-living",
keywords="physical activity",
keywords="sleep",
keywords="validity",
abstract="Background: Smartwatches are increasingly popular for physical activity and health promotion. However, ongoing validation studies on commercial smartwatches are still needed to ensure their accuracy in assessing daily activity levels, which is important for both promoting activity-related health behaviors and serving research purposes. Objective: This study aimed to evaluate the accuracy of a popular smartwatch, the Huawei Watch GT2, in measuring step count (SC), total daily activity energy expenditure (TDAEE), and total sleep time (TST) during daily activities among Chinese adults, and test whether there are population differences. Methods: A total of 102 individuals were recruited and divided into 2 age groups: young adults (YAs) and middle-aged and older (MAAO) adults. Participants' daily activity data were collected for 1 week by wearing the Huawei Watch GT2 on their nondominant wrist and the Actigraph GT3X+ (ActiGraph) on their right hip as the reference measure. The accuracy of the GT2 was examined using the intraclass correlation coefficient (ICC), Pearson product-moment correlation coefficient (PPMCC), Bland-Altman analysis, mean percentage error, and mean absolute percentage error (MAPE). Results: The GT2 demonstrated reasonable agreement with the Actigraph, as evidenced by a consistency test ICC of 0.88 (P<.001) and an MAPE of 25.77\% for step measurement, an ICC of 0.75 (P<.001) and an MAPE of 33.79\% for activity energy expenditure estimation, and an ICC of 0.25 (P<.001) and an MAPE of 23.29\% for sleep time assessment. Bland-Altman analysis revealed that the GT2 overestimated SC and underestimated TDAEE and TST. The GT2 was better at measuring SC and TDAEE among YAs than among MAAO adults, and there was no significant difference between these 2 groups in measuring TST (P=.12). Conclusions: The Huawei Watch GT2 demonstrates good accuracy in step counting. However, its accuracy in assessing activity energy expenditure and sleep time measurement needs further examination. The GT2 demonstrated higher accuracy in measuring SC and TDAEE in the YA group than in the MAAO group. However, the measurement errors for TST did not differ significantly between the 2 age groups. Therefore, the watch may be suitable for monitoring several key parameters (eg, SC) of daily activity, yet caution is advised for its use in research studies that require high accuracy. ",
doi="10.2196/59521",
url="https://formative.jmir.org/2024/1/e59521"
}


@Article{info:doi/10.2196/62959,
author="Cochran, M. Jeffrey",
title="Developing a Sleep Algorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study",
journal="JMIR Ment Health",
year="2024",
month="Dec",
day="20",
volume="11",
pages="e62959",
keywords="actigraphy",
keywords="machine learning",
keywords="accelerometer",
keywords="sleep-wake cycles",
keywords="sleep monitoring",
keywords="sleep quality",
keywords="sleep disorder",
keywords="polysomnography",
keywords="wearable sensor",
keywords="electrocardiogram",
abstract="Background: Sleep-wake patterns are important behavioral biomarkers for patients with serious mental illness (SMI), providing insight into their well-being. The gold standard for monitoring sleep is polysomnography (PSG), which requires a sleep lab facility; however, advances in wearable sensor technology allow for real-world sleep-wake monitoring. Objective: The goal of this study was to develop a PSG-validated sleep algorithm using accelerometer (ACC) and electrocardiogram (ECG) data from a wearable patch to accurately quantify sleep in a real-world setting. Methods: In this noninterventional, nonsignificant-risk, abbreviated investigational device exemption, single-site study, participants wore the reusable wearable sensor version 2 (RW2) patch. The RW2 patch is part of a digital medicine system (aripiprazole with sensor) designed to provide objective records of medication ingestion for patients with schizophrenia, bipolar I disorder, and major depressive disorder. This study developed a sleep algorithm from patch data and did not contain any study-related or digitized medication. Patch-acquired ACC and ECG data were compared against PSG data to build machine learning classification models to distinguish periods of wake from sleep. The PSG data provided sleep stage classifications at 30-second intervals, which were combined into 5-minute windows and labeled as sleep or wake based on the majority of sleep stages within the window. ACC and ECG features were derived for each 5-minute window. The algorithm that most accurately predicted sleep parameters against PSG data was compared to commercially available wearable devices to further benchmark model performance. Results: Of 80 participants enrolled, 60 had at least 1 night of analyzable ACC and ECG data (25 healthy volunteers and 35 participants with diagnosed SMI). Overall, 10,574 valid 5-minute windows were identified (5854 from participants with SMI), and 84\% (n=8830) were classified as greater than half sleep. Of the 3 models tested, the conditional random field algorithm provided the most robust sleep-wake classification. Performance was comparable to the middle 50\% of commercial devices evaluated in a recent publication, providing a sleep detection performance of 0.93 (sensitivity) and wake detection performance of 0.60 (specificity) at a prediction probability threshold of 0.75. The conditional random field algorithm retained this performance for individual sleep parameters, including total sleep time, sleep efficiency, and wake after sleep onset (within the middle 50\% to top 25\% of the assessed devices). The only parameter where the model performance was lower was sleep onset latency (within the bottom 25\% of all comparator devices). Conclusions: Using industry-best practices, we developed a sleep algorithm for use with the RW2 patch that can accurately detect sleep and wake windows compared to PSG-labeled sleep data. This algorithm may be used for a more complete understanding of well-being for patients with SMI in a real-world setting, without the need for PSG and a sleep lab. ",
doi="10.2196/62959",
url="https://mental.jmir.org/2024/1/e62959"
}


@Article{info:doi/10.2196/51615,
author="Kuo, Nai-Yu
and Tsai, Hsin-Jung
and Tsai, Shih-Jen
and Yang, C. Albert",
title="Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="19",
volume="26",
pages="e51615",
keywords="sleep apnea",
keywords="machine learning",
keywords="questionnaire",
keywords="oxygen saturation",
keywords="polysomnography",
keywords="screening",
keywords="sleep disorder",
keywords="insomnia",
keywords="utilization",
keywords="dataset",
keywords="training",
keywords="diagnostic",
abstract="Background: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time consuming and labor intensive, thus limiting diagnostic efficiency. Objective: This study aims to develop 2 sequential machine learning models to efficiently screen and differentiate OSA. Methods: We used 2 datasets comprising 8444 cases from the Sleep Heart Health Study (SHHS) and 1229 cases from Taipei Veterans General Hospital (TVGH). The Questionnaire Model (Model-Questionnaire) was designed to distinguish OSA from primary insomnia using demographic information and Pittsburgh Sleep Quality Index questionnaires, while the Saturation Model (Model-Saturation) categorized OSA severity based on multiple blood oxygen saturation parameters. The performance of the sequential machine learning models in screening and assessing the severity of OSA was evaluated using an independent test set derived from TVGH. Results: The Model-Questionnaire achieved an F1-score of 0.86, incorporating demographic data and the Pittsburgh Sleep Quality Index. Model-Saturation training by the SHHS dataset displayed an F1-score of 0.82 when using the power spectrum of blood oxygen saturation signals and reached the highest F1-score of 0.85 when considering all saturation-related parameters. Model-saturation training by the TVGH dataset displayed an F1-score of 0.82. The independent test set showed stable results for Model-Questionnaire and Model-Saturation training by the TVGH dataset, but with a slightly decreased F1-score (0.78) in Model-Saturation training by the SHHS dataset. Despite reduced model accuracy across different datasets, precision remained at 0.89 for screening moderate to severe OSA. Conclusions: Although a composite model using multiple saturation parameters exhibits higher accuracy, optimizing this model by identifying key factors is essential. Both models demonstrated adequate at-home screening capabilities for sleep disorders, particularly for patients unsuitable for in-laboratory sleep studies. ",
doi="10.2196/51615",
url="https://www.jmir.org/2024/1/e51615",
url="http://www.ncbi.nlm.nih.gov/pubmed/39699950"
}


@Article{info:doi/10.2196/53522,
author="Shao, Heng
and Chen, Hui
and Xu, Kewang
and Gan, Quan
and Chen, Meiling
and Zhao, Yanyu
and Yu, Shun
and Li, Kelly Yutong
and Chen, Lihua
and Cai, Bibo",
title="Investigating the Associations Between COVID-19, Long COVID, and Sleep Disturbances: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Dec",
day="13",
volume="10",
pages="e53522",
keywords="COVID-19",
keywords="long COVID",
keywords="sleep disturbances",
keywords="psychological outcomes",
keywords="socioeconomic factors",
keywords="cross-sectional study",
abstract="Background: COVID-19 has not only resulted in acute health issues but also led to persistent symptoms known as long COVID, which have been linked to disruptions in sleep quality. Objective: This study aims to investigate the associations between COVID-19, long COVID, and sleep disturbances, focusing on demographic, socioeconomic, and psychological factors among a Chinese population. Methods: This cross-sectional study included 1062 participants from China. Demographic, socioeconomic, and clinical data were collected through web-based questionnaires. Participants were divided into 2 groups based on COVID-19 infection status: infected and noninfected. Within the infected group, participants were further categorized into those with long COVID and those without long COVID. Noninfected participants were included in the non--long COVID group for comparison. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while depression and anxiety were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) scales, respectively. Multivariable linear regression was conducted to examine the associations between COVID-19, long COVID, and sleep quality, adjusting for demographic and psychosocial factors. Results: COVID-19 infection was confirmed in 857 participants, with 273 of them developing long COVID. No significant sex disparities were observed in infection rates (P=.63). However, a marginal statistical difference was noted in the prevalence of long COVID among females (P=.051). Age was significantly associated with both infection rates (P<.001) and long COVID (P=.001). Participants aged 60?70 years were particularly vulnerable to both outcomes. Sleep latency was significantly longer in the infected group (mean 1.73, SD 0.83) compared to the uninfected group (mean 1.57, SD 0.78; P=.01), and PSQI scores were higher (mean 8.52, SD 4.10 vs. 7.76, SD 4.31; P=.02). Long COVID participants had significantly worse sleep outcomes across all metrics (P<.001), except for sleep medication use (P=.17). Conclusions: Our findings indicate that long COVID is strongly associated with significant sleep disturbances, while initial COVID-19 infection shows a more moderate association with sleep issues. Long COVID--related sleep disturbances were exacerbated by factors such as age, income, and chronic health conditions. The study highlights the need for targeted interventions that address the multifaceted impacts of long COVID on sleep, especially among vulnerable groups such as older adults and those with lower socioeconomic status. Future research should use longitudinal designs to better establish the temporal relationships and causal pathways between COVID-19 and sleep disturbances. ",
doi="10.2196/53522",
url="https://publichealth.jmir.org/2024/1/e53522"
}


@Article{info:doi/10.2196/57748,
author="Bragazzi, Luigi Nicola
and Garbarino, Sergio",
title="The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: Call for Comprehensive Research on Sleep Infodemiology and Infoveillance",
journal="JMIR Infodemiology",
year="2024",
month="Dec",
day="13",
volume="4",
pages="e57748",
keywords="sleep health",
keywords="sleep-related clinical public health",
keywords="sleep information",
keywords="health information",
keywords="infodemiology",
keywords="infoveillance",
keywords="social media",
keywords="myth",
keywords="misconception",
keywords="circadian",
keywords="chronobiology",
keywords="insomnia",
keywords="eHealth",
keywords="digital health",
keywords="public health informatics",
keywords="sleep data",
keywords="health data",
keywords="well-being",
keywords="patient information",
keywords="lifestyle",
doi="10.2196/57748",
url="https://infodemiology.jmir.org/2024/1/e57748",
url="http://www.ncbi.nlm.nih.gov/pubmed/39475424"
}


@Article{info:doi/10.2196/59288,
author="Mariappan, Vijandran
and Mukhtar, Firdaus",
title="Effects of Internet Cognitive Behavioral Therapy for Insomnia and Internet Sleep Hygiene Education on Sleep Quality and Executive Function Among Medical Students in Malaysia: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Dec",
day="11",
volume="13",
pages="e59288",
keywords="sleep quality",
keywords="cognitive behavioral therapy",
keywords="sleep hygiene",
keywords="medical students",
keywords="executive function",
keywords="Malaysia",
keywords="insomnia",
abstract="Background: Medical students are frequently affected by poor sleep quality. Since poor sleep quality has negative physiological and psychological consequences such as on executive function, there is an opportunity to improve sleep quality and executive functions using non-pharmacological intervention such as cognitive behavioural therapy. Objective: The aim of this study therefore is to determine if improving sleep quality could improve executive functions in medical students with poor sleep quality by comparing cognitive behavioural therapy for insomnia (CBT-I) with sleep hygiene education (SHE) in a randomized controlled trial (RCT). Methods: A parallel group, RCT with a target sample of 120 medical students recruited from government-based medical universities in Malaysia. Eligible participants will be randomized to internet group CBT-I or internet group SHE in a 1:1 ratio. Assessments will be performed at baseline, post-intervention, 1 month, 3-months, and 6-months. The primary outcome is between-group differences in sleep quality and executive function post-baseline. The secondary outcomes include pre-sleep worry, attitude about sleep, sleep hygiene and sleep parameters. Results: This study received approval from the Research Ethics Committee in Universiti Putra Malaysia (JKEUPM-2023-1446) and Universiti Kebangsaan Malaysia (JEP-2024-669). The clinical trial was also registered in Australian New Zealand Clinical Trial Registry (ACTRN1264000243516). As of June 2024, the recruitment process is ongoing and a total of 48 and 49 students have been enrolled from the universities into the CBT-I and ISHE groups, respectively. All the participants provided signed and informed consent to participate in the study. Data collection has been completed for the baseline (pre-treatment assessment), and follow-up assessments for T1 and T2 for all the participants in both groups, while T3 and T4 assessments will be completed by July 2025. Data analysis will be performed by August 2025 and the research will be completed by December 2025. Conclusions: This study is the first attempt to design a CBT intervention to ameliorate poor sleep quality and its related negative effects among medical students. This research is also the first large-scale exploring the relationship between health status and CBT-mediated sleep improvement among medical students. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12624000243516; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=387030 International Registered Report Identifier (IRRID): PRR1-10.2196/59288 ",
doi="10.2196/59288",
url="https://www.researchprotocols.org/2024/1/e59288",
url="http://www.ncbi.nlm.nih.gov/pubmed/39661437"
}


@Article{info:doi/10.2196/56951,
author="Turley, Kate
and Rafferty, Joseph
and Bond, Raymond
and Mulvenna, Maurice
and Ryan, Assumpta
and Crawford, Lloyd",
title="Evaluating the Impact of a Daylight-Simulating Luminaire on Mood, Agitation, Rest-Activity Patterns, and Social Well-Being Parameters in a Care Home for People With Dementia: Cohort Study",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Nov",
day="29",
volume="12",
pages="e56951",
keywords="digital health",
keywords="dementia",
keywords="dynamic lighting",
keywords="sensors",
keywords="circadian rhythm",
keywords="daylight",
keywords="wellbeing",
keywords="mood",
keywords="agitation",
keywords="sleep",
keywords="social wellbeing",
keywords="care home",
keywords="older adults",
keywords="elderly",
keywords="cardiac",
keywords="psychological",
keywords="monitoring",
abstract="Background: Living with a diagnosis of dementia can involve managing certain behavioral and psychological symptoms. Alongside cognitive decline, this cohort expresses a suppression in melatonin production which can negatively influence their alignment of sleep or wake timings with the 24 hour day and night cycle. As a result, their circadian rhythms become disrupted. Since daylight has the capacity to stimulate the circadian rhythm and humans spend approximately 90\% of their time indoors, research has shifted toward the use of indoor lighting to achieve this same effect. This type of lighting is programmed in a daylight-simulating manner; mimicking the spectral changes of the sun throughout the day. As such, this paper focuses on the use of a dynamic lighting and sensing technology used to support the circadian rhythm, behavioral and psychological symptoms, and well-being of people living with dementia. Objective: This study aimed to understand how dynamic lighting, as opposed to static lighting, may impact the well-being of those who are living with dementia. Methods: An ethically approved trial was conducted within a care home for people with dementia. Data were collected in both quantitative and qualitative formats using environmentally deployed radar sensing technology and the validated QUALIDEM (Quality of Life for People With Dementia) well-being scale, respectively. An initial 4 weeks of static baseline lighting was used before switching out for 12 weeks of dynamic lighting. Metrics were collected for 11 participants on mood, social interactions, agitation, sense of feeling, and sleep and rest-activity over a period of 16 weeks. Results: Dynamic lighting showed significant improvement with a moderate effect size in well-being parameters including positive affect (P=.03), social isolation (P=.048), and feeling at home (P=.047) after 5?10 weeks of dynamic lighting exposure. The results also highlight statistically significant improvements in rest-activity--related parameters of interdaily stability (P<.001), intradaily variation (P<.001), and relative amplitude (P=.03) from baseline to weeks 5?10, with the effect propagating for interdaily stability at weeks 10?16 as well (P<.001). Nonsignificant improvements are also noted for sleep metrics with a small effect size; however, the affect in agitation does not reflect this improvement. Conclusions: Dynamic lighting has the potential to support well-being in dementia, with seemingly stronger influence in earlier weeks where the dynamic lighting initially follows the static lighting contrast, before proceeding to aggregate as marginal gains over time. Future longitudinal studies are recommended to assess the additional impact that varying daylight availability throughout the year may have on the measured parameters. ",
doi="10.2196/56951",
url="https://mhealth.jmir.org/2024/1/e56951"
}


@Article{info:doi/10.2196/56777,
author="Shang, Chen
and Yang, Ya
and He, Chengcheng
and Feng, Junqi
and Li, Yan
and Tian, Meimei
and Zhao, Zhanqi
and Gao, Yuan
and Li, Zhe",
title="Quantitative Impact of Traditional Open Surgery and Minimally Invasive Surgery on Patients' First-Night Sleep Status in the Intensive Care Unit: Prospective Cohort Study",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="22",
volume="26",
pages="e56777",
keywords="sleep quality",
keywords="wearable sleep monitoring wristband",
keywords="intensive care unit",
keywords="minimally invasive surgery",
keywords="traditional open surgery",
abstract="Background: The sleep status of patients in the surgical intensive care unit (ICU) significantly impacts their recoveries. However, the effects of surgical procedures on sleep are rarely studied. Objective: This study aimed to investigate quantitatively the impact of traditional open surgery (TOS) versus minimally invasive surgery (MIS) on patients' first-night sleep status in a surgical ICU. Methods: Patients transferred to the ICU after surgery were prospectively screened. The sleep status on the night of surgery was assessed by the patient- and nurse-completed Richards-Campbell Sleep Questionnaire (RCSQ) and Huawei wearable sleep monitoring wristband. Surgical types and sleep parameters were analyzed. Results: A total of 61 patients were enrolled. Compared to patients in the TOS group, patients in the MIS group had a higher nurse-RCSQ score (mean 60.9, SD 16.9 vs mean 51.2, SD 17.3; P=.03), self-RCSQ score (mean 58.6, SD 16.2 vs mean 49.5, SD 14.8; P=.03), and Huawei sleep score (mean 77.9, SD 4.5 vs mean 68.6, SD 11.1; P<.001). Quantitative sleep analysis of Huawei wearable data showed a longer total sleep period (mean 503.0, SD 91.4 vs mean 437.9, SD 144.0 min; P=.04), longer rapid eye movement sleep period (mean 81.0, 52.1 vs mean 55.8, SD 44.5 min; P=.047), and higher deep sleep continuity score (mean 56.4, SD 7.0 vs mean 47.5, SD 12.1; P=.001) in the MIS group. Conclusions: MIS, compared to TOS, contributed to higher sleep quality for patients in the ICU after surgery. ",
doi="10.2196/56777",
url="https://www.jmir.org/2024/1/e56777"
}


@Article{info:doi/10.2196/54792,
author="Knowlden, P. Adam
and Winchester, J. Lee
and MacDonald, V. Hayley
and Geyer, D. James
and Higginbotham, C. John",
title="Associations Among Cardiometabolic Risk Factors, Sleep Duration, and Obstructive Sleep Apnea in a Southeastern US Rural Community: Cross-Sectional Analysis From the SLUMBRx-PONS Study",
journal="JMIR Form Res",
year="2024",
month="Nov",
day="8",
volume="8",
pages="e54792",
keywords="obstructive sleep apnea",
keywords="obesity",
keywords="adiposity",
keywords="cardiometabolic",
keywords="cardiometabolic disease",
keywords="risk factors",
keywords="sleep",
keywords="sleep duration",
keywords="sleep apnea",
keywords="Short Sleep Undermines Cardiometabolic Health-Public Health Observational study",
keywords="SLUMBRx study",
abstract="Background: Short sleep and obstructive sleep apnea are underrecognized strains on the public health infrastructure. In the United States, over 35\% of adults report short sleep and more than 80\% of individuals with obstructive sleep apnea remain undiagnosed. The associations between inadequate sleep and cardiometabolic disease risk factors have garnered increased attention. However, challenges persist in modeling sleep-associated cardiometabolic disease risk factors. Objective: This study aimed to report early findings from the Short Sleep Undermines Cardiometabolic Health-Public Health Observational study (SLUMBRx-PONS). Methods: Data for the SLUMBRx-PONS study were collected cross-sectionally and longitudinally from a nonclinical, rural community sample (n=47) in the southeast United States. Measures included 7 consecutive nights of wrist-based actigraphy (eg, mean of 7 consecutive nights of total sleep time [TST7N]), 1 night of sleep apnea home testing (eg, apnea-hypopnea index [AHI]), and a cross-sectional clinical sample of anthropometric (eg, BMI), cardiovascular (eg, blood pressure), and blood-based biomarkers (eg, triglycerides and glucose). Correlational analyses and regression models assessed the relationships between the cardiometabolic disease risk factors and the sleep indices (eg, TST7N and AHI). Linear regression models were constructed to examine associations between significant cardiometabolic indices of TST7N (model 1) and AHI (model 2). Results: Correlational assessment in model 1 identified significant associations between TST7N and AHI (r=--0.45, P=.004), BMI (r=--0.38, P=.02), systolic blood pressure (r=0.40, P=.01), and diastolic blood pressure (r=0.32, P=.049). Pertaining to model 1, composite measures of AHI, BMI, systolic blood pressure, and diastolic blood pressure accounted for 25.1\% of the variance in TST7N (R2adjusted=0.25; F2,38=7.37; P=.002). Correlational analyses in model 2 revealed significant relationships between AHI and TST7N (r=--0.45, P<.001), BMI (r=0.71, P<.001), triglycerides (r=0.36, P=.03), and glucose (r=0.34, P=.04). Results from model 2 found that TST7N, triglycerides, and glucose accounted for 37.6\% of the variance in the composite measure of AHI and BMI (R2adjusted=0.38; F3,38=8.63; P<.001). Conclusions: Results from the SLUMBRx-PONS study highlight the complex interplay between sleep-associated risk factors for cardiometabolic disease. Early findings underscore the need for further investigations incorporating the collection of clinical, epidemiological, and ambulatory measures to inform public health, health promotion, and health education interventions addressing the cardiometabolic consequences of inadequate sleep. International Registered Report Identifier (IRRID): RR2-10.2196/27139 ",
doi="10.2196/54792",
url="https://formative.jmir.org/2024/1/e54792"
}


@Article{info:doi/10.2196/63341,
author="Bennett, E. Sarah
and Johnston, H. Milly
and Treneman-Evans, Georgia
and Denison-Day, James
and Duffy, Anthony
and Brigden, Amberly
and Kuberka, Paula
and Christoforou, Nicholas
and Ritterband, Lee
and Koh, Jewel
and Meadows, Robert
and Alamoudi, Doaa
and Nabney, Ian
and Yardley, Lucy",
title="Using the Person-Based Approach to Co-Create and Optimize an App-Based Intervention to Support Better Sleep for Adolescents in the United Kingdom: Mixed Methods Study",
journal="JMIR Hum Factors",
year="2024",
month="Oct",
day="31",
volume="11",
pages="e63341",
keywords="behavior change",
keywords="digital intervention",
keywords="insomnia",
keywords="depression",
keywords="anxiety",
keywords="sleep",
keywords="qualitative research",
keywords="mobile phone",
abstract="Background: Poor sleep is a common problem in adolescents aged 14 to 18 years. Difficulties with sleep have been found to have a bidirectional link to mental health problems. Objective: This new research sought to involve young people in the co-creation of a new app, particularly those from underserved communities. The Sleep Solved app uses science-based advice to improve sleep-related behaviors and well-being. The app was developed using the person-based approach, underpinned by the social cognitive theory and the social-ecological model of sleep health. Methods: Young people (aged 14-18 y) were recruited from across the United Kingdom to contribute to patient and public involvement (PPI) activities. In partnership with our peer researcher (MHJ), we used a multitude of methods to engage with PPI contributors, including web-based workshops, surveys, think-aloud interviews, focus groups, and app beta testing. Results: A total of 85 young people provided PPI feedback: 54 (64\%) young women, 27 (32\%) young men, 2 (2\%) genderfluid people, 1 (1\%) nonbinary person, and 1 (1\%) who reported ``prefer not to say.'' Their levels of deprivation ranged from among the 40\% most deprived to the 20\% least deprived areas. Most had self-identified sleep problems, ranging from 2 to 3 times per week to >4 times per week. Attitudes toward the app were positive, with praise for its usability and use of science-based yet accessible information. Think-aloud interviews and a focus group identified a range of elements that may influence the use of the app, including the need to pay attention to language choices and readability. User experiences in the form of narrated audio clips were used to normalize sleep problems and provide examples of how the app had helped these users. Conclusions: Young people were interested in using an app to better support their sleep and mental health. The app was co-created with strong links to theory- and evidence-based sleep hygiene behaviors. Future work to establish the effectiveness of the intervention, perhaps in a randomized controlled trial, would provide support for potential UK-wide rollout. ",
doi="10.2196/63341",
url="https://humanfactors.jmir.org/2024/1/e63341"
}


@Article{info:doi/10.2196/51322,
author="Moghadam, Shokraneh
and Husted, Margaret
and Aznar, Ana
and Gray, Debra",
title="A Person-Based Web-Based Sleep Intervention Aimed at Adolescents (SleepWise): Randomized Controlled Feasibility Study",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="23",
volume="8",
pages="e51322",
keywords="web-based health interventions",
keywords="sleep",
keywords="adolescence",
keywords="behavior change",
keywords="person-based approach",
keywords="sleep intervention",
keywords="detrimental health outcome",
keywords="SleepWise",
abstract="Background: Adolescents are advised to sleep 8-10 hours per night; however, most do not sleep for this recommended amount. Poor adolescent sleep is associated with detrimental health outcomes, including reduced physical activity, risk-taking behaviors, and increased depression and anxiety levels, making this an important public health concern. Existing interventions targeting adolescent sleep are often unsuccessful or their effectiveness unclear, as they are frequently noninteractive, time-consuming, and lack a strong theoretical foundation; highlighting an urgent need for innovative interventions deemed acceptable by adolescents. Objective: The main objective of this study was to determine the acceptability, feasibility, and preliminary impact of a web-based person-based sleep intervention (SleepWise) on adolescent sleep quality. Participant incentivization was also explored to understand its impact on engagement, acceptability, and sleep quality. Methods: A feasibility trial was conducted to test the feasibility, acceptability, and preliminary impact of SleepWise on adolescent sleep quality, developed based on the person-based approach to intervention development. In total, 90 participants (aged 13-17 years) from further education institutions and secondary schools were recruited for two 2-arm randomized controlled trials. One trial (trial 1) was incentivized to understand the impact of incentivization. Acceptability and sleep quality were assessed via questionnaires, and a mixed methods process evaluation was undertaken to assess participant engagement and experience with SleepWise. Engagement was automatically tracked by SleepWise, which collected data on the date and time, pages viewed, and the number of goals and sleep logs completed per participant. Semistructured interviews were carried out to gain participant feedback. Results: Participants in both trials reported high levels of acceptability (trial 1: mean 21.00, SD 2.74; trial 2: mean 20.82, SD 2.48) and demonstrated similar levels of engagement with SleepWise. Participants in trial 1 viewed slightly more pages of the intervention, and those in trial 2 achieved their set goals more frequently. Improvements in sleep quality were found in both trials 1 and 2, with medium (trial 1) and large (trial 2) effect sizes. A larger effect size for improvement in sleep quality was found in the nonincentivized trial (d=0.87), suggesting that incentivization may not impact engagement or sleep quality. Both trials achieved acceptable recruitment (trial 1, N=48; trial 2, N=42), and retention at 5 weeks (trial 1: N=30; trial 2: N=30). Qualitative findings showed that adolescents lead busy lifestyles, which may hinder engagement; however, participants deemed SleepWise acceptable in length and content, and made attempts at behavior change. Conclusions: SleepWise is an acceptable and potentially efficacious web-based sleep intervention aimed at adolescents. Findings from this study showed that incentivization did not greatly impact engagement, acceptability, or sleep quality. Subject to a full trial, SleepWise has the potential to address the urgent need for innovative, personalized, and acceptable sleep interventions for adolescents. Trial Registration: OSF Registries osf.io/yanb2; https://osf.io/yanb2 ",
doi="10.2196/51322",
url="https://formative.jmir.org/2024/1/e51322"
}


@Article{info:doi/10.2196/52977,
author="Baek, Kwangyeol
and Jeong, Jake
and Kim, Hyun-Woo
and Shin, Dong-Hyeon
and Kim, Jiyoung
and Lee, Gha-Hyun
and Cho, Wook Jae",
title="Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="11",
volume="8",
pages="e52977",
keywords="insomnia",
keywords="sleep",
keywords="internet search",
keywords="adolescents",
keywords="school",
keywords="seasonal",
keywords="weekly",
keywords="NAVER",
keywords="infodemiology",
keywords="inforveillance",
abstract="Background: Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population. Objective: We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults. Methods: We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation). Results: In the annual trend, 2-way ANOVA revealed a significant (group) {\texttimes} (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) {\texttimes} (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults. Conclusions: Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend--weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups. ",
doi="10.2196/52977",
url="https://formative.jmir.org/2024/1/e52977",
url="http://www.ncbi.nlm.nih.gov/pubmed/39311496"
}


@Article{info:doi/10.2196/57437,
author="Dong, Xing-Xuan
and Huang, Yueqing
and Miao, Yi-Fan
and Hu, Hui-Hui
and Pan, Chen-Wei
and Zhang, Tianyang
and Wu, Yibo",
title="Personality and Health-Related Quality of Life of Older Chinese Adults: Cross-Sectional Study and Moderated Mediation Model Analysis",
journal="JMIR Public Health Surveill",
year="2024",
month="Sep",
day="12",
volume="10",
pages="e57437",
keywords="personality",
keywords="health-related quality of life",
keywords="older adults",
keywords="sleep quality",
keywords="quality of life",
keywords="old",
keywords="older",
keywords="Chinese",
keywords="China",
keywords="mechanisms",
keywords="psychology",
keywords="behavior",
keywords="analysis",
keywords="hypothesis",
keywords="neuroticism",
keywords="mediation analysis",
keywords="health care providers",
keywords="aging",
abstract="Background: Personality has an impact on the health-related quality of life (HRQoL) of older adults. However, the relationship and mechanisms of the 2 variables are controversial, and few studies have been conducted on older adults. Objective: The aim of this study was to explore the relationship between personality and HRQoL and the mediating and moderating roles of sleep quality and place of residence in this relationship. Methods: A total of 4123 adults 60 years and older were from the Psychology and Behavior Investigation of Chinese Residents survey. Participants were asked to complete the Big Five Inventory, the Brief version of the Pittsburgh Sleep Quality Index, and EQ-5D-5L. A backpropagation neural network was used to explore the order of factors contributing to HRQoL. Path analysis was performed to evaluate the mediation hypothesis. Results: As of August 31, 2022, we enrolled 4123 older adults 60 years and older. Neuroticism and extraversion were strong influencing factors of HRQoL (normalized importance >50\%). The results of the mediation analysis suggested that neuroticism and extraversion may enhance and diminish, respectively, HRQoL (index: $\beta$=?.262, P<.001; visual analog scale: $\beta$=?.193, P<.001) by increasing and decreasing brief version of the Pittsburgh Sleep Quality Index scores (neuroticism: $\beta$=.17, P<.001; extraversion: $\beta$=?.069, P<.001). The multigroup analysis suggested a significant moderating effect of the place of residence (EQ-5D-5L index: P<.001; EQ-5D-5L visual analog scale: P<.001). No significant direct effect was observed between extraversion and EQ-5D-5L index in urban older residents ($\beta$=.037, P=.73). Conclusions: This study sheds light on the potential mechanisms of personality and HRQoL among older Chinese adults and can help health care providers and relevant departments take reasonable measures to promote healthy aging. ",
doi="10.2196/57437",
url="https://publichealth.jmir.org/2024/1/e57437"
}


@Article{info:doi/10.2196/58187,
author="Abd-alrazaq, Alaa
and Aslam, Hania
and AlSaad, Rawan
and Alsahli, Mohammed
and Ahmed, Arfan
and Damseh, Rafat
and Aziz, Sarah
and Sheikh, Javaid",
title="Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="10",
volume="26",
pages="e58187",
keywords="sleep apnea",
keywords="hypopnea",
keywords="artificial intelligence",
keywords="wearable devices",
keywords="machine learning",
keywords="systematic review",
keywords="mobile phone",
abstract="Background: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. Objective: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. Methods: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. Results: Among 615 studies, 38 (6.2\%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. Conclusions: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses. ",
doi="10.2196/58187",
url="https://www.jmir.org/2024/1/e58187",
url="http://www.ncbi.nlm.nih.gov/pubmed/39255014"
}


@Article{info:doi/10.2196/53389,
author="Luong, Nguyen
and Mark, Gloria
and Kulshrestha, Juhi
and Aledavood, Talayeh",
title="Sleep During the COVID-19 Pandemic: Longitudinal Observational Study Combining Multisensor Data With Questionnaires",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Sep",
day="3",
volume="12",
pages="e53389",
keywords="computational social science",
keywords="digital health",
keywords="COVID-19",
keywords="sleep",
keywords="longitudinal",
keywords="wearables",
keywords="surveys",
keywords="observational study",
keywords="isolation",
keywords="sleep patterns",
keywords="sleep pattern",
keywords="questionnaires",
keywords="Finland",
keywords="fitness trackers",
keywords="fitness tracker",
keywords="wearable",
keywords="sleeping habits",
keywords="sleeping habit",
keywords="work from home",
abstract="Background: The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people's sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep. Objective: This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period. Methods: We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables. Results: The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) ($\beta$=.003, 95\% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) ($\beta$=.02, 95\% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST ($\beta$=.15, 95\% CI 0.05-0.27; P=.006) and MS ($\beta$=.17, 95\% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST ($\beta$=.37, 95\% CI 0.14-0.61; P=.004) and lower variability in TST ($\beta$=--.15, 95\% CI --0.27 to --0.05; P<.001). Engaging in PA later in the day was associated with longer TST ($\beta$=.03, 95\% CI 0.02-0.04; P<.001) and less variability in TST ($\beta$=--.01, 95\% CI --0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST ($\beta$=--.26, 95\% CI --0.29 to --0.23; P<.001), earlier MS ($\beta$=--.29, 95\% CI --0.33 to --0.26; P<.001), and reduced variability in TST ($\beta$=--.16, 95\% CI --0.23 to --0.09; P<.001). Conclusions: Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce. ",
doi="10.2196/53389",
url="https://mhealth.jmir.org/2024/1/e53389",
url="http://www.ncbi.nlm.nih.gov/pubmed/39226100"
}


@Article{info:doi/10.2196/53643,
author="G Ravindran, K. Kiran
and della Monica, Ciro
and Atzori, Giuseppe
and Lambert, Damion
and Hassanin, Hana
and Revell, Victoria
and Dijk, Derk-Jan",
title="Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Aug",
day="27",
volume="12",
pages="e53643",
keywords="Withings Sleep Analyzer",
keywords="Emfit",
keywords="Somnofy",
keywords="contactless technologies",
keywords="vital signs",
keywords="evaluation",
keywords="apnea-hypopnea index",
keywords="wearables",
keywords="nearables",
abstract="Background: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. Objective: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. Methods: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60\%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57\%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49\% (17) had moderate to severe sleep apnea, while 29\% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. Results: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5\%) and breathing rate (mean absolute error ?1.6 cycles per minute and mean absolute percentage error <12\%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). Conclusions: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. International Registered Report Identifier (IRRID): RR2-10.3390/clockssleep6010010 ",
doi="10.2196/53643",
url="https://mhealth.jmir.org/2024/1/e53643",
url="http://www.ncbi.nlm.nih.gov/pubmed/39190477"
}


@Article{info:doi/10.2196/58217,
author="Knutzen, M{\o}gelberg Sofie
and Christensen, Skj{\ae}rlund Dinne
and Cairns, Patrick
and Damholdt, Flensborg Malene
and Amidi, Ali
and Zachariae, Robert",
title="Efficacy of eHealth Versus In-Person Cognitive Behavioral Therapy for Insomnia: Systematic Review and Meta-Analysis of Equivalence",
journal="JMIR Ment Health",
year="2024",
month="Aug",
day="26",
volume="11",
pages="e58217",
keywords="sleep disturbance",
keywords="digital",
keywords="telehealth",
keywords="face-to-face",
keywords="head-to-head comparison",
keywords="CBTI",
keywords="cognitive behavioral therapy for insomnia",
keywords="mobile phone",
abstract="Background: Insomnia is a prevalent condition with significant health, societal, and economic impacts. Cognitive behavioral therapy for insomnia (CBTI) is recommended as the first-line treatment. With limited accessibility to in-person--delivered CBTI (ipCBTI), electronically delivered eHealth CBTI (eCBTI), ranging from telephone- and videoconference-delivered interventions to fully automated web-based programs and mobile apps, has emerged as an alternative. However, the relative efficacy of eCBTI compared to ipCBTI has not been conclusively determined. Objective: This study aims to test the comparability of eCBTI and ipCBTI through a systematic review and meta-analysis of equivalence based on randomized controlled trials directly comparing the 2 delivery formats. Methods: A comprehensive search across multiple databases was conducted, leading to the identification and analysis of 15 unique randomized head-to-head comparisons of ipCBTI and eCBTI. Data on sleep and nonsleep outcomes were extracted and subjected to both conventional meta-analytical methods and equivalence testing based on predetermined equivalence margins derived from previously suggested minimal important differences. Supplementary Bayesian analyses were conducted to determine the strength of the available evidence. Results: The meta-analysis included 15 studies with a total of 1083 participants. Conventional comparisons generally favored ipCBTI. However, the effect sizes were small, and the 2 delivery formats were statistically significantly equivalent (P<.05) for most sleep and nonsleep outcomes. Additional within-group analyses showed that both formats led to statistically significant improvements (P<.05) in insomnia severity; sleep quality; and secondary outcomes such as fatigue, anxiety, and depression. Heterogeneity analyses highlighted the role of treatment duration and dropout rates as potential moderators of the differences in treatment efficacy. Conclusions: eCBTI and ipCBTI were found to be statistically significantly equivalent for treating insomnia for most examined outcomes, indicating eCBTI as a clinically relevant alternative to ipCBTI. This supports the expansion of eCBTI as a viable option to increase accessibility to effective insomnia treatment. Nonetheless, further research is needed to address the limitations noted, including the high risk of bias in some studies and the potential impact of treatment duration and dropout rates on efficacy. Trial Registration: PROSPERO CRD42023390811; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=390811 ",
doi="10.2196/58217",
url="https://mental.jmir.org/2024/1/e58217"
}


@Article{info:doi/10.2196/48148,
author="Moebus, Max
and Hilty, Marc
and Oldrati, Pietro
and Barrios, Liliana
and 
and Holz, Christian",
title="Assessing the Role of the Autonomic Nervous System as a Driver of Sleep Quality in Patients With Multiple Sclerosis: Observation Study",
journal="JMIR Neurotech",
year="2024",
month="Aug",
day="21",
volume="3",
pages="e48148",
keywords="sleep quality",
keywords="multiple sclerosis",
keywords="autonomic nervous system",
keywords="wearable sensors",
keywords="mobile phone",
abstract="Background: Low sleep quality is a common symptom of multiple sclerosis (MS) and substantially decreases patients' quality of life. The autonomic nervous system (ANS) is crucial to healthy sleep, and the transition from wake to sleep produces the largest shift in autonomic activity we experience every day. For patients with MS, the ANS is often impaired. The relationship between the ANS and perceived sleep quality in patients with MS remains elusive. Objective: In this study, we aim to quantify the impact of the ANS and MS on perceived sleep quality. Methods: We monitored 77 participants over 2 weeks using an arm-worn wearable sensor and a custom smartphone app. Besides recording daily perceived sleep quality, we continuously recorded participants' heart rate (HR) and HR variability on a per-second basis, as well as stress, activity, and the weather (20,700 hours of sensor data). Results: During sleep, we found that reduced HR variability and increased motion led to lower perceived sleep quality in patients with MS (n=53) as well as the age- and gender-matched control group (n=24). An activated stress response (high sympathetic activity and low parasympathetic activity) while asleep resulted in lower perceived sleep quality. For patients with MS, an activated stress response while asleep reduced perceived sleep quality more heavily than in the control group. Similarly, the effect of increased stress levels throughout the day is particularly severe for patients with MS. For patients with MS, we found that stress correlated negatively with minimal observed HR while asleep and might even affect their daily routine. We found that patients with MS with more severe impairments generally recorded lower perceived sleep quality than patients with MS with less severe disease progression. Conclusions: For patients with MS, stress throughout the day and an activated stress response while asleep play a crucial role in determining sleep quality, whereas this is less important for healthy individuals. Besides ensuring an adequate sleep duration, patients with MS might thus work to reduce stressors, which seem to have a particularly negative effect on sleep quality. Generally, however, sleep quality decreases with MS disease progression. ",
doi="10.2196/48148",
url="https://neuro.jmir.org/2024/1/e48148"
}


@Article{info:doi/10.2196/63692,
author="Jackson, Rosie
and Gu, Chao
and Haszard, Jillian
and Meredith-Jones, Kim
and Galland, Barbara
and Camp, Justine
and Brown, Deirdre
and Taylor, Rachael",
title="The Effect of Prebedtime Behaviors on Sleep Duration and Quality in Children: Protocol for a Randomized Crossover Trial",
journal="JMIR Res Protoc",
year="2024",
month="Aug",
day="20",
volume="13",
pages="e63692",
keywords="screen time",
keywords="digital device",
keywords="diet",
keywords="physical activity",
keywords="objective measurement",
keywords="wearable camera",
keywords="sleep",
keywords="mobile phone",
abstract="Background: It is recommended that children should avoid eating dinner, being physically active, or using screens in the hour before bed to ensure good sleep health. However, the evidence base behind these guidelines is weak and limited to cross-sectional studies using questionnaires. Objective: The aim of this randomized crossover trial was to use objective measures to experimentally determine whether recommendations to improve sleep by banning electronic media, physical activity, or food intake in the hour before bed, impact sleep quantity and quality in the youth. Methods: After a baseline week to assess usual behavior, 72 children (10-14.9 years old) will be randomized to four conditions, which are (1) avoid all 3 behaviors, (2) use screens for at least 30 minutes, (3) be physically active for at least 30 minutes, and (4) eat a large meal, during the hour before bed on days 5 to 7 of weeks 2 to 5. Families can choose which days of the week they undertake the intervention, but they must be the same days for each intervention week. Guidance on how to undertake each intervention will be provided. Interventions will only be undertaken during the school term to avoid known changes in sleep during school holidays. Intervention adherence and shuteye latency (time from getting into bed until attempting sleep) will be measured by wearable and stationary PatrolEyes video cameras (StuntCams). Sleep (total sleep time, sleep onset, and wake after sleep onset) will be measured using actigraphy (baseline, days 5 to 7 of each intervention week). Mixed effects regression models with a random effect for participants will be used to estimate mean differences (95\% CI) for conditions 2 to 4 compared with condition 1. Results: Recruitment started in March 2024, and is anticipated to finish in April 2025. Following data analysis, we expect that results will be available later in 2026. Conclusions: Using objective measures, we will be able to establish if causal relationships exist between prebedtime behaviors and sleep in children. Such information is critical to ensure appropriate and achievable sleep guidelines. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12624000206527; https://tinyurl.com/3kcjmfnj International Registered Report Identifier (IRRID): DERR1-10.2196/63692 ",
doi="10.2196/63692",
url="https://www.researchprotocols.org/2024/1/e63692",
url="http://www.ncbi.nlm.nih.gov/pubmed/39163119"
}


@Article{info:doi/10.2196/39554,
author="Armitage, Tanya Bianca
and Potts, W. Henry W.
and Irwin, R. Michael
and Fisher, Abi",
title="Exploring the Impact of a Sleep App on Sleep Quality in a General Population Sample: Pilot Randomized Controlled Trial",
journal="JMIR Form Res",
year="2024",
month="Aug",
day="13",
volume="8",
pages="e39554",
keywords="sleep",
keywords="mobile app",
keywords="app optimization",
keywords="intervention",
keywords="smartphone",
keywords="general population",
keywords="mindfulness",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="mobile phone",
abstract="Background: A third of adults in Western countries have impaired sleep quality. A possible solution involves distributing sleep aids through smartphone apps, but most empirical studies are limited to small pilot trials in distinct populations (eg, soldiers) or individuals with clinical sleep disorders; therefore, general population data are required. Furthermore, recent research shows that sleep app users desire a personalized approach, offering an individually tailored choice of techniques. One such aid is Peak Sleep, a smartphone app based on scientifically validated principles for improving sleep quality, such as mindfulness meditation and cognitive behavioral therapy. Objective: We aimed to test the impact of the smartphone app Peak Sleep on sleep quality and collect user experience data to allow for future app development. Methods: This was a 2-arm pilot randomized controlled trial. Participants were general population adults in the United Kingdom (aged ?18 years) who were interested in improving their sleep quality and were not undergoing clinical treatment for sleep disorder or using sleep medication ?1 per week. Participants were individually randomized to receive the intervention (3 months of app use) versus a no-treatment control. The intervention involved free access to Peak Sleep, an app that offered a choice of behavioral techniques to support better sleep (mindfulness, cognitive behavioral therapy, and acceptance commitment therapy). The primary outcome was sleep quality assessed using the Insomnia Severity Index at baseline and 1-, 2-, and 3-month follow-ups. Assessments were remote using web-based questionnaires. Objective sleep data collection using the Oura Ring (?ura Health Oy) was planned; however, because the COVID-19 pandemic lockdowns began just after recruitment started, this plan could not be realized. Participant engagement with the app was assessed using the Digital Behavior Change Intervention Engagement Scale and qualitative telephone interviews with a subsample. Results: A total of 101 participants were enrolled in the trial, and 21 (21\%) were qualitatively interviewed. Sleep quality improved in both groups over time, with Insomnia Severity Index scores of the intervention group improving by a mean of 2.5 and the control group by a mean of 1.6 (between-group mean difference 0.9, 95\% CI --2.0 to 3.8), with was no significant effect of group (P=.91). App users' engagement was mixed, with qualitative interviews supporting the view of a polarized sample who either strongly liked or disliked the app. Conclusions: In this trial, self-reported sleep improved over time in both intervention and control arms, with no impact by group, suggesting no effect of the sleep app. Qualitative data suggested polarized views on liking or not liking the app, features that people engaged with, and areas for improvement. Future work could involve developing the app features and then testing the app using objective measures of sleep in a larger sample. Trial Registration: ClinicalTrials.gov NCT04487483; https://www.clinicaltrials.gov/study/NCT04487483 ",
doi="10.2196/39554",
url="https://formative.jmir.org/2024/1/e39554",
url="http://www.ncbi.nlm.nih.gov/pubmed/39137016"
}


@Article{info:doi/10.2196/51716,
author="Chan, Sze Wai
and Cheng, Yee Wing
and Lok, Chun Samson Hoi
and Cheah, Mun Amanda Kah
and Lee, Win Anna Kai
and Ng, Ying Albe Sin
and Kowatsch, Tobias",
title="Assessing the Short-Term Efficacy of Digital Cognitive Behavioral Therapy for Insomnia With Different Types of Coaching: Randomized Controlled Comparative Trial",
journal="JMIR Ment Health",
year="2024",
month="Aug",
day="7",
volume="11",
pages="e51716",
keywords="insomnia",
keywords="cognitive behavioral therapy",
keywords="digital intervention",
keywords="mobile health",
keywords="mHealth",
keywords="chatbot-based coaching",
keywords="human support",
keywords="mobile phone",
abstract="Background: Digital cognitive behavioral therapy for insomnia (dCBTi) is an effective intervention for treating insomnia. The findings regarding its efficacy compared to face-to-face cognitive behavioral therapy for insomnia are inconclusive but suggest that dCBTi might be inferior. The lack of human support and low treatment adherence are believed to be barriers to dCBTi achieving its optimal efficacy. However, there has yet to be a direct comparative trial of dCBTi with different types of coaching support. Objective: This study examines whether adding chatbot-based and human coaching would improve the treatment efficacy of, and adherence to, dCBTi. Methods: Overall, 129 participants (n=98, 76\% women; age: mean 34.09, SD 12.05 y) whose scores on the Insomnia Severity Index [ISI] were greater than 9 were recruited. A randomized controlled comparative trial with 5 arms was conducted: dCBTi with chatbot-based coaching and therapist support (dCBTi-therapist), dCBTi with chatbot-based coaching and research assistant support, dCBTi with chatbot-based coaching only, dCBTi without any coaching, and digital sleep hygiene and self-monitoring control. Participants were blinded to the condition assignment and study hypotheses, and the outcomes were self-assessed using questionnaires administered on the web. The outcomes included measures of insomnia (the ISI and the Sleep Condition Indicator), mood disturbances, fatigue, daytime sleepiness, quality of life, dysfunctional beliefs about sleep, and sleep-related safety behaviors administered at baseline, after treatment, and at 4-week follow-up. Treatment adherence was measured by the completion of video sessions and sleep diaries. An intention-to-treat analysis was conducted. Results: Significant condition-by-time interaction effects showed that dCBTi recipients, regardless of having any coaching, had greater improvements in insomnia measured by the Sleep Condition Indicator (P=.003; d=0.45) but not the ISI (P=.86; d=--0.28), depressive symptoms (P<.001; d=--0.62), anxiety (P=.01; d=--0.40), fatigue (P=.02; d=--0.35), dysfunctional beliefs about sleep (P<.001; d=--0.53), and safety behaviors related to sleep (P=.001; d=--0.50) than those who received digital sleep hygiene and self-monitoring control. The addition of chatbot-based coaching and human support did not improve treatment efficacy. However, adding human support promoted greater reductions in fatigue (P=.03; d=--0.33) and sleep-related safety behaviors (P=.05; d=--0.30) than dCBTi with chatbot-based coaching only at 4-week follow-up. dCBTi-therapist had the highest video and diary completion rates compared to other conditions (video: 16/25, 60\% in dCBTi-therapist vs <3/21, <25\% in dCBTi without any coaching), indicating greater treatment adherence. Conclusions: Our findings support the efficacy of dCBTi in treating insomnia, reducing thoughts and behaviors that perpetuate insomnia, reducing mood disturbances and fatigue, and improving quality of life. Adding chatbot-based coaching and human support did not significantly improve the efficacy of dCBTi after treatment. However, adding human support had incremental benefits on reducing fatigue and behaviors that could perpetuate insomnia, and hence may improve long-term efficacy. Trial Registration: ClinicalTrials.gov NCT05136638; https://www.clinicaltrials.gov/study/NCT05136638 ",
doi="10.2196/51716",
url="https://mental.jmir.org/2024/1/e51716"
}


@Article{info:doi/10.2196/50555,
author="Shin, Jiyoon
and Kim, Sujin
and Lee, Jooyoung
and Gu, Hyerin
and Ahn, Jihye
and Park, Chowon
and Seo, Mincheol
and Jeon, Eun Jeong
and Lee, Young Ha
and Yeom, Won Ji
and Kim, Sojeong
and Yoon, Yeaseul
and Lee, Heon-Jeong
and Kim, Ju Seog
and Lee, Jin Yu",
title="Efficacy of Mobile App--Based Cognitive Behavioral Therapy for Insomnia: Multicenter, Single-Blind Randomized Clinical Trial",
journal="J Med Internet Res",
year="2024",
month="Jul",
day="26",
volume="26",
pages="e50555",
keywords="digital therapeutics",
keywords="mobile app--based cognitive behavioral therapy for insomnia",
keywords="cognitive behavioral therapy",
keywords="insomnia",
keywords="mental health",
keywords="mobile phone",
abstract="Background: Cognitive behavioral therapy for insomnia (CBTi) is the first-line therapy for chronic insomnia. Mobile app--based CBTi (MCBTi) can enhance the accessibility of CBTi treatment; however, few studies have evaluated the effectiveness of MCBTi using a multicenter, randomized controlled trial design. Objective: We aimed to assess the efficacy of Somzz, an MCBTi that provides real-time and tailored feedback to users, through comparison with an active comparator app. Methods: In our multicenter, single-blind randomized controlled trial study, participants were recruited from 3 university hospitals and randomized into a Somzz group and a sleep hygiene education (SHE) group at a 1:1 ratio. The intervention included 6 sessions for 6 weeks, with follow-up visits over a 4-month period. The Somzz group received audiovisual sleep education, guidance on relaxation therapy, and real-time feedback on sleep behavior. The primary outcome was the Insomnia Severity Index score, and secondary outcomes included sleep diary measures and mental health self-reports. We analyzed the outcomes based on the intention-to-treat principle. Results: A total of 98 participants were randomized into the Somzz (n=49, 50\%) and SHE (n=49, 50\%) groups. Insomnia Severity Index scores for the Somzz group were significantly lower at the postintervention time point (9.0 vs 12.8; t95=3.85; F2,95=22.76; $\eta$p2=0.13; P<.001) and at the 3-month follow-up visit (11.3 vs 14.7; t68=2.61; F2,68=5.85; $\eta$p2=0.03; P=.01) compared to those of the SHE group. The Somzz group maintained their treatment effect at the postintervention time point and follow-ups, with a moderate to large effect size (Cohen d=--0.62 to --1.35; P<.01 in all cases). Furthermore, the Somzz group showed better sleep efficiency (t95=--3.32; F2,91=69.87; $\eta$p2=0.41; P=.001), wake after sleep onset (t95=2.55; F2,91=51.81; $\eta$p2=0.36; P=.01), satisfaction (t95=--2.05; F2,91=26.63; $\eta$p2=0.20; P=.04) related to sleep, and mental health outcomes, including depression (t95=2.11; F2,94=29.64; $\eta$p2=0.21; P=.04) and quality of life (t95=--3.13; F2,94=54.20; $\eta$p2=0.33; P=.002), compared to the SHE group after the intervention. The attrition rate in the Somzz group was 12\% (6/49). Conclusions: Somzz outperformed SHE in improving insomnia, mental health, and quality of life. The MCBTi can be a highly accessible, time-efficient, and effective treatment option for chronic insomnia, with high compliance. Trial Registration: Clinical Research Information Service (CRiS) KCT0007292; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=22214\&search\_page=L ",
doi="10.2196/50555",
url="https://www.jmir.org/2024/1/e50555"
}


@Article{info:doi/10.2196/55408,
author="Iliakis, Ioannis
and Anagnostouli, Maria
and Chrousos, George",
title="Assessing the Impact of the Mindfulness-Based Body Scan Technique on Sleep Quality in Multiple Sclerosis Using Objective and Subjective Assessment Tools: Single-Case Study",
journal="JMIR Form Res",
year="2024",
month="Jul",
day="25",
volume="8",
pages="e55408",
keywords="multiple sclerosis",
keywords="MS",
keywords="sleep problems",
keywords="electronic portable device",
keywords="EPD",
keywords="mindfulness-based body scan technique",
keywords="sleep quality",
keywords="neurodegenerative disease",
keywords="quality of life",
keywords="anxiety",
keywords="pain",
keywords="nocturia",
keywords="assessment tools",
keywords="single-case study",
keywords="effectiveness",
abstract="Background: Multiple sclerosis (MS) is a chronic inflammatory disease affecting the central nervous system, often leading to poor sleep quality and diminished quality of life (QoL) for affected patients. Sleep disturbances in MS do not always correlate linearly with other symptoms such as anxiety, depression, fatigue, or pain. Various approaches, including stress reduction techniques such as mindfulness-based interventions, have been proposed to manage MS-related sleep issues. Objective: The aim of this study was to evaluate the effects of the mindfulness-based body scan technique on sleep quality and QoL in patients with MS using both subjective (questionnaires) and objective (electronic portable device) measures. Methods: A single-case study was performed involving a 31-year-old woman diagnosed with relapsing-remitting MS. The patient practiced the mindfulness-based body scan technique daily before bedtime and outcomes were compared to measures evaluated at baseline. Results: The mindfulness-based body scan intervention demonstrated positive effects on both sleep quality and overall QoL. Biometric data revealed a notable dissociation between daily stress levels and sleep quality during the intervention period. Although self-report instruments indicated significant improvement, potential biases were noted. Conclusions: While this study is limited to a single patient, the promising outcomes suggest the need for further investigation on a larger scale. These findings underscore the potential benefits of the mindfulness-based body scan technique in managing sleep disturbances and enhancing QoL among patients with MS. ",
doi="10.2196/55408",
url="https://formative.jmir.org/2024/1/e55408"
}


@Article{info:doi/10.2196/55834,
author="Sato, Daisuke
and Ikarashi, Koyuki
and Nakajima, Fumiko
and Fujimoto, Tomomi",
title="Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping: Validity and Effectiveness Study",
journal="JMIR Form Res",
year="2024",
month="Jul",
day="5",
volume="8",
pages="e55834",
keywords="menstrual cycle",
keywords="ovulation",
keywords="biphasic temperature shift",
keywords="estimation method",
keywords="women",
abstract="Background: Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However, issues related to its low accuracy are still under discussion. Objective: This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual cycle. We investigated whether core body temperature (CBT) estimation can improve the accuracy of temperature biphasic shift discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the CBT estimation method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts. Methods: In experiment 1, healthy women aged between 18 and 40 years had their true CBT measured using an ingestible thermometer and their CBT estimated from skin temperature and ambient temperature measured during sleep in both the follicular and luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations in temperature between the 2 phases, and the repeated measures correlation between the true and estimated CBT. Experiment 2 followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches (estimated CBT and traditional oral basal body temperature [BBT]) for identifying ovulatory cycles. This was performed using urine luteinizing hormone (LH) as the reference standard. Menstrual cycles were categorized based on the results of the LH tests, and a temperature shift was identified using a specific criterion called the ``three-over-six rule.'' This rule and the nested design of the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity. Results: The main findings showed that CBT estimated from skin temperature and ambient temperature during sleep was consistently lower than directly measured CBT in both the follicular and luteal phases of the menstrual cycle. Despite this, the pattern of temperature variation between these phases was comparable for both the estimated and true CBT measurements, suggesting that the estimated CBT accurately reflected the cyclical variations in the true CBT. Significantly, the CBT estimation method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral BBT measurements, highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the CBT provides a practical and noninvasive method for monitoring CBT, which is essential for identifying biphasic shifts in the BBT throughout the menstrual cycle. Conclusions: This study demonstrated that the estimated CBT derived from skin temperature and ambient temperature during sleep accurately captures variations in true CBT and is more accurate in determining the presence or absence of ovulation than traditional oral BBT measurements. This method holds promise for improving reproductive health monitoring and understanding of menstrual cycle dynamics. ",
doi="10.2196/55834",
url="https://formative.jmir.org/2024/1/e55834",
url="http://www.ncbi.nlm.nih.gov/pubmed/38967967"
}


@Article{info:doi/10.2196/56616,
author="Togo, Eiichi
and Takami, Miki
and Ishigaki, Kyoko",
title="Evaluation of Autonomic Nervous System Function During Sleep by Mindful Breathing Using a Tablet Device: Randomized Controlled Trial",
journal="JMIR Nursing",
year="2024",
month="Jun",
day="12",
volume="7",
pages="e56616",
keywords="mindfulness",
keywords="sleep",
keywords="cardiac potential",
keywords="low frequency",
keywords="high frequency",
keywords="mobile phone",
abstract="Background: One issue to be considered in universities is the need for interventions to improve sleep quality and educational systems for university students. However, sleep problems remain unresolved. As a clinical practice technique, a mindfulness-based stress reduction method can help students develop mindfulness skills to cope with stress, self-healing skills, and sleep. Objective: We aim to verify the effectiveness of mindful breathing exercises using a tablet device. Methods: In total, 18 nursing students, aged 18-22 years, were randomly assigned and divided equally into mindfulness (Mi) and nonmindfulness (nMi) implementation groups using tablet devices. During the 9-day experimental period, cardiac potentials were measured on days 1, 5, and 9. In each sleep stage (sleep with sympathetic nerve dominance, shallow sleep with parasympathetic nerve dominance, and deep sleep with parasympathetic nerve dominance), low frequency (LF) value, high frequency (HF) value, and LF/HF ratios obtained from the cardiac potentials were evaluated. Results: On day 5, a significant correlation was observed between sleep duration and each sleep stage in both groups. In comparison to each experimental day, the LF and LF/HF ratios of the Mi group were significantly higher on day 1 than on days 5 and 10. LF and HF values in the nMi group were significantly higher on day 1 than on day 5. Conclusions: The correlation between sleep duration and each sleep stage on day 5 suggested that sleep homeostasis in both groups was activated on day 5, resulting in similar changes in sleep stages. During the experimental period, the cardiac potentials in the nMi group showed a wide range of fluctuations, whereas the LF values and LF/HF ratio in the Mi group showed a decreasing trend over time. This finding suggests that implementing mindful breathing exercises using a tablet device may suppress sympathetic activity during sleep. Trial Registration: UMIN-CTR Clinical Trials Registry UMIN000054639; https://tinyurl.com/mu2vdrks ",
doi="10.2196/56616",
url="https://nursing.jmir.org/2024/1/e56616",
url="http://www.ncbi.nlm.nih.gov/pubmed/38865177"
}


@Article{info:doi/10.2196/51585,
author="Gauld, Christophe
and Hartley, Sarah
and Micoulaud-Franchi, Jean-Arthur
and Royant-Parola, Sylvie",
title="Sleep Health Analysis Through Sleep Symptoms in 35,808 Individuals Across Age and Sex Differences: Comparative Symptom Network Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Jun",
day="11",
volume="10",
pages="e51585",
keywords="symptom",
keywords="epidemiology",
keywords="age",
keywords="sex",
keywords="diagnosis",
keywords="network approach",
keywords="sleep",
keywords="sleep health",
abstract="Background: Sleep health is a multidimensional construct that includes objective and subjective parameters and is influenced by individual sleep-related behaviors and sleep disorders. Symptom network analysis allows modeling of the interactions between variables, enabling both the visualization of relationships between different factors and the identification of the strength of those relationships. Given the known influence of sex and age on sleep health, network analysis can help explore sets of mutually interacting symptoms relative to these demographic variables. Objective: This study aimed to study the centrality of symptoms and compare age and sex differences regarding sleep health using a symptom network approach in a large French population that feels concerned about their sleep. Methods: Data were extracted from a questionnaire provided by the R{\'e}seau Morph{\'e}e health network. A network analysis was conducted on 39 clinical variables related to sleep disorders and sleep health. After network estimation, statistical analyses consisted of calculating inferences of centrality, robustness (ie, testifying to a sufficient effect size), predictability, and network comparison. Sleep clinical variable centralities within the networks were analyzed by both sex and age using 4 age groups (18-30, 31-45, 46-55, and >55 years), and local symptom-by-symptom correlations determined. Results: Data of 35,808 participants were obtained. The mean age was 42.7 (SD 15.7) years, and 24,964 (69.7\%) were women. Overall, there were no significant differences in the structure of the symptom networks between sexes or age groups. The most central symptoms across all groups were nonrestorative sleep and excessive daytime sleepiness. In the youngest group, additional central symptoms were chronic circadian misalignment and chronic sleep deprivation (related to sleep behaviors), particularly among women. In the oldest group, leg sensory discomfort and breath abnormality complaint were among the top 4 central symptoms. Symptoms of sleep disorders thus became more central with age than sleep behaviors. The high predictability of central nodes in one of the networks underlined its importance in influencing other nodes. Conclusions: The absence of structural difference between networks is an important finding, given the known differences in sleep between sexes and across age groups. These similarities suggest comparable interactions between clinical sleep variables across sexes and age groups and highlight the implication of common sleep and wake neural circuits and circadian rhythms in understanding sleep health. More precisely, nonrestorative sleep and excessive daytime sleepiness are central symptoms in all groups. The behavioral component is particularly central in young people and women. Sleep-related respiratory and motor symptoms are prominent in older people. These results underscore the importance of comprehensive sleep promotion and screening strategies tailored to sex and age to impact sleep health. ",
doi="10.2196/51585",
url="https://publichealth.jmir.org/2024/1/e51585",
url="http://www.ncbi.nlm.nih.gov/pubmed/38861716"
}


@Article{info:doi/10.2196/49669,
author="Takeuchi, Hiroki
and Ishizawa, Tetsuro
and Kishi, Akifumi
and Nakamura, Toru
and Yoshiuchi, Kazuhiro
and Yamamoto, Yoshiharu",
title="Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial",
journal="J Med Internet Res",
year="2024",
month="Jun",
day="11",
volume="26",
pages="e49669",
keywords="objective push-type sleep feedback",
keywords="stability of habitual sleep behaviors",
keywords="just-in-time adaptive intervention",
keywords="microrandomized trial",
keywords="mobile phone",
abstract="Background: Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention). Objective: This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2). Methods: Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50\% chance for each day (microrandomization). Results: In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood ($\beta$=3.83; P=.004), anxiety ($\beta$=5.70; P=.03), and subjective sleep quality ($\beta$=?3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001). Conclusions: This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback. ",
doi="10.2196/49669",
url="https://www.jmir.org/2024/1/e49669",
url="http://www.ncbi.nlm.nih.gov/pubmed/38861313"
}


@Article{info:doi/10.2196/53548,
author="Luo, Minjing
and Dong, Yue
and Fan, Bingbing
and Zhang, Xinyue
and Liu, Hao
and Liang, Changhao
and Rong, Hongguo
and Fei, Yutong",
title="Sleep Duration and Functional Disability Among Chinese Older Adults: Cross-Sectional Study",
journal="JMIR Aging",
year="2024",
month="Jun",
day="10",
volume="7",
pages="e53548",
keywords="sleep duration",
keywords="functional disability",
keywords="activity of daily living disability",
keywords="instrumental activity of daily living",
keywords="older population",
abstract="Background: The duration of sleep plays a crucial role in the development of physiological functions that impact health. However, little is known about the associations between sleep duration and functional disability among older adults in China. Objective: This study aimed to explore the associations between sleep duration and functional disabilities in the older population (aged?65 years) in China. Methods: The data for this cross-sectional study were gathered from respondents 65 years and older who participated in the 2018 survey of the China Health and Retirement Longitudinal Study, an ongoing nationwide longitudinal investigation of Chinese adults. The duration of sleep per night was obtained through face-to-face interviews. Functional disability was assessed according to activities of daily living (ADL) and instrumental activities of daily living (IADL) scales. The association between sleep duration and functional disability was assessed by multivariable generalized linear models. A restricted cubic-spline model was used to explore the dose-response relationship between sleep duration and functional disability. Results: In total, 5519 participants (n=2471, 44.77\% men) were included in this study with a mean age of 73.67 years, including 2800 (50.73\%) respondents with a functional disability, 1978 (35.83\%) with ADL disability, and 2299 (41.66\%) with IADL disability. After adjusting for potential confounders, the older adults reporting shorter (?4, 5, or 6 hours) or longer (8, 9, or ?10 hours) sleep durations per night exhibited a notably increased risk of functional disability compared to that of respondents who reported having 7 hours of sleep per night (all P<.05), which revealed a U-shaped association between sleep duration and dysfunction. When the sleep duration fell below 7 hours, increased sleep duration was associated with a significantly lower risk of functional disability (odds ratio [OR] 0.85, 95\% CI 0.79-0.91; P<.001). When the sleep duration exceeded 7 hours, the risk of functional disability associated with a prolonged sleep duration increased (OR 1.16, 95\% CI 1.05-1.29; P<.001). Conclusions: Sleep durations shorter and longer than 7 hours were associated with a higher risk of functional disability among Chinese adults 65 years and older. Future studies are needed to explore intervention strategies for improving sleep duration with a particular focus on functional disability. ",
doi="10.2196/53548",
url="https://aging.jmir.org/2024/1/e53548",
url="http://www.ncbi.nlm.nih.gov/pubmed/38771907"
}


@Article{info:doi/10.2196/54595,
author="Soltero, G. Erica
and Musaad, M. Salma
and O'Connor, M. Teresia
and Thompson, Debbe
and Norris, Keith
and Beech, M. Bettina",
title="Feasibility of Fit24, a Digital Diabetes Prevention Program for Hispanic Adolescents: Qualitative Evaluation Study",
journal="JMIR Form Res",
year="2024",
month="May",
day="17",
volume="8",
pages="e54595",
keywords="health disparities",
keywords="diabetes prevention",
keywords="Mexican youth",
keywords="physical activity",
keywords="sleep",
keywords="digital health",
abstract="Background: Digital health interventions are promising for reaching and engaging high-risk youth in disease prevention opportunities; however, few digital prevention interventions have been developed for Hispanic youth, limiting our knowledge of these strategies among this population. Objective: This study qualitatively assessed the feasibility and acceptability of Fit24, a 12-week goal-setting intervention that uses a Fitbit watch (Fitbit Inc) and theoretically grounded SMS text messages to promote physical activity and sleep among Hispanic adolescents (aged between 14 and 16 years) with obesity. Methods: After completing the intervention, a subsample of youth (N=15) participated in an in-depth interview. We categorized the themes into dimensions based on participant perspectives using the Practical, Robust Implementation, and Sustainability Model (PRISM) framework. Results: Participants shared positive perceptions of wearing the Fitbit and receiving SMS text messages. Youth were highly engaged in monitoring their behaviors and perceived increased activity and sleep. Almost all youth organically received social support from a peer or family member and suggested the use of a group chat or team challenge for integrating peers into future interventions. However, most youth also expressed the need to take personal responsibility for the change in their behavior. Barriers that impacted the feasibility of the study included the skin-irritating material on the Fitbit watch band and environmental barriers (eg, lack of resources and school schedules), that limited participation in activity suggestions. Additionally, sync issues with the Fitbit limited the transmission of data, leading to inaccurate feedback. Conclusions: Fit24 is a promising approach for engaging Hispanic youth in a diabetes prevention program. Strategies are needed to address technical issues with the Fitbit and environmental issues such as message timing. While integrating peer social support may be desired by some, peer support strategies should be mindful of youth's desire to foster personal motivation for behavior change. Findings from this study will inform future diabetes prevention trials of Fit24 and other digital health interventions for high-risk pediatric populations. ",
doi="10.2196/54595",
url="https://formative.jmir.org/2024/1/e54595",
url="http://www.ncbi.nlm.nih.gov/pubmed/38758584"
}


@Article{info:doi/10.2196/49396,
author="Kinoshita, Shotaro
and Hanashiro, Sayaka
and Tsutsumi, Shiori
and Shiga, Kiko
and Kitazawa, Momoko
and Wada, Yasuyo
and Inaishi, Jun
and Kashiwagi, Kazuhiro
and Fukami, Toshikazu
and Mashimo, Yasumasa
and Minato, Kazumichi
and Kishimoto, Taishiro",
title="Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study",
journal="JMIR Form Res",
year="2024",
month="May",
day="2",
volume="8",
pages="e49396",
keywords="wearable device",
keywords="sleep feedback",
keywords="well-being",
keywords="stress",
keywords="ecological momentary assessment",
keywords="feasibility study",
abstract="Background: Poor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking. Objective: This study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being. Methods: A total of 40 office workers (female, 20/40, 50\%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants' stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation. Results: The EMA achieved a total response rate of 87.3\%, while the Fitbit Inspire 2 wear rate reached 88.0\%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P<.001 and P=.01, respectively), whereas those in the highest stress quartile exhibited greater variation in the midpoint of sleep (P<.001). A multilevel analysis unveiled notable relationships: intraindividual variability analysis indicated that higher energy levels were associated with lower deviation of heart rate during sleep on the preceding day ($\beta$=--.12, P<.001), and decreased sleepiness was observed on days following longer sleep durations ($\beta$=--.10, P<.001). Furthermore, interindividual variability analysis revealed that individuals with earlier midpoints of sleep tended to exhibit higher energy levels ($\beta$=--.26, P=.04). Conclusions: Increased sleep variabilities, characterized by unstable bedtime or midpoint of sleep, were correlated with elevated stress levels and diminished well-being. Conversely, improved sleep indices (eg, lower heart rate during sleep and earlier average bedtime) were associated with heightened daytime energy levels. Further research with a larger sample size using these methodologies, particularly focusing on specific phenomena such as social jet lag, has the potential to yield valuable insights. Trial Registration: UMIN-CTR UMIN000046858; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr\_view.cgi?recptno=R000053392 ",
doi="10.2196/49396",
url="https://formative.jmir.org/2024/1/e49396",
url="http://www.ncbi.nlm.nih.gov/pubmed/38696237"
}


@Article{info:doi/10.2196/53441,
author="Vidal Bustamante, M. Constanza
and Coombs III, Garth
and Rahimi-Eichi, Habiballah
and Mair, Patrick
and Onnela, Jukka-Pekka
and Baker, T. Justin
and Buckner, L. Randy",
title="Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="30",
volume="8",
pages="e53441",
keywords="deep phenotyping",
keywords="individualized models",
keywords="intensive longitudinal data",
keywords="sleep",
keywords="stress",
keywords="actigraphy",
keywords="accelerometer",
keywords="wearable",
keywords="mobile phone",
keywords="digital health",
abstract="Background: Heightened stress and insufficient sleep are common in the transition to college, often co-occur, and have both been linked to negative health outcomes. A challenge concerns disentangling whether perceived stress precedes or succeeds changes in sleep. These day-to-day associations may vary across individuals, but short study periods and group-level analyses in prior research may have obscured person-specific phenotypes. Objective: This study aims to obtain stable estimates of lead-lag associations between perceived stress and objective sleep duration in the individual, unbiased by the group, by developing an individual-level linear model that can leverage intensive longitudinal data while remaining parsimonious. Methods: In total, 55 college students (n=6, 11\% second-year students and n=49, 89\% first-year students) volunteered to provide daily self-reports of perceived stress via a smartphone app and wore an actigraphy wristband for the estimation of daily sleep duration continuously throughout the academic year (median usable daily observations per participant: 178, IQR 65.5). The individual-level linear model, developed in a Bayesian framework, included the predictor and outcome of interest and a covariate for the day of the week to account for weekly patterns. We validated the model on the cohort of second-year students (n=6, used as a pilot sample) by applying it to variables expected to correlate positively within individuals: objective sleep duration and self-reported sleep quality. The model was then applied to the fully independent target sample of first-year students (n=49) for the examination of bidirectional associations between daily stress levels and sleep duration. Results: Proof-of-concept analyses captured expected associations between objective sleep duration and subjective sleep quality in every pilot participant. Target analyses revealed negative associations between sleep duration and perceived stress in most of the participants (45/49, 92\%), but their temporal association varied. Of the 49 participants, 19 (39\%) showed a significant association (probability of direction>0.975): 8 (16\%) showed elevated stress in the day associated with shorter sleep later that night, 5 (10\%) showed shorter sleep associated with elevated stress the next day, and 6 (12\%) showed both directions of association. Of note, when analyzed using a group-based multilevel model, individual estimates were systematically attenuated, and some even reversed sign. Conclusions: The dynamic interplay of stress and sleep in daily life is likely person specific. Paired with intensive longitudinal data, our individual-level linear model provides a precision framework for the estimation of stable real-world behavioral and psychological dynamics and may support the personalized prioritization of intervention targets for health and well-being. ",
doi="10.2196/53441",
url="https://formative.jmir.org/2024/1/e53441",
url="http://www.ncbi.nlm.nih.gov/pubmed/38687600"
}


@Article{info:doi/10.2196/55402,
author="Arring, Noel
and Barton, L. Debra
and Lafferty, Carolyn
and Cox, Bryana
and Conroy, A. Deirdre
and An, Lawrence",
title="Mi Sleep Coach Mobile App to Address Insomnia Symptoms Among Cancer Survivors: Single-Arm Feasibility Study",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="26",
volume="8",
pages="e55402",
keywords="cognitive behavioral therapy",
keywords="insomnia",
keywords="mobile health",
keywords="breast cancer",
keywords="prostate cancer",
keywords="colon cancer",
keywords="cancer survivor",
abstract="Background: Rates of sleep disturbance among survivors of cancer are more than 3 times higher than the general population. Causes of sleep disturbance among survivors are many and multifaceted, including anxiety and fear related to cancer diagnosis and treatments. Cognitive behavioral therapy for insomnia (CBT-I) is considered a first-line treatment for insomnia; However, a lack of access to trained professionals and limited insurance coverage for CBT-I services has limited patient access to these effective treatments. Evidence supports digital delivery of CBT-I (dCBT-I), but there is only limited evidence to support its use among survivors of cancer. Broad adoption of smartphone technology provides a new channel to deliver dCBT-I, but no prior studies have evaluated mobile dCBT-I interventions for survivors. To address the need for accessible and efficacious CBT-I for survivors of cancer, the Mi Sleep Coach program was developed to adapt CBT-I for delivery to survivors of cancer as a self-directed mobile health app. Objective: This single-arm feasibility study assessed the adherence, attrition, usefulness, and satisfaction of the Mi Sleep Coach app for insomnia. Methods: A 7-week, single-arm study was conducted, enrolling adult survivors of breast, prostate, or colon cancer reporting sleep disturbances. Results: In total, 30 participants were enrolled, with 100\% completing the study and providing data through week 7. Further, 9 out of 10 app features were found to be useful by 80\% (n=24) to 93\% (n=28) of the 30 participants. Furthermore, 27 (90\%) participants were satisfied with the Mi Sleep Coach app and 28 (93\%) would recommend the use of the Mi Sleep Coach app for those with insomnia. The Insomnia Severity Index showed a decrease from baseline (18.5, SD 4.6) to week 7 (10.4, SD 4.2) of 8.1 (P<.001; Cohen d=1.5). At baseline, 25 (83\%) participants scored in the moderate (n=19; 15-21) or severe (n=6; 22-28) insomnia range. At week 7, a total of 4 (13\%) patients scored in the moderate (n=4) or severe (n=0) range. The number of patients taking prescription sleep medications decreased from 7 (23\%) at baseline to 1 (3\%; P<.001) at week 7. The number of patients taking over-the-counter sleep medications decreased from 14 (47\%) at baseline to 9 (30\%; P=.03) at week 7. Conclusions: The Mi Sleep Coach app demonstrated high levels of program adherence and user satisfaction and had large effects on the severity of insomnia among survivors of cancer. The Mi Sleep Coach app is a promising intervention for cancer-related insomnia, and further clinical trials are warranted. If proven to significantly decrease insomnia in survivors of cancer in future randomized controlled clinical trials, this intervention would provide more survivors of cancer with easy access to evidence-based CBT-I treatment. Trial Registration: ClinicalTrials.gov NCT04827459; https://clinicaltrials.gov/study/NCT04827459 ",
doi="10.2196/55402",
url="https://formative.jmir.org/2024/1/e55402",
url="http://www.ncbi.nlm.nih.gov/pubmed/38669678"
}


@Article{info:doi/10.2196/48356,
author="Han, Xiaoning
and Zhou, Enze
and Liu, Dong",
title="Electronic Media Use and Sleep Quality: Updated Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2024",
month="Apr",
day="23",
volume="26",
pages="e48356",
keywords="electronic media",
keywords="sleep quality",
keywords="meta-analysis",
keywords="media types",
keywords="cultural difference",
abstract="Background: This paper explores the widely discussed relationship between electronic media use and sleep quality, indicating negative effects due to various factors. However, existing meta-analyses on the topic have some limitations. Objective: The study aims to analyze and compare the impacts of different digital media types, such as smartphones, online games, and social media, on sleep quality. Methods: Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study performed a systematic meta-analysis of literature across multiple databases, including Web of Science, MEDLINE, PsycINFO, PubMed, Science Direct, Scopus, and Google Scholar, from January 2018 to October 2023. Two trained coders coded the study characteristics independently. The effect sizes were calculated using the correlation coefficient as a standardized measure of the relationship between electronic media use and sleep quality across studies. The Comprehensive Meta-Analysis software (version 3.0) was used to perform the meta-analysis. Statistical methods such as funnel plots were used to assess the presence of asymmetry and a p-curve test to test the p-hacking problem, which can indicate publication bias. Results: Following a thorough screening process, the study involved 55 papers (56 items) with 41,716 participants from over 20 countries, classifying electronic media use into ``general use'' and ``problematic use.'' The meta-analysis revealed that electronic media use was significantly linked with decreased sleep quality and increased sleep problems with varying effect sizes across subgroups. A significant cultural difference was also observed in these effects. General use was associated with a significant decrease in sleep quality (P<.001). The pooled effect size was 0.28 (95\% CI 0.21-0.35; k=20). Problematic use was associated with a significant increase in sleep problems (P?.001). The pooled effect size was 0.33 (95\% CI 0.28-0.38; k=36). The subgroup analysis indicated that the effect of general smartphone use and sleep problems was r=0.33 (95\% CI 0.27-0.40), which was the highest among the general group. The effect of problematic internet use and sleep problems was r=0.51 (95\% CI 0.43-0.59), which was the highest among the problematic groups. There were significant differences among these subgroups (general: Qbetween=14.46, P=.001; problematic: Qbetween=27.37, P<.001). The results of the meta-regression analysis using age, gender, and culture as moderators indicated that only cultural difference in the relationship between Eastern and Western culture was significant (Qbetween=6.69; P=.01). All funnel plots and p-curve analyses showed no evidence of publication and selection bias. Conclusions: Despite some variability, the study overall confirms the correlation between increased electronic media use and poorer sleep outcomes, which is notably more significant in Eastern cultures. ",
doi="10.2196/48356",
url="https://www.jmir.org/2024/1/e48356",
url="http://www.ncbi.nlm.nih.gov/pubmed/38533835"
}


@Article{info:doi/10.2196/55762,
author="Bragazzi, Luigi Nicola
and Garbarino, Sergio",
title="Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="16",
volume="8",
pages="e55762",
keywords="sleep",
keywords="sleep health",
keywords="sleep-related disbeliefs",
keywords="generative conversational artificial intelligence",
keywords="chatbot",
keywords="ChatGPT",
keywords="misinformation",
keywords="artificial intelligence",
keywords="comparative study",
keywords="expert analysis",
keywords="adequate sleep",
keywords="well-being",
keywords="sleep trackers",
keywords="sleep health education",
keywords="sleep-related",
keywords="chronic disease",
keywords="healthcare cost",
keywords="sleep timing",
keywords="sleep duration",
keywords="presleep behaviors",
keywords="sleep experts",
keywords="healthy behavior",
keywords="public health",
keywords="conversational agents",
abstract="Background: Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths. Objective: This study aims to examine ChatGPT's capability to debunk sleep-related disbeliefs. Methods: A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted. Results: ChatGPT labeled a significant portion (n=17, 85\%) of the statements as ``false'' (n=9, 45\%) or ``generally false'' (n=8, 40\%), with varying accuracy across different domains. For instance, it correctly identified most myths about ``sleep timing,'' ``sleep duration,'' and ``behaviors during sleep,'' while it had varying degrees of success with other categories such as ``pre-sleep behaviors'' and ``brain function and sleep.'' ChatGPT's assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85\% and a positive predictive value of 100\%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT's appraisals and expert opinions could be found, suggesting that the AI's ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations. Conclusions: ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors. ",
doi="10.2196/55762",
url="https://formative.jmir.org/2024/1/e55762",
url="http://www.ncbi.nlm.nih.gov/pubmed/38501898"
}


@Article{info:doi/10.2196/51901,
author="Roberge, Patrice
and Ruel, Jean
and B{\'e}gin-Drolet, Andr{\'e}
and Lemay, Jean
and Gakwaya, Simon
and Masse, Jean-Fran{\c{c}}ois
and S{\'e}ri{\`e}s, Fr{\'e}d{\'e}ric",
title="Preliminary Assessment of an Ambulatory Device Dedicated to Upper Airway Muscle Training in Patients With Sleep Apnea: Proof-of-Concept Study",
journal="JMIR Biomed Eng",
year="2024",
month="Apr",
day="15",
volume="9",
pages="e51901",
keywords="obstructive sleep apnea/hypopnea syndrome",
keywords="OSAHS",
keywords="myofunctional therapy",
keywords="myotherapy",
keywords="oral",
keywords="orofacial",
keywords="myology",
keywords="musculature",
keywords="labial",
keywords="buccal",
keywords="lingual",
keywords="speech therapy",
keywords="physiotherapy",
keywords="physical therapy",
keywords="oropharyngeal exercises",
keywords="oropharyngeal",
keywords="pharyngeal",
keywords="pharynx",
keywords="hypopnea",
keywords="lip",
keywords="home-based",
keywords="portable device",
keywords="devices",
keywords="ambulatory",
keywords="portable",
keywords="monitoring",
keywords="apnea",
keywords="mouth",
keywords="lips",
keywords="tongue",
keywords="facial",
keywords="exercise",
keywords="exercises",
keywords="myofunctional",
keywords="continuous monitoring",
keywords="sleep-disordered breathing",
keywords="sleep",
keywords="breathing",
keywords="tongue exercise",
keywords="lip exercise",
keywords="mHealth",
keywords="muscle",
keywords="muscles",
keywords="muscular",
keywords="airway",
keywords="sleep apnea",
abstract="Background: Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a prevalent condition affecting a substantial portion of the global population, with its prevalence increasing over the past 2 decades. OSAHS is characterized by recurrent upper airway (UA) closure during sleep, leading to significant impacts on quality of life and heightened cardiovascular and metabolic morbidity. Despite continuous positive airway pressure (CPAP) being the gold standard treatment, patient adherence remains suboptimal due to various factors, such as discomfort, side effects, and treatment unacceptability. Objective: Considering the challenges associated with CPAP adherence, an alternative approach targeting the UA muscles through myofunctional therapy was explored. This noninvasive intervention involves exercises of the lips, tongue, or both to improve oropharyngeal functions and mitigate the severity of OSAHS. With the goal of developing a portable device for home-based myofunctional therapy with continuous monitoring of exercise performance and adherence, the primary outcome of this study was the degree of completion and adherence to a 4-week training session. Methods: This proof-of-concept study focused on a portable device that was designed to facilitate tongue and lip myofunctional therapy and enable precise monitoring of exercise performance and adherence. A clinical study was conducted to assess the effectiveness of this program in improving sleep-disordered breathing. Participants were instructed to perform tongue protrusion, lip pressure, and controlled breathing as part of various tasks 6 times a week for 4 weeks, with each session lasting approximately 35 minutes. Results: Ten participants were enrolled in the study (n=8 male; mean age 48, SD 22 years; mean BMI 29.3, SD 3.5 kg/m2; mean apnea-hypopnea index [AHI] 20.7, SD 17.8/hour). Among the 8 participants who completed the 4-week program, the overall compliance rate was 91\% (175/192 sessions). For the tongue exercise, the success rate increased from 66\% (211/320 exercises; SD 18\%) on the first day to 85\% (272/320 exercises; SD 17\%) on the last day (P=.05). AHI did not change significantly after completion of training but a noteworthy correlation between successful lip exercise improvement and AHI reduction in the supine position was observed (Rs=--0.76; P=.03). These findings demonstrate the potential of the device for accurately monitoring participants' performance in lip and tongue pressure exercises during myofunctional therapy. The diversity of the training program (it mixed exercises mixed training games), its ability to provide direct feedback for each exercise to the participants, and the easy measurement of treatment adherence are major strengths of our training program. Conclusions: The study's portable device for home-based myofunctional therapy shows promise as a noninvasive alternative for reducing the severity of OSAHS, with a notable correlation between successful lip exercise improvement and AHI reduction, warranting further development and investigation. ",
doi="10.2196/51901",
url="https://biomedeng.jmir.org/2024/1/e51901",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875673"
}


@Article{info:doi/10.2196/52652,
author="Gabb, Grace Victoria
and Blackman, Jonathan
and Morrison, Duncan Hamish
and Biswas, Bijetri
and Li, Haoxuan
and Turner, Nicholas
and Russell, M. Georgina
and Greenwood, Rosemary
and Jolly, Amy
and Trender, William
and Hampshire, Adam
and Whone, Alan
and Coulthard, Elizabeth",
title="Remote Evaluation of Sleep and Circadian Rhythms in Older Adults With Mild Cognitive Impairment and Dementia: Protocol for a Feasibility and Acceptability Mixed Methods Study",
journal="JMIR Res Protoc",
year="2024",
month="Mar",
day="22",
volume="13",
pages="e52652",
keywords="feasibility",
keywords="sleep",
keywords="mild cognitive impairment",
keywords="dementia",
keywords="Lewy body disease",
keywords="Alzheimer disease",
keywords="Parkinson",
keywords="wearable devices",
keywords="research",
keywords="mobile phone",
keywords="electroencephalography",
keywords="accelerometery",
keywords="mobile applications",
keywords="application",
keywords="app",
keywords="cognitive",
keywords="cognitive impairment",
keywords="sleeping",
keywords="sleep disturbance",
keywords="risk factor",
keywords="Alzheimer",
keywords="wearable",
keywords="wearables",
keywords="acceptability",
keywords="smart device",
abstract="Background: Sleep disturbances are a potentially modifiable risk factor for neurodegenerative dementia secondary to Alzheimer disease (AD) and Lewy body disease (LBD). Therefore, we need to identify the best methods to study sleep in this population. Objective: This study will assess the feasibility and acceptability of various wearable devices, smart devices, and remote study tasks in sleep and cognition research for people with AD and LBD. Methods: We will deliver a feasibility and acceptability study alongside a prospective observational cohort study assessing sleep and cognition longitudinally in the home environment. Adults aged older than 50 years who were diagnosed with mild to moderate dementia or mild cognitive impairment (MCI) due to probable AD or LBD and age-matched controls will be eligible. Exclusion criteria include lack of capacity to consent to research, other causes of MCI or dementia, and clinically significant sleep disorders. Participants will complete a cognitive assessment and questionnaires with a researcher and receive training and instructions for at-home study tasks across 8 weeks. At-home study tasks include remote sleep assessments using wearable devices (electroencephalography headband and actigraphy watch), app-based sleep diaries, online cognitive assessments, and saliva samples for melatonin- and cortisol-derived circadian markers. Feasibility outcomes will be assessed relating to recruitment and retention, data completeness, data quality, and support required. Feedback on acceptability and usability will be collected throughout the study period and end-of-study interviews will be analyzed using thematic analysis. Results: Recruitment started in February 2022. Data collection is ongoing, with final data expected in February 2024 and data analysis and publication of findings scheduled for the summer of 2024. Conclusions: This study will allow us to assess if remote testing using smart devices and wearable technology is a viable alternative to traditional sleep measurements, such as polysomnography and questionnaires, in older adults with and without MCI or dementia due to AD or LBD. Understanding participant experience and the barriers and facilitators to technology use for research purposes and remote research in this population will assist with the development of, recruitment to, and retention within future research projects studying sleep and cognition outside of the clinic or laboratory. International Registered Report Identifier (IRRID): DERR1-10.2196/52652 ",
doi="10.2196/52652",
url="https://www.researchprotocols.org/2024/1/e52652",
url="http://www.ncbi.nlm.nih.gov/pubmed/38517469"
}


@Article{info:doi/10.2196/53347,
author="Xie, Fangfang
and You, Yanli
and Gu, Yuanjia
and Xu, Jiatuo
and Yao, Fei",
title="Effects of the Prolong Life With Nine Turn-Method Qigong on Fatigue, Insomnia, Anxiety, and Gastrointestinal Disorders in Patients With Chronic Fatigue Syndrome: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Feb",
day="26",
volume="13",
pages="e53347",
keywords="chronic fatigue syndrome",
keywords="prolong life with nine turn method Qigong",
keywords="fMRI",
keywords="gut microbiota",
keywords="gastrointestinal",
keywords="fatigue",
keywords="insomnia",
keywords="CFS",
keywords="study protocol",
keywords="Qigong",
keywords="efficacy",
keywords="safety",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="randomized trial",
abstract="Background: Chronic fatigue syndrome (CFS) is a debilitating multisystem disorder that can lead to various pathophysiological abnormalities and symptoms, including insomnia, gastrointestinal disorders, and anxiety. Due to the side effects of currently available drugs, there is a growing need for safe and effective nondrug therapies. The Prolong Life With Nine Turn (PLWNT) Qigong method is a system of mind-body exercise with restorative benefits that can alleviate the clinical symptoms of CFS and impart a significant inhibitory effect. Various studies have proven the treatment efficacy of PLWNT; however, the impact on insomnia, gastrointestinal disorders, and anxiety in patients with CFS has not yet been?investigated. Objective: This study aims to evaluate the efficacy and safety of the PLWNT method in terms of its effects on fatigue, insomnia, anxiety, and gastrointestinal symptoms in patients with CFS. Methods: We will conduct a randomized, analyst-blinded, parallel-controlled trial with a 12-week intervention and 8-week follow-up. A total of 208 patients of age 20-60 years will be recruited. The patients will be randomly divided into a PLWNT Qigong exercise group (PLWNT Group) and a control group treated with cognitive behavioral therapy at a ratio of 1:1. Participants from the treatment groups will be taught by a highly qualified professor at the Shanghai University of Traditional Chinese Medicine once a week and will be supervised via web during the remaining 6 days at home, over 12 consecutive weeks. The primary outcome will be the Multidimensional Fatigue Inventory 20, while the secondary outcomes include the Pittsburgh Sleep Quality Index, Gastrointestinal Symptom Rating Scale, Hospital Anxiety and Depression Scale, functional magnetic resonance imaging, gut microbiota, and peripheral blood. Results: The study was approved by the ethics committee of Shanghai Municipal Hospital of Traditional Chinese Medicine in March 2022 (Ethics Approval Number 2022SHL-KY-05). Recruitment started in July 2022. The intervention is scheduled to be completed in December 2024, and data collection will be completed by the end of January 2025. Over the 3-year recruitment period, 208 participants will be recruited. Data management is still in progress; therefore, data analysis has yet to be performed. Conclusions: This randomized trial will evaluate the effectiveness of the PLWNT method in relieving fatigue, insomnia, anxiety, and gastrointestinal symptoms in patients with CFS. If proven effective, it will provide a promising alternative intervention for patients with CFS. Trial Registration: China Clinical Trials Registry ChiCTR2200061229; https://www.chictr.org.cn/showproj.html?proj=162803 International Registered Report Identifier (IRRID): PRR1-10.2196/53347 ",
doi="10.2196/53347",
url="https://www.researchprotocols.org/2024/1/e53347",
url="http://www.ncbi.nlm.nih.gov/pubmed/38407950"
}


@Article{info:doi/10.2196/52905,
author="Jiang, Shan
and Ng, Y. Johan Y.
and Chong, Hau Kar
and Peng, Bo
and Ha, S. Amy",
title="Effects of eHealth Interventions on 24-Hour Movement Behaviors Among Preschoolers: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2024",
month="Feb",
day="21",
volume="26",
pages="e52905",
keywords="preschooler",
keywords="movement behaviors",
keywords="eHealth",
keywords="physical activity",
keywords="sedentary behavior",
keywords="sleep",
keywords="mobile phone",
keywords="review",
keywords="systematic review",
abstract="Background: The high prevalence of unhealthy movement behaviors among young children remains a global public health issue. eHealth is considered a cost-effective approach that holds great promise for enhancing health and related behaviors. However, previous research on eHealth interventions aimed at promoting behavior change has primarily focused on adolescents and adults, leaving a limited body of evidence specifically pertaining to preschoolers. Objective: This review aims to examine the effectiveness of eHealth interventions in promoting 24-hour movement behaviors, specifically focusing on improving physical activity (PA) and sleep duration and reducing sedentary behavior among preschoolers. In addition, we assessed the moderating effects of various study characteristics on intervention effectiveness. Methods: We searched 6 electronic databases (PubMed, Ovid, SPORTDiscus, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials) for experimental studies with a randomization procedure that examined the effectiveness of eHealth interventions on 24-hour movement behaviors among preschoolers aged 2 to 6 years in February 2023. The study outcomes included PA, sleep duration, and sedentary time. A meta-analysis was conducted to assess the pooled effect using a random-effects model, and subgroup analyses were conducted to explore the potential effects of moderating factors such as intervention duration, intervention type, and risk of bias (ROB). The included studies underwent a rigorous ROB assessment using the Cochrane ROB tool. Moreover, the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment. Results: Of the 7191 identified records, 19 (0.26\%) were included in the systematic review. The meta-analysis comprised a sample of 2971 preschoolers, which was derived from 13 included studies. Compared with the control group, eHealth interventions significantly increased moderate to vigorous PA (Hedges g=0.16, 95\% CI 0.03-0.30; P=.02) and total PA (Hedges g=0.37, 95\% CI 0.02-0.72; P=.04). In addition, eHealth interventions significantly reduced sedentary time (Hedges g=?0.15, 95\% CI ?0.27 to ?0.02; P=.02) and increased sleep duration (Hedges g=0.47, 95\% CI 0.18-0.75; P=.002) immediately after the intervention. However, no significant moderating effects were observed for any of the variables assessed (P>.05). The quality of evidence was rated as ``moderate'' for moderate to vigorous intensity PA and sedentary time outcomes and ``low'' for sleep outcomes. Conclusions: eHealth interventions may be a promising strategy to increase PA, improve sleep, and reduce sedentary time among preschoolers. To effectively promote healthy behaviors in early childhood, it is imperative for future studies to prioritize the development of rigorous comparative trials with larger sample sizes. In addition, researchers should thoroughly examine the effects of potential moderators. There is also a pressing need to comprehensively explore the long-term effects resulting from these interventions. Trial Registration: PROSPERO CRD42022365003; http://tinyurl.com/3nnfdwh3 ",
doi="10.2196/52905",
url="https://www.jmir.org/2024/1/e52905",
url="http://www.ncbi.nlm.nih.gov/pubmed/38381514"
}


@Article{info:doi/10.2196/51862,
author="Bjelkar{\o}y, Torheim Maria
and Simonsen, Breines Tone
and Siddiqui, Ghazal Tahreem
and Halset, Sigrid
and Cheng, Socheat
and Grambaite, Ramune
and Benth, {\vS}altyt? J?rat?
and Gerwing, Jennifer
and Kristoffersen, Saxhaug Espen
and Lundqvist, Christofer",
title="Brief Intervention as a Method to Reduce Z-Hypnotic Use by Older Adults: Feasibility Case Series",
journal="JMIR Form Res",
year="2024",
month="Feb",
day="8",
volume="8",
pages="e51862",
keywords="prescription medication misuse",
keywords="older adults",
keywords="brief intervention",
keywords="z-drugs",
keywords="benzodiazepine-related drugs",
keywords="BZD-related drugs",
keywords="z-hypnotic",
keywords="intervention",
keywords="feasibility",
keywords="case series",
keywords="insomnia",
keywords="sleep",
keywords="substance overuse",
keywords="older adult",
keywords="treatment",
keywords="reduction",
keywords="benzodiazepine",
keywords="hypnotics",
abstract="Background: Z-hypnotics or z-drugs are commonly prescribed for insomnia and sleep difficulties in older adults. These drugs are associated with adverse events and dependence and are not recommended for long-term use. Despite evidence of older adults being more sensitive to a wide array of adverse events and clinical guidelines advocating limiting use, inappropriate use in this population is still prevalent. Previous intervention studies have focused mainly on prescriber information. Simple, individually focused intervention designs are less studied. Brief intervention (BI) is a simple, easily transferable method mainly used to treat patients at risk of alcohol overuse. Objective: Our objective was to design and test the feasibility and acceptability of a BI intervention adapted to address individual, inappropriate use of z-hypnotics among older adults. This preparatory study aimed to optimize the intervention in advance of a quantitative randomized controlled trial investigating the treatment effect in a larger population. Methods: This feasibility case series was conducted at Akershus University Hospital, Norway, in autumn 2021. We included 5 adults aged ?65 years with long-term (?4 weeks) use of z-hypnotics and 2 intervening physicians. Additionally, 2 study investigators contributed with process evaluation notes. The BI consists of information on the risk of inappropriate use and individualized advice on how to reduce use. The focus of the intervention is behavioral and aims, in cooperation with the patient and based on shared decision-making, to change patient behavior regarding sleep medication rather than physician-based detoxification and termination of z-hypnotic prescriptions. Qualitative and descriptive quantitative data were collected from intervening physicians, study investigators, and participants at baseline, immediately after the intervention, and at the 6-week follow-up. Results: Data were obtained from 2 physicians, 2 study investigators, and 5 participants (4 women) with a median age of 84 years. The average time spent on the BI consultation was 15 minutes. All 5 participants completed the intervention without problems. The participants and 2 intervening physicians reported the intervention as acceptable and were satisfied with the delivery of the intervention. After the intervention, 2 participants stopped their use of z-hypnotics completely and participated in the follow-up interview. Study investigators identified logistical challenges regarding location and time requirements. Identified aspects that may improve the intervention and reduce dropouts included revising the intervention content, focusing on rebound insomnia, adding an information leaflet, and supporting the patient in the period between the intervention and follow-up. The notion that the intervention should best be located and conducted by the patient's own general practitioner was supported by the participants. Conclusions: We identified important aspects to improve the designed intervention and found that the BI is feasible and acceptable for incorporation into a larger randomized trial investigating the treatment effect of BI for reducing z-hypnotic use by older adults. Trial Registration: ClinicalTrials.gov NCT03162081; http://tinyurl.com/rmzx6brn ",
doi="10.2196/51862",
url="https://formative.jmir.org/2024/1/e51862",
url="http://www.ncbi.nlm.nih.gov/pubmed/38329779"
}


@Article{info:doi/10.2196/53597,
author="Ahn, Chan-Young
and Lee, Jong-Sun",
title="Digital Phenotyping for Real-Time Monitoring of Nonsuicidal Self-Injury: Protocol for a Prospective Observational Study",
journal="JMIR Res Protoc",
year="2024",
month="Feb",
day="8",
volume="13",
pages="e53597",
keywords="nonsuicidal self-injury",
keywords="NSSI",
keywords="digital phenotyping",
keywords="digital phenotype",
keywords="wearable device",
keywords="wearable",
keywords="wearables",
keywords="wrist worn",
keywords="mood",
keywords="emotion",
keywords="emotions",
keywords="heart rate",
keywords="step",
keywords="sleep",
keywords="machine learning",
keywords="multilevel modeling",
keywords="ecological momentary assessment",
keywords="EMA",
keywords="self-injury",
keywords="self-harm",
keywords="psychiatry",
keywords="psychiatric",
keywords="mental health",
keywords="predict",
keywords="prediction",
keywords="predictions",
keywords="predictor",
keywords="predictors",
keywords="predictive",
abstract="Background: Nonsuicidal self-injury (NSSI) is a major global health concern. The limitations of traditional clinical and laboratory-based methodologies are recognized, and there is a pressing need to use novel approaches for the early detection and prevention of NSSI. Unfortunately, there is still a lack of basic knowledge of a descriptive nature on NSSI, including when, how, and why self-injury occurs in everyday life. Digital phenotyping offers the potential to predict and prevent NSSI by assessing objective and ecological measurements at multiple points in time. Objective: This study aims to identify real-time predictors and explain an individual's dynamic course of NSSI. Methods: This study will use a hybrid approach, combining elements of prospective observational research with non--face-to-face study methods. This study aims to recruit a cohort of 150 adults aged 20 to 29 years who have self-reported engaging in NSSI on 5 or more days within the past year. Participants will be enrolled in a longitudinal study conducted at 3-month intervals, spanning 3 long-term follow-up phases. The ecological momentary assessment (EMA) technique will be used via a smartphone app. Participants will be prompted to complete a self-injury and suicidality questionnaire and a mood appraisal questionnaire 3 times a day for a duration of 14 days. A wrist-worn wearable device will be used to collect heart rate, step count, and sleep patterns from participants. Dynamic structural equation modeling and machine learning approaches will be used. Results: Participant recruitment and data collection started in October 2023. Data collection and analysis are expected to be completed by December 2024. The results will be published in a peer-reviewed journal and presented at scientific conferences. Conclusions: The insights gained from this study will not only shed light on the underlying mechanisms of NSSI but also pave the way for the development of tailored and culturally sensitive treatment options that can effectively address this major mental health concern. International Registered Report Identifier (IRRID): DERR1-10.2196/53597 ",
doi="10.2196/53597",
url="https://www.researchprotocols.org/2024/1/e53597",
url="http://www.ncbi.nlm.nih.gov/pubmed/38329791"
}


@Article{info:doi/10.2196/50836,
author="Liu, Chenan
and Zhang, Qingsong
and Liu, Chenning
and Liu, Tong
and Song, Mengmeng
and Zhang, Qi
and Xie, Hailun
and Lin, Shiqi
and Ren, Jiangshan
and Chen, Yue
and Zheng, Xin
and Shi, Jinyu
and Deng, Li
and Shi, Hanping
and Wu, Shouling",
title="Age Differences in the Association of Sleep Duration Trajectory With Cancer Risk and Cancer-Specific Mortality: Prospective Cohort Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Feb",
day="7",
volume="10",
pages="e50836",
keywords="sleep duration",
keywords="aging",
keywords="cancer risk",
keywords="mortality",
keywords="sleep",
keywords="trajectory",
keywords="adult",
abstract="Background: Baseline sleep duration is associated with cancer risk and cancer-specific mortality; however, the association between longitudinal patterns of sleep duration and these risks remains unknown. Objective: This study aimed to elucidate the association between sleep duration trajectory and cancer risk and cancer-specific mortality. Methods: The participants recruited in this study were from the Kailuan cohort, with all participants aged between 18 and 98 years and without cancer at baseline. The sleep duration of participants was continuously recorded in 2006, 2008, and 2010. Latent mixture modeling was used to identify shared sleep duration trajectories. Furthermore, the Cox proportional risk model was used to examine the association of sleep duration trajectory with cancer risk and cancer-specific mortality. Results: A total of 53,273 participants were included in the present study, of whom 40,909 (76.79\%) were men and 12,364 (23.21\%) were women. The average age of the participants was 49.03 (SD 11.76) years. During a median follow-up of 10.99 (IQR 10.27-11.15) years, 2705 participants developed cancers. Three sleep duration trajectories were identified: normal-stable (44,844/53,273, 84.18\%), median-stable (5877/53,273, 11.03\%), and decreasing low-stable (2552/53,273, 4.79\%). Compared with the normal-stable group, the decreasing low-stable group had increased cancer risk (hazard ratio [HR] 1.39, 95\% CI 1.16-1.65) and cancer-specific mortality (HR 1.54, 95\% CI 1.18-2.06). Dividing the participants by an age cutoff of 45 years revealed an increase in cancer risk (HR 1.88, 95\% CI 1.30-2.71) and cancer-specific mortality (HR 2.52, 95\% CI 1.22-5.19) only in participants younger than 45 years, rather than middle-aged or older participants. Joint analysis revealed that compared with participants who had a stable sleep duration within the normal range and did not snore, those with a shortened sleep duration and snoring had the highest cancer risk (HR 2.62, 95\% CI 1.46-4.70). Conclusions: Sleep duration trajectories and quality are closely associated with cancer risk and cancer-specific mortality. However, these associations differ with age and are more pronounced in individuals aged <45 years. Trial Registration: Chinese Clinical Trial Registry ChiCTR--TNRC--11001489; http://tinyurl.com/2u89hrhx ",
doi="10.2196/50836",
url="https://publichealth.jmir.org/2024/1/e50836",
url="http://www.ncbi.nlm.nih.gov/pubmed/38324354"
}


@Article{info:doi/10.2196/45910,
author="Rajput, Gargi
and Gao, Andy
and Wu, Tzu-Chun
and Tsai, Ching-Tzu
and Molano, Jennifer
and Wu, Y. Danny T.",
title="Sleep Patterns of Premedical Undergraduate Students: Pilot Study and Protocol Evaluation",
journal="JMIR Form Res",
year="2024",
month="Feb",
day="2",
volume="8",
pages="e45910",
keywords="patient-generated health data",
keywords="Fitbit wearables",
keywords="sleep quality",
keywords="premedical college students",
keywords="sleep",
keywords="sleep hygiene",
keywords="student",
keywords="colleges",
keywords="university",
keywords="postsecondary",
keywords="higher education",
keywords="survey",
keywords="sleep pattern",
keywords="medical student",
keywords="adolescence",
keywords="behavior change",
abstract="Background: Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns. Objective: This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students? Methods: An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments. Results: According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI?5) across cohorts. When students perceived no problem at all in their sleep quality, 50\% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26\% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app's feature description was vague and could lead to confusion. Conclusions: Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors. ",
doi="10.2196/45910",
url="https://formative.jmir.org/2024/1/e45910",
url="http://www.ncbi.nlm.nih.gov/pubmed/38306175"
}


@Article{info:doi/10.2196/51212,
author="Hildebrand, Lindsey
and Huskey, Alisa
and Dailey, Natalie
and Jankowski, Samantha
and Henderson-Arredondo, Kymberly
and Trapani, Christopher
and Patel, Imran Salma
and Chen, Yu-Chin Allison
and Chou, Ying-Hui
and Killgore, S. William D.",
title="Transcranial Magnetic Stimulation of the Default Mode Network to Improve Sleep in Individuals With Insomnia Symptoms: Protocol for a Double-Blind Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Jan",
day="26",
volume="13",
pages="e51212",
keywords="continuous theta burst stimulation",
keywords="transcranial magnetic stimulation",
keywords="default mode network",
keywords="sleep",
keywords="insomnia",
keywords="cTBS",
keywords="randomized controlled trial",
abstract="Background: Cortical hyperarousal and ruminative thinking are common aspects of insomnia that have been linked with greater connectivity in the default mode network (DMN). Therefore, disrupting network activity within the DMN may reduce cortical and cognitive hyperarousal and facilitate better sleep. Objective: This trial aims to establish a novel, noninvasive method for treating insomnia through disruption of the DMN with repetitive transcranial magnetic stimulation, specifically with continuous theta burst stimulation (cTBS). This double-blind, pilot randomized controlled trial will assess the efficacy of repetitive transcranial magnetic stimulation as a novel, nonpharmacological approach to improve sleep through disruption of the DMN prior to sleep onset for individuals with insomnia. Primary outcome measures will include assessing changes in DMN functional connectivity before and after stimulation. Methods: A total of 20 participants between the ages of 18 to 50 years with reported sleep disturbances will be recruited as a part of the study. Participants will then conduct an in-person screening and follow-on enrollment visit. Eligible participants then conduct at-home actigraphic collection until their first in-residence overnight study visit. In a double-blind, counterbalanced, crossover study design, participants will receive a 40-second stimulation to the left inferior parietal lobule of the DMN during 2 separate overnight in-residence visits. Participants are randomized to the order in which they receive the active stimulation and sham stimulation. Study participants will undergo a prestimulation functional magnetic resonance imaging scan and a poststimulation functional magnetic resonance imaging scan prior to sleep for each overnight study visit. Sleep outcomes will be measured using clinical polysomnography. After their first in-residence study visit, participants conduct another at-home actigraphic collection before returning for their second in-residence overnight study visit. Results: Our study was funded in September 2020 by the Department of Defense (W81XWH2010173). We completed the enrollment of our target study population in the October 2022 and are currently working on neuroimaging processing and analysis. We aim to publish the results of our study by 2024. Primary neuroimaging outcome measures will be tested using independent components analysis, seed-to-voxel analyses, and region of interest to region of interest analyses. A repeated measures analysis of covariance (ANCOVA) will be used to assess the effects of active and sham stimulation on sleep variables. Additionally, we will correlate changes in functional connectivity to polysomnography-graded sleep. Conclusions: The presently proposed cTBS protocol is aimed at establishing the initial research outcomes of the effects of a single burst of cTBS on disrupting the network connectivity of the DMN to improve sleep. If effective, future work could determine the most effective stimulation sites and administration schedules to optimize this potential intervention for sleep problems. Trial Registration: ClinicalTrials.gov NCT04953559; https://clinicaltrials.gov/ct2/show/NCT04953559 International Registered Report Identifier (IRRID): DERR1-10.2196/51212 ",
doi="10.2196/51212",
url="https://www.researchprotocols.org/2024/1/e51212",
url="http://www.ncbi.nlm.nih.gov/pubmed/38277210"
}


@Article{info:doi/10.2196/49253,
author="Pang, Mingli
and Wang, Jieru
and Zhao, Mingyue
and Chen, Rui
and Liu, Hui
and Xu, Xixing
and Li, Shixue
and Kong, Fanlei",
title="The Migrant-Local Difference in the Relationship Between Social Support, Sleep Disturbance, and Loneliness Among Older Adults in China: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Jan",
day="9",
volume="10",
pages="e49253",
keywords="loneliness",
keywords="social support",
keywords="sleep disturbance",
keywords="older adults",
keywords="migrant-local difference",
keywords="structural equation modeling",
abstract="Background: Driven by the accelerated aging of the population of China, the number of older adults has increased rapidly in the country. Meanwhile, following children, migrant older adults (MOA) have emerged as a vulnerable group in the process of fast urbanization. Existed studies have illustrated the association between social support and loneliness and the relationship between sleep disturbance and loneliness; however, the underlying mechanisms and the migrant-local difference in the association between social support, sleep disturbance, and loneliness have not been identified. Objective: This study aimed to clarify the migrant-local difference in the relationship between social support, sleep disturbance, and loneliness in older adults in China. Methods: Multistage cluster random sampling was used to select participants: 1205 older adults (n=613, 50.9\%, MOA and n=592, 49.1\%, local older adults [LOA]) were selected in Weifang City, China, in August 2021. Loneliness was assessed with the 6-item short-form University of California, Los Angeles Loneliness Scale, social support was evaluated with the Social Support Rating Scale, and sleep disturbance was measured with the Pittsburgh Sleep Quality Index. The chi-square test, t test, and structural equation modeling (SEM) were adopted to explore the migrant-local difference between social support, sleep disturbance, and loneliness among the MOA and LOA. Results: The mean score of loneliness was 8.58 (SD 3.03) for the MOA and 8.00 (SD 2.79) for the LOA. SEM analysis showed that social support exerts a direct negative effect on both sleep disturbance (standardized coefficient=--0.24 in the MOA and --0.20 in the LOA) and loneliness (standardized coefficient=--0.44 in the MOA and --0.40 in the LOA), while sleep disturbance generates a direct positive effect on loneliness (standardized coefficient=0.13 in the MOA and 0.22 in the LOA). Conclusions: Both MOA and LOA have a low level of loneliness, but the MOA show higher loneliness than the LOA. There is a negative correlation between social support and loneliness as well as between social support and sleep disturbance among the MOA and LOA (MOA>LOA), while loneliness is positively associated with sleep disturbance in both populations (MOA<LOA). Measures should be taken by the government, society, and families to increase social support, decrease sleep disturbance, and further reduce the loneliness among older adults, especially the MOA. ",
doi="10.2196/49253",
url="https://publichealth.jmir.org/2024/1/e49253",
url="http://www.ncbi.nlm.nih.gov/pubmed/38194253"
}


@Article{info:doi/10.2196/48044,
author="Lin, Chen
and Chen, I-Ming
and Chuang, Hai-Hua
and Wang, Zih-Wen
and Lin, Hsiao-Han
and Lin, Yu-Hsuan",
title="Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study",
journal="J Med Internet Res",
year="2023",
month="Dec",
day="15",
volume="25",
pages="e48044",
keywords="actigraphy",
keywords="circadian rhythm",
keywords="digital biomarkers",
keywords="human-smartphone interaction",
keywords="insomnia",
keywords="intradaily variability",
keywords="mobile apps",
abstract="Background: The sleep and circadian rhythm patterns associated with smartphone use, which are influenced by mental activities, might be closely linked to sleep quality and depressive symptoms, similar to the conventional actigraphy-based assessments of physical activity. Objective: The primary objective of this study was to develop app-defined circadian rhythm and sleep indicators and compare them with actigraphy-derived measures. Additionally, we aimed to explore the clinical correlations of these indicators in individuals with insomnia and healthy controls. Methods: The mobile app ``Rhythm'' was developed to record smartphone use time stamps and calculate circadian rhythms in 33 patients with insomnia and 33 age- and gender-matched healthy controls, totaling 2097 person-days. Simultaneously, we used standard actigraphy to quantify participants' sleep-wake cycles. Sleep indicators included sleep onset, wake time (WT), wake after sleep onset (WASO), and the number of awakenings (NAWK). Circadian rhythm metrics quantified the relative amplitude, interdaily stability, and intradaily variability based on either smartphone use or physical activity data. Results: Comparisons between app-defined and actigraphy-defined sleep onsets, WTs, total sleep times, and NAWK did not reveal any significant differences (all P>.05). Both app-defined and actigraphy-defined sleep indicators successfully captured clinical features of insomnia, indicating prolonged WASO, increased NAWK, and delayed sleep onset and WT in patients with insomnia compared with healthy controls. The Pittsburgh Sleep Quality Index scores were positively correlated with WASO and NAWK, regardless of whether they were measured by the app or actigraphy. Depressive symptom scores were positively correlated with app-defined intradaily variability ($\beta$=9.786, SD 3.756; P=.01) and negatively correlated with actigraphy-based relative amplitude ($\beta$=--21.693, SD 8.214; P=.01), indicating disrupted circadian rhythmicity in individuals with depression. However, depressive symptom scores were negatively correlated with actigraphy-based intradaily variability ($\beta$=--7.877, SD 3.110; P=.01) and not significantly correlated with app-defined relative amplitude ($\beta$=--3.859, SD 12.352; P=.76). Conclusions: This study highlights the potential of smartphone-derived sleep and circadian rhythms as digital biomarkers, complementing standard actigraphy indicators. Although significant correlations with clinical manifestations of insomnia were observed, limitations in the evidence and the need for further research on predictive utility should be considered. Nonetheless, smartphone data hold promise for enhancing sleep monitoring and mental health assessments in digital health research. ",
doi="10.2196/48044",
url="https://www.jmir.org/2023/1/e48044",
url="http://www.ncbi.nlm.nih.gov/pubmed/38100195"
}


@Article{info:doi/10.2196/44002,
author="Schipp, Jasmine
and Hendrieckx, Christel
and Braune, Katarina
and Knoll, Christine
and O'Donnell, Shane
and Ballhausen, Hanne
and Cleal, Bryan
and W{\"a}ldchen, Mandy
and Lewis, M. Dana
and Gajewska, A. Katarzyna
and Skinner, C. Timothy
and Speight, Jane",
title="Psychosocial Outcomes Among Users and Nonusers of Open-Source Automated Insulin Delivery Systems: Multinational Survey of Adults With Type 1 Diabetes",
journal="J Med Internet Res",
year="2023",
month="Dec",
day="14",
volume="25",
pages="e44002",
keywords="artificial",
keywords="diabetes mellitus",
keywords="hypoglycaemia",
keywords="pancreas",
keywords="patient-reported outcome measures, surveys, and questionnaires",
keywords="quality of life",
keywords="sleep quality",
keywords="type 1",
abstract="Background: Emerging research suggests that open-source automated insulin delivery (AID) may reduce diabetes burden and improve sleep quality and quality of life (QoL). However, the evidence is mostly qualitative or uses unvalidated, study-specific, single items. Validated person-reported outcome measures (PROMs) have demonstrated the benefits of other diabetes technologies. The relative lack of research investigating open-source AID using PROMs has been considered a missed opportunity. Objective: This study aimed to examine the psychosocial outcomes of adults with type 1 diabetes using and not using open-source AID systems using a comprehensive set of validated PROMs in a real-world, multinational, cross-sectional study. Methods: Adults with type 1 diabetes completed 8 validated measures of general emotional well-being (5-item World Health Organization Well-Being Index), sleep quality (Pittsburgh Sleep Quality Index), diabetes-specific QoL (modified DAWN Impact of Diabetes Profile), diabetes-specific positive well-being (4-item subscale of the 28-item Well-Being Questionnaire), diabetes treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire), diabetes distress (20-item Problem Areas in Diabetes scale), fear of hypoglycemia (short form of the Hypoglycemia Fear Survey II), and a measure of the impact of COVID-19 on QoL. Independent groups 2-tailed t tests and Mann-Whitney U tests compared PROM scores between adults with type 1 diabetes using and not using open-source AID. An analysis of covariance was used to adjust for potentially confounding variables, including all sociodemographic and clinical characteristics that differed by use of open-source AID. Results: In total, 592 participants were eligible (attempting at least 1 questionnaire), including 451 using open-source AID (mean age 43, SD 13 years; n=189, 41.9\% women) and 141 nonusers (mean age 40, SD 13 years; n=90, 63.8\% women). Adults using open-source AID reported significantly better general emotional well-being and subjective sleep quality, as well as better diabetes-specific QoL, positive well-being, and treatment satisfaction. They also reported significantly less diabetes distress, fear of hypoglycemia, and perceived less impact of the COVID-19 pandemic on their QoL. All were medium-to-large effects (Cohen d=0.5-1.5). The differences between groups remained significant after adjusting for sociodemographic and clinical characteristics. Conclusions: Adults with type 1 diabetes using open-source AID report significantly better psychosocial outcomes than those not using these systems, after adjusting for sociodemographic and clinical characteristics. Using validated, quantitative measures, this real-world study corroborates the beneficial psychosocial outcomes described previously in qualitative studies or using unvalidated study-specific items. ",
doi="10.2196/44002",
url="https://www.jmir.org/2023/1/e44002",
url="http://www.ncbi.nlm.nih.gov/pubmed/38096018"
}


@Article{info:doi/10.2196/51336,
author="Kawai, Keita
and Iwamoto, Kunihiro
and Miyata, Seiko
and Okada, Ippei
and Fujishiro, Hiroshige
and Noda, Akiko
and Nakagome, Kazuyuki
and Ozaki, Norio
and Ikeda, Masashi",
title="Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study",
journal="J Med Internet Res",
year="2023",
month="Dec",
day="13",
volume="25",
pages="e51336",
keywords="consumer sleep-tracking device",
keywords="polysomnography",
keywords="portable sleep EEG monitor",
keywords="electroencephalography",
keywords="EEG",
keywords="psychiatric disorders",
keywords="sleep logs",
keywords="sleep state misperception",
keywords="sleep study",
keywords="wearable",
keywords="psychiatric disorder",
keywords="sleep",
keywords="disturbances",
keywords="quality of sleep",
keywords="Fitbit",
keywords="mHealth",
keywords="wearables",
keywords="psychiatry",
keywords="electroencephalogram",
abstract="Background: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. Objective: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. Methods: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. Results: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=--0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=--0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. Conclusions: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders. ",
doi="10.2196/51336",
url="https://www.jmir.org/2023/1/e51336",
url="http://www.ncbi.nlm.nih.gov/pubmed/38090797"
}


@Article{info:doi/10.2196/48713,
author="Kulkarni, A. Vishnutheertha
and Mojica, Isaiah
and Gamsarian, Vahram
and Tahjian, Michelle
and Liu, David
and Grewal, Tjinder
and Liu, Yuyang
and Sivesind, E. Torunn
and Lio, Peter",
title="Integrative Approaches to Sleep Management in Skin Disease: Systematic Review",
journal="JMIR Dermatol",
year="2023",
month="Dec",
day="13",
volume="6",
pages="e48713",
keywords="sleep",
keywords="dermatology",
keywords="atopic dermatitis",
keywords="chronic idiopathic urticaria",
keywords="quality of life",
keywords="literature review",
keywords="parameter",
keywords="teledermatology",
keywords="dermatologist",
keywords="skin",
keywords="epidermis",
keywords="review",
keywords="polysomnography",
keywords="polysomnographic",
keywords="sleep medicine",
abstract="Background: Dermatological conditions, especially when severe, can lead to sleep disturbances that affect a patient's quality of life. However, limited research exists on the efficacy of treatments for improving sleep parameters in skin conditions. Objective: The objective was to perform a systematic review of the literature on dermatological conditions and the treatments available for improving sleep parameters. Methods: A literature review was performed using the PubMed, Ovid MEDLINE, Embase, Cochrane, and ClinicalTrials.gov databases from 1945 to 2021. After filtering based on our exclusion criteria, studies were graded using the SORT (Strength of Recommendation Taxonomy) algorithm, and only those receiving a grade of ``2'' or better were included. Results: In total, 25 treatment studies (n=11,025) assessing sleep parameters related to dermatological conditions were found. Dupilumab appeared to be the best-supported and most effective treatment for improving sleep in atopic dermatitis (AD) but had frequent adverse effects. Topical treatments for AD were mostly ineffective, but procedural treatments showed some promise. Treatments for other conditions appeared efficacious. Conclusions: The evaluation of sleep parameter changes in dermatological treatments is predominantly restricted to AD. Systemic interventions such as dupilumab and procedural interventions were the most efficacious. Sleep changes in other dermatoses were limited by a paucity of available studies. The inclusion of a sleep assessment component to a broader range of dermatological treatment studies is warranted. ",
doi="10.2196/48713",
url="https://derma.jmir.org/2023/1/e48713",
url="http://www.ncbi.nlm.nih.gov/pubmed/38090791"
}


@Article{info:doi/10.2196/53501,
author="Xie, Chaoqun
and Xie, Fangfang
and Ma, Jianwen
and Yue, Hongyu
and You, Yanli
and Yao, Fei",
title="Effect of Baduanjin Qigong on Sleep Quality and Hyperarousal State in Adults With Chronic Insomnia: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Dec",
day="12",
volume="12",
pages="e53501",
keywords="Baduanjin qigong",
keywords="chronic insomnia",
keywords="functional magnetic resonance imaging",
keywords="hyperarousal",
keywords="randomized controlled trial",
abstract="Background: Chronic insomnia (CI) is a mind-body disease that is commonly defined as a state of having disturbed daytime activities due to poor nighttime sleep quality. Baduanjin qigong (BDJQG) is widely used for CI in China. However, there is little scientific evidence to evaluate its effects on the hyperarousal state, which is closely associated with improved sleep quality. Objective: The objective of the trial is to assess the therapeutic effects of BDJQG on sleep quality in patients with CI. Methods: A randomized controlled trial will be conducted on 86 patients, who will be divided into a BDJQG group and a cognitive behavioral therapy for insomnia group at a ratio of 1:1. Interventions in both groups will be given to the participants 7 times a week for 8 weeks, and the participants will be followed up for 4 weeks. The primary outcome is the change in the Pittsburgh Sleep Quality Index from baseline to week 8. The secondary outcomes are the changes in the Hyperarousal Scale, Insomnia Severity Index, Fatigue Scale-14, wrist actigraphy, salivary cortisol level, and functional magnetic resonance imaging from baseline to week 8. All main analyses will be carried out on the basis of the intention-to-treat principle. Results: This study was funded from January 2023. As of the submission of the manuscript, there were 86 participants. Data collection began in April 2023 and will end in January 2024. Data analysis is expected to begin in January 2024, with the publication of results expected in February 2024. Conclusions: This study will present data concerning the clinical effects of BDJQG on CI. The results will help to demonstrate whether BDJQG is an effective therapy for improving sleep quality in association with a decreased hyperarousal level as a possible underlying mechanism. This study will provide much-needed knowledge for complementary and alternative therapy for patients with CI. Trial Registration: China Clinical Registration Agency ChiCTR2300069241; https://chictr.org.cn/bin/project/ChiCTR2300069241 International Registered Report Identifier (IRRID): PRR1-10.2196/53501 ",
doi="10.2196/53501",
url="https://www.researchprotocols.org/2023/1/e53501",
url="http://www.ncbi.nlm.nih.gov/pubmed/38085570"
}


@Article{info:doi/10.2196/52315,
author="Michaud, L. Alexis
and Bice, Briana
and Miklos, Eva
and McCormick, Katherine
and Medeiros-Nancarrow, Cheryl
and Zhou, S. Eric
and Recklitis, J. Christopher",
title="Sleep Treatment Education Program for Young Adult Cancer Survivors (STEP-YA): Protocol for an Efficacy Trial",
journal="JMIR Res Protoc",
year="2023",
month="Nov",
day="29",
volume="12",
pages="e52315",
keywords="insomnia",
keywords="cancer survivors",
keywords="young adults",
keywords="protocol",
keywords="coaching",
keywords="mood",
abstract="Background: Young adult cancer survivors (YACS) are at elevated risk for chronic insomnia, even years after completing treatment. In addition to potential health consequences, insomnia can interrupt social, educational, and vocational development just as they are trying to ``make up'' for time lost to cancer. Cognitive behavioral therapy for insomnia (CBTI) is recommended as first-line treatment for insomnia but remains largely unavailable to YACS due to several barriers (ie, shortage of trained providers, geographic limitations, financial limitations). Traditional CBTI has not been adapted to meet YACS' unique developmental and circadian challenges. To improve availability of effective behavioral insomnia treatment for this population, we developed the Sleep Treatment Education Program for Young Adult Cancer Survivors (STEP-YA), a low-intensity educational intervention delivered virtually online. Objective: In this phase 2 ``proof of concept'' trial, primary aims are to test the efficacy of STEP-YA to improve insomnia symptoms and mood in YACS and assess the utility of individualized coaching to improve treatment effects. A secondary aim will explore participant variables associated with clinically significant response to STEP-YA. Methods: This 2-arm randomized prospective trial will enroll 74 off-treatment YACS aged 20 years to 39 years with clinically significant insomnia. Each participant completes the STEP-YA intervention in a 1-on-1 synchronous online session led by a trained interventionist following a structured outline. The 90-minute intervention presents educational information on the development of insomnia after cancer and offers specific suggestions for improving insomnia symptoms. During the session, participants review the suggestions and develop a personalized sleep action plan for implementing them. After the session, participants are randomized to either the coaching condition, in which they receive 2 telephone coaching sessions, or the no-coaching condition, which offers no subsequent coaching. The Insomnia Severity Index (ISI) and the Profile of Mood States: Short Form (POMS-SF) are assessed at baseline and 4 and 8 weeks postintervention. Results: Enrollment began in November 2022, with 28 participants currently enrolled. We anticipate recruitment will be completed in 2024. The primary endpoint is a change in ISI score from baseline to 8 weeks postintervention. The secondary endpoint is change in mood symptoms (POMS-SF) from baseline to 8 weeks postintervention. Change scores will be treated as continuous variables. Primary analyses will use ANOVA methods. A within-subjects analysis will examine if the STEP-YA intervention is associated with significant changes in insomnia and mood over time. A 2-way ANOVA will be used to evaluate the utility of coaching sessions to improve treatment effects. Conclusions: Chronic insomnia has significant negative effects on YACS' medical, educational, and psychological functioning. STEP-YA aims to address their needs; study results will determine if the intervention warrants future effectiveness and dissemination studies and if individualized coaching is necessary for adequate treatment response. Trial Registration: ClinicalTrials.gov NCT05358951: https://clinicaltrials.gov/study/NCT05358951 International Registered Report Identifier (IRRID): DERR1-10.2196/52315 ",
doi="10.2196/52315",
url="https://www.researchprotocols.org/2023/1/e52315/"
}


@Article{info:doi/10.2196/50516,
author="Nazem, Sarra
and Barnes, M. Sean
and Forster, E. Jeri
and Hostetter, A. Trisha
and Monteith, L. Lindsey
and Kramer, B. Emily
and Gaeddert, A. Laurel
and Brenner, A. Lisa",
title="Efficacy of an Internet-Delivered Intervention for Improving Insomnia Severity and Functioning in Veterans: Randomized Controlled Trial",
journal="JMIR Ment Health",
year="2023",
month="Nov",
day="24",
volume="10",
pages="e50516",
keywords="cognitive behavioral therapy",
keywords="insomnia",
keywords="internet intervention",
keywords="online intervention",
keywords="randomized controlled trial",
keywords="RCT",
keywords="RCTs",
keywords="sleep",
keywords="treatment",
keywords="veteran",
keywords="veterans",
keywords="veterans' health",
abstract="Background: Despite a growing evidence base that internet-delivered cognitive behavioral therapy for insomnia (iCBT-I) is associated with decreased insomnia severity, its efficacy has been minimally examined in veterans. Objective: The objective of this study was to evaluate the efficacy of an unguided iCBT-I (Sleep Healthy Using the Internet [SHUTi]) among veterans. Methods: We conducted a single-blind, randomized controlled trial in Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn veterans eligible for Veterans Health Administration care. Participants were randomly assigned (1:1) to receive SHUTi (a self-guided and interactive program) or an Insomnia Education Website (IEW) that provided nontailored and fixed insomnia information. Web-based assessments were administered at baseline, postintervention, 6 months postintervention, and 1 year postintervention. The primary outcome was self-reported insomnia severity (Insomnia Severity Index [ISI]). Secondary outcomes were self-reported mental and physical health functioning (Veterans RAND 36-item Health Survey). Exploratory outcomes comprised sleep diary parameters. Results: Of the 231 randomized participants (mean age 39.3, SD 7.8 years; 170/231, 73.5\% male sex; 26/231, 11.3\% Black; 172/231, 74.5\% White; 10/231, 4.3\% multiracial; and 17/231, 7.4\% other; 36/231, 15.6\% Hispanic) randomized between April 2018 and January 2019, a total of 116 (50.2\%) were randomly assigned to SHUTi and 115 (49.8\%) to the IEW. In intent-to-treat analyses, SHUTi participants experienced significantly larger ISI decreases compared with IEW participants at all time points (generalized $\eta$2 values of 0.13, 0.12, and 0.10, respectively; all P<.0001). These corresponded to estimated larger differences in changes of --3.47 (95\% CI --4.78 to --2.16), --3.80 (95\% CI --5.34 to --2.27), and --3.42 (95\% CI --4.97 to 1.88) points on the ISI for the SHUTi group. SHUTi participants experienced significant improvements in physical (6-month generalized $\eta$2=0.04; P=.004) and mental health functioning (6-month and 1-year generalized $\eta$2=0.04; P=.009 and P=.005, respectively). Significant sleep parameter improvements were noted for SHUTi (all P<.05), though the pattern and magnitude of these reductions varied by parameter. No adverse events were reported. Conclusions: Self-administered iCBT-I was associated with immediate and long-term improvements in insomnia severity. Findings suggest that leveraging technology to meet insomnia treatment demands among veterans may be a promising approach. Trial Registration: ClinicalTrials.gov NCT03366870; https://clinicaltrials.gov/ct2/show/NCT03366870 ",
doi="10.2196/50516",
url="https://mental.jmir.org/2023/1/e50516",
url="http://www.ncbi.nlm.nih.gov/pubmed/37999953"
}


@Article{info:doi/10.2196/49144,
author="Jang, Hyunchul
and Lee, Siwoo
and Son, Yunhee
and Seo, Sumin
and Baek, Younghwa
and Mun, Sujeong
and Kim, Hoseok
and Kim, Icktae
and Kim, Junho",
title="Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Nov",
day="21",
volume="11",
pages="e49144",
keywords="sleep",
keywords="sleep time",
keywords="chat",
keywords="self-report",
keywords="sleep log",
keywords="sleep diary",
keywords="wearables",
keywords="Fitbit",
keywords="patient-generated health data",
keywords="PGHD",
abstract="Background: Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices. Objective: To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data. Methods: Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared. Results: In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within {\textpm}30 and {\textpm}60 minutes of the Fitbit TST-WASO was 50.7\% and 74.3\%, respectively. Conclusions: The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices. ",
doi="10.2196/49144",
url="https://mhealth.jmir.org/2023/1/e49144",
url="http://www.ncbi.nlm.nih.gov/pubmed/37988148"
}


@Article{info:doi/10.2196/51767,
author="Wang, Huixian
and Yu, Xintong
and Hu, Jing
and Zheng, Yanting
and Hu, Jia
and Sun, Xuqiu
and Ren, Ying
and Chen, Yunfei",
title="Effect of Electroacupuncture Versus Cognitive Behavioral Therapy for Perimenopausal Insomnia: Protocol for a Noninferiority Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Nov",
day="9",
volume="12",
pages="e51767",
keywords="perimenopausal insomnia",
keywords="acupuncture",
keywords="electroacupuncture",
keywords="cognitive behavioral therapy",
keywords="randomized controlled trial",
keywords="CBT",
keywords="sleep disorder",
keywords="insomnia",
keywords="perimenoupause",
keywords="effectiveness",
abstract="Background: Perimenopausal insomnia (PMI) has a high global incidence, which is common in middle-aged women and is more severe than nonmenopausal insomnia. Effective treatments with fewer side effects and more consistent repeatable results are needed. Acupuncture, a therapy based on traditional Chinese medicine, is safe and may be effective for PMI. It is widely accepted in Western countries, and evidence supports the use of acupuncture as a main or supplementary therapy. Cognitive behavioral therapy is also used to improve sleep quality. It has structured sessions and has been recommended as a first-line treatment for insomnia (cognitive behavioral therapy for insomnia [CBT-I]) by the American Association of Physicians. However, few randomized controlled trials have been conducted to compare the effectiveness of these 2 therapies. This study will be performed in perimenopausal women with insomnia to determine the efficacy of electroacupuncture (EA) versus CBT-I. Objective: This study aimed to compare the preliminary effectiveness and safety of EA and CBT-I for PMI through a randomized controlled noninferiority study design. Methods: This study is designed as an assessor-blinded, noninferiority, randomized controlled trial. A total of 160 eligible participants with PMI will be randomly divided into 2 groups to receive either EA or CBT-I. Participants in the EA group will receive electroacupuncture for 8 weeks. The intervention will be delivered 3 times weekly for a total of 12 sessions and 2 times weekly for the next 4 weeks. Meanwhile, participants in the control group will undergo CBT-I (once a week) for 8 weeks. Treatment will use 7 main acupoints (GV20, DU24, EX-HN3, EX-HN18, EX-CA1, RN6, and RN4) and an extra 4 acupoints based on syndrome differentiation. The primary outcome is the Insomnia Severity Index. The secondary outcome measures are the Pittsburgh Sleep Quality Index; Menopause-Specific Quality of Life; Menopause Rating Scale; Hamilton Depression Scale; Hamilton Anxiety Scale; hot flash score; and the level of estradiol, follicle-stimulating hormone, and luteinizing hormone in serum. Sleep architecture will be assessed using polysomnograms. Results: Participants are currently being recruited. The first participant was enrolled in January 2023, marking the initiation of the recruitment phase. The recruitment process is expected to continue until January 2025, at which point data collection will commence. Conclusions: This trial represents a pioneering effort to investigate the efficacy and safety of EA and CBT-I as interventions for PMI. It is noteworthy that this study is conducted solely within a single center and involves Chinese participants, which is a limitation. Nonetheless, the findings of this study are expected to contribute valuable insights for clinicians engaged in the management of PMI. Trial Registration: Chinese Clinical Trial Registry ChiCTR2300070981; https://www.chictr.org.cn/showprojEN.html?proj=194561 International Registered Report Identifier (IRRID): DERR1-10.2196/51767 ",
doi="10.2196/51767",
url="https://www.researchprotocols.org/2023/1/e51767",
url="http://www.ncbi.nlm.nih.gov/pubmed/37943587"
}


@Article{info:doi/10.2196/46385,
author="Lei, Ting
and Li, Mingqing
and Qian, Hu
and Yang, Junxiao
and Hu, Yihe
and Hua, Long",
title="The Effect of Sleep on Metabolism, Musculoskeletal Disease, and Mortality in the General US Population: Analysis of Results From the National Health and Nutrition Examination Survey",
journal="JMIR Public Health Surveill",
year="2023",
month="Nov",
day="7",
volume="9",
pages="e46385",
keywords="sleep duration",
keywords="mortality",
keywords="clinical outcomes",
keywords="threshold effect",
keywords="National Health and Nutrition Examination Survey",
abstract="Background: Sleep is an important physiological behavior in humans that is associated with the occurrence and development of various diseases. However, the association of sleep duration with health-related outcomes, including obesity-related factors, musculoskeletal diseases, and mortality because of different causes, has not been systematically reported. Objective: This study aims to systematically investigate the effect of sleep duration on health-related outcomes. Methods: Overall, 54,664 participants with sleep information from 8 survey cycles of the National Health and Nutrition Examination Survey (2005-2020) were included in the analysis. Health-related outcomes comprised obesity-related outcomes (ie, BMI, obesity, waist circumference, and abdominal obesity), metabolism-related outcomes (ie, uric acid, hyperuricemia, and bone mineral density [BMD]), musculoskeletal diseases (ie, osteoarthritis [OA] and rheumatoid arthritis [RA]), and mortality because of different causes. The baseline information of participants including age, sex, race, educational level, marital status, total energy intake, physical activity, alcohol consumption, smoking, hypertension, and diabetes was also collected as covariates. Information about the metabolism index, disease status, and covariates was acquired from the laboratory, examination, and questionnaire data. Survival information, including survival status, duration, and cause of death, was obtained from the National Death Index records. Quantile regression models and Cox regression models were used for association analysis between sleep duration and health-related outcomes. In addition, the threshold effect analysis, along with smooth curve fitting method, was applied for the nonlinear association analysis. Results: Participants were divided into 4 groups with different sleep durations. The 4 groups showed significant differences in terms of baseline data (P<.001). The quantile regression analysis indicated that participants with increased sleep duration showed decreased BMI ($\beta$=?.176, 95\% CI ?.220 to ?.133; P<.001), obesity (odds ratio [OR] 0.964, 95\% CI 0.950-0.977; P<.001), waist circumference ($\beta$=?.219, 95\% CI ?.320 to ?.117; P<.001), abdominal obesity (OR 0.975, 95\% CI 0.960-0.990; P<.001), OA (OR 0.965, 95\% CI 0.942-0.990; P=.005), and RA (OR 0.940, 95\% CI 0.912-0.968; P<.001). Participants with increased sleep duration also showed increased BMD ($\beta$=.002, 95\% CI .001-.003; P=.005), as compared with participants who slept <5.5 hours. A significant saturation effect of sleep duration on obesity, abdominal obesity, and hyperuricemia was detected through smooth curve fitting and threshold effect analysis (sleep duration>inflection point). In addition, a significant threshold effect of sleep duration on BMD (P<.001); OA (P<.001); RA (P<.001); and all-cause (P<.001), cardiovascular disease?cause (P<.001), cancer-cause (P=.005), and diabetes-cause mortality (P<.001) was found. The inflection point was between 6.5 hours and 9 hours. Conclusions: The double-edged sword effect of sleep duration on obesity-related outcomes, embolism-related diseases, musculoskeletal diseases, and mortality because of different causes was detected in this study. These findings provided epidemiological evidence that proper sleep duration may be an important factor in the prevention of multisystem diseases. ",
doi="10.2196/46385",
url="https://publichealth.jmir.org/2023/1/e46385",
url="http://www.ncbi.nlm.nih.gov/pubmed/37934562"
}


@Article{info:doi/10.2196/50983,
author="Lee, Taeyoung
and Cho, Younghoon
and Cha, Su Kwang
and Jung, Jinhwan
and Cho, Jungim
and Kim, Hyunggug
and Kim, Daewoo
and Hong, Joonki
and Lee, Dongheon
and Keum, Moonsik
and Kushida, A. Clete
and Yoon, In-Young
and Kim, Jeong-Whun",
title="Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Nov",
day="2",
volume="11",
pages="e50983",
keywords="consumer sleep trackers",
keywords="wearables",
keywords="nearables",
keywords="airables",
keywords="sleep monitoring",
keywords="sleep stage",
keywords="comparative study",
keywords="polysomnography",
keywords="multicenter study",
keywords="deep learning",
keywords="artificial intelligence",
keywords="Fitbit Sense 2, Amazon Halo Rise, SleepRoutine",
abstract="Background: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. Objective: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. Methods: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. Results: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. Conclusions: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep. ",
doi="10.2196/50983",
url="https://mhealth.jmir.org/2023/1/e50983",
url="http://www.ncbi.nlm.nih.gov/pubmed/37917155"
}


@Article{info:doi/10.2196/46338,
author="G Ravindran, K. Kiran
and della Monica, Ciro
and Atzori, Giuseppe
and Lambert, Damion
and Hassanin, Hana
and Revell, Victoria
and Dijk, Derk-Jan",
title="Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Oct",
day="25",
volume="11",
pages="e46338",
keywords="contactless sleep technologies",
keywords="evaluation",
keywords="nearables",
keywords="polysomnography",
keywords="older adults",
keywords="sleep",
keywords="Withings sleep analyzer",
keywords="Emfit",
keywords="Somnofy",
abstract="Background: Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. Objective: This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. Methods: Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40\%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. Results: The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9\% (SD 10.4\%; range 52.27\%-92.60\%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13\%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95\% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95\% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95\% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21). Conclusions: These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals. ",
doi="10.2196/46338",
url="https://mhealth.jmir.org/2023/1/e46338",
url="http://www.ncbi.nlm.nih.gov/pubmed/37878360"
}


@Article{info:doi/10.2196/46520,
author="Ha, Seokmin
and Choi, Jung Su
and Lee, Sujin
and Wijaya, Hansel Reinatt
and Kim, Hyun Jee
and Joo, Yeon Eun
and Kim, Kyoung Jae",
title="Predicting the Risk of Sleep Disorders Using a Machine Learning--Based Simple Questionnaire: Development and Validation Study",
journal="J Med Internet Res",
year="2023",
month="Sep",
day="21",
volume="25",
pages="e46520",
keywords="obstructive sleep apnea",
keywords="insomnia",
keywords="comorbid insomnia and sleep apnea",
keywords="polysomnography",
keywords="questionnaires",
keywords="risk prediction",
keywords="XGBoost",
keywords="machine learning",
keywords="risk",
keywords="sleep",
abstract="Background: Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients' hesitancy to undergo demanding and costly overnight polysomnography tests. Objective: We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test. Methods: We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve. Results: In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age. Conclusions: SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia. ",
doi="10.2196/46520",
url="https://www.jmir.org/2023/1/e46520",
url="http://www.ncbi.nlm.nih.gov/pubmed/37733411"
}


@Article{info:doi/10.2196/47460,
author="Hnatiak, Jakub
and Zikmund Galkova, Lujza
and Winnige, Petr
and Batalik, Ladislav
and Dosbaba, Filip
and Ludka, Ondrej
and Krejci, Jan",
title="Obstructive Sleep Apnea and a Comprehensive Remotely Supervised Rehabilitation Program: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Sep",
day="18",
volume="12",
pages="e47460",
keywords="obstructive sleep apnea",
keywords="telerehabilitation",
keywords="telemonitoring",
keywords="CPAP",
keywords="apnea-hypopnea index",
keywords="telehealth",
keywords="telemedicine",
keywords="sleep",
keywords="respiratory",
keywords="home based",
keywords="rehabilitation",
keywords="RCT",
keywords="randomized controlled trial",
abstract="Background: Obstructive sleep apnea (OSA) is characterized by recurrent, intermittent partial or complete obstruction of the upper respiratory tract during sleep, which negatively affects the patient's daily quality of life (QoL). Middle-aged and older men who smoke and have obesity are most at risk. Even though the use of continuous positive airway pressure (CPAP) during sleep remains the gold standard treatment, various rehabilitation methods, such as exercise, respiratory therapy, myofunctional therapy, and nutritional lifestyle interventions, also appear to be effective. Moreover, it is increasingly recommended to use alternative or additional therapy options in combination with CPAP therapy. Objective: This study aims to evaluate if a comprehensive home-based, remotely supervised rehabilitation program (tele-RHB), in combination with standard therapy, can improve OSA severity by decreasing the apnea-hypopnea index (AHI); improve objective parameters of polysomnographic, spirometric, anthropometric, and body composition examinations; improve lipid profile, maximal mouth pressure, and functional capacity tests; and enhance the subjective perception of QoL, as well as daytime sleepiness in male participants with moderate to severe OSA. Our hypothesis is that a combination of the tele-RHB program and CPAP therapy will be more effective by improving OSA severity and the abovementioned parameters. Methods: This randomized controlled trial aims to recruit 50 male participants between the ages of 30 and 60 years with newly diagnosed moderate to severe OSA. Participants will be randomized 1:1, either to a 12-week tele-RHB program along with CPAP therapy or to CPAP therapy alone. After the completion of the intervention, the participants will be invited to complete a 1-year follow-up. The primary outcomes will be the polysomnographic value of AHI, Epworth Sleepiness Scale score, 36-Item Short Form Health Survey (SF-36) score, percentage of body fat, 6-minute walk test distance covered, as well as maximal inspiratory and expiratory mouth pressure values. Secondary outcomes will include polysomnographic values of oxygen desaturation index, supine AHI, total sleep time, average heart rate, mean oxygen saturation, and the percentage of time with oxygen saturation below 90\%; anthropometric measurements of neck, waist, and hip circumference; BMI values; forced vital capacity; forced expiratory volume in 1 second; World Health Organization's tool to measure QoL (WHOQOL-BREF) score; and lipid profile values. Results: Study recruitment began on October 25, 2021, and the estimated study completion date is December 2024. Analyses will be performed to examine whether the combination of the tele-RHB program and CPAP therapy will be more effective in the reduction of OSA severity and improvement of QoL, body composition and circumferences, exercise tolerance, lipid profile, as well as respiratory muscle and lung function, compared to CPAP therapy alone. Conclusions: The study will evaluate the effect of a comprehensive tele-RHB program on selected parameters mentioned above in male participants. The results of this intervention could help the further development of novel additional therapeutic home-based options for OSA. Trial Registration: ClinicalTrials.gov NCT04759456; https://clinicaltrials.gov/ct2/show/NCT04759456 International Registered Report Identifier (IRRID): DERR1-10.2196/47460 ",
doi="10.2196/47460",
url="https://www.researchprotocols.org/2023/1/e47460",
url="http://www.ncbi.nlm.nih.gov/pubmed/37721786"
}


@Article{info:doi/10.2196/46735,
author="Jia, Yang Xiao
and Andreou, Pantelis
and Brown, Cary
and Constantin, Evelyn
and Godbout, Roger
and Hanlon-Dearman, Ana
and Ipsiroglu, Osman
and Reid, Graham
and Shea, Sarah
and Smith, M. Isabel
and Zwicker, D. Jennifer
and Weiss, K. Shelly
and Corkum, Penny",
title="An eHealth Program for Insomnia in Children With Neurodevelopmental Disorders (Better Nights, Better Days): Protocol for an
Economic Evaluation of a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Sep",
day="12",
volume="12",
pages="e46735",
keywords="eHealth intervention",
keywords="pediatric insomnia",
keywords="neurodevelopmental disorders",
keywords="attention-deficit/hyperactivity disorder",
keywords="autism spectrum disorder",
keywords="cerebral palsy",
keywords="fetal alcohol spectrum disorder",
keywords="economic evaluation",
keywords="cost-effectiveness",
abstract="Background: Children with neurodevelopmental disorders have a high risk of sleep disturbances, with insomnia being the most common sleep disorder (ie, chronic and frequent difficulties with going and staying asleep). Insomnia adversely affects the well-being of these children and their caregivers. Pediatric sleep experts recommend behavioral interventions as the first-line treatment option for children. Better Nights, Better Days for Children with Neurodevelopmental Disorders (BNBD-NDD) is a 5-session eHealth behavioral intervention delivered to parents to improve outcomes (eg, Pediatric Quality of Life Inventory [PedsQL]) for their children (ages 4-12 years) with insomnia and who have a diagnosis of mild to moderate attention-deficit/hyperactivity disorder, autism spectrum disorder, cerebral palsy, or fetal alcohol spectrum disorder. If cost-effective, BNBD-NDD can be a scalable intervention that provides value to an underserved population. Objective: This protocol outlines an economic evaluation conducted alongside the BNBD-NDD randomized controlled trial (RCT) that aims to assess its costs, efficacy, and cost-effectiveness compared to usual care. Methods: The BNBD-NDD RCT evaluates the impacts of the intervention on children's sleep and quality of life, as well as parents' daytime functioning and psychosocial health. Parent participants were randomized to the BNBD-NDD treatment or to usual care. The economic evaluation assesses outcomes at baseline and 8 months later, which include the PedsQL as the primary measure. Quality of life outcomes facilitate the comparison of competing interventions across different populations and medical conditions. Cost items include the BNBD-NDD intervention and parent-reported usage of private and publicly funded resources for their children's insomnia. The economic evaluation involves a reference case cost-effectiveness analysis to examine the incremental cost of BNBD-NDD per units gained in the PedsQL from the family payer perspective and a cost-consequence analysis from a societal perspective. These analyses will be conducted over an 8-month time horizon. Results: Research funding was obtained from the Kids Brain Health Network in 2015. Ethics were approved by the IWK Health Research Ethics Board and the University of Calgary Conjoint Health Research Ethics Board in January 2019 and June 2022, respectively. The BNBD-NDD RCT data collection commenced in June 2019 and ended in April 2022. The RCT data are currently being analyzed, and data relevant to the economic analysis will be analyzed concurrently. Conclusions: To our knowledge, this will be the first economic evaluation of an eHealth intervention for insomnia in children with neurodevelopmental disorders. This evaluation's findings can inform users and stakeholders regarding the costs and benefits of BNBD-NDD. Trial Registration: ClinicalTrial.gov NCT02694003; https://clinicaltrials.gov/study/NCT02694003 International Registered Report Identifier (IRRID): DERR1-10.2196/46735 ",
doi="10.2196/46735",
url="https://www.researchprotocols.org/2023/1/e46735",
url="http://www.ncbi.nlm.nih.gov/pubmed/37698915"
}


@Article{info:doi/10.2196/40640,
author="Germain, Anne
and Wolfson, Megan
and Pulantara, Wayan I.
and Wallace, L. Meredith
and Nugent, Katie
and Mesias, George
and Clarke-Walper, Kristina
and Quartana, J. Phillip
and Wilk, Joshua",
title="Prototyping Apps for the Management of Sleep, Fatigue, and Behavioral Health in Austere Far-Forward Environments: Development Study",
journal="J Med Internet Res",
year="2023",
month="Aug",
day="28",
volume="25",
pages="e40640",
keywords="military digital health technology",
keywords="operational environment",
keywords="self-monitoring",
keywords="self-management",
keywords="connectivity protocol",
keywords="evidence-based practice",
keywords="deployment health",
keywords="military",
keywords="army",
keywords="smartphone app",
keywords="mHealth",
keywords="mobile health",
keywords="health app",
keywords="feasibility",
keywords="prototype",
keywords="digital health",
keywords="health technology",
keywords="eHealth",
keywords="decision support",
keywords="medic",
keywords="soldier",
keywords="sleep",
keywords="fatigue",
keywords="behavioral health",
keywords="operational setting",
keywords="mental health",
keywords="mental well-being",
abstract="Background: Military service inherently includes frequent periods of high-stress training, operational tempo, and sustained deployments to austere far-forward environments. These occupational requirements can contribute to acute and chronic sleep disruption, fatigue, and behavioral health challenges related to acute and chronic stress and disruption of team dynamics. To date, there is no centralized mobile health platform that supports self- and supervised detection, monitoring, and management of sleep and behavioral health issues in garrison and during and after deployments. Objective: The objective of this study was to adapt a clinical decision support platform for use outside clinical settings, in garrison, and during field exercises by medics and soldiers to monitor and manage sleep and behavioral health in operational settings. Methods: To adapt an existing clinical decision support digital health platform, we first gathered system, content, and context-related requirements for a sleep and behavioral health management system from experts. Sleep and behavioral health assessments were then adapted for prospective digital data capture. Evidence-based and operationally relevant educational and interventional modules were formatted for digital delivery. These modules addressed the management and mitigation of sleep, circadian challenges, fatigue, stress responses, and team communication. Connectivity protocols were adapted to accommodate the absence of cellular or Wi-Fi access in deployed settings. The resulting apps were then tested in garrison and during 2 separate field exercises. Results: Based on identified requirements, 2 Android smartphone apps were adapted for self-monitoring and management for soldiers (Soldier app) and team supervision and intervention by medics (Medic app). A total of 246 soldiers, including 28 medics, received training on how to use the apps. Both apps function as expected under conditions of limited connectivity during field exercises. Areas for future technology enhancement were also identified. Conclusions: We demonstrated the feasibility of adapting a clinical decision support platform into Android smartphone--based apps to collect, save, and synthesize sleep and behavioral health data, as well as share data using adaptive data transfer protocols when Wi-Fi or cellular data are unavailable. The AIRE (Autonomous Connectivity Independent System for Remote Environments) prototype offers a novel self-management and supervised tool to augment capabilities for prospective monitoring, detection, and intervention for emerging sleep, fatigue, and behavioral health issues that are common in military and nonmilitary high-tempo occupations (eg, submarines, long-haul flights, space stations, and oil rigs) where medical expertise is limited. ",
doi="10.2196/40640",
url="https://www.jmir.org/2023/1/e40640",
url="http://www.ncbi.nlm.nih.gov/pubmed/37639304"
}


@Article{info:doi/10.2196/44145,
author="Tadros, Michelle
and Li, Sophie
and Upton, Emily
and Newby, Jill
and Werner-Seidler, Aliza",
title="Preferences of University Students for a Psychological Intervention Designed to Improve Sleep: Focus Group Study",
journal="JMIR Hum Factors",
year="2023",
month="Aug",
day="24",
volume="10",
pages="e44145",
keywords="university students",
keywords="sleep difficulties",
keywords="intervention",
keywords="student needs",
keywords="insomnia",
keywords="treatment",
keywords="focus group",
keywords="intervention design",
keywords="sleep",
keywords="sleep medicine",
keywords="student",
keywords="university",
keywords="college",
keywords="post secondary",
keywords="psychological",
keywords="psychotherapy",
keywords="help-seeking",
keywords="polysomnography",
abstract="Background: Many university students have difficulties with sleep; therefore, effective psychological treatments are needed. Most research on psychological treatments to improve sleep has been conducted with middle-aged and older adults, which means it is unclear whether existing psychological treatments are helpful for young adult university students. Objective: This study aimed to discover university student preferences for a psychological intervention to improve sleep quality. Methods: Focus groups were conducted over 3 stages to examine students' views regarding content, format, and session duration for a psychological intervention to improve sleep. A thematic analysis was conducted to analyze participant responses. Results: In total, 30 participants attended small focus group discussions. Three key themes were identified: (1) program development, (2) help-seeking, and (3) student sleep characteristics. Program development subthemes were program format, program content, and engagement facilitators. Help-seeking subthemes were when to seek help, where to access help, stigma, and barriers. Student sleep characteristics subthemes were factors disturbing sleep and consequences of poor sleep. Conclusions: Students emphasized the need for a sleep intervention with an in-person and social component, individualized content, and ways to monitor their progress. Participants did not think there was a stigma associated with seeking help for sleep problems. Students identified the lack of routine in their lifestyle, academic workload, and the pressure of multiple demands as key contributors to sleep difficulties. ",
doi="10.2196/44145",
url="https://humanfactors.jmir.org/2023/1/e44145",
url="http://www.ncbi.nlm.nih.gov/pubmed/37616036"
}


@Article{info:doi/10.2196/41719,
author="Kreibig, D. Sylvia
and ten Brink, Maia
and Mehta, Ashish
and Talmon, Anat
and Zhang, Jin-Xiao
and Brown, S. Alan
and Lucas-Griffin, S. Sawyer
and Axelrod, K. Ariel
and Manber, Rachel
and Lavigne, J. Gilles
and Gross, J. James",
title="The Role of Emotion Regulation, Affect, and Sleep in Individuals With Sleep Bruxism and Those Without: Protocol for a Remote Longitudinal Observational Study",
journal="JMIR Res Protoc",
year="2023",
month="Aug",
day="24",
volume="12",
pages="e41719",
keywords="sleep bruxism",
keywords="emotion regulation",
keywords="ecological momentary assessment",
keywords="rhythmic masticatory muscle activity",
keywords="heart rate variability",
keywords="wrist actigraphy",
abstract="Background: Sleep bruxism (SB) is an oral behavior characterized by high levels of repetitive jaw muscle activity during sleep, leading to teeth grinding and clenching, and may develop into a disorder. Despite its prevalence and negative outcomes on oral health and quality of life, there is currently no cure for SB. The etiology of SB remains poorly understood, but recent research suggests a potential role of negative emotions and maladaptive emotion regulation (ER). Objective: This study's primary aim investigates whether ER is impaired in individuals with SB, while controlling for affective and sleep disturbances. The secondary aim tests for the presence of cross-sectional and longitudinal mediation pathways in the bidirectional relationships among SB, ER, affect, and sleep. Methods: The study used a nonrandomized repeated-measures observational design and was conducted remotely. Participants aged 18-49 years underwent a 14-day ambulatory assessment. Data collection was carried out using electronic platforms. We assessed trait and state SB and ER alongside affect and sleep variables. We measured SB using self-reported trait questionnaires, ecological momentary assessment (EMA) for real-time reports of SB behavior, and portable electromyography for multinight assessment of rhythmic masticatory muscle activity. We assessed ER through self-reported trait questionnaires, EMA for real-time reports of ER strategies, and heart rate variability derived from an electrocardiography wireless physiological sensor as an objective physiological measure. Participants' trait affect and real-time emotional experiences were obtained using self-reported trait questionnaires and EMA. Sleep patterns and quality were evaluated using self-reported trait questionnaires and sleep diaries, as well as actigraphy as a physiological measure. For the primary objective, analyses will test for maladaptive ER in terms of strategy use frequency and effectiveness as a function of SB using targeted contrasts in the general linear model. Control analyses will be conducted to examine the persistence of the SB-ER relationship after adjusting for affective and sleep measures, as well as demographic variables. For the secondary objective, cross-sectional and longitudinal mediation analyses will test various competing models of directional effects among self-reported and physiological measures of SB, ER, affect, and sleep. Results: This research received funding in April 2017. Data collection took place from August 2020 to March 2022. In all, 237 participants were eligible and completed the study. Data analysis has not yet started. Conclusions: We hope that the effort to thoroughly measure SB and ER using gold standard methods and cutting-edge technology will advance the knowledge of SB. The findings of this study may contribute to a better understanding of the relationship among SB, ER, affect, and sleep disturbances. By identifying the role of ER in SB, the results may pave the way for the development of targeted interventions for SB management to alleviate the pain and distress of those affected. International Registered Report Identifier (IRRID): DERR1-10.2196/41719 ",
doi="10.2196/41719",
url="https://www.researchprotocols.org/2023/1/e41719",
url="http://www.ncbi.nlm.nih.gov/pubmed/37616042"
}


@Article{info:doi/10.2196/45834,
author="Ito-Masui, Asami
and Sakamoto, Ryota
and Matsuo, Eri
and Kawamoto, Eiji
and Motomura, Eishi
and Tanii, Hisashi
and Yu, Han
and Sano, Akane
and Imai, Hiroshi
and Shimaoka, Motomu",
title="Effect of an Internet--Delivered Cognitive Behavioral Therapy--Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial",
journal="J Med Internet Res",
year="2023",
month="Aug",
day="22",
volume="25",
pages="e45834",
keywords="shift worker sleep disorder",
keywords="internet-based cognitive behavioral therapy",
keywords="mobile apps",
keywords="fitness tracker",
keywords="subjective well-being",
keywords="machine learning",
keywords="mobile phone",
abstract="Background: Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. Objective: This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning--based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. Methods: A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ?5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network--based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. Results: In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. Conclusions: The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work--related sleep disorders. International Registered Report Identifier (IRRID): RR2-10.2196/24799. ",
doi="10.2196/45834",
url="https://www.jmir.org/2023/1/e45834",
url="http://www.ncbi.nlm.nih.gov/pubmed/37606971"
}


@Article{info:doi/10.2196/45313,
author="Butler, Mark
and D'Angelo, Stefani
and Perrin, Alexandra
and Rodillas, Jordyn
and Miller, Danielle
and Arader, Lindsay
and Chandereng, Thevaa
and Cheung, Kuen Ying
and Shechter, Ari
and Davidson, W. Karina",
title="A Series of Remote Melatonin Supplement Interventions for Poor Sleep: Protocol for a Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials",
journal="JMIR Res Protoc",
year="2023",
month="Aug",
day="3",
volume="12",
pages="e45313",
keywords="feasibility",
keywords="insomnia",
keywords="melatonin",
keywords="N-of-1",
keywords="personalized trial",
keywords="personalized",
keywords="placebo",
keywords="poor sleep",
keywords="sleep duration",
keywords="sleep quality",
keywords="supplements",
keywords="virtual",
abstract="Background: Poor sleep, defined as short-duration or poor-quality sleep, is a frequently reported condition with many deleterious effects including poorer cognitive functioning, increased accidents, and poorer health. Melatonin has been shown to be an efficacious treatment to manage symptoms of poor sleep. However, the treatment effects of melatonin on sleep can vary greatly between participants. Personalized, or N-of-1, trial designs represent a method for identifying the best treatment for individual participants. Although using N-of-1 trials of melatonin to treat poor sleep is possible, the feasibility, acceptability, and effectiveness of N-of-1 trials using melatonin are unknown. Using the National Institutes of Health Stage Model for Behavioral Intervention Development, a stage IB (intervention refinement, modification, and adaptation and pilot testing) design appeared to be needed to address these feasibility questions. Objective: This trial series evaluates the feasibility, acceptability, and effectiveness of a series of personalized interventions for remote delivery of melatonin dose (3 and 0.5 mg) versus placebo supplements for self-reported poor sleep among 60 participants. The goal of this study is to provide valuable information about implementing remote N-of-1 randomized controlled trials to improve poor sleep. Methods: Participants will complete a 2-week baseline followed by six 2-week alternating intervention periods of 3 mg of melatonin, 0.5 mg of melatonin, and placebo. Participants will be randomly assigned to 2 intervention orders. The feasibility and acceptability of the personalized trial approach will be determined with participants' ratings of usability and satisfaction with the remote, personalized intervention delivery system. The effectiveness of the intervention will be measured using participants' self-reported sleep quality and duration and Fitbit tracker--measured sleep duration and efficiency. Additional measures will include ecological momentary assessment measures of fatigue, stress, pain, mood, concentration, and confidence as well as measures of participant adherence to the intervention, use of the Fitbit tracker, and survey data collection. Results: As of the submission of this protocol, recruitment for this National Institutes of Health stage IB personalized trial series is approximately 78.3\% complete (47/60). We expect recruitment and data collection to be finalized by June 2023. Conclusions: Evaluating the feasibility, acceptability, and effectiveness of a series of personalized interventions of melatonin will address the longer term aim of this program of research---is integrating N-of-1 trials useful patient care? The personalized trial series results will be published in a peer-reviewed journal and will follow the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015) reporting guidelines. This trial series was approved by the Northwell Health institutional review board. Trial Registration: ClinicalTrials.gov NCT05349188; https://www.clinicaltrials.gov/study/NCT05349188 International Registered Report Identifier (IRRID): DERR1-10.2196/45313 ",
doi="10.2196/45313",
url="https://www.researchprotocols.org/2023/1/e45313",
url="http://www.ncbi.nlm.nih.gov/pubmed/37535419"
}


@Article{info:doi/10.2196/48032,
author="Fan, Si
and Wang, Qianning
and Zheng, Feiyang
and Wu, Yuanyang
and Yu, Tiantian
and Wang, Yanting
and Zhang, Xinping
and Zhang, Dexing",
title="Depression as a Mediator and Social Participation as a Moderator in the Bidirectional Relationship Between Sleep Disorders and Pain: Dynamic Cohort Study",
journal="JMIR Public Health Surveill",
year="2023",
month="Jul",
day="26",
volume="9",
pages="e48032",
keywords="depression",
keywords="dynamic cohort",
keywords="longitudinal mediation",
keywords="pain",
keywords="sleep disorders",
keywords="social participation",
abstract="Background: Chronic pain, sleep disorders, and depression are major global health concerns. Recent studies have revealed a strong link between sleep disorders and pain, and each of them is bidirectionally correlated with depressive symptoms, suggesting a complex relationship between these conditions. Social participation has been identified as a potential moderator in this complex relationship, with implications for treatment. However, the complex interplay among sleep disorders, pain, depressive symptoms, and social participation in middle- and old-aged Asians remains unclear. Objective: This study aimed to examine the bidirectional relationship between sleep disorders and pain in middle- and old-aged Chinese and measure the role of depression as a mediator and social participation as a moderator in this bidirectional relationship through a dynamic cohort study. Methods: We used data from the China Health and Retirement Longitudinal Study across 5 years and included a total of 7998 middle- and old-aged people (?45 years old) with complete data in 2011 (T1), 2015 (T2), and 2018 (T3). The cross-lag model was used to assess the interplay among sleep disorders, pain, depressive symptoms, and social participation. Depressive symptoms were assessed by the 10-item Centre for Epidemiological Studies Depression scale. Sleep disorders were assessed by a single-item sleep quality scale and nighttime sleep duration. The pain score was the sum of all pain locations reported. Social participation was measured using self-reported activity. Results: Our results showed significant cross-lagged effects of previous sleep disorders on subsequent pain at T2 ($\beta$=.141; P<.001) and T3 ($\beta$=.117; P<.001) and previous pain on subsequent poor sleep at T2 ($\beta$=.080; P<.001) and T3 ($\beta$=.093; P<.001). The indirect effects of previous sleep disorders on pain through depressive symptoms ($\beta$=.020; SE 0.004; P<.001; effect size 21.98\%), as well as previous pain on sleep disorders through depressive symptoms ($\beta$=.012; SE 0.002; P<.001; effect size 20.69\%), were significant across the 3 time intervals. Among participants with high levels of social participation, there were no statistically significant effects of previous sleep disorders on subsequent pain at T2 ($\beta$=.048; P=.15) and T3 ($\beta$=.085; P=.02), nor were there statistically significant effects of previous pain on subsequent sleep disorders at T2 ($\beta$=.037; P=.15) and T3 ($\beta$=.039; P=.24). Additionally, the mediating effects of depressive symptoms on the sleep disorders-to-pain pathway (P=.14) and the pain-to-sleep disorders pathway (P=.02) were no longer statistically significant. Conclusions: There is a bidirectional relationship between sleep disorders and pain in middle- and old-aged Asians; depression plays a longitudinal mediating role in the bidirectional relationship between them; and social participation moderates the bidirectional relationship between them directly and indirectly by affecting depression. Future interventions may consider the complex relationship between these conditions and adopt a comprehensive treatment regime. ",
doi="10.2196/48032",
url="https://publichealth.jmir.org/2023/1/e48032",
url="http://www.ncbi.nlm.nih.gov/pubmed/37494109"
}


@Article{info:doi/10.2196/47636,
author="Bramoweth, D. Adam
and Hough, E. Caroline
and McQuillan, D. Amanda
and Spitznogle, L. Brittany
and Thorpe, T. Carolyn
and Lickel, J. James
and Boudreaux-Kelly, Monique
and Hamm, E. Megan
and Germain, Anne",
title="Reduction of Sleep Medications via a Combined Digital Insomnia and Pharmacist-Led Deprescribing Intervention: Protocol for a Feasibility Trial",
journal="JMIR Res Protoc",
year="2023",
month="Jul",
day="20",
volume="12",
pages="e47636",
keywords="insomnia",
keywords="sedatives and hypnotics",
keywords="mHealth",
keywords="deprescribing",
keywords="cognitive behavioral therapy",
keywords="clinical pharmacist",
keywords="veterans",
abstract="Background: Chronic insomnia is one of the most common health problems among veterans and negatively impacts their health, function, and quality of life. Although cognitive behavioral therapy for insomnia (CBT-I) is the first-line recommended treatment, sedative-hypnotic medications remain the most common. Sedative-hypnotics, however, have mixed effectiveness, are frequently prescribed longer than recommended, and are associated with numerous risks and adverse effects that negatively impact veteran function. Meeting the treatment needs of veterans impacted by insomnia requires delivering gold standard behavioral care, like CBT-I, and the reduction of sedative-hypnotics through innovative methods. Objective: The objective of this feasibility clinical trial is to test a digital CBT-I approach combined with deprescribing to improve the success of sedative-hypnotic reduction among veterans. The intervention combines Noctem Health Clinician Operated Assistive Sleep Technology (COAST), an effective and efficient, scalable, and adaptable digital platform to deliver CBT-I, with clinical pharmacy practitioner (CPP)--led deprescribing of sedative-hypnotic medications. Methods: In this nonrandomized single-group clinical trial, 50 veterans will be recruited and enrolled to receive CBT-I delivered via Noctem COAST and CPP-led deprescribing for up to 12 weeks. Assessments will occur at baseline, posttreatment, and 3-month follow-up. The aims are to (1) assess the feasibility of recruiting veterans with chronic sedative-hypnotic use to participate in the combined intervention, (2) evaluate veterans' acceptability and usability of the COAST platform, and (3) measure changes in veterans' sleep, sedative-hypnotic use, and function at baseline, posttreatment, and 3-month follow-up. Results: The institutional review board approved the study in October 2021 and the trial was initiated in May 2022. Recruitment and data collection began in September 2022 and is anticipated to be completed in April 2024. Aim 1 will be measured by tracking the response to a mail-centric recruitment approach using electronic medical records to identify potentially eligible veterans based on sedative-hypnotic use. Aim 2 will be measured using the Post-Study System Usability Questionnaire, assessing overall usability as well as system usefulness, information quality, and interface quality. Aim 3 will use the Insomnia Severity Index and sleep diaries to measure change in insomnia outcomes, the Patient-Reported Outcome Measurement Information System Profile to measure change in physical function, anxiety, depression, fatigue, sleep disturbance, participation in social roles, pain, cognitive function, and self-reported sedative-hypnotic use to measure change in dose and frequency of use. Conclusions: Findings will inform the utility of a combined digital CBT-I and CPP-led deprescribing intervention and the development of an adequately powered clinical trial to test the effectiveness in a diverse sample of veterans. Further, findings will help inform potential new approaches to deliver care and improve access to care for veterans with insomnia, many of whom use sedative-hypnotics that may be ineffective and increase the risk for negative outcomes. Trial Registration: ClinicalTrials.gov NCT05027438; https://classic.clinicaltrials.gov/ct2/show/NCT05027438 International Registered Report Identifier (IRRID): DERR1-10.2196/47636 ",
doi="10.2196/47636",
url="https://www.researchprotocols.org/2023/1/e47636",
url="http://www.ncbi.nlm.nih.gov/pubmed/37471122"
}


@Article{info:doi/10.2196/42750,
author="Hoang, Huyen Nhung
and Liang, Zilu",
title="Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Jun",
day="28",
volume="11",
pages="e42750",
keywords="sleep tracking",
keywords="knowledge discovery",
keywords="data mining",
keywords="personal informatics",
keywords="self-experimentation",
keywords="sleep health",
keywords="scoping review",
keywords="mobile phone",
abstract="Background: Over the past few decades, there has been a rapid increase in the number of wearable sleep trackers and mobile apps in the consumer market. Consumer sleep tracking technologies allow users to track sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to be identified through visual inspection and reflection. Advanced analytical methods are needed to discover new insights into the rapidly growing volume of personal sleep tracking data. Objective: This review aimed to summarize and analyze the existing literature that applies formal analytical methods to discover insights in the context of personal informatics. Guided by the problem-constraints-system framework for literature review in computer science, we framed 4 main questions regarding general research trends, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic. Methods: Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that met the inclusion criteria. After full-text screening, 14 publications were included. Results: The research on knowledge discovery in sleep tracking is limited. More than half of the studies (8/14, 57\%) were conducted in the United States, followed by Japan (3/14, 21\%). Only a few of the publications (5/14, 36\%) were journal articles, whereas the remaining were conference proceeding papers. The most used sleep metrics were subjective sleep quality (4/14, 29\%), sleep efficiency (4/14, 29\%), sleep onset latency (4/14, 29\%), and time at lights off (3/14, 21\%). Ratio parameters such as deep sleep ratio and rapid eye movement ratio were not used in any of the reviewed studies. A dominant number of the studies applied simple correlation analysis (3/14, 21\%), regression analysis (3/14, 21\%), and statistical tests or inferences (3/14, 21\%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (1/14, 7\%) or anomaly detection (2/14, 14\%). Exercise, digital device use, caffeine and alcohol consumption, places visited before sleep, and sleep environments were important contextual factors substantially correlated to various dimensions of sleep quality. Conclusions: This scoping review shows that knowledge discovery methods have great potential for extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights. ",
doi="10.2196/42750",
url="https://mhealth.jmir.org/2023/1/e42750",
url="http://www.ncbi.nlm.nih.gov/pubmed/37379057"
}


@Article{info:doi/10.2196/43067,
author="Purnell, Larissa
and Sierra, Maribel
and Lisker, Sarah
and Lim, S. Melissa
and Bailey, Emma
and Sarkar, Urmimala
and Lyles, R. Courtney
and Nguyen, H. Kim",
title="Acceptability and Usability of a Wearable Device for Sleep Health Among English- and Spanish-Speaking Patients in a Safety Net Clinic: Qualitative Analysis",
journal="JMIR Form Res",
year="2023",
month="Jun",
day="5",
volume="7",
pages="e43067",
keywords="health equity",
keywords="medical informatics",
keywords="sleep disorders",
keywords="user-centered design",
keywords="wearable electronic devices",
abstract="Background: Sleep disorders are common and disproportionately affect marginalized populations. Technology, such as wearable devices, holds the potential to improve sleep quality and reduce sleep disparities, but most devices have not been designed or tested with racially, ethnically, and socioeconomically diverse patients. Inclusion and engagement of diverse patients throughout digital health development and implementation are critical to achieving health equity. Objective: This study aims to evaluate the usability and acceptability of a wearable sleep monitoring device---SomnoRing---and its accompanying mobile app among patients treated in a safety net clinic. Methods: The study team recruited English- and Spanish-speaking patients from a mid-sized pulmonary and sleep medicine practice serving publicly insured patients. Eligibility criteria included initial evaluation of obstructed sleep apnea, which is most appropriate for limited cardiopulmonary testing. Patients with primary insomnia or other suspected sleep disorders were not included. Patients tested the SomnoRing over a 7-night period and participated in a 1-hour semistructured web-based qualitative interview covering perceptions of the device, motivators and barriers to use, and general experiences with digital health tools. The study team used inductive or deductive processes to code interview transcripts, guided by the Technology Acceptance Model. Results: A total of 21 individuals participated in the study. All participants owned a smartphone, almost all (19/21) felt comfortable using their phone, and few already owned a wearable (6/21). Almost all participants wore the SomnoRing for 7 nights and found it comfortable. The following four themes emerged from qualitative data: (1) the SomnoRing was easy to use compared to other wearable devices or traditional home sleep testing alternatives, such as the standard polysomnogram technology for sleep studies; (2) the patient's context and environment, such as family and peer influence, housing status, access to insurance, and device cost affected the overall acceptance of the SomnoRing; (3) clinical champions motivated use in supporting effective onboarding, interpretation of data, and, ongoing technical support; and (4) participants desired more assistance and information to best interpret their own sleep data summarized in the companion app. Conclusions: Racially, ethnically, and socioeconomically diverse patients with sleep disorders perceived a wearable as useful and acceptable for sleep health. Participants also uncovered external barriers related to the perceived usefulness of the technology, such as housing status, insurance coverage, and clinical support. Future studies should further examine how to best address these barriers so that wearables, such as the SomnoRing, can be successfully implemented in the safety net health setting. ",
doi="10.2196/43067",
url="https://formative.jmir.org/2023/1/e43067",
url="http://www.ncbi.nlm.nih.gov/pubmed/37098152"
}


@Article{info:doi/10.2196/46216,
author="Tran, Hong Hai
and Hong, Kyung Jung
and Jang, Hyeryung
and Jung, Jinhwan
and Kim, Jongmok
and Hong, Joonki
and Lee, Minji
and Kim, Jeong-Whun
and Kushida, A. Clete
and Lee, Dongheon
and Kim, Daewoo
and Yoon, In-Young",
title="Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="1",
volume="25",
pages="e46216",
keywords="respiratory sounds",
keywords="sleep stages",
keywords="deep learning",
keywords="smartphone",
keywords="home environment",
abstract="Background: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. Objective: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. Methods: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. Results: The accuracy of the model was 76.2\% (63.4\% for wake, 64.9\% for rapid-eye movement [REM], and 83.6\% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4\%-79.4\%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2\% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3\%) than adding transfer learning (+0.1\%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0\%). Conclusions: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only. ",
doi="10.2196/46216",
url="https://www.jmir.org/2023/1/e46216",
url="http://www.ncbi.nlm.nih.gov/pubmed/37261889"
}


@Article{info:doi/10.2196/40193,
author="Lacroix, Joyca
and Tatousek, Jan
and Den Teuling, Niek
and Visser, Thomas
and Wells, Charles
and Wylie, Paul
and Rosenberg, Russell
and Bogan, Richard",
title="Effectiveness of an Intervention Providing Digitally Generated Personalized Feedback and Education on Adherence to Continuous Positive Airway Pressure: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2023",
month="May",
day="22",
volume="25",
pages="e40193",
keywords="therapy adherence",
keywords="personalized feedback",
keywords="personalized education",
keywords="tailored communication",
keywords="psychological profile",
keywords="continuous positive airway pressure therapy",
keywords="CPAP therapy",
keywords="obstructive sleep apnea",
abstract="Background: Many people worldwide experience obstructive sleep apnea, which is associated with medical and psychological problems. Continuous positive airway pressure (CPAP) is an efficacious therapy for obstructive sleep apnea, but its effect is limited by nonadherence. Studies show that personalized education and feedback can increase CPAP adherence. Moreover, tailoring the style of information to the psychological profile of a patient has been shown to enhance the impact of interventions. Objective: This study aimed to assess the effect of an intervention providing digitally generated personalized education and feedback on CPAP adherence and the additional effect of tailoring the style of the education and feedback to an individual's psychological profile. Methods: This study was a 90-day, multicenter, parallel, single-blinded, and randomized controlled trial with 3 conditions: personalized content in a tailored style (PT) in addition to usual care (UC), personalized content in a nontailored style (PN) in addition to UC, and UC. To test the effect of personalized education and feedback, the PN + PT group was compared with the UC group. To test the additional effect of tailoring the style to psychological profiles, the PN and PT groups were compared. Overall, 169 participants were recruited from 6 US sleep clinics. The primary outcome measures were adherence based on minutes of use per night and on nights of use per week. Results: We found a significant positive effect of personalized education and feedback on both primary adherence outcome measures. The difference in the estimated average adherence based on minutes of use per night between the PT + PN and UC groups on day 90 was 81.3 minutes in favor of the PT + PN group (95\% CI ?134.00 to ?29.10; P=.002). The difference in the average adherence based on nights of use per week between the PT + PN and UC groups at week 12 was 0.9 nights per week in favor of the PT + PN group (difference in odds ratio 0.39, 95\% CI 0.21-0.72; P=.003). We did not find an additional effect of tailoring the style of the intervention to psychological profiles on the primary outcomes. The difference in nightly use between the PT and PN groups on day 90 (95\% CI ?28.20 to 96.50; P=.28) and the difference in nights of use per week between the PT and PN groups at week 12 (difference in odds ratio 0.85, 95\% CI 0.51-1.43; P=.054) were both nonsignificant. Conclusions: The results show that personalized education and feedback can increase CPAP adherence substantially. Tailoring the style of the intervention to the psychological profiles of patients did not further increase adherence. Future research should investigate how the impact of interventions can be enhanced by catering to differences in psychological profiles. Trial Registration: ClinicalTrials.gov NCT02195531; https://clinicaltrials.gov/ct2/show/NCT02195531 ",
doi="10.2196/40193",
url="https://www.jmir.org/2023/1/e40193",
url="http://www.ncbi.nlm.nih.gov/pubmed/37213195"
}


@Article{info:doi/10.2196/45543,
author="Oh, Won Jae
and Kim, Mi Sun
and Lee, Deokjong
and Son, Nak-Hoon
and Uh, Jinsun
and Yoon, Hong Ju
and Choi, Yukyung
and Lee, San",
title="Evaluating the Modified Patient Health Questionnaire-2 and Insomnia Severity Index-2 for Daily Digital Screening of Depression and Insomnia: Validation Study",
journal="JMIR Ment Health",
year="2023",
month="May",
day="22",
volume="10",
pages="e45543",
keywords="Patient Health Questionnaire-2",
keywords="PHQ-2",
keywords="Insomnia Severity Index",
keywords="ISI-2",
keywords="depression",
keywords="insomnia",
keywords="mobile health",
keywords="mobile phone",
abstract="Background: The Patient Health Questionnaire-2 (PHQ-2) and Insomnia Severity Index-2 (ISI-2) are screening assessments that reflect the past 2-week experience of depression and insomnia, respectively. Retrospective assessment has been associated with reduced accuracy owing to recall bias. Objective: This study aimed to increase the reliability of responses by validating the use of the PHQ-2 and ISI-2 for daily screening. Methods: A total of 167 outpatients from the psychiatric department at the Yongin Severance Hospital participated in this study, of which 63 (37.7\%) were male and 104 (62.3\%) were female with a mean age of 35.1 (SD 12.1) years. Participants used a mobile app (``Mental Protector'') for 4 weeks and rated their depressive and insomnia symptoms daily on the modified PHQ-2 and ISI-2 scales. The validation assessments were conducted in 2 blocks, each with a fortnight response from the participants. The modified version of the PHQ-2 was evaluated against the conventional scales of the Patient Health Questionnaire-9 and the Korean version of the Center for Epidemiologic Studies Depression Scale--Revised. Results: According to the sensitivity and specificity analyses, an average score of 3.29 on the modified PHQ-2 was considered valid for screening for depressive symptoms. Similarly, the ISI-2 was evaluated against the conventional scale, Insomnia Severity Index, and a mean score of 3.50 was determined to be a valid threshold for insomnia symptoms when rated daily. Conclusions: This study is one of the first to propose a daily digital screening measure for depression and insomnia delivered through a mobile app. The modified PHQ-2 and ISI-2 were strong candidates for daily screening of depression and insomnia, respectively. ",
doi="10.2196/45543",
url="https://mental.jmir.org/2023/1/e45543",
url="http://www.ncbi.nlm.nih.gov/pubmed/37213186"
}


@Article{info:doi/10.2196/45752,
author="Su, Ting
and Calvo, A. Rafael
and Jouaiti, Melanie
and Daniels, Sarah
and Kirby, Pippa
and Dijk, Derk-Jan
and della Monica, Ciro
and Vaidyanathan, Ravi",
title="Assessing a Sleep Interviewing Chatbot to Improve Subjective and Objective Sleep: Protocol for an Observational Feasibility Study",
journal="JMIR Res Protoc",
year="2023",
month="May",
day="11",
volume="12",
pages="e45752",
keywords="automated chatbot",
keywords="behavior analysis",
keywords="conversational agents",
keywords="older adults",
keywords="sleep disorders",
keywords="sleep interview",
abstract="Background: Sleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases. Objective: This work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants' self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep. Methods: We will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrupted sleep. We will analyze questionnaire responses before and after the study to assess their perception of our proposed chatbot; questionnaire responses before, during, and after the study to assess their subjective sleep quality changes; and sleep parameters recorded by the sleep analyzer throughout the study to assess their objective sleep quality changes. Results: Recruitment will begin in May 2023 through UK Dementia Research Institute Care Research and Technology Centre organized community outreach. Data collection will run from May 2023 until December 2023. We hypothesize that participants will perceive our proposed chatbot as intelligent and trustworthy; we also hypothesize that our proposed chatbot can help improve participants' subjective and objective sleep assessment throughout the study. Conclusions: The MotivSleep automated chatbot has the potential to provide additional care to older adults who wish to improve their sleep in more accessible and less costly ways than conventional face-to-face therapy. International Registered Report Identifier (IRRID): PRR1-10.2196/45752 ",
doi="10.2196/45752",
url="https://www.researchprotocols.org/2023/1/e45752",
url="http://www.ncbi.nlm.nih.gov/pubmed/37166964"
}


@Article{info:doi/10.2196/41049,
author="Fonseca, Catarina
and Cavadas, Francisca
and Fonseca, Patr{\'i}cia",
title="Upper Airway Assessment in Cone-Beam Computed Tomography for Screening of Obstructive Sleep Apnea Syndrome: Development of an Evaluation Protocol in Dentistry",
journal="JMIR Res Protoc",
year="2023",
month="May",
day="5",
volume="12",
pages="e41049",
keywords="cone-beam computed tomography",
keywords="three-dimensional image",
keywords="3D image",
keywords="airway obstructions",
keywords="sleep medicine specialty",
keywords="dentistry",
keywords="obstructive sleep apnea",
keywords="protocol",
abstract="Background: The upper airways are formed by the nasal cavities, pharynx, and larynx. There are several radiographic methods that allow evaluation of the craniofacial structure. Upper airway analysis in cone-beam computed tomography (CBCT) may be useful in diagnosing some pathologies such as obstructive sleep apnea syndrome (OSAS). OSAS prevalence has increased significantly in recent decades, justified by increased obesity and average life expectancy. It can be associated with cardiovascular, respiratory, and neurovascular diseases, diabetes, and hypertension. In some individuals with OSAS, the upper airway is compromised and narrowed. Nowadays, CBCT is widely used in dentistry by clinicians. Its use for upper airway assessment would be an advantage for screening some abnormalities related to an increased risk of pathologies such as OSAS. CBCT helps to calculate the total volume of the airways and their area in different anatomical planes (sagittal, coronal, and transverse). It also helps identify regions with the highest anteroposterior and laterolateral constriction of the airways. Despite its undoubted advantages, airway assessment is not routinely performed in dentistry. There is no protocol that allows comparisons between studies, which makes it difficult to obtain scientific evidence in this area. Hence, there is an urgent need to standardize the protocol for upper airway measurement to help clinicians identify at-risk patients. Objective: Our main aim is to develop a standard protocol for upper airway evaluation in CBCT for OSAS screening in dentistry. Methods: To measure and evaluate the upper airways, data are obtained using Planmeca ProMax 3D (Planmeca). Patient orientation is performed in accordance with the manufacturer's indications at the time of image acquisition. The exposure corresponds to 90 kV, 8 mA, and 13,713 seconds. The software used for upper airway analysis is Romexis (version 5.1.O.R; Planmeca). The images are exhibited in accordance with the field of view of 20.1{\texttimes}17.4 cm, size of 502{\texttimes}502{\texttimes}436 mm, and voxel size of 400 $\mu$m. Results: The protocol described and illustrated here allows for automatic calculation of the total volume of the pharyngeal airspace, its area of greatest narrowing, its location, and the smallest anteroposterior and laterolateral dimensions of the pharynx. These measurements are carried out automatically by the imaging software whose reliability is proven by the existing literature. Thus, we could reduce the possible bias of manual measurement, aiming at data collection. Conclusions: The use of this protocol by dentists will allow for standardization of the measurements and constitutes a valuable screening tool for OSAS. This protocol may also be suitable for other imaging software. The anatomical points used as reference are most relevant for standardizing studies in this field. International Registered Report Identifier (IRRID): RR1-10.2196/41049 ",
doi="10.2196/41049",
url="https://www.researchprotocols.org/2023/1/e41049",
url="http://www.ncbi.nlm.nih.gov/pubmed/37145857"
}


@Article{info:doi/10.2196/43617,
author="Ke Wang, Will
and Cesnakova, Lucia
and Goldsack, C. Jennifer
and Dunn, Jessilyn",
title="Defining the Digital Measurement of Scratching During Sleep or Nocturnal Scratching: Review of the Literature",
journal="J Med Internet Res",
year="2023",
month="Apr",
day="18",
volume="25",
pages="e43617",
keywords="atopic dermatitis",
keywords="ontologies",
keywords="nocturnal scratching",
keywords="quality of life",
keywords="outcomes measurement",
keywords="dermatitis",
keywords="scratch",
keywords="review method",
keywords="systematic review",
keywords="literature review",
keywords="pruritis",
abstract="Background: Digital sensing solutions represent a convenient, objective, relatively inexpensive method that could be leveraged for assessing symptoms of various health conditions. Recent progress in the capabilities of digital sensing products has targeted the measurement of scratching during sleep, traditionally referred to as nocturnal scratching, in patients with atopic dermatitis or other skin conditions. Many solutions measuring nocturnal scratch have been developed; however, a lack of efforts toward standardization of the measure's definition and contextualization of scratching during sleep hampers the ability to compare different technologies for this purpose. Objective: We aimed to address this gap and bring forth unified measurement definitions for nocturnal scratch. Methods: We performed a narrative literature review of definitions of scratching in patients with skin inflammation and a targeted literature review of sleep in the context of the period during which such scratching occurred. Both searches were limited to English language studies in humans. The extracted data were synthesized into themes based on study characteristics: scratch as a behavior, other characterization of the scratching movement, and measurement parameters for both scratch and sleep. We then developed ontologies for the digital measurement of sleep scratching. Results: In all, 29 studies defined inflammation-related scratching between 1996 and 2021. When cross-referenced with the results of search terms describing the sleep period, only 2 of these scratch-related papers also described sleep-related variables. From these search results, we developed an evidence-based and patient-centric definition of nocturnal scratch: an action of rhythmic and repetitive skin contact movement performed during a delimited time period of intended and actual sleep that is not restricted to any specific time of the day or night. Based on the measurement properties identified in the searches, we developed ontologies of relevant concepts that can be used as a starting point to develop standardized outcome measures of scratching during sleep in patients with inflammatory skin conditions. Conclusions: This work is intended to serve as a foundation for the future development of unified and well-described digital health technologies measuring nocturnal scratching and should enable better communication and sharing of results between various stakeholders taking part in research in atopic dermatitis and other inflammatory skin conditions. ",
doi="10.2196/43617",
url="https://www.jmir.org/2023/1/e43617",
url="http://www.ncbi.nlm.nih.gov/pubmed/37071460"
}


@Article{info:doi/10.2196/45721,
author="Zheng, Wanyu
and Chen, Qingquan
and Yao, Ling
and Zhuang, Jiajing
and Huang, Jiewei
and Hu, Yiming
and Yu, Shaoyang
and Chen, Tebin
and Wei, Nan
and Zeng, Yifu
and Zhang, Yixiang
and Fan, Chunmei
and Wang, Youjuan",
title="Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="24",
volume="25",
pages="e45721",
keywords="artificial neural network",
keywords="college students",
keywords="COVID-19",
keywords="internet new media",
keywords="logistic regression",
keywords="machine learning",
keywords="sleep quality",
abstract="Background: COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic's effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. Objective: This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. Methods: From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70\%) and an independent validation set (n=6194, 30\%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and na{\"i}ve Bayes were developed and validated. Results: In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9\% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52\%, 25.51\%, 92.58\%, 57.71\%, and 75.79\%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41\%, 94.77\%, and 86.40\%, respectively. Conclusions: The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions. ",
doi="10.2196/45721",
url="https://www.jmir.org/2023/1/e45721",
url="http://www.ncbi.nlm.nih.gov/pubmed/36961495"
}


@Article{info:doi/10.2196/39052,
author="Vollert, Bianka
and M{\"u}ller, Luise
and Jacobi, Corinna
and Trockel, Mickey
and Beintner, Ina",
title="Effectiveness of an App-Based Short Intervention to Improve Sleep: Randomized Controlled Trial",
journal="JMIR Ment Health",
year="2023",
month="Mar",
day="21",
volume="10",
pages="e39052",
keywords="sleep",
keywords="insomnia",
keywords="cognitive behavioral treatment for insomnia",
keywords="eHealth",
keywords="mobile app",
abstract="Background: A growing body of evidence for digital interventions to improve sleep shows promising effects. The interventions investigated so far have been primarily web-based; however, app-based interventions may reach a wider audience and be more suitable for daily use. Objective: This study aims to evaluate the intervention effects, adherence, and acceptance of an unguided app-based intervention for individuals who wish to improve their sleep. Methods: In a randomized controlled trial, we evaluated the effects of an app-based short intervention (Refresh) to improve sleep compared with a waitlist condition. Refresh is an 8-week unguided intervention covering the principles of cognitive behavioral therapy for insomnia (CBT-I) and including a sleep diary. The primary outcome was sleep quality (insomnia symptoms) as self-assessed by the Regensburg Insomnia Scale (RIS). The secondary outcomes were depression (9-item Patient Health Questionnaire [PHQ-9] score) and perceived insomnia-related impairment. Results: We included 371 participants, of which 245 reported poor sleep at baseline. About 1 in 3 participants who were allocated to the intervention group never accessed the intervention. Active participants completed on average 4 out of 8 chapters. Retention rates were 67.4\% (n=250) at postassessment and 57.7\% (n=214) at the 6-month follow-up. At postintervention, insomnia symptoms in the intervention group had improved more than those in the waitlist group, with a small effect (d=0.26) in the whole sample and a medium effect (d=0.45) in the subgroup with poor sleep. Effects in the intervention group were maintained at follow-up. Perceived insomnia-related impairment also improved from pre- to postassessment. No significant intervention effect on depression was detected. Working alliance and acceptance were moderate to good. Conclusions: An app-based, unguided intervention is a feasible and effective option to scale-up CBT-I-based treatment, but intervention uptake and adherence need to be carefully addressed. Trial Registration: ISRCTN Registry ISRCTN53553517; https://www.isrctn.com/ISRCTN53553517 ",
doi="10.2196/39052",
url="https://mental.jmir.org/2023/1/e39052",
url="http://www.ncbi.nlm.nih.gov/pubmed/36943337"
}


@Article{info:doi/10.2196/44123,
author="Alamoudi, Doaa
and Breeze, Emma
and Crawley, Esther
and Nabney, Ian",
title="The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Feb",
day="17",
volume="11",
pages="e44123",
keywords="mHealth",
keywords="digital",
keywords="health",
keywords="mental health",
keywords="insomnia",
keywords="technology",
keywords="sleep",
keywords="risk",
keywords="cardiovascular disease",
keywords="diabetes",
keywords="men",
keywords="mortality",
keywords="sleep disorder",
keywords="anxiety",
keywords="depression",
keywords="heart disease",
keywords="obesity",
keywords="dementia",
keywords="sensor",
keywords="intervention",
keywords="young adult",
abstract="Background: Since the era of smartphones started in early 2007, they have steadily turned into an accepted part of our lives. Poor sleep is a health problem that needs to be closely monitored before it causes severe mental health problems, such as anxiety or depression. Sleep disorders (eg, acute insomnia) can also develop to chronic insomnia if not treated early. More specifically, mental health problems have been recognized to have casual links to anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. Several researchers have used mobile sensors to monitor sleep and to study changes in individual mood that may cause depression and anxiety. Objective: Extreme sleepiness and insomnia not only influence physical health, they also have a significant impact on mental health, such as by causing depression, which has a prevalence of 18\% to 21\% among young adults aged 16 to 24 in the United Kingdom. The main body of this narrative review explores how passive data collection through smartphone sensors can be used in predicting anxiety and depression. Methods: A narrative review of the English language literature was performed. We investigated the use of smartphone sensors as a method of collecting data from individuals, regardless of whether the data source was active or passive. Articles were found from a search of Google Scholar records (from 2013 to 2020) with keywords including ``mobile phone,'' ``mobile applications,'' ``health apps,'' ``insomnia,'' ``mental health,'' ``sleep monitoring,'' ``depression,'' ``anxiety,'' ``sleep disorder,'' ``lack of sleep,'' ``digital phenotyping,'' ``mobile sensing,'' ``smartphone sensors,'' and ``sleep detector.'' Results: The 12 articles presented in this paper explain the current practices of using smartphone sensors for tracking sleep patterns and detecting changes in mental health, especially depression and anxiety over a period of time. Several researchers have been exploring technological methods to detect sleep using smartphone sensors. Researchers have also investigated changes in smartphone sensors and linked them with mental health and well-being. Conclusions: The conducted review provides an overview of the possibilities of using smartphone sensors unobtrusively to collect data related to sleeping pattern, depression, and anxiety. This provides a unique research opportunity to use smartphone sensors to detect insomnia and provide early detection or intervention for mental health problems such as depression and anxiety if insomnia is detected. ",
doi="10.2196/44123",
url="https://mhealth.jmir.org/2023/1/e44123",
url="http://www.ncbi.nlm.nih.gov/pubmed/36800211"
}


@Article{info:doi/10.2196/41298,
author="Zhang, Xuan
and Li, Yang
and Wang, Juan
and Mao, Fangxiang
and Wu, Liuliu
and Huang, Yongqi
and Sun, Jiwei
and Cao, Fenglin",
title="Effectiveness of Digital Guided Self-help Mindfulness Training During Pregnancy on Maternal Psychological Distress and Infant Neuropsychological Development: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2023",
month="Feb",
day="10",
volume="25",
pages="e41298",
keywords="digital",
keywords="mobile health",
keywords="mHealth",
keywords="guided self-help",
keywords="psychological distress",
keywords="pregnancy",
keywords="psychosocial intervention",
keywords="mindfulness",
keywords="infant",
keywords="neuropsychological performance",
abstract="Background: Maternal psychological distress during pregnancy is associated with unfavorable outcomes in infants. Mindfulness-based interventions (MBIs) can effectively alleviate psychological distress, but there are often barriers to the access of face-to-face interventions. Objective: This study aimed to investigate the effectiveness of a digital guided self-help (GSH) MBI (GSH-MBI) in reducing maternal psychological distress and improving infant neuropsychological performance. Methods: This was a randomized controlled trial. We recruited 160 women who were 12 to 20 weeks pregnant and exhibited psychological distress. We randomized them into a digital GSH-MBI group and a control group (usual perinatal care). The digital GSH-MBI consisted of a 6-week intervention through a WeChat mini program, with a daily reminder sent to the participants by a research assistant via WeChat. The primary outcomes consisted of maternal psychological distress, including depression, anxiety, and pregnancy-related anxiety symptoms, which were assessed at 6 time points from baseline to 6 months post partum (only pregnancy-related anxiety symptoms were assessed 3 times during pregnancy). The secondary outcomes were infant neuropsychological outcomes, including temperament and developmental behaviors, which were assessed at 6 weeks and 6 months post partum. Results: Compared with the control group, the digital GSH-MBI group showed a significant reduction in depression, anxiety, and pregnancy-related anxiety symptoms. In addition, the scores of the digital GSH-MBI group were lower than those of the control group for the 3 types of infant temperament at 6 weeks post partum, including quality of mood, distractibility, and adaptability. Conclusions: Digital GSH-MBIs are effective in alleviating psychological distress among pregnant women and protecting infant outcomes. Trial Registration: Chinese Clinical Trial Register ChiCTR2000040717; https://www.chictr.org.cn/showproj.aspx?proj=65376 ",
doi="10.2196/41298",
url="https://www.jmir.org/2023/1/e41298",
url="http://www.ncbi.nlm.nih.gov/pubmed/36763452"
}


@Article{info:doi/10.2196/40836,
author="Yoshizaki, Arika
and Murata, Emi
and Yamamoto, Tomoka
and Fujisawa, X. Takashi
and Hanaie, Ryuzo
and Hirata, Ikuko
and Matsumoto, Sayuri
and Mohri, Ikuko
and Taniike, Masako",
title="Improving Children's Sleep Habits Using an Interactive Smartphone App: Community-Based Intervention Study",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Feb",
day="10",
volume="11",
pages="e40836",
keywords="infant sleep",
keywords="app",
keywords="mHealth",
keywords="mobile health",
keywords="behavioral intervention",
keywords="sleep health",
keywords="social implementation",
keywords="mobile phone",
abstract="Background: Sleep problems are quite common among young children and are often a challenge for parents and a hinderance to children's development. Although behavioral therapy has proven effective in reducing sleep problems in children, a lack of access to professionals who can provide effective support is a major barrier for many caregivers. Therefore, pediatric sleep experts have begun developing apps and web-based services for caregivers. Despite the substantial influence of cultural and familial factors on children's sleep, little effort has gone into developing cultural or family-tailored interventions. Objective: This study aimed to examine the effectiveness of the interactive smartphone app ``Nenne Navi,'' which provides culturally and family-tailored suggestions for improving sleep habits in young Japanese children through community-based long-term trials. The study also aimed to investigate the association between app-driven improvements in sleep and mental development in children. Methods: This study adopted a community-based approach to recruit individuals from the Higashi-Osaka city (Japan) who met ?1 of the following eligibility criteria for sleep problems: sleeping after 10 PM, getting <9 hours of nighttime sleep, and experiencing frequent nighttime awakenings. A total of 87 Japanese caregivers with young children (mean 19.50, SD 0.70 months) were recruited and assigned to the app use group (intervention group) or the video-only group (control group). Both groups received educational video content regarding sleep health literacy. The caregivers in the intervention group used the app, which provides family-tailored suggestions, once per month for 1 year. Results: A total of 92\% (33/36) of the caregivers in the app use group completed 1 year of the intervention. The participants' overall evaluation of the app was positive. The wake-up time was advanced (base mean 8:06 AM; post mean 7:48 AM; F1,65=6.769; P=.01 and sleep onset latency was decreased (base mean 34.45 minutes; post mean 20.05 minutes; F1,65=23.219; P<.001) significantly in the app use group at the 13th month compared with the video-only group. Moreover, multiple regression analysis showed that decreased social jetlag ($\beta$=?0.302; P=.03) and increased sleep onset latency SD ($\beta$=.426; P=.02) in children predicted a significant enhancement in the development of social relationships with adults. At 6 months after the completion of the app use, all the caregivers reported continuation of the new lifestyle. Conclusions: The present findings suggest that the app ``Nenne Navi'' has high continuity in community use and can improve sleep habits in young Japanese children and that interventions for sleep habits of young children may lead to the enhancement of children's social development. Future studies must focus on the effectiveness of the app in other regions with different regional characteristics and neuroscientific investigations on how changes in sleep impact brain development. ",
doi="10.2196/40836",
url="https://mhealth.jmir.org/2023/1/e40836",
url="http://www.ncbi.nlm.nih.gov/pubmed/36641237"
}


@Article{info:doi/10.2196/42856,
author="Nuo, Mingfu
and Zheng, Shaojiang
and Wen, Qinglian
and Fang, Hongjuan
and Wang, Tong
and Liang, Jun
and Han, Hongbin
and Lei, Jianbo",
title="Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling",
journal="J Med Internet Res",
year="2023",
month="Jan",
day="31",
volume="25",
pages="e42856",
keywords="sleep disorder",
keywords="mobile health applications",
keywords="topic modeling",
keywords="Herzberg's 2-factor theory",
keywords="machine learning",
abstract="Background: Sleep disorders are a global challenge, affecting a quarter of the global population. Mobile health (mHealth) sleep apps are a potential solution, but 25\% of users stop using them after a single use. User satisfaction had a significant impact on continued use intention. Objective: This China-US comparison study aimed to mine the topics discussed in user-generated reviews of mHealth sleep apps, assess the effects of the topics on user satisfaction and dissatisfaction with these apps, and provide suggestions for improving users' intentions to continue using mHealth sleep apps. Methods: An unsupervised clustering technique was used to identify the topics discussed in user reviews of mHealth sleep apps. On the basis of the two-factor theory, the Tobit model was used to explore the effect of each topic on user satisfaction and dissatisfaction, and differences in the effects were analyzed using the Wald test. Results: A total of 488,071 user reviews of 10 mainstream sleep apps were collected, including 267,589 (54.8\%) American user reviews and 220,482 (45.2\%) Chinese user reviews. The user satisfaction rates of sleep apps were poor (China: 56.58\% vs the United States: 45.87\%). We identified 14 topics in the user-generated reviews for each country. In the Chinese data, 13 topics had a significant effect on the positive deviation (PD) and negative deviation (ND) of user satisfaction. The 2 variables (PD and ND) were defined by the difference between the user rating and the overall rating of the app in the app store. Among these topics, the app's sound recording function ($\beta$=1.026; P=.004) had the largest positive effect on the PD of user satisfaction, and the topic with the largest positive effect on the ND of user satisfaction was the sleep improvement effect of the app ($\beta$=1.185; P<.001). In the American data, all 14 topics had a significant effect on the PD and ND of user satisfaction. Among these, the topic with the largest positive effect on the ND of user satisfaction was the app's sleep promotion effect ($\beta$=1.389; P<.001), whereas the app's sleep improvement effect ($\beta$=1.168; P<.001) had the largest positive effect on the PD of user satisfaction. The Wald test showed that there were significant differences in the PD and ND models of user satisfaction in both countries (all P<.05), indicating that the influencing factors of user satisfaction with mHealth sleep apps were asymmetrical. Using the China-US comparison, hygiene factors (ie, stability, compatibility, cost, and sleep monitoring function) and 2 motivation factors (ie, sleep suggestion function and sleep promotion effects) of sleep apps were identified. Conclusions: By distinguishing between the hygiene and motivation factors, the use of sleep apps in the real world can be effectively promoted. ",
doi="10.2196/42856",
url="https://www.jmir.org/2023/1/e42856",
url="http://www.ncbi.nlm.nih.gov/pubmed/36719730"
}


@Article{info:doi/10.2196/38112,
author="Meyerson, U. William
and Fineberg, K. Sarah
and Song, Kyung Ye
and Faber, Adam
and Ash, Garrett
and Andrade, C. Fernanda
and Corlett, Philip
and Gerstein, B. Mark
and Hoyle, H. Rick",
title="Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys",
journal="JMIR Form Res",
year="2023",
month="Jan",
day="17",
volume="7",
pages="e38112",
keywords="social media",
keywords="sleep",
keywords="parametric models",
keywords="Reddit",
keywords="observational model",
keywords="research tool",
keywords="sleep patterns",
keywords="usage data",
keywords="model",
keywords="bedtime",
abstract="Background: Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community. Objective: This study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation). Methods: We included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model's form was chosen by visualizing the aggregate distribution of the timing of users' posts relative to their reported bedtimes. The chosen model represents a user's frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user's bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts. Results: The Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87\% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6\%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5\%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach. Conclusions: We uncovered a simple, reproducible relationship between Reddit users' reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users' bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available. ",
doi="10.2196/38112",
url="https://formative.jmir.org/2023/1/e38112",
url="http://www.ncbi.nlm.nih.gov/pubmed/36649054"
}


@Article{info:doi/10.2196/32705,
author="Kutzer, Yvonne
and Whitehead, Lisa
and Quigley, Eimear
and Stanley, Mandy",
title="The Role of Dysfunctional Sleep Beliefs in Mediating the Outcomes of Web-Based Cognitive Behavioral Therapy for Insomnia in Community-Dwelling Older Adults: Protocol for a Single-Group, Nonrandomized Trial",
journal="JMIR Res Protoc",
year="2022",
month="Dec",
day="27",
volume="11",
number="12",
pages="e32705",
keywords="older adults",
keywords="insomnia",
keywords="cognitive therapy",
keywords="digital literacy",
keywords="cognitive behavioral therapy for insomnia (CBT-I)",
keywords="online psychological intervention",
abstract="Background: Sleeping well is an essential part of good health. Older adult populations report a high rate of sleep problems, with recent studies suggesting that cognitive processes as well as behavioral and hyperarousal-related mechanisms could be important factors in the development and maintenance of insomnia. Individuals who have an asynchronous or uncoupled sleep pattern and sleep appraisal---those who complain about their sleep but do not have poor sleep quality, and vice versa---might show differences in subjective sleep and sleep perceptions and other characteristics that could impact their treatment outcomes following cognitive behavioral therapy for insomnia (CBT-I). Objective: The purpose of this protocol is to describe the rationale and methods for a nonrandomized, single-arm trial assessing objective and subjective sleep quality in community-dwelling older adults aged 60-80 years with synchronous sleep patterns and sleep appraisal compared to those in older adults with asynchronous sleep patterns and sleep appraisal. The trial will further examine the role of cognitive, behavioral, and hyperarousal processes in mediating the treatment outcomes of web-based CBT-I. Methods: This trial aims to recruit a sample of 60 participants, who will be assigned to 1 of 4 sleep groups based on their sleep pattern and sleep appraisal status: complaining good sleepers, complaining poor sleepers, noncomplaining good sleepers, and noncomplaining poor sleepers, respectively. The trial will be completed in 2 phases: phase 1 will assess objective sleep (measured via wrist actigraphy) and subjective (self-reported) sleep. Phase 2 will investigate the impact of a web-based CBT-I program on the sleep outcomes of individuals with uncoupled sleep compared to that of individuals without uncoupled sleep, as well as the mediators of CBT-I. Results: Recruitment began in March 2020, and the last participants were recruited by March 2021. A total of 65 participants completed phases 1 and 2. Data analysis for phase 1 was finished in December 2021, and data analysis for phase 2 was finalized in July 2022. The results for phase 1 were submitted for publication in March 2022, and those for phase 2 will be submitted by the end of December 2022. Conclusions: This trial will provide guidance on factors that contribute to the variability of sleep in older adults and their sleep outcomes following CBT-I. The outcomes of this study could be valuable for future research attempting to tailor CBT-I to individual needs. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619001509156; https://tinyurl.com/69hhdu2w International Registered Report Identifier (IRRID): DERR1-10.2196/32705 ",
doi="10.2196/32705",
url="https://www.researchprotocols.org/2022/12/e32705",
url="http://www.ncbi.nlm.nih.gov/pubmed/36574272"
}


@Article{info:doi/10.2196/39489,
author="Pfammatter, Fidler Angela
and Hughes, Olivia Bonnie
and Tucker, Becky
and Whitmore, Harry
and Spring, Bonnie
and Tasali, Esra",
title="The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="5",
volume="24",
number="12",
pages="e39489",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="CPAP adherence",
keywords="weight loss",
keywords="lifestyle",
abstract="Background: Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA. Objective: This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA. Methods: We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback. Results: During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal (``Goal: 100\%''). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning. Conclusions: We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted. ",
doi="10.2196/39489",
url="https://www.jmir.org/2022/12/e39489",
url="http://www.ncbi.nlm.nih.gov/pubmed/36469406"
}


@Article{info:doi/10.2196/37371,
author="Scher, Nathaniel
and Guetta, Liath
and Draghi, Cl{\'e}ment
and Yahiaoui, Safia
and Terzioglu, Mathilde
and Butaye, Emilie
and Henriques, Kathy
and Alavoine, Marie
and Elharar, Ayala
and Guetta, Andre
and Toledano, Alain",
title="Sleep Disorders and Quality of Life in Patients With Cancer: Prospective Observational Study of the Rafael Institute",
journal="JMIR Form Res",
year="2022",
month="Nov",
day="24",
volume="6",
number="11",
pages="e37371",
keywords="cancer",
keywords="sleep disorder",
keywords="sleep",
keywords="fatigue",
keywords="nocturnal",
keywords="oncology",
keywords="cancer care",
keywords="patient-centred approach",
keywords="patient-centered",
keywords="personalized",
keywords="personalization",
keywords="customized",
keywords="customization",
keywords="care plan",
keywords="quality of life",
keywords="mood",
keywords="pain",
keywords="cancer treatment",
keywords="overweight",
keywords="obese",
keywords="hormone therapy",
keywords="breast",
keywords="prostate",
abstract="Background: Sleep disorders are a common occurrence in the general population. Yet today, it is clearly agreed that sleep disorders represent both a cancer risk factor and a biological consequence of the of the activation of the immuno-inflammatory system induced by cancer itself. Objective: The aim of this study was to assess the impact of sleep disorders on quality of life and identify the type of disorder and its causes in order to offer an adapted and personalized care plan. Methods: In a survey completed during the COVID-19 lockdown, 2000 hours of interviews were collected by remote consultations. During these calls, we administered a sleep questionnaire. This questionnaire was inspired by the STOP-BANG questionnaire and enquired about 6 items. The demographic details of each patient (eg, age and sex), the nature of the pathology, their past treatments, the ongoing cancer treatment, the mood, whether or not the patient is anxious or depressed, and the use of sleeping drug pills were analyzed. A univariate analysis was performed according to the presence or absence of fatigue. Chi-square test was applied to assess possible differences of variables' link to sleep disturbance between patients complaining of fatigue and those without fatigue. The same test was then used to analyze patients on hormone therapy and those with no hormone therapy for 2 types of cancer---breast cancer and prostate cancer. Results: A total of 905 patients were prospectively included in this study. The average age was 66.7 (5 SD) years, and 606 (67\%) patients were women; 142 patients declared being overweight. Breast cancer was the most frequently reported cancer. Nocturnal awakening was reported by 70\% (n=633), fatigue by 50\% (n=452), difficulty falling asleep by 38\% (n=343), snoring reported by an independent observer in 38\% (n=343), and apnea reported by an independent observer in 9\% (n=81) of the patients. The univariate analysis showed that the feeling of tiredness was significantly greater in patients reporting difficulty falling asleep (P?.99), pain (P<.001), and frequent awakening (P<.001), as well as in patients who were not receiving cancer treatment (P<.001). The univariate analysis showed that patients who were receiving breast cancer treatment and were under hormone therapy reported difficulty falling asleep (P=.04) and pain (P=.05). In a univariate analysis of patients treated for prostate cancer, being overweight was the only factor reported that had a statistically significant value. Conclusions: Our preliminary data support and are consistent with data in the literature regarding the importance of sleep disorders in oncology. This justifies the usefulness of a diagnosis and early treatment of sleep disorders in patients with cancer. The Rafael Institute sleep observatory will enable patients to be identified and treated. ",
doi="10.2196/37371",
url="https://formative.jmir.org/2022/11/e37371",
url="http://www.ncbi.nlm.nih.gov/pubmed/36422866"
}


@Article{info:doi/10.2196/41288,
author="Cohen Zion, Mairav
and Gescheit, Iddo
and Levy, Nir
and Yom-Tov, Elad",
title="Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire",
journal="J Med Internet Res",
year="2022",
month="Nov",
day="23",
volume="24",
number="11",
pages="e41288",
keywords="sleep disorders",
keywords="search engine queries",
keywords="search advertising",
keywords="internet",
keywords="Bing",
keywords="sleep",
keywords="machine learning",
keywords="questionnaire",
abstract="Background: Sleep disorders are experienced by up to 40\% of the population but their diagnosis is often delayed by the availability of specialists. Objective: We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders. Methods: Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire. Results: A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder. Conclusions: Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do. ",
doi="10.2196/41288",
url="https://www.jmir.org/2022/11/e41288",
url="http://www.ncbi.nlm.nih.gov/pubmed/36416870"
}


@Article{info:doi/10.2196/40500,
author="Espel-Huynh, Hallie
and Baldwin, Matthew
and Puzia, Megan
and Huberty, Jennifer",
title="The Indirect Effects of a Mindfulness Mobile App on Productivity Through Changes in Sleep Among Retail Employees: Secondary Analysis",
journal="JMIR Mhealth Uhealth",
year="2022",
month="Sep",
day="28",
volume="10",
number="9",
pages="e40500",
keywords="mindfulness",
keywords="mobile apps",
keywords="workforce",
keywords="workplace",
keywords="sleep",
keywords="presenteeism",
keywords="mobile phone",
abstract="Background: Chronic sleep disturbance is prevalent among United States employees and associated with costly productivity impairment. Mindfulness interventions improve sleep (ie, insomnia and daytime sleepiness) and productivity outcomes, and mobile apps provide scalable means of intervention delivery. However, few studies have examined the effects of mindfulness mobile apps on employees, and no research to date has tested the role of sleep improvement as a potential mechanism of action for productivity outcomes. Objective: This study examined the effects of Calm, a consumer-based mindfulness app, and sleep coaching, on productivity impairment among retail employees through the indirect effects of changes in insomnia and daytime sleepiness. Methods: This study was a secondary analysis of data from a randomized controlled trial (N=1029) comparing the use of Calm (n=585, 56.9\%) to a waitlist control (n=444, 43.2\%) for 8 weeks among employees of a large retail employer in the United States. A subset of individuals with elevated insomnia symptoms also had access to brief sleep coaching with Calm (n=101, 9.8\%). Insomnia symptom severity, daytime sleepiness, and productivity impairment (ie, absenteeism, presenteeism, overall productivity impairment, and non--work activity impairment) were assessed at baseline and weeks 2, 4, 6, and 8. Indirect effects were evaluated with latent growth curve modeling to test whether the Calm intervention (Calm group vs waitlist control) was effective in reducing work productivity impairment through changes in sleep disturbance. Results: No significant main effects of Calm intervention on productivity impairment were detected for any outcome at $\alpha$ level of .05, with the exception of non--work activity impairment models, in which Calm intervention reduced non--work activity impairment over time (P=.01 and P=.02 for insomnia and sleepiness models, respectively). Significant indirect effects of insomnia were detected for presenteeism (P=.002), overall work productivity (P=.01), and non--work activity impairment (P=.002); Calm intervention produced significantly greater reductions in insomnia symptoms (relative to waitlist control), and decreases in insomnia were associated with decreases in work productivity impairment. There was no significant indirect effect of change in insomnia on changes in absenteeism (P=.20). Furthermore, we detected no significant indirect effects of daytime sleepiness on productivity impairment. Conclusions: We found that Calm (plus sleep coaching for a small subset of individuals) had beneficial effects on employee sleep, and these benefits on sleep were related to indirect effects on productivity impairment (ie, presenteeism, overall work productivity impairment, and non--work activity impairment). There were no overall main effects of Calm intervention on productivity impairment; however, insomnia appears to be a mechanism associated with benefits for employee productivity. This is one of the first studies to suggest that sleep benefits of a mindfulness mobile app may also indirectly relate to benefits for workplace productivity. Trial Registration: ClinicalTrials.gov NCT05120310; https://clinicaltrials.gov/ct2/show/NCT05120310 ",
doi="10.2196/40500",
url="https://mhealth.jmir.org/2022/9/e40500",
url="http://www.ncbi.nlm.nih.gov/pubmed/36169994"
}


@Article{info:doi/10.2196/38608,
author="Ludwig, Rebecca
and Rippee, Michael
and D'Silva, J. Linda
and Radel, Jeff
and Eakman, M. Aaron
and Morris, Jill
and Drerup, Michelle
and Siengsukon, Catherine",
title="Assessing Cognitive Behavioral Therapy for Insomnia to Improve Sleep Outcomes in Individuals With a Concussion: Protocol for a Delayed Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2022",
month="Sep",
day="23",
volume="11",
number="9",
pages="e38608",
keywords="sleep",
keywords="concussion",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="insomnia",
keywords="brain",
keywords="injury",
keywords="RCT",
keywords="randomized controlled trial",
keywords="protocol",
keywords="recovery",
keywords="pilot study",
abstract="Background: Sleep disturbances post concussion have been associated with more frequent and severe concussion symptoms and may contribute to poorer recovery. Cognitive behavioral therapy for insomnia (CBT-I) is an effective treatment for insomnia; however, it remains unclear if this treatment method is effective in improving sleep outcomes and reducing concomitant postconcussion symptoms. Objective: The hypotheses for this study are that (1) CBT-I will improve sleep outcomes and (2) CBT-I will improve concomitant postconcussion symptoms. Methods: In total, 40 individuals who are within ?4 weeks of postconcussion injury and have insomnia symptoms will be enrolled in this randomized controlled trial. Participants will be randomized into either a group that starts a 6-week CBT-I program immediately after baseline or a waitlist control group that starts CBT-I following a 6-week waiting period. All participants will be reassessed 6, 12, and 18 weeks after baseline. Standardized assessments measuring sleep outcomes, postconcussion symptoms, and mood will be used. Linear regression and t tests will be used for statistical analyses. Results: Enrollment of 40 participants was completed July 2022, data collection will be completed in November 2022, and publication of main findings is anticipated in May 2023. It is anticipated that participants experience reduced insomnia symptoms and postconcussion symptoms following CBT-I and these improvements will be retained for at least 12 weeks. Additionally, we expect to observe a positive correlation between sleep and postconcussion symptom improvement. Conclusions: Successful completion of this pilot study will allow for a better understanding of the treatment of insomnia and postconcussion symptoms in individuals following a concussion. Trial Registration: ClinicalTrials.gov NCT04885205; https://clinicaltrials.gov/ct2/show/NCT04885205 International Registered Report Identifier (IRRID): DERR1-10.2196/38608 ",
doi="10.2196/38608",
url="https://www.researchprotocols.org/2022/9/e38608",
url="http://www.ncbi.nlm.nih.gov/pubmed/36149737"
}


@Article{info:doi/10.2196/36618,
author="Toften, St{\aa}le
and Kjellstadli, T. Jonas
and Thu, Forstr{\o}nen Ole Kristian
and Ellingsen, Ole-Johan",
title="Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study",
journal="JMIR Biomed Eng",
year="2022",
month="Aug",
day="12",
volume="7",
number="2",
pages="e36618",
keywords="noncontact",
keywords="monitoring",
keywords="radar technology",
keywords="respiratory rate",
keywords="Somnofy",
keywords="validation",
abstract="Background: Respiratory rate (RR) is arguably the most important vital sign to detect clinical deterioration. Change in RR can also, for example, be associated with the onset of different diseases, opioid overdoses, intense workouts, or mood. However, unlike for most other vital parameters, an easy and accurate measuring method is lacking. Objective: This study aims to validate the radar-based sleep monitor, Somnofy, for measuring RRs and investigate whether events affecting RR can be detected from personalized baselines calculated from nightly averages. Methods: First, RRs from Somnofy for 37 healthy adults during full nights of sleep were extensively validated against respiratory inductance plethysmography. Then, the night-to-night consistency of a proposed filtered average RR was analyzed for 6 healthy participants in a pilot study in which they used Somnofy at home for 3 months. Results: Somnofy measured RR 84\% of the time, with mean absolute error of 0.18 (SD 0.05) respirations per minute, and Bland-Altman 95\% limits of agreement adjusted for repeated measurements ranged from --0.99 to 0.85. The accuracy and coverage were substantially higher in deep and light sleep than in rapid eye movement sleep and wake. The results were independent of age, sex, and BMI, but dependent on supine sleeping position for some radar orientations. For nightly filtered averages, the 95\% limits of agreement ranged from ?0.07 to ?0.04 respirations per minute. In the longitudinal part of the study, the nightly average was consistent from night to night, and all substantial deviations coincided with self-reported illnesses. Conclusions: RRs from Somnofy were more accurate than those from any other alternative method suitable for longitudinal measurements. Moreover, the nightly averages were consistent from night to night. Thus, several factors affecting RR should be detectable as anomalies from personalized baselines, enabling a range of applications. More studies are necessary to investigate its potential in children and older adults or in a clinical setting. ",
doi="10.2196/36618",
url="https://biomedeng.jmir.org/2022/2/e36618",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875674"
}


@Article{info:doi/10.2196/33964,
author="Mousavi, Avah Zahra
and Lai, Jocelyn
and Simon, Katharine
and Rivera, P. Alexander
and Yunusova, Asal
and Hu, Sirui
and Labbaf, Sina
and Jafarlou, Salar
and Dutt, D. Nikil
and Jain, C. Ramesh
and Rahmani, M. Amir
and Borelli, L. Jessica",
title="Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study",
journal="JMIR Form Res",
year="2022",
month="Aug",
day="5",
volume="6",
number="8",
pages="e33964",
keywords="sleep",
keywords="objective sleep outcomes",
keywords="COVID-19",
keywords="affect variability",
keywords="affect dynamics",
abstract="Background: Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant. Objective: In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic. Methods: College student participants (N=20; female: 13/20, 65\%) wore an Oura ring (?ura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day. Results: Participants with a higher sleep onset latency (b=?1.09, SE 0.36; P=.006) and TST (b=?0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=?0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04). Conclusions: Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health. ",
doi="10.2196/33964",
url="https://formative.jmir.org/2022/8/e33964",
url="http://www.ncbi.nlm.nih.gov/pubmed/35816447"
}


@Article{info:doi/10.2196/36862,
author="Shimamoto, Tomonari
and Furihata, Ryuji
and Nakagami, Yukako
and Tateyama, Yukiko
and Kobayashi, Daisuke
and Kiyohara, Kosuke
and Iwami, Taku",
title="Providing Brief Personalized Therapies for Insomnia Among Workers Using a Sleep Prompt App: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2022",
month="Jul",
day="25",
volume="24",
number="7",
pages="e36862",
keywords="sleep prompt app",
keywords="smartphone",
keywords="brief personalized therapies for insomnia",
keywords="worker",
keywords="randomized controlled trial",
keywords="Japan",
abstract="Background: Insomnia is the most common sleep disorder and the foremost health concern among workers. We developed a new sleep prompt app (SPA) for smartphones to positively alter the users' consciousness and behavior by sending timely short messages for mild sleep problems at an early stage. Objective: The aim of this study is to investigate the effectiveness of the SPA in providing brief personalized therapy for insomnia among workers. Methods: We conducted a 2-arm parallel randomized controlled trial. The intervention group used the SPA, and the control group received no intervention. Participants were recruited between November 2020 and January 2021. The researcher sent emails for recruitment to more than 3000 workers of 2 companies and 1 university in Japan. The SPA provided personalized prompt messages, sleep diaries, sleep hygiene education, stimulus control therapy, and sleep restriction therapy. The prompt messages were sent automatically to the participants to encourage them to improve their sleep habits and sleep status and were optimized to the individual's daily rhythm. The intervention program duration was 4 weeks. The primary outcome was a change in the Insomnia Severity Index (ISI) for the study period. The ISI was obtained weekly using a web questionnaire. Results: A total of 116 Japanese workers (intervention group n=60, control group n=56) with sleep disorders were recruited. Two participants in the intervention group were excluded from the analyses because of challenges in installing the SPA. The mean ISI scores at baseline were 9.2 for both groups; however, after 4 weeks, the mean ISI scores declined to 6.8 and 8.0 for the intervention and control groups, respectively. Primary analysis using a linear mixed model showed a significant improvement in the temporal trends of the ISI in the SPA group and in the total population (P=.03). Subgroup analyses of ISI-8-insomniacs revealed a significant improvement in the temporal trends of ISI in the SPA group (P=.01), and the CFS score for physical condition significantly improved following the intervention (P=.02). Conclusions: This study demonstrates the effectiveness of the SPA in providing brief personalized therapy for insomnia among Japanese workers with mild insomnia. The physical fatigue score significantly improved in ISI-8-insomniacs. Thus, SPA could play an important role in reducing the adverse effects of sleep disorders in workers. To promote the wide use of the SPA in the future, further studies are required to examine its effectiveness in other age groups and individuals with health problems. Trial Registration: University Medical Information Network Clinical Trials Registry (UMIN-CTR) UMIN000042263; https://center6.umin.ac.jp/cgi-open-bin/ctr\_e/ctr\_view.cgi?recptno=R000046295 ",
doi="10.2196/36862",
url="https://www.jmir.org/2022/7/e36862",
url="http://www.ncbi.nlm.nih.gov/pubmed/35877164"
}


@Article{info:doi/10.2196/38000,
author="Mekhael, Mario
and Lim, Ho Chan
and El Hajjar, Hadi Abdel
and Noujaim, Charbel
and Pottle, Christopher
and Makan, Noor
and Dagher, Lilas
and Zhang, Yichi
and Chouman, Nour
and Li, L. Dan
and Ayoub, Tarek
and Marrouche, Nassir",
title="Studying the Effect of Long COVID-19 Infection on Sleep Quality Using Wearable Health Devices: Observational Study",
journal="J Med Internet Res",
year="2022",
month="Jul",
day="5",
volume="24",
number="7",
pages="e38000",
keywords="COVID-19",
keywords="digital health",
keywords="wearables",
keywords="sleep",
keywords="long COVID-19",
keywords="wearable device",
keywords="demographic",
keywords="biometric",
keywords="patient data",
keywords="sleep architecture",
keywords="health data",
keywords="health monitoring",
abstract="Background: Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data. Objective: In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands. Methods: Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject's total sleep time and sleep phases per night was calculated and compared between the 2 groups. Results: This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO2). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO2. Increased light sleep time was correlated with increased RR and SpO2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO2. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P=.02). Conclusions: Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep. ",
doi="10.2196/38000",
url="https://www.jmir.org/2022/7/e38000",
url="http://www.ncbi.nlm.nih.gov/pubmed/35731968"
}


@Article{info:doi/10.2196/39007,
author="Huberty, Jennifer
and Bhuiyan, Nishat
and Eckert, Ryan
and Larkey, Linda
and Petrov, Megan
and Todd, Michael
and Mesa, Ruben",
title="Insomnia as an Unmet Need in Patients With Chronic Hematological Cancer: Protocol for a Randomized Controlled Trial Evaluating a Consumer-Based Meditation App for Treatment of Sleep Disturbance",
journal="JMIR Res Protoc",
year="2022",
month="Jul",
day="1",
volume="11",
number="7",
pages="e39007",
keywords="hematological cancers",
keywords="mobile health",
keywords="mHealth",
keywords="meditation",
keywords="sleep disturbance",
keywords="mobile phone",
abstract="Background: To address the need for long-term, accessible, nonpharmacologic interventions targeting sleep in patients with chronic hematological cancer, we propose the first randomized controlled trial to determine the effects of a consumer-based mobile meditation app, Calm, on sleep disturbance in this population. Objective: This study aims to test the efficacy of daily meditation delivered via Calm compared with a health education podcast control group in improving the primary outcome of self-reported sleep disturbance, as well as secondary sleep outcomes, including sleep impairment and sleep efficiency; test the efficacy of daily meditation delivered via Calm compared with a health education podcast control group on inflammatory markers, fatigue, and emotional distress; and explore free-living use during a 12-week follow-up period and the sustained effects of Calm in patients with chronic hematological cancer. Methods: In a double-blinded randomized controlled trial, we will recruit 276 patients with chronic hematological cancer to an 8-week app-based wellness intervention---the active, daily, app-based meditation intervention or the health education podcast app control group, followed by a 12-week follow-up period. Participants will be asked to use their assigned app for at least 10 minutes per day during the 8-week intervention period; complete web-based surveys assessing self-reported sleep disturbance, fatigue, and emotional distress at baseline, 8 weeks, and 20 weeks; complete sleep diaries and wear an actigraphy device during the 8-week intervention period and at 20 weeks; and complete blood draws to assess inflammatory markers (tumor necrosis factor-$\alpha$, interleukin-6, interleukin-8, and C-reactive protein) at baseline, 8 weeks, and 20 weeks. Results: This project was funded by the National Institutes of Health National Cancer Institute (R01CA262041). The projects began in April 2022, and study recruitment is scheduled to begin in October 2022, with a total project duration of 5 years. We anticipate that we will be able to achieve our enrollment goal of 276 patients with chronic hematological cancers within the allotted project time frame. Conclusions: This research will contribute to broader public health efforts by providing researchers and clinicians with an evidence-based commercial product to improve sleep in the long term in an underserved and understudied cancer population with a high incidence of sleep disturbance. Trial Registration: ClinicalTrials.gov NCT05294991; https://clinicaltrials.gov/ct2/show/NCT05294991 International Registered Report Identifier (IRRID): PRR1-10.2196/39007 ",
doi="10.2196/39007",
url="https://www.researchprotocols.org/2022/7/e39007",
url="http://www.ncbi.nlm.nih.gov/pubmed/35776489"
}


@Article{info:doi/10.2196/39198,
author="Carney, E. Colleen
and Carmona, E. Nicole",
title="Authors' Response to: Additional Measurement Approaches for Sleep Disturbances. Comment on ``Transdiagnostic Self-management Web-Based App for Sleep Disturbance in Adolescents and Young Adults: Feasibility and Acceptability Study''",
journal="JMIR Form Res",
year="2022",
month="Jun",
day="13",
volume="6",
number="6",
pages="e39198",
keywords="youth",
keywords="sleep",
keywords="technology",
keywords="mHealth",
keywords="self-management",
keywords="adolescents",
keywords="young adults",
keywords="mobile phone",
keywords="smartphone",
keywords="polysomnography",
doi="10.2196/39198",
url="https://formative.jmir.org/2022/6/e39198",
url="http://www.ncbi.nlm.nih.gov/pubmed/35699990"
}


@Article{info:doi/10.2196/35959,
author="Tsai, Wan-Tong
and Liu, Tzung-Liang",
title="Additional Measurement Approaches for Sleep Disturbances. Comment on ``A Transdiagnostic Self-management Web-Based App for Sleep Disturbance in Adolescents and Young Adults: Feasibility and Acceptability Study''",
journal="JMIR Form Res",
year="2022",
month="Jun",
day="13",
volume="6",
number="6",
pages="e35959",
keywords="youth",
keywords="sleep",
keywords="technology",
keywords="mHealth",
keywords="self-management",
keywords="adolescents",
keywords="young adults",
keywords="mobile phone",
keywords="smartphone",
keywords="polysomnography",
doi="10.2196/35959",
url="https://formative.jmir.org/2022/6/e35959",
url="http://www.ncbi.nlm.nih.gov/pubmed/35700003"
}


@Article{info:doi/10.2196/27248,
author="Henriksen, Andr{\'e}
and Svartdal, Frode
and Grimsgaard, Sameline
and Hartvigsen, Gunnar
and Hopstock, Arnesdatter Laila",
title="Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study",
journal="JMIR Form Res",
year="2022",
month="May",
day="27",
volume="6",
number="5",
pages="e27248",
keywords="actigraphy",
keywords="fitness trackers",
keywords="motor activity",
keywords="energy expenditure",
keywords="steps",
keywords="activity tracker",
abstract="Background: Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed. Objective: The objective of this study was to compare the Polar Vantage watch (Polar Electro Oy) and Oura ring (generation 2; ?ura Health Oy) activity trackers to research-based instruments for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration in free-living adults. Methods: A total of 21 participants wore 2 consumer-based activity trackers (Polar watch and Oura ring), an ActiGraph accelerometer (ActiGraph LLC), and an Actiheart accelerometer and heart rate monitor (CamNtech Ltd) and completed a sleep diary for up to 7 days. We assessed Polar watch and Oura ring validity and comparability for measuring physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analyzed repeated measures correlations, Bland-Altman plots, and mean absolute percentage errors. Results: The Polar watch and Oura ring values strongly correlated (P<.001) with the ActiGraph values for steps (Polar: r=0.75, 95\% CI 0.54-0.92; Oura: r=0.77, 95\% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar: r=0.76, 95\% CI 0.62-0.88; Oura: r=0.70, 95\% CI 0.49-0.82), and total energy expenditure (Polar: r=0.69, 95\% CI 0.48-0.88; Oura: r=0.70, 95\% CI 0.51-0.83) and strongly or very strongly correlated (P<.001) with the sleep diary--derived sleep durations (Polar: r=0.74, 95\% CI 0.56-0.88; Oura: r=0.82, 95\% CI 0.68-0.91). Oura ring--derived resting heart rates had a very strong correlation (P<.001) with the Actiheart-derived resting heart rates (r=0.9, 95\% CI 0.85-0.96). However, the mean absolute percentage error was high for all variables except Oura ring--derived sleep duration (10\%) and resting heart rate (3\%), which the Oura ring underreported on average by 1 beat per minute. Conclusions: The Oura ring can potentially be used as an alternative to the Actiheart to measure resting heart rate. As for sleep duration, the Polar watch and Oura ring can potentially be used as replacements for a manual sleep diary, depending on the acceptable error. Neither the Polar watch nor the Oura ring can replace the ActiGraph when it comes to measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but they may be used as additional sources of physical activity measures in some settings. On average, the Polar Vantage watch reported higher outputs compared to those reported by the Oura ring for steps, moderate-to-vigorous physical activity, and total energy expenditure. ",
doi="10.2196/27248",
url="https://formative.jmir.org/2022/5/e27248",
url="http://www.ncbi.nlm.nih.gov/pubmed/35622397"
}


@Article{info:doi/10.2196/33817,
author="Dewa, H. Lindsay
and Pappa, Sofia
and Greene, Talya
and Cooke, James
and Mitchell, Lizzie
and Hadley, Molly
and Di Simplicio, Martina
and Woodcock, Thomas
and Aylin, Paul",
title="The Association Between Sleep Disturbance and Suicidality in Psychiatric Inpatients Transitioning to the Community: Protocol for an Ecological Momentary Assessment Study",
journal="JMIR Res Protoc",
year="2022",
month="May",
day="17",
volume="11",
number="5",
pages="e33817",
keywords="sleep",
keywords="suicide",
keywords="psychiatric inpatient",
keywords="ecological momentary assessment",
keywords="EMA",
keywords="experience sampling",
keywords="coproduction",
keywords="sleep disturbance",
keywords="discharge",
abstract="Background: Patients are at high risk of suicidal behavior and death by suicide immediately following discharge from inpatient psychiatric hospitals. Furthermore, there is a high prevalence of sleep problems in inpatient settings, which is associated with worse outcomes following hospitalization. However, it is unknown whether poor sleep is associated with suicidality following initial hospital discharge. Objective: Our study objective is to describe a protocol for an ecological momentary assessment (EMA) study that aims to examine the relationship between sleep and suicidality in discharged patients. Methods: Our study will use an EMA design based on a wearable device to examine the sleep-suicide relationship during the transition from acute inpatient care to the community. Prospectively discharged inpatients 18 to 35 years old with mental disorders (N=50) will be assessed for eligibility and recruited across 2 sites. Data on suicidal ideation, behavior, and imagery; nonsuicidal self-harm and imagery; defeat, entrapment, and hopelessness; affect; and sleep will be collected on the Pro-Diary V wrist-worn electronic watch for up to 14 days. Objective sleep and daytime activity will be measured using the inbuilt MotionWare software. Questionnaires will be administered face-to-face at baseline and follow up, and data will also be collected on the acceptability and feasibility of using the Pro-Diary V watch to monitor the transition following discharge. The study has been, and will continue to be, coproduced with young people with experience of being in an inpatient setting and suicidality. Results: South Birmingham Research Ethics Committee (21/WM/0128) approved the study on June 28, 2021. We expect to see a relationship between poor sleep and postdischarge suicidality. Results will be available in 2022. Conclusions: This protocol describes the first coproduced EMA study to examine the relationship between sleep and suicidality and to apply the integrated motivational volitional model in young patients transitioning from a psychiatric hospital to the community. We expect our findings will inform coproduction in suicidology research and clarify the role of digital monitoring of suicidality and sleep before and after initial hospital discharge. International Registered Report Identifier (IRRID): PRR1-10.2196/33817 ",
doi="10.2196/33817",
url="https://www.researchprotocols.org/2022/5/e33817",
url="http://www.ncbi.nlm.nih.gov/pubmed/35579920"
}


@Article{info:doi/10.2196/37002,
author="Moreno, P. Jennette
and Dadabhoy, Hafza
and Musaad, Salma
and Baranowski, Tom
and Thompson, Debbe
and Alfano, A. Candice
and Crowley, J. Stephanie",
title="Evaluation of a Circadian Rhythm and Sleep-Focused Mobile Health Intervention for the Prevention of Accelerated Summer Weight Gain Among Elementary School--Age Children: Protocol for a Randomized Controlled Feasibility Study",
journal="JMIR Res Protoc",
year="2022",
month="May",
day="16",
volume="11",
number="5",
pages="e37002",
keywords="summer",
keywords="circadian rhythms",
keywords="sleep",
keywords="child obesity",
keywords="elementary school",
abstract="Background: The i?rhythm project is a mobile health adaptation of interpersonal and social rhythm therapy designed to promote healthy sleep and behavioral rhythms among 5-8-year olds during summer for the prevention of accelerated summer weight gain. Objective: This pilot study will examine the feasibility, acceptability, and preliminary efficacy of the i?rhythm intervention. This will ensure that the research protocol and procedures work as desired and are acceptable to families in preparation for the fully powered randomized controlled trial. The proposed study will examine the willingness of participants to participate in the intervention and determine whether modifications to the intervention, procedures, and measures are needed before conducting a fully powered study. We will assess our ability to (1) recruit, consent, and retain participants; (2) deliver the intervention; (3) implement the study and assessment procedures; (4) assess the reliability of the proposed measures; and (5) assess the acceptability of the intervention and assessment protocol. Methods: This study will employ a single-blinded 2-group randomized control design (treatment and no-treatment control) with randomization occurring after baseline (Time 0) and 3 additional evaluation periods (postintervention [Time 1], and 9 months [Time 2] and 12 months after intervention [Time 3]). A sample of 40 parent-child dyads will be recruited. Results: This study was approved by the institutional review board of Baylor College of Medicine (H-47369). Recruitment began in March 2021. As of March 2022, data collection and recruitment are ongoing. Conclusions: This study will address the role of sleep and circadian rhythms in the prevention of accelerated summer weight gain and assess the intervention's effects on the long-term prevention of child obesity. Trial Registration: ClinicalTrials.gov NCT04445740; https://clinicaltrials.gov/ct2/show/NCT04445740. International Registered Report Identifier (IRRID): DERR1-10.2196/37002 ",
doi="10.2196/37002",
url="https://www.researchprotocols.org/2022/5/e37002",
url="http://www.ncbi.nlm.nih.gov/pubmed/35576573"
}


@Article{info:doi/10.2196/29258,
author="Wiklund, Tobias
and Molander, Peter
and Lindner, Philip
and Andersson, Gerhard
and Gerdle, Bj{\"o}rn
and Dragioti, Elena",
title="Internet-Delivered Cognitive Behavioral Therapy for Insomnia Comorbid With Chronic Pain: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="29",
volume="24",
number="4",
pages="e29258",
keywords="insomnia",
keywords="chronic pain",
keywords="comorbid",
keywords="CBT-i",
keywords="RCT",
keywords="web-based CBT",
keywords="pain",
keywords="online health",
keywords="online treatment",
keywords="digital health",
keywords="mental health",
keywords="rehabilitation",
abstract="Background: Patients with chronic pain often experience insomnia symptoms. Pain initiates, maintains, and exacerbates insomnia symptoms, and vice versa, indicating a complex situation with an additional burden for these patients. Hence, the evaluation of insomnia-related interventions for patients with chronic pain is important. Objective: This randomized controlled trial examined the effectiveness of internet-based cognitive behavioral therapy for insomnia (ICBT-i) for reducing insomnia severity and other sleep- and pain-related parameters in patients with chronic pain. Participants were recruited from the Swedish Quality Registry for Pain Rehabilitation. Methods: We included 54 patients (mean age 49.3, SD 12.3 years) who were randomly assigned to the ICBT-i condition and 24 to an active control condition (applied relaxation). Both treatment conditions were delivered via the internet. The Insomnia Severity Index (ISI), a sleep diary, and a battery of anxiety, depression, and pain-related parameter measurements were assessed at baseline, after treatment, and at a 6-month follow-up (only ISI, anxiety, depression, and pain-related parameters). For the ISI and sleep diary, we also recorded weekly measurements during the 5-week treatment. Negative effects were also monitored and reported. Results: Results showed a significant immediate interaction effect (time by treatment) on the ISI and other sleep parameters, namely, sleep efficiency, sleep onset latency, early morning awakenings, and wake time after sleep onset. Participants in the applied relaxation group reported no significant immediate improvements, but both groups exhibited a time effect for anxiety and depression at the 6-month follow-up. No significant improvements on pain-related parameters were found. At the 6-month follow-up, both the ICBT-i and applied relaxation groups had similar sleep parameters. For both treatment arms, increased stress was the most frequently reported negative effect. Conclusions: In patients with chronic pain, brief ICBT-i leads to a more rapid decline in insomnia symptoms than does applied relaxation. As these results are unique, further research is needed to investigate the effect of ICBT-i on a larger sample size of people with chronic pain. Using both treatments might lead to an even better outcome in patients with comorbid insomnia and chronic pain. Trial Registration: ClinicalTrials.gov NCT03425942; https://clinicaltrials.gov/ct2/show/NCT03425942 ",
doi="10.2196/29258",
url="https://www.jmir.org/2022/4/e29258",
url="http://www.ncbi.nlm.nih.gov/pubmed/35486418"
}


@Article{info:doi/10.2196/30089,
author="Greysen, Ryan S.
and Waddell, J. Kimberly
and Patel, S. Mitesh",
title="Exploring Wearables to Focus on the ``Sweet Spot'' of Physical Activity and Sleep After Hospitalization: Secondary Analysis",
journal="JMIR Mhealth Uhealth",
year="2022",
month="Apr",
day="27",
volume="10",
number="4",
pages="e30089",
keywords="sleep",
keywords="physical activity",
keywords="hospitalization",
keywords="wearables",
keywords="health care",
keywords="digital health",
keywords="patient reported outcomes",
keywords="hospital",
abstract="Background: Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge. Objective: This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge. Methods: This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activit{\'e} wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup. Results: For the full sample of 120 participants, we observed a ``T-shaped'' distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95\% CI 0.3 to 1.5; P=.02), community mobility (z-score --0.77, 95\% CI --1.3 to --0.15; P=.02), and perceived physical health (z-score --0.73, 95\% CI --1.3 to --0.13; P=.003), compared with those who were more physically active (?5000 steps/day). Conclusions: Participants within the ``sweet spot'' that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the ``sweet spot.'' Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a ``sweet spot'' of recommended levels for both sleep and activity needed for optimal recovery. Trial Registration: ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279 ",
doi="10.2196/30089",
url="https://mhealth.jmir.org/2022/4/e30089",
url="http://www.ncbi.nlm.nih.gov/pubmed/35476034"
}


@Article{info:doi/10.2196/32825,
author="McBeth, John
and Dixon, G. William
and Moore, Mary Susan
and Hellman, Bruce
and James, Ben
and Kyle, D. Simon
and Lunt, Mark
and Cordingley, Lis
and Yimer, Birlie Belay
and Druce, L. Katie",
title="Sleep Disturbance and Quality of Life in Rheumatoid Arthritis: Prospective mHealth Study",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="22",
volume="24",
number="4",
pages="e32825",
keywords="mobile health",
keywords="sleep",
keywords="rheumatoid arthritis",
keywords="pain",
keywords="fatigue",
keywords="mood",
keywords="sleep disturbance",
keywords="HRQoL",
keywords="quality of life",
keywords="health-related quality of life",
keywords="QoL",
keywords="sleep efficiency",
keywords="WHOQoL-BREF",
keywords="mobile phone",
abstract="Background: Sleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies. Objective: This mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL. Methods: The quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social). Results: Of the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants' scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points ($\beta$=1.11, 95\% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF. Conclusions: Optimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA. ",
doi="10.2196/32825",
url="https://www.jmir.org/2022/4/e32825",
url="http://www.ncbi.nlm.nih.gov/pubmed/35451978"
}


@Article{info:doi/10.2196/30102,
author="Danoff-Burg, Sharon
and Rus, M. Holly
and Weaver, A. Morgan
and Raymann, M. Roy J. E.",
title="Sleeping in an Inclined Position to Reduce Snoring and Improve Sleep: In-home Product Intervention Study",
journal="JMIR Form Res",
year="2022",
month="Apr",
day="6",
volume="6",
number="4",
pages="e30102",
keywords="snoring",
keywords="sleep",
keywords="sleep tracker",
keywords="snoring tracker",
keywords="adjustable bed",
keywords="digital health",
keywords="health technology",
keywords="digital tracker",
keywords="intervention",
keywords="measurement",
abstract="Background: Accurately and unobtrusively testing the effects of snoring and sleep interventions at home has become possible with recent advances in digital measurement technologies. Objective: The aim of this study was to examine the effectiveness of using an adjustable bed base to sleep with the upper body in an inclined position to reduce snoring and improve sleep, measured at home using commercially available trackers. Methods: Self-reported snorers (N=25) monitored their snoring and sleep nightly and completed questionnaires daily for 8 weeks. They slept flat for the first 4 weeks, then used an adjustable bed base to sleep with the upper body at a 12-degree incline for the next 4 weeks. Results: Over 1000 nights of data were analyzed. Objective snoring data showed a 7\% relative reduction in snoring duration (P=.001) in the inclined position. Objective sleep data showed 4\% fewer awakenings (P=.04) and a 5\% increase in the proportion of time spent in deep sleep (P=.02) in the inclined position. Consistent with these objective findings, snoring and sleep measured by self-report improved. Conclusions: New measurement technologies allow intervention studies to be conducted in the comfort of research participants' own bedrooms. This study showed that sleeping at an incline has potential as a nonobtrusive means of reducing snoring and improving sleep in a nonclinical snoring population. ",
doi="10.2196/30102",
url="https://formative.jmir.org/2022/4/e30102",
url="http://www.ncbi.nlm.nih.gov/pubmed/35384849"
}


@Article{info:doi/10.2196/33527,
author="Arroyo, Carmen Amber
and Zawadzki, J. Matthew",
title="The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review",
journal="JMIR Mhealth Uhealth",
year="2022",
month="Apr",
day="4",
volume="10",
number="4",
pages="e33527",
keywords="behavior change techniques",
keywords="sleep",
keywords="mHealth",
keywords="apps",
keywords="digital health",
keywords="mobile phone",
abstract="Background: Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep. Objective: This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep? Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020. Results: A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92\%), shaping knowledge (n=11, 92\%), goals and planning (n=10, 83\%), and antecedents (n=10, 83\%), whereas the least common were scheduled consequences (n=0, 0\%), self-belief (n=0, 0\%), and covert learning (n=0, 0\%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes. Conclusions: Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes). ",
doi="10.2196/33527",
url="https://mhealth.jmir.org/2022/4/e33527",
url="http://www.ncbi.nlm.nih.gov/pubmed/35377327"
}


@Article{info:doi/10.2196/28811,
author="Jalali, Niloofar
and Sahu, Sundar Kirti
and Oetomo, Arlene
and Morita, Pelegrini Plinio",
title="Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study",
journal="JMIR Mhealth Uhealth",
year="2022",
month="Apr",
day="1",
volume="10",
number="4",
pages="e28811",
keywords="public health",
keywords="Internet of Things (IoT)",
keywords="big data",
keywords="sleep monitoring",
keywords="health monitoring",
keywords="mobile phone",
abstract="Background: Sleep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. Objective: The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations. Methods: From the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ?6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household's record was divided into different subsets based on seasonal, weekday, and seasonal weekday scales. Results: Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450; P<.001; odds ratio [OR] 1.8, 95\% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450; P<.001; OR 1.8, 95\% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450; P<.001; OR 5.6, 95\% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445; P<.001; OR 2.06, 95\% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455; P<.001; OR 1.6, 95\% CI 1.3-2.3). These results are in accordance with existing literature. Conclusions: This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change). ",
doi="10.2196/28811",
url="https://mhealth.jmir.org/2022/4/e28811",
url="http://www.ncbi.nlm.nih.gov/pubmed/35363147"
}


@Article{info:doi/10.2196/35040,
author="Wu, Yi-Syuan
and Wang, Wei-Yun
and Chan, Ta-Chien
and Chiu, Yu-Lung
and Lin, Hung-Che
and Chang, Yu-Tien
and Wu, Hao-Yi
and Liu, Tzu-Chi
and Chuang, Yu-Cheng
and Wu, Jonan
and Chang, Wen-Yen
and Sun, Chien-An
and Lin, Meng-Chiung
and Tseng, S. Vincent
and Hu, Je-Ming
and Li, Yuan-Kuei
and Hsiao, Po-Jen
and Chen, Chao-Wen
and Kao, Hao-Yun
and Lee, Chia-Cheng
and Hsieh, Chung-Bao
and Wang, Chih-Hung
and Chu, Chi-Ming",
title="Effect of the Nintendo Ring Fit Adventure Exergame on Running Completion Time and Psychological Factors Among University Students Engaging in Distance Learning During the COVID-19 Pandemic: Randomized Controlled Trial",
journal="JMIR Serious Games",
year="2022",
month="Mar",
day="22",
volume="10",
number="1",
pages="e35040",
keywords="exergaming",
keywords="cardiac force index",
keywords="running",
keywords="physical activity",
keywords="sleep quality",
keywords="mood disorders",
keywords="digital health",
keywords="physical fitness",
keywords="Nintendo Ring Fit Adventure",
keywords="COVID-19 pandemic",
abstract="Background: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. Objective: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). Methods: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. Results: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)\_walk, CFIPA\_run, or sleep quality. Conclusions: RFA could either maintain or improve an individual's physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. Trial Registration: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040 ",
doi="10.2196/35040",
url="https://games.jmir.org/2022/1/e35040",
url="http://www.ncbi.nlm.nih.gov/pubmed/35315780"
}


@Article{info:doi/10.2196/28353,
author="Duggan, M. Nicole
and Hasdianda, Adrian M.
and Baker, Olesya
and Jambaulikar, Guruprasad
and Goldsmith, J. Andrew
and Condella, Anna
and Azizoddin, Desiree
and Landry, I. Adaira
and Boyer, W. Edward
and Eyre, J. Andrew",
title="The Effect of Noise-Masking Earbuds (SleepBuds) on Reported Sleep Quality and Tension in Health Care Shift Workers: Prospective Single-Subject Design Study",
journal="JMIR Form Res",
year="2022",
month="Mar",
day="22",
volume="6",
number="3",
pages="e28353",
keywords="shift work",
keywords="sleep",
keywords="sleep aid",
keywords="alertness",
keywords="earbud",
keywords="SleepBuds",
keywords="healthcare worker",
keywords="physician",
keywords="health care",
abstract="Background: Shift work is associated with sleep disorders, which impair alertness and increase risk of chronic physical and mental health disease. In health care workers, shift work and its associated sleep loss decrease provider wellness and can compromise patient care. Pharmacological sleep aids or substances such as alcohol are often used to improve sleep with variable effects on health and well-being. Objective: We tested whether use of noise-masking earbuds can improve reported sleep quality, sleepiness, and stress level in health care shift workers, and increase alertness and reaction time post night shift. Methods: Emergency medicine resident physicians were recruited for a prospective, single-subject design study. Entrance surveys on current sleep habits were completed. For 14 days, participants completed daily surveys reporting sleep aid use and self-rated perceived sleepiness, tension level, and last nights' sleep quality using an 8-point Likert scale. After overnight shifts, 3-minute psychomotor vigilance tests (PVT) measuring reaction time were completed. At the end of 14 days, participants were provided noise-masking earbuds, which they used in addition to their baseline sleep regimens as they were needed for sleep for the remainder of the study period. Daily sleep surveys, post--overnight shift PVT, and earbud use data were collected for an additional 14 days. A linear mixed effects regression model was used to assess changes in the pre- and postintervention outcomes with participants serving as their own controls. Results: In total, 36 residents were recruited, of whom 26 participants who completed daily sleep surveys and used earbuds at least once during the study period were included in the final analysis. The median number of days of earbud use was 5 (IQR 2-9) days of the available 14 days. On days when residents reported earbud use, previous nights' sleep quality increased by 0.5 points (P<.001, 95\% CI 0.23-0.80), daily sleepiness decreased by 0.6 points (P<.001, 95\% CI --0.90 to --0.34), and total daily tension decreased by 0.6 points (P<.001, 95\% CI --0.81 to --0.32). These effects were more pronounced in participants who reported worse-than-average preintervention sleep scores. Conclusions: Nonpharmacological noise-masking interventions such as earbuds may improve daily sleepiness, tension, and perceived sleep quality in health care shift workers. Larger-scale studies are needed to determine this interventions' effect on other populations of shift workers' post--night shift alertness, users' long-term physical and mental health, and patient outcomes. ",
doi="10.2196/28353",
url="https://formative.jmir.org/2022/3/e28353",
url="http://www.ncbi.nlm.nih.gov/pubmed/35315781"
}


@Article{info:doi/10.2196/30231,
author="Jensen, Skov Esben
and Ladegaard, Nicolai
and Mellentin, Isabella Angelina
and Ebert, Daniel David
and Titzler, Ingrid
and Araya, Ricardo
and Cerga Pashoja, Arlinda
and Hazo, Jean-Baptiste
and Holtzmann, J{\'e}r{\^o}me
and Cieslak, Roman
and Smoktunowicz, Ewelina
and Ba{\~n}os, Rosa
and Herrero, Rocio
and Garc{\'i}a-Palacios, Azucena
and Botella, Cristina
and Berger, Thomas
and Krieger, Tobias
and Holmberg, Theresa Trine
and Topooco, Naira
and Andersson, Gerhard
and van Straten, Annemieke
and Kemmeren, Lise
and Kleiboer, Annet
and Riper, Heleen
and Mathiasen, Kim",
title="Effect of Sleep Disturbance Symptoms on Treatment Outcome in Blended Cognitive Behavioral Therapy for Depression (E-COMPARED Study): Secondary Analysis",
journal="J Med Internet Res",
year="2022",
month="Mar",
day="21",
volume="24",
number="3",
pages="e30231",
keywords="blended care",
keywords="bCBT",
keywords="cognitive behavioral therapy",
keywords="digital intervention",
keywords="major depressive disorder",
keywords="sleep disturbance",
keywords="sleep disorder",
keywords="mental health",
keywords="digital health",
keywords="mobile phone",
abstract="Background: Sleep disturbance symptoms are common in major depressive disorder (MDD) and have been found to hamper the treatment effect of conventional face-to-face psychological treatments such as cognitive behavioral therapy. To increase the dissemination of evidence-based treatment, blended cognitive behavioral therapy (bCBT) consisting of web-based and face-to-face treatment is on the rise for patients with MDD. To date, no study has examined whether sleep disturbance symptoms have an impact on bCBT treatment outcomes and whether it affects bCBT and treatment-as-usual (TAU) equally. Objective: The objectives of this study are to investigate whether baseline sleep disturbance symptoms have an impact on treatment outcomes independent of treatment modality and whether sleep disturbance symptoms impact bCBT and TAU in routine care equally. Methods: The study was based on data from the E-COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment Versus Treatment-as-Usual) study, a 2-arm, multisite, parallel randomized controlled, noninferiority trial. A total of 943 outpatients with MDD were randomized to either bCBT (476/943, 50.5\%) or TAU consisting of routine clinical MDD treatment (467/943, 49.5\%). The primary outcome of this study was the change in depression symptom severity at the 12-month follow-up. The secondary outcomes were the change in depression symptom severity at the 3- and 6-month follow-up and MDD diagnoses at the 12-month follow-up, assessed using the Patient Health Questionnaire-9 and Mini-International Neuropsychiatric Interview, respectively. Mixed effects models were used to examine the association of sleep disturbance symptoms with treatment outcome and treatment modality over time. Results: Of the 943 patients recruited for the study, 558 (59.2\%) completed the 12-month follow-up assessment. In the total sample, baseline sleep disturbance symptoms did not significantly affect change in depressive symptom severity at the 12-month follow-up ($\beta$=.16, 95\% CI --0.04 to 0.36). However, baseline sleep disturbance symptoms were negatively associated with treatment outcome for bCBT ($\beta$=.49, 95\% CI 0.22-0.76) but not for TAU ($\beta$=--.23, 95\% CI ?0.50 to 0.05) at the 12-month follow-up, even when adjusting for baseline depression symptom severity. The same result was seen for the effect of sleep disturbance symptoms on the presence of depression measured with Mini-International Neuropsychiatric Interview at the 12-month follow-up. However, for both treatment formats, baseline sleep disturbance symptoms were not associated with depression symptom severity at either the 3- ($\beta$=.06, 95\% CI ?0.11 to 0.23) or 6-month ($\beta$=.09, 95\% CI ?0.10 to 0.28) follow-up. Conclusions: Baseline sleep disturbance symptoms may have a negative impact on long-term treatment outcomes in bCBT for MDD. This effect was not observed for TAU. These findings suggest that special attention to sleep disturbance symptoms might be warranted when MDD is treated with bCBT. Future studies should investigate the effect of implementing modules specifically targeting sleep disturbance symptoms in bCBT for MDD to improve long-term prognosis. ",
doi="10.2196/30231",
url="https://www.jmir.org/2022/3/e30231",
url="http://www.ncbi.nlm.nih.gov/pubmed/35311687"
}


@Article{info:doi/10.2196/25643,
author="Niemeijer, Koen
and Mestdagh, Merijn
and Kuppens, Peter",
title="Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study",
journal="J Med Internet Res",
year="2022",
month="Mar",
day="18",
volume="24",
number="3",
pages="e25643",
keywords="mobile sensing",
keywords="sleep",
keywords="subjective sleep quality",
keywords="negative affect",
keywords="depression",
keywords="multiverse",
keywords="multilevel modeling",
keywords="machine learning",
keywords="mood",
keywords="mood disorder",
keywords="mobile sensors",
keywords="sleep quality",
keywords="clinical applications",
abstract="Background: Sleep influences moods and mood disorders. Existing methods for tracking the quality of people's sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications. Objective: Our goal was to evaluate the potential of mobile sensing data to obtain information about a person's sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model. Methods: We used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting. Results: Most models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2?0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set. Conclusions: The approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic. ",
doi="10.2196/25643",
url="https://www.jmir.org/2022/3/e25643",
url="http://www.ncbi.nlm.nih.gov/pubmed/35302502"
}


@Article{info:doi/10.2196/34409,
author="O'Hora, Patricia Kathleen
and Osorno, A. Raquel
and Sadeghi-Bahmani, Dena
and Lopez, Mateo
and Morehouse, Allison
and Kim, P. Jane
and Manber, Rachel
and Goldstein-Piekarski, N. Andrea",
title="Viability of an Early Sleep Intervention to Mitigate Poor Sleep and Improve Well-being in the COVID-19 Pandemic: Protocol for a Feasibility Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2022",
month="Mar",
day="14",
volume="11",
number="3",
pages="e34409",
keywords="insomnia",
keywords="COVID-19",
keywords="pandemic",
keywords="telehealth",
keywords="cognitive behavioral therapy",
keywords="CBT-I",
keywords="sleep",
keywords="depression",
keywords="well-being",
keywords="telemedicine",
keywords="impact",
keywords="mental health",
keywords="therapy",
abstract="Background: The COVID-19 pandemic has led to drastic increases in the prevalence and severity of insomnia symptoms. These increases in insomnia complaints have been paralleled by significant decreases in well-being, including increased symptoms of depression, anxiety, and suicidality and decreased quality of life. However, the efficacy and impact of early treatment of insomnia symptoms on future sleep and well-being remain unknown. Objective: Here, we present the framework and protocol for a novel feasibility, pilot study that aims to investigate whether a brief telehealth insomnia intervention targeting new insomnia that developed during the pandemic prevents deterioration of well-being, including symptoms of insomnia, depression, anxiety, suicidality, and quality of life. Methods: The protocol details a 2-arm randomized controlled feasibility trial to investigate the efficacy of a brief, telehealth-delivered, early treatment of insomnia and evaluate its potential to prevent deterioration of well-being. Participants with clinically significant insomnia symptoms that began during the pandemic were randomized to either a treatment group or a 28-week waitlist control group. Treatment consists of 4 telehealth sessions of cognitive behavioral therapy for insomnia (CBT-I) delivered over 5 weeks. All participants will complete assessments of insomnia symptom severity, well-being, and daily habits checklist at baseline (week 0) and at weeks 1-6, 12, 28, and 56. Results: The trial began enrollment on June 3, 2020 and closed enrollment on June 17, 2021. As of October 2021, 49 participants had been randomized to either immediate treatment or a 28-week waitlist; 23 participants were still active in the protocol. Conclusions: To our knowledge, this protocol would represent the first study to test an early sleep intervention for improving insomnia that emerged during the COVID-19 pandemic. The findings of this feasibility study could provide information about the utility of CBT-I for symptoms that emerge in the context of other stressors before they develop a chronic course and deepen understanding of the relationship between sleep and well-being. Trial Registration: ClinicalTrials.gov NCT04409743; https://clinicaltrials.gov/ct2/show/NCT04409743 International Registered Report Identifier (IRRID): DERR1-10.2196/34409 ",
doi="10.2196/34409",
url="https://www.researchprotocols.org/2022/3/e34409",
url="http://www.ncbi.nlm.nih.gov/pubmed/34995204"
}


@Article{info:doi/10.2196/32129,
author="Simon, L. Stacey
and Kaar, L. Jill
and Talker, Ishaah
and Reich, Jennifer",
title="Evidence-Based Behavioral Strategies in Smartphone Apps for Children's Sleep: Content Analysis",
journal="JMIR Pediatr Parent",
year="2022",
month="Mar",
day="3",
volume="5",
number="1",
pages="e32129",
keywords="pediatrics",
keywords="technology",
keywords="smartphones",
keywords="health behavior",
keywords="sleep applications",
keywords="children",
keywords="mobile health",
keywords="mHealth",
keywords="smartphone applications",
keywords="health applications",
keywords="sleep disorders",
keywords="sleep problems",
keywords="developer descriptions",
keywords="apps",
abstract="Background: Empirically supported treatments for pediatric sleep problems exist, but many families turn to other sources for help with their children's sleep, such as smartphone apps. Sleep apps are easy for families to access, but little evidence exists regarding the validity of the services and information provided in the developer descriptions of the apps. Objective: The goal of this study was to examine the features and claims of developer descriptions of sleep apps for children. Methods: A search of the Apple iTunes store and Google Play was conducted using the terms ``kids sleep,'' ``child sleep,'' and ``baby sleep.'' Data on the type of app, price, user rating, and number of users were collected. Apps were analyzed in comparison with evidence-based behavioral strategies and were thematically coded on the basis of claims provided in developer descriptions. Results: A total of 83 app descriptions were examined, of which only 2 (2.4\%) offered sleep improvement strategies. The majority were sound and light apps (78\%) and 19\% were bedtime games or stories. Only 18 of 83 (21.6\%) apps were identified as containing empirically supported behavioral sleep strategies. Despite this, many apps asserted claims that they will help children ``fall asleep instantly,'' ``cry less and sleep better,'' or improve child development. Conclusions: A large variety of sleep apps exist for use among children, but few include evidence-based behavioral strategies according to the developer descriptions of the apps. Addressing sleep difficulties in children is important to promote physical, cognitive, and emotional development. Collaboration between sleep researchers and technology developers may be beneficial for creating evidence-supported apps to help with children's sleep in the future. ",
doi="10.2196/32129",
url="https://pediatrics.jmir.org/2022/1/e32129",
url="http://www.ncbi.nlm.nih.gov/pubmed/35238787"
}


@Article{info:doi/10.2196/31807,
author="{\'O}skarsd{\'o}ttir, Mar{\'i}a
and Islind, Sigridur Anna
and August, Elias
and Arnard{\'o}ttir, Sif Erna
and Patou, Fran{\c{c}}ois
and Maier, M. Anja",
title="Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study",
journal="JMIR Form Res",
year="2022",
month="Feb",
day="22",
volume="6",
number="2",
pages="e31807",
keywords="wearable technology",
keywords="nearable technology",
keywords="internet of health care things",
keywords="sleep",
keywords="Withings",
keywords="study duration",
keywords="establishing standards",
keywords="seasonality",
keywords="mHealth",
keywords="digital health",
abstract="Background: The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used. Objective: The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary. Methods: Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels. Results: Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time. Conclusions: We demonstrate that >2 months' worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future. ",
doi="10.2196/31807",
url="https://formative.jmir.org/2022/2/e31807",
url="http://www.ncbi.nlm.nih.gov/pubmed/35191850"
}


@Article{info:doi/10.2196/29595,
author="Reifman, Jaques
and Kumar, Kamal
and Hartman, Luke
and Frock, Andrew
and Doty, J. Tracy
and Balkin, J. Thomas
and Ramakrishnan, Sridhar
and Vital-Lopez, G. Francisco",
title="2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study",
journal="J Med Internet Res",
year="2022",
month="Jan",
day="27",
volume="24",
number="1",
pages="e29595",
keywords="alertness-prediction model",
keywords="caffeine intervention",
keywords="neurobehavioral performance",
keywords="psychomotor vigilance test",
keywords="PVT",
keywords="sleep loss",
abstract="Background: One-third of the US population experiences sleep loss, with the potential to impair physical and cognitive performance, reduce productivity, and imperil safety during work and daily activities. Computer-based fatigue-management systems with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. Objective: In this study, we aim to enhance the capability of the 2B-Alert Web tool by providing the means for it to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times under any sleep-loss condition. Methods: We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web tool, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm's recommendations with those based on the US Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. Results: For the 4 simulated conditions, the 2B-Alert Web--proposed interventions increased mean alertness by 36\% to 94\% and decreased peak alertness impairment by 31\% to 71\% while using equivalent or smaller doses of caffeine as the corresponding US Army guidelines. Conclusions: The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work and rest schedules. ",
doi="10.2196/29595",
url="https://www.jmir.org/2022/1/e29595",
url="http://www.ncbi.nlm.nih.gov/pubmed/35084336"
}


@Article{info:doi/10.2196/34792,
author="Shaffer, M. Kelly
and Ritterband, M. Lee
and You, Wen
and Buysse, J. Daniel
and Mattos, K. Meghan
and Camacho, Fabian
and Glazer, V. Jillian
and Klinger, Julie
and Donovan, Heidi",
title="Single-Group Trial of an Internet-Delivered Insomnia Intervention Among Higher-Intensity Family Caregivers: Rationale and Protocol for a Mixed Methods Study",
journal="JMIR Res Protoc",
year="2022",
month="Jan",
day="12",
volume="11",
number="1",
pages="e34792",
keywords="family caregiver",
keywords="cognitive behavioral therapy",
keywords="insomnia",
keywords="sleep initiation and maintenance disorders",
keywords="eHealth",
keywords="protocol",
keywords="mobile phone",
abstract="Background: Family caregivers are more likely to experience insomnia relative to noncaregivers but have significant barriers to accessing gold standard cognitive behavioral therapy for insomnia treatment. Delivering interventions to caregivers through the internet may help increase access to care, particularly among higher-intensity caregivers who provide assistance with multiple care tasks over many hours per week. Although there are existing internet interventions that have been thoroughly studied and demonstrated as effective in the general population, the extent to which these interventions may be effective for caregivers without tailoring to address this population's unique psychosocial needs has not been studied. Objective: The goal of this trial is to determine what tailoring may be necessary for which caregivers to ensure they receive optimal benefit from an existing evidence-based, internet-delivered cognitive behavioral therapy for insomnia program named Sleep Healthy Using the Internet (SHUTi). Specifically, we will test the association between caregivers' engagement with SHUTi and their caregiving context characteristics (ie, caregiving strain, self-efficacy, and guilt) and environment (ie, proximity to care recipient; functional status, cognitive status, and problem behavior of care recipient; and type of care provided). Among caregivers using the program, we will also test the associations between change in known treatment mechanisms (sleep beliefs and sleep locus of control) and caregiving context factors. Methods: A total of 100 higher-intensity caregivers with significant insomnia symptoms will be recruited from across the United States to receive access to SHUTi in an open-label trial with mixed methods preassessments and postassessments. At postassessment (9 weeks following preassessment completion), participants will be categorized according to their engagement with the program (nonusers, incomplete users, or complete users). Study analyses will address 3 specific aims: to examine the association between caregivers' engagement with SHUTi and their caregiving context (aim 1a); to describe caregivers' barriers to and motivations for SHUTi engagement from open-ended survey responses (aim 1b); and among caregivers using SHUTi, to determine whether cognitive mechanisms of change targeted by SHUTi are associated with differences in caregiving context (aim 2). Results: Institutional review board approvals have been received. Data collection is anticipated to begin in December 2021 and is expected to be completed in 2023. Conclusions: Findings will inform the next research steps for tailoring and testing SHUTi for optimal impact and reach among caregivers. Beyond implication to the SHUTi program, the findings will be translatable across intervention programs and will hold significant promise to reduce inefficiencies in developing digital health interventions for caregivers while also increasing their impact and reach for this underserved population. Trial Registration: ClinicalTrials.gov; NCT04986904; https://clinicaltrials.gov/ct2/show/NCT04986904?term=NCT04986904 International Registered Report Identifier (IRRID): PRR1-10.2196/34792 ",
doi="10.2196/34792",
url="https://www.researchprotocols.org/2022/1/e34792",
url="http://www.ncbi.nlm.nih.gov/pubmed/35019846"
}


@Article{info:doi/10.2196/31698,
author="Kumar, Shefali
and Rudie, Emma
and Dorsey, Cynthia
and Blase, Amy
and Benjafield, V. Adam
and Sullivan, S. Shannon",
title="Assessment of Patient Journey Metrics for Users of a Digital Obstructive Sleep Apnea Program: Single-Arm Feasibility Pilot Study",
journal="JMIR Form Res",
year="2022",
month="Jan",
day="12",
volume="6",
number="1",
pages="e31698",
keywords="obstructive sleep apnea",
keywords="virtual care",
keywords="remote care",
keywords="OSA diagnosis",
keywords="sleep apnea",
keywords="OSA",
keywords="underdiagnosed",
keywords="feasibility",
keywords="patient-centered",
keywords="treatment pathway",
keywords="diagnostic",
keywords="eHealth",
abstract="Background: Despite the importance of diagnosis and treatment, obstructive sleep apnea (OSA) remains a vastly underdiagnosed condition; this is partially due to current OSA identification methods and a complex and fragmented diagnostic pathway. Objective: This prospective, single-arm, multistate feasibility pilot study aimed to understand the journey in a nonreferred sample of participants through the fully remote OSA screening and diagnostic and treatment pathway, using the Primasun Sleep Apnea Program (formally, Verily Sleep Apnea Program). Methods: Participants were recruited online from North Carolina and Texas to participate in the study entirely virtually. Eligible participants were invited to schedule a video telemedicine appointment with a board-certified sleep physician who could order a home sleep apnea test (HSAT) to be delivered to the participant's home. The results were interpreted by the sleep physician and communicated to the participant during a second video telemedicine appointment. The participants who were diagnosed with OSA during the study and prescribed a positive airway pressure (PAP) device were instructed to download an app that provides educational and support-related content and access to personalized coaching support during the study's 90-day PAP usage period. Surveys were deployed throughout the study to assess baseline characteristics, prior knowledge of sleep apnea, and satisfaction with the program. Results: For the 157 individuals who were ordered an HSAT, it took a mean of 7.4 (SD 2.6) days and median 7.1 days (IQR 2.0) to receive their HSAT after they completed their first televisit appointment. For the 114 individuals who were diagnosed with OSA, it took a mean of 13.9 (SD 9.6) days and median 11.7 days (IQR 10.1) from receiving their HSAT to being diagnosed with OSA during their follow-up televisit appointment. Overall, the mean and median time from the first televisit appointment to receiving an OSA diagnosis was 21.4 (SD 9.6) days and 18.9 days (IQR 9.2), respectively. For those who were prescribed PAP therapy, it took a mean of 8.1 (SD 9.3) days and median 6.0 days (IQR 4.0) from OSA diagnosis to PAP therapy initiation. Conclusions: These results demonstrate the possibility of a highly efficient, patient-centered pathway for OSA workup and treatment. Such findings support pathways that could increase access to care, reduce loss to follow-up, and reduce health burden and overall cost. The program's ability to efficiently diagnose patients who otherwise may have not been diagnosed with OSA is important, especially during a pandemic, as the United States shifted to remote care models and may sustain this direction. The potential economic and clinical impact of the program's short and efficient journey time and low attrition rate should be further examined in future analyses. Future research also should examine how a fast and positive diagnosis experience impacts success rates for PAP therapy initiation and adherence. Trial Registration: ClinicalTrials.gov NCT04599803; https://clinicaltrials.gov/ct2/show/NCT04599803 ",
doi="10.2196/31698",
url="https://formative.jmir.org/2022/1/e31698",
url="http://www.ncbi.nlm.nih.gov/pubmed/34792470"
}


@Article{info:doi/10.2196/29573,
author="Reilly, Dawna Erin
and Robinson, A. Stephanie
and Petrakis, Ann Beth
and Gardner, M. Melissa
and Wiener, Soylemez Renda
and Castaneda-Sceppa, Carmen
and Quigley, S. Karen",
title="Mobile Intervention to Improve Sleep and Functional Health of Veterans With Insomnia: Randomized Controlled Trial",
journal="JMIR Form Res",
year="2021",
month="Dec",
day="9",
volume="5",
number="12",
pages="e29573",
keywords="cognitive behavioral therapy",
keywords="mobile app",
keywords="physical activity",
keywords="insomnia",
abstract="Background: Insomnia is a prevalent and debilitating disorder among veterans. Cognitive behavioral therapy for insomnia (CBTI) can be effective for treating insomnia, although many cannot access this care. Technology-based solutions and lifestyle changes, such as physical activity (PA), offer affordable and accessible self-management alternatives to in-person CBTI. Objective: This study aims to extend and replicate prior pilot work to examine whether the use of a mobile app for CBTI (cognitive behavioral therapy for insomnia coach app [CBT-i Coach]) improves subjective and objective sleep outcomes. This study also aims to investigate whether the use of the CBT-i Coach app with adjunctive PA improves sleep outcomes more than CBT-i Coach alone. Methods: A total of 33 veterans (mean age 37.61 years, SD 9.35 years) reporting chronic insomnia were randomized to use either the CBT-i Coach app alone or the CBT-i Coach app with a PA intervention over 6 weeks, with outcome measures of objective and subjective sleep at pre- and posttreatment. Results: Although the PA manipulation was unsuccessful, both groups of veterans using the CBT-i Coach app showed significant improvement from baseline to postintervention on insomnia (P<.001), sleep quality (P<.001), and functional sleep outcomes (P=.002). Improvements in subjective sleep outcomes were similar in those with and without posttraumatic stress disorder and mild-to-moderate sleep apnea. We also observed a significant but modest increase in objective sleep efficiency (P=.02). Conclusions: These findings suggest that the use of a mobile app--delivered CBTI is feasible and beneficial for improving sleep outcomes in veterans with insomnia, including those with comorbid conditions such as posttraumatic stress disorder or mild-to-moderate sleep apnea. Trial Registration: ClinicalTrials.gov NCT03305354; https://clinicaltrials.gov/ct2/show/NCT03305354 ",
doi="10.2196/29573",
url="https://formative.jmir.org/2021/12/e29573",
url="http://www.ncbi.nlm.nih.gov/pubmed/34889746"
}


@Article{info:doi/10.2196/27613,
author="Sakib, Shahriar Ahmed
and Mukta, Hossain Md Saddam
and Huda, Rowshan Fariha
and Islam, Najmul A. K. M.
and Islam, Tohedul
and Ali, Eunus Mohammed",
title="Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets",
journal="J Med Internet Res",
year="2021",
month="Dec",
day="9",
volume="23",
number="12",
pages="e27613",
keywords="insomnia",
keywords="Twitter",
keywords="word embedding",
keywords="Big 5 personality traits",
keywords="classification",
keywords="social media",
keywords="prediction model",
keywords="psycholinguistics",
abstract="Background: Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users' thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users' insomnia and their Big 5 personality traits as derived from social media interactions. Objective: The purpose of this study is to build an insomnia prediction model from users' psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods: In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users' personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results: Our classification model showed strong prediction potential (78.8\%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, ``no,'' ``not,'' ``never''). Some people frequently use swear words (eg, ``damn,'' ``piss,'' ``fuck'') with strong temperament. They also use anxious (eg, ``worried,'' ``fearful,'' ``nervous'') and sad (eg, ``crying,'' ``grief,'' ``sad'') words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions: Our model can help predict insomnia from users' social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients. ",
doi="10.2196/27613",
url="https://www.jmir.org/2021/12/e27613",
url="http://www.ncbi.nlm.nih.gov/pubmed/34889758"
}


@Article{info:doi/10.2196/31908,
author="Ahlers-Schmidt, R. Carolyn
and Schunn, Christy
and Hervey, M. Ashley
and Torres, Maria
and Nelson, V. Jill Elizabeth",
title="Promoting Safe Sleep, Tobacco Cessation, and Breastfeeding to Rural Women During the COVID-19 Pandemic: Quasi-Experimental Study",
journal="JMIR Pediatr Parent",
year="2021",
month="Nov",
day="22",
volume="4",
number="4",
pages="e31908",
keywords="COVID-19",
keywords="SIDS",
keywords="sudden infant death syndrome",
keywords="safe sleep",
keywords="tobacco cessation",
keywords="breastfeeding",
keywords="virtual education",
abstract="Background: Safe Sleep Community Baby Showers address strategies to prevent sleep-related infant deaths. Due to the COVID-19 pandemic, these events transitioned from in-person to virtual. Objective: This study describes outcomes of transitioning Safe Sleep Community Baby Showers to a virtual format and compares outcomes to previous in-person events. Methods: Participants from four rural Kansas counties were emailed the presurvey, provided educational materials (videos, livestream, or digital documents), and completed a postsurvey. Those who completed both surveys received a portable crib and wearable blanket. Within-group comparisons were assessed between pre- and postsurveys; between-group comparisons (virtual vs in-person) were assessed by postsurveys. Results: Based on data from 145 in-person and 74 virtual participants, virtual participants were more likely to be married (P<.001) and have private insurance (P<.001), and were less likely to report tobacco use (P<.001). Both event formats significantly increased knowledge and intentions regarding safe sleep and avoidance of secondhand smoke (all P?.001). Breastfeeding intentions did not change. Differences were observed between in-person and virtual meetings regarding confidence in the ability to avoid secondhand smoke (in-person: 121/144, 84\% vs virtual: 53/74, 72\%; P=.03), intention to breastfeed ?6 months (in-person: 79/128, 62\% vs virtual: 52/66, 79\%; P=.008), and confidence in the ability to breastfeed ?6 months (in-person: 58/123, 47\% vs virtual: 44/69, 64\%; P=.02). Conclusions: Although both event formats demonstrated increased knowledge/intentions to follow safe sleep recommendations, virtual events may further marginalize groups who are at high risk for poor birth outcomes. Strategies to increase technology access, recruit priority populations, and ensure disparities are not exacerbated will be critical for the implementation of future virtual events. ",
doi="10.2196/31908",
url="https://pediatrics.jmir.org/2021/4/e31908",
url="http://www.ncbi.nlm.nih.gov/pubmed/34550075"
}


@Article{info:doi/10.2196/30991,
author="Jimah, Tamara
and Borg, Holly
and Kehoe, Priscilla
and Pimentel, Pamela
and Turner, Arlene
and Labbaf, Sina
and Asgari Mehrabadi, Milad
and Rahmani, M. Amir
and Dutt, Nikil
and Guo, Yuqing",
title="A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study",
journal="JMIR Form Res",
year="2021",
month="Nov",
day="17",
volume="5",
number="11",
pages="e30991",
keywords="ecological momentary assessment",
keywords="heart rate",
keywords="mHealth",
keywords="physical activity",
keywords="pregnancy",
keywords="sleep",
keywords="wearable electronic device",
abstract="Background: The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. Objective: The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user's engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components. Methods: A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app--based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data. Results: Decreased resting heart rate was significantly correlated with increased heart rate variability (r=--0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery. Conclusions: The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women. ",
doi="10.2196/30991",
url="https://formative.jmir.org/2021/11/e30991",
url="http://www.ncbi.nlm.nih.gov/pubmed/34787576"
}


@Article{info:doi/10.2196/27297,
author="Chin, Samuel
and Carlin, Rebecca
and Mathews, Anita
and Moon, Rachel",
title="Infant Safe Sleep Practices as Portrayed on Instagram: Observational Study",
journal="JMIR Pediatr Parent",
year="2021",
month="Nov",
day="15",
volume="4",
number="4",
pages="e27297",
keywords="sleep position",
keywords="bed-sharing",
keywords="social norms",
keywords="social media",
keywords="safe sleep",
keywords="bedding",
abstract="Background: Parenting practices are highly influenced by perceived social norms. Social norms and American Academy of Pediatrics (AAP) guidelines for infant safe sleep practices are often inconsistent. Instagram has become one of the most popular social media websites among young adults (including many expectant and new parents). We hypothesized that the majority of Instagram images of infant sleep and sleep environments are inconsistent with AAP guidelines, and that the number of ``likes'' for each image would not correlate with adherence of the image to these guidelines. Objective: The objective of this study was to determine the extent of adherence of Instagram images of infant sleep and sleep environments to safe infant sleep guidelines. Methods: We searched Instagram using hashtags that were relevant to infant sleeping practices and environments. We then used an open-source web scraper to collect images and the number of ``likes'' for each image from 27 hashtags. Images were analyzed for adherence to AAP safe sleep guidelines. Results: A total of 1563 images (1134 of sleeping infant; 429 of infant sleep environment without sleeping infant) met inclusion criteria and were analyzed. Only 117 (7.49\%) of the 1563 images were consistent with AAP guidelines. The most common reasons for inconsistency with AAP guidelines were presence of bedding (1173/1563, 75.05\%) and nonrecommended sleep position (479/1134, 42.24\%). The number of ``likes'' was not correlated with adherence of the image to AAP guidelines. Conclusions: Although individuals who use Instagram and post pictures of sleeping infants or infant sleep environments may not actually use these practices regularly, the consistent portrayal of images inconsistent with AAP guidelines reinforces that these practices are normative and may influence the practice of young parents. ",
doi="10.2196/27297",
url="https://pediatrics.jmir.org/2021/4/e27297",
url="http://www.ncbi.nlm.nih.gov/pubmed/34779783"
}


@Article{info:doi/10.2196/26524,
author="Akbarian, Sina
and Ghahjaverestan, Montazeri Nasim
and Yadollahi, Azadeh
and Taati, Babak",
title="Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation",
journal="J Med Internet Res",
year="2021",
month="Nov",
day="1",
volume="23",
number="11",
pages="e26524",
keywords="sleep apnea",
keywords="deep learning",
keywords="noncontact monitoring",
keywords="computer vision",
keywords="positional sleep apnea",
keywords="3D convolutional neural network",
keywords="3D-CNN",
abstract="Background: Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective: The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods: A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results: The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83\% accuracy and an F1-score of 86\%. Conclusions: This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app. ",
doi="10.2196/26524",
url="https://www.jmir.org/2021/11/e26524",
url="http://www.ncbi.nlm.nih.gov/pubmed/34723817"
}


@Article{info:doi/10.2196/25392,
author="Carmona, E. Nicole
and Usyatynsky, Aleksandra
and Kutana, Samlau
and Corkum, Penny
and Henderson, Joanna
and McShane, Kelly
and Shapiro, Colin
and Sidani, Souraya
and Stinson, Jennifer
and Carney, E. Colleen",
title="A Transdiagnostic Self-management Web-Based App for Sleep Disturbance in Adolescents and Young Adults: Feasibility and Acceptability Study",
journal="JMIR Form Res",
year="2021",
month="Nov",
day="1",
volume="5",
number="11",
pages="e25392",
keywords="youth",
keywords="sleep",
keywords="technology",
keywords="mHealth",
keywords="self-management",
keywords="adolescents",
keywords="young adults",
keywords="mobile phone",
abstract="Background: Sleep disturbance and its daytime sequelae, which comprise complex, transdiagnostic sleep problems, are pervasive problems in adolescents and young adults (AYAs) and are associated with negative outcomes. Effective interventions must be both evidence based and individually tailored. Some AYAs prefer self-management and digital approaches. Leveraging these preferences is helpful, given the dearth of AYA treatment providers trained in behavioral sleep medicine. We involved AYAs in the co-design of a behavioral, self-management, transdiagnostic sleep app called DOZE (Delivering Online Zzz's with Empirical Support). Objective: This study tests the feasibility and acceptability of DOZE in a community AYA sample aged 15-24 years. The secondary objective is to evaluate sleep and related outcomes in this nonclinical sample. Methods: Participants used DOZE for 4 weeks (2 periods of 2 weeks). They completed sleep diaries, received feedback on their sleep, set goals in identified target areas, and accessed tips to help them achieve their goals. Measures of acceptability and credibility were completed at baseline and end point. Google Analytics was used to understand the patterns of app use to assess feasibility. Participants completed questionnaires assessing fatigue, sleepiness, chronotype, depression, anxiety, and quality of life at baseline and end point. Results: In total, 83 participants created a DOZE account, and 51 completed the study. During the study, 2659 app sessions took place with an average duration of 3:02 minutes. AYAs tracked most days in period 1 (mean 10.52, SD 4.87) and period 2 (mean 9.81, SD 6.65), with a modal time of 9 AM (within 2 hours of waking). DOZE was appraised as highly acceptable (mode?4) on the items ``easy to use,'' ``easy to understand,'' ``time commitment,'' and ``overall satisfaction'' and was rated as credible (mode?4) at baseline and end point across all items (logic, confident it would work, confident recommending it to a friend, willingness to undergo, and perceived success in treating others). The most common goals set were decreasing schedule variability (34/83, 41\% of participants), naps (17/83, 20\%), and morning lingering in bed (16/83, 19\%). AYAs accessed tips on difficulty winding down (24/83, 29\% of participants), being a night owl (17/83, 20\%), difficulty getting up (13/83, 16\%), and fatigue (13/83, 16\%). There were significant improvements in morning lingering in bed (P=.03); total wake time (P=.02); sleep efficiency (P=.002); total sleep time (P=.03); and self-reported insomnia severity (P=.001), anxiety (P=.002), depression (P=.004), and energy (P=.01). Conclusions: Our results support the feasibility, acceptability, credibility, and preliminary efficacy of DOZE. AYAs are able to set and achieve goals based on tailored feedback on their sleep habits, which is consistent with research suggesting that AYAs prefer autonomy in their health care choices and produce good results when given tools that support their autonomy. Trial Registration: ClinicalTrials.gov NCT03960294; https://clinicaltrials.gov/ct2/show/NCT03960294 ",
doi="10.2196/25392",
url="https://formative.jmir.org/2021/11/e25392",
url="http://www.ncbi.nlm.nih.gov/pubmed/34723820"
}


@Article{info:doi/10.2196/29001,
author="Cho, Eunhee
and Kim, Sujin
and Hwang, Sinwoo
and Kwon, Eunji
and Heo, Seok-Jae
and Lee, Hong Jun
and Ye, Seok Byoung
and Kang, Bada",
title="Factors Associated With Behavioral and Psychological Symptoms of Dementia: Prospective Observational Study Using Actigraphy",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="29",
volume="23",
number="10",
pages="e29001",
keywords="behavioral and psychological symptoms",
keywords="dementia",
keywords="older adults",
keywords="actigraphy",
keywords="sleep",
keywords="activity",
keywords="risk factors",
abstract="Background: Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors---circadian rhythm--related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states---on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures. Objective: The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model. Methods: A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3\%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing. Results: Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day's sleep and nighttime behaviors (odds ratio [OR] 0.9, 95\% CI 0.8-1.0; P=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95\% CI 0.0-0.5; P=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes. Conclusions: BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care. ",
doi="10.2196/29001",
url="https://www.jmir.org/2021/10/e29001",
url="http://www.ncbi.nlm.nih.gov/pubmed/34714244"
}


@Article{info:doi/10.2196/30169,
author="Wong, Jill
and Foussat, C. Agathe
and Ting, Steven
and Acerbi, Enzo
and van Elburg, M. Ruurd
and Mei Chien, Chua",
title="A Chatbot to Engage Parents of Preterm and Term Infants on Parental Stress, Parental Sleep, and Infant Feeding: Usability and Feasibility Study",
journal="JMIR Pediatr Parent",
year="2021",
month="Oct",
day="26",
volume="4",
number="4",
pages="e30169",
keywords="chatbot",
keywords="parental stress",
keywords="parental sleep",
keywords="infant feeding",
keywords="preterm infants",
keywords="term infants",
keywords="sleep",
keywords="stress",
keywords="eHealth",
keywords="support",
keywords="anxiety",
keywords="usability",
abstract="Background: Parents commonly experience anxiety, worry, and psychological distress in caring for newborn infants, particularly those born preterm. Web-based therapist services may offer greater accessibility and timely psychological support for parents but are nevertheless labor intensive due to their interactive nature. Chatbots that simulate humanlike conversations show promise for such interactive applications. Objective: The aim of this study is to explore the usability and feasibility of chatbot technology for gathering real-life conversation data on stress, sleep, and infant feeding from parents with newborn infants and to investigate differences between the experiences of parents with preterm and term infants. Methods: Parents aged ?21 years with infants aged ?6 months were enrolled from November 2018 to March 2019. Three chatbot scripts (stress, sleep, feeding) were developed to capture conversations with parents via their mobile devices. Parents completed a chatbot usability questionnaire upon study completion. Responses to closed-ended questions and manually coded open-ended responses were summarized descriptively. Open-ended responses were analyzed using the latent Dirichlet allocation method to uncover semantic topics. Results: Of 45 enrolled participants (20 preterm, 25 term), 26 completed the study. Parents rated the chatbot as ``easy'' to use (mean 4.08, SD 0.74; 1=very difficult, 5=very easy) and were ``satisfied'' (mean 3.81, SD 0.90; 1=very dissatisfied, 5 very satisfied). Of 45 enrolled parents, those with preterm infants reported emotional stress more frequently than did parents of term infants (33 vs 24 occasions). Parents generally reported satisfactory sleep quality. The preterm group reported feeding problems more frequently than did the term group (8 vs 2 occasions). In stress domain conversations, topics linked to ``discomfort'' and ``tiredness'' were more prevalent in preterm group conversations, whereas the topic of ``positive feelings'' occurred more frequently in the term group conversations. Interestingly, feeding-related topics dominated the content of sleep domain conversations, suggesting that frequent or irregular feeding may affect parents' ability to get adequate sleep or rest. Conclusions: The chatbot was successfully used to collect real-time conversation data on stress, sleep, and infant feeding from a group of 45 parents. In their chatbot conversations, term group parents frequently expressed positive emotions, whereas preterm group parents frequently expressed physical discomfort and tiredness, as well as emotional stress. Overall, parents who completed the study gave positive feedback on their user experience with the chatbot as a tool to express their thoughts and concerns. Trial Registration: ClinicalTrials.gov NCT03630679; https://clinicaltrials.gov/ct2/show/NCT03630679 ",
doi="10.2196/30169",
url="https://pediatrics.jmir.org/2021/4/e30169",
url="http://www.ncbi.nlm.nih.gov/pubmed/34544679"
}


@Article{info:doi/10.2196/24072,
author="Turino, Cecilia
and Ben{\'i}tez, D. Ivan
and Rafael-Palou, Xavier
and Mayoral, Ana
and Lopera, Alejandro
and Pascual, Lydia
and Vaca, Rafaela
and Cortijo, Anunciaci{\'o}n
and Moncus{\'i}-Moix, Anna
and Dalmases, Mireia
and Vargiu, Eloisa
and Blanco, Jordi
and Barb{\'e}, Ferran
and de Batlle, Jordi",
title="Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="18",
volume="23",
number="10",
pages="e24072",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="patient compliance",
keywords="remote monitoring",
keywords="machine learning",
abstract="Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. Objective: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. Methods: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea--Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient's CPAP compliance from the very first days of usage; (2) machine learning--based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient's compliance, satisfaction, and health care costs. Results: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13\% (8/60) were women. Patients in the intervention arm had a mean (95\% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients' satisfaction was excellent in both arms, and up to 88\% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean {\texteuro}90.2 (SD 53.14) (US \$105.76 [SD 62.31]); intervention: mean {\texteuro}96.2 (SD 62.13) (US \$112.70 [SD 72.85]); P=.70; {\texteuro}1=US \$1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. Conclusions: A machine learning--based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients' empowerment in the management of chronic diseases. Trial Registration: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958 ",
doi="10.2196/24072",
url="https://www.jmir.org/2021/10/e24072",
url="http://www.ncbi.nlm.nih.gov/pubmed/34661550"
}


@Article{info:doi/10.2196/18403,
author="Li, Xinyue
and Kane, Michael
and Zhang, Yunting
and Sun, Wanqi
and Song, Yuanjin
and Dong, Shumei
and Lin, Qingmin
and Zhu, Qi
and Jiang, Fan
and Zhao, Hongyu",
title="Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="14",
volume="23",
number="10",
pages="e18403",
keywords="wearable device",
keywords="actigraphy",
keywords="circadian rhythm",
keywords="physical activity",
keywords="early childhood development",
abstract="Background: Wearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers. Objective: This study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development. Methods: We developed a machine learning--based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis. Results: The PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third--day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth--day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third--day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third--day periodicity (P<.001). Conclusions: The proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood. ",
doi="10.2196/18403",
url="https://www.jmir.org/2021/10/e18403",
url="http://www.ncbi.nlm.nih.gov/pubmed/34647895"
}


@Article{info:doi/10.2196/30757,
author="Kaufmann, G. Peter
and Havens, S. Donna
and Mensinger, L. Janell
and Bradley, K. Patricia
and Brom, M. Heather
and Copel, C. Linda
and Costello, Alexander
and D'Annunzio, Christine
and Dean Durning, Jennifer
and Maldonado, Linda
and Barrow McKenzie, Ann
and Smeltzer, C. Suzanne
and Yost, Jennifer
and ",
title="The COVID-19 Study of Healthcare and Support Personnel (CHAMPS): Protocol for a Longitudinal Observational Study",
journal="JMIR Res Protoc",
year="2021",
month="Oct",
day="7",
volume="10",
number="10",
pages="e30757",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="stress",
keywords="depression",
keywords="anxiety",
keywords="sleep",
keywords="social support",
keywords="resilience",
keywords="mental health",
keywords="physical health",
abstract="Background: Early in the development of the COVID-19 pandemic, it was evident that health care workers, first responders, and other essential workers would face significant stress and workplace demands related to equipment shortages and rapidly growing infections in the general population. Although the effects of other sources of stress on health have been documented, the effects of these unique conditions of the COVID-19 pandemic on the long-term health and well-being of the health care workforce are not known. Objective: The COVID-19 Study of Healthcare and Support Personnel (CHAMPS) was designed to document early and longitudinal effects of the pandemic on the mental and physical health of essential workers engaged in health care. We will investigate mediators and moderators of these effects and evaluate the influence of exposure to stress, including morbidity and mortality, over time. We will also examine the effect of protective factors and resilience on health outcomes. Methods: The study cohort is a convenience sample recruited nationally through communities, professional organizations, networks, social media, and snowball sampling. Recruitment took place for 13 months to obtain an estimated sample of 2762 adults who provided self-reported information administered on the web through structured questionnaires about their work environment, mental and physical health, and psychosocial factors. Follow-up questionnaires will be administered after 6 months and annually thereafter to ascertain changes in health, well-being, and lifestyle. Participants who consented to be recontacted form the longitudinal cohort and the CHAMPS Registry may be contacted to ascertain their interest in ancillary studies for which they may be eligible. Results: The study was approved by the Institutional Review Board and launched in May 2020, with grants from Travere Therapeutics Inc, McKesson Corporation, anonymous donors, and internal funding from the M. Louise Fitzpatrick College of Nursing at Villanova University.  Recruitment ended in June 2021 after enrolling 2762 participants, 1534 of whom agreed to participate in the longitudinal study and the registry as well as to be contacted about eligibility for future studies. Conclusions: The CHAMPS Study and Registry will enable the acquisition of detailed data on the effects of extended psychosocial and workplace stress on morbidity and mortality and serve as a platform for ancillary studies related to the COVID-19 pandemic. Trial Registration: ClinicalTrials.gov NCT04370821; https://clinicaltrials.gov/ct2/show/NCT04370821 International Registered Report Identifier (IRRID): DERR1-10.2196/30757 ",
doi="10.2196/30757",
url="https://www.researchprotocols.org/2021/10/e30757",
url="http://www.ncbi.nlm.nih.gov/pubmed/34582354"
}


@Article{info:doi/10.2196/29849,
author="Rahimi-Eichi, Habiballah
and Coombs III, Garth
and Vidal Bustamante, M. Constanza
and Onnela, Jukka-Pekka
and Baker, T. Justin
and Buckner, L. Randy",
title="Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Oct",
day="6",
volume="9",
number="10",
pages="e29849",
keywords="actigraphy",
keywords="accelerometer",
keywords="sleep",
keywords="deep-phenotyping",
keywords="smartphone",
keywords="mobile phone",
abstract="Background: Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Objective: This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. Methods: The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward--sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. Results: Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. Conclusions: We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments. ",
doi="10.2196/29849",
url="https://mhealth.jmir.org/2021/10/e29849",
url="http://www.ncbi.nlm.nih.gov/pubmed/34612831"
}


@Article{info:doi/10.2196/25662,
author="Qanash, Sultan
and Al-Husayni, Faisal
and Falata, Haneen
and Halawani, Ohud
and Jahra, Enas
and Murshed, Boshra
and Alhejaili, Faris
and Ghabashi, Ala'a
and Alhashmi, Hashem",
title="Effect of Electronic Device Addiction on Sleep Quality and Academic Performance Among Health Care Students: Cross-sectional Study",
journal="JMIR Med Educ",
year="2021",
month="Oct",
day="6",
volume="7",
number="4",
pages="e25662",
keywords="electronic devices",
keywords="addiction",
keywords="sleep quality",
keywords="grade point average",
keywords="academic performance",
keywords="health care students",
keywords="medical education",
keywords="sleep",
keywords="student performance",
keywords="screen time",
keywords="well-being",
abstract="Background: Sleep quality ensures better physical and psychological well-being. It is regulated through endogenous hemostatic, neurogenic, and circadian processes. Nonetheless, environmental and behavioral factors also play a role in sleep hygiene. Electronic device use is increasing rapidly and has been linked to many adverse effects, raising public health concerns. Objective: This study aimed to investigate the impact of electronic device addiction on sleep quality and academic performance among health care students in Saudi Arabia. Methods: A descriptive cross-sectional study was conducted from June to December 2019 at 3 universities in Jeddah. Of the 1000 students contacted, 608 students from 5 health sciences disciplines completed the questionnaires. The following outcome measures were used: Smartphone Addiction Scale for Adolescents--short version (SAS-SV), Pittsburgh Sleep Quality Index (PSQI), and grade point average (GPA). Results: The median age of participants was 21 years, with 71.9\% (437/608) being female. Almost all of the cohort used smartphones, and 75.0\% (456/608) of them always use them at bedtime. Half of the students (53\%) have poor sleep quality, while 32\% are addicted to smartphone use. Using multivariable logistic regression, addiction to smartphones (SAS-SV score >31 males and >33 females) was significantly associated with poor sleep quality (PSQI >5) with an odds ratio of 1.8 (1.2-2.7). In addition, male gender and older students (age ?21 years) were significantly associated with lower GPA (<4.5), with an odds ratio of 1.6 (1.1-2.3) and 2.3 (1.5-3.6), respectively; however, addiction to smartphones and poor sleep quality were not significantly associated with a lower GPA. Conclusions: Electronic device addiction is associated with increased risk for poor sleep quality; however, electronic device addiction and poor sleep quality are not associated with increased risk for a lower GPA. ",
doi="10.2196/25662",
url="https://mededu.jmir.org/2021/4/e25662",
url="http://www.ncbi.nlm.nih.gov/pubmed/34612827"
}


@Article{info:doi/10.2196/31273,
author="Summers, Charlotte
and Wu, Philip
and Taylor, G. Alisdair J.",
title="Supporting Mental Health During the COVID-19 Pandemic Using a Digital Behavior Change Intervention: An Open-Label, Single-Arm, Pre-Post Intervention Study",
journal="JMIR Form Res",
year="2021",
month="Oct",
day="6",
volume="5",
number="10",
pages="e31273",
keywords="stress",
keywords="mental health",
keywords="COVID-19",
keywords="digital therapy",
keywords="mHealth",
keywords="support",
keywords="behavior",
keywords="intervention",
keywords="online intervention",
keywords="outcome",
keywords="wellbeing",
keywords="sleep",
keywords="activity",
keywords="nutrition",
abstract="Background: The COVID-19 pandemic is taking a toll on people's mental health, particularly as people are advised to adhere to social distancing, self-isolation measures, and government-imposed national lockdowns. Digital health technologies have an important role to play in keeping people connected and in supporting their mental health and well-being. Even before the COVID-19 pandemic, mental health and social services were already strained. Objective: Our objective was to evaluate the 12-week outcomes of the digitally delivered Gro Health intervention, a holistic digital behavior change app designed for self-management of mental well-being, sleep, activity, and nutrition. Methods: The study used a quasi-experimental research design consisting of an open-label, single-arm, pre-post intervention engagement using a convenience sample. Adults who had joined the Gro Health app (intervention) and had a complete baseline dataset (ie, 7-item Generalized Anxiety Disorder scale, Perceived Stress Scale, and 9-item Patient Health Questionnaire) were followed up at 12 weeks (n=273), including 33 (12.1\%) app users who reported a positive COVID-19 diagnosis during the study period. User engagement with the Gro Health platform was tracked by measuring total minutes of app engagement. Paired t tests were used to compare pre-post intervention scores. Linear regression analysis was performed to assess the relationship between minutes of active engagement with the Gro Health app and changes in scores across the different mental health measures. Results: Of the 347 study participants, 273 (78.67\%) completed both the baseline and follow-up surveys. Changes in scores for anxiety, perceived stress, and depression were predicted by app engagement, with the strongest effect observed for changes in perceived stress score (F1,271=251.397; R2=0.479; P<.001). Conclusions: A digital behavior change platform that provides remote mental well-being support can be effective in managing depression, anxiety, and perceived stress during times of crisis such as the current COVID-19 pandemic. The outcomes of this study may also support the implementation of remote digital health apps supporting behavior change and providing support for low levels of mental health within the community. ",
doi="10.2196/31273",
url="https://formative.jmir.org/2021/10/e31273",
url="http://www.ncbi.nlm.nih.gov/pubmed/34459740"
}


@Article{info:doi/10.2196/29199,
author="Kuczy?ski, Wojciech
and Wibowo, Erik
and Hoshino, Tetsuro
and Kudrycka, Aleksandra
and Ma?olepsza, Aleksandra
and Karwowska, Urszula
and Pruszkowska, Milena
and Wasiak, Jakub
and Kuczy?ska, Aleksandra
and Spa?ka, Jakub
and Pruszkowska-Przybylska, Paulina
and Mokros, ?ukasz
and Bia?as, Adam
and Bia?asiewicz, Piotr
and Sasanabe, Ryujiro
and Blagrove, Mark
and Manning, John",
title="Understanding the Associations of Prenatal Androgen Exposure on Sleep Physiology, Circadian Proteins, Anthropometric Parameters, Hormonal Factors, Quality of Life, and Sex Among Healthy Young Adults: Protocol for an International, Multicenter Study",
journal="JMIR Res Protoc",
year="2021",
month="Oct",
day="6",
volume="10",
number="10",
pages="e29199",
keywords="digit ratio",
keywords="sleep",
keywords="sex hormones",
keywords="testosterone",
keywords="estrogen",
keywords="circadian proteins",
keywords="circadian rhythm",
keywords="chronotype",
keywords="miRNA",
abstract="Background: The ratio of the second finger length to the fourth finger length (2D:4D ratio) is considered to be negatively correlated with prenatal androgen exposure (PAE) and positively correlated with prenatal estrogen. Coincidentally, various brain regions are sensitive to PAE, and their functions in adults may be influenced by the prenatal actions of sex hormones. Objective: This study aims to assess the relationship between PAE (indicated by the 2D:4D ratio) and various physiological (sex hormone levels and sleep-wake parameters), psychological (mental health), and sexual parameters in healthy young adults. Methods: This study consists of two phases. In phase 1, we will conduct a survey-based study and anthropometric assessments (including 2D:4D ratio and BMI) in healthy young adults. Using validated questionnaires, we will collect self-reported data on sleep quality, sexual function, sleep chronotype, anxiety, and depressive symptoms. In phase 2, a subsample of phase 1 will undergo polysomnography and physiological and genetic assessments. Sleep architecture data will be obtained using portable polysomnography. The levels of testosterone, estradiol, progesterone, luteinizing hormone, follicle-stimulating hormone, prolactin, melatonin, and circadian regulatory proteins (circadian locomotor output cycles kaput [CLOCK], timeless [TIM], and period [PER]) and the expression levels of some miRNAs will be measured using blood samples. The rest and activity cycle will be monitored using actigraphy for a 7-day period. Results: In Poland, 720 participants were recruited for phase 1. Among these, 140 completed anthropometric measurements. In addition, 25 participants joined and completed phase 2 data collection. Recruitment from other sites will follow. Conclusions: Findings from our study may help to better understand the plausible role of PAE in sleep physiology, mental health, and sexual quality of life in young adults. International Registered Report Identifier (IRRID): DERR1-10.2196/29199 ",
doi="10.2196/29199",
url="https://www.researchprotocols.org/2021/10/e29199",
url="http://www.ncbi.nlm.nih.gov/pubmed/34612837"
}


@Article{info:doi/10.2196/26476,
author="Stucky, Benjamin
and Clark, Ian
and Azza, Yasmine
and Karlen, Walter
and Achermann, Peter
and Kleim, Birgit
and Landolt, Hans-Peter",
title="Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="5",
volume="23",
number="10",
pages="e26476",
keywords="wearables",
keywords="actigraphy",
keywords="polysomnography",
keywords="validation",
keywords="multisensory",
keywords="mobile phone",
abstract="Background: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. Objective: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. Methods: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. Results: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non--rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. Conclusions: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data. ",
doi="10.2196/26476",
url="https://www.jmir.org/2021/10/e26476",
url="http://www.ncbi.nlm.nih.gov/pubmed/34609317"
}


@Article{info:doi/10.2196/28731,
author="de Vries, Herman
and Kamphuis, Wim
and Oldenhuis, Hilbrand
and van der Schans, Cees
and Sanderman, Robbert",
title="Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring",
journal="JMIR Cardio",
year="2021",
month="Oct",
day="4",
volume="5",
number="2",
pages="e28731",
keywords="stress",
keywords="strain",
keywords="burnout",
keywords="resilience",
keywords="heart rate variability",
keywords="sleep",
keywords="wearables",
keywords="digital health",
keywords="sensors",
keywords="ecological momentary assessment",
keywords="mobile phone",
abstract="Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual's resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes. ",
doi="10.2196/28731",
url="https://cardio.jmir.org/2021/2/e28731",
url="http://www.ncbi.nlm.nih.gov/pubmed/34319877"
}


@Article{info:doi/10.2196/26273,
author="Hamilton, Leigh Jessica
and Lee, Woanjun",
title="Associations Between Social Media, Bedtime Technology Use Rules, and Daytime Sleepiness Among Adolescents: Cross-sectional Findings From a Nationally Representative Sample",
journal="JMIR Ment Health",
year="2021",
month="Sep",
day="15",
volume="8",
number="9",
pages="e26273",
keywords="adolescents",
keywords="social media",
keywords="daytime sleepiness",
keywords="parenting",
keywords="bedtime",
keywords="mental health",
keywords="mobile phone",
abstract="Background: Social media use is associated with poor sleep among adolescents, including daytime sleepiness, which affects adolescents' mental health. Few studies have examined the associations among specific aspects of social media, such as frequency of checking and posting, perceived importance of social media for social belonging, and daytime sleepiness. Identifying whether certain adolescents are more at risk or protected from the effects of social media on sleepiness may inform future interventions for social media, sleep, and mental health. Objective: This study aims to examine the association between social media use frequency and importance, daytime sleepiness, and whether the perceived importance of social media for social interactions and parental rules around bedtime technology moderated these relationships. Methods: This cross-sectional survey study was conducted with a sample of 4153 adolescents from across the United States. Qualtrics was used to collect data via panel recruitment from a national sample representing the US demographics of teens aged 12 to 17 years. Participants completed measures of daytime sleepiness, frequency of social media checking and posting, and the importance of social media for social interactions. Parents reported whether they had a household rule around bedtime media and screen use. Hierarchical regressions and moderation analyses were conducted, covarying for age, gender, and age at first smartphone use. Results: Participants had a mean age of 14.64 (SD 1.66) years in grades 6 to 12, 46.45\% (1929/4153) identified as female, and 67.93\% (2821/4153) identified as White. The results indicated that adolescents who posted (B=0.70, SE 0.04; P<.001) or checked (B=0.76, SE 0.04; P<.001) social media more frequently or who perceived social media to be more important for social belonging (B=0.36, SE 0.02; P<.001) had higher levels of daytime sleepiness. Moderation analyses indicated that the relationship between social media use frequency and daytime sleepiness was exacerbated by higher levels of perceived social media importance (B=0.04, SE 0.01; P<.001). Adolescents without household rules around bedtime technology use were more likely to be affected by social media checking (B=?0.34, SE 0.09; P<.001) and importance (B=?0.16, SE 0.04; P<.001) on daytime sleepiness. Conclusions: The findings suggest that social media use frequency and perceived importance of social interactions are associated with daytime sleepiness among adolescents. It is important to consider youth's perceptions of social media when assessing the potential effects of social media use frequency on youth well-being. Furthermore, youth who did not have parental rules around bedtime technology use were most likely to be affected by social media use and perceived importance. The findings may extend to other mental health outcomes and may guide future prevention and intervention programs designed to improve social media use, sleep, and mental health. ",
doi="10.2196/26273",
url="https://mental.jmir.org/2021/9/e26273",
url="http://www.ncbi.nlm.nih.gov/pubmed/34524967"
}


@Article{info:doi/10.2196/17411,
author="Patel, Vikas
and Orchanian-Cheff, Ani
and Wu, Robert",
title="Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Aug",
day="18",
volume="9",
number="8",
pages="e17411",
keywords="wearable",
keywords="inpatient",
keywords="continuous monitoring",
abstract="Background: The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients. Objective: This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients. Methods: This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. Results: Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67\%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90\%. Conclusions: The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients' digital biomarker readings and eventually determine whether these wearable devices improve health outcomes. ",
doi="10.2196/17411",
url="https://mhealth.jmir.org/2021/8/e17411",
url="http://www.ncbi.nlm.nih.gov/pubmed/34406121"
}


@Article{info:doi/10.2196/30500,
author="Borrmann, Francisca Paz
and O'Connor-Reina, Carlos
and Ignacio, M. Jose
and Rodriguez Ruiz, Elisa
and Rodriguez Alcala, Laura
and Dzembrovsky, Florencia
and Baptista, Peter
and Garcia Iriarte, T. Maria
and Casado Alba, Carlos
and Plaza, Guillermo",
title="Muscular Assessment in Patients With Severe Obstructive Sleep Apnea Syndrome: Protocol for a Case-Control Study",
journal="JMIR Res Protoc",
year="2021",
month="Aug",
day="6",
volume="10",
number="8",
pages="e30500",
keywords="myofunctional therapy",
keywords="sleep apnea",
keywords="sleep disordered breathing",
keywords="speech therapy",
keywords="phenotype",
keywords="sleep",
keywords="therapy",
keywords="protocol",
keywords="muscle",
keywords="assessment",
keywords="case study",
keywords="exercise",
keywords="airway",
keywords="respiratory",
abstract="Background: Myofunctional therapy is currently a reasonable therapeutic option to treat obstructive sleep apnea-hypopnea syndrome (OSAHS). This therapy is based on performing regular exercises of the upper airway muscles to increase their tone and prevent their collapse. Over the past decade, there has been an increasing number of publications in this area; however, to our knowledge, there are no studies focused on patients who can most benefit from this therapy. Objective: This protocol describes a case-control clinical trial aimed at determining the muscular features of patients recently diagnosed with severe OSAHS compared with those of healthy controls. Methods: Patients meeting set criteria will be sequentially enrolled up to a sample size of 40. Twenty patients who meet the inclusion criteria for controls will also be evaluated. Patients will be examined by a qualified phonoaudiologist who will take biometric measurements and administer the Expanded Protocol of Orofacial Myofunctional Evaluation with Scores (OMES), Friedman Staging System, Epworth Sleepiness Scale, and Pittsburgh Sleep Quality Index questionnaires. Measures of upper airway muscle tone will also be performed using the Iowa Oral Performance Instrument and tongue digital spoon devices. Evaluation will be recorded and reevaluated by a second specialist to determine concordance between observers. Results: A total of 60 patients will be enrolled. Both the group with severe OSAHS (40 patients) and the control group (20 subjects) will be assessed for differences between upper airway muscle tone and OMES questionnaire responses. Conclusions: This study will help to determine muscle patterns in patients with severe OSAHS and can be used to fill the gap currently present in the assessment of patients suitable to be treated with myofunctional therapy. Trial Registration: ISRCTN Registry ISRCTN12596010; https://www.isrctn.com/ISRCTN12596010 International Registered Report Identifier (IRRID): PRR1-10.2196/30500 ",
doi="10.2196/30500",
url="https://www.researchprotocols.org/2021/8/e30500",
url="http://www.ncbi.nlm.nih.gov/pubmed/34115605"
}


@Article{info:doi/10.2196/24171,
author="Zhang, Zhongxing
and Qi, Ming
and H{\"u}gli, Gordana
and Khatami, Ramin",
title="The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="29",
volume="23",
number="7",
pages="e24171",
keywords="obstructive sleep apnea",
keywords="wearable devices",
keywords="smartwatch",
keywords="oxygen saturation",
keywords="near-infrared spectroscopy",
keywords="continuous positive airway pressure therapy",
keywords="photoplethysmography",
abstract="Background: Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9\% to 38\% of the general population. About 90\% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ?30\% drop in breathing and an at least 3\% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. Objective: We aimed to compare event-by-event arm OD (arm\_OD) with fingertip OD (finger\_OD) in sleep hypopneas during both na{\"i}ve sleep and CPAP therapy. Methods: Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm\_OD of the left biceps muscle and finger\_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm\_OD and finger\_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. Results: In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57\%) were collected during baseline sleep and 1517 (69.43\%), during CPAP sleep. The mean difference between finger\_OD and arm\_OD was 2.86\% (95\% CI 2.67\%-3.06\%, t667=28.28; P<.001; 95\% limits of agreement [LoA] --2.27\%, 8.00\%) during baseline sleep and 1.83\% (95\% CI 1.72\%-1.94\%, t1516=31.99; P<.001; 95\% LoA --2.54\%, 6.19\%) during CPAP. Using the standard criterion of 3\% saturation drop, arm\_OD only recognized 16.32\% (109/668) and 14.90\% (226/1517) of hypopneas at baseline and during CPAP, respectively. Conclusions: arm\_OD is 2\% to 3\% lower than standard finger\_OD in sleep hypopnea, probably because the measured arm\_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ?3\% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness. ",
doi="10.2196/24171",
url="https://www.jmir.org/2021/7/e24171",
url="http://www.ncbi.nlm.nih.gov/pubmed/34326039"
}


@Article{info:doi/10.2196/26297,
author="Woolf, B. Thomas
and Goheer, Attia
and Holzhauer, Katherine
and Martinez, Jonathan
and Coughlin, W. Janelle
and Martin, Lindsay
and Zhao, Di
and Song, Shanshan
and Ahmad, Yanif
and Sokolinskyi, Kostiantyn
and Remayeva, Tetyana
and Clark, M. Jeanne
and Bennett, Wendy
and Lehmann, Harold",
title="Development of a Mobile App for Ecological Momentary Assessment of Circadian Data: Design Considerations and Usability Testing",
journal="JMIR Form Res",
year="2021",
month="Jul",
day="23",
volume="5",
number="7",
pages="e26297",
keywords="mhealth",
keywords="circadian",
keywords="sleep",
keywords="ecological momentary assessment",
keywords="timing of eating",
keywords="mobile applications",
keywords="habits",
keywords="body weight",
keywords="surveys and questionnaires",
abstract="Background: Collecting data on daily habits across a population of individuals is challenging. Mobile-based circadian ecological momentary assessment (cEMA) is a powerful frame for observing the impact of daily living on long-term health. Objective: In this paper, we (1) describe the design, testing, and rationale for specifications of a mobile-based cEMA app to collect timing of eating and sleeping data and (2) compare cEMA and survey data collected as part of a 6-month observational cohort study. The ultimate goal of this paper is to summarize our experience and lessons learned with the Daily24 mobile app and to highlight the pros and cons of this data collection modality. Methods: Design specifications for the Daily24 app were drafted by the study team based on the research questions and target audience for the cohort study. The associated backend was optimized to provide real-time data to the study team for participant monitoring and engagement. An external 8-member advisory board was consulted throughout the development process, and additional test users recruited as part of a qualitative study provided feedback through in-depth interviews. Results: After ?4 days of at-home use, 37 qualitative study participants provided feedback on the app. The app generally received positive feedback from test users for being fast and easy to use. Test users identified several bugs and areas where modifications were necessary to in-app text and instructions and also provided feedback on the engagement strategy. Data collected through the mobile app captured more variability in eating windows than data collected through a one-time survey, though at a significant cost. Conclusions: Researchers should consider the potential uses of a mobile app beyond the initial data collection when deciding whether the time and monetary expenditure are advisable for their situation and goals. ",
doi="10.2196/26297",
url="https://formative.jmir.org/2021/7/e26297",
url="http://www.ncbi.nlm.nih.gov/pubmed/34296999"
}


@Article{info:doi/10.2196/27062,
author="Laursen, Hjorth Ditte
and Rom, Gitte
and Bangh{\o}j, Margareta Anne
and Tarnow, Lise
and Schou, Lone",
title="Improving Diabetes Self-management by Providing Continuous Positive Airway Pressure Treatment to Patients With Obstructive Sleep Apnea and Type 2 Diabetes: Qualitative Exploratory Interview Study",
journal="JMIR Form Res",
year="2021",
month="Jul",
day="20",
volume="5",
number="7",
pages="e27062",
keywords="diabetes",
keywords="diabetes self-management",
keywords="obstructive sleep apnea",
keywords="continued positive airway pressure",
keywords="sleep patterns",
keywords="sleepiness in daily life",
keywords="sleep apnea",
keywords="elderly",
keywords="sleep",
abstract="Background: There is a high prevalence of unexplained and unexplored obstructive sleep apnea (OSA) among patients with type 2 diabetes. The daytime symptoms of OSA include severe fatigue, cognitive problems, a decreased quality of life, and the reduced motivation to perform self-care. These symptoms impair the management of both diabetes and daily life. OSA may therefore have negative implications for diabetes self-management. Continuous positive airway pressure (CPAP) therapy is used to treat OSA. This treatment improves sleep quality, insulin resistance, and glycemic control. Although the benefits of using CPAP as a treatment for OSA are clear, the noncompliance rate is high, and the evidence for the perceived effect that CPAP treatment has on patients with type 2 diabetes and OSA is poor. Objective: The purpose of this study was to analyze the impacts that comorbid diabetes and OSA have on the daily lives of older adults and to investigate the perceived effect that CPAP treatment for OSA has on patients' diabetes self-management. Methods: A qualitative follow-up study that involved in-depth, semistructured dyad interviews with couples before and after CPAP treatment (N=22) was conducted. Patients were recruited from the Hilleroed Hospital in Denmark and were all diagnosed with type 2 diabetes, aged >18 years, and had an apnea-hypopnea index of ?15. All interviews were coded and analyzed via thematic analysis. Results: The results showed that patients and their partners did not consider OSA to be a serious disorder, as they believed that OSA symptoms were similar to those of the process of aging. Patients experienced poor nocturnal sleep, took frequent daytime naps, exhibited reduced cognitive function, and had low levels of physical activity and a high-calorie diet. These factors negatively influenced their diabetes self-management. Despite the immediate benefit of CPAP treatment, most patients (11/12, 92\%) faced technical challenges when using the CPAP device. Only the patients with severe OSA symptoms that affected their daily lives overcame the challenges of using the CPAP device and thereby improved their diabetes self-management. Patients with less severe symptoms rated CPAP-related challenges as more burdensome than their symptoms. Conclusions: If used correctly, CPAP has the potential to significantly improve OSA, resulting in better sleep quality; improved physical activity; improved diet; and, in the end, better diabetes self-management. However, there are many barriers to undergoing CPAP treatment, and only few patients manage to overcome these barriers and comply with correct treatment. ",
doi="10.2196/27062",
url="https://formative.jmir.org/2021/7/e27062",
url="http://www.ncbi.nlm.nih.gov/pubmed/34283032"
}


@Article{info:doi/10.2196/26059,
author="Mulgund, Pavankumar
and Sharman, Raj
and Rifkin, Daniel
and Marrazzo, Sam",
title="Design, Development, and Evaluation of a Telemedicine Platform for Patients With Sleep Apnea (Ognomy): Design Science Research Approach",
journal="JMIR Form Res",
year="2021",
month="Jul",
day="19",
volume="5",
number="7",
pages="e26059",
keywords="design science research",
keywords="telemedicine platform",
keywords="sleep apnea care",
keywords="mHealth",
keywords="telemedicine",
keywords="sleep apnea",
keywords="mobile health",
keywords="web application",
keywords="mobile phone",
abstract="Background: With an aging population and the escalating cost of care, telemedicine has become a societal imperative. Telemedicine alternatives are especially relevant to patients seeking care for sleep apnea, with its prevalence approaching one billion cases worldwide. Increasing awareness has led to a surge in demand for sleep apnea care; however, there is a shortage of the resources and expertise necessary to cater to the rising demand. Objective: The aim of this study is to design, develop, and evaluate a telemedicine platform, called Ognomy, for the consultation, diagnosis, and treatment of patients with sleep apnea. Methods: Using the design science research methodology, we developed a telemedicine platform for patients with sleep apnea. To explore the problem, in the analysis phase, we conducted two brainstorming workshops and structured interviews with 6 subject matter experts to gather requirements. Following that, we conducted three design and architectural review sessions to define and evaluate the system architecture. Subsequently, we conducted 14 formative usability assessments to improve the user interface of the system. In addition, 3 trained test engineers performed end-to-end system testing to comprehensively evaluate the platform. Results: Patient registration and data collection, physician appointments, video consultation, and patient progress tracking have emerged as critical functional requirements. A telemedicine platform comprising four artifacts---a mobile app for patients, a web app for providers, a dashboard for reporting, and an artificial intelligence--based chatbot for customer onboarding and support---was developed to meet these requirements. Design reviews emphasized the need for a highly cohesive but loosely coupled interaction among the platform's components, which was achieved through a layered modular architecture using third-party application programming interfaces. In contrast, critical findings from formative usability assessments focused on the need for a more straightforward onboarding process for patients, better status indicators during patient registration, and reorganization of the appointment calendar. Feedback from the design reviews and usability assessments was translated into technical improvements and design enhancements that were implemented in subsequent iterations. Conclusions: Sleep apnea is an underdiagnosed and undertreated condition. However, with increasing awareness, the demand for quality sleep apnea care is likely to surge, and creative alternatives are needed. The results of this study demonstrate the successful application of a framework using a design science research paradigm to design, develop, and evaluate a telemedicine platform for patients with sleep apnea and their providers. ",
doi="10.2196/26059",
url="https://formative.jmir.org/2021/7/e26059",
url="http://www.ncbi.nlm.nih.gov/pubmed/34279237"
}


@Article{info:doi/10.2196/24666,
author="Sch{\"u}tz, Narayan
and Saner, Hugo
and Botros, Angela
and Pais, Bruno
and Santschi, Val{\'e}rie
and Buluschek, Philipp
and Gatica-Perez, Daniel
and Urwyler, Prabitha
and M{\"u}ri, M. Ren{\'e}
and Nef, Tobias",
title="Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Jun",
day="11",
volume="9",
number="6",
pages="e24666",
keywords="sleep restlessness",
keywords="telemonitoring",
keywords="digital biomarkers",
keywords="contactless sensing",
keywords="pervasive computing",
keywords="home-monitoring",
keywords="older adults",
keywords="toss and turns",
keywords="sleep monitoring",
keywords="body movements in bed",
abstract="Background: Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective: In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods: We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results: Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed---measured by the number toss-and-turn events---as the most predictive sleep parameter (t score=--0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions: Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options. ",
doi="10.2196/24666",
url="https://mhealth.jmir.org/2021/6/e24666",
url="http://www.ncbi.nlm.nih.gov/pubmed/34114966"
}


@Article{info:doi/10.2196/16304,
author="Mascheroni, Alessandro
and Choe, Kyoung Eun
and Luo, Yuhan
and Marazza, Michele
and Ferlito, Clara
and Caverzasio, Serena
and Mezzanotte, Francesco
and Kaelin-Lang, Alain
and Faraci, Francesca
and Puiatti, Alessandro
and Ratti, Luca Pietro",
title="The SleepFit Tablet Application for Home-Based Clinical Data Collection in Parkinson Disease: User-Centric Development and Usability Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Jun",
day="8",
volume="9",
number="6",
pages="e16304",
keywords="Parkinson disease",
keywords="ecological momentary assessment",
keywords="finger-tapping test",
keywords="subjective scales",
keywords="sleep diaries",
keywords="tablet application",
keywords="home-based system",
abstract="Background: Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. Objective: The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients' homes, and as a remote tool for researchers to monitor patients and integrate and manage data. Methods: An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. Results: From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89\% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86\% (2070/2356) to 89.92\% (2899/3224; P=.04). Among the patients who used v2.0, 96\% (25/26) declared they would use SleepFit again. Conclusions: SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. Trial Registration: ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396 ",
doi="10.2196/16304",
url="https://mhealth.jmir.org/2021/6/e16304",
url="http://www.ncbi.nlm.nih.gov/pubmed/34100767"
}


@Article{info:doi/10.2196/26462,
author="Wei, Jing
and Boger, Jennifer",
title="Sleep Detection for Younger Adults, Healthy Older Adults, and Older Adults Living With Dementia Using Wrist Temperature and Actigraphy: Prototype Testing and Case Study Analysis",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Jun",
day="1",
volume="9",
number="6",
pages="e26462",
keywords="sleep monitoring",
keywords="wearables",
keywords="accelerometer",
keywords="wrist temperature",
keywords="circadian rhythm",
keywords="younger adults",
keywords="older adults",
keywords="dementia",
keywords="mobile phone",
abstract="Background: Sleep is essential for one's health and quality of life. Wearable technologies that use motion and temperature sensors have made it possible to self-monitor sleep. Although there is a growing body of research on sleep monitoring using wearable devices for healthy young-to-middle-aged adults, few studies have focused on older adults, including those living with dementia. Objective: This study aims to investigate the impact of age and dementia on sleep detection through movement and wrist temperature. Methods: A total of 10 younger adults, 10 healthy older adults, and 8 older adults living with dementia (OAWD) were recruited. Each participant wore a Mi Band 2 (accemetry-based sleep detection) and our custom-built wristband (actigraphy and wrist temperature) 24 hours a day for 2 weeks and was asked to keep a daily sleep journal. Sleep parameters detected by the Mi Band 2 were compared with sleep journals, and visual analysis of actigraphy and temperature data was performed. Results: The absolute differences in sleep onset and offset between the sleep journals and Mi Band 2 were 39 (SD 51) minutes and 31 (SD 52) minutes for younger adults, 49 (SD 58) minutes and 33 (SD 58) minutes for older adults, and 253 (SD 104) minutes and 161 (SD 94) minutes for OAWD. The Mi Band 2 was unable to accurately detect sleep in 3 healthy older adults and all OAWDs. The average sleep and wake temperature difference of OAWD (1.26 {\textdegree}C, SD 0.82 {\textdegree}C) was significantly lower than that of healthy older adults (2.04 {\textdegree}C, SD 0.70 {\textdegree}C) and healthy younger adults (2.48 {\textdegree}C, SD 0.88 {\textdegree}C). Actigraphy data showed that older adults had more movement during sleep compared with younger adults and that this trend appears to increase for those with dementia. Conclusions: The Mi Band 2 did not accurately detect sleep in older adults who had greater levels of nighttime movement. As more nighttime movement appears to be a phenomenon that increases in prevalence with age and even more so with dementia, further research needs to be conducted with a larger sample size and greater diversity of commercially available wearable devices to explore these trends more conclusively. All participants, including older adults and OAWD, had a distinct sleep and wake wrist temperature contrast, which suggests that wrist temperature could be leveraged to create more robust and broadly applicable sleep detection algorithms. ",
doi="10.2196/26462",
url="https://mhealth.jmir.org/2021/6/e26462",
url="http://www.ncbi.nlm.nih.gov/pubmed/34061038"
}


@Article{info:doi/10.2196/27331,
author="Stewart, H. Nancy
and Koza, Anya
and Dhaon, Serena
and Shoushtari, Christiana
and Martinez, Maylyn
and Arora, M. Vineet",
title="Sleep Disturbances in Frontline Health Care Workers During the COVID-19 Pandemic: Social Media Survey Study",
journal="J Med Internet Res",
year="2021",
month="May",
day="19",
volume="23",
number="5",
pages="e27331",
keywords="social media",
keywords="sleep disorders",
keywords="frontline health care workers",
keywords="burnout",
keywords="insomnia",
keywords="sleep",
keywords="health care worker",
keywords="stress",
keywords="survey",
keywords="demographic",
keywords="outcome",
keywords="COVID-19",
abstract="Background: During the COVID-19 pandemic, health care workers are sharing their challenges, including sleep disturbances, on social media; however, no study has evaluated sleep in predominantly US frontline health care workers during the COVID-19 pandemic. Objective: The aim of this study was to assess sleep among a sample of predominantly US frontline health care workers during the COVID-19 pandemic using validated measures through a survey distributed on social media. Methods: A self-selection survey was distributed on Facebook, Twitter, and Instagram for 16 days (August 31 to September 15, 2020), targeting health care workers who were clinically active during the COVID-19 pandemic. Study participants completed the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI), and they reported their demographic and career information. Poor sleep quality was defined as a PSQI score ?5. Moderate-to-severe insomnia was defined as an ISI score >14. The Mini-Z Burnout Survey was used to measure burnout. Multivariate logistic regression tested associations between demographics, career characteristics, and sleep outcomes. Results: A total of 963 surveys were completed. Participants were predominantly White (894/963, 92.8\%), female (707/963, 73.4\%), aged 30-49 years (692/963, 71.9\%), and physicians (620/963, 64.4\%). Mean sleep duration was 6.1 hours (SD 1.2). Nearly 96\% (920/963, 95.5\%) of participants reported poor sleep (PSQI). One-third (288/963, 30\%) reported moderate or severe insomnia. Many participants (554/910, 60.9\%) experienced sleep disruptions due to device use or had nightmares at least once per week (420/929, 45.2\%). Over 50\% (525/932, 56.3\%) reported burnout. In multivariable logistic regressions, nonphysician (odds ratio [OR] 2.4, 95\% CI 1.7-3.4), caring for patients with COVID-19 (OR 1.8, 95\% CI 1.2-2.8), Hispanic ethnicity (OR 2.2, 95\% CI 1.4-3.5), female sex (OR 1.6, 95\% CI 1.1-2.4), and having a sleep disorder (OR 4.3, 95\% CI 2.7-6.9) were associated with increased odds of insomnia. In open-ended comments (n=310), poor sleep was mapped to four categories: children and family, work demands, personal health, and pandemic-related sleep disturbances. Conclusions: During the COVID-19 pandemic, nearly all the frontline health care workers surveyed on social media reported poor sleep, over one-third reported insomnia, and over half reported burnout. Many also reported sleep disruptions due to device use and nightmares. Sleep interventions for frontline health care workers are urgently needed. ",
doi="10.2196/27331",
url="https://www.jmir.org/2021/5/e27331",
url="http://www.ncbi.nlm.nih.gov/pubmed/33875414"
}


@Article{info:doi/10.2196/24566,
author="Bowling, B. April
and Slavet, James
and Hendrick, Chelsea
and Beyl, Robbie
and Nauta, Phillip
and Augustyn, Marilyn
and Mbamalu, Mediatrix
and Curtin, Carol
and Bandini, Linda
and Must, Aviva
and Staiano, E. Amanda",
title="The Adaptive GameSquad Xbox-Based Physical Activity and Health Coaching Intervention for Youth With Neurodevelopmental and Psychiatric Diagnoses: Pilot Feasibility Study",
journal="JMIR Form Res",
year="2021",
month="May",
day="14",
volume="5",
number="5",
pages="e24566",
keywords="exercise",
keywords="diet",
keywords="sleep",
keywords="mental health",
keywords="children",
keywords="adolescent",
keywords="health promotion",
keywords="telehealth",
keywords="exergaming",
abstract="Background: The prevalence of neurodevelopmental and psychiatric diagnoses (NPDs) in youth is increasing, and unhealthy physical activity (PA), diet, screen time, and sleep habits contribute to the chronic disease disparities and behavioral challenges this population experiences. Objective: This pilot study aims to adapt a proven exergaming and telehealth PA coaching intervention for typically developing youth with overweight or obesity; expand it to address diet, screen, and sleep behaviors; and then test its feasibility and acceptability, including PA engagement, among youth with NPDs. Methods: Participants (N=23; mean age 15.1 years, SD 1.5; 17 males, 9 people of color) recruited in person from clinic and special education settings were randomized to the Adaptive GameSquad (AGS) intervention or wait-list control. The 10-week adapted intervention included 3 exergaming sessions per week and 6 real-time telehealth coaching sessions. The primary outcomes included feasibility (adherence to planned sessions), engagement (uptake and acceptability as reported on process questionnaires), and PA level (combined light, moderate, and vigorous as measured by accelerometer). Descriptive statistics summarized feasibility and engagement data, whereas paired, two-tailed t tests assessed group differences in pre-post PA. Results: Of the 6 coaching sessions, AGS participants (n=11; mean age 15.3 years, SD 1.2; 7 males, 4 people of color) completed an average of 5 (83\%), averaging 81.2 minutes per week of exergaming. Of 9 participants who completed the exit questionnaire, 6 (67\%) reported intention to continue, and 8 (89\%) reported feeling that the coaching sessions were helpful. PA and sleep appeared to increase during the course of the intervention over baseline, video game use appeared to decrease, and pre-post intervention PA per day significantly decreased for the control (?58.8 min; P=.04) but not for the intervention group (?5.3 min; P=.77), despite potential seasonality effects. However, beta testers and some intervention participants indicated a need for reduced complexity of technology and more choice in exergames. Conclusions: AGS shows promise in delivering a health behavior intervention remotely to youth with NPDs, but a full-scale efficacy trial with a larger sample size is needed to confirm this finding. On the basis of feedback from beta testers and intervention participants, the next steps should include reduced technology burden and increased exergame choice before efficacy testing. Trial Registration: ClinicalTrials.gov NCT03665415; https://clinicaltrials.gov/ct2/show/NCT03665415. ",
doi="10.2196/24566",
url="https://formative.jmir.org/2021/5/e24566",
url="http://www.ncbi.nlm.nih.gov/pubmed/33988508"
}


@Article{info:doi/10.2196/26186,
author="Lai, Jocelyn
and Rahmani, Amir
and Yunusova, Asal
and Rivera, P. Alexander
and Labbaf, Sina
and Hu, Sirui
and Dutt, Nikil
and Jain, Ramesh
and Borelli, L. Jessica",
title="Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study",
journal="JMIR Form Res",
year="2021",
month="May",
day="11",
volume="5",
number="5",
pages="e26186",
keywords="COVID-19",
keywords="emerging adulthood",
keywords="multimodal personal chronicles",
keywords="case study",
keywords="wearable internet of things",
keywords="individualized mHealth",
keywords="college students",
keywords="mental health",
abstract="Background: The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as multimodal personal chronicles, present an opportunity to examine indicators of health in an ongoing and personalized way using mobile sensing devices and wearable internet of things. Objective: To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September. Methods: The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual's psychological well-being across the pandemic. Results: Over the course of the pandemic, Lee's depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee's positive affect and negative affect were positively correlated in April (r=0.53, P=.04) whereas they were negatively correlated in September (r=--0.57, P=.03). Only in the month of January was sleep negatively associated with negative affect (r=--0.58, P=.03) and diurnal beats per minute (r=--0.54, P=.04), and then positively associated with heart rate variability (resting root mean square of successive differences between normal heartbeats) (r=0.54, P=.04). When looking at his available contextual data, Lee noted certain situations as supportive coping factors and other situations as potential stressors. Conclusions: We observed more pandemic concerns in April and noticed other contextual events relating to this individual's well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health. ",
doi="10.2196/26186",
url="https://formative.jmir.org/2021/5/e26186",
url="http://www.ncbi.nlm.nih.gov/pubmed/33882022"
}


@Article{info:doi/10.2196/20779,
author="Kooij, Laura
and Vos, JE Petra
and Dijkstra, Antoon
and Roovers, A. Elisabeth
and van Harten, H. Wim",
title="Video Consultation as an Adequate Alternative to Face-to-Face Consultation in Continuous Positive Airway Pressure Use for Newly Diagnosed Patients With Obstructive Sleep Apnea: Randomized Controlled Trial",
journal="JMIR Form Res",
year="2021",
month="May",
day="11",
volume="5",
number="5",
pages="e20779",
keywords="video consultation",
keywords="eHealth",
keywords="obstructive sleep apnea",
keywords="continuous positive airway pressure",
keywords="randomized controlled trial",
abstract="Background: The effectiveness of continuous positive airway pressure (CPAP) is dependent on the degree of use, so adherence is essential. Cognitive components (eg, self-efficacy) and support during treatment have been found to be important in CPAP use. Video consultation may be useful to support patients during treatment. So far, video consultation has rarely been evaluated in thorough controlled research, with only a limited number of outcomes assessed. Objective: The aim of the study was to evaluate the superiority of video consultation over face-to-face consultation for patients with obstructive sleep apnea (OSA) on CPAP use (minutes per night), adherence, self-efficacy, risk outcomes, outcome expectancies, expectations and experiences with video consultation, and satisfaction of patients and nurses. Methods: A randomized controlled trial was conducted with an intervention (video consultation) and a usual care group (face-to-face consultation). Patients with confirmed OSA (apnea-hypopnea index >15), requiring CPAP treatment, no history of CPAP treatment, having access to a tablet or smartphone, and proficient in the Dutch language were recruited from a large teaching hospital. CPAP use was monitored remotely, with short-term (weeks 1 to 4) and long-term (week 4, week 12, and week 24) assessments. Questionnaires were completed at baseline and after 4 weeks on self-efficacy, risk perception, outcome expectancies (Self-Efficacy Measure for Sleep Apnea), expectations and experiences with video consultation (covering constructs of the unified theory of acceptance and use of technology), and satisfaction. Nurse satisfaction was evaluated using questionnaires. Results: A total of 140 patients were randomized (1:1 allocation). The use of video consultation for OSA patients does not lead to superior results on CPAP use and adherence compared with face-to-face consultation. A significant difference in change over time was found between groups for short-term (P-interaction=.008) but not long-term (P-interaction=.68) CPAP use. CPAP use decreased in the long term (P=.008), but no significant difference was found between groups (P=.09). Change over time for adherence was not significantly different in the short term (P-interaction=.17) or long term (P-interaction=.51). A relation was found between CPAP use and self-efficacy (P=.001), regardless of the intervention arm (P=.25). No significant difference between groups was found for outcome expectancies (P=.64), self-efficacy (P=.41), and risk perception (P=.30). The experiences were positive, and 95\% (60/63) intended to keep using video consultation. Patients in both groups rated the consultations on average with an 8.4. Overall, nurses (n=3) were satisfied with the video consultation system. Conclusions: Support of OSA patients with video consultation does not lead to superior results on CPAP use and adherence compared with face-to-face consultation. The findings of this research suggest that self-efficacy is an important factor in improving CPAP use and that video consultation may be a feasible way to support patients starting CPAP. Future research should focus on blended care approaches in which self-efficacy receives greater emphasis. Trial Registration: Clinicaltrials.gov NCT04563169; https://clinicaltrials.gov/show/NCT04563169 ",
doi="10.2196/20779",
url="https://formative.jmir.org/2021/5/e20779",
url="http://www.ncbi.nlm.nih.gov/pubmed/33973866"
}


@Article{info:doi/10.2196/23432,
author="McKeon, Grace
and Steel, Zachary
and Wells, Ruth
and Newby, Jill
and Hadzi-Pavlovic, Dusan
and Vancampfort, Davy
and Rosenbaum, Simon",
title="A Mental Health--Informed Physical Activity Intervention for First Responders and Their Partners Delivered Using Facebook: Mixed Methods Pilot Study",
journal="JMIR Form Res",
year="2021",
month="Apr",
day="22",
volume="5",
number="4",
pages="e23432",
keywords="physical activity",
keywords="PTSD",
keywords="social media",
keywords="first responders",
keywords="mental health",
keywords="families",
keywords="online",
keywords="exercise",
abstract="Background: First responders (eg, police, firefighters, and paramedics) are at high risk of experiencing poor mental health. Physical activity interventions can help reduce symptoms and improve mental health in this group. More research is needed to evaluate accessible, low-cost methods of delivering programs. Social media may be a potential platform for delivering group-based physical activity interventions. Objective: This study aims to examine the feasibility and acceptability of delivering a mental health--informed physical activity program for first responders and their self-nominated support partners. This study also aims to assess the feasibility of applying a novel multiple time series design and to explore the impact of the intervention on mental health symptoms, sleep quality, quality of life, and physical activity levels. Methods: We co-designed a 10-week web-based physical activity program delivered via a private Facebook group. We provided education and motivation around different topics weekly (eg, goal setting, overcoming barriers to exercise, and reducing sedentary behavior) and provided participants with a Fitbit. A multiple time series design was applied to assess psychological distress levels, with participants acting as their own control before the intervention. Results: In total, 24 participants (12 first responders and 12 nominated support partners) were recruited, and 21 (88\%) completed the postassessment questionnaires. High acceptability was observed in the qualitative interviews. Exploratory analyses revealed significant reductions in psychological distress during the intervention. Preintervention and postintervention analysis showed significant improvements in quality of life (P=.001; Cohen d=0.60); total depression, anxiety, and stress scores (P=.047; Cohen d=0.35); and minutes of walking (P=.04; Cohen d=0.55). Changes in perceived social support from family (P=.07; Cohen d=0.37), friends (P=.10; Cohen d=0.38), and sleep quality (P=.28; Cohen d=0.19) were not significant. Conclusions: The results provide preliminary support for the use of social media and a multiple time series design to deliver mental health--informed physical activity interventions for first responders and their support partners. Therefore, an adequately powered trial is required. Trial Registration: Australian New Zealand Clinical Trials Registry (ACTRN): 12618001267246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618001267246. ",
doi="10.2196/23432",
url="https://formative.jmir.org/2021/4/e23432",
url="http://www.ncbi.nlm.nih.gov/pubmed/33885376"
}


@Article{info:doi/10.2196/24604,
author="Zhang, Yuezhou
and Folarin, A. Amos
and Sun, Shaoxiong
and Cummins, Nicholas
and Bendayan, Rebecca
and Ranjan, Yatharth
and Rashid, Zulqarnain
and Conde, Pauline
and Stewart, Callum
and Laiou, Petroula
and Matcham, Faith
and White, M. Katie
and Lamers, Femke
and Siddi, Sara
and Simblett, Sara
and Myin-Germeys, Inez
and Rintala, Aki
and Wykes, Til
and Haro, Maria Josep
and Penninx, WJH Brenda
and Narayan, A. Vaibhav
and Hotopf, Matthew
and Dobson, JB Richard
and ",
title="Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Apr",
day="12",
volume="9",
number="4",
pages="e24604",
keywords="mobile health (mHealth)",
keywords="mental health",
keywords="depression",
keywords="sleep",
keywords="wearable device",
keywords="monitoring",
abstract="Background: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. Conclusions: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant. ",
doi="10.2196/24604",
url="https://mhealth.jmir.org/2021/4/e24604",
url="http://www.ncbi.nlm.nih.gov/pubmed/33843591"
}


@Article{info:doi/10.2196/22498,
author="Li, H. Sophie
and Graham, M. Bronwyn
and Werner-Seidler, Aliza",
title="Gender Differences in Adolescent Sleep Disturbance and Treatment Response to Smartphone App--Delivered Cognitive Behavioral Therapy for Insomnia: Exploratory Study",
journal="JMIR Form Res",
year="2021",
month="Mar",
day="23",
volume="5",
number="3",
pages="e22498",
keywords="insomnia",
keywords="gender differences",
keywords="adolescents",
keywords="sleep disturbance",
keywords="sleep quality",
keywords="sleep",
keywords="gender",
keywords="digital interventions",
abstract="Background: Insomnia and sleep disturbance are pervasive and debilitating conditions affecting up to 40\% of adolescents. Women and girls are at greater risk of insomnia, yet differences in treatment responsiveness between genders have not been adequately investigated. Additionally, while women report greater symptom severity and burden of illness than men, this discrepancy requires further examination in adolescents. Objective: The purpose of this study was to examine gender differences in sleep symptom profiles and treatment response in adolescents. Methods: Digital cognitive behavioral therapy for insomnia (CBT-I) treatment responsiveness, as indexed by changes in Insomnia Severity Index (ISI) and Global Pittsburgh Sleep Quality Index (PSQI) scores, was compared in boys and girls (aged 12-16 years; N=49) who participated in a pilot evaluation of the Sleep Ninja smartphone app. Gender differences in self-reported baseline insomnia symptom severity (ISI), sleep quality (PSQI), and sleep characteristics derived from sleep diaries were also examined. Results: Compared with boys, we found that girls reported greater symptom severity (P=.04) and nighttime wakefulness (P=.01 and P=.04) and reduced sleep duration (P=.02) and efficiency (P=.03), but not poorer sleep quality (P=.07), more nighttime awakenings (P=.16), or longer time to get to sleep (P=.21). However, gender differences in symptom severity and sleep duration were accounted for by boys being marginally younger in age. Treatment response to CBT-I was equivalent between boys and girls when comparing reductions in symptom severity (P=.32); there was a trend showing gender differences in improvements in sleep quality, but this was not statistically significant (P=.07). Conclusions: These results demonstrate the presence of gender differences in insomnia symptoms and severity in adolescents and suggest further research is required to understand gender differences in insomnia symptom profiles to inform the development of gender-specific digital interventions delivered to adolescents. ",
doi="10.2196/22498",
url="https://formative.jmir.org/2021/3/e22498",
url="http://www.ncbi.nlm.nih.gov/pubmed/33755029"
}


@Article{info:doi/10.2196/24799,
author="Ito-Masui, Asami
and Kawamoto, Eiji
and Sakamoto, Ryota
and Yu, Han
and Sano, Akane
and Motomura, Eishi
and Tanii, Hisashi
and Sakano, Shoko
and Esumi, Ryo
and Imai, Hiroshi
and Shimaoka, Motomu",
title="Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study",
journal="JMIR Res Protoc",
year="2021",
month="Mar",
day="18",
volume="10",
number="3",
pages="e24799",
keywords="shift work sleep disorders",
keywords="health care workers",
keywords="wearable sensors",
keywords="shift work",
keywords="sleep disorder",
keywords="medical safety",
keywords="safety issue",
keywords="shift workers",
keywords="sleep",
keywords="safety",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="online intervention",
keywords="pilot study",
keywords="machine learning",
keywords="well-being",
abstract="Background: Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective: In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods: This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Results: Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. Conclusions: iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. Trial Registration: UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr\_e/ctr\_view.cgi?recptno=R000046284 International Registered Report Identifier (IRRID): DERR1-10.2196/24799 ",
doi="10.2196/24799",
url="https://www.researchprotocols.org/2021/3/e24799",
url="http://www.ncbi.nlm.nih.gov/pubmed/33626497"
}


@Article{info:doi/10.2196/27139,
author="Knowlden, P. Adam
and Higginbotham, C. John
and Grandner, A. Michael
and Allegrante, P. John",
title="Modeling Risk Factors for Sleep- and Adiposity-Related Cardiometabolic Disease: Protocol for the Short Sleep Undermines Cardiometabolic Health (SLUMBRx) Observational Study",
journal="JMIR Res Protoc",
year="2021",
month="Mar",
day="9",
volume="10",
number="3",
pages="e27139",
keywords="abdominal obesity-metabolic syndrome",
keywords="adiposity",
keywords="body composition",
keywords="body fat distribution",
keywords="insufficient sleep syndrome",
keywords="observational study",
keywords="short sleeper syndrome",
keywords="sleep deprivation",
abstract="Background: Obesity and short sleep duration are significant public health issues. Current evidence suggests that these conditions are associated with cardiovascular disease, metabolic syndrome, inflammation, and premature mortality. Increased interest in the potential link between obesity and short sleep duration, and its health consequences, has been driven by the apparent parallel increase in the prevalence of both conditions in recent decades, their overlapping association with cardiometabolic outcomes, and the potential causal connection between the two health issues. The SLUMBRx (Short Sleep Undermines Cardiometabolic Health) study seeks to contribute to the development of a comprehensive adiposity-sleep model while laying the groundwork for a future research program that will be designed to prevent and treat adiposity- and sleep-related cardiometabolic disease risk factors. Objective: This SLUMBRx study aims to address 4 topics pertinent to the adiposity-sleep hypothesis: the relationship between adiposity and sleep duration; sex-based differences in the relationship between adiposity and sleep duration; the influence of adiposity indices and sleep duration on cardiometabolic outcomes; and the role of socioecological factors as effect modifiers in the relationship between adiposity indices, sleep, and cardiometabolic outcomes. Methods: SLUMBRx will employ a large-scale survey (n=1000), recruiting 159 participants (53 normal weight, 53 overweight, and 53 obese) to be assessed in 2 phases. Results: SLUMBRx was funded by the National Institutes of Health, Heart, Lung, and Blood Institute through a K01 grant award mechanism (1K01HL145128-01A1) on July 23, 2019. Institutional Review Board (IRB) approval for the research project was sought and obtained on July 10, 2019. Phase 1 of SLUMBRx, the laboratory-based component of the study, will gather objective adiposity indices (air displacement plethysmography and anthropometrics) and cardiometabolic data (blood pressure, pulse wave velocity and pulse wave analysis, and a blood-based biomarker). Phase 2 of SLUMBRx, a 1-week, home-based component of the study, will gather sleep-related data (home sleep testing or sleep apnea, actigraphy, and sleep diaries). During phase 2, detailed demographic and socioecological data will be collected to contextualize hypothesized adiposity and sleep-associated cardiometabolic disease risk factors. Collection and analyses of these data will yield information necessary to customize future observational and intervention research. Conclusions: Precise implementation of the SLUMBRx protocol promises to provide objective and empirical data on the interaction between body composition and sleep duration. The hypotheses that will be tested by SLUMBRx are important for understanding the pathogenesis of cardiometabolic disease and for developing future public health interventions to prevent its conception and treat its consequences. International Registered Report Identifier (IRRID): PRR1-10.2196/27139 ",
doi="10.2196/27139",
url="https://www.researchprotocols.org/2021/3/e27139",
url="http://www.ncbi.nlm.nih.gov/pubmed/33687340"
}


@Article{info:doi/10.2196/25775,
author="Yunusova, Asal
and Lai, Jocelyn
and Rivera, P. Alexander
and Hu, Sirui
and Labbaf, Sina
and Rahmani, M. Amir
and Dutt, Nikil
and Jain, C. Ramesh
and Borelli, L. Jessica",
title="Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study",
journal="JMIR Res Protoc",
year="2021",
month="Mar",
day="2",
volume="10",
number="3",
pages="e25775",
keywords="ecological momentary assessment",
keywords="stress",
keywords="digital mental health",
keywords="college student",
keywords="mental health",
keywords="protocol",
keywords="prospective",
keywords="feasibility",
keywords="individual",
keywords="factors",
keywords="sleepy",
keywords="physiology",
keywords="activity",
keywords="COVID-19",
abstract="Background: Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation. Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health. Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months. Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19--related stress assessments. Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health. International Registered Report Identifier (IRRID): DERR1-10.2196/25775 ",
doi="10.2196/25775",
url="https://www.researchprotocols.org/2021/3/e25775",
url="http://www.ncbi.nlm.nih.gov/pubmed/33513124"
}


@Article{info:doi/10.2196/26557,
author="Fucito, M. Lisa
and Ash, I. Garrett
and DeMartini, S. Kelly
and Pittman, Brian
and Barnett, P. Nancy
and Li, R. Chiang-Shan
and Redeker, S. Nancy
and O'Malley, S. Stephanie",
title="A Multimodal Mobile Sleep Intervention for Young Adults Engaged in Risky Drinking: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2021",
month="Feb",
day="26",
volume="10",
number="2",
pages="e26557",
keywords="sleep",
keywords="binge drinking",
keywords="young adults",
keywords="mHealth",
keywords="biosensor",
keywords="behavior therapy",
keywords="mobile phone",
abstract="Background: This paper describes the research protocol for a randomized controlled trial of a multimodal mobile sleep intervention for heavy-drinking young adults. Young adults report the highest rates of heavy, risky alcohol consumption and are a priority population for alcohol prevention and intervention efforts. Alcohol strategies that leverage other health concerns and use technology may offer an innovative solution. Poor sleep is common among young adults and is a risk factor for developing an alcohol use disorder. Moreover, young adults are interested in information to help them sleep better, and behavioral sleep interventions address alcohol use as a standard practice. Objective: The primary aim of this study is to assess the effectiveness of a 2-week multimodal mobile sleep intervention for reducing drinks consumed per week among heavy-drinking young adults. We will explore the effects on alcohol-related consequences, assessing quantitative and qualitative sleep characteristics as secondary aims. The study's goals are to identify the optimal combination of sleep intervention components for improving drinking outcomes, the feasibility and acceptability of these components, and the potential mechanisms by which these components may promote alcohol behavior change. Methods: Young adults (aged 18-25 years) who report recent heavy drinking will be randomly assigned to one of three conditions: mobile sleep hygiene advice (n=30), mobile sleep hygiene advice and sleep and alcohol diary self-monitoring (n=30), or mobile sleep hygiene advice, sleep and alcohol diary self-monitoring, and sleep and alcohol data feedback (n=60). For the feedback component, participants will complete two web-based sessions with a health coach during which they will receive summaries of their sleep and alcohol data, and the potential association between them along with brief advice tailored to their data. All participants will wear sleep and alcohol biosensors daily for 2 weeks for objective assessments of these outcomes. Results: The study was funded by the National Institutes of Health in May 2018. Recruitment began in December 2018 and will be concluded in Spring 2021. As of February 4, 2021, we have enrolled 110 participants. Conclusions: Ultimately, this research could result in an efficacious, low-cost intervention with broad population reach through the use of technology. In addition, this intervention may substantially impact public health by reducing alcohol use disorder risk at a crucial developmental stage. Trial Registration: ClinicalTrials.gov NCT03658954; https://clinicaltrials.gov/ct2/show/NCT03658954 International Registered Report Identifier (IRRID): DERR1-10.2196/26557 ",
doi="10.2196/26557",
url="https://www.researchprotocols.org/2021/2/e26557",
url="http://www.ncbi.nlm.nih.gov/pubmed/33635276"
}


@Article{info:doi/10.2196/23936,
author="Kalanadhabhatta, Manasa
and Rahman, Tauhidur
and Ganesan, Deepak",
title="Effect of Sleep and Biobehavioral Patterns on Multidimensional Cognitive Performance: Longitudinal, In-the-Wild Study",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="18",
volume="23",
number="2",
pages="e23936",
keywords="fitness trackers",
keywords="cognitive performance",
keywords="alertness",
keywords="cognitive throughput",
keywords="sleep",
keywords="activity",
keywords="circadian rhythms",
abstract="Background: With nearly 20\% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood. Objective: We seek to bridge this gap by evaluating prevailing theories on the effects of a variety of sleep, activity, and heart rate parameters on cognitive performance against data collected in real-world settings. Methods: We used a Fitbit Charge 3 and a smartphone app to collect different physiological and neurobehavioral task data, respectively, as part of our 6-week-long in-the-wild study. We collected data from 24 participants across multiple population groups (shift workers, regular workers, and graduate students) on different performance measures (vigilant attention and cognitive throughput). Simultaneously, we used a fitness tracker to unobtrusively obtain physiological measures that could influence these performance measures, including over 900 nights of sleep and over 1 million minutes of heart rate and physical activity metrics. We performed a repeated measures correlation (rrm) analysis to investigate which sleep and physiological markers show association with each performance measure. We also report how our findings relate to existing theories and previous observations from controlled studies. Results: Daytime alertness was found to be significantly correlated with total sleep duration on the previous night (rrm=0.17, P<.001) as well as the duration of rapid eye movement (rrm=0.12, P<.001) and light sleep (rrm=0.15, P<.001). Cognitive throughput, by contrast, was not found to be significantly correlated with sleep duration but with sleep timing---a circadian phase shift toward a later sleep time corresponded with lower cognitive throughput on the following day (rrm=--0.13, P<.001). Both measures show circadian variations, but only alertness showed a decline (rrm=--0.1, P<.001) as a result of homeostatic pressure. Both heart rate and physical activity correlate positively with alertness as well as cognitive throughput. Conclusions: Our findings reveal that there are significant differences in terms of which sleep-related physiological metrics influence each of the 2 performance measures. This makes the case for more targeted in-the-wild studies investigating how physiological measures from self-tracking data influence, or can be used to predict, specific aspects of cognitive performance. ",
doi="10.2196/23936",
url="http://www.jmir.org/2021/2/e23936/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33599622"
}


@Article{info:doi/10.2196/24339,
author="Hendriks, S. Maartje M.
and van Lotringen, H. Jaap
and Vos-van der Hulst, Marije
and Keijsers, W. No{\"e}l L.",
title="Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Feb",
day="8",
volume="9",
number="2",
pages="e24339",
keywords="continuous sleep monitoring device",
keywords="bed sensor technology",
keywords="mHealth",
keywords="nocturnal heart rate",
keywords="nocturnal respiratory rate",
keywords="nocturnal movement activity",
keywords="neurological disorders",
keywords="incomplete spinal cord injury",
keywords="stroke",
keywords="inpatient rehabilitation",
keywords="clinical application",
abstract="Background: Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective: The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods: Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results: In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8\%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97\% and 93\% for iSCI and 99\% and 97\% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ?120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=?2.1, P=.04) and movement activity (t18=?1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26\% and 38\% of the nights for iSCI and stroke patients, respectively. Conclusions: It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants. ",
doi="10.2196/24339",
url="http://mhealth.jmir.org/2021/2/e24339/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33555268"
}


@Article{info:doi/10.2196/24704,
author="Kuula, Liisa
and Pesonen, Anu-Katriina",
title="Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Feb",
day="3",
volume="9",
number="2",
pages="e24704",
keywords="electroencephalogram",
keywords="actigraphy",
keywords="polysomnography",
keywords="sleep",
keywords="heart rate",
keywords="rapid eye movements",
abstract="Background: Polysomnography (PSG) is considered the only reliable way to distinguish between different sleep stages. Wearable devices provide objective markers of sleep; however, these devices often rely only on accelerometer data, which do not enable reliable sleep stage detection. The alteration between sleep stages correlates with changes in physiological measures such as heart rate variability (HRV). Utilizing HRV measures may thus increase accuracy in wearable algorithms. Objective: We examined the validity of the Firstbeat sleep analysis method, which is based on HRV and accelerometer measurements. The Firstbeat method was compared against PSG in a sample of healthy adults. Our aim was to evaluate how well Firstbeat distinguishes sleep stages, and which stages are most accurately detected with this method. Methods: Twenty healthy adults (mean age 24.5 years, SD 3.5, range 20-37 years; 50\% women) wore a Firstbeat Bodyguard 2 measurement device and a Geneactiv actigraph, along with taking ambulatory SomnoMedics PSG measurements for two consecutive nights, resulting in 40 nights of sleep comparisons. We compared the measures of sleep onset, wake, combined stage 1 and stage 2 (light sleep), stage 3 (slow wave sleep), and rapid eye movement (REM) sleep between Firstbeat and PSG. We calculated the sensitivity, specificity, and accuracy from the 30-second epoch-by-epoch data. Results: In detecting wake, Firstbeat yielded good specificity (0.77), and excellent sensitivity (0.95) and accuracy (0.93) against PSG. Light sleep was detected with 0.69 specificity, 0.67 sensitivity, and 0.69 accuracy. Slow wave sleep was detected with 0.91 specificity, 0.72 sensitivity, and 0.87 accuracy. REM sleep was detected with 0.92 specificity, 0.60 sensitivity, and 0.84 accuracy. There were two measures that differed significantly between Firstbeat and PSG: Firstbeat underestimated REM sleep (mean 18 minutes, P=.03) and overestimated wake time (mean 14 minutes, P<.001). Conclusions: This study supports utilizing HRV alongside an accelerometer as a means for distinguishing sleep from wake and for identifying sleep stages. The Firstbeat method was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation. ",
doi="10.2196/24704",
url="http://mhealth.jmir.org/2021/2/e24704/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33533726"
}


@Article{info:doi/10.2196/15524,
author="Kitsaras, George
and Goodwin, Michaela
and Allan, Julia
and Kelly, Michael
and Pretty, Iain",
title="An Interactive Text Message Survey as a Novel Assessment for Bedtime Routines in Public Health Research: Observational Study",
journal="JMIR Public Health Surveill",
year="2020",
month="Dec",
day="21",
volume="6",
number="4",
pages="e15524",
keywords="digital technologies",
keywords="mobile health",
keywords="child",
keywords="well-being",
keywords="development",
keywords="assessment",
keywords="bedtime routines",
keywords="P4 health care",
keywords="text survey",
abstract="Background: Traditional research approaches, especially questionnaires and paper-based assessments, limit in-depth understanding of the fluid dynamic processes associated with child well-being and development. This includes bedtime routine activities such as toothbrushing and reading a book before bed. The increase in innovative digital technologies alongside greater use and familiarity among the public creates unique opportunities to use these technical developments in research. Objective: This study aimed to (1) examine the best way of assessing bedtime routines in families and develop an automated, interactive, text message survey assessment delivered directly to participants' mobile phones and (2) test the assessment within a predominately deprived sociodemographic sample to explore retention, uptake, feedback, and effectiveness. Methods: A public and patient involvement project showed clear preference for interactive text surveys regarding bedtime routines. The developed interactive text survey included questions on bedtime routine activities and was delivered for seven consecutive nights to participating parents' mobile phones. A total of 200 parents participated. Apart from the completion of the text survey, feedback was provided by participants, and data on response, completion, and retention rates were captured. Results: There was a high retention rate (185/200, 92.5\%), and the response rate was high (160/185, 86.5\%). In total, 114 participants provided anonymized feedback. Only a small percentage (5/114, 4.4\%) of participants reported problems associated with completing the assessment. The majority (99/114, 86.8\%) of participants enjoyed their participation in the study, with an average satisfaction score of 4.6 out of 5. Conclusions: This study demonstrated the potential of deploying SMS text message--based surveys to capture and quantify real-time information on recurrent dynamic processes in public health research. Changes and adaptations based on recommendations are crucial next steps in further exploring the diagnostic and potential intervention properties of text survey and text messaging approaches. ",
doi="10.2196/15524",
url="http://publichealth.jmir.org/2020/4/e15524/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33346734"
}


@Article{info:doi/10.2196/24268,
author="Philip, Pierre
and Dupuy, Lucile
and Morin, M. Charles
and de Sevin, Etienne
and Bioulac, St{\'e}phanie
and Taillard, Jacques
and Serre, Fuschia
and Auriacombe, Marc
and Micoulaud-Franchi, Jean-Arthur",
title="Smartphone-Based Virtual Agents to Help Individuals With Sleep Concerns During COVID-19 Confinement: Feasibility Study",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="18",
volume="22",
number="12",
pages="e24268",
keywords="COVID-19",
keywords="virtual agent",
keywords="sleep disorders",
keywords="technology acceptance",
keywords="agent",
keywords="sleep",
keywords="smartphone",
keywords="mobile phone",
keywords="eHealth",
keywords="feasibility",
keywords="stress",
keywords="app",
keywords="intervention",
keywords="behavior",
abstract="Background: The COVID-19 crisis and consequent confinement restrictions have caused significant psychosocial stress and reports of sleep complaints, which require early management, have increased during recent months. To help individuals concerned about their sleep, we developed a smartphone-based app called KANOPEE that allows users to interact with a virtual agent dedicated to autonomous screening and delivering digital behavioral interventions. Objective: Our objective was to assess the feasibility of this app, in terms of inclusion rate, follow-up rate, perceived trust and acceptance of the virtual agent, and effects of the intervention program, in the context of COVID-19 confinement in France. Methods: The virtual agent is an artificial intelligence program using decision tree architecture and interacting through natural body motion and natural voice. A total of 2069 users aged 18 years and above downloaded the free app during the study period (April 22 to May 5, 2020). These users first completed a screening interview based on the Insomnia Severity Index (ISI) conducted by the virtual agent. If the users were positive for insomnia complaints (ISI score >14), they were eligible to join the 2-stage intervention program: (1) complete an electronic sleep diary for 1 week and (2) follow personalized sleep recommendations for 10 days. We collected and analyzed the following measures: sociodemographic information, ISI scores and sleep/wake schedules, and acceptance and trust of the agent. Results: Approximately 76\% (1574/2069) of the app users completed the screening interview with the virtual agent. The virtual agent was well accepted by 27.4\% (431/1574) of the users who answered the acceptance and trust questionnaires on its usability, satisfaction, benevolence, and credibility. Of the 773 screened users who reported sleep complaints (ISI score >14), 166 (21.5\%) followed Step 1 of the intervention, and only 47 of those (28.3\%) followed Step 2. Users who completed Step 1 found that their insomnia complaints (baseline mean ISI score 18.56, mean ISI score after Step 1 15.99; P<.001) and nocturnal sleep quality improved significantly after 1 week. Users who completed Step 2 also showed an improvement compared to the initial measures (baseline mean ISI score 18.87, mean ISI score after Step 2 14.68; P<.001). Users that were most severely affected (ISI score >21) did not respond to either intervention. Conclusions: These preliminary results suggest that the KANOPEE app is a promising solution to screen populations for sleep complaints and that it provides acceptable and practical behavioral advice for individuals reporting moderately severe insomnia. ",
doi="10.2196/24268",
url="http://www.jmir.org/2020/12/e24268/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33264099"
}


@Article{info:doi/10.2196/20597,
author="Kim, Ki-Hun
and Kim, Kwang-Jae",
title="Missing-Data Handling Methods for Lifelogs-Based Wellness Index Estimation: Comparative Analysis With Panel Data",
journal="JMIR Med Inform",
year="2020",
month="Dec",
day="17",
volume="8",
number="12",
pages="e20597",
keywords="lifelogs-based wellness index",
keywords="missing-data handling",
keywords="health behavior lifelogs",
keywords="panel data",
keywords="smart wellness service",
abstract="Background: A lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch). A wellness score intuitively shows the users of smart wellness services the overall condition of their health behaviors. LWI development includes estimation (ie, estimating coefficients in LWI with data). A panel data set comprising health behavior lifelogs allows LWI estimation to control for unobserved variables, thereby resulting in less bias. However, these data sets typically have missing data due to events that occur in daily life (eg, smart devices stop collecting data when batteries are depleted), which can introduce biases into LWI coefficients. Thus, the appropriate choice of method to handle missing data is important for reducing biases in LWI estimations with panel data. However, there is a lack of research in this area. Objective: This study aims to identify a suitable missing-data handling method for LWI estimation with panel data. Methods: Listwise deletion, mean imputation, expectation maximization--based multiple imputation, predictive-mean matching--based multiple imputation, k-nearest neighbors--based imputation, and low-rank approximation--based imputation were comparatively evaluated by simulating an existing case of LWI development. A panel data set comprising health behavior lifelogs of 41 college students over 4 weeks was transformed into a reference data set without any missing data. Then, 200 simulated data sets were generated by randomly introducing missing data at proportions from 1\% to 80\%. The missing-data handling methods were each applied to transform the simulated data sets into complete data sets, and coefficients in a linear LWI were estimated for each complete data set. For each proportion for each method, a bias measure was calculated by comparing the estimated coefficient values with values estimated from the reference data set. Results: Methods performed differently depending on the proportion of missing data. For 1\% to 30\% proportions, low-rank approximation--based imputation, predictive-mean matching--based multiple imputation, and expectation maximization--based multiple imputation were superior. For 31\% to 60\% proportions, low-rank approximation--based imputation and predictive-mean matching--based multiple imputation performed best. For over 60\% proportions, only low-rank approximation--based imputation performed acceptably. Conclusions: Low-rank approximation--based imputation was the best of the 6 data-handling methods regardless of the proportion of missing data. This superiority is generalizable to other panel data sets comprising health behavior lifelogs given their verified low-rank nature, for which low-rank approximation--based imputation is known to perform effectively. This result will guide missing-data handling in reducing coefficient biases in new development cases of linear LWIs with panel data. ",
doi="10.2196/20597",
url="http://medinform.jmir.org/2020/12/e20597/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33331831"
}


@Article{info:doi/10.2196/22102,
author="Yoshizaki, Arika
and Mohri, Ikuko
and Yamamoto, Tomoka
and Shirota, Ai
and Okada, Shiho
and Murata, Emi
and Hoshino, Kyoko
and Kato-Nishimura, Kumi
and Matsuzawa, Shigeyuki
and Kato, Takafumi
and Taniike, Masako",
title="An Interactive Smartphone App, Nenne Navi, for Improving Children's Sleep: Pilot Usability Study",
journal="JMIR Pediatr Parent",
year="2020",
month="Dec",
day="1",
volume="3",
number="2",
pages="e22102",
keywords="infant sleep",
keywords="app",
keywords="mHealth",
keywords="behavioral intervention",
keywords="sleep health, PDCA cycle",
abstract="Background: Healthy sleep is important not only for physical health but also for brain development in children. Several reports have revealed that Japanese adults and children have later bedtimes and shorter sleep durations compared with those in other countries, possibly because of Japanese culture and lifestyles. Therefore, an intervention tool that is suitable to the Japanese sociocultural environment is urgently needed to improve children's sleep problems in their early years. Objective: To provide appropriate sleep health literacy to caregivers and change their parenting behavior, we developed a smartphone app that allows reciprocal interaction between caregivers and pediatric sleep experts. This paper describes a preliminary study to examine the app's basic design and functions and to establish its acceptability and usability in a small sample. Methods: A total of 10 caregivers and 10 infants (aged 18-28 months; 4/10, 40\% boys) living in Japan participated in the study. At the start of the trial, the e-learning content regarding sleep health literacy was delivered via a smartphone. Thereafter, caregivers manually inputted recorded data about their own and their infant's sleep habits for 8 consecutive days per month for 2 months. After pediatric sleep experts retrieved this information from the Osaka University server, they specified the problems and provided multiple sleep habit improvement suggestions to caregivers. Caregivers then selected one of the feasible pieces of advice to practice and reported their child's sleep-related behaviors via the app. Actigraphy was used to monitor children's sleep behaviors objectively. The concordance between the information provided by caregivers and the actigraphy data was assessed. The acceptability and usability of the app were evaluated using self-report questionnaires completed by caregivers; qualitative feedback was obtained via semistructured interviews after the intervention. Results: There was no significant difference between the information provided by the caregivers and the actigraphy data for bedtimes and wake-up times (P=.13 to P=.97). However, there was a difference between the actigraphy data and the caregivers' reports of nighttime sleep duration and nighttime awakenings (P<.001 each), similar to prior findings. User feedback showed that 6 and 5 of the 10 caregivers rated the app easy to understand and easy to continue to use, respectively. Additionally, 6 of the 10 caregivers rated the app's operativity as satisfactory. Although this was a short-term trial, children's sleep habits, caregivers' sleep health consciousness, and parenting behaviors improved to some extent. Conclusions: The present findings suggest that the app can easily be used and is acceptable by Japanese caregivers. Given the user feedback, the app has the potential to improve children's sleep habits by sending individualized advice that fits families' backgrounds and home lives. Further studies are needed to confirm the efficacy of the app and facilitate social implementation. ",
doi="10.2196/22102",
url="http://pediatrics.jmir.org/2020/2/e22102/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33122163"
}


@Article{info:doi/10.2196/21209,
author="Jalali, Niloofar
and Sahu, Sundar Kirti
and Oetomo, Arlene
and Morita, Pelegrini Plinio",
title="Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Nov",
day="13",
volume="8",
number="11",
pages="e21209",
keywords="public health",
keywords="IoT",
keywords="anomaly detection",
keywords="behavioral monitoring",
keywords="deep learning",
keywords="variational autoencoder",
keywords="LSTM",
abstract="Background: One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective: The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods: From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results: The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions: This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment. ",
doi="10.2196/21209",
url="http://mhealth.jmir.org/2020/11/e21209/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33185562"
}


@Article{info:doi/10.2196/19508,
author="Huberty, Jennifer
and Puzia, E. Megan
and Larkey, Linda
and Irwin, R. Michael
and Vranceanu, Ana-Maria",
title="Use of the Consumer-Based Meditation App Calm for Sleep Disturbances: Cross-Sectional Survey Study",
journal="JMIR Form Res",
year="2020",
month="Nov",
day="13",
volume="4",
number="11",
pages="e19508",
keywords="insomnia",
keywords="mental health",
keywords="mindfulness",
keywords="meditation",
keywords="mobile apps",
keywords="consumer behavior",
keywords="mobile phone",
abstract="Background: Over 30\% of Americans report regular sleep disturbance, and consumers are increasingly seeking strategies to improve sleep. Self-guided mindfulness mobile apps may help individuals improve their sleep. Despite the recent proliferation of sleep content within commercially available mindfulness apps, there is little research on how consumers are using these apps for sleep. Objective: We conducted a cross-sectional survey among subscribers to Calm, a popular, consumer-based, mindfulness-based meditation app, and described and compared how good sleepers, poor sleepers, and those with self-reported insomnia use the app for sleep. Methods: Participants who were paying subscribers of Calm and had used a sleep component of Calm in the last 90 days were invited to complete an investigator-developed survey that included questions about sleep disturbance and the use of Calm for sleep. Based on self-reports of sleep disturbances and of insomnia diagnosis, participants were categorized as ``good sleepers,'' ``poor sleepers,'' or ``those with insomnia diagnosis.'' Chi-square tests compared reasons for downloading the app and usage patterns across participants with and without sleep disturbance. Results: There was a total of 9868 survey respondents. Approximately 10\% of participants (1008/9868, 10.21\%) were good sleepers, 78\% were poor sleepers (7565/9868, 77.66\%), and 11\% reported a diagnosis of insomnia (1039/9868, 10.53\%). The sample was mostly White (8185/9797, 83.55\%), non-Hispanic (8929/9423, 94.76\%), and female (8166/9578, 85.26\%). The most common reasons for sleep disturbances were racing thoughts (7084/8604, 82.33\%), followed by stress or anxiety (6307/8604, 73.30\%). Poor sleepers and those with insomnia were more likely than good sleepers to have downloaded Calm to improve sleep ($\chi$22=1548.8, P<.001), reduce depression or anxiety ($\chi$22=15.5, P<.001), or improve overall health ($\chi$22=57.6, P<.001). Respondents with insomnia used Calm most often (mean 5.417 days/week, SD 1.936), followed by poor sleepers (mean 5.043 days/week, SD 2.027; F2=21.544, P<.001). The most common time to use Calm was while lying down to sleep (7607/9686, 78.54\%), and bedtime use was more common among poor sleepers and those with insomnia ($\chi$22=382.7, P<.001). Compared to good and poor sleepers, those with insomnia were more likely to use Calm after waking up at night ($\chi$22=410.3, P<.001). Most participants tried to use Calm on a regular basis (5031/8597, 58.52\%), but regular nighttime use was most common among those with insomnia (646/977, 66.1\%), followed by poor sleepers (4040/6930, 58.30\%; $\chi$22=109.3, P<.001). Conclusions: Of the paying subscribers to Calm who have used one of the sleep components, approximately 90\% have sleep difficulties, and 77\% started using Calm primarily for sleep. These descriptive data point to areas of focus for continued refinement of app features and content, followed by prospective trials testing efficacy of consumer-based meditation mobile apps for improving sleep. ",
doi="10.2196/19508",
url="http://formative.jmir.org/2020/11/e19508/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33185552"
}


@Article{info:doi/10.2196/15037,
author="Edmonds, R. Michael
and Hadjistavropoulos, D. Heather
and Gullickson, M. Kirsten
and Asmundson, JN Aleiia
and Dear, F. Blake
and Titov, Nickolai",
title="Understanding Problems With Sleep, Sexual Functioning, Energy, and Appetite Among Patients Who Access Transdiagnostic Internet-Delivered Cognitive Behavioral Therapy for Anxiety and Depression: Qualitative Exploratory Study",
journal="JMIR Form Res",
year="2020",
month="Oct",
day="13",
volume="4",
number="10",
pages="e15037",
keywords="cognitive behavioral therapy",
keywords="anxiety",
keywords="depression",
keywords="internet-based intervention",
keywords="sleep",
keywords="sexual health",
keywords="motivation",
keywords="appetite",
abstract="Background: Transdiagnostic internet-delivered cognitive behavioral therapy (T-ICBT) is an effective treatment for anxiety and depression, and nowadays, there is interest in exploring ways to optimize T-ICBT in routine care. T-ICBT programs are designed to address the primary cognitive-affective and behavioral symptoms of anxiety and depression (eg, low mood, worry, anhedonia, and avoidance). Treatment also has the potential to resolve other symptom concerns (eg, sleep disruption, sexual dysfunction, lack of energy, and appetite or weight changes). Having additional information regarding the extent of these concerns and how concerns change over time could prove beneficial for further development of T-ICBT in routine care. Objective: This exploratory formative study aims to better understand sleep, sexual functioning, energy, and appetite concerns among T-ICBT clients seeking treatment for depression and anxiety. A qualitative analytic approach was used to identify themes in the symptom concerns reported by patients in the areas of sleep, sexual functioning, energy, and appetite at the time of enrollment. Patient responses to related items from screening measures for anxiety and depression were also examined pre- and posttreatment. Methods: Patients in routine care who applied for a T-ICBT program for depression and anxiety over a 1-year period were included in this study. As part of the application and screening process, participants completed depression and anxiety symptom measures (ie, 9-item Patient Health Questionnaire and 7-item Generalized Anxiety Disorder scale). These same measures were administered posttreatment. Subsequently, they were asked if they were experiencing any problems with sleep, sexual activity, energy, or appetite (yes or no). If their response was yes, they were presented with an open-ended comment box that asked them to describe the problems they had experienced in those areas. Results: A total of 462 patients were admitted to T-ICBT during the study period, of which 438 endorsed having some problems with sleep, sexual activity, energy, or appetite. The analysis of open-ended responses indicated that 73.4\% (339/462) of patients reported sleep problems (eg, difficulty initiating or maintaining sleep), 69.3\% (320/462) of patients reported problems with energy or motivation (eg, tiredness and low motivation), 57.4\% (265/462) of patients reported appetite or body weight concerns (eg, changes in appetite and weight loss or gain), and 30.1\% (139/462) of patients described concerns with sexual functioning (eg, loss of interest in sex and difficulty with arousal). Item analysis of symptom measures demonstrated that T-ICBT produced improvements in sleep, energy, and appetite in 8 weeks. Sexual dysfunction and weight changes were not represented in the screening measures, so it remains unclear what effect T-ICBT has on these symptoms. Conclusions: Sleep disruption, lack of energy, appetite or weight changes, and sexual dysfunction are common concerns reported by clients enrolled in T-ICBT in routine practice and may deserve greater attention in T-ICBT program development and administration. ",
doi="10.2196/15037",
url="http://formative.jmir.org/2020/10/e15037/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33048054"
}


@Article{info:doi/10.2196/20501,
author="Sieverdes, C. John
and Treiber, A. Frank
and Kline, E. Christopher
and Mueller, Martina
and Brunner-Jackson, Brenda
and Sox, Luke
and Cain, Mercedes
and Swem, Maria
and Diaz, Vanessa
and Chandler, Jessica",
title="Ethnicity Differences in Sleep Changes Among Prehypertensive Adults Using a Smartphone Meditation App: Dose-Response Trial",
journal="JMIR Form Res",
year="2020",
month="Oct",
day="6",
volume="4",
number="10",
pages="e20501",
keywords="meditation",
keywords="sleep",
keywords="mobile phone",
keywords="prehypertension",
keywords="ethnicity",
abstract="Background: African Americans (AAs) experience greater sleep quality problems than non-Hispanic Whites (NHWs). Meditation may aid in addressing this disparity, although the dosage levels needed to achieve such benefits have not been adequately studied. Smartphone apps present a novel modality for delivering, monitoring, and measuring adherence to meditation protocols. Objective: This 6-month dose-response feasibility trial investigated the effects of a breathing awareness meditation (BAM) app, Tension Tamer, on the secondary outcomes of self-reported and actigraphy measures of sleep quality and the modulating effects of ethnicity of AAs and NHWs. Methods: A total of 64 prehypertensive adults (systolic blood pressure <139 mm Hg; 31 AAs and 33 NHWs) were randomized into 3 different Tension Tamer dosage conditions (5,10, or 15 min twice daily). Sleep quality was assessed at baseline and at 1, 3, and 6 months using the Pittsburgh Sleep Quality Index (PSQI) and 1-week bouts of continuous wrist actigraphy monitoring. The study was conducted between August 2014 and October 2016 (IRB \#Pro00020894). Results: At baseline, PSQI and actigraphy data indicated that AAs had shorter sleep duration, greater sleep disturbance, poorer efficiency, and worse quality of sleep (range P=.03 to P<.001). Longitudinal generalized linear mixed modeling revealed a dose effect modulated by ethnicity (P=.01). Multimethod assessment showed a consistent pattern of NHWs exhibiting the most favorable responses to the 5-min dose; they reported greater improvements in sleep efficiency and quality as well as the PSQI global value than with the 10-min and 15-min doses (range P=.04 to P<.001). Actigraphy findings revealed a consistent, but not statistically significant, pattern in the 5-min group, showing lower fragmentation, longer sleep duration, and higher efficiency than the other 2 dosage conditions. Among AAs, actigraphy indicated lower sleep fragmentation with the 5-min dose compared with the 10-min and 15-min doses (P=.03 and P<.001, respectively). The 10-min dose showed longer sleep duration than the 5-min and 15-min doses (P=.02 and P<.001, respectively). The 5-min dose also exhibited significantly longer average sleep than the 15-min dose (P=.03). Conclusions: These findings indicate the need for further study of the potential modulating influence of ethnicity on the impact of BAM on sleep indices and user-centered exploration to ascertain the potential merits of refining the Tension Tamer app with attention to cultural tailoring among AAs and NHWs with pre-existing sleep complaints. ",
doi="10.2196/20501",
url="https://formative.jmir.org/2020/10/e20501",
url="http://www.ncbi.nlm.nih.gov/pubmed/33021484"
}


@Article{info:doi/10.2196/20590,
author="Lunsford-Avery, R. Jessica
and Keller, Casey
and Kollins, H. Scott
and Krystal, D. Andrew
and Jackson, Leah
and Engelhard, M. Matthew",
title="Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Oct",
day="1",
volume="8",
number="10",
pages="e20590",
keywords="sleep",
keywords="wearable",
keywords="mHealth",
keywords="adolescents",
keywords="EEG",
keywords="feasibility",
keywords="acceptability",
keywords="tolerability",
keywords="actigraphy",
abstract="Background: Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group. Objective: The goal of the research was to investigate the feasibility and acceptability of sleep EEG wearable devices among adolescents aged 11 to 17 years. Methods: A total of 104 adolescents aged 11 to 17 years participated in 7 days of at-home sleep recording using a self-administered wearable sleep EEG device (Zmachine Insight+, General Sleep Corporation) as well as a wristworn actigraph. Feasibility was assessed as the number of full nights of successful recording completed by adolescents, and acceptability was measured by the wearable acceptability survey for sleep. Feasibility and acceptability were assessed separately for the sleep EEG device and wristworn actigraph. Results: A total of 94.2\% (98/104) of adolescents successfully recorded at least 1 night of data using the sleep EEG device (mean number of nights 5.42; SD 1.71; median 6, mode 7). A total of 81.6\% (84/103) rated the comfort of the device as falling in the comfortable to mildly uncomfortable range while awake. A total of 40.8\% (42/103) reported typical sleep while using the device, while 39.8\% (41/103) indicated minimal to mild device-related sleep disturbances. A minority (32/104, 30.8\%) indicated changes in their sleep position due to device use, and very few (11/103, 10.7\%) expressed dissatisfaction with their experience with the device. A similar pattern was observed for the wristworn actigraph device. Conclusions: Wearable sleep EEG appears to represent a feasible, acceptable method for sleep assessment among adolescents and may have utility for assessing and treating sleep disturbances at a population level. Future studies with adolescents should evaluate strategies for further improving usability of such devices, assess relationships between sleep EEG--derived metrics and health outcomes, and investigate methods for incorporating data from these devices into emerging digital interventions and applications. Trial Registration: ClinicalTrials.gov NCT03843762; https://clinicaltrials.gov/ct2/show/NCT03843762 ",
doi="10.2196/20590",
url="https://mhealth.jmir.org/2020/10/e20590",
url="http://www.ncbi.nlm.nih.gov/pubmed/33001035"
}


@Article{info:doi/10.2196/18086,
author="Thota, Darshan",
title="Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study",
journal="JMIR Form Res",
year="2020",
month="Sep",
day="29",
volume="4",
number="9",
pages="e18086",
keywords="Fitbit",
keywords="sleep",
keywords="healthy",
keywords="mood",
keywords="context",
keywords="waking",
abstract="Background: Mental health disorders can disrupt a person's sleep, resulting in lower quality of life. Early identification and referral to mental health services are critical for active duty service members returning from forward-deployed missions. Although technologies like wearable computing devices have the potential to help address this problem, research on the role of technologies like Fitbit in mental health services is in its infancy. Objective: If Fitbit proves to be an appropriate clinical tool in a military setting, it could provide potential cost savings, improve clinician access to patient data, and create real-time treatment options for the greater active duty service member population. The purpose of this study was to determine if the Fitbit device can be used to identify indicators of mental health disorders by measuring the relationship between Fitbit sleep data, self-reported mood, and environmental contextual factors that may disrupt sleep. Methods: This observational cohort study was conducted at the Madigan Army Medical Center. The study included 17 healthy adults who wore a Fitbit Flex for 2 weeks and completed a daily self-reported mood and sleep log. Daily Fitbit data were obtained for each participant. Contextual factors were collected with interim and postintervention surveys. This study had 3 specific aims: (1) Determine the correlation between daily Fitbit sleep data and daily self-reported sleep, (2) Determine the correlation between number of waking events and self-reported mood, and (3) Explore the qualitative relationships between Fitbit waking events and self-reported contextual factors for sleep. Results: There was no significant difference in the scores for the pre-intevention Pittsburg Sleep Quality Index (PSQI; mean 5.88 points, SD 3.71 points) and postintervention PSQI (mean 5.33 points, SD 2.83 points). The Wilcoxon signed-ranks test showed that the difference between the pre-intervention PSQI and postintervention PSQI survey data was not statistically significant (Z=0.751, P=.05). The Spearman correlation between Fitbit sleep time and self-reported sleep time was moderate (r=0.643, P=.005). The Spearman correlation between number of waking events and self-reported mood was weak (r=0.354, P=.163). Top contextual factors disrupting sleep were ``pain,'' ``noises,'' and ``worries.'' A subanalysis of participants reporting ``worries'' found evidence of potential stress resilience and outliers in waking events. Conclusions: Findings contribute valuable evidence on the strength of the Fitbit Flex device as a proxy that is consistent with self-reported sleep data. Mood data alone do not predict number of waking events. Mood and Fitbit data combined with further screening tools may be able to identify markers of underlying mental health disease. ",
doi="10.2196/18086",
url="http://formative.jmir.org/2020/9/e18086/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32990631"
}


@Article{info:doi/10.2196/18297,
author="Sadek, Ibrahim
and Heng, Soon Terry Tan
and Seet, Edwin
and Abdulrazak, Bessam",
title="A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="18",
volume="22",
number="9",
pages="e18297",
keywords="ballistocardiography",
keywords="sleep apnea",
keywords="vital signs",
keywords="eHealth",
keywords="mobile health",
keywords="home care",
abstract="Background: At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient's care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient's everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective: This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods: In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient's mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results: For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96\% (SD 6.39), a sensitivity of 57.07\% (SD 12.63), and a specificity of 45.26\% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42\% (SD 0.57), an NRMSE of 6.54\% (SD 0.56), and an MAPE of 5.41\% (SD 0.58) for HR, whereas an NMAE of 11.42\% (SD 2.62), an NRMSE of 13.85\% (SD 2.78), and an MAPE of 11.60\% (SD 2.84) for RR. Conclusions: Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection. ",
doi="10.2196/18297",
url="http://www.jmir.org/2020/9/e18297/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32945773"
}


@Article{info:doi/10.2196/20921,
author="Pan, Qiang
and Brulin, Damien
and Campo, Eric",
title="Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review",
journal="JMIR Biomed Eng",
year="2020",
month="Aug",
day="26",
volume="5",
number="1",
pages="e20921",
keywords="EEG",
keywords="ECG",
keywords="classification",
keywords="mobile phone",
abstract="Background: Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective: This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods: This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results: By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions: Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research. ",
doi="10.2196/20921",
url="http://biomedeng.jmir.org/2020/1/e20921/"
}


@Article{info:doi/10.2196/17755,
author="Chung, Kyungmi
and Kim, Seoyoung
and Lee, Eun
and Park, Young Jin",
title="Mobile App Use for Insomnia Self-Management in Urban Community-Dwelling Older Korean Adults: Retrospective Intervention Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Aug",
day="24",
volume="8",
number="8",
pages="e17755",
keywords="sleep hygiene",
keywords="cognitive behavioral therapy",
keywords="sleep initiation and maintenance disorders",
keywords="telemedicine",
keywords="mobile apps",
keywords="treatment adherence and compliance",
keywords="health education",
keywords="health services for the aged",
keywords="community mental health services",
keywords="health care quality, access, and evaluation",
abstract="Background: As an evidence-based psychotherapy for treating insomnia, cognitive behavioral therapy for insomnia (CBT-I), which helps people with sleep problems to change their unhelpful sleep-related beliefs and habits, has been well-established in older adults. Recently, the utilization of mobile CBT-I apps has been getting attention from mental health professionals and researchers; however, whether mobile CBT-I apps are usable among older users has yet to be determined. Objective: The aims of this study were to explore the relationships between subjective sleep quality and subjective memory complaints and depressive symptoms; to explore the relationship between perceived difficulty in mobile app use and usability of the mobile phone--based self-help CBT-I app, named MIND MORE, in urban community-dwelling Korean older adults; to compare changes in subjective sleep quality from pre-intervention to post-intervention, during which they used the mobile app over a 1-week intervention period; and evaluate adherence to the app. Methods: During the 2-hour training program delivered on 1 day titled ``Overcoming insomnia without medication: How to use the `MIND MORE' mobile app for systematic self-management of insomnia'' (pre-intervention), 41 attendants were asked to gain hands-on experience with the app facilitated by therapists and volunteer workers. They were then asked to complete questionnaires on sociodemographic characteristics, subjective evaluation of mental health status (ie, depression, memory loss and impairment, and sleep problems), and app usability. For the 1-week home-based self-help CBT-I using the app (post-intervention), 9 of the 41 program attendants, who had already signed up for the pre-intervention, were guided to complete the given questionnaires on subjective evaluation of sleep quality after the 1-week intervention, specifically 8 days after the training program ended. Results: Due to missing data, 40 of 41 attendants were included in the data analysis. The main findings of this study were as follows. First, poor subjective sleep quality was associated with higher ratings of depressive symptoms (40/40; $\rho$=.60, P<.001) and memory complaints (40/40; $\rho$=.46, P=.003) at baseline. Second, significant improvements in subjective sleep quality from pre-intervention to post-intervention were observed in the older adults who used the MIND MORE app only for the 1-week intervention period (9/9; t8=3.74, P=.006). Third, apart from the program attendants who did not have a smartphone (2/40) or withdrew from their MIND MORE membership (3/40), those who attended the 1-day sleep education program adhered to the app from at least 2 weeks (13/35, 37\%) to 8 weeks (2/35, 6\%) without any further contact. Conclusions: This study provides empirical evidence that the newly developed MIND MORE app not only is usable among older users but also could improve subjective sleep quality after a 1-week self-help intervention period. ",
doi="10.2196/17755",
url="http://mhealth.jmir.org/2020/8/e17755/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32831177"
}


@Article{info:doi/10.2196/15697,
author="Eke, Ransome
and Li, Tong
and Bond, Kiersten
and Ho, Arlene
and Graves, Lisa",
title="Viewing Trends and Users' Perceptions of the Effect of Sleep-Aiding Music on YouTube: Quantification and Thematic Content Analysis",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="24",
volume="22",
number="8",
pages="e15697",
keywords="insomnia",
keywords="sleep deprivation",
keywords="YouTube",
keywords="utilization",
keywords="pattern",
keywords="perception",
keywords="content analysis",
abstract="Background: Sleep plays an essential role in the psychological and physiological functioning of humans. A report from the Centers for Disease Control and Prevention (CDC) found that sleep duration was significantly reduced among US adults in 2012 compared to 1985. Studies have described a significant association between listening to soothing music and an improvement in sleep quality and sleep duration. YouTube is a platform where users can access sleep-aiding music videos. No literature exists pertaining to the use of sleep-aiding music on YouTube. Objective: This study aimed to examine the patterns of viewing sleep-aiding music videos on YouTube. We also performed a content analysis of the comments left on sleep-aiding music video posts, to describe the perception of users regarding the effects of these music videos on their sleep quality. Methods: We searched for sleep-aiding music videos published on YouTube between January 1, 2012, and December 31, 2017. We sorted videos by view number (highest to lowest) and used a targeted sampling approach to select eligible videos for qualitative content analysis. To perform the content analysis, we imported comments into a mixed-method analytical software. We summarized variables including total views, likes, dislikes, play duration, and age of published music videos. All descriptive statistics were completed with SAS statistical software. Results: We found a total of 238 sleep-aiding music videos on YouTube that met the inclusion criteria. The total view count was 1,467,747,018 and the total playtime was 84,252 minutes. The median play length was 186 minutes (IQR 122 to 480 minutes) and the like to dislike ratio was approximately 9 to 1. In total, 135 (56.7\%) videos had over 1 million views, and 124 (52.1\%) of the published sleep-aiding music videos had stayed active for 1 to 2 years. Overall, 4023 comments were extracted from 20 selected sleep-aiding music videos. Five overarching themes emerged in the reviewed comments, including viewers experiencing a sleep problem, perspective on the positive impact of the sleep-aiding music videos, no effect of the sleep-aiding music videos, time to initiation of sleep or sleep duration, and location of viewers. The overall $\kappa$ statistic for the codes was 0.87 (range 0.85-0.96). Conclusions: This is the first study to examine the patterns of viewing sleep-aiding music videos on YouTube. We observed a substantial increase in the number of people using sleep-aiding music videos, with a wide variation in viewer location. This study supports the hypothesis that listening to soothing music has a positive impact on sleep habits. ",
doi="10.2196/15697",
url="http://www.jmir.org/2020/8/e15697/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32831182"
}


@Article{info:doi/10.2196/18370,
author="Liu, Jiaxing
and Zhao, Yang
and Lai, Boya
and Wang, Hailiang
and Tsui, Leung Kwok",
title="Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Aug",
day="5",
volume="8",
number="8",
pages="e18370",
keywords="sleep/wake identification",
keywords="hidden Markov model",
keywords="personalized health",
keywords="unsupervised learning",
keywords="sleep",
keywords="physical activity",
keywords="wearables",
keywords="heart rate",
abstract="Background: The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns. Objective: We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach's output with that of an existing commercial wearable device's algorithms. Methods: In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual's data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified. Results: When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants. Conclusions: Our unsupervised approach can be effectively implemented based on an individual's multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters. ",
doi="10.2196/18370",
url="https://mhealth.jmir.org/2020/8/e18370",
url="http://www.ncbi.nlm.nih.gov/pubmed/32755887"
}


@Article{info:doi/10.2196/16880,
author="Miyashita, Hirotaka
and Nakamura, Mitsuteru
and Svensson, Kishi Akiko
and Nakamura, Masahiro
and Tokuno, Shinichi
and Chung, Ung-Il
and Svensson, Thomas",
title="Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study",
journal="JMIR Form Res",
year="2020",
month="Jun",
day="9",
volume="4",
number="6",
pages="e16880",
keywords="voice analysis",
keywords="emotional status",
keywords="vitality",
keywords="sleep",
keywords="mobile phone",
abstract="Background: Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective: The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods: A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep ($\Delta$vitality) and specific sleep measures. Results: A significant association was identified between total sleep time and $\Delta$vitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and $\Delta$vitality (regression coefficient: --0.026, P=.001). There was no significant association between $\Delta$vitality and sleep efficiency or number of awakenings. Conclusions: Total sleep time and sleep onset latency are significantly associated with $\Delta$vitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures. ",
doi="10.2196/16880",
url="https://formative.jmir.org/2020/6/e16880",
url="http://www.ncbi.nlm.nih.gov/pubmed/32515745"
}


@Article{info:doi/10.2196/16674,
author="Degroote, Laurent
and Hamerlinck, Gilles
and Poels, Karolien
and Maher, Carol
and Crombez, Geert
and De Bourdeaudhuij, Ilse
and Vandendriessche, Ann
and Curtis, G. Rachel
and DeSmet, Ann",
title="Low-Cost Consumer-Based Trackers to Measure Physical Activity and Sleep Duration Among Adults in Free-Living Conditions: Validation Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="May",
day="19",
volume="8",
number="5",
pages="e16674",
keywords="fitness trackers",
keywords="mobile phone",
keywords="accelerometry",
keywords="physical activity",
keywords="sleep",
abstract="Background: Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. Objective: This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. Methods: A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. Results: Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. Conclusions: This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers. ",
doi="10.2196/16674",
url="http://mhealth.jmir.org/2020/5/e16674/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32282332"
}


@Article{info:doi/10.2196/17071,
author="Lorenz, Noah
and Sander, Christian
and Ivanova, Galina
and Hegerl, Ulrich",
title="Temporal Associations of Daily Changes in Sleep and Depression Core Symptoms in Patients Suffering From Major Depressive Disorder: Idiographic Time-Series Analysis",
journal="JMIR Ment Health",
year="2020",
month="Apr",
day="23",
volume="7",
number="4",
pages="e17071",
keywords="depression",
keywords="sleep",
keywords="time series",
keywords="idiographic",
keywords="self-management",
abstract="Background: There is a strong link between sleep and major depression; however, the causal relationship remains unclear. In particular, it is unknown whether changes in depression core symptoms precede or follow changes in sleep, and whether a longer or shorter sleep duration is related to improvements of depression core symptoms. Objective: The aim of this study was to investigate temporal associations between sleep and depression in patients suffering from major depressive disorder using an idiographic research approach. Methods: Time-series data of daily sleep assessments (time in bed and total sleep time) and self-rated depression core symptoms for an average of 173 days per patient were analyzed in 22 patients diagnosed with recurrent major depressive disorder using a vector autoregression model. Granger causality tests were conducted to test for possible causality. Impulse response analysis and forecast error variance decomposition were performed to quantify the temporal mutual impact of sleep and depression. Results: Overall, 11 positive and 5 negative associations were identified between time in bed/total sleep time and depression core symptoms. Granger analysis showed that time in bed/total sleep time caused depression core symptoms in 9 associations, whereas this temporal order was reversed for the other 7 associations. Most of the variance (10\%) concerning depression core symptoms could be explained by time in bed. Changes in sleep or depressive symptoms of 1 SD had the greatest impact on the other variable in the following 2 to 4 days. Conclusions: Longer rather than shorter bedtimes were associated with more depression core symptoms. However, the temporal orders of the associations were heterogeneous. ",
doi="10.2196/17071",
url="http://mental.jmir.org/2020/4/e17071/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32324147"
}


@Article{info:doi/10.2196/17544,
author="Chong, L. Kimberly P.
and Guo, Z. Julia
and Deng, Xiaomeng
and Woo, P. Benjamin K.",
title="Consumer Perceptions of Wearable Technology Devices: Retrospective Review and Analysis",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Apr",
day="20",
volume="8",
number="4",
pages="e17544",
keywords="wearable technology devices",
keywords="Fitbit",
keywords="Amazon",
keywords="sleep",
abstract="Background: Individuals of all ages are becoming more health conscious, and wearable technology devices (eg, Fitbit and Apple Watch) are becoming increasingly popular in encouraging healthy lifestyles. Objective: The aim of this paper was to explore how consumers use wearable devices. Methods: A retrospective review was done on the top-rated verified purchase reviews of the Fitbit One posted on Amazon.com between January 2014 and August 2018. Relevant themes were identified by qualitatively analyzing open-ended reviews. Results: On retrieval, there were 9369 reviews with 7706 positive reviews and 1663 critical reviews. The top 100 positive and top 100 critical comments were subsequently analyzed. Four major themes were identified: sleep hygiene (``charts when you actually fall asleep, when you wake up during the night, when you're restless--and gives you a cumulative time of ``actual sleep'' as well as weekly averages.''), motivation (``25 lbs lost after 8 months -- best motivator ever!''), accountability (``platform to connect with people you know and set little competitions or group\ldotsfun accountability if you set a goal with a friend/family.''), and discretion (``able to be clipped to my bra without being seen.''). Alternatively, negative reviewers felt that the wearable device's various tracking functions, specifically steps and sleep, were inaccurate. Conclusions: Wearable technology devices are an affordable, user-friendly application that can support all individuals throughout their everyday lives and potentially be implemented into medical surveillance, noninvasive medical care, and mobile health and wellness monitoring. This study is the first to explore wearable technology device use among consumers, and further studies are needed to examine the limitless possibilities of wearable devices in health care. ",
doi="10.2196/17544",
url="http://mhealth.jmir.org/2020/4/e17544/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32310148"
}


@Article{info:doi/10.2196/14508,
author="Fortunato, Michael
and Adusumalli, Srinath
and Chokshi, Neel
and Harrison, Joseph
and Rareshide, Charles
and Patel, Mitesh",
title="Usability of Wearable Devices to Remotely Monitor Sleep Patterns Among Patients With Ischemic Heart Disease: Observational Study",
journal="JMIR Form Res",
year="2020",
month="Apr",
day="7",
volume="4",
number="4",
pages="e14508",
keywords="sleep",
keywords="wearable devices",
keywords="ischemic heart disease",
abstract="Background: There is growing interest in using wearable devices to remotely monitor patient behaviors. However, there has been little evaluation of how often these technologies are used to monitor sleep patterns over longer term periods, particularly among more high-risk patients. Objective: The goal of the research was to evaluate the proportion of time that patients with ischemic heart disease used wearable devices to monitor their sleep and identify differences in characteristics of patients with higher versus lower use. Methods: We evaluated wearable device data from a previously conducted clinical trial testing the use of wearable devices with personalized goal-setting and financial incentives. Patients with ischemic heart disease established a sleep baseline and were then followed for 24 weeks. The proportion of days that sleep data was collected was compared over the 24 weeks and by study arm. Characteristics of patients were compared to groups with high, low, or no sleep data. Results: The sample comprised 99 patients with ischemic heart disease, among which 79\% (78/99) used the wearable device to track their sleep. During the 6-month trial, sleep data were collected on 60\% (10,024/16,632) of patient-days. These rates declined over time from 77\% (4292/5544) in months 1 and 2 to 58\% (3188/5544) in months 3 and 4 to 46\% (2544/5544) in months 5 and 6. Sleep data were collected at higher rates among the intervention group compared with control (67\% vs 55\%, P<.001). In the main intervention period (months 3 and 4), patients with higher rates of sleep data were on average older (P=.03), had a history of smoking (P=.007), and had higher rates of commercial health insurance (P=.03). Conclusions: Among patients with ischemic heart disease in a physical activity trial, a high proportion used wearable devices to track their sleep; however, rates declined over time. Future research should consider larger evaluations coupled with behavioral interventions. Trial Registration: ClinicalTrials.gov NCT02531022; https://clinicaltrials.gov/ct2/show/NCT02531022 ",
doi="10.2196/14508",
url="https://formative.jmir.org/2020/4/e14508",
url="http://www.ncbi.nlm.nih.gov/pubmed/32254044"
}


@Article{info:doi/10.2196/10733,
author="Guillodo, Elise
and Lemey, Christophe
and Simonnet, Mathieu
and Walter, Michel
and Baca-Garc{\'i}a, Enrique
and Masetti, Vincent
and Moga, Sorin
and Larsen, Mark
and 
and Ropars, Juliette
and Berrouiguet, Sofian",
title="Clinical Applications of Mobile Health Wearable--Based Sleep Monitoring: Systematic Review",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Apr",
day="1",
volume="8",
number="4",
pages="e10733",
keywords="sleep",
keywords="eHealth",
keywords="telemedicine",
keywords="review",
keywords="medicine",
keywords="wearable electronic devices",
abstract="Background: Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health. Objective: The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. Methods: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults. Results: The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 \% (11/19) were comparison studies with the gold standard, 21\% (4/19) were feasibility studies, 15\% (3/19) were population comparison studies, and 5\% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable--based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography. Conclusions: This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring. ",
doi="10.2196/10733",
url="https://mhealth.jmir.org/2020/4/e10733",
url="http://www.ncbi.nlm.nih.gov/pubmed/32234707"
}


@Article{info:doi/10.2196/14735,
author="Robbins, Rebecca
and Affouf, Mahmoud
and Seixas, Azizi
and Beaugris, Louis
and Avirappattu, George
and Jean-Louis, Girardin",
title="Four-Year Trends in Sleep Duration and Quality: A Longitudinal Study Using Data from a Commercially Available Sleep Tracker",
journal="J Med Internet Res",
year="2020",
month="Feb",
day="20",
volume="22",
number="2",
pages="e14735",
keywords="big data",
keywords="sleep health",
keywords="fitness tracker",
keywords="mHealth",
abstract="Background: Population estimates of sleep duration and quality are inconsistent because they rely primarily on self-reported data. Passive and ubiquitous digital tracking and wearable devices may provide more accurate estimates of sleep duration and quality. Objective: This study aimed to identify trends in sleep duration and quality in New York City based on 2 million nights of data from users of a popular mobile sleep app. Methods: We examined sleep duration and quality using 2,161,067 nights of data captured from 2015 to 2018 by Sleep Cycle, a popular sleep-tracking app. In this analysis, we explored differences in sleep parameters based on demographic factors, including age and sex. We used graphical matrix representations of data (heat maps) and geospatial analyses to compare sleep duration (in hours) and sleep quality (based on time in bed, deep sleep time, sleep consistency, and number of times fully awake), considering potential effects of day of the week and seasonality. Results: Women represented 46.43\% (1,003,421/2,161,067) of the sample, and men represented 53.57\% (1,157,646/2,161,067) of individuals in the sample. The average age of the sample was 31.0 years (SD 10.6). The mean sleep duration of the total sample was 7.11 hours (SD 1.4). Women slept longer on average (mean 7.27 hours, SD 1.4) than men (mean 7 hours, SD 1.3; P<.001). Trend analysis indicated longer sleep duration and higher sleep quality among older individuals than among younger (P<.001). On average, sleep duration was longer on the weekend nights (mean 7.19 hours, SD 1.5) than on weeknights (mean 7.09 hours, SD 1.3; P<.001). Conclusions: Our study of data from a commercially available sleep tracker showed that women experienced longer sleep duration and higher sleep quality in nearly every age group than men, and a low proportion of young adults obtained the recommended sleep duration. Future research may compare sleep measures obtained via wearable sleep trackers with validated research-grade measures of sleep. ",
doi="10.2196/14735",
url="https://www.jmir.org/2020/2/e14735",
url="http://www.ncbi.nlm.nih.gov/pubmed/32078573"
}


@Article{info:doi/10.2196/14939,
author="Quaedackers, Laury
and De Wit, Jan
and Pillen, Sigrid
and Van Gilst, Merel
and Batalas, Nikolaos
and Lammers, Jan Gert
and Markopoulos, Panos
and Overeem, Sebastiaan",
title="A Mobile App for Longterm Monitoring of Narcolepsy Symptoms: Design, Development, and Evaluation",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Jan",
day="7",
volume="8",
number="1",
pages="e14939",
keywords="outcome measure",
keywords="hypersomnia",
keywords="patient-related outcome measure",
keywords="PROM",
keywords="mHealth",
keywords="symptom monitoring",
abstract="Background: Narcolepsy is a chronic sleep disorder with a broad variety of symptoms. Although narcolepsy is primarily characterized by excessive daytime sleepiness and cataplexy (loss of muscle control triggered by emotions), patients may suffer from hypnagogic hallucinations, sleep paralysis, and fragmented night sleep. However, the spectrum of narcolepsy also includes symptoms not related to sleep, such as cognitive or psychiatric problems. Symptoms vary greatly among patients and day-to-day variance can be considerable. Available narcolepsy questionnaires do not cover the whole symptom spectrum and may not capture symptom variability. Therefore, there is a clinical need for tools to monitor narcolepsy symptoms over time to evaluate their burden and the effect of treatment. Objective: This study aimed to describe the design, development, implementation, and evaluation of the Narcolepsy Monitor, a companion app for long-term symptom monitoring in narcolepsy patients. Methods: After several iterations during which content, interaction design, data management, and security were critically evaluated, a complete version of the app was built. The Narcolepsy Monitor allows patients to report a broad spectrum of experienced symptoms and rate their severity based on the level of burden that each symptom imposes. The app emphasizes the reporting of changes in relative severity of the symptoms. A total of 7 patients with narcolepsy were recruited and asked to use the app for 30 days. Evaluation was done by using in-depth interviews and user experience questionnaire. Results: We designed and developed a final version of the Narcolepsy Monitor after which user evaluation took place. Patients used the app on an average of 45.3 (SD 19.2) days. The app was opened on 35\% of those days. Daytime sleepiness was the most dynamic symptom, with a mean number of changes of 5.5 (SD 3.7) per month, in contrast to feelings of anxiety or panic, which was only moved 0.3 (SD 0.7) times per month. Mean symptom scores were highest for daytime sleepiness (1.8 [SD 1.0]), followed by lack of energy (1.6 [SD 1.4]) and often awake at night (1.5 [SD 1.0]). The personal in-depth interviews revealed 3 major themes: (1) reasons to use, (2) usability, and (3) features. Overall, patients appreciated the concept of ranking symptoms on subjective burden and found the app easy to use. Conclusions: The Narcolepsy Monitor appears to be a helpful tool to gain more insight into the individual burden of narcolepsy symptoms over time and may serve as a patient-reported outcome measure for this debilitating disorder. ",
doi="10.2196/14939",
url="https://mhealth.jmir.org/2020/1/e14939",
url="http://www.ncbi.nlm.nih.gov/pubmed/31909723"
}


@Article{info:doi/10.2196/13346,
author="Behrendt, Doerte
and Ebert, Daniel David
and Spiegelhalder, Kai
and Lehr, Dirk",
title="Efficacy of a Self-Help Web-Based Recovery Training in Improving Sleep in Workers: Randomized Controlled Trial in the General Working Population",
journal="J Med Internet Res",
year="2020",
month="Jan",
day="7",
volume="22",
number="1",
pages="e13346",
keywords="occupational health",
keywords="e-mental-health",
keywords="insomnia",
keywords="Web-based, cognitive behavioral therapy",
keywords="mediators",
abstract="Background: Sleep complaints are among the most prevalent health concerns, especially among workers, which may lead to adverse effects on health and work. Internet-delivered cognitive behavioral therapy for insomnia (iCBT-I) offers the opportunity to deliver effective solutions on a large scale. The efficacy of iCBT-I for clinical samples has been demonstrated in recent meta-analyses, and there is evidence that iCBT-I is effective in the working population with severe sleep complaints. However, to date, there is limited evidence from randomized controlled trials that iCBT-I could also be an effective tool for universal prevention among the general working population regardless of symptom severity. Although increasing evidence suggests that negatively toned cognitive activity may be a key factor for the development and maintenance of insomnia, little is known about how iCBT-I improves sleep by reducing presleep cognitive activity. Objective: This study aimed to examine the efficacy of a self-help internet-delivered recovery training, based on principles of iCBT-I tailored to the work-life domain, among the general working population. General and work-related cognitive activities were investigated as potential mediators of the intervention's effect. Methods: A sample of 177 workers were randomized to receive either the iCBT-I (n=88) or controls (n=89). The intervention is a Web-based training consisting of six 1-week modules. As the training was self-help, participants received nothing but technical support via email. Web-based self-report assessments were scheduled at baseline, at 8 weeks, and at 6 months following randomization. The primary outcome was insomnia severity. Secondary outcomes included measures of mental health and work-related health and cognitive activity. In an exploratory analysis, general and work-related cognitive activities, measured as worry and work-related rumination, were investigated as mediators. Results: Analysis of the linear mixed effects model showed that, relative to controls, participants who received iCBT-I reported significantly lower insomnia severity scores at postintervention (between-group mean difference ?4.36; 95\% CI ?5.59 to ? 3.03; Cohen d=0.97) and at 6-month follow-up (between-group difference: ?3.64; 95\% CI ?4.89 to ?2.39; Cohen d=0.86). The overall test of group-by-time interaction was significant (P<.001). Significant differences, with small-to-large effect sizes, were also detected for cognitive activity and for mental and work-related health, but not for absenteeism. Mediation analysis demonstrated that work-related rumination (indirect effect: a1b1=?0.80; SE=0.34; 95\% boot CI ?1.59 to ?0.25) and worry (indirect effect: a2b2=?0.37; SE=0.19; 95\% boot CI ?0.85 to ?0.09) mediate the intervention's effect on sleep. Conclusions: A self-help Web-based recovery training, grounded in the principles of iCBT-I, can be effective in the general working population, both short and long term. Work-related rumination may be a particularly crucial mediator of the intervention's effect, suggesting that tailoring interventions to the workplace, including components to reduce the work-related cognitive activity, might be important when designing recovery interventions for workers. Trial Registration: German Clinical Trials Register DRKS00007142; https://www.drks.de/DRKS00007142 ",
doi="10.2196/13346",
url="https://www.jmir.org/2020/1/e13346",
url="http://www.ncbi.nlm.nih.gov/pubmed/31909725"
}


@Article{info:doi/10.2196/15707,
author="DeSmet, Ann
and De Bourdeaudhuij, Ilse
and Chastin, Sebastien
and Crombez, Geert
and Maddison, Ralph
and Cardon, Greet",
title="Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Dec",
day="20",
volume="7",
number="12",
pages="e15707",
keywords="physical activity",
keywords="sleep",
keywords="sedentary behavior",
keywords="24-hour movement",
keywords="mobile health",
keywords="mobile apps",
keywords="behavior change technique",
keywords="engagement",
keywords="adult",
abstract="Background: There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features. Objective: This study aimed to examine adult users' preferences for techniques and features in mobile apps for 24-hour movement behaviors. Methods: A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57\% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample t tests were used to examine associations and differences between and within users by the type of health domain and users' behavioral intention and adoption. Results: Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users' intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (P=.03), information on behavior health outcome (P=.048), and feedback (P=.04) and incorporate social support (P=.048) to help those who are further removed from healthy sleep. A virtual coach (P<.001) and video modeling (P=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (P=.03). Social comparison and support features are not high on users' agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method. Conclusions: The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines. ",
doi="10.2196/15707",
url="http://mhealth.jmir.org/2019/12/e15707/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31859680"
}


@Article{info:doi/10.2196/13076,
author="Melvin, Sara
and Jamal, Amanda
and Hill, Kaitlyn
and Wang, Wei
and Young, D. Sean",
title="Identifying Sleep-Deprived Authors of Tweets: Prospective Study",
journal="JMIR Ment Health",
year="2019",
month="Dec",
day="6",
volume="6",
number="12",
pages="e13076",
keywords="wearable electronic devices",
keywords="safety",
keywords="natural language processing",
keywords="information storage and retrieval",
keywords="sleep deprivation",
keywords="neural networks (computer)",
keywords="sleep",
keywords="social media",
abstract="Background: Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep. Objective: This study aimed to determine whether social media data can be used to monitor sleep deprivation. Methods: The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting. Results: Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet's author with an average area under the curve of 0.68. Conclusions: It is feasible to use social media to identify students' sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health. ",
doi="10.2196/13076",
url="https://mental.jmir.org/2019/12/e13076",
url="http://www.ncbi.nlm.nih.gov/pubmed/31808747"
}


@Article{info:doi/10.2196/13084,
author="Tedesco, Salvatore
and Sica, Marco
and Ancillao, Andrea
and Timmons, Suzanne
and Barton, John
and O'Flynn, Brendan",
title="Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jun",
day="19",
volume="7",
number="6",
pages="e13084",
keywords="aging",
keywords="fitness trackers",
keywords="wristbands",
keywords="older adults",
keywords="wearable activity trackers",
keywords="Fitbit",
keywords="Garmin",
keywords="energy expenditure",
keywords="physical activity",
keywords="sleep",
abstract="Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. Methods: Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. Results: For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36\%), the Garmin and ActiGraph undercounted (MPE 9.36\% and 11.53\%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25\%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13\%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7\%. Conclusions: The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes. ",
doi="10.2196/13084",
url="https://mhealth.jmir.org/2019/6/e13084/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31219048"
}


@Article{info:doi/10.2196/13482,
author="Zhang, Zhongxing
and Cajochen, Christian
and Khatami, Ramin",
title="Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices",
journal="J Med Internet Res",
year="2019",
month="May",
day="11",
volume="21",
number="6",
pages="e13482",
keywords="chronotypes",
keywords="social jetlag",
keywords="wearable devices",
keywords="nap",
keywords="cardiopulmonary coupling",
keywords="sleep",
keywords="big data",
abstract="Background: Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China. Objective: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. Methods: We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc. Results: The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76\% (13,266/49,573), 58.59\% (29,045/49,573), and 14.64\% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07\% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53\%) of Chinese have SJL<0. The results showed that 53.72\% (7127/13,266), 25.46\% (7396/29,045), and 12.71\% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively). Conclusions: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL. ",
doi="10.2196/13482",
url="https://www.jmir.org/2019/6/e13482/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31199292"
}


@Article{info:doi/10.2196/13384,
author="Liang, Zilu
and Chapa-Martell, Alberto Mario",
title="Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jun",
day="06",
volume="7",
number="6",
pages="e13384",
keywords="wearable electronic devices",
keywords="sleep",
keywords="validation studies",
abstract="Background: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. Objective: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. Methods: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed--rank test was performed to investigate the effect of user-specific factors. Results: Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0\% to 60\%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90\% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. Conclusions: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE. ",
doi="10.2196/13384",
url="https://mhealth.jmir.org/2019/6/e13384/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31172956"
}


@Article{info:doi/10.2196/12455,
author="Hawkins, Marquis
and Iradukunda, Favorite
and Paterno, Mary",
title="Feasibility of a Sleep Self-Management Intervention in Pregnancy Using a Personalized Health Monitoring Device: Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2019",
month="May",
day="29",
volume="8",
number="5",
pages="e12455",
keywords="eHealth",
keywords="pregnancy",
keywords="personal health monitoring",
keywords="behavior",
keywords="maternal health",
abstract="Background: Sleep disruptions are common during pregnancy and associated with increased risk of adverse maternal outcomes such as preeclampsia, gestational diabetes, prolonged labor, and cesarean birth. Given the morbidity associated with poor sleep, cost-effective approaches to improving sleep that can be disseminated in community or clinical settings are needed. Personal health monitor (PHM) devices offer an opportunity to promote behavior change, but their acceptability and efficacy at improving sleep in pregnant women are unknown. Objective: The goal of the paper is to describe the protocol for an ongoing pilot randomized controlled trial that aims to establish the feasibility, acceptability, and preliminary efficacy of using a PHM device (Shine 2, Misfit) to promote sleep during pregnancy. Methods: The proposed pilot study is a 12-week, parallel arm, randomized controlled trial. Pregnant women, at 24 weeks gestation, will be randomized at a 1:1 ratio to a 12-week sleep education plus PHM device group or a sleep education alone comparison group. The primary outcomes will be measures of feasibility (ie, recruitment, enrollment, adherence) and acceptability (ie, participant satisfaction). The secondary outcomes will be self-reported sleep quality and duration, excessive daytime sleepiness, fatigue, and depressive symptoms. Results: Recruitment for this study began in September 2017 and ended in March 2018. Data collection for the primary and secondary aims was completed in August 2018. We anticipate that the data analysis for primary and secondary aims will be completed by December 2019. The results from this trial will inform the development of a larger National Institutes of Health grant application to test the efficacy of an enhanced version of the sleep intervention that we plan to submit in the year 2020. Conclusions: This study will be the first to apply a PHM device as a tool for promoting self-management of sleep among pregnant women. PHM devices have the potential to facilitate behavioral interventions because they include theory-driven, self-regulatory techniques such as behavioral self-monitoring. The results of the study will inform the development of a sleep health intervention for pregnant women. Trial Registration: ClinicalTrials.gov NCT03783663; https://clinicaltrials.gov/ct2/show/NCT03783663 (Archived by WebCite at http://www.webcitation.org/779Ou8hon) International Registered Report Identifier (IRRID): DERR1-10.2196/12455 ",
doi="10.2196/12455",
url="https://www.researchprotocols.org/2019/5/e12455/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31144670"
}


@Article{info:doi/10.2196/13421,
author="Lin, Yu-Hsuan
and Wong, Bo-Yu
and Pan, Yuan-Chien
and Chiu, Yu-Chuan
and Lee, Yang-Han",
title="Validation of the Mobile App--Recorded Circadian Rhythm by a Digital Footprint",
journal="JMIR Mhealth Uhealth",
year="2019",
month="May",
day="16",
volume="7",
number="5",
pages="e13421",
keywords="circadian rhythm",
keywords="sleep",
keywords="smartphone",
keywords="mobile applications",
abstract="Background: Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. Objective: This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. Methods: The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. Results: No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. Conclusions: The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm. ",
doi="10.2196/13421",
url="https://mhealth.jmir.org/2019/5/e13421/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31099340"
}


@Article{info:doi/10.2196/12613,
author="Triantafillou, Sofia
and Saeb, Sohrab
and Lattie, G. Emily
and Mohr, C. David
and Kording, Paul Konrad",
title="Relationship Between Sleep Quality and Mood: Ecological Momentary Assessment Study",
journal="JMIR Ment Health",
year="2019",
month="Mar",
day="27",
volume="6",
number="3",
pages="e12613",
keywords="sleep",
keywords="affect",
keywords="ecological momentary assessment",
keywords="smartphone",
keywords="depression",
keywords="causality",
abstract="Background: Sleep disturbances play an important role in everyday affect and vice versa. However, the causal day-to-day interaction between sleep and mood has not been thoroughly explored, partly because of the lack of daily assessment data. Mobile phones enable us to collect ecological momentary assessment data on a daily basis in a noninvasive manner. Objective: This study aimed to investigate the relationship between self-reported daily mood and sleep quality. Methods: A total of 208 adult participants were recruited to report mood and sleep patterns daily via their mobile phones for 6 consecutive weeks. Participants were recruited in 4 roughly equal groups: depressed and anxious, depressed only, anxious only, and controls. The effect of daily mood on sleep quality and vice versa were assessed using mixed effects models and propensity score matching. Results: All methods showed a significant effect of sleep quality on mood and vice versa. However, within individuals, the effect of sleep quality on next-day mood was much larger than the effect of previous-day mood on sleep quality. We did not find these effects to be confounded by the participants' past mood and sleep quality or other variables such as stress, physical activity, and weather conditions. Conclusions: We found that daily sleep quality and mood are related, with the effect of sleep quality on mood being significantly larger than the reverse. Correcting for participant fixed effects dramatically affected results. Causal analysis suggests that environmental factors included in the study and sleep and mood history do not mediate the relationship. ",
doi="10.2196/12613",
url="http://mental.jmir.org/2019/3/e12613/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30916663"
}


@Article{info:doi/10.2196/jmir.9058,
author="Meyer, Denny
and Jayawardana, W. Madawa
and Muir, D. Samuel
and Ho, Yen-Teh David
and Sackett, Olivia",
title="Promoting Psychological Well-Being at Work by Reducing Stress and Improving Sleep: Mixed-Methods Analysis",
journal="J Med Internet Res",
year="2018",
month="Oct",
day="19",
volume="20",
number="10",
pages="e267",
keywords="exercise",
keywords="productivity",
keywords="healthy lifestyle",
abstract="Background: Workplace programs designed to improve the health and psychological well-being of employees are becoming increasingly popular. However, there are mixed reports regarding the effectiveness of such programs and little analysis of what helps people to engage with such programs. Objective: This evaluation of a particularly broad, team-based, digital health and well-being program uses mixed methods to identify the elements of the program that reduce work stress and promote psychological well-being, sleep quality, and productivity of employees. Methods: Participation in the Virgin Pulse Global Challenge program during May to September 2016 was studied. Self-reported stress, sleep quality, productivity, and psychological well-being data were collected both pre- and postprogram. Participant experience data were collected through a third final survey. However, the response rates for the last 2 surveys were only 48\% and 10\%, respectively. A random forest was used to estimate the probability of the completion of the last 2 surveys based on the preprogram assessment data and the demographic data for the entire sample (N=178,350). The inverse of these estimated probabilities were used as weights in hierarchical linear models in an attempt to address any estimation bias caused by the low response rates. These linear models described changes in psychological well-being, stress, sleep, and productivity over the duration of the program in relation to gender and age, engagement with each of the modules, each of the program features, and participant descriptions of the Virgin Pulse Global Challenge. A 0.1\% significance level was used due to the large sample size for the final survey (N=18,653). Results: The final analysis suggested that the program is more beneficial for older people, with 2.9\% greater psychological well-being improvements observed on average in the case of women than men (P<.001). With one exception, all the program modules contributed significantly to the outcome measures with the following average improvements observed: psychological well-being, 4.1\%-6.0\%; quality of sleep, 3.2\%-6.9\%; work-related stress, 1.7\%-6.8\%; and productivity, 1.9\%-4.2\%. However, only 4 of the program features were found to have significant associations with the outcome measures with the following average improvements observed: psychological well-being, 3.7\%-5.6\%; quality of sleep, 3.4\%-6.5\%; work-related stress, 4.1\%-6.4\%; and productivity, 1.6\%-3.2\%. Finally, descriptions of the Virgin Pulse Global Challenge produced 5 text topics that were related to the outcome measures. Healthy lifestyle descriptions showed a positive association with outcomes, whereas physical activity and step count tracking descriptions showed a negative association with outcomes. Conclusions: The complementary use of qualitative and quantitative survey data in a mixed-methods analysis provided rich information that will inform the development of this and other programs designed to improve employee health. However, the low response rates and the lack of a control group are limitations, despite the attempts to address these problems in the analysis. ",
doi="10.2196/jmir.9058",
url="https://www.jmir.org/2018/10/e267/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30341045"
}


@Article{info:doi/10.2196/jmir.9410,
author="Sano, Akane
and Taylor, Sara
and McHill, W. Andrew
and Phillips, JK Andrew
and Barger, K. Laura
and Klerman, Elizabeth
and Picard, Rosalind",
title="Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study",
journal="J Med Internet Res",
year="2018",
month="Jun",
day="08",
volume="20",
number="6",
pages="e210",
keywords="mobile health",
keywords="mood",
keywords="machine learning",
keywords="wearable electronic devices",
keywords="smartphone",
keywords="mobile phone",
keywords="mental health",
keywords="psychological stress",
abstract="Background: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. Objective: We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. Methods: We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69\%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. Results: We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3\% (148/189) accuracy for classifying students into high or low stress groups and 87\% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5\% (139/189) accuracy for stress classification and 79\% (37/47) accuracy for mental health classification. Conclusions: New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping. ",
doi="10.2196/jmir.9410",
url="http://www.jmir.org/2018/6/e210/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29884610"
}