@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/10.2196/67646,
author="Lin, Sheng-Hsuan
and Su, Kuan-Pin
and Tsou, Hsiao-Hui
and Hsia, Pei-Hsuan
and Lin, Yu-Hsuan",
title="Search Volume of Insomnia and Suicide as Digital Footprints of Global Mental Health During the COVID-19 Pandemic: 3-Year Infodemiology Study",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="17",
volume="27",
pages="e67646",
keywords="mediation analysis",
keywords="internet searches",
keywords="stay-at-home measures",
keywords="insomnia",
keywords="suicide",
keywords="COVID-19",
abstract="Background: The global COVID-19 pandemic's mental health impact was primarily studied in the initial year of lockdowns but remained underexplored in subsequent years despite evolving conditions. This study aimed to address this gap by investigating how COVID-19--related factors, including nationwide COVID-19 deaths and incidence rates, influenced mental health indicators over time. Objective: This study aimed to examine the interplay among national COVID-19 pandemic deaths, incidence rates, stay-at-home behaviors, and mental health indicators across different income-level countries. Specifically, we assessed the mediating role of stay-at-home behaviors in the relationship between the COVID-19 pandemic deaths and mental health indicators. Methods: We analyzed data from 45 countries spanning March 2020 to October 2022. COVID-19--related factors included national COVID-19 pandemic deaths and incidence rates, obtained from publicly available datasets. Stay-at-home behaviors were assessed using Google Location History data, which captured residence-based cell phone activity as a proxy for mobility patterns. Mental health indicators were evaluated through Google Trends data, measuring changes in search volumes for ``insomnia'' and ``suicide.'' The interplay among these variables was assessed using mediation analysis to quantify the proportion mediated by stay-at-home behaviors in the association between COVID-19 deaths and mental health indicators. Results: In high-income countries, during the first pandemic year (March 2020 to February 2021), a higher monthly COVID-19 death count was associated with increased searches for ``insomnia,'' with a total effect estimate of 2.1{\texttimes}10-4 (95\% CI 4.3{\texttimes}10-5 to 3.9{\texttimes}10-4; P=.01). Stay-at-home behaviors mediated 31.9\% of this effect (95\% CI 9.8\% to 127.5\%, P=.02). This association weakened and became nonsignificant in the second and third years (P=.25 and P=.54, respectively). For middle-income countries, a different pattern emerged regarding ``suicide'' searches. Higher COVID-19 death counts were linked to a decline in ``suicide'' searches in the first (estimate: --3.5{\texttimes}10-4, 95\% CI --6.1{\texttimes}10-4 to --9.8{\texttimes}10-5; P=.006) and second years (P=.01). Mediation analysis indicated that this effect was not significantly explained by stay-at-home behaviors, suggesting the influence of other societal factors. In high-income countries, no significant association between COVID-19 deaths and ``suicide'' searches was observed in the first year (P=.86). However, a positive association emerged in the second year, approaching statistical significance (estimate: 2.2{\texttimes}10-4, 95\% CI --9.5{\texttimes}10-7 to 4.2{\texttimes}10-4; P=.05), and became significant in the third year (estimate: 5.0{\texttimes}10-4, 95\% CI 5.0{\texttimes}10-5 to 1.0{\texttimes}10-3; P=.03,), independent of stay-at-home behaviors. Conclusions: Our findings highlight how the mental health impact of the pandemic varied across income groups and evolved over time. The mediating effect of stay-at-home behaviors was significant in the early phases but diminished in later stages, particularly in high-income countries. Meanwhile, middle-income countries exhibited unique patterns that suggest alternative protective factors. These insights can inform tailored mental health interventions and policy strategies in future public health crises. ",
doi="10.2196/67646",
url="https://www.jmir.org/2025/1/e67646"
}


@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/10.2196/65412,
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/10.2196/65228,
author="Isaac, Fadia
and Klein, Britt
and Nguyen, Huy
and Watson, Shaun
and Kennedy, A. Gerard",
title="Digital Cognitive Behavioral Therapy--Based Treatment for Insomnia, Nightmares, and Posttraumatic Stress Disorder Symptoms in Survivors of Wildfires: Pilot Randomized Feasibility Trial",
journal="JMIR Hum Factors",
year="2025",
month="Mar",
day="14",
volume="12",
pages="e65228",
keywords="insomnia",
keywords="nightmares",
keywords="posttraumatic stress disorder",
keywords="PTSD",
keywords="wildfires",
keywords="cognitive behavioral therapy for insomnia",
keywords="CBTi",
keywords="exposure, relaxation, and rescripting therapy",
keywords="ERRT",
keywords="Sleep Best-i",
keywords="mobile health",
keywords="mHealth",
keywords="digital health",
keywords="computer",
keywords="eHealth",
keywords="bushfires",
abstract="Background: Symptoms of insomnia, nightmares, and trauma are highly prevalent. However, there are significant barriers to accessing evidence-based treatments for these conditions, leading to poor mental health outcomes. Objective: This pilot trial evaluated the feasibility of a 4-week, digital self-paced intervention combining cognitive behavioral therapy for insomnia and exposure, relaxation, and rescripting therapy for nightmares in survivors of wildfires from Australia, Canada, and the United States. Methods: Study participants were recruited between May 2023 and December 2023 through social media platforms, workshops, conferences, and radio interviews. Participants had to meet at least one of the following criteria: a score of ?8 on the Insomnia Severity Index, a score of ?3 on the Nightmare Disorder Index, or a score of ?31 on the PTSD Checklist for DSM-5. In total, 30 survivors of wildfires were allocated to either the treatment group (n=16, 53\%) or the waitlist control group (n=14, 47\%) in a sequential manner. Participants' ages ranged from 18 to 79 years, with a mean age of 52.50 (SD 16.26) years. The cohort consisted of 63\% (19/30) female and 37\% (11/30) male participants. Participants also completed self-report secondary outcome measures, including the Generalized Anxiety Disorder--7, the Patient Health Questionnaire--9, and the Pittsburgh Sleep Quality Index, via the HealthZone digital platform. Assessments were conducted at baseline, the posttreatment time point, and the 3-month follow-up, with the waitlist group undergoing an additional assessment at the pretreatment time point, after 4 weeks of waiting and before crossing over to treatment. This study used intention-to-treat analysis as a primary analysis and per-protocol analysis as a secondary analysis. Results: Mixed-effects linear regression models and difference-in-differences analyses were used to assess the intervention's effects. The intention-to-treat analysis revealed significant improvements over time (main effect of time), with a 1.64-point reduction (P=.001) on the Nightmare Disorder Index and 10.64-point reduction (P=.009) on the PTSD Checklist for DSM-5 at the postintervention time point. No significant changes were observed in insomnia symptoms. On the secondary measures, there was an interaction effect of condition {\texttimes} time, with a 2.22-point reduction (P<.001) on the Pittsburgh Sleep Quality Index, and a main effect of time, with a 6.48-point reduction (P<.001) on the Patient Health Questionnaire--9. No changes were detected on the Generalized Anxiety Disorder--7. The per-protocol analysis yielded comparable results for both the primary and secondary measures. Conclusions: The findings of this pilot trial demonstrated a reduction in nightmares and trauma symptoms. Future research studies should aim at evaluating the intervention in a more definitive trial with a larger sample size. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12623000415606; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=385054 ",
doi="10.2196/65228",
url="https://humanfactors.jmir.org/2025/1/e65228"
}


@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/10.2196/67000,
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/10.2196/69417,
author="Li, Yadi
and Zhou, Jianlong
and Wei, Zheng
and Liang, Lizhu
and Xu, Hualing
and Lv, Caihong
and Liu, Gang
and Li, Wenlin
and Wu, Xin
and Xiao, Yunhui
and Sunzi, Kejimu",
title="Efficacy and Safety of Acupuncture for Post--COVID-19 Insomnia: Protocol for a Systematic Review and Meta-Analysis",
journal="JMIR Res Protoc",
year="2025",
month="Mar",
day="3",
volume="14",
pages="e69417",
keywords="acupuncture",
keywords="traditional Chinese medicine",
keywords="post--COVID-19 condition",
keywords="long COVID-19",
keywords="insomnia",
keywords="sleep disorder",
keywords="depression",
keywords="complementary and alternative medicine",
keywords="treatment",
keywords="public health",
keywords="study protocol",
keywords="systematic review",
abstract="Background: The COVID-19 pandemic has had a profound global impact, leading to a range of persistent sequelae referred to as post--COVID-19 condition or ``long COVID'' that continue to affect patients worldwide. Among these sequelae, post--COVID-19 insomnia (PCI) has emerged as a significant issue. Conventional treatments, including cognitive behavioral therapy and pharmacological interventions, face limitations such as variable efficacy, potential side effects, and substantial costs. Recently, acupuncture has gained traction due to its efficacy, cost-effectiveness, and safety profile. Objective: This study aims to conduct a meta-analysis and systematic review evaluating the efficacy and safety of acupuncture for the treatment of PCI to delineate the optimal modality, intervention frequency, and duration for achieving the most beneficial outcomes, thereby providing a comprehensive understanding of acupuncture's role in managing PCI, contributing to evidence-based clinical practice, and informing clinical decision-making. Methods: Electronic searches will be performed in 12 databases from inception to October 2024 without language restrictions. This includes both English databases (PubMed, Cochrane Library, Web of Science, Embase, OVID and Scopus), as well as Chinese databases (China National Knowledge Infrastructure, Wan-Fang Data, Chinese Biomedical Literature Database, Chinese Scientific Journal Database, Duxiu Database and the Chinese Clinical Trial Registry Center). Randomized controlled trials on acupuncture for PCI will be included. Primary outcomes will include the response rate and insomnia severity; secondary outcomes will include the Traditional Chinese Medicine Symptom Scale (TCMSS) and adverse event rates. Data synthesis will use risk ratios for dichotomous data and mean differences for continuous data. Study selection, data extraction, and quality assessment will be conducted independently by 2 reviewers. Methodological quality of eligible studies will be evaluated following the Cochrane Handbook for Systematic Reviews of Interventions (version 6.3). Meta-analysis will be performed with RevMan 5.3. Results: Based on the data on response rate, insomnia severity, TCMSS score, and adverse event rates, this study will provide an evidence-based review of the efficacy and safety of acupuncture for PCI treatment. Conclusions: This systematic review will present the current evidence for acupuncture for PCI, aiming to inform clinical practices and decision-making and to enhance the understanding of acupuncture's role in managing PCI. Furthermore, it will identify research gaps and suggest potential areas for future investigation. Trial Registration: PROSPERO CRD42024499284; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=499284 International Registered Report Identifier (IRRID): DERR1-10.2196/69417 ",
doi="10.2196/69417",
url="https://www.researchprotocols.org/2025/1/e69417",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053784"
}


@Article{info:doi/10.2196/54608,
author="Parsons, Marie E.
and Figueroa, G. Zo{\"e}
and Hiserodt, Michele
and Cornelius, Talea
and Otto, W. Michael",
title="Relative Preference for In-Person, Telehealth, Digital, and Pharmacologic Mental Health Care After the COVID-19 Pandemic: Cross-Sectional Questionnaire Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="13",
volume="27",
pages="e54608",
keywords="stigma",
keywords="digital CBT",
keywords="age",
keywords="generalized anxiety disorder",
keywords="insomnia",
keywords="adult",
keywords="telehealth",
keywords="digital health",
abstract="Background: Most adults and children in the United States fail to receive timely care for mental health symptoms, with even worse rates of care access for individuals who belong to racial and ethnic minority groups. Digital (ie, app-based) care has proven to be an efficacious and empirically supported treatment option with the potential to address low rates of care and reduce care disparities, yet little is known about the relative preference for such treatment. Furthermore, the rapid adoption of telehealth care during the COVID-19 pandemic may have shifted care preferences. Objective: This study aimed to examine relative treatment preferences for 4 different types of mental health care: in-person psychological care, telehealth psychological care, digital treatment, or pharmacologic care. Care preferences were also examined relative to potential predictors of care use (ie, gender, race, age, stigma, discrimination, and level of shame). Methods: In this cross-sectional online survey study of adults (N=237, mean age 35 years, range 19-68 years), we ranked 4 mental health care modalities based on care preference: (1) in-person care, (2) telehealth care, (3) digital care, and (4) pharmacologic care. Preference for treatment modality was assessed based on vignette presentation for generalized anxiety disorder and insomnia. In addition, participants completed self-report questionnaires for demographics, symptom severity, and psychological and stigma-related variables. Results: We found no difference in overall preference for in-person versus both telehealth and digital care. For both generalized anxiety disorder and insomnia, participants preferred in-person care to telehealth care, although this finding was attenuated amongst older participants for insomnia treatment. Participants' depressed mood was associated with a greater relative preference for pharmacologic care. There was no evidence of differential preference for digital care according to demographics, symptom severity, or psychological and stigma-related variables. Conclusions: These results indicate that digital care now competes well in terms of treatment preference with in-person, telehealth, and pharmacologic treatment options. ",
doi="10.2196/54608",
url="https://www.jmir.org/2025/1/e54608"
}


@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/10.2196/70168,
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/10.2196/63139,
author="Taguchi, Kayoko
and Miyoshi, Mirai
and Seki, Yoichi
and Baba, Shiori
and Shimizu, Eiji",
title="Effectiveness of Cognitive Behavioral Therapy Provided Through a Web Application for Subthreshold Depression, Subthreshold Insomnia, and Subthreshold Panic: Open-Labeled 6-Arm Randomized Clinical Trial Pilot Study",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="3",
volume="9",
pages="e63139",
keywords="minimally important change",
keywords="nonguided cognitive behavioral therapy",
keywords="subthreshold depression",
keywords="subthreshold insomnia",
keywords="subthreshold panic",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="psychiatric disease",
keywords="primary care",
keywords="interventions",
keywords="depression",
keywords="anxiety",
keywords="insomnia",
keywords="psychological therapy",
abstract="Background: A common definition of ``subthreshold'' is that the diagnostic threshold is not met but the individuals are not asymptomatic. Some symptoms are present, causing significant difficulty in functioning and negatively impacting quality of life. Despite the attention given to subthreshold symptoms and the interventions for subthreshold symptoms being efficient in preventing the transition to psychiatric disease in primary care, reports on specific interventions are insufficient. Objective: This study aimed to verify the effectiveness of internet-delivered cognitive behavioral therapy (ICBT) for subthreshold depression (SD), subthreshold insomnia (SI), and subthreshold panic (SP). Additionally, this study aimed to explore the minimally important change (MIC) of each subthreshold group's effectiveness outcome. Methods: Participants aged 18-70 years from internet research monitors were categorized into SD, SI, and SP groups based on screening assessment. They were randomly assigned to intervention or control groups within each subthreshold symptom. The intervention groups worked on 4 weeks of nonguided ICBT (``Mentre''), while the control groups worked on a sham app. The primary outcome was the score change from screening (T1) to 4-week follow-up (T4) using the Center for Epidemiologic Studies Depression Scale (CESD) in the SD group, the Pittsburgh Sleep Quality Index (PSQI) in the SI group, and the Panic and Agoraphobia Scale (PAS) in the SP group. Secondary outcomes were score changes in the Generalized Anxiety Disorder-7 (GAD-7) scale, the Patient Health Questionnaire 9 (PHQ-9), the CESD, the PSQI, and the PAS, except the primary outcome in each group. Secondary outcomes were analyzed using complete-case analysis and repeated-measures ANOVA. Additionally, the MIC in the primary endpoint for each group was also calculated as an exploratory outcome. Results: The SD, SP, and SI groups contained 846, 597, and 1106 participants, respectively. In the SD group, the difference in the CESD score change from baseline to follow-up between the intervention and control groups was significant (difference=0.52, 95\% CI 1.29-4.66, P<.001). In the SI group, the difference in the PSQI score change was also significant (difference=0.53, 95\% CI 0.11-0.94, P=.01). However, in the SP group, the difference in the PAS score change was not significant (difference=0.07, 95\% CI --2.00 to 2.15, P=.94). Conclusions: Our ICBT program Mentre contributes to the improvement of SI and SD. This suggests that nonguided ICBT may be effective in preventing SI and SD from progressing to the full threshold. However, appropriate definitions of subthreshold symptoms are necessary. In particular, it is difficult to define SP, and further research that considers the specific factors of each subthreshold symptom is necessary to accumulate evidence. Trial Registration: University Hospital Medical Information Network (UMIN) UMIN000051280; https://tinyurl.com/2wyahhe3 ",
doi="10.2196/63139",
url="https://formative.jmir.org/2025/1/e63139",
url="http://www.ncbi.nlm.nih.gov/pubmed/39899369"
}


@Article{info:doi/10.2196/65840,
author="Alami, Sarah
and Schaller, Manuella
and Blais, Sylvie
and Taupin, Henry
and Hern{\'a}ndez Gonz{\'a}lez, Marta
and Gagnadoux, Fr{\'e}d{\'e}ric
and Pinto, Paula
and Cano-Pumarega, Irene
and Bedert, Lieven
and Braithwaite, Ben
and Servy, Herv{\'e}
and Ouary, St{\'e}phane
and Fabre, C{\'e}line
and Bazin, Fabienne
and Texereau, Jo{\"e}lle",
title="Evaluating the Benefit of Home Support Provider Services for Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea: Protocol for an Ambispective International Real-World Study",
journal="JMIR Res Protoc",
year="2025",
month="Jan",
day="31",
volume="14",
pages="e65840",
keywords="obstructive sleep apnea",
keywords="positive airway pressure",
keywords="real-world evidence",
keywords="home support provider",
keywords="adherence",
keywords="electronic patient-reported outcome",
keywords="comparative real-world study",
abstract="Background: Adherence and persistence to positive airway pressure (PAP) therapy are key factors for positive health outcomes. Home support providers participate in the home implementation and follow-up of PAP therapy for patients with obstructive sleep apnea (OSA). In Europe, home support provider service levels are country (or area) specific, resulting in differences in content and frequency of patient interactions. However, no robust evaluation of the impact of these differences on clinical and patient outcomes has been performed. Objective: The AWAIR study aims to evaluate and compare the impact of different home support provider service levels on PAP adherence and persistence in 4 European countries. Methods: This real-world, ambispective, cohort study---conducted in France, Belgium, Spain, and Portugal---will recruit adults with OSA who started PAP therapy between 2019 and 2023 and were followed by an Air Liquide Healthcare home support provider. Given the large number of eligible participants (around 150,000), the study will use a decentralized and digital approach. A patient video will present the study objectives and the participation process. A secure electronic solution will be used to manage patient information and consent, as well as to administer a web-based questionnaire. Retrospective data, collected during routine patient follow-up by home support providers, include the level of service and device data, notably PAP use. Prospective data collected using an electronic patient-reported outcome tool include health status, OSA-related factors, patient-reported outcomes including quality of life and symptoms, OSA and PAP literacy, patient-reported experience, and satisfaction with PAP therapy and service. Hierarchical models, adjusted for preidentified confounding factors, will be used to assess the net effect of home support provider services on PAP adherence and persistence while minimizing real-world study biases and considering the influence of country-level contextual factors. We hypothesize that higher levels of home support provider services will be positively associated with adherence and persistence to PAP therapy. Results: As of December 2024, the study has received approval in France, Portugal, and 2 regions of Spain. The study began enrollment in France in October 2024. Results are expected in the second quarter of 2025. Conclusions: The AWAIR study has a unique design, leveraging an unprecedented number of eligible participants, decentralized technologies, and a real-world comparative methodology across multiple countries. This approach will highlight intercountry differences in terms of patient characteristics, PAP adherence, and persistence, as well as patient-reported outcomes, patient-reported experiences, and satisfaction with the home service provider. By assessing the added value of home support provider services, the results will support best practices for patient management and for decision-making by payers and authorities. International Registered Report Identifier (IRRID): PRR1-10.2196/65840 ",
doi="10.2196/65840",
url="https://www.researchprotocols.org/2025/1/e65840"
}


@Article{info:doi/10.2196/65471,
author="Chen, Shu-Cheng
and Lo, Kwai-Ching
and Li, Han
and Wong, Pong-Ming
and Pang, Lok-Yi
and Qin, Jing
and Yeung, Wing-Fai",
title="Parental Experiences of Administering Pediatric Tuina for Sleep and Appetite in Early School-Aged Children With Attention-Deficit/Hyperactivity Disorder: Qualitative Study in Hong Kong",
journal="JMIR Pediatr Parent",
year="2025",
month="Jan",
day="30",
volume="8",
pages="e65471",
keywords="pediatric massage",
keywords="child",
keywords="traditional Chinese medicine",
keywords="TCM",
keywords="ADHD",
keywords="qualitative study",
keywords="complementary medicine",
keywords="attention deficit",
keywords="hyperactivity",
keywords="massage",
keywords="tuina",
keywords="tui na",
keywords="mental health",
keywords="sleep",
keywords="appetite",
keywords="parent",
keywords="parenting",
keywords="interview",
keywords="focus group",
keywords="anmo",
keywords="attention-deficit/hyperactivity disorder",
abstract="Background: Previous research suggested that parent-administered pediatric tuina could improve symptoms of attention-deficit/hyperactivity disorder (ADHD), such as sleep quality and appetite. Objective: This study aimed to explore the experiences and perceptions of parents administering pediatric tuina to school-aged children with ADHD in Hong Kong. Methods: This qualitative study was embedded in a pilot randomized controlled trial on parent-administered pediatric tuina for improving sleep and appetite in school-aged children diagnosed with ADHD. Purposive sampling was used to invite 12 parents who attended a pediatric tuina training program and delivered the intervention to their children at home for at least 8 weeks. Data were collected through semistructured focus group interviews and individual interviews, which were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Results: Two main themes emerged: (1) effects of parent-administered pediatric tuina and (2) parents' experience of administering pediatric tuina. Parents reported significant improvements in children's sleep quality, appetite, behavior, mental state, and academic performance. Facilitators provided professional guidance and applied a user-friendly course design. Challenges included difficulties in mastering techniques, locating acupuncture points, and time management. Participants suggested the need for more traditional Chinese medicine pattern diagnostic sessions, real-time supervision methods, and extended follow-up to better observe long-term effects. Conclusions: Parent-administered pediatric tuina was perceived to improve children's sleep quality and appetite significantly, along with other aspects of well-being. Professional guidance and a structured training program facilitated implementation, and challenges highlighted the need for more frequent diagnostic sessions, real-time supervision, and extended follow-up. Trial Registration: ClinicalTrials.gov NCT06007742; https://clinicaltrials.gov/study/NCT06007742 ",
doi="10.2196/65471",
url="https://pediatrics.jmir.org/2025/1/e65471"
}


@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/10.2196/67478,
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/10.2196/51022,
author="Sanchez Ortu{\~n}o, Montserrat Mar{\'i}a
and Pecune, Florian
and Coelho, Julien
and Micoulaud-Franchi, Arthur Jean
and Salles, Nathalie
and Auriacombe, Marc
and Serre, Fuschia
and Levavasseur, Yannick
and De Sevin, Etienne
and Sagaspe, Patricia
and Philip, Pierre",
title="Determinants of Dropout From a Virtual Agent--Based App for Insomnia Management in a Self-Selected Sample of Users With Insomnia Symptoms: Longitudinal Study",
journal="JMIR Ment Health",
year="2025",
month="Jan",
day="15",
volume="12",
pages="e51022",
keywords="insomnia",
keywords="digital behavioral therapy",
keywords="mobile health",
keywords="dropout",
keywords="virtual agent--based app",
keywords="virtual agent",
keywords="user",
keywords="digital intervention",
keywords="smartphone",
keywords="mental health",
keywords="implementation",
keywords="cognitive behavioral therapy",
keywords="CBT",
abstract="Background: Fully automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important aspect is that they are accessible at a low cost, thereby increasing their potential public impact and reducing disparities. However, a major challenge to their successful implementation is the phenomenon of users dropping out early. Objective: The purpose of this study was to pinpoint the factors influencing early dropout in a sample of self-selected users of a virtual agent (VA)--based behavioral intervention for managing insomnia, named KANOPEE, which is freely available in France. Methods: From January 2021 to December 2022, of the 9657 individuals, aged 18 years or older, who downloaded and completed the KANOPEE screening interview and had either subclinical or clinical insomnia symptoms, 4295 (44.5\%) dropped out (ie, did not return to the app to continue filling in subsequent assessments). The primary outcome was a binary variable: having dropped out after completing the screening assessment (early dropout) or having completed all the treatment phases (n=551). Multivariable logistic regression analysis was used to identify predictors of dropout among a set of sociodemographic, clinical, and sleep diary variables, and users' perceptions of the treatment program, collected during the screening interview. Results: The users' mean age was 47.95 (SD 15.21) years. Of those who dropped out early and those who completed the treatment, 65.1\% (3153/4846) were women and 34.9\% (1693/4846) were men. Younger age (adjusted odds ratio [AOR] 0.98, 95\% CI 0.97?0.99), lower education level (compared to middle school; high school: AOR 0.56, 95\% CI 0.35?0.90; bachelor's degree: AOR 0.35, 95\% CI 0.23?0.52; master's degree or higher: AOR 0.35, 95\% CI 0.22?0.55), poorer nocturnal sleep (sleep efficiency: AOR 0.64, 95\% CI 0.42?0.96; number of nocturnal awakenings: AOR 1.13, 95\% CI 1.04?1.23), and more severe depression symptoms (AOR 1.12, 95\% CI 1.04?1.21) were significant predictors of dropping out. When measures of perceptions of the app were included in the model, perceived benevolence and credibility of the VA decreased the odds of dropout (AOR 0.91, 95\%\thinspaceCI 0.85?0.97). Conclusions: As in traditional face-to-face cognitive behavioral therapy for insomnia, the presence of significant depression symptoms plays an important role in treatment dropout. This variable represents an important target to address to increase early engagement with fully automated insomnia management programs. Furthermore, our results support the contention that a VA can provide relevant user stimulation that will eventually pay out in terms of user engagement. Trial Registration: ClinicalTrials.gov NCT05074901; https://clinicaltrials.gov/study/NCT05074901?a=1 ",
doi="10.2196/51022",
url="https://mental.jmir.org/2025/1/e51022"
}


@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/10.2196/58461,
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/10.2196/62959,
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/10.2196/63311,
author="Shetty, A. Vishal
and Gregor, M. Christina
and Tusing, D. Lorraine
and Pradhan, M. Apoorva
and Romagnoli, M. Katrina
and Piper, J. Brian
and Wright, A. Eric",
title="Discussions of Cannabis Over Patient Portal Secure Messaging: Content Analysis",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="12",
volume="26",
pages="e63311",
keywords="patient portal",
keywords="secure message",
keywords="marijuana",
keywords="patient-provider communication",
keywords="message content",
keywords="content analysis",
keywords="United States",
keywords="pain",
keywords="anxiety",
keywords="depression",
keywords="insomnia",
keywords="electronic messaging",
keywords="electronic health record",
keywords="EHR",
keywords="cannabis",
abstract="Background: Patient portal secure messaging allows patients to describe health-related behaviors in ways that may not be sufficiently captured in standard electronic health record (EHR) documentation, but little is known about how cannabis is discussed on this platform. Objective: This study aimed to identify patient and provider secure messages that discussed cannabis and contextualize these discussions over periods before and after its legalization for medical purposes in Pennsylvania. Methods: We examined 382,982 secure messages sent by 15,340 patients and 6101 providers from an integrated health delivery system in Pennsylvania, United States, from January 2012 to June 2022. We used an unsupervised natural language processing approach to construct a lexicon that identified messages explicitly discussing cannabis. We then conducted a qualitative content analysis on a random sample of identified messages to understand the medical reasons behind patients' use, the primary purposes of the cannabis-related discussions, and changes in these purposes over time. Results: We identified 1782 messages sent by 1098 patients (7.2\% of total patients in the study) and 800 messages sent by 430 providers (7\% of total providers in the study) as explicitly discussing cannabis. The most common medical reasons for use stated by patients in 190 sampled messages included pain or a pain-related condition (50.5\% of messages), anxiety (13.7\% of messages), and sleep (11.1\% of messages). We coded 56 different purposes behind the mentions of cannabis in patient messages and 33 purposes in 100 sampled provider messages. In years before the legalization (2012-2016), patient and provider messages (n=20 for both) were primarily driven by discussions about cannabis screening results (38.9\% and 76.5\% of messages, respectively). In the years following legalization (2017-2022), patient messages (n=170) primarily involved seeking assistance to facilitate medical use (35.2\% of messages) and reporting current use (25.3\% of messages). Provider messages (n=80) were driven by giving assistance with medical marijuana access (27.5\% of messages) and stating that they were unable to refer, prescribe or recommend medical marijuana (26.3\% of messages). Conclusions: Patients showed a willingness to discuss cannabis use over patient portal secure messages and expressed interest in use after the legalization of medical marijuana. Some providers responded to patient inquiries with assistance in obtaining access to medical marijuana, while others cautioned patients on the risks of use. Insight into cannabis-related discussions through secure messages can help health systems determine opportunities to improve care processes around patients' cannabis use, and providers should be supported to communicate accurate and consistent information. ",
doi="10.2196/63311",
url="https://www.jmir.org/2024/1/e63311",
url="http://www.ncbi.nlm.nih.gov/pubmed/39666375"
}


@Article{info:doi/10.2196/54321,
author="Walsh, Julia
and Cave, Jonathan
and Griffiths, Frances",
title="Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="11",
volume="26",
pages="e54321",
keywords="unstructured text",
keywords="natural language processing",
keywords="NLP",
keywords="topic modeling",
keywords="sentiment analysis",
keywords="corpus linguistics",
keywords="social media data",
keywords="patient experience",
keywords="unsupervised",
keywords="modafinil",
abstract="Background: Patient experience data from social media offer patient-centered perspectives on disease, treatments, and health service delivery. Current guidelines typically rely on systematic reviews, while qualitative health studies are often seen as anecdotal and nongeneralizable. This study explores combining personal health experiences from multiple sources to create generalizable evidence. Objective: The study aims to (1) investigate how combining unsupervised natural language processing (NLP) and corpus linguistics can explore patient perspectives from a large unstructured dataset of modafinil experiences, (2) compare findings with Cochrane meta-analyses on modafinil's effectiveness, and (3) develop a methodology for analyzing such data. Methods: Using 69,022 posts from 790 sources, we used a variety of NLP and corpus techniques to analyze the data, including data cleaning techniques to maximize post context, Python for NLP techniques, and Sketch Engine for linguistic analysis. We used multiple topic mining approaches, such as latent Dirichlet allocation, nonnegative matrix factorization, and word-embedding methods. Sentiment analysis used TextBlob and Valence Aware Dictionary and Sentiment Reasoner, while corpus methods including collocation, concordance, and n-gram generation. Previous work had mapped topic mining to themes, such as health conditions, reasons for taking modafinil, symptom impacts, dosage, side effects, effectiveness, and treatment comparisons. Results: Key findings of the study included modafinil use across 166 health conditions, most frequently narcolepsy, multiple sclerosis, attention-deficit disorder, anxiety, sleep apnea, depression, bipolar disorder, chronic fatigue syndrome, fibromyalgia, and chronic disease. Word-embedding topic modeling mapped 70\% of posts to predefined themes, while sentiment analysis revealed 65\% positive responses, 6\% neutral responses, and 28\% negative responses. Notably, the perceived effectiveness of modafinil for various conditions strongly contrasts with the findings of existing randomized controlled trials and systematic reviews, which conclude insufficient or low-quality evidence of effectiveness. Conclusions: This study demonstrated the value of combining NLP with linguistic techniques for analyzing large unstructured text datasets. Despite varying opinions, findings were methodologically consistent and challenged existing clinical evidence. This suggests that patient-generated data could potentially provide valuable insights into treatment outcomes, potentially improving clinical understanding and patient care. ",
doi="10.2196/54321",
url="https://www.jmir.org/2024/1/e54321"
}


@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/10.2196/50835,
author="Oh, Taek Kyue
and Ko, Jisu
and ?Jin, Nayoung
and Han, Sangbin
and Yoon, Yul Chan
and Shin, Jaemyung
and Ko, Minsam",
title="Understanding Morning Emotions by Analyzing Daily Wake-Up Alarm Usage: Longitudinal Observational Study",
journal="JMIR Hum Factors",
year="2024",
month="Nov",
day="29",
volume="11",
pages="e50835",
keywords="morning emotion",
keywords="wake-up alarm usage",
keywords="morning context",
keywords="emotion monitoring",
keywords="longitudinal observational study",
abstract="Background: Morning emotions can significantly affect daily wellness. While many studies have analyzed daily survey responses to identify factors influencing morning emotions, these methods require additional time and effort from individuals for emotional monitoring. Objective: This study aims to identify daily alarm usage patterns related to morning emotions. Methods: We recruited 373 users of the Alarmy app (DelightRoom) in the United States and South Korea and surveyed their demographics and usual behaviors related to morning emotions. Participants described their morning emotions over a 2-week period, during which we collected daily alarm app logs. We used a generalized estimating equation (GEE) method to identify factors affecting morning emotions. Results: The findings indicate that varied alarm usage is related to morning emotions. Alarm set time was positively associated with feelings of peacefulness and refreshment in the morning, while task-based alarms were related to nervousness. The time taken to deactivate the alarm after it rang was negatively correlated with happiness. In addition, usual behaviors and demographic factors were found to be related to morning emotions, consistent with previous studies. Conclusions: The study reveals that daily alarm usage is related to morning emotions, suggesting that daily alarm logs can supplement survey methods to facilitate daily emotion monitoring. ",
doi="10.2196/50835",
url="https://humanfactors.jmir.org/2024/1/e50835"
}


@Article{info:doi/10.2196/60762,
author="Bice, L. Briana
and Michaud, L. Alexis
and McCormick, G. Katherine
and Miklos, M. Eva
and Descombes, D. Indiana
and Medeiros-Nancarrow, Cheryl
and Zhou, S. Eric
and Recklitis, J. Christopher",
title="Sleep Treatment Education Program for Cancer Survivors: Protocol for an Efficacy Trial",
journal="JMIR Res Protoc",
year="2024",
month="Nov",
day="28",
volume="13",
pages="e60762",
keywords="insomnia",
keywords="mood",
keywords="cancer survivors",
keywords="online interventions",
keywords="protocol",
keywords="cognitive behavioral therapy",
keywords="CBT",
keywords="cognitive behavioral therapy for insomnia",
keywords="CBTI",
keywords="digital health",
keywords="sleep disorders",
keywords="sleep treatment education program",
keywords="STEP-1",
abstract="Background: Cancer survivors are at increased risk for chronic insomnia, even years after treatment completion. As insomnia is associated with a variety of long-term health consequences, access to insomnia treatment is critically important for the survivor population. Cognitive behavioral therapy for insomnia (CBTI) is the recommended first-line treatment for insomnia but remains largely unavailable to survivors. Treatment barriers include geographic limitations, a shortage of trained providers, and demanding treatment regimens. Designed with these limitations in mind, the Sleep Treatment Education Program (STEP-1) delivers components of CBTI in a low-intensity educational intervention delivered online. Objective: This is a phase II pilot randomized controlled trial. The primary aims are to test the efficacy of STEP-1 to improve (1) insomnia symptoms and (2) mood in cancer survivors compared to a control condition. The secondary aims will (1) explore participant factors associated with clinically significant response, (2) evaluate acceptability of the control intervention, (3) explore feasibility of delivering individualized coaching sessions for participants who do not have a significant response to STEP-1, and (4) describe participants' satisfaction with STEP-1 and suggestions for improvement. Methods: This 2-arm randomized controlled trial enrolled 70 off-treatment cancer survivors aged 40-89 years with clinically significant insomnia. Participants are randomized to receive either the STEP-1 intervention or control condition (relaxation education); interventions are delivered in one-on-one, synchronous, virtual videoconference sessions by trained interventionists. The STEP-1 intervention presents educational information on the development of insomnia after cancer and offers suggestions for improving insomnia symptoms based on the CBTI elements of sleep hygiene, stimulus control, and cognitive restructuring. With the interventionist, participants review the suggestions and develop a personalized sleep action plan for implementation. The relaxation education session provides information on the potential benefits of relaxation and how to independently access online relaxation exercises. The Insomnia Severity Index is used to measure insomnia symptoms, and the Profile of Mood States Short Form is used to measure mood at baseline and 4 and 8 weeks after intervention. The primary end point is change in the Insomnia Severity Index score at 8 weeks, and the secondary end point is change in mood symptoms (Profile of Mood States Short Form) at 8 weeks. Results: This trial was funded in July 2022. Enrollment and data collection began in February 2023 and concluded in October 2024, with 70 participants enrolled. The analysis will begin in fall 2024, and the results are expected in winter 2025. Conclusions: Trial results will determine if STEP-1 effects go beyond those that could be attributed to placebo and other nonspecific treatment factors. Should results support the efficacy of STEP-1 to improve mood and insomnia symptoms, we anticipate developing efficacy and implementation trials of STEP-1 in larger and more diverse samples. Trial Registration: ClinicalTrials.gov NCT05519982; https://clinicaltrials.gov/study/NCT05519982 International Registered Report Identifier (IRRID): DERR1-10.2196/60762 ",
doi="10.2196/60762",
url="https://www.researchprotocols.org/2024/1/e60762"
}


@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/10.2196/51110,
author="Chiu, Yi-Hang
and Lee, Yen-Fen
and Lin, Huang-Li
and Cheng, Li-Chen",
title="Exploring the Role of Mobile Apps for Insomnia in Depression: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="18",
volume="26",
pages="e51110",
keywords="depression",
keywords="insomnia",
keywords="chatbots",
keywords="conversational agents",
keywords="medical apps",
keywords="systematic review",
keywords="technical aspects",
keywords="PRISMA",
abstract="Background: The COVID-19 pandemic has profoundly affected mental health, leading to an increased prevalence of depression and insomnia. Currently, artificial intelligence (AI) and deep learning have thoroughly transformed health care--related mobile apps, offered more effective mental health support, and alleviated the psychological stress that may have emerged during the pandemic. Early reviews outlined the use of mobile apps for dealing with depression and insomnia separately. However, there is now an urgent need for a systematic evaluation of mobile apps that address both depression and insomnia to reveal new applications and research gaps. Objective: This study aims to systematically review and evaluate mobile apps targeting depression and insomnia, highlighting their features, effectiveness, and gaps in the current research. Methods: We systematically searched PubMed, Scopus, and Web of Science for peer-reviewed journal articles published between 2017 and 2023. The inclusion criteria were studies that (1) focused on mobile apps addressing both depression and insomnia, (2) involved young people or adult participants, and (3) provided data on treatment efficacy. Data extraction was independently conducted by 2 reviewers. Title and abstract screening, as well as full-text screening, were completed in duplicate. Data were extracted by a single reviewer and verified by a second reviewer, and risk of bias assessments were completed accordingly. Results: Of the initial 383 studies we found, 365 were excluded after title, abstract screening, and removal of duplicates. Eventually, 18 full-text articles met our criteria and underwent full-text screening. The analysis revealed that mobile apps related to depression and insomnia were primarily utilized for early detection, assessment, and screening (n=5 studies); counseling and psychological support (n=3 studies); and cognitive behavioral therapy (CBT; n=10 studies). Among the 10 studies related to depression, our findings showed that chatbots demonstrated significant advantages in improving depression symptoms, a promising development in the field. Additionally, 2 studies evaluated the effectiveness of mobile apps as alternative interventions for depression and sleep, further expanding the potential applications of this technology. Conclusions: The integration of AI and deep learning into mobile apps, particularly chatbots, is a promising avenue for personalized mental health support. Through innovative features, such as early detection, assessment, counseling, and CBT, these apps significantly contribute toward improving sleep quality and addressing depression. The reviewed chatbots leveraged advanced technologies, including natural language processing, machine learning, and generative dialog, to provide intelligent and autonomous interactions. Compared with traditional face-to-face therapies, their feasibility, acceptability, and potential efficacy highlight their user-friendly, cost-effective, and accessible nature with the aim of enhancing sleep and mental health outcomes. ",
doi="10.2196/51110",
url="https://www.jmir.org/2024/1/e51110",
url="http://www.ncbi.nlm.nih.gov/pubmed/39423009"
}


@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/10.2196/64063,
author="Liang, Zilu
and Melcer, Edward
and Khotchasing, Kingkarn
and Chen, Samantha
and Hwang, Daeun
and Hoang, Huyen Nhung",
title="The Role of Relevance in Shaping Perceptions of Sleep Hygiene Games Among University Students: Mixed Methods Study",
journal="JMIR Serious Games",
year="2024",
month="Oct",
day="8",
volume="12",
pages="e64063",
keywords="serious games",
keywords="sleep hygiene",
keywords="sleep technologies",
keywords="co-design",
keywords="relevance",
keywords="self-determination theory",
keywords="digital health",
keywords="persuasive technology",
keywords="behavior change",
abstract="Background: Sleep games are an emerging topic in the realm of serious health game research. However, designing features that are both enjoyable and effective at engaging users, particularly university students, to develop healthy sleep habits remains a challenge. Objective: This study aims to investigate user preferences for 3 sleep game prototypes, that is, Hero's Sleep Journey, Sleep Tamagotchi, and Sleepland, and to explore their popularity and perceived utility in promoting sleep health. Methods: A mixed methods approach was used in this study. Quantitative and qualitative data were collected through a co-design workshop involving 47 university students. Participants were presented with storyboard cards of game features and were asked to provide an overall rating on each game, as well as ratings for individual features. They were also encouraged to provide free-form comments on the features and suggest improvements. In addition, participants were asked to express their preferences among the 3 games regarding which game they would most like to play and which one they found most useful for promoting sleep health. Results: Surprisingly, while Hero's Sleep Journey was the most popular choice among participants, Sleep Tamagotchi was perceived as the most beneficial for improving sleep health. Relevance emerged as an overarching theme in the qualitative data analysis, with 3 interconnected dimensions: psychological relevance to users' personal lives, logical relevance to sleep health, and situational relevance to users' circumstantial context. We discussed how the 3 dimensions of relevance address the autonomy and relatedness constructs outlined in the self-determination theory and proposed 3 design recommendations. Conclusions: Our serious sleep game prototypes demonstrated the potential to engage university students to develop healthy sleep hygiene. Future sleep game designs should aim to create a sense of relevance to users' personal lives, sleep health goals, and situational contexts. Rather than a one-size-fits-all approach, it is essential to develop a wide range of game genres and features to cater to diverse users. Aligning game features with sleep health goals and educating users on the design rationale through sleep knowledge are also important aspects. Furthermore, allowing users to customize their game experience and manage technology boundaries is necessary to nurture a sense of control and autonomy in the process of forming good sleep hygiene. ",
doi="10.2196/64063",
url="https://games.jmir.org/2024/1/e64063"
}


@Article{info:doi/10.2196/54066,
author="Feng, Yonggang
and Xue, Qihui
and Yu, Peng
and Peng, Lanxiang",
title="The Relationship Between Epidemic Perception and Cyberbullying Behaviors of Chinese Adolescents During the COVID-19 Pandemic: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Oct",
day="2",
volume="10",
pages="e54066",
keywords="COVID-19",
keywords="epidemic perception",
keywords="cyberbullying behaviors",
keywords="insomnia",
keywords="anxiety and depression",
abstract="Background: In response to the COVID-19 outbreak, the government initiated measures for social distancing, leading to a gradual transition of adolescents' social interactions toward web-based platforms. Consequently, web-based behaviors, particularly cyberbullying, have become a prominent concern. Considering that adolescents experience more intense feelings, the widely increased negative emotions and strains perceived from the COVID-19 pandemic may end up engaging in cyberbullying behaviors. In addition, during the COVID-19 pandemic, adolescents experiencing insomnia and negative affect are more prone to diminished self-control, which is associated with cyberbullying behaviors. Objective: This study aims to investigate the relationship between epidemic perception and cyberbullying behaviors, while also examining the serial mediating roles of insomnia and negative affect on the relationship between epidemic perception and cyberbullying behaviors. Methods: This study presents a large-scale web-based survey conducted during the period of concentrated COVID-19 outbreaks, encompassing 20,000 Chinese adolescents. A total of 274 submitted questionnaires were discarded because of high levels of missing data or their answers were clearly fictitious or inconsistent. The final count of valid participants amounted to 19,726 (10,371 boys, age range: 12?18 years; mean 14.80, SD 1.63 years). The Perceptions of COVID-19 Scale, Negative Affect Scale, Insomnia Scale, and Cyberbullying Behavior Scale were used to assess participants' responses on the Questionnaire Star platform. Results: The results show that epidemic perception is positively correlated with cyberbullying behaviors (r=0.13; P<.001), insomnia (r=0.19; P<.001), and negative affect (r=0.25; P<.001). Insomnia is positively correlated with negative affect (r=0.44; P<.001) and cyberbullying behaviors (r=0.30; P<.001). Negative affect is positively correlated with cyberbullying behaviors (r=0.25; P<.001). And insomnia and negative affect play independent mediating and serial mediating roles in epidemic perception and cyberbullying behaviors. Conclusions: This study provides additional empirical evidence on the relationship between the perception of COVID-19 pandemic and cyberbullying in adolescents. In addition, the study offers recommendations for implementing interventions targeted at mitigating cyberbullying in adolescents during the COVID-19 pandemic. ",
doi="10.2196/54066",
url="https://publichealth.jmir.org/2024/1/e54066"
}


@Article{info:doi/10.2196/60769,
author="Mak, Selene
and Ash, Garrett
and Liang, Li-Jung
and Der-McLeod, Erin
and Ghadimi, Sara
and Kewalramani, Anjali
and Naeem, Saadia
and Zeidler, Michelle
and Fung, Constance",
title="Testing a Consumer Wearables Program to Promote the Use of Positive Airway Pressure Therapy in Patients With Obstructive Sleep Apnea: Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Sep",
day="19",
volume="13",
pages="e60769",
keywords="sleep apnea",
keywords="consumer wearables",
keywords="adherence",
keywords="self-management",
keywords="mobile phone",
abstract="Background: Although positive airway pressure (PAP) therapy is considered first-line treatment for obstructive sleep apnea (OSA), nonadherence is common. Numerous factors influence PAP use, including a belief that the therapy is important and effective. In theory, providing information to patients about their blood oxygen levels during sleep (which may be low when PAP is not used), juxtaposed to information about their PAP use, may influence a patient's beliefs about therapy and increase PAP use. With the advent of consumer wearable smartwatches' blood oxygen saturation monitoring capability (and the existing routine availability of PAP use data transmitted via modem to clinical dashboards), there is an opportunity to provide this combination of information to patients. Objective: This study aims to test the feasibility, acceptability, and preliminary efficacy of the Chronic Care Management With Wearable Devices in Patients Prescribed Positive Airway Pressure Therapy (mPAP), a program that augments current PAP therapy data with consumer-grade wearable device to promote self-management of PAP therapy for OSA in a pilot randomized waitlist-controlled clinical trial. Methods: This is a single-blinded randomized controlled trial. We will randomize 50 individuals with a history of OSA, who receive care from a Department of Veterans Affairs medical center in the Los Angeles area and are nonadherent to prescribed PAP therapy, into either an immediate intervention group or a waitlist control group. During a 28-day intervention, the participants will wear a study-provided consumer wearable device and complete a weekly survey about their OSA symptoms. A report that summarizes consumer wearable--provided oxygen saturation values, PAP use derived from modem data, and patient-reported OSA symptoms will be prepared weekly and shared with the patient. The immediate intervention group will begin intervention immediately after randomization (T1). Assessments will occur at week 5 (T3; 1 week after treatment for the immediate intervention group and repeat baseline for the waitlist control group) and week 11 (T5; follow-up for the immediate intervention group and 1 week after treatment for the waitlist control group). The primary outcome will be the change in 7-day PAP adherence (average minutes per night) from T1 to T3. The primary analysis will be a comparison of the primary outcome between the immediate intervention and the waitlist control groups (intention-to-treat design), using a 2-sample, 2-sided t test on change scores (unadjusted). Results: Recruitment began in October 2023. Data analysis is expected to begin in October 2024 when all follow-ups are complete, and a manuscript summarizing trial results will be submitted following completion of data analysis. Conclusions: Findings from the study may provide additional insights on how patients with OSA might use patient-generated health data collected by consumer wearables to inform self-management of OSA and possibly increase their use of PAP therapy. Trial Registration: ClinicalTrials.gov NCT06039865; https://clinicaltrials.gov/study/NCT06039865 International Registered Report Identifier (IRRID): DERR1-10.2196/60769 ",
doi="10.2196/60769",
url="https://www.researchprotocols.org/2024/1/e60769",
url="http://www.ncbi.nlm.nih.gov/pubmed/39207912"
}


@Article{info:doi/10.2196/58344,
author="Zhou, Peng
and Song, Huiqi
and Lau, C. Patrick W.
and Shi, Lei
and Wang, Jingjing",
title="Effectiveness of a Parent-Based eHealth Intervention for Physical Activity, Dietary Behavior, and Sleep Among Preschoolers: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Sep",
day="12",
volume="13",
pages="e58344",
keywords="physical activity",
keywords="dietary behavior",
keywords="sleep",
keywords="electronic health",
keywords="eHealth",
keywords="preschoolers",
keywords="parenting",
abstract="Background: Preschoolers' lifestyles have become physically inactive and sedentary, their eating habits have become unhealthy, and their sleep routines have become increasingly disturbed. Parent-based interventions have shown promise to improve physical activity (PA), improve dietary behavior (DB), and reduce sleep problems among preschoolers. However, because of the recognized obstacles of face-to-face approaches (eg, travel costs and time commitment), easy access and lower costs make eHealth interventions appealing. Previous studies that examined the effectiveness of parent-based eHealth for preschoolers' PA, DB, and sleep have either emphasized 1 variable or failed to balance PA, DB, and sleep modules and consider the intervention sequence during the intervention period. There is an acknowledged gap in parent-based eHealth interventions that target preschoolers raised in Chinese cultural contexts. Objective: This study aims to investigate the effectiveness of a parent-based eHealth intervention for PA, DB, and sleep problems among Chinese preschoolers. Methods: This 2-arm, parallel, randomized controlled trial comprises a 12-week intervention with a 12-week follow-up. A total of 206 parent-child dyads will be randomized to either an eHealth intervention group or a control group. Participants allocated to the eHealth intervention group will receive 12 interactive modules on PA, DB, and sleep, with each module delivered on a weekly basis to reduce the sequence effect on variable outcomes. The intervention is grounded in social cognitive theory. It will be delivered through social media, where parents can obtain valid and updated educational information, have a social rapport, and interact with other group members and facilitators. Participants in the control group will receive weekly brochures on PA, DB, and sleep recommendations from kindergarten teachers, but they will not receive any interactive components. Data will be collected at baseline, 3 months, and 6 months. The primary outcome will be preschoolers' PA. The secondary outcomes will be preschoolers' DB, preschoolers' sleep duration, preschoolers' sleep problems, parents' PA, parenting style, and parental feeding style. Results: Parent-child dyads were recruited in September 2023. Baseline and posttest data collection occurred from October 2023 to March 2024. The follow-up data will be obtained in June 2024. The results of the study are expected to be published in 2025. Conclusions: The parent-based eHealth intervention has the potential to overcome the barriers of face-to-face interventions and will offer a novel approach for promoting a healthy lifestyle among preschoolers. If this intervention is found to be efficacious, the prevalence of unhealthy lifestyles among preschoolers may be alleviated at a low cost, which not only has a positive influence on the health of individuals and the well-being of the family but also reduces the financial pressure on society to treat diseases caused by poor lifestyle habits. Trial Registration: ClinicalTrials.gov NCT06025019; https://clinicaltrials.gov/study/NCT06025019 International Registered Report Identifier (IRRID): DERR1-10.2196/58344 ",
doi="10.2196/58344",
url="https://www.researchprotocols.org/2024/1/e58344"
}


@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/10.2196/58217,
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/10.2196/51585,
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/10.2196/49396,
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/10.2196/47809,
author="Haverinen, Jari
and Harju, Terttu
and Mikkonen, Hanna
and Liljamo, Pia
and Turpeinen, Miia
and Reponen, Jarmo",
title="Digital Care Pathway for Patients With Sleep Apnea in Specialized Care: Mixed Methods Study",
journal="JMIR Hum Factors",
year="2024",
month="Feb",
day="22",
volume="11",
pages="e47809",
keywords="health services",
keywords="telehealth",
keywords="telemedicine",
keywords="health personnel",
keywords="sleep apnea syndromes",
keywords="mobile phone",
abstract="Background: Sleep apnea is a significant public health disorder in Finland, with a prevalence of 3.7\%. Continuous positive airway pressure (CPAP) therapy is the first-line treatment for moderate or severe sleep apnea. From November 18, 2019, all patients who started their CPAP therapy at Oulu University Hospital were attached to a sleep apnea digital care pathway (SA-DCP) and were instructed on its use. Some patients still did not use the SA-DCP although they had started their CPAP therapy. Objective: We aimed to study health care professionals' (HCPs') perspectives on the SA-DCP and its usefulness for their work; whether the main targets of SA-DCP can be reached: shortening the initial guiding sessions of CPAP therapy, reducing patient calls and contact with HCPs, and improving patients' adherence to CPAP therapy; and patients' perspectives on the SA-DCP and its usefulness to them. Methods: Overall, 6 HCPs were interviewed in May and June 2021. The survey for SA-DCP users (58/91, 64\%) and SA-DCP nonusers (33/91, 36\%) was conducted in 2 phases: from May to August 2021 and January to June 2022. CPAP device remote monitoring data were collected from SA-DCP users (80/170, 47.1\%) and SA-DCP nonusers (90/170, 52.9\%) in May 2021. The registered phone call data were collected during 2019, 2020, and 2021. Feedback on the SA-DCP was collected from 446 patients between February and March 2022. Results: According to HCPs, introducing the SA-DCP had not yet significantly improved their workload and work practices, but it had brought more flexibility in some communication situations. A larger proportion of SA-DCP users familiarized themselves with prior information about CPAP therapy before the initial guiding session than nonusers (43/58, 74\% vs 16/33, 49\%; P=.02). Some patients still had not received prior information about CPAP therapy; therefore, most of the sessions were carried out according to their needs. According to the patient survey and remote monitoring data of CPAP devices, adherence to CPAP therapy was high for both SA-DCP users and nonusers. The number of patients' phone calls to HCPs did not decrease during the study. SA-DCP users perceived their abilities to use information and communications technology to be better than nonusers (mean 4.2, SD 0.8 vs mean 3.2, SD 1.2; P<.001). Conclusions: According to this study, not all the goals set for the introduction of the SA-DCP have been achieved. Despite using the SA-DCP, some patients still wanted to communicate with HCPs by phone. The most significant factors explaining the nonuse of the SA-DCP were lower digital literacy and older age of the patients. In the future, more attention should be paid to these user groups when designing and introducing upcoming digital care pathways. ",
doi="10.2196/47809",
url="https://humanfactors.jmir.org/2024/1/e47809",
url="http://www.ncbi.nlm.nih.gov/pubmed/38386368"
}


@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/10.2196/50836,
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/10.2196/50072,
author="Aggestrup, Sofie Anne
and Svendsen, Dunker Signe
and Pr{\ae}stegaard, Anne
and L{\o}ventoft, Philip
and N{\o}rregaard, Lasse
and Knorr, Ulla
and Dam, Henrik
and Fr{\o}kj{\ae}r, Erik
and Danilenko, Konstantin
and Hageman, Ida
and Faurholt-Jepsen, Maria
and Kessing, Vedel Lars
and Martiny, Klaus",
title="Circadian Reinforcement Therapy in Combination With Electronic Self-Monitoring to Facilitate a Safe Postdischarge Period for Patients With Major Depression: Randomized Controlled Trial",
journal="JMIR Ment Health",
year="2023",
month="Nov",
day="27",
volume="10",
pages="e50072",
keywords="major depression",
keywords="internet interventions",
keywords="self-monitoring",
keywords="sleep",
keywords="circadian",
keywords="chronobiology",
keywords="chronotherapy",
keywords="clinician assisted",
abstract="Background: Patients with major depression exhibit circadian disturbance of sleep and mood, and when they are discharged from inpatient wards, this disturbance poses a risk of relapse. We developed a circadian reinforcement therapy (CRT) intervention to facilitate the transition from the inpatient ward to the home for these patients. CRT focuses on increasing the zeitgeber strength for the circadian clock through social contact, physical activity, diet, daylight exposure, and sleep timing. Objective: In this study, we aimed to prevent the worsening of depression after discharge by using CRT, supported by an electronic self-monitoring system, to advance and stabilize sleep and improve mood. The primary outcome, which was assessed by a blinded rater, was the change in the Hamilton Depression Rating Scale scores from baseline to the end point. Methods: Participants were contacted while in the inpatient ward and randomized 1:1 to the CRT or the treatment-as-usual (TAU) group. For 4 weeks, participants in both groups electronically self-monitored their daily mood, physical activity, sleep, and medication using the Monsenso Daybuilder (MDB) system. The MDB allowed investigators and participants to simultaneously view a graphical display of registrations. An investigator phoned all participants weekly to coinspect data entry. In the CRT group, participants were additionally phoned between the scheduled calls if specific predefined trigger points for mood and sleep were observed during the daily inspection. Participants in the CRT group were provided with specialized CRT psychoeducation sessions immediately after inclusion, focusing on increasing the zeitgeber input to the circadian system; a PowerPoint presentation was presented; paper-based informative materials and leaflets were reviewed with the participants; and the CRT principles were used during all telephone consultations. In the TAU group, phone calls focused on data entry in the MDB system. When discharged, all patients were treated at a specialized affective disorders service. Results: Overall, 103 participants were included. Participants in the CRT group had a significantly larger reduction in Hamilton Depression Scale score (P=.04) than those in the TAU group. The self-monitored MDB data showed significantly improved evening mood (P=.02) and sleep quality (P=.04), earlier sleep onset (P=.009), and longer sleep duration (P=.005) in the CRT group than in the TAU group. The day-to-day variability of the daily and evening mood, sleep offset, sleep onset, and sleep quality were significantly lower in the CRT group (all P<.001) than in the TAU group. The user evaluation was positive for the CRT method and the MDB system. Conclusions: We found significantly lower depression levels and improved sleep quality in the CRT group than in the TAU group. We also found significantly lower day-to-day variability in daily sleep, mood parameters, and activity parameters in the CRT group than in the TAU group. The delivery of the CRT intervention should be further refined and tested. Trial Registration: ClinicalTrials.gov NCT02679768; https://clinicaltrials.gov/study/NCT02679768 International Registered Report Identifier (IRRID): RR2-10.1186/s12888-019-2101-z ",
doi="10.2196/50072",
url="https://mental.jmir.org/2023/1/e50072",
url="http://www.ncbi.nlm.nih.gov/pubmed/37800194"
}


@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/10.2196/47356,
author="Wickwire, M. Emerson
and Collen, Jacob
and Capaldi, F. Vincent
and Williams, G. Scott
and Assefa, Z. Samson
and Adornetti, P. Julianna
and Huang, Kathleen
and Venezia, M. Janet
and Jones, L. Rachell
and Johnston, W. Christine
and Thomas, Connie
and Thomas, Ann Mary
and Mounts, Charles
and Drake, L. Christopher
and Businelle, S. Michael
and Grandner, A. Michael
and Manber, Rachel
and Albrecht, S. Jennifer",
title="Patient Engagement and Provider Effectiveness of a Novel Sleep Telehealth Platform and Remote Monitoring Assessment in the US Military: Pilot Study Providing Evidence-Based Sleep Treatment Recommendations",
journal="JMIR Form Res",
year="2023",
month="Nov",
day="16",
volume="7",
pages="e47356",
keywords="sleep",
keywords="sleep disorders",
keywords="insomnia",
keywords="obstructive sleep apnea",
keywords="telehealth",
keywords="remote monitoring",
keywords="monitoring",
keywords="patient engagement",
keywords="effectiveness",
keywords="effective care",
keywords="behavioral",
keywords="care",
keywords="application",
keywords="wearables",
abstract="Background: Sleep problems are common and costly in the US military. Yet, within the military health system, there is a gross shortage of trained specialist providers to address sleep problems. As a result, demand for sleep medicine care far exceeds the available supply. Telehealth including telemedicine, mobile health, and wearables represents promising approaches to increase access to high-quality and cost-effective care. Objective: The purpose of this study was to evaluate patient engagement and provider perceived effectiveness of a novel sleep telehealth platform and remote monitoring assessment in the US military. The platform includes a desktop web portal, native mobile app, and integrated wearable sensors (ie, a commercial off-the-shelf sleep tracker [Fitbit]). The goal of the remote monitoring assessment was to provide evidence-based sleep treatment recommendations to patients and providers. Methods: Patients with sleep problems were recruited from the Internal Medicine clinic at Walter Reed National Military Medical Center. Patients completed intensive remote monitoring assessments over 10 days (including a baseline intake questionnaire, daily sleep diaries, and 2 daily symptom surveys), and wore a Fitbit sleep tracker. Following the remote monitoring period, patients received assessment results and personalized sleep education in the mobile app. In parallel, providers received a provisional patient assessment report in an editable electronic document format. Patient engagement was assessed via behavioral adherence metrics that were determined a priori. Patients also completed a brief survey regarding ease of completion. Provider effectiveness was assessed via an anonymous survey. Results: In total, 35 patients with sleep problems participated in the study. There were no dropouts. Results indicated a high level of engagement with the sleep telehealth platform, with all participants having completed the baseline remote assessment, reviewed their personalized sleep assessment report, and completed the satisfaction survey. Patients completed 95.1\% of sleep diaries and 95.3\% of symptom surveys over 10 days. Patients reported high levels of satisfaction with most aspects of the remote monitoring assessment. In total, 24 primary care providers also participated and completed the anonymous survey. The results indicate high levels of perceived effectiveness and identified important potential benefits from adopting a sleep telehealth approach throughout the US military health care system. Conclusions: Military patients with sleep problems and military primary care providers demonstrated high levels of engagement and satisfaction with a novel sleep telehealth platform and remote monitoring assessment. Sleep telehealth approaches represent a potential pathway to increase access to evidence-based sleep medicine care in the US military. Further evaluation is warranted. ",
doi="10.2196/47356",
url="https://formative.jmir.org/2023/1/e47356",
url="http://www.ncbi.nlm.nih.gov/pubmed/37971788"
}


@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/10.2196/46338,
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/10.2196/45859,
author="McCrae, S. Christina
and Curtis, F. Ashley
and Stearns, A. Melanie
and Nair, Neetu
and Golzy, Mojgan
and Shenker, I. Joel
and Beversdorf, Q. David
and Cottle, Amelia
and Rowe, A. Meredeth",
title="Development and Initial Evaluation of Web-Based Cognitive Behavioral Therapy for Insomnia in Rural Family Caregivers of People With Dementia (NiteCAPP): Mixed Methods Study",
journal="JMIR Aging",
year="2023",
month="Aug",
day="24",
volume="6",
pages="e45859",
keywords="arousal",
keywords="caregivers",
keywords="cognitive behavioral therapy",
keywords="CBT: cognitive behavioral therapy for insomnia",
keywords="CBT-I",
keywords="dementia",
keywords="insomnia",
keywords="internet",
abstract="Background: Informal caregivers of people with dementia frequently experience chronic insomnia, contributing to stress and poor health outcomes. Rural caregivers are particularly vulnerable but have limited access to cognitive behavioral therapy for insomnia (CBT-I), a recommended frontline treatment for chronic insomnia. Web-based delivery promises to improve insomnia, particularly for rural caregivers who have limited access to traditional in-person treatments. Our team translated an efficacious 4-session standard CBT-I content protocol into digital format to create NiteCAPP. Objective: This study aimed to (1) adapt NiteCAPP for dementia caregivers to create NiteCAPP CARES, a tailored digital format with standard CBT-I content plus caregiver-focused modifications; (2) conduct usability testing and evaluate acceptability of NiteCAPP CARES' content and features; and (3) pilot-test the adapted intervention to evaluate feasibility and preliminary effects on sleep and related health outcomes. Methods: We followed Medical Research Council recommendations for evaluating complex medical interventions to explore user needs and adapt and validate content using a stepwise approach: (1) a rural dementia caregiver (n=5) and primary care provider (n=5) advisory panel gave feedback that was used to adapt NiteCAPP; (2) caregiver (n=5) and primary care provider (n=7) focus groups reviewed the newly adapted NiteCAPP CARES and provided feedback that guided further adaptations; and (3) NiteCAPP CARES was pilot-tested in caregivers (n=5) for feasibility and to establish preliminary effects. Self-report usability measures were collected following intervention. Before and after treatment, 14 daily electronic sleep diaries and questionnaires were collected to evaluate arousal, health, mood, burden, subjective cognition, and interpersonal processes. Results: The stepped approach provided user and expert feedback on satisfaction, usefulness, and content, resulting in a new digital CBT-I tailored for rural dementia caregivers: NiteCAPP CARES. The advisory panel recommended streamlining content, eliminating jargon, and including caregiver-focused content. Focus groups gave NiteCAPP CARES high usefulness ratings (mean score 4.4, SD 0.79, scored from 1=least to 5=most favorable; score range 4.2-4.8). Multiple features were evaluated positively, including the intervention's comprehensive and engaging information, caregiver focus, good layout, easy-to-access intervention material, and easy-to-understand sleep graphs. Suggestions for improvement included the provision of day and night viewing options, collapsible text, font size options, tabbed access to videos, and a glossary of terms. Pilot-test users rated usefulness (mean score 4.3, SD 0.83; range 4.1-4.5) and satisfaction (mean score 8.4, SD 1.41, scored from 1=least to 10=most satisfied; range 7.4-9.0) highly. Preliminary effects on caregiver sleep, arousal, health, mood, burden, cognition, and interpersonal processes (all P<.05) were promising. Conclusions: Adaptations made to standard digital CBT-I created a feasible, tailored digital intervention for rural dementia caregivers. Important next steps include further examination of feasibility and efficacy in a randomized controlled trial with an active control condition, a multisite effectiveness trial, and eventual broad dissemination. Trial Registration: ClinicalTrials.gov NCT04632628; https://clinicaltrials.gov/ct2/show/NCT04632628 ",
doi="10.2196/45859",
url="https://aging.jmir.org/2023/1/e45859",
url="http://www.ncbi.nlm.nih.gov/pubmed/37616032"
}


@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/10.2196/47735,
author="Duarte, Miguel
and Pereira-Rodrigues, Pedro
and Ferreira-Santos, Daniela",
title="The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review",
journal="J Med Internet Res",
year="2023",
month="Jul",
day="26",
volume="25",
pages="e47735",
keywords="obstructive sleep apnea",
keywords="diagnosis",
keywords="digital tools",
keywords="smartphone",
keywords="wearables",
keywords="sensor",
keywords="polysomnography",
keywords="systematic review",
keywords="mobile phone",
abstract="Background: Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard. Objective: This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. Methods: We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures. Results: We retrieved 1714 articles, 41 (2.39\%) of which were included in the study. From these 41 articles, we found 7 (17\%) smartphone-based tools, 10 (24\%) wearables, 11 (27\%) bed or mattress sensors, 5 (12\%) nasal airflow devices, and 8 (20\%) other sensors that did not fit the previous categories. Only 8 (20\%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96\%, and 92\%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)?30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100\%, and 96\%, respectively, for a clinical cutoff AHI?30. It uses the Sonomat---a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events. Conclusions: These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings. Trial Registration: PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=387748 ",
doi="10.2196/47735",
url="https://www.jmir.org/2023/1/e47735",
url="http://www.ncbi.nlm.nih.gov/pubmed/37494079"
}


@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/10.2196/43194,
author="Sahoo, Astha
and Starr, Leah Savannah
and Osadchiy, Vadim
and Desai, Sophia
and Iyer, Neha
and Luff, Marie
and Sollender, E. Grace
and Sturm, Renea",
title="Identifying Targets for Innovation in Amazon Reviews of Bedwetting Alarms: Thematic Analysis",
journal="Interact J Med Res",
year="2023",
month="Jul",
day="6",
volume="12",
pages="e43194",
keywords="Amazon",
keywords="bedwetting alarms",
keywords="nocturnal enuresis",
keywords="online reviews",
keywords="early customer discovery",
keywords="online",
keywords="diagnosis",
keywords="pediatric",
keywords="teen",
keywords="adolescent",
keywords="sensor",
keywords="treatment",
keywords="age",
keywords="sex",
keywords="device",
keywords="user",
keywords="adaptability",
keywords="efficacy",
keywords="child health",
keywords="bedwet",
keywords="enuresis",
keywords="Urology",
keywords="pediatric urology",
keywords="alarm",
keywords="alert",
keywords="notification",
keywords="sleeping practice",
keywords="sleep practice",
keywords="sleep disorder",
keywords="polysomnography",
keywords="thematic analysis",
keywords="natural language processing",
keywords="NLP",
abstract="Background: Nocturnal enuresis (NE) is a frequent diagnosis in pediatric and adolescent populations with an estimated prevalence of around 15\% at the age of 6 years. NE can have a substantial impact on multiple health domains. Bedwetting alarms, which typically consist of a sensor and moisture-activated alarm, are a common treatment. Objective: This study aimed to determine areas of satisfaction versus dissatisfaction reported by the parents and caregivers of children using current bedwetting alarms. Methods: Using the search term ``bedwetting alarms'' on the Amazon marketplace, products with >300 reviews were included. For each product, the 5 reviews ranked the ``most helpful'' for each star category were selected for analysis. Meaning extraction method was applied to identify major themes and subthemes. A percent skew was calculated by summing the total number of mentions of each subtheme,+1 for a positive mention, 0 for a neutral mention, and --1 for a negative mention, and dividing this total by the number of reviews in which that particular subtheme was observed. Subanalyses were performed for age and gender. Results: Of 136 products identified, 10 were evaluated based on the selection criteria. The main themes identified across products were long-term concerns, marketing, alarm systems, and device mechanics and features. The subthemes identified as future targets for innovation included alarm accuracy, volume variability, durability, user-friendliness, and adaptability to girls. In general, durability, alarm accuracy, and comfort were the most negatively skewed subthemes (with a negative skew of --23.6\%, --20.0\%, and --12.4\% respectively), which are indicative of potential areas for improvement. Effectiveness was the only substantially positively skewed subtheme (16.8\%). Alarm sound and device features were positively skewed for older children, whereas ease of use had a negative skew for younger children. Girls and their caretakers reported negative experiences with devices that featured cords, arm bands, and sensor pads. Conclusions: This analysis provides an innovation roadmap for future device design to improve patient and caregiver satisfaction and compliance with bedwetting alarms. Our results highlight the need for additional options in alarm sound features, as children of different ages have divergent preferences in this domain. Additionally, girls and their parents and caretakers provided more negative overall reviews regarding the range of current device features compared to boys, indicating a potential focus area for future development. The percent skew showed that subthemes were often more negatively skewed toward girls, with the ease of use being --10.7\% skewed for boys versus --20.5\% for girls, and comfort being --7.1\% skewed for boys versus --29.4\% for girls. Put together, this review highlights multiple device features that are targets for innovation to ensure translational efficacy regardless of age, gender, or specific family needs. ",
doi="10.2196/43194",
url="https://www.i-jmr.org/2023/1/e43194",
url="http://www.ncbi.nlm.nih.gov/pubmed/37410523"
}


@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/10.2196/45543,
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/10.2196/42073,
author="Concheiro-Moscoso, Patricia
and Groba, Betania
and Alvarez-Estevez, Diego
and Miranda-Duro, Carmen Mar{\'i}a del
and Pousada, Thais
and Nieto-Riveiro, Laura
and Mejuto-Mui{\~n}o, Javier Francisco
and Pereira, Javier",
title="Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study",
journal="J Med Internet Res",
year="2023",
month="May",
day="19",
volume="25",
pages="e42073",
keywords="sleep",
keywords="health promotion",
keywords="occupation",
keywords="polysomnography",
keywords="Xiaomi Mi Band 5",
keywords="Internet of Things",
abstract="Background: Polysomnography is the gold standard for measuring and detecting sleep patterns. In recent years, activity wristbands have become popular because they record continuous data in real time. Hence, comprehensive validation studies are needed to analyze the performance and reliability of these devices in the recording of sleep parameters. Objective: This study compared the performance of one of the best-selling activity wristbands, the Xiaomi Mi Band 5, against polysomnography in measuring sleep stages. Methods: This study was carried out at a hospital in A Coru{\~n}a, Spain. People who were participating in a polysomnography study at a sleep unit were recruited to wear a Xiaomi Mi Band 5 simultaneously for 1 night. The total sample consisted of 45 adults, 25 (56\%) with sleep disorders (SDis) and 20 (44\%) without SDis. Results: Overall, the Xiaomi Mi Band 5 displayed 78\% accuracy, 89\% sensitivity, 35\% specificity, and a Cohen $\kappa$ value of 0.22. It significantly overestimated polysomnography total sleep time (P=.09), light sleep (N1+N2 stages of non--rapid eye movement [REM] sleep; P=.005), and deep sleep (N3 stage of non-REM sleep; P=.01). In addition, it underestimated polysomnography wake after sleep onset and REM sleep. Moreover, the Xiaomi Mi Band 5 performed better in people without sleep problems than in those with sleep problems, specifically in detecting total sleep time and deep sleep. Conclusions: The Xiaomi Mi Band 5 can be potentially used to monitor sleep and to detect changes in sleep patterns, especially for people without sleep problems. However, additional studies are necessary with this activity wristband in people with different types of SDis. Trial Registration: ClinicalTrials.gov NCT04568408; https://clinicaltrials.gov/ct2/show/NCT04568408 International Registered Report Identifier (IRRID): RR2-10.3390/ijerph18031106 ",
doi="10.2196/42073",
url="https://www.jmir.org/2023/1/e42073",
url="http://www.ncbi.nlm.nih.gov/pubmed/37204853"
}


@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/10.2196/43738,
author="Coteur, Kristien
and Van Nuland, Marc
and Schoenmakers, Birgitte
and Anthierens, Sibyl
and Van den Broeck, Kris",
title="Implementing Blended Care to Discontinue Benzodiazepine Receptor Agonist Use for Insomnia: Process Evaluation of a Pragmatic Cluster Randomized Controlled Trial",
journal="JMIR Form Res",
year="2023",
month="Apr",
day="7",
volume="7",
pages="e43738",
keywords="benzodiazepines",
keywords="long-term use",
keywords="deprescriptions",
keywords="deprescribing",
keywords="telemedicine",
keywords="general practice",
keywords="insomnia",
keywords="cognitive behavioral therapy",
keywords="eHealth",
abstract="Background: Long-term use of benzodiazepine receptor agonists (BZRAs) remains common despite European guidelines advising that these drugs be used in the lowest possible dose and for the shortest possible duration. Half of all BZRAs are prescribed in family practice. This creates a window of opportunity for discontinuation in primary care. Therefore, the effectiveness of blended care for the discontinuation of long-term BZRA use in adult primary care patients with chronic insomnia disorder was tested in a multicenter, pragmatic, and cluster randomized controlled superiority trial in Belgium. In the literature, information on implementing blended care in a primary care setting is scarce. Objective: The study aimed to contribute to a framework for the successful implementation of blended care in a primary care setting by increasing our understanding of this complex intervention through an evaluation of e-tool use and views and ideas of participants in a BZRA discontinuation trial. Methods: Based on a theoretical framework, this study evaluated the processes of recruitment, delivery, and response using 4 components: a survey on recruitment (n=76), semistructured in-depth interviews with patients (n=18), web-based asynchronous focus groups with general practitioners (GPs; n=19), and usage data of the web-based tool. Quantitative data were analyzed descriptively, and qualitative data were analyzed thematically. Results: For recruitment, the most common barriers were refusal by the patient and the lack of digital literacy, while facilitators were starting the conversation and the curiosity of patients. The delivery of the intervention to the patients was diverse, ranging from GPs who never informed the patient about their access to the e-tool to GPs consulting the e-tool in between consultations to have discussion points when the patient visited. Concerning response, patients' and GPs' narratives also showed much variety. For some GPs, daily practice changed because they received more positive reactions than expected and felt empowered to talk more often about BZRA discontinuation. Conversely, some GPs reported no changes in practice or among patients. In general, patients found follow-up by an expert to be the most important component in blended care, whereas GPs deemed the intrinsic motivation of patients to be the key element of success. An important barrier to implementation by the GP was time. Conclusions: Overall, the participants who had used the e-tool were positive about its structure and content. Nevertheless, many patients desired a more tailored application with feedback from an expert and personal tapering schedules. Strict pragmatic implementation of blended care seems to only reach GPs with an interest in digitalization. Although not superior to usual care, blended care could be a complementary tool that allows tailoring the discontinuation process to the personal style of the GP and the needs of the patient. Trial Registration: ClinicalTrials.gov NCT03937180; https://clinicaltrials.gov/ct2/show/NCT03937180 ",
doi="10.2196/43738",
url="https://formative.jmir.org/2023/1/e43738",
url="http://www.ncbi.nlm.nih.gov/pubmed/37027198"
}


@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/10.2196/39250,
author="Deng, Wenrui
and M J J van der Kleij, Rianne
and Shen, Hongxia
and Wei, Junjie
and Brakema, A. Evelyn
and Guldemond, Nick
and Song, Xiaoyue
and Li, Xiaoming
and van Tol, Marie-Jos{\'e}
and Aleman, Andr{\'e}
and Chavannes, H. Niels",
title="eHealth-Based Psychosocial Interventions for Adults With Insomnia: Systematic Review and Meta-analysis of Randomized Controlled Trials",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="14",
volume="25",
pages="e39250",
keywords="eHealth",
keywords="psychosocial interventions",
keywords="insomnia",
keywords="adults",
keywords="meta-analysis",
keywords="mobile phone",
abstract="Background: Worldwide, insomnia remains a highly prevalent public health problem. eHealth presents a novel opportunity to deliver effective, accessible, and affordable insomnia treatments on a population-wide scale. However, there is no quantitative integration of evidence regarding the effectiveness of eHealth-based psychosocial interventions on insomnia. Objective: We aimed to evaluate the effectiveness of eHealth-based psychosocial interventions for insomnia and investigate the influence of specific study characteristics and intervention features on these effects. Methods: We searched PubMed, Embase, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials from database inception to February 16, 2021, for publications investigating eHealth-based psychosocial interventions targeting insomnia and updated the search of PubMed to December 6, 2021. We also screened gray literature for unpublished data. Eligible studies were randomized controlled trials of eHealth-based psychosocial interventions targeting adults with insomnia. Random-effects meta-analysis models were used to assess primary and secondary outcomes. Primary outcomes were insomnia severity and sleep quality. Meta-analyses were performed by pooling the effects of eHealth-based psychosocial interventions on insomnia compared with inactive and in-person conditions. We performed subgroup analyses and metaregressions to explore specific factors that affected the effectiveness. Secondary outcomes included sleep diary parameters and mental health--related outcomes. Results: Of the 19,980 identified records, 37 randomized controlled trials (13,227 participants) were included. eHealth-based psychosocial interventions significantly reduced insomnia severity (Hedges g=?1.01, 95\% CI ?1.12 to ?0.89; P<.001) and improved sleep quality (Hedges g=?0.58, 95\% CI ?0.75 to ?0.41; P<.001) compared with inactive control conditions, with no evidence of publication bias. We found no significant difference compared with in-person treatment in alleviating insomnia severity (Hedges g=0.41, 95\% CI ?0.02 to 0.85; P=.06) and a significant advantage for in-person treatment in enhancing sleep quality (Hedges g=0.56, 95\% CI 0.24-0.88; P<.001). eHealth-based psychosocial interventions had significantly larger effects (P=.01) on alleviating insomnia severity in clinical samples than in subclinical samples. eHealth-based psychosocial interventions that incorporated guidance from trained therapists had a significantly greater effect on insomnia severity (P=.05) and sleep quality (P=.02) than those with guidance from animated therapists or no guidance. Higher baseline insomnia severity and longer intervention duration were associated with a larger reduction in insomnia severity (P=.004). eHealth-based psychosocial interventions significantly improved each secondary outcome. Conclusions: eHealth interventions for insomnia are effective in improving sleep and mental health and can be considered a promising treatment for insomnia. Our findings support the wider dissemination of eHealth interventions and their further promotion in a stepped-care model. Offering blended care could improve treatment effectiveness. Future research needs to elucidate which specific intervention components are most important to achieve intervention effectiveness. Blended eHealth interventions may be tailored to benefit people with low socioeconomic status, limited access to health care, or lack of eHealth literacy. ",
doi="10.2196/39250",
url="https://www.jmir.org/2023/1/e39250",
url="http://www.ncbi.nlm.nih.gov/pubmed/36917145"
}


@Article{info:doi/10.2196/40104,
author="Ryan, Thomas Arthur
and Brenner, Anne Lisa
and Ulmer, S. Christi
and Mackintosh, Margaret-Anne
and Greene, J. Carolyn",
title="The Use of Evaluation Panels During the Development of a Digital Intervention for Veterans Based on Cognitive Behavioral Therapy for Insomnia: Qualitative Evaluation Study",
journal="JMIR Form Res",
year="2023",
month="Mar",
day="6",
volume="7",
pages="e40104",
keywords="cognitive behavioral therapy for insomnia",
keywords="CBT-I",
keywords="insomnia",
keywords="digital mental health intervention",
keywords="digital MH intervention",
keywords="internet-delivered",
keywords="veterans",
keywords="Path to Better Sleep",
abstract="Background: Individuals enrolling in the Veterans Health Administration frequently report symptoms consistent with insomnia disorder. Cognitive behavioral therapy for insomnia (CBT-I) is a gold standard treatment for insomnia disorder. While the Veterans Health Administration has successfully implemented a large dissemination effort to train providers in CBT-I, the limited number of trained CBT-I providers continues to restrict the number of individuals who can receive CBT-I. Digital mental health intervention adaptations of CBT-I have been found to have similar efficacy as traditional CBT-I. To help address the unmet need for insomnia disorder treatment, the VA commissioned the creation of a freely available, internet-delivered digital mental health intervention adaptation of CBT-I known as Path to Better Sleep (PTBS). Objective: We aimed to describe the use of evaluation panels composed of veterans and spouses of veterans during the development of PTBS. Specifically, we report on the methods used to conduct the panels, the feedback they provided on elements of the course relevant to user engagement, and how their feedback influenced the design and content of PTBS. Methods: A communications firm was contracted to recruit 3 veteran (n=27) and 2 spouse of veteran (n=18) panels and convene them for three 1-hour meetings. Members of the VA team identified key questions for the panels, and the communications firm prepared facilitator guides to elicit feedback on these key questions. The guides provided a script for facilitators to follow while convening the panels. The panels were telephonically conducted, with visual content displayed via remote presentation software. The communications firm prepared reports summarizing the panelists' feedback during each panel meeting. The qualitative feedback described in these reports served as the raw material for this study. Results: The panel members provided markedly consistent feedback on several elements of PTBS, including recommendations to emphasize the efficacy of CBT-I techniques; clarify and simplify written content as much as possible; and ensure that content is consistent with the lived experiences of veterans. Their feedback was congruent with previous studies on the factors influencing user engagement with digital mental health interventions. Panelist feedback influenced multiple course design decisions, including reducing the effort required to use the course's sleep diary function, making written content more concise, and selecting veteran testimonial videos that emphasized the benefits of treating chronic insomnia symptoms. Conclusions: The veteran and spouse evaluation panels provided useful feedback during the design of PTBS. This feedback was used to make concrete revisions and design decisions consistent with existing research on improving user engagement with digital mental health interventions. We believe that many of the key feedback messages provided by these evaluation panels could prove useful to other digital mental health intervention designers. ",
doi="10.2196/40104",
url="https://formative.jmir.org/2023/1/e40104",
url="http://www.ncbi.nlm.nih.gov/pubmed/36877553"
}


@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/10.2196/33964,
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/10.2196/39198,
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/10.2196/37002,
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/10.2196/30231,
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/10.2196/34409,
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/10.2196/31116,
author="Silang, A. Katherine
and Sohal, R. Pooja
and Bright, S. Katherine
and Leason, Jennifer
and Roos, Leslie
and Lebel, Catherine
and Giesbrecht, F. Gerald
and Tomfohr-Madsen, M. Lianne",
title="eHealth Interventions for Treatment and Prevention of Depression, Anxiety, and Insomnia During Pregnancy: Systematic Review and Meta-analysis",
journal="JMIR Ment Health",
year="2022",
month="Feb",
day="21",
volume="9",
number="2",
pages="e31116",
keywords="eHealth",
keywords="pregnancy",
keywords="depression",
keywords="anxiety",
keywords="insomnia",
keywords="mobile phone",
abstract="Background: Pregnancy is associated with an increased risk for depression, anxiety, and insomnia. eHealth interventions provide a promising and accessible treatment alternative to face-to-face interventions. Objective: The objective of this systematic review and meta-analysis is to determine the effectiveness of eHealth interventions in preventing and treating depression, anxiety, and insomnia during pregnancy. Secondary aims are to identify demographic and intervention moderators of effectiveness. Methods: A total of 5 databases (PsycINFO, Medline, CINAHL, Embase, and Cochrane) were searched from inception to May 2021. Terms related to eHealth, pregnancy, randomized controlled trials (RCTs), depression, anxiety, and insomnia were included. RCTs and pilot RCTs were included if they reported an eHealth intervention for the prevention or treatment of depression, anxiety, or insomnia in pregnant women. Study screening, data extractions, and quality assessment were conducted independently by 2 reviewers from an 8-member research team (KAS, PRS, Hangsel Sanguino, Roshni Sohail, Jasleen Kaur, Songyang (Mark) Jin, Makayla Freeman, and Beatrice Valmana). Random-effects meta-analyses of pooled effect sizes were conducted to determine the effect of eHealth interventions on prenatal mental health. Meta-regression analyses were conducted to identify potential moderators. Results: In total, 17 studies were included in this review that assessed changes in depression (11/17, 65\%), anxiety (10/17, 59\%), and insomnia (3/17, 18\%). Several studies included both depression and anxiety symptoms as outcomes (7/17, 41\%). The results indicated that during pregnancy, eHealth interventions showed small effect sizes for preventing and treating symptoms of anxiety and depression and a moderate effect size for treating symptoms of insomnia. With the exception of intervention type for the outcome of depressive symptoms, where mindfulness interventions outperformed other intervention types, no significant moderators were detected. Conclusions: eHealth interventions are an accessible and promising resource for treating symptoms of anxiety, depression, and insomnia during pregnancy. However, more research is necessary to identify ways to increase the efficacy of eHealth interventions for this population. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42020205954; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=205954 ",
doi="10.2196/31116",
url="https://mental.jmir.org/2022/2/e31116",
url="http://www.ncbi.nlm.nih.gov/pubmed/35188471"
}


@Article{info:doi/10.2196/32355,
author="Shakeri Hossein Abad, Zahra
and Butler, P. Gregory
and Thompson, Wendy
and Lee, Joon",
title="Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research",
journal="JMIR Public Health Surveill",
year="2022",
month="Feb",
day="14",
volume="8",
number="2",
pages="e32355",
keywords="digital public health surveillance",
keywords="social media analysis",
keywords="physical activity",
keywords="sedentary behavior",
keywords="sleep",
keywords="machine learning",
keywords="online health information",
keywords="infodemiology",
keywords="public health database",
abstract="Background: Advances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public's trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage. Objective: The main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata. Methods: We collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set. Results: LPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet. Conclusions: Publicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners. ",
doi="10.2196/32355",
url="https://publichealth.jmir.org/2022/2/e32355",
url="http://www.ncbi.nlm.nih.gov/pubmed/35156938"
}


@Article{info:doi/10.2196/27584,
author="W Beukes, Eldr{\'e}
and Andersson, Gerhard
and Fagelson, Marc
and Manchaiah, Vinaya",
title="Internet-Based Audiologist-Guided Cognitive Behavioral Therapy for Tinnitus: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2022",
month="Feb",
day="14",
volume="24",
number="2",
pages="e27584",
keywords="tinnitus",
keywords="cognitive behavioral therapy",
keywords="internet intervention",
keywords="web-based intervention",
keywords="randomized controlled trial",
keywords="telehealth",
keywords="teleaudiology",
keywords="eHealth",
abstract="Background: Tinnitus is a symptom that can be very distressing owing to hearing sounds not related to any external sound source. Managing tinnitus is notoriously difficult, and access to evidence-based care is limited. Cognitive behavioral therapy (CBT) is a tinnitus management strategy with the most evidence of effectiveness but is rarely offered to those distressed by tinnitus. The provision of internet-based CBT for tinnitus overcomes accessibility barriers; however, it is not currently readily available in the United States. Objective: The aim of this study is to investigate the efficacy of internet-based CBT compared with that of weekly monitoring for the management of tinnitus in reducing tinnitus distress; reducing tinnitus-related comorbidities, including tinnitus cognitions, insomnia, anxiety, and depression; and assessing the stability of the intervention effects 2 months after the intervention. Methods: A 2-arm randomized clinical trial comparing audiologist-guided internet-based CBT (n=79) to a weekly monitoring group (n=79) with a 2-month follow-up assessed the efficacy of internet-based CBT. Eligible participants included adults seeking help for tinnitus. Recruitment was conducted on the web using an open-access website. Participants were randomized via 1:1 allocation, but blinding was not possible. The study was undertaken by English or Spanish speakers on the web. The primary outcome was a change in tinnitus distress as measured using the Tinnitus Functional Index. Secondary outcome measures included anxiety, depression, insomnia, tinnitus cognition, hearing-related difficulties, and quality of life. Results: Internet-based CBT led to a greater reduction in tinnitus distress (mean 36.57, SD 22) compared with that in weekly monitoring (mean 46.31, SD 20.63; effect size: Cohen d=0.46, 95\% CI 0.14-0.77) using an intention-to-treat analysis. For the secondary outcomes, there was a greater reduction in negative tinnitus cognition and insomnia. The results remained stable over the 2-month follow-up period. No important adverse events were observed. Further, 16\% (10/158) of participants withdrew, with low overall compliance rates for questionnaire completion of 72.3\% (107/148) at T1, 61\% (91/148) at T2, and 42\% (62/148) at T3. Conclusions: This study is the first to evaluate and indicate the efficacy of audiologist-delivered internet-based CBT in reducing tinnitus distress in a US population. It was also the first study to offer internet-based CBT in Spanish to accommodate the large Hispanic population in the United States. The results have been encouraging, and further work is indicated in view of making such an intervention applicable to a wider population. Further work is required to improve compliance and attract more Spanish speakers. Trial Registration: ClinicalTrials.gov NCT04004260; https://clinicaltrials.gov/ct2/show/NCT04004260 ",
doi="10.2196/27584",
url="https://www.jmir.org/2022/2/e27584",
url="http://www.ncbi.nlm.nih.gov/pubmed/35156936"
}


@Article{info:doi/10.2196/28747,
author="Sato, Daisuke
and Sekizawa, Yoichi
and Sutoh, Chihiro
and Hirano, Yoshiyuki
and Okawa, Sho
and Hirose, Motohisa
and Takemura, Ryo
and Shimizu, Eiji",
title="Effectiveness of Unguided Internet-Based Cognitive Behavioral Therapy and the Three Good Things Exercise for Insomnia: 3-Arm Randomized Controlled Trial",
journal="J Med Internet Res",
year="2022",
month="Feb",
day="9",
volume="24",
number="2",
pages="e28747",
keywords="insomnia",
keywords="internet-based treatment",
keywords="cognitive behavioral therapy",
keywords="positive psychology",
keywords="randomized controlled trial",
keywords="mobile phone",
abstract="Background: The treatment of insomnia with sleep medication causes problems such as long-term use, dependence, and significant economic losses, including medical expenses. Evidence-based lifestyle guidance is required to improve insomnia symptoms not only in person but also in easy-to-use web-based formats. Objective: This study aims to clarify whether unguided internet-based cognitive behavioral therapy (ICBT) or the Three Good Things (TGT) exercise, both administered as self-help internet interventions without email support, could improve insomnia symptoms compared with a waiting list control (WLC) group. Methods: A 4-week program was implemented, and participants were randomly allocated to 1 of the 3 groups. The primary outcome measure was the Pittsburgh Sleep Questionnaire (PSQI) score at 4 weeks compared with baseline. Results: Of the 21,394 individuals invited to participate, 312 (1.46\%) met the eligibility criteria and were randomly assigned to 1 of the 3 groups. Of these 312 individuals, 270 (86.5\%; ICBT 79/270, 29.3\%; TGT 88/270, 32.6\%; and WLC 103/270, 38.1\%) completed a postintervention survey at 4 and 8 weeks. The adjusted mean changes of the primary outcome measure (PSQI) in the ICBT (?1.56, 95\% CI ?2.52 to ?0.59; P<.001) and TGT (?1.15, 95\% CI ?2.08 to ?0.23; P=.002) groups at 4 weeks from baseline showed a significant improvement compared with the WLC group. The adjusted mean changes in the secondary outcome measures of sleep onset latency, total sleep time, Athens Insomnia Scale score, and Patient Health Questionnaire-9 score at 4 weeks from baseline, as well as in the PSQI at 8 weeks from baseline, showed significant improvement for ICBT. Moreover, total sleep time, Athens Insomnia Scale, and Patient Health Questionnaire-9 scores at 4 weeks from baseline showed a significant improvement in the TGT group compared with the WLC group. Conclusions: A total of 4 weeks of unguided ICBT and TGT exercises improved insomnia. Trial Registration: University Hospital Medical Information Network Clinical Trial Registry UMIN000034927; https://center6.umin.ac.jp/cgi-open-bin/ctr\_e/ctr\_view.cgi?recptno=R000039814 ",
doi="10.2196/28747",
url="https://www.jmir.org/2022/2/e28747",
url="http://www.ncbi.nlm.nih.gov/pubmed/35138259"
}


@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/10.2196/29573,
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/10.2196/26524,
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/10.2196/25730,
author="Kuhathasan, Nirushi
and Minuzzi, Luciano
and MacKillop, James
and Frey, N. Benicio",
title="The Use of Cannabinoids for Insomnia in Daily Life: Naturalistic Study",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="27",
volume="23",
number="10",
pages="e25730",
keywords="medicinal cannabis",
keywords="insomnia",
keywords="symptom management",
keywords="linear mixed-effects",
abstract="Background: Insomnia is a prevalent condition that presents itself at both the symptom and diagnostic levels. Although insomnia is one of the main reasons individuals seek medicinal cannabis, little is known about the profile of cannabinoid use or the perceived benefit of the use of cannabinoids in daily life. Objective: We conducted a retrospective study of medicinal cannabis users to investigate the use profile and perceived efficacy of cannabinoids for the management of insomnia. Methods: Data were collected using the Strainprint app, which allows medicinal cannabis users to log conditions and symptoms, track cannabis use, and monitor symptom severity pre- and postcannabis use. Our analyses examined 991 medicinal cannabis users with insomnia across 24,189 tracked cannabis use sessions. Sessions were analyzed, and both descriptive statistics and linear mixed-effects modeling were completed to examine use patterns and perceived efficacy. Results: Overall, cannabinoids were perceived to be efficacious across all genders and ages, and no significant differences were found among product forms, ingestion methods, or gender groups. Although all strain categories were perceived as efficacious, predominant indica strains were found to reduce insomnia symptomology more than cannabidiol (CBD) strains (estimated mean difference 0.59, SE 0.11; 95\% CI 0.36-0.81; adjusted P<.001) and predominant sativa strains (estimated mean difference 0.74, SE 0.16; 95\% CI 0.43-1.06; adjusted P<.001). Indica hybrid strains also presented a greater reduction in insomnia symptomology than CBD strains (mean difference 0.52, SE 0.12; 95\% CI 0.29-0.74; adjusted P<.001) and predominant sativa strains (mean difference 0.67, SE 0.16; 95\% CI 0.34-1.00; adjusted P=.002). Conclusions: Medicinal cannabis users perceive a significant improvement in insomnia with cannabinoid use, and this study suggests a possible advantage with the use of predominant indica strains compared with predominant sativa strains and exclusively CBD in this population. This study emphasizes the need for randomized placebo-controlled trials assessing the efficacy and safety profile of cannabinoids for the treatment of insomnia. ",
doi="10.2196/25730",
url="https://www.jmir.org/2021/10/e25730",
url="http://www.ncbi.nlm.nih.gov/pubmed/34704957"
}


@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/10.2196/24208,
author="Smith, L. Jeremy
and Allen, W. Jason
and Haack, I. Carla
and Wehrmeyer, L. Kathryn
and Alden, G. Kayley
and Lund, B. Maha
and Mascaro, S. Jennifer",
title="Impact of App-Delivered Mindfulness Meditation on Functional Connectivity, Mental Health, and Sleep Disturbances Among Physician Assistant Students: Randomized, Wait-list Controlled Pilot Study",
journal="JMIR Form Res",
year="2021",
month="Oct",
day="19",
volume="5",
number="10",
pages="e24208",
keywords="mindfulness",
keywords="meditation",
keywords="resting state",
keywords="fMRI",
keywords="connectivity",
keywords="mobile phone",
abstract="Background: Health care provider and trainee burnout results in substantial national and institutional costs and profound social effects. Identifying effective solutions and interventions to cultivate resilience among health care trainees is critical. Although less is known about the mental health needs of physician assistants (PAs) or PA students, accumulating research indicates that they experience similarly alarming rates of burnout, depression, and emotional exhaustion. Mobile app--delivered mindfulness meditation may be an effective part of salubrious programming to bolster long-term resilience and health among PA students. Objective: This study aims to examine the impact of app-delivered mindfulness meditation on self-reported mental health symptoms among PA students. A secondary aim is to investigate changes in brain connectivity to identify neurobiological changes related to changes in mental health symptoms. Methods: We recruited PA students enrolled in their third semester of PA school and used a longitudinal, randomized, wait-list--controlled design. Participants randomized to the mindfulness group were provided 1-year subscriptions to the 10\% Happier app, a consumer-based meditation app, and asked to practice every day for 8 weeks. Before randomization and again after completion of the 8-week program, all participants completed resting-state functional magnetic resonance imaging as well as self-report assessments of burnout, depression, anxiety, and sleep impairment. App use was acquired as a measure of mindfulness practice time. Results: PA students randomized to the mindfulness group reported improvements in sleep impairment compared with those randomized to the wait-list control group ($\eta$p2=0.42; P=.01). Sleep impairment decreased significantly in the mindfulness group (19\% reduction; P=.006) but not in the control group (1\% reduction; P=.71). There were no other significant changes in mental health for those randomized to app-delivered mindfulness. Across all students, changes in sleep impairment were associated with increased resting-state functional connectivity between the medial prefrontal cortex (a component of the default mode network) and the superior temporal gyrus, as well as between areas important for working memory. Changes in connectivity predicted categorical conversion from impaired to nonimpaired sleep in the mindfulness group. Conclusions: This pilot study is the first to examine app-based mindfulness for PA students' mental health and investigate the impact of mindfulness on PA students' brain function. These findings suggest that app-delivered mindfulness may be an effective tool to improve sleep dysfunction and that it may be an important part of the programming necessary to reduce the epidemic of suffering among health profession trainees. ",
doi="10.2196/24208",
url="https://formative.jmir.org/2021/10/e24208",
url="http://www.ncbi.nlm.nih.gov/pubmed/34665153"
}


@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/10.2196/28731,
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/10.2196/27062,
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/10.2196/24666,
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/10.2196/26462,
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/10.2196/25609,
author="Buntrock, Claudia
and Lehr, Dirk
and Smit, Filip
and Horvath, Hanne
and Berking, Matthias
and Spiegelhalder, Kai
and Riper, Heleen
and Ebert, Daniel David",
title="Guided Internet-Based Cognitive Behavioral Therapy for Insomnia: Health-Economic Evaluation From the Societal and Public Health Care Perspective Alongside a Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="May",
day="24",
volume="23",
number="5",
pages="e25609",
keywords="insomnia",
keywords="internet-based cognitive behavioural therapy",
keywords="iCBT-I",
keywords="economic evaluation",
keywords="cost-effectiveness",
keywords="cost-utility",
keywords="cognitive behavioral therapy",
keywords="behavior",
keywords="sleep",
keywords="economics",
keywords="public health",
keywords="perspective",
abstract="Background: The evidence base for internet-based cognitive behavioral therapy for insomnia (iCBT-I) is firm; however, little is known about iCBT-I's health-economic effects. Objective: This study aimed to evaluate the cost-effectiveness and cost--utility of iCBT-I in reducing insomnia among schoolteachers. Methods: Schoolteachers (N=128) with clinically significant insomnia symptoms and work-related rumination were randomized to guided iCBT-I or a wait list control group, both with unrestricted access to treatment as usual. Health care use, patient and family expenditures, and productivity losses were self-assessed and used for costing from a societal and a public health care perspective. Costs were related to symptom-free status (score <8 on the insomnia severity index) and quality-adjusted life years (QALYs) gained. Sampling error was handled using nonparametric bootstrapping. Results: Statistically significant differences favoring the intervention group were found for both health outcomes (symptom-free status yes or no: $\beta$=.30; 95\% CI 0.16-0.43; QALYs: $\beta$=.019, 95\% CI 0.01-0.03). From a societal perspective, iCBT-I had a 94\% probability of dominating the wait list control for both health outcomes. From a public health care perspective, iCBT-I was more effective but also more expensive than the wait list control, resulting in an incremental cost-effectiveness ratio of {\texteuro}650 per symptom-free individual. In terms of QALYs, the incremental cost-effectiveness ratio was {\texteuro}11,285. At a willingness-to-pay threshold of {\texteuro}20,000 per QALY gained, the intervention's probability of being cost-effective was 89\%. Conclusions: Our trial indicates that iCBT could be considered as a good value-for-money intervention for insomnia. Trial Registration: German Clinical Trial Registry: DRKS00004700; https://tinyurl.com/2nnk57jm International Registered Report Identifier (IRRID): RR2-10.1186/1745-6215-14-169 ",
doi="10.2196/25609",
url="https://www.jmir.org/2021/5/e25609",
url="http://www.ncbi.nlm.nih.gov/pubmed/34028361"
}


@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/10.2196/21678,
author="Hungerbuehler, Ines
and Daley, Kate
and Cavanagh, Kate
and Garcia Claro, Helo{\'i}sa
and Kapps, Michael",
title="Chatbot-Based Assessment of Employees' Mental Health: Design Process and Pilot Implementation",
journal="JMIR Form Res",
year="2021",
month="Apr",
day="21",
volume="5",
number="4",
pages="e21678",
keywords="chatbot",
keywords="conversational agent",
keywords="online",
keywords="digital health",
keywords="mobile phone",
keywords="mental health",
keywords="workplace",
keywords="work stress",
keywords="survey",
keywords="response rate",
abstract="Background: Stress, burnout, and mental health problems such as depression and anxiety are common, and can significantly impact workplaces through absenteeism and reduced productivity. To address this issue, organizations must first understand the extent of the difficulties by mapping the mental health of their workforce. Online surveys are a cost-effective and scalable approach to achieve this but typically have low response rates, in part due to a lack of interactivity. Chatbots offer one potential solution, enhancing engagement through simulated natural human conversation and use of interactive features. Objective: The aim of this study was to explore if a text-based chatbot is a feasible approach to engage and motivate employees to complete a workplace mental health assessment. This paper describes the design process and results of a pilot implementation. Methods: A fully automated chatbot (``Viki'') was developed to evaluate employee risks of suffering from depression, anxiety, stress, insomnia, burnout, and work-related stress. Viki uses a conversation style and gamification features to enhance engagement. A cross-sectional analysis was performed to gain first insights of a pilot implementation within a small to medium--sized enterprise (120 employees). Results: The response rate was 64.2\% (77/120). In total, 98 employees started the assessment, 77 of whom (79\%) completed it. The majority of participants scored in the mild range for anxiety (20/40, 50\%) and depression (16/28, 57\%), in the moderate range for stress (10/22, 46\%), and at the subthreshold level for insomnia (14/20, 70\%) as defined by their questionnaire scores. Conclusions: A chatbot-based workplace mental health assessment seems to be a highly engaging and effective way to collect anonymized mental health data among employees with response rates comparable to those of face-to-face interviews. ",
doi="10.2196/21678",
url="https://formative.jmir.org/2021/4/e21678",
url="http://www.ncbi.nlm.nih.gov/pubmed/33881403"
}


@Article{info:doi/10.2196/26330,
author="Taeymans, Jan
and Luijckx, Eefje
and Rogan, Slavko
and Haas, Karin
and Baur, Heiner",
title="Physical Activity, Nutritional Habits, and Sleeping Behavior in Students and Employees of a Swiss University During the COVID-19 Lockdown Period: Questionnaire Survey Study",
journal="JMIR Public Health Surveill",
year="2021",
month="Apr",
day="13",
volume="7",
number="4",
pages="e26330",
keywords="COVID-19",
keywords="healthy lifestyle",
keywords="pandemics",
keywords="public health",
keywords="universities",
abstract="Background: The new coronavirus SARS-CoV-2 led to the COVID-19 pandemic starting in January 2020. The Swiss Federal Council prescribed a lockdown of nonessential businesses. Students and employees of higher education institutions had to install home offices and participate in online lectures. Objective: The aim of this survey study was to evaluate lifestyle habits, such as physical activity (PA), sitting time, nutritional habits (expressed as median modified Mediterranean Diet Score [mMDS]),  alcohol consumption habits,  and sleeping behavior during a 2-month period of confinement and social distancing due to the COVID-19 pandemic. Survey participants were students and employees of a Swiss university of applied sciences. Methods: All students and employees from Bern University of Applied Sciences, Department of Health Professions (ie, nursing, nutrition and dietetics, midwifery, and physiotherapy divisions) were invited to complete an anonymous online survey during the COVID-19 confinement period. Information on the lifestyle dimensions of PA, sitting time, nutritional and alcohol consumption habits, and sleep behavior was gathered using adaptations of validated questionnaires. Frequency analyses and nonparametric statistical methods were used for data analysis. Significance was set at 5\% $\alpha$ level of error. Results: Prevalence of non-health-enhancing PA was 37.1\%, with participants of the division of physiotherapy showing the lowest prevalence. Prevalence of long sitting time (>8 hours/day) was 36.1\%. The median mMDS was 9, where the maximal score was 15, with participants of the division of nutrition and dietetics being more adherent to a Mediterranean diet as compared to the other groups. Prevalence of nonadherence to the Swiss alcohol consumption recommendations was 8.3\%. Prevalence of low sleeping quality was 44.7\%, while the median sleeping duration was 8 hours, which is considered healthy for adult populations. Conclusions: In the group analysis, differences in PA, sitting time, and mMDS were observed between different divisions of health professions as well as between Bachelor of Science students,  Master of Science students, and employees. Therefore, public health messages regarding healthy lifestyle habits during home confinement should be more group specific. The results of this study may provide support for the implementation of group-specific health promotion interventions at universities in pandemic conditions. Trial Registration: ClinicalTrials.gov NCT04502108; https://www.clinicaltrials.gov/ct2/show/NCT04502108 ",
doi="10.2196/26330",
url="https://publichealth.jmir.org/2021/4/e26330",
url="http://www.ncbi.nlm.nih.gov/pubmed/33630747"
}


@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/10.2196/21757,
author="Lim, Julian
and Leow, Zaven
and Ong, Jason
and Pang, Ly-Shan
and Lim, Eric",
title="Effects of Web-Based Group Mindfulness Training on Stress and Sleep Quality in Singapore During the COVID-19 Pandemic: Retrospective Equivalence Analysis",
journal="JMIR Ment Health",
year="2021",
month="Mar",
day="15",
volume="8",
number="3",
pages="e21757",
keywords="mindfulness",
keywords="COVID-19",
keywords="videoconference",
keywords="perceived stress",
keywords="sleep quality",
keywords="intervention",
keywords="telehealth",
keywords="mental health",
keywords="psychology",
abstract="Background: The COVID-19 pandemic has negatively impacted psychological health. Mindfulness training, which helps individuals attend to the present moment with a nonjudgmental attitude, improves sleep and reduces stress during regular times. Mindfulness training may also be relevant to the mitigation of harmful health consequences during acute crises. However, certain restrictions may necessitate the web-based delivery of mindfulness training (ie, rather than in-person group training settings). Objective: The objective of our study was to examine the effects of mindfulness interventions during the COVID-19 pandemic and to evaluate the effectiveness of web-based interventions. Methods: Data from an ongoing study were used for this retrospective equivalence analysis. Recruited participants were enrollees from mindfulness courses at a local charity organization that promoted mental wellness. This study had no exclusion criteria. We created three groups; two groups received their training during the COVID-19 pandemic (in-person training group: n=36; videoconferencing group: n=38), and a second control group included participants who were trained before the pandemic (n=86). Our primary outcomes were self-reported stress and sleep quality. Baseline levels and changes in these variables due to mindfulness training were compared among the groups via an analysis of covariance test and two one-tailed t tests. Results: Baseline perceived stress (P=.50) and sleep quality (P=.22) did not differ significantly among the three groups. Mindfulness training significantly reduced stress in all three groups (P<.001), and this effect was statistically significant when comparing videoconferencing to in-person training (P=.002). Sleep quality improved significantly in the prepandemic training group (P<.001). However, sleep quality did not improve in the groups that received training during the pandemic. Participants reported that they required shorter times to initiate sleep following prepandemic mindfulness training (P<.001), but this was not true for those who received training during the pandemic. Course attendance was high and equivalent across the videoconferencing and comparison groups (P=.02), and participants in the videoconferencing group engaged in marginally more daily practice than the in-person training group. Conclusions: Web-based mindfulness training via videoconferencing may be a useful intervention for reducing stress during times when traditional, in-person training is not feasible. However, it may not be useful for improving sleep quality. ",
doi="10.2196/21757",
url="https://mental.jmir.org/2021/3/e21757",
url="http://www.ncbi.nlm.nih.gov/pubmed/33482627"
}


@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/10.2196/23936,
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/10.2196/24607,
author="Aji, Melissa
and Gordon, Christopher
and Stratton, Elizabeth
and Calvo, A. Rafael
and Bartlett, Delwyn
and Grunstein, Ronald
and Glozier, Nick",
title="Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="17",
volume="23",
number="2",
pages="e24607",
keywords="mobile applications",
keywords="sleep",
keywords="insomnia",
keywords="internet-based intervention",
keywords="mHealth",
keywords="mobile health",
keywords="systematic review",
abstract="Background: Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps. Objective: The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. Methods: A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL). Results: A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial. Conclusions: This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation. ",
doi="10.2196/24607",
url="http://www.jmir.org/2021/2/e24607/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33595441"
}


@Article{info:doi/10.2196/20319,
author="Varghese, Elsem Nirosha
and Santoro, Eugenio
and Lugo, Alessandra
and Madrid-Valero, J. Juan
and Ghislandi, Simone
and Torbica, Aleksandra
and Gallus, Silvano",
title="The Role of Technology and Social Media Use in Sleep-Onset Difficulties Among Italian Adolescents: Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Jan",
day="21",
volume="23",
number="1",
pages="e20319",
keywords="sleep-onset difficulties",
keywords="adolescents",
keywords="social media",
keywords="electronic device use",
abstract="Background: The use of technology and social media among adolescents is an increasingly prevalent phenomenon. However, there is a paucity of evidence on the relationship between frequency of use of electronic devices and social media and sleep-onset difficulties among the Italian population. Objective: The aim of this study is to investigate the association between the use of technology and social media, including Facebook and YouTube, and sleep-onset difficulties among adolescents from Lombardy, the most populous region in Italy. Methods: The relationship between use of technology and social media and sleep-onset difficulties was investigated. Data came from the 2013-2014 wave of the Health Behavior in School-aged Children survey, a school-based cross-sectional study conducted on 3172 adolescents aged 11 to 15 years in Northern Italy. Information was collected on difficulties in falling asleep over the last 6 months. We estimated the odds ratios (ORs) for sleep-onset difficulties and corresponding 95\% CIs using logistic regression models after adjustment for major potential confounders. Results: The percentage of adolescents with sleep-onset difficulties was 34.3\% (1081/3151) overall, 29.7\% (483/1625) in boys and 39.2\% (598/1526) in girls. It was 30.3\% (356/1176) in 11-year-olds, 36.2\% (389/1074) in 13-year-olds, and 37.3\% (336/901) in 15-year-olds. Sleep-onset difficulties were more frequent among adolescents with higher use of electronic devices, for general use (OR 1.50 for highest vs lowest tertile of use; 95\% CI 1.21-1.85), use for playing games (OR 1.35; 95\% CI 1.11-1.64), use of online social networks (OR 1.40 for always vs never or rarely; 95\% CI 1.09-1.81), and YouTube (OR 2.00; 95\% CI 1.50-2.66). Conclusions: This study adds novel information about the relationship between sleep-onset difficulties and technology and social media in a representative sample of school-aged children from a geographical location that has not been included in studies of this type previously. Exposure to screen-based devices and online social media is significantly associated with adolescent sleep-onset difficulties. Interventions to create a well-coordinated parent- and school-centered strategy, thereby increasing awareness on the unfavorable effect of evolving technologies on sleep among adolescents, are needed. ",
doi="10.2196/20319",
url="http://www.jmir.org/2021/1/e20319/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33475517"
}


@Article{info:doi/10.2196/24206,
author="Gupta, Bhawna
and Sharma, Vyom
and Kumar, Narinder
and Mahajan, Akanksha",
title="Anxiety and Sleep Disturbances Among Health Care Workers During the COVID-19 Pandemic in India: Cross-Sectional Online Survey",
journal="JMIR Public Health Surveill",
year="2020",
month="Dec",
day="22",
volume="6",
number="4",
pages="e24206",
keywords="occupational epidemiology",
keywords="anxiety",
keywords="GAD-7",
keywords="sleep quality",
keywords="health care worker",
keywords="pandemic",
keywords="COVID-19",
keywords="online survey",
keywords="sleep",
keywords="mental health, personal protective equipment",
abstract="Background: The COVID-19 pandemic caused by SARS-CoV-2 has become a serious concern among the global medical community and has resulted in an unprecedented psychological impact on health care workers, who were already working under stressful conditions. Objective: In this study, we aimed to evaluate and measure the effects of the COVID-19 pandemic on the anxiety levels and sleep quality among health care workers in India, as well as to determine how the unavailability of personal protective equipment affects their willingness to provide patient-related care. Methods: We conducted an online cross-sectional study using piloted, structured questionnaires with self-reported responses from 368 volunteer male and female health care workers in India. Study participants were identified through social networking platforms such as Facebook and WhatsApp. The survey evaluated the participants' degree of signs and symptoms of anxiety and sleep quality based on the 7-item Generalized Anxiety Disorder (GAD-7) scale and single-item Sleep Quality Scale, respectively. Information on the availability of personal protective equipment was collected based on responses to relevant survey questions. Results: The majority of health care workers (126/368, 34.2\%) were in the age group 45-60 years, and 52.2\% (192/368) were doctors. Severe anxiety (ie, GAD-7 score >10) was observed among 7.3\% (27/368) health care workers, whereas moderate, mild, and minimal anxiety was observed among 12.5\% (46/368), 29.3\% (108/368), and 50.8\% (187/368) health care workers, respectively. Moreover, 31.5\% (116/368) of the health care workers had poor-to-fair sleep quality (ie, scores <6). Univariate analysis showed female gender and inadequate availability of personal protective equipment was significantly associated with higher anxiety levels (P=.01 for both). Sleep disturbance was significantly associated with age <30 years (P=.04) and inadequate personal protective equipment (P<.001). Multivariable analysis showed that poorer quality of sleep was associated with higher anxiety levels (P<.001). Conclusions: The COVID-19 pandemic has potentially caused significant levels of anxiety and sleep disturbances among health care workers, particularly associated with the female gender, younger age group, and inadequate availability of personal protective equipment. These factors put health care workers at constant risk of contracting the infection themselves or transmitting it to their families. Early identification of at-risk health care workers and implementation of situation-tailored mitigation measures could help alleviate the risk of long-term, serious psychological sequelae as well as reduce current anxiety levels among health care workers. ",
doi="10.2196/24206",
url="https://publichealth.jmir.org/2020/4/e24206",
url="http://www.ncbi.nlm.nih.gov/pubmed/33284784"
}


@Article{info:doi/10.2196/25051,
author="Rogan, Slavko
and Luijckx, Eefje
and Taeymans, Jan
and Haas, Karin
and Baur, Heiner",
title="Physical Activity, Nutritional Habits, and Sleep Behavior Among Health Profession Students and Employees of a Swiss University During and After COVID-19 Confinement: Protocol for a Longitudinal Observational Study",
journal="JMIR Res Protoc",
year="2020",
month="Dec",
day="22",
volume="9",
number="12",
pages="e25051",
keywords="healthy lifestyle",
keywords="pandemic",
keywords="public health",
keywords="universities",
keywords="COVID-19",
keywords="SARS-CoV-2",
abstract="Background: SARS-CoV-2, a novel coronavirus strain, has resulted in the COVID-19 pandemic since early 2020. To contain the transmission of this virus, the Swiss Federal Council ordered a nationwide lockdown of all nonessential businesses. Accordingly, students and employees of institutions for higher education were informed to continue their academic programs through home-office settings and online lectures. Objective: This longitudinal survey aims to evaluate various lifestyle habits such as physical activity, nutritional habits, and sleep behavior among students and employees of a Swiss University of Applied Sciences during a 2-month period of confinement and social distancing due to the COVID-19 pandemic and 1 year thereafter. Methods: This paper describes a protocol for a retrospective and prospective observational cohort study. Students and employees of Bern University of Applied Sciences, Department of Health Professions, were invited to anonymously complete a web-based survey during the COVID-19 confinement period. This will be followed by a second survey, scheduled 1 year after the lockdown. Information on various lifestyle aspects, including physical activity, nutritional habits, and sleep behavior, will be collected using adaptations of existing validated questionnaires. Results: This longitudinal study started during the government-ordered confinement period in Switzerland in mid-April 2020 and will end in mid-2021. Conclusions: The findings of this survey will provide information about the impact of confinement during the COVID-19 crisis on the physical activity, nutritional habits, and sleep behavior of students and employees of a Swiss institute. Trial Registration: ClinicalTrials.gov NCT04502108; https://www.clinicaltrials.gov/ct2/show/NCT04502108 International Registered Report Identifier (IRRID): DERR1-10.2196/25051 ",
doi="10.2196/25051",
url="http://www.researchprotocols.org/2020/12/e25051/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33296868"
}


@Article{info:doi/10.2196/20529,
author="Zhao, Zhi Sheng
and Guo, Ningyuan
and Wang, Ping Man
and Fong, Tak Daniel Yee
and Lai, Kwan Agnes Yuen
and Chan, Siu-Chee Sophia
and Lam, Hing Tai
and Ho, Yin Daniel Sai",
title="The Association Between Electronic Device Use During Family Time and Family Well-Being: Population-Based Cross-Sectional Study",
journal="J Med Internet Res",
year="2020",
month="Oct",
day="14",
volume="22",
number="10",
pages="e20529",
keywords="eDevice",
keywords="smartphone",
keywords="mobile phone",
keywords="well-being",
keywords="family dinner",
keywords="family communication",
keywords="public health",
abstract="Background: Electronic devices (eDevices) may have positive or negative influences on family communication and well-being depending on how they are used. Objective: We examined eDevice use during family time and its association with the quality of family communication and well-being in Hong Kong Chinese adults. Methods: In 2017, a probability-based 2-stage random sampling landline telephone survey collected data on eDevice use in daily life and during family time (eg, family dinner) and the presence of rules banning eDevice use during family dinner. Family communication quality was rated from 0 to 10 with higher scores being favorable. Family well-being was calculated as a composite mean score of 3 items each using the same scale from 0 to 10. The associations of family communication quality and well-being with eDevice use in daily life and during family time were estimated using beta-coefficient ($\beta$) adjusting for sociodemographics. The mediating role of family communication quality in the association between eDevice use and family well-being was analyzed. Results: Of the 2064 respondents (mean age 56.4 [SD 19.2] years, 1269/2064 [61.48\%] female), 1579/2059 (76.69\%) used an eDevice daily for a mean of 3.6 hours (SD 0.1) and 257/686 (37.5\%) used it for 30+ minutes before sleep. As much as 794/2046 (38.81\%) often or sometimes used an eDevice during family time including dinner (311/2017, 15.42\%); 713/2012 (35.44\%) reported use of an eDevice by family members during dinner. Lower family communication quality was associated with hours of eDevice use before sleep (adjusted $\beta$=--.25; 95\% CI --0.44 to --0.05), and often use (vs never use) of eDevice during family dinner by oneself (adjusted $\beta$=--.51; 95\% CI --0.91 to --0.10) and family members (adjusted $\beta$=--.54; 95\% CI --0.79 to --0.29). Similarly, lower family well-being was associated with eDevice use before sleep (adjusted $\beta$=--.26; 95\% CI --0.42 to --0.09), and often use during family dinner by oneself (adjusted $\beta$=--.48; 95\% CI --0.83 to --0.12) and family members (adjusted $\beta$=--.50; 95\% CI --0.72 to --0.28). Total ban of eDevice use during family dinner was negatively associated with often use by oneself (adjusted odds ratio 0.49; 95\% CI 0.29 to 0.85) and family members (adjusted odds ratio 0.41; 95\% CI 0.28, 0.60) but not with family communication and well-being. Lower family communication quality substantially mediated the total effect of the association of eDevice use time before sleep (61.2\%) and often use at family dinner by oneself (87.0\%) and by family members (67.8\%) with family well-being. Conclusions: eDevice use before sleep and during family dinner was associated with lower family well-being, and the association was substantially mediated by family communication quality. Our results suggest that interventions on smart use of eDevice may improve family communication and well-being. ",
doi="10.2196/20529",
url="http://www.jmir.org/2020/10/e20529/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33052120"
}


@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/10.2196/22181,
author="Lin, Yu-Hsuan
and Chiang, Ting-Wei
and Lin, Yu-Lun",
title="Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="21",
volume="22",
number="9",
pages="e22181",
keywords="internet search",
keywords="Google Trends",
keywords="infodemiology",
keywords="infoveillance",
keywords="COVID-19",
keywords="insomnia",
keywords="mental health",
abstract="Background: Real-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic. Objective: This study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: ``insomnia,'' ``depression,'' and ``suicide.'' Methods: We examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95\% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19--related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations. Results: The countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19--related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries ($\rho$=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression ($\rho$=--0.12, P=.63) or suicide ($\rho$=--0.07, P=.79). Conclusions: Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic. ",
doi="10.2196/22181",
url="http://www.jmir.org/2020/9/e22181/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32924951"
}


@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/10.2196/17449,
author="Ash, I. Garrett
and Robledo, S. David
and Ishii, Momoko
and Pittman, Brian
and DeMartini, S. Kelly
and O'Malley, S. Stephanie
and Redeker, S. Nancy
and Fucito, M. Lisa",
title="Using Web-Based Social Media to Recruit Heavy-Drinking Young Adults for Sleep Intervention: Prospective Observational Study",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="11",
volume="22",
number="8",
pages="e17449",
keywords="substance abuse",
keywords="social media",
keywords="alcohol drinking",
keywords="sleep",
keywords="mobile phone",
abstract="Background: Novel alcohol prevention strategies are needed for heavy-drinking young adults. Sleep problems are common among young adults who drink heavily and are a risk factor for developing an alcohol use disorder (AUD). Young adults, interested in the connection between sleep and alcohol, are open to getting help with their sleep. Therefore, sleep interventions may offer an innovative solution. This study evaluates social media advertising for reaching young adults and recruiting them for a new alcohol prevention program focused on sleep. Objective: This study aims to evaluate the effectiveness and cost of using Facebook, Instagram, and Snapchat advertising to reach young adults who drink heavily for a sleep intervention; characterize responders' sleep, alcohol use, and related concerns and interests; and identify the most appealing advertising content. Methods: In study 1, advertisements targeting young adults with sleep concerns, heavy alcohol use, or interest in participating in a sleep program ran over 3 months. Advertisements directed volunteers to a brief web-based survey to determine initial sleep program eligibility and characterize the concerns or interests that attracted them to click the advertisement. In study 2, three advertisements ran simultaneously for 2 days to enable us to compare the effectiveness of specific advertising themes. Results: In study 1, advertisements generated 13,638 clicks, 909 surveys, and 27 enrolled volunteers in 3 months across the social media platforms. Fees averaged US \$0.27 per click, US \$3.99 per completed survey, US \$11.43 per volunteer meeting initial screening eligibility, and US \$106.59 per study enrollee. On average, those who completed the web-based survey were 21.1 (SD 2.3) years of age, and 69.4\% (631/909) were female. Most reported sleep concerns (725/909, 79.8\%) and an interest in the connection between sleep and alcohol use (547/909, 60.2\%), but few had drinking concerns (49/909, 5.4\%). About one-third (317/909, 34.9\%) were identified as being at risk for developing an AUD based on a validated alcohol screener. Among this subsample, 8.5\% (27/317) met the final criteria and were enrolled in the trial. Some volunteers also referred additional volunteers by word of mouth. In study 2, advertisements targeting sleep yielded a higher response rate than advertisements targeting alcohol use (0.91\% vs 0.56\% click rate, respectively; P<.001). Conclusions: Social media advertisements designed to target young adults with sleep concerns reached those who also drank alcohol heavily, despite few being concerned about their drinking. Moreover, advertisements focused on sleep were more effective than those focused on drinking. Compared with previous studies, cost-effectiveness was moderate for engagement (impressions to clicks), excellent for conversion (clicks to survey completion), and reasonable for enrollment. These data demonstrate the utility of social media advertising focused on sleep to reach young adults who drink heavily and recruit them for intervention. ",
doi="10.2196/17449",
url="https://www.jmir.org/2020/8/e17449",
url="http://www.ncbi.nlm.nih.gov/pubmed/32780027"
}


@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/10.2196/17096,
author="Cheng, Ya-Wen Vivian
and Huang, Chiu-Mieh
and Liao, Jung-Yu
and Hsu, Hsiao-Pei
and Wang, Shih-Wen
and Huang, Su-Fei
and Guo, Jong-Long",
title="Combination of 3-Dimensional Virtual Reality and Hands-On Aromatherapy in Improving Institutionalized Older Adults' Psychological Health: Quasi-Experimental Study",
journal="J Med Internet Res",
year="2020",
month="Jul",
day="23",
volume="22",
number="7",
pages="e17096",
keywords="three-dimensional",
keywords="virtual reality",
keywords="aromatherapy",
keywords="older adult",
keywords="happiness",
keywords="stress",
keywords="sleep quality",
keywords="meditation",
keywords="life satisfaction",
abstract="Background: In Taiwan, which has one of the most rapidly aging populations in the world, it is becoming increasingly critical to promote successful aging strategies that are effective, easily usable, and acceptable to institutionalized older adults. Although many practitioners and professionals have explored aromatherapy and identified its psychological benefits, the effectiveness of combining 3-dimensional (3D) virtual reality and hands-on aromatherapy remains unknown. Objective: A quasi-experimental trial was designed to evaluate the effectiveness of this combination in lowering perceived stress and promoting happiness, sleep quality, meditation experience, and life satisfaction among institutionalized older adults in Taiwan. Methods: A total of 60 institutionalized elderly participants either received the combined intervention or were in a control group. Weekly 2-hour sessions were implemented over 9 weeks. The outcome variables were happiness, perceived stress, sleep quality, meditation experience, and life satisfaction, which were assessed at baseline and after the intervention. Results: Generalized estimating equation (GEE) analyses indicated that the experimental group showed significant post-intervention improvements in terms of scores for happiness, perceived stress, sleep quality, meditation experience, and life satisfaction (n=48; all P<.001). Another GEE analysis showed that the significant improvements in the 5 outcome variables persisted in participants aged 80 years and older (n=35; all P<.001). Conclusions: This is the first trial to explore the effectiveness of a combination of 3D virtual reality and hands-on aromatherapy in improving older adults' psychological health. The results are promising for the promotion of psychological health in institutionalized older adults. Trial Registration: ClinicalTrials.gov NCT04324216; https://clinicaltrials.gov/ct2/show/NCT04324216. ",
doi="10.2196/17096",
url="http://www.jmir.org/2020/7/e17096/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32706660"
}


@Article{info:doi/10.2196/18095,
author="Xie, Jie Yao
and Cheung, SK Daphne
and Loke, Y. Alice
and Nogueira, L. Bernice
and Liu, M. Karry
and Leung, YM Angela
and Tsang, SM Alice
and Leong, SU Cindy
and Molassiotis, Alex",
title="Relationships Between the Usage of Televisions, Computers, and Mobile Phones and the Quality of Sleep in a Chinese Population: Community-Based Cross-Sectional Study",
journal="J Med Internet Res",
year="2020",
month="Jul",
day="7",
volume="22",
number="7",
pages="e18095",
keywords="electronic device",
keywords="screen-based",
keywords="sleep",
keywords="Chinese",
keywords="digital",
keywords="mobile phone",
abstract="Background: No study has comprehensively investigated the association between the usage of typical screen-based electronic media devices and sleep quality in a Chinese population with individuals in a wide range of ages. Objective: This study aimed to understand the characteristics of television (TV) viewing, computer usage, and mobile phone usage in a representative Chinese population in Macau and to examine their roles in predicting the variations in sleep quality. Methods: This cross-sectional study was an analysis of 1500 Macau residents aged 15 to 90 years based on a community-based health needs assessment study entitled, ``Healthy Living, Longer Lives.'' Data collection was conducted in 7 districts of Macau from 2017 to 2018 through face-to-face interviews. The durations of daily TV viewing, computer usage, and mobile phone usage were recorded in a self-administered questionnaire. The Chinese version of the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality. Results: The prevalence of TV, computer, and mobile phone usage was 78.4\% (1176/1500), 51.6\% (769/1490), and 85.5\% (1276/1492), respectively. The average daily hours of usage were 1.75 (1.62), 1.53 (2.26), and 2.85 (2.47) hours, respectively. Females spent more time watching TV (P=.03) and using mobile phones (P=.02) and less time on the computer (P=.04) as compared to males. Older adults were more likely to watch TV while young people spent more time using the computer and mobile phones (P for all trends<.001). The mean PSQI global score was 4.79 (2.80) among the participants. Females exhibited significantly higher PSQI scores than males (5.04 vs 4.49, respectively; P<.001). No linear association was observed between the PSQI score and the amount of time spent on the 3 electronic devices (P=.58 for PSQI-TV, P=.05 for PSQI-computer, and P=.52 for PSQI-mobile phone). Curve estimation showed significant quadratic curvilinear associations in PSQI-TV (P=.003) and PSQI-computer (P<.001) among all the participants and in PSQI-mobile phone among youths (age, 15-24 years; P=.04). After adjustment of the gender, age, body mass index, demographics, and lifestyle factors, more than 3 hours of TV viewing and 4 hours of computer usage or mobile phone usage was associated with 85\% (95\% CI 1.04-1.87; P=.008), 72\% (95\% CI 1.01-2.92; P=.045), and 53\% (95\% CI 1.06-2.22; P=.03) greater odds of having poor sleep quality (PSQI score>5), respectively. Conclusions: The mobile phone was the most popular screen-based electronic device used in the Macau population, especially among young people. ``J'' shape associations were observed between sleep quality and the duration of TV viewing, computer usage, and mobile phone usage, indicating that the extreme use of screen-based electronic devices predicted poorer sleep status, whereas moderate use would be acceptable. ",
doi="10.2196/18095",
url="https://www.jmir.org/2020/7/e18095",
url="http://www.ncbi.nlm.nih.gov/pubmed/32369439"
}


@Article{info:doi/10.2196/15403,
author="Lee, Jae Jung
and Wang, Ping Man
and Luk, Tsun Tzu
and Guo, Ningyuan
and Chan, Siu-Chee Sophia
and Lam, Hing Tai",
title="Associations of Electronic Device Use Before and After Sleep With Psychological Distress Among Chinese Adults in Hong Kong: Cross-Sectional Study",
journal="JMIR Ment Health",
year="2020",
month="Jun",
day="11",
volume="7",
number="6",
pages="e15403",
keywords="addictive behavior",
keywords="anxiety",
keywords="computers",
keywords="depression",
keywords="devices",
keywords="internet",
keywords="smartphone",
keywords="withdrawal symptoms",
abstract="Background: Hong Kong has a high rate of electronic device (e-device; computer, smartphone, and tablet) use. However, little is known about the associations of the duration of e-device use before and after sleep with psychological symptoms. Objective: This study aimed to investigate the associations of the duration of e-device use before and after sleep with psychological distress. Methods: A probability-based telephone survey was conducted on 3162 Hong Kong adults (54.6\% female; mean age 47.4 years, SD 18.3 years) in 2016. Multivariate linear and Poisson regressions were used to calculate adjusted regression coefficients (aBs) and prevalence ratios (aPRs) of anxiety and depressive symptoms (measured by Patient Health Questionnaire-4) for the duration from waking to the first e-device use (?61, 31-60, 6-30, and ?5 minutes) and the duration of e-device use before sleeping (?5, 6-30, 31-60, and ?61 minutes). Results: The first e-device use in ?5 (vs ?61) minutes after waking was associated with anxiety (aB 0.35, 95\% CI 0.24-0.46; aPR 1.74, 95\% CI 1.34-2.25) and depressive symptoms (aB 0.27, 95\% CI 0.18-0.37; aPR 1.84, 95\% CI 1.33-2.54). Using e-devices for ?61 (vs ?5) minutes before sleeping was also associated with anxiety (aB 0.17, 95\% CI 0.04-0.31; aPR 1.32, 95\% CI 1.01-1.73) and depressive symptoms (aB 0.17, 95\% CI 0.05-0.28; aPR 1.47, 95\% CI 1.07-2.02). E-device use both ?5 minutes after waking and for ?61 minutes before sleeping was strongly associated with anxiety (aB 0.68, 95\% CI 0.47-0.90; aPR 2.64, 95\% CI 1.90-3.67) and depressive symptoms (aB 0.55, 95\% CI 0.36-0.74; aPR 2.56, 95\% CI 1.69-3.88). Conclusions: E-device use immediately (?5 minutes) after waking and use for a long duration (?61 minutes) before sleeping were associated with anxiety and depressive symptoms among Chinese adults in Hong Kong. ",
doi="10.2196/15403",
url="http://mental.jmir.org/2020/6/e15403/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32525489"
}


@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/10.2196/15859,
author="Arem, Hannah
and Scott, Remle
and Greenberg, Daniel
and Kaltman, Rebecca
and Lieberman, Daniel
and Lewin, Daniel",
title="Assessing Breast Cancer Survivors' Perceptions of Using Voice-Activated Technology to Address Insomnia: Feasibility Study Featuring Focus Groups and In-Depth Interviews",
journal="JMIR Cancer",
year="2020",
month="May",
day="26",
volume="6",
number="1",
pages="e15859",
keywords="artificial intelligence",
keywords="breast neoplasms",
keywords="survivors",
keywords="insomnia",
keywords="cognitive behavioral therapy",
keywords="mobile phones",
abstract="Background: Breast cancer survivors (BCSs) are a growing population with a higher prevalence of insomnia than women of the same age without a history of cancer. Cognitive behavioral therapy for insomnia (CBT-I) has been shown to be effective in this population, but it is not widely available to those who need it. Objective: This study aimed to better understand BCSs' experiences with insomnia and to explore the feasibility and acceptability of delivering CBT-I using a virtual assistant (Amazon Alexa). Methods: We first conducted a formative phase with 2 focus groups and 3 in-depth interviews to understand BCSs' perceptions of insomnia as well as their interest in and comfort with using a virtual assistant to learn about CBT-I. We then developed a prototype incorporating participant preferences and CBT-I components and demonstrated it in group and individual settings to BCSs to evaluate acceptability, interest, perceived feasibility, educational potential, and usability of the prototype. We also collected open-ended feedback on the content and used frequencies to describe the quantitative data. Results: We recruited 11 BCSs with insomnia in the formative phase and 14 BCSs in the prototype demonstration. In formative work, anxiety, fear, and hot flashes were identified as causes of insomnia. After prototype demonstration, nearly 79\% (11/14) of participants reported an interest in and perceived feasibility of using the virtual assistant to record sleep patterns. Approximately two-thirds of the participants thought lifestyle modification (9/14, 64\%) and sleep restriction (9/14, 64\%) would be feasible and were interested in this feature of the program (10/14, 71\% and 9/14, 64\%, respectively). Relaxation exercises were rated as interesting and feasible using the virtual assistant by 71\% (10/14) of the participants. Usability was rated as better than average, and all women reported that they would recommend the program to friends and family. Conclusions: This virtual assistant prototype delivering CBT-I components by using a smart speaker was rated as feasible and acceptable, suggesting that this prototype should be fully developed and tested for efficacy in the BCS population. If efficacy is shown in this population, the prototype should also be adapted for other high-risk populations. ",
doi="10.2196/15859",
url="http://cancer.jmir.org/2020/1/e15859/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32348274"
}


@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/10.2196/16749,
author="Hu, Fang
and Li, Liuhuan
and Huang, Xiaoyu
and Yan, Xingyu
and Huang, Panpan",
title="Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis",
journal="JMIR Med Inform",
year="2020",
month="Apr",
day="16",
volume="8",
number="4",
pages="e16749",
keywords="insomnia",
keywords="core symptom",
keywords="symptom community",
keywords="symptom embedding representation",
keywords="spectral clustering algorithm",
abstract="Background: Recent research in machine-learning techniques has led to signi?cant progress in various research ?elds. In particular, knowledge discovery using this method has become a hot topic in traditional Chinese medicine. As the key clinical manifestations of patients, symptoms play a signi?cant role in clinical diagnosis and treatment, which evidently have their underlying traditional Chinese medicine mechanisms. Objective: We aimed to explore the core symptoms and potential regularity of symptoms for diagnosing insomnia to reveal the key symptoms, hidden relationships underlying the symptoms, and their corresponding syndromes. Methods: An insomnia dataset with 807 samples was extracted from real-world electronic medical records. After cleaning and selecting the theme data referring to the syndromes and symptoms, the symptom network analysis model was constructed using complex network theory. We used four evaluation metrics of node centrality to discover the core symptom nodes from multiple aspects. To explore the hidden relationships among symptoms, we trained each symptom node in the network to obtain the symptom embedding representation using the Skip-Gram model and node embedding theory. After acquiring the symptom vocabulary in a digital vector format, we calculated the similarities between any two symptom embeddings, and clustered these symptom embeddings into five communities using the spectral clustering algorithm. Results: The top five core symptoms of insomnia diagnosis, including difficulty falling asleep, easy to wake up at night, dysphoria and irascibility, forgetful, and spiritlessness and weakness, were identified using evaluation metrics of node centrality. The symptom embeddings with hidden relationships were constructed, which can be considered as the basic dataset for future insomnia research. The symptom network was divided into five communities, and these symptoms were accurately categorized into their corresponding syndromes. Conclusions: These results highlight that network and clustering analyses can objectively and effectively find the key symptoms and relationships among symptoms. Identification of the symptom distribution and symptom clusters of insomnia further provide valuable guidance for clinical diagnosis and treatment. ",
doi="10.2196/16749",
url="http://medinform.jmir.org/2020/4/e16749/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32297869"
}


@Article{info:doi/10.2196/15841,
author="Rajabi Majd, Nilofar
and Brostr{\"o}m, Anders
and Ulander, Martin
and Lin, Chung-Ying
and Griffiths, D. Mark
and Imani, Vida
and Ahorsu, Kwasi Daniel
and Ohayon, M. Maurice
and Pakpour, H. Amir",
title="Efficacy of a Theory-Based Cognitive Behavioral Technique App-Based Intervention for Patients With Insomnia: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="1",
volume="22",
number="4",
pages="e15841",
keywords="app-based intervention",
keywords="cognitive behavioral therapy, insomnia",
keywords="sleep hygiene",
keywords="theory of planned behavior",
abstract="Background: Sleep hygiene is important for maintaining good sleep and reducing insomnia. Objective: This study examined the long-term efficacy of a theory-based app (including cognitive behavioral therapy [CBT], theory of planned behavior [TPB], health action process approach [HAPA], and control theory [CT]) on sleep hygiene among insomnia patients. Methods: The study was a 2-arm single-blind parallel-group randomized controlled trial (RCT). Insomnia patients were randomly assigned to a treatment group that used an app for 6 weeks (ie, CBT for insomnia [CBT-I], n=156) or a control group that received only patient education (PE, n=156) through the app. Outcomes were assessed at baseline and 1 month, 3 months, and 6 months postintervention. Primary outcomes were sleep hygiene, insomnia, and sleep quality. Secondary outcomes included attitudes toward sleep hygiene behavior, perceived behavioral control, behavioral intention, action and coping planning, self-monitoring, behavioral automaticity, and anxiety and depression. Linear mixed models were used to evaluate the magnitude of changes in outcomes between the two groups and across time. Results: Sleep hygiene was improved in the CBT-I group compared with the PE group (P=.02 at 1 month, P=.04 at 3 months, and P=.02 at 6 months) as were sleep quality and severity of insomnia. Mediation analyses suggested that perceived behavioral control on sleep hygiene as specified by TPB along with self-regulatory processes from HAPA and CT mediated the effect of the intervention on outcomes. Conclusions: Health care providers might consider using a CBT-I app to improve sleep among insomnia patients. Trial Registration: ClinicalTrials.gov NCT03605732; https://clinicaltrials.gov/ct2/show/NCT03605732 ",
doi="10.2196/15841",
url="http://www.jmir.org/2020/4/e15841/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32234700"
}


@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/10.2196/14842,
author="Cliffe, Bethany
and Croker, Abigail
and Denne, Megan
and Smith, Jacqueline
and Stallard, Paul",
title="Digital Cognitive Behavioral Therapy for Insomnia for Adolescents With Mental Health Problems: Feasibility Open Trial",
journal="JMIR Ment Health",
year="2020",
month="Mar",
day="3",
volume="7",
number="3",
pages="e14842",
keywords="insomnia",
keywords="internet-based intervention",
keywords="cognitive therapy",
keywords="mental health",
abstract="Background: Insomnia in adolescents is common, persistent, and associated with poor mental health including anxiety and depression. Insomnia in adolescents attending child mental health services is seldom directly treated, and the effects of digital cognitive behavioral therapy (CBT) for insomnia (CBTi) on the mental health of adolescents with significant mental health problems are unknown. Objective: This open study aimed to assess the feasibility of adding supported Web-based CBT for insomnia to the usual care of young people aged 14 to 17 years attending specialist child and adolescent mental health services (CAMHS). Methods: A total of 39 adolescents with insomnia aged 14 to 17 years attending specialist CAMHS were assessed and offered digital CBTi. The digital intervention was Sleepio, an evidence-based, self-directed, fully automated CBTi that has proven effective in multiple randomized controlled trials with adults. Self-report assessments of sleep (Sleep Condition Indicator [SCI], Insomnia Severity Scale, and Web- or app-based sleep diaries), anxiety (Revised Child Anxiety and Depression Scale [RCADS]), and depression (Mood and Feelings Questionnaire [MFQ]) were completed at baseline and post intervention. Postuse interviews assessed satisfaction with digital CBTi. Results: Average baseline sleep efficiency was very poor (53\%), with participants spending an average of 9.6 hours in bed but only 5.1 hours asleep. All participants scored less than 17 on the SCI, with 92\% (36/39) participants scoring 15 or greater on the Insomnia Severity Scale, suggesting clinical insomnia. Of the 39 participants, 36 (92\%) scored 27 or greater on the MFQ for major depression and 20 (51\%) had clinically elevated symptoms of anxiety. The majority of participants (38/49, 78\%) were not having any treatment for their insomnia, with the remaining 25\% (12/49) receiving medication. Sleepio was acceptable, with 77\% (30/39) of the participants activating their account and 54\% (21/39) completing the program. Satisfaction was high, with 84\% (16/19) of the participants finding Sleepio helpful, 95\% (18/19) indicating that they would recommend it to a friend, and 37\% (7/19) expressing a definite preference for a digital intervention. Statistically significant pre-post improvements were found in weekly diaries of sleep efficiency (P=.005) and sleep quality (P=.001) and on measures of sleep (SCI: P=.001 and Insomnia Severity Index: P=.001), low mood (MFQ: P=.03), and anxiety (RCADS: P=.005). Conclusions: Our study has a number of methodological limitations, particularly the small sample size, absence of a comparison group and no follow-up assessment. Nonetheless, our findings are encouraging and suggest that digital CBTi for young people with mental health problems might offer an acceptable and an effective way to improve both sleep and mental health. International Registered Report Identifier (IRRID): RR2-10.2196/11324 ",
doi="10.2196/14842",
url="https://mental.jmir.org/2020/3/e14842",
url="http://www.ncbi.nlm.nih.gov/pubmed/32134720"
}


@Article{info:doi/10.2196/15750,
author="Chow, I. Philip
and Drago, Fabrizio
and Kennedy, M. Erin
and Cohn, F. Wendy",
title="A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women's Cancer: Pre-Post Pilot Study",
journal="JMIR Cancer",
year="2020",
month="Feb",
day="6",
volume="6",
number="1",
pages="e15750",
keywords="mobile apps",
keywords="mental health",
keywords="mHealth",
keywords="women",
keywords="cancer survivors",
abstract="Background: Psychological distress is a major issue among survivors of women's cancer who face numerous barriers to accessing in-person mental health treatments. Mobile phone app--based interventions are scalable and have the potential to increase access to mental health care among survivors of women's cancer worldwide. Objective: This study aimed to evaluate the acceptability and preliminary efficacy of a novel app-based intervention with phone coaching in a sample of survivors of women's cancer. Methods: In a single-group, pre-post, 6-week pilot study in the United States, 28 survivors of women's cancer used iCanThrive, a novel app intervention that teaches skills for coping with stress and enhancing well-being, with added phone coaching. The primary outcome was self-reported symptoms of depression (Center for Epidemiologic Studies Depression Scale). Emotional self-efficacy and sleep disruption were also assessed at baseline, 6-week postintervention, and 4 weeks after the intervention period. Feedback obtained at the end of the study focused on user experience of the intervention. Results: There were significant decreases in symptoms of depression and sleep disruption from baseline to postintervention. Sleep disruption remained significantly lower at 4-week postintervention compared with baseline. The iCanThrive app was launched a median of 20.5 times over the intervention period. The median length of use was 2.1 min. Of the individuals who initiated the intervention, 87\% (20/23) completed the 6-week intervention. Conclusions: This pilot study provides support for the acceptability and preliminary efficacy of the iCanThrive intervention. Future work should validate the intervention in a larger randomized controlled study. It is important to develop scalable interventions that meet the psychosocial needs of different cancer populations. The modular structure of the iCanThrive app and phone coaching could impact a large population of survivors of women's cancer. ",
doi="10.2196/15750",
url="http://cancer.jmir.org/2020/1/e15750/"
}


@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/10.2196/14647,
author="Alshehri, M. Mohammed
and Alenazi, M. Aqeel
and Hoover, C. Jeffrey
and Alothman, A. Shaima
and Phadnis, A. Milind
and Rucker, L. Jason
and Befort, A. Christie
and Miles, M. John
and Kluding, M. Patricia
and Siengsukon, F. Catherine",
title="Effect of Cognitive Behavioral Therapy for Insomnia on Insomnia Symptoms for Individuals With Type 2 Diabetes: Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2019",
month="Dec",
day="19",
volume="8",
number="12",
pages="e14647",
keywords="insomnia",
keywords="type 2 diabetes",
keywords="cognitive behavioral therapy",
keywords="sleep variability",
keywords="self-care",
keywords="fatigue",
abstract="Background: Insomnia symptoms are a common form of sleep difficulty among people with type 2 diabetes (T2D) affecting sleep quality and health outcomes. Several interventional approaches have been used to improve sleep outcomes in people with T2D. Nonpharmacological approaches, such as cognitive behavioral therapy for insomnia (CBT-I), show promising results regarding safety and sustainability of improvements, although CBT-I has not been examined in people with T2D. Promoting sleep for people with insomnia and T2D could improve insomnia severity and diabetes outcomes. Objective: The objective of this study is to establish a protocol for a pilot randomized controlled trial (RCT) to examine the effect of 6 sessions of CBT-I on insomnia severity (primary outcome), sleep variability, and other health-related outcomes in individuals with T2D and insomnia symptoms. Methods: This RCT will use random mixed block size randomization with stratification to assign 28 participants with T2D and insomnia symptoms to either a CBT-I group or a health education group. Outcomes including insomnia severity; sleep variability; diabetes self-care behavior (DSCB); glycemic control (A1c); glucose level; sleep quality; daytime sleepiness; and symptoms of depression, anxiety, and pain will be gathered before and after the 6-week intervention. Chi-square and independent t tests will be used to test for between-group differences at baseline. Independent t tests will be used to examine the effect of the CBT-I intervention on change score means for insomnia severity, sleep variability, DSCB, A1c, fatigue, sleep quality, daytime sleepiness, and severity of depression, anxiety, and pain. For all analyses, alpha level will be set at .05. Results: This study recruitment began in February 2019 and was completed in September 2019. Conclusions: The intervention, including 6 sessions of CBT-I, will provide insight about its effect in improving insomnia symptoms, sleep variability, fatigue, and diabetes-related health outcomes in people with T2D and those with insomnia symptoms when compared with control. Trial Registration: ClinicalTrials.gov NCT03713996; https://clinicaltrials.gov/ct2/show/NCT03713996 International Registered Report Identifier (IRRID): DERR1-10.2196/14647 ",
doi="10.2196/14647",
url="https://www.researchprotocols.org/2019/12/e14647",
url="http://www.ncbi.nlm.nih.gov/pubmed/31855189"
}


@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/10.2196/16273,
author="Haghayegh, Shahab
and Khoshnevis, Sepideh
and Smolensky, H. Michael
and Diller, R. Kenneth
and Castriotta, J. Richard",
title="Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2019",
month="Nov",
day="28",
volume="21",
number="11",
pages="e16273",
keywords="Fitbit",
keywords="polysomnography",
keywords="sleep tracker",
keywords="wearable",
keywords="actigraphy",
keywords="sleep diary",
keywords="sleep stages",
keywords="accuracy",
keywords="validation",
keywords="comparison of performance",
abstract="Background: Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. Objective: We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. Methods: In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. Results: The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8\%, P=.36) and sleep efficiency (SE; range from approximately 2\% to 15\%; effect size=-0.74, P<.001; heterogenicity: I2=24.0\%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0\%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0\%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0\%, P=.92), TST (P=.29; heterogenicity: I2=0\%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0\%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. Conclusions: Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG. ",
doi="10.2196/16273",
url="http://www.jmir.org/2019/11/e16273/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31778122"
}


@Article{info:doi/10.2196/13371,
author="Borchert, S. Jill
and Wang, Bo
and Ramzanali, Muzaina
and Stein, B. Amy
and Malaiyandi, M. Latha
and Dineley, E. Kirk",
title="Adverse Events Due to Insomnia Drugs Reported in a Regulatory Database and Online Patient Reviews: Comparative Study",
journal="J Med Internet Res",
year="2019",
month="Nov",
day="8",
volume="21",
number="11",
pages="e13371",
keywords="drug safety",
keywords="drug ineffective",
keywords="postmarketing",
keywords="pharmacovigilance",
keywords="internet",
keywords="pharmacoepidemiology",
keywords="adverse effect",
keywords="hypnotic",
keywords="insomnia",
keywords="patient-reported outcomes",
abstract="Background: Patient online drug reviews are a resource for other patients seeking information about the practical benefits and drawbacks of drug therapies. Patient reviews may also serve as a source of postmarketing safety data that are more user-friendly than regulatory databases. However, the reliability of online reviews has been questioned, because they do not undergo professional review and lack means of verification. Objective: We evaluated online reviews of hypnotic medications, because they are commonly used and their therapeutic efficacy is particularly amenable to patient self-evaluation. Our primary objective was to compare the types and frequencies of adverse events reported to the Food and Drug Administration Adverse Event Reporting System (FAERS) with analogous information in patient reviews on the consumer health website Drugs.com. The secondary objectives were to describe patient reports of efficacy and adverse events and assess the influence of medication cost, effectiveness, and adverse events on user ratings of hypnotic medications. Methods: Patient ratings and narratives were retrieved from 1407 reviews on Drugs.com between February 2007 and March 2018 for eszopiclone, ramelteon, suvorexant, zaleplon, and zolpidem. Reviews were coded to preferred terms in the Medical Dictionary for Regulatory Activities. These reviews were compared to 5916 cases in the FAERS database from January 2015 to September 2017. Results: Similar adverse events were reported to both Drugs.com and FAERS. Both resources identified a lack of efficacy as a common complaint for all five drugs. Both resources revealed that amnesia commonly occurs with eszopiclone, zaleplon, and zolpidem, while nightmares commonly occur with suvorexant. Compared to FAERS, online reviews of zolpidem reported a much higher frequency of amnesia and partial sleep activities. User ratings were highest for zolpidem and lowest for suvorexant. Statistical analyses showed that patient ratings are influenced by considerations of efficacy and adverse events, while drug cost is unimportant. Conclusions: For hypnotic medications, online patient reviews and FAERS emphasized similar adverse events. Online reviewers rated drugs based on perception of efficacy and adverse events. We conclude that online patient reviews of hypnotics are a valid source that can supplement traditional adverse event reporting systems. ",
doi="10.2196/13371",
url="http://www.jmir.org/2019/11/e13371/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31702558"
}


@Article{info:doi/10.2196/12500,
author="Perucho, Isabel
and Vijayakumar, M. Kamalakannan
and Talamas, N. Sean
and Chee, Wei-Liang Michael
and Perrett, I. David
and Liu, J. Jean C.",
title="A Web-Based Photo-Alteration Intervention to Promote Sleep: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2019",
month="Sep",
day="26",
volume="21",
number="9",
pages="e12500",
keywords="sleep",
keywords="public health",
keywords="physical appearance",
keywords="outward appearance",
abstract="Background: Receiving insufficient sleep has wide-ranging consequences for health and well-being. Although educational programs have been developed to promote sleep, these have had limited success in extending sleep duration. To address this gap, we developed a Web-based program emphasizing how physical appearances change with varying amounts of sleep. Objective: The aims of this study were to evaluate (1) whether participants can detect changes in appearances as a function of sleep and (2) whether this intervention can alter habitual sleep patterns. Methods: We conducted a 5-week, parallel-group, randomized controlled trial among 70 habitual short sleepers (healthy adults who reported having <7 hours of sleep routinely). Upon study enrollment, participants were randomly assigned (1:1) to receive either standard information or an appearance-based intervention. Both groups received educational materials about sleep, but those in the appearance group also viewed a website containing digitally edited photographs that showed how they would look with varying amounts of sleep. As the outcome variables, sleep duration was monitored objectively via actigraphy (at baseline and at postintervention weeks 1 and 4), and participants completed a measure of sleep hygiene (at baseline and at postintervention weeks 2, 4, and 5). For each outcome, we ran intention-to-treat analyses using linear mixed-effects models. Results: In total, 35 participants were assigned to each group. Validating the intervention, participants in the appearance group (1) were able to identify what they looked like at baseline and (2) judged that they would look more attractive with a longer sleep duration (t26=10.35, P<.001). In turn, this translated to changes in sleep hygiene. Whereas participants in the appearance group showed improvements following the intervention (F1,107.99=9.05, P=.003), those in the information group did not (F1,84.7=0.19, P=.66). Finally, there was no significant effect of group nor interaction of group and time on actigraphy-measured sleep duration (smallest P=.26). Conclusions: Our findings suggest that an appearance-based intervention, while not sufficient as a stand-alone, could have an adjunctive role in sleep promotion. Trial Registration: ClinicalTrials.gov NCT02491138; https://clinicaltrials.gov/ct2/show/study/NCT02491138. ",
doi="10.2196/12500",
url="https://www.jmir.org/2019/9/e12500",
url="http://www.ncbi.nlm.nih.gov/pubmed/31573913"
}


@Article{info:doi/10.2196/12408,
author="Reilly, D. Erin
and Robinson, A. Stephanie
and Petrakis, Ann Beth
and Kuhn, Eric
and Pigeon, R. Wilfred
and Wiener, Soylemez Renda
and McInnes, Keith D.
and Quigley, S. Karen",
title="Mobile App Use for Insomnia Self-Management: Pilot Findings on Sleep Outcomes in Veterans",
journal="Interact J Med Res",
year="2019",
month="Jul",
day="24",
volume="8",
number="3",
pages="e12408",
keywords="cognitive behavioral therapy",
keywords="mobile apps",
keywords="insomnia",
keywords="sleep apnea",
abstract="Background: Sleep disturbance is a major health concern among US veterans who have served since 2001 in a combat theater in Iraq or Afghanistan. We report subjective and objective sleep results from a pilot trial assessing self-management--guided use of a mobile app (CBT-i Coach, which is based on cognitive behavioral therapy for insomnia) as an intervention for insomnia in military veterans. Objective: The primary aim of this study was to evaluate changes in subjective and objective sleep outcomes from pre to postintervention. Methods: Subjective outcomes included the Insomnia Severity Index, the Pittsburgh Sleep Quality Inventory, and sleep-related functional status. A wearable sleep monitor (WatchPAT) measured objective sleep outcomes, including sleep efficiency, percent rapid eye movement (REM) during sleep, sleep time, and sleep apnea. A total of 38 participants were enrolled in the study, with 18 participants being withdrawn per the protocol because of moderate or severe sleep apnea and 9 others who dropped out or withdrew. Thus, 11 participants completed the full 6-week CBT-i Coach self-management intervention (ie, completers). Results: Completer results indicated significant changes in subjective sleep measures, including reduced reports of insomnia (Z=--2.68, P=.007) from pre (mean 16.63, SD 5.55) to postintervention (mean 12.82, SD 3.74), improved sleep quality (Z=--2.37, P=.02) from pre (mean 12.82, SD 4.60) to postintervention (mean 10.73, SD 3.32), and sleep-related functioning (Z=2.675, P=.007) from pre (mean 13.86, SD 3.69) to postintervention (mean 15.379, SD 2.94). Among the objective measures, unexpectedly, objective sleep time significantly decreased from pre to postintervention ($\chi$22=7.8, P=.02). There were no significant changes in percent REM sleep or sleep efficiency. Conclusions: These findings suggest that the CBT-i Coach app can improve subjective sleep and that incorporating objective sleep measures into future, larger clinical trials or clinical practice may yield important information, particularly by detecting previously undetected sleep apnea. Trial Registration: ClinicalTrials.gov NCT02392000;?http://clinicaltrials.gov/ct2/show/NCT02392000 ",
doi="10.2196/12408",
url="http://www.i-jmr.org/2019/3/e12408/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31342904"
}


@Article{info:doi/10.2196/14290,
author="Shimoga, V. Sandhya
and Erlyana, Erlyana
and Rebello, Vida",
title="Associations of Social Media Use With Physical Activity and Sleep Adequacy Among Adolescents: Cross-Sectional Survey",
journal="J Med Internet Res",
year="2019",
month="Jun",
day="18",
volume="21",
number="6",
pages="e14290",
keywords="adolescent",
keywords="social media",
keywords="exercise",
keywords="sleep",
abstract="Background: Adolescents' use of social media, which has increased considerably in the past decade, has both positive and negative influences on adolescents' health and health behaviors. As social media is the most prominent communication tool of choice for adolescents, it is important to understand the relationship between the frequency of social media use and health behaviors among this population. Objective: The objective of our study was to examine the associations between the frequency of social media use and physical activity and sleep adequacy among middle and high school students. Methods: We used data from the Monitoring the Future survey (2014 and 2015), a nationally representative, annual, cross-sectional survey of American 8th-, 10th-, and 12th-grade students (N=43,994). Health behaviors examined were frequency of vigorous physical activity and frequency of getting 7 hours of sleep (never/seldom, sometimes, and every day/nearly every day). We measured frequency of social media use using a Likert-like scale (never, a few times a year, 1-2 times a month, once a week, or every day). Multivariable generalized ordered logistic regressions examined the association of social media use with different levels of physical activity and sleep. We estimated marginal effects (MEs) for the main independent variable (social media use frequency) by holding all other variables at their observed values. Results: The study population comprised 51.13\% (21,276/42,067) female students, 37.48\% (17,160/43,994) from the South, and 80.07\% (34,953/43,994) from a metropolitan area, with 76.90\% (33,831/43,994) reporting using social media every day. Among physically active students, frequent social media use was associated with a higher likelihood of vigorous daily exercise (ME 50.1\%, 95\% CI 49.2\%-51.0\%). Among sedentary students, frequent social media use was associated with a lower likelihood of vigorous daily exercise (ME 15.8\%, 95\% CI 15.1\%-16.4\%). Moderately active students who used social media once or twice a month had the highest likelihood of reporting vigorous daily exercise (ME 42.0\%, 95\% CI 37.6\%-46.3\%). Among those who normally got adequate sleep, daily social media users were least likely to report adequate sleep (ME 41.3\%, 95\% CI 40.4\%-42.1\%). Among those who were usually sleep deprived, daily social media users were more likely to report adequate sleep (ME 18.3\%, 95\% CI 17.6\%-19.0\%). Conclusions: Regular social media use every day was associated with a reinforcement of health behaviors at both extremes of health behaviors, whereas a medium intensity of social media use was associated with the highest levels of physical activity and lowest sleep adequacy among those with moderate health behaviors. Hence, finding an optimal level of social media use that is beneficial to a variety of health behaviors would be most beneficial to adolescents who are in the middle of the health behavior spectrum. ",
doi="10.2196/14290",
url="http://www.jmir.org/2019/6/e14290/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31215512"
}


@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/10.2196/12455,
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/10.2196/12635,
author="Sato, Daisuke
and Sutoh, Chihiro
and Seki, Yoichi
and Nagai, Eiichi
and Shimizu, Eiji",
title="Treatment Preferences for Internet-Based Cognitive Behavioral Therapy for Insomnia in Japan: Online Survey",
journal="JMIR Form Res",
year="2019",
month="May",
day="15",
volume="3",
number="2",
pages="e12635",
keywords="patient preference",
keywords="insomnia",
keywords="internet-based cognitive behavioral therapy",
abstract="Background: The internet has the potential to increase individuals' access to cognitive behavioral therapy (CBT) for insomnia at low cost. However, treatment preferences regarding internet-based computerized CBT for insomnia have not been fully examined. Objective: The aim was to conduct an anonymous online survey to evaluate treatment preferences for insomnia among patients with insomnia and individuals without insomnia. Methods: We developed an online survey to recruit a total of 600 participants living in the Kanto district in Japan. There were three subgroups: 200 medicated individuals with insomnia, 200 unmedicated individuals with insomnia, and 200 individuals without insomnia. The survey asked questions about the severity of the respondent's insomnia (using the Athens Insomnia Scale), the frequency of sleep medication use and the level of satisfaction with sleep medication use, the respondent's knowledge of CBT, his or her preference for CBT for insomnia before drug therapy, preference for CBT versus drug therapy, and preference for internet-based CBT versus face-to-face CBT. Results: Of the 600 respondents, 47.7\% (286/600) indicated that they received CBT before drug therapy, and 57.2\% (343/600) preferred CBT for insomnia to drug therapy. In addition, 47.0\% (282/600) preferred internet-based CBT for insomnia to face-to-face CBT. Although the respondents with insomnia who were taking an insomnia medication had a relatively lower preference for internet-based CBT (40.5\%, 81/200), the respondents with insomnia who were not taking an insomnia medication had a relatively higher preference for internet-based CBT (55.5\%, 111/200). Conclusions: The results of our online survey suggest that approximately half of the people queried preferred CBT for insomnia to drug therapy, and half of the respondents preferred internet-based CBT for insomnia to face-to-face CBT. ",
doi="10.2196/12635",
url="http://formative.jmir.org/2019/2/e12635/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31094319"
}


@Article{info:doi/10.2196/12686,
author="Sato, Daisuke
and Yoshinaga, Naoki
and Nagai, Eiichi
and Nagai, Kazue
and Shimizu, Eiji",
title="Effectiveness of Internet-Delivered Computerized Cognitive Behavioral Therapy for Patients With Insomnia Who Remain Symptomatic Following Pharmacotherapy: Randomized Controlled Exploratory Trial",
journal="J Med Internet Res",
year="2019",
month="Apr",
day="11",
volume="21",
number="4",
pages="e12686",
keywords="insomnia",
keywords="cognitive behavioral therapy",
keywords="randomized controlled trial",
keywords="internet",
keywords="benzodiazepines",
keywords="residual symptoms",
abstract="Background: In reality, pharmacotherapy still remains the most common treatment for insomnia. Objective: This study aimed to examine the effectiveness of our internet-delivered computerized cognitive behavioral therapy (ICBT) program as an adjunct to usual care (UC) compared with UC alone in patients with insomnia who remain symptomatic following hypnotics. Methods: We recruited 23 patients with insomnia who remained symptomatic following pharmacologic treatment including benzodiazepines, and we conducted an exploratory randomized controlled trial. The primary outcome was the Pittsburgh Sleep Quality Index (PSQI) at week 6 of the treatment. Secondary outcomes were sleep onset latency, total sleep time, sleep efficiency, number of awakenings, refreshment and soundness of sleep, anxiety by Hospital Anxiety and Depression Scale, depression measured by the Center for Epidemiologic Studies Depression Scale, and quality of life (QOL) measured by the EuroQol-5D. All parameters were measured at weeks 0 (baseline), 6 (postintervention), and 12 (follow-up). Results: The adjusted mean reduction (?6.11) in PSQI at week 6 from baseline in the ICBT plus UC group was significantly (P<.001) larger than the adjusted mean reduction (0.40) in the UC alone group. Significant differences were also found in favor of ICBT plus UC for PSQI, sleep onset latency, sleep efficiency, number of awakenings, and depression at all assessment points. Refreshment, soundness of sleep, anxiety, and QOL improved by week 6 in ICBT plus UC compared with UC alone. There were no reports of adverse events in either group during the study. Conclusions: These results indicated that our 6-week ICBT program is an effective treatment adjunct to UC for improving insomnia and related symptoms even after unsuccessful pharmacotherapy. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry: UMIN000021509; https://upload.umin.ac.jp/cgi-open-bin/ctr\_e/ctr\_view.cgi?recptno=R000023545 (Archived by WebCite at http://www.webcitation. org/75tCmwnYt). ",
doi="10.2196/12686",
url="http://www.jmir.org/2019/4/e12686/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30973344"
}


@Article{info:doi/10.2196/mhealth.9929,
author="Holtz, Bree
and Vasold, Kerri
and Cotten, Shelia
and Mackert, Michael
and Zhang, Mi",
title="Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jan",
day="22",
volume="7",
number="1",
pages="e9929",
keywords="physicians",
keywords="primary care",
keywords="APRN",
keywords="nurse practitioners",
keywords="technology",
abstract="Background: The use of Web- or mobile phone--based apps for tracking health indicators has increased greatly. However, provider perceptions of consumer-grade devices have not been widely explored. Objective: The purpose of this study was to determine primary care physicians' and advanced practice registered nurses' perceptions of consumer-grade sensor devices and Web- or mobile phone--based apps that allow patients to track physical activity, diet, and sleep. Methods: We conducted a cross-sectional mailed survey with a random sample of 300 primary care physicians and 300 advanced practice registered nurses from Michigan, USA. Providers' use and recommendation of these types of technologies, and their perceptions of the benefits of and barriers to patients' use of the technologies for physical activity, diet, and sleep tracking were key outcomes assessed. Results: Most of the respondents (189/562, 33.6\% response rate) were advanced practice registered nurses (107/189, 56.6\%). Almost half of the sample (93/189, 49.2\%) owned or used behavioral tracking technologies. Providers found these technologies to be helpful in clinical encounters, trusted the data, perceived their patients to be interested in them, and did not have concerns over the privacy of the data. However, the providers did perceive patient barriers to using these technologies. Additionally, those who owned or used these technologies were up to 6.5 times more likely to recommend them to their patients. Conclusions: Our study demonstrated that many providers perceived benefits for their patients to use these technologies, including improved communication. Providers' concerns included their patients' access and the usability of these technologies. Providers who encountered data from these technologies during patient visits generally perceive this to be helpful. We additionally discuss the barriers perceived by the providers and offer suggestions and future research to realize the potential benefits to using these data in clinical encounters. ",
doi="10.2196/mhealth.9929",
url="https://mhealth.jmir.org/2018/1/e9929/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30668515"
}


@Article{info:doi/10.2196/11331,
author="Parsey, M. Carolyn
and Schmitter-Edgecombe, Maureen",
title="Using Actigraphy to Predict the Ecological Momentary Assessment of Mood, Fatigue, and Cognition in Older Adulthood: Mixed-Methods Study",
journal="JMIR Aging",
year="2019",
month="Jan",
day="18",
volume="2",
number="1",
pages="e11331",
keywords="actigraphy",
keywords="aging",
keywords="ecological momentary assessment",
keywords="mood",
keywords="sleep",
abstract="Background: Sleep quality has been associated with cognitive and mood outcomes in otherwise healthy older adults. However, most studies have evaluated sleep quality as aggregate and mean measures, rather than addressing the impact of previous night's sleep on next-day functioning. Objective: This study aims to evaluate the ability of previous night's sleep parameters on self-reported mood, cognition, and fatigue to understand short-term impacts of sleep quality on next-day functioning. Methods: In total, 73 cognitively healthy older adults (19 males, 54 females) completed 7 days of phone-based self-report questions, along with 24-hour actigraph data collection. We evaluated a model of previous night's sleep parameters as predictors of mood, fatigue, and perceived thinking abilities the following day. Results: Previous night's sleep predicted fatigue in the morning and midday, as well as sleepiness or drowsiness in the morning; however, sleep measures did not predict subjective report of mood or perceived thinking abilities the following day. Conclusions: This study suggests that objectively measured sleep quality from the previous night may not have a direct or substantial relationship with subjective reporting of cognition or mood the following day, despite frequent patient reports. Continued efforts to examine the relationship among cognition, sleep, and everyday functioning are encouraged. ",
doi="10.2196/11331",
url="http://aging.jmir.org/2019/1/e11331/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518282"
}


@Article{info:doi/10.2196/mhealth.9286,
author="R{\"o}nkk{\"o}, Kari",
title="An Activity Tracker and Its Accompanying App as a Motivator for Increased Exercise and Better Sleeping Habits for Youths in Need of Social Care: Field Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Dec",
day="21",
volume="6",
number="12",
pages="e193",
keywords="mHealth",
keywords="social work",
keywords="youths",
keywords="activity trackers",
keywords="mobile applications",
keywords="motivation",
keywords="self-care",
keywords="sleep hygiene",
keywords="goals",
abstract="Background: The number of mobile self-tracking devices connected to the Web has exploded in today's society. With these wearable activity trackers related to Web 2.0 apps and social media have come new ways of monitoring, measuring, representing, and sharing experiences of the human body. New opportunities related to health and new areas of implementation for professionals have appeared, and one identified area that can benefit from mobile health technologies is social work. Objective: There are still only a small number of papers reporting the results from studying wearable activity trackers and accompanying apps in the context of agency-based social work. This study aimed to contribute to the identified shortage by presenting results from a research project framed by the following overarching question: What effects will the studied youths in need of social care experience in relation to exercise and sleep as the result of using a wearable activity tracker and its accompanying app? Methods: A field study framed by action research was performed. The study concerned vulnerable youths living in a Swedish municipality's care and accommodation home that tried out an activity tracker and its accompanying app. Results: The results from the study confirm previously published research results reporting that instant graphical feedback, sharing information, and being part of a social community can have a positive impact on lifestyle changes. In addition, this study's main results are that (1) the most important factor for positive health-related lifestyle changes was the establishment of personal long-term goals and (2) professional social workers found the studied technology to function as a valuable counseling tool, opening up avenues for lifestyle talks that otherwise were hard to undertake. Conclusions: This study demonstrates how an activity tracker and its accompanying app can open up a topic for discussion regarding how vulnerable youths can achieve digital support for changing unhealthy lifestyle patterns, and it shows that the technology might be a valuable counseling tool for professionals in social work. ",
doi="10.2196/mhealth.9286",
url="https://mhealth.jmir.org/2018/12/e193/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30578186"
}


@Article{info:doi/10.2196/11193,
author="Tougas, E. Michelle
and Chambers, T. Christine
and Corkum, Penny
and Robillard, M. Julie
and Gruzd, Anatoliy
and Howard, Vivian
and Kampen, Andrea
and Boerner, E. Katelynn
and Hundert, S. Amos",
title="Social Media Content About Children's Pain and Sleep: Content and Network Analysis",
journal="JMIR Pediatr Parent",
year="2018",
month="Dec",
day="11",
volume="1",
number="2",
pages="e11193",
keywords="child health",
keywords="knowledge translation",
keywords="pain",
keywords="sleep",
keywords="social media",
abstract="Background: Social media is often used for health communication and can facilitate fast information exchange. Despite its increasing use, little is known about child health information sharing and engagement over social media. Objective: The primary objectives of this study are to systematically describe the content of social media posts about child pain and sleep and identify the level of research evidence in these posts. The secondary objective is to examine user engagement with information shared over social media. Methods: Twitter, Instagram, and Facebook were searched by members of the research team over a 2-week period using a comprehensive search strategy. Codes were used to categorize the content of posts to identify the frequency of content categories shared over social media platforms. Posts were evaluated by content experts to determine the frequency of posts consistent with existing research evidence. User engagement was analyzed using Netlytic, a social network analysis program, to examine visual networks illustrating the level of user engagement. Results: From the 2-week period, nearly 1500 pain-related and 3800 sleep-related posts were identified and analyzed. Twitter was used most often to share knowledge about child pain (639/1133, 56.40\% of posts), and personal experiences for child sleep (2255/3008, 75.00\% of posts). For both topics, Instagram posts shared personal experiences (53/68, 78\% pain; 413/478, 86.4\% sleep), Facebook group posts shared personal experiences (30/49, 61\% pain; 230/345, 66.7\% sleep) and Facebook pages shared knowledge (68/198, 34.3\% pain; 452/1026, 44.05\% sleep). Across platforms, research evidence was shared in 21.96\% (318/1448) of pain- and 9.16\% (445/4857) of sleep-related posts; 5.38\% (61/1133) of all pain posts and 2.82\% (85/3008) of all sleep posts shared information inconsistent with the evidence, while the rest were absent of evidence. User interactions were indirect, with mostly one-way, rather than reciprocal conversations. Conclusions: Social media is commonly used to discuss child health, yet the majority of posts do not contain research evidence, and user engagement is primarily one-way. These findings represent an opportunity to expand engagement through open conversations with credible sources. Research and health care communities can benefit from incorporating specific information about evidence within social media posts to improve communication with the public and empower users to distinguish evidence-based content better. Together, these findings have identified potential gaps in social media communication that may be informative targets to guide future strategies for improving the translation of child health evidence over social media. ",
doi="10.2196/11193",
url="http://pediatrics.jmir.org/2018/2/e11193/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518292"
}


@Article{info:doi/10.2196/10124,
author="Pulantara, Wayan I.
and Parmanto, Bambang
and Germain, Anne",
title="Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study",
journal="J Med Internet Res",
year="2018",
month="Dec",
day="07",
volume="20",
number="12",
pages="e10124",
keywords="just-in-time adaptive intervention",
keywords="insomnia",
keywords="sleep",
keywords="mHealth",
keywords="mobile health",
keywords="interactive Resilience Enhancing Sleep Tactics (iREST)",
keywords="behavioral therapy",
keywords="brief behavioral therapy for insomnia",
keywords="cognitive behavioral therapy for insomnia",
abstract="Background: Although evidence-based cognitive behavioral sleep treatments have been shown to be safe and effective, these treatments have limited scalability. Mobile health tools can address this scalability challenge. iREST, or interactive Resilience Enhancing Sleep Tactics, is a mobile health platform designed to provide a just-in-time adaptive intervention (JITAI) in the assessment, monitoring, and delivery of evidence-based sleep recommendations in a scalable and personalized manner. The platform includes a mobile phone--based patient app linked to a clinician portal. Objective: The first aim of the pilot study was to evaluate the effectiveness of JITAI using the iREST platform for delivering evidence-based sleep interventions in a sample of military service members and veterans. The second aim was to explore the potential effectiveness of this treatment delivery form relative to habitual in-person delivery. Methods: In this pilot study, military service members and veterans between the ages of 18 and 60 years who reported clinically significant service-related sleep disturbances were enrolled as participants. Participants were asked to use iREST for a period of 4 to 6 weeks during which time they completed a daily sleep/wake diary. Through the clinician portal, trained clinicians offered recommendations consistent with evidence-based behavioral sleep treatments on weeks 2 through 4. To explore potential effectiveness, self-report measures were used, including the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), and the PSQI Addendum for Posttraumatic Stress Disorder. Results: A total of 27 participants completed the posttreatment assessments. Between pre- and postintervention, clinically and statistically significant improvements in primary and secondary outcomes were detected (eg, a mean reduction on the ISI of 9.96, t26=9.99, P<.001). At posttreatment, 70\% (19/27) of participants met the criteria for treatment response and 59\% (16/27) achieved remission. Comparing these response and remission rates with previously published results for in-person trials showed no significant differences. Conclusion: Participants who received evidence-based recommendations from their assigned clinicians through the iREST platform showed clinically significant improvements in insomnia severity, overall sleep quality, and disruptive nocturnal disturbances. These findings are promising, and a larger noninferiority clinical trial is warranted. ",
doi="10.2196/10124",
url="https://www.jmir.org/2018/12/e10124/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30530452"
}


@Article{info:doi/10.2196/humanfactors.8905,
author="Pulantara, Wayan I.
and Parmanto, Bambang
and Germain, Anne",
title="Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study",
journal="JMIR Hum Factors",
year="2018",
month="May",
day="17",
volume="5",
number="2",
pages="e21",
keywords="Just-in-Time Adaptive Intervention",
keywords="JITAI",
keywords="mobile health",
keywords="mHealth",
keywords="sleep",
keywords="insomnia",
keywords="usability",
keywords="smartphone",
keywords="iREST",
abstract="Background: Healthy sleep is a fundamental component of physical and brain health. Insomnia, however, is a prevalent sleep disorder that compromises functioning, productivity, and health. Therefore, developing efficient treatment delivery methods for insomnia can have significant societal and personal health impacts. Cognitive behavioral therapy for insomnia (CBTI) is the recommended first-line treatment of insomnia but access is currently limited for patients, since treatment must occur in specialty sleep clinics, which suffer from an insufficient number of trained clinicians. Smartphone-based interventions offer a promising means for improving the delivery of CBTI. Furthermore, novel features such as real-time monitoring and assessment, personalization, dynamic adaptations of the intervention, and context awareness can enhance treatment personalization and effectiveness, and reduce associated costs. Ultimately, this ``Just in Time Adaptive Intervention'' for insomnia---an intervention approach that is acceptable to patients and clinicians, and is based on mobile health (mHealth) platform and tools---can significantly improve patient access and clinician delivery of evidence-based insomnia treatments. Objective: This study aims to develop and assess the usability of a Just in Time Adaptive Intervention application platform called iREST (``interactive Resilience Enhancing Sleep Tactics'') for use in behavioral insomnia interventions. iREST can be used by both patients and clinicians. Methods: The development of iREST was based on the Iterative and Incremental Development software development model. Requirement analysis was based on the case study's description, workflow and needs, clinician inputs, and a previously conducted BBTI military study/implementation of the Just in Time Adaptive Intervention architecture. To evaluate the usability of the iREST mHealth tool, a pilot usability study was conducted. Additionally, this study explores the feasibility of using an off-the-shelf wearable device to supplement the subjective assessment of patient sleep patterns. Results: The iREST app was developed from the mobile logical architecture of Just in Time Adaptive Intervention. It consists of a cross-platform smartphone app, a clinician portal, and secure 2-way communications platform between the app and the portal. The usability study comprised 19 Active Duty Service Members and Veterans between the ages of 18 and 60. Descriptive statistics based on in-app questionnaires indicate that on average, 12 (mean 12.23, SD 8.96) unique devices accessed the clinician portal per day for more than two years, while the app was rated as ``highly usable'', achieving a mean System Usability Score score of 85.74 (SD 12.37), which translates to an adjective rating of ``Excellent''. The participants also gave high scores on ``ease of use and learnability'' with an average score of 4.33 (SD 0.65) on a scale of 1 to 5. Conclusions: iREST provides a feasible platform for the implementation of Just in Time Adaptive Intervention in mHealth-based and remote intervention settings. The system was rated highly usable and its cross-platformness made it readily implemented within the heavily segregated smartphone market. The use of wearables to track sleep is promising; yet the accuracy of this technology needs further improvement. Ultimately, iREST demonstrates that mHealth-based Just in Time Adaptive Intervention is not only feasible, but also works effectively. ",
doi="10.2196/humanfactors.8905",
url="http://humanfactors.jmir.org/2018/2/e21/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29773529"
}


@Article{info:doi/10.2196/resprot.8348,
author="Corkum, V. Penny
and Reid, J. Graham
and Hall, A. Wendy
and Godbout, Roger
and Stremler, Robyn
and Weiss, K. Shelly
and Gruber, Reut
and Witmans, Manisha
and Chambers, T. Christine
and Begum, Ara Esmot
and Andreou, Pantelis
and Rigney, Gabrielle",
title="Evaluation of an Internet-Based Behavioral Intervention to Improve Psychosocial Health Outcomes in Children With Insomnia (Better Nights, Better Days): Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2018",
month="Mar",
day="26",
volume="7",
number="3",
pages="e76",
keywords="sleep",
keywords="insomnia",
keywords="children",
keywords="randomized controlled trial",
keywords="eHealth",
keywords="Internet",
keywords="treatment",
abstract="Background: Up to 25\% of 1- to 10-year-old children experience insomnia (ie, resisting bedtime, trouble falling asleep, night awakenings, and waking too early in the morning). Insomnia can be associated with excessive daytime sleepiness and negative effects on daytime functioning across multiple domains (eg, behavior, mood, attention, and learning). Despite robust evidence supporting the effectiveness of behavioral treatments for insomnia in children, very few children with insomnia receive these treatments, primarily due to a shortage of available treatment resources. Objective: The Better Nights, Better Days (BNBD) internet-based program provides a readily accessible electronic health (eHealth) intervention to support parents in providing evidence-based care for insomnia in typically developing children. The purpose of the randomized controlled trial (RCT) is to evaluate the effectiveness of BNBD in treating insomnia in children aged between 1 and 10 years. Methods: BNBD is a fully automated program, developed based on evidence-based interventions previously tested by the investigators, as well as on the extant literature on this topic. We describe the 2-arm RCT in which participants (500 primary caregivers of children with insomnia residing in Canada) are assigned to intervention or usual care. Results: The effects of this behavioral sleep eHealth intervention will be assessed at 4 and 8 months postrandomization. Assessment includes both sleep (actigraphy, sleep diary) and daytime functioning of the children and daytime functioning of their parents. Results will be reported using the standards set out in the Consolidated Standards of Reporting Trials statement. Conclusions: If the intervention is supported by the results of the RCT, we plan to commercialize this program so that it is sustainable and available at a low cost to all families with internet access. Trial Registration: ClinicalTrials.gov NCT02243501; https://clinicaltrials.gov/show/NCT02243501 (Archived by WebCite at http://www.webcitation.org/6x8Z5pBui) ",
doi="10.2196/resprot.8348",
url="http://www.researchprotocols.org/2018/3/e76/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29581089"
}


@Article{info:doi/10.2196/mental.8634,
author="Baron, Glazer Kelly
and Duffecy, Jennifer
and Reid, Kathryn
and Begale, Mark
and Caccamo, Lauren",
title="Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App",
journal="JMIR Ment Health",
year="2018",
month="Jan",
day="10",
volume="5",
number="1",
pages="e3",
keywords="sleep duration",
keywords="wearable",
keywords="obesity",
keywords="technology",
keywords="behavioral intervention",
abstract="Background: Despite the high prevalence of short sleep duration (29.2\% of adults sleep <6 hours on weekdays), there are no existing theory-based behavioral interventions to extend sleep duration. The popularity of wearable sleep trackers provides an opportunity to engage users in interventions. Objective: The objective of this study was to outline the theoretical foundation and iterative process of designing the ``Sleep Bunny,'' a technology-assisted sleep extension intervention including a mobile phone app, wearable sleep tracker, and brief telephone coaching. We conducted a two-step process in the development of this intervention, which was as follows: (1) user testing of the app and (2) a field trial that was completed by 2 participants with short sleep duration and a cardiovascular disease risk factor linked to short sleep duration (body mass index [BMI] >25). Methods: All participants had habitual sleep duration <6.5 hours verified by 7 days of actigraphy. A total of 6 individuals completed initial user testing in the development phase, and 2 participants completed field testing. Participants in the user testing and field testing responded to open-ended surveys about the design and utility of the app. Participants in the field testing completed the Epworth Sleepiness Scale and also wore an actigraph for a 1-week baseline period and during the 4-week intervention period. Results: The feedback suggests that users enjoyed the wearable sleep tracker and found the app visually pleasing, but they suggested improvements to the notification and reminder features of the app. The 2 participants who completed the field test demonstrated significant improvements in sleep duration and daytime sleepiness. Conclusions: Further testing is needed to determine effects of this intervention in populations at risk for the mental and physical consequences of sleep loss. ",
doi="10.2196/mental.8634",
url="http://mental.jmir.org/2018/1/e3/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29321122"
}


@Article{info:doi/10.2196/jmir.7930,
author="Fagherazzi, Guy
and El Fatouhi, Douae
and Bellicha, Alice
and El Gareh, Amin
and Affret, Aur{\'e}lie
and Dow, Courtney
and Delrieu, Lidia
and Vegreville, Matthieu
and Normand, Alexis
and Oppert, Jean-Michel
and Severi, Gianluca",
title="An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices",
journal="J Med Internet Res",
year="2017",
month="Oct",
day="23",
volume="19",
number="10",
pages="e363",
keywords="connected devices",
keywords="sleep",
keywords="Withings",
keywords="Nokia",
keywords="determinants",
keywords="Internet of Things",
keywords="epidemiology",
keywords="wearables",
keywords="lifestyle",
keywords="blood pressure",
keywords="steps",
keywords="heart rate",
keywords="weight",
abstract="Background: Sleep is a modifiable lifestyle factor that can be a target for efficient intervention studies to improve the quality of life and decrease the risk or burden of some chronic conditions. Knowing the profiles of individuals with poor sleep patterns is therefore a prerequisite. Wearable devices have recently opened new areas in medical research as potential efficient tools to measure lifestyle factors such as sleep quantity and quality. Objectives: The goal of our research is to identify the determinants of poor sleep based on data from a large population of users of connected devices. Methods: We analyzed data from 15,839 individuals (13,658 males and 2181 females) considered highly connected customers having purchased and used at least 3 connected devices from the consumer electronics company Withings (now Nokia). Total and deep sleep durations as well as the ratio of deep/total sleep as a proxy of sleep quality were analyzed in association with available data on age, sex, weight, heart rate, steps, and diastolic and systolic blood pressures. Results: With respect to the deep/total sleep duration ratio used as a proxy of sleep quality, we have observed that those at risk of having a poor ratio (?0.40) were more frequently males (odds ratio [OR]female vs male=0.45, 95\% CI 0.38-0.54), younger individuals (OR>60 years vs 18-30 years=0.47, 95\% CI 0.35-0.63), and those with elevated heart rate (OR>78 bpm vs ?61 bpm=1.18, 95\% CI 1.04-1.34) and high systolic blood pressure (OR>133 mm Hg vs ?116 mm Hg=1.22, 95\% CI 1.04-1.43). A direct association with weight was observed for total sleep duration exclusively. Conclusions: Wearables can provide useful information to target individuals at risk of poor sleep. Future alert or mobile phone notification systems based on poor sleep determinants measured with wearables could be tested in intervention studies to evaluate the benefits. ",
doi="10.2196/jmir.7930",
url="http://www.jmir.org/2017/10/e363/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29061551"
}


@Article{info:doi/10.2196/mhealth.7244,
author="Shin, Cheol Jong
and Kim, Julia
and Grigsby-Toussaint, Diana",
title="Mobile Phone Interventions for Sleep Disorders and Sleep Quality: Systematic Review",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Sep",
day="07",
volume="5",
number="9",
pages="e131",
keywords="mHealth",
keywords="apps",
keywords="mobile health",
keywords="sleep",
abstract="Background: Although mobile health technologies have been developed for interventions to improve sleep disorders and sleep quality, evidence of their effectiveness remains limited. Objective: A systematic literature review was performed to determine the effectiveness of mobile technology interventions for improving sleep disorders and sleep quality. Methods: Four electronic databases (EBSCOhost, PubMed/Medline, Scopus, and Web of Science) were searched for articles on mobile technology and sleep interventions published between January 1983 and December 2016. Studies were eligible for inclusion if they met the following criteria: (1) written in English, (2) adequate details on study design, (3) focus on sleep intervention research, (4) sleep index measurement outcome provided, and (5) publication in peer-reviewed journals. Results: An initial sample of 2679 English-language papers were retrieved from five electronic databases. After screening and review, 16 eligible studies were evaluated to examine the impact of mobile phone interventions on sleep disorders and sleep quality. These included one case study, three pre-post studies, and 12 randomized controlled trials. The studies were categorized as (1) conventional mobile phone support and (2) utilizing mobile phone apps. Based on the results of sleep outcome measurements, 88\% (14/16) studies showed that mobile phone interventions have the capability to attenuate sleep disorders and to enhance sleep quality, regardless of intervention type. In addition, mobile phone intervention methods (either alternatively or as an auxiliary) provide better sleep solutions in comparison with other recognized treatments (eg, cognitive behavioral therapy for insomnia). Conclusions: We found evidence to support the use of mobile phone interventions to address sleep disorders and to improve sleep quality. Our findings suggest that mobile phone technologies can be effective for future sleep intervention research. ",
doi="10.2196/mhealth.7244",
url="http://mhealth.jmir.org/2017/9/e131/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28882808"
}


@Article{info:doi/10.2196/mental.7614,
author="Werner-Seidler, Aliza
and O'Dea, Bridianne
and Shand, Fiona
and Johnston, Lara
and Frayne, Anna
and Fogarty, S. Andrea
and Christensen, Helen",
title="A Smartphone App for Adolescents With Sleep Disturbance: Development of the Sleep Ninja",
journal="JMIR Ment Health",
year="2017",
month="Jul",
day="28",
volume="4",
number="3",
pages="e28",
keywords="insomnia",
keywords="sleep",
keywords="adolescence",
keywords="depression",
keywords="cognitive behavioral therapy",
keywords="smartphone",
abstract="Background: Sleep disturbances are common in young people and have consequences for academic, social, emotional, and behavioral development. The most effective treatment is cognitive behavioral therapy for insomnia (CBT-I), with evidence suggesting that it is efficacious even when delivered digitally. Objective: There are no commercially available digitally delivered CBT-I programs for use by young people. The aim of this project was to develop a smartphone app that delivers CBT-I to young people to improve sleep. Methods: To inform the development of the app, young people (N=21) aged between 12 and 16 years attended one of the 3 focus groups (each with 4-10 participants). These focus groups were conducted at different stages of the development process such that the process could be iterative. Participants were asked the reasons why they might use an app to help them sleep, the kinds of features or functions that they would like to see in such an app, and any concerns they may have in using the app. Data were analyzed using a thematic analysis approach. Of the issues discussed by the participants, the researchers selected themes associated with content, functionality, and accessibility and user experience to examine, as these were most informative for the app design process. Results: In terms of content, young people were interested in receiving information about recommended sleep guidelines and personalized information for their age group. They reported that keeping a sleep diary was acceptable, but they should be able to complete it flexibly, in their own time. They reported mixed views about the use of the phone's accelerometer. Young people felt that the functionality of the app should include elements of game playing if they were to remain engaged with the app. Flexibility of use and personalized features were also desirable, and there were mixed views about the schedule of notifications and reminders. Participants reported that for the app to be accessible and usable, it should be from a trusted developer, have engaging aesthetics, have a layout that is easy to navigate, not rely on Internet coverage, and preferably be free. Participants felt that being able to conceal the purpose of the app from peers was an advantage and were willing to provide personal information to use the app if the purpose and use of that information was made clear. Overall, participants endorsed the use of the app for sleep problems among their age group and reported motivation to use it. Conclusions: The Sleep Ninja is a fully-automated app that delivers CBT-I to young people, incorporating the features and information that young people reported they would expect from this app. A pilot study testing the feasibility, acceptability, and efficacy of the Sleep Ninja is now underway. ",
doi="10.2196/mental.7614",
url="http://mental.jmir.org/2017/3/e28/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28754651"
}


@Article{info:doi/10.2196/mhealth.7938,
author="Ichikawa, Daisuke
and Kashiyama, Makiko
and Ueno, Taro",
title="Tamper-Resistant Mobile Health Using Blockchain Technology",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Jul",
day="26",
volume="5",
number="7",
pages="e111",
keywords="telemedicine",
keywords="electronic health records",
keywords="sleep",
keywords="cognitive therapy",
keywords="computer security",
abstract="Background: Digital health technologies, including telemedicine, mobile health (mHealth), and remote monitoring, are playing a greater role in medical practice. Safe and accurate management of medical information leads to the advancement of digital health, which in turn results in a number of beneficial effects. Furthermore, mHealth can help lower costs by facilitating the delivery of care and connecting people to their health care providers. Mobile apps help empower patients and health care providers to proactively address medical conditions through near real-time monitoring and treatment, regardless of the location of the patient or the health care provider. Additionally, mHealth data are stored in servers, and consequently, data management that prevents all forms of manipulation is crucial for both medical practice and clinical trials. Objective: The aim of this study was to develop and evaluate a tamper-resistant mHealth system using blockchain technology, which enables trusted and auditable computing using a decentralized network. Methods: We developed an mHealth system for cognitive behavioral therapy for insomnia using a smartphone app. The volunteer data collected with the app were stored in JavaScript Object Notation format and sent to the blockchain network. Thereafter, we evaluated the tamper resistance of the data against the inconsistencies caused by artificial faults. Results: Electronic medical records collected using smartphones were successfully sent to a private Hyperledger Fabric blockchain network. We verified the data update process under conditions where all the validating peers were running normally. The mHealth data were successfully updated under network faults. We further ensured that any electronic health record registered to the blockchain network was resistant to tampering and revision. The mHealth data update was compatible with tamper resistance in the blockchain network. Conclusions: Blockchain serves as a tamperproof system for mHealth. Combining mHealth with blockchain technology may provide a novel solution that enables both accessibility and data transparency without a third party such as a contract research organization. ",
doi="10.2196/mhealth.7938",
url="http://mhealth.jmir.org/2017/7/e111/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28747296"
}


@Article{info:doi/10.2196/jmir.6821,
author="Saeb, Sohrab
and Cybulski, R. Thaddeus
and Schueller, M. Stephen
and Kording, P. Konrad
and Mohr, C. David",
title="Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles",
journal="J Med Internet Res",
year="2017",
month="Apr",
day="18",
volume="19",
number="4",
pages="e118",
keywords="sleep monitoring",
keywords="mobile phones",
keywords="decision trees",
keywords="classification",
abstract="Background: Sleep is a critical aspect of people's well-being and as such assessing sleep is an important indicator of a person's health. Traditional methods of sleep assessment are either time- and resource-intensive or suffer from self-reporting biases. Recently, researchers have started to use mobile phones to passively assess sleep in individuals' daily lives. However, this work remains in its early stages, having only examined relatively small and homogeneous populations in carefully controlled contexts. Thus, it remains an open question as to how well mobile device-based sleep monitoring generalizes to larger populations in typical use cases. Objective: The aim of this study was to assess the ability of machine learning algorithms to detect the sleep start and end times for the main sleep period in a 24-h cycle using mobile devices in a diverse sample. Methods: We collected mobile phone sensor data as well as daily self-reported sleep start and end times from 208 individuals (171 females; 37 males), diverse in age (18?66 years; mean 39.3), education, and employment status, across the United States over 6 weeks. Sensor data consisted of geographic location, motion, light, sound, and in-phone activities. No specific instructions were given to the participants regarding phone placement. We used random forest classifiers to develop both personalized and global predictors of sleep state from the phone sensor data. Results: Using all available sensor features, the average accuracy of classifying whether a 10-min segment was reported as sleep was 88.8\%. This is somewhat better than using the time of day alone, which gives an average accuracy of 86.9\%. The accuracy of the model considerably varied across the participants, ranging from 65.1\% to 97.3\%. We found that low accuracy in some participants was due to two main factors: missing sensor data and misreports. After correcting for these, the average accuracy increased to 91.8\%, corresponding to an average median absolute deviation (MAD) of 38 min for sleep start time detection and 36 min for sleep end time. These numbers are close to the range reported by previous research in more controlled situations. Conclusions: We find that mobile phones provide adequate sleep monitoring in typical use cases, and that our methods generalize well to a broader population than has previously been studied. However, we also observe several types of data artifacts when collecting data in uncontrolled settings. Some of these can be resolved through corrections, but others likely impose a ceiling on the accuracy of sleep prediction for certain subjects. Future research will need to focus more on the understanding of people's behavior in their natural settings in order to develop sleep monitoring tools that work reliably in all cases for all people. ",
doi="10.2196/jmir.6821",
url="http://www.jmir.org/2017/4/e118/"
}


@Article{info:doi/10.2196/jmir.6524,
author="Horsch, HG Corine
and Lancee, Jaap
and Griffioen-Both, Fiemke
and Spruit, Sandor
and Fitrianie, Siska
and Neerincx, A. Mark
and Beun, Jan Robbert
and Brinkman, Willem-Paul",
title="Mobile Phone-Delivered Cognitive Behavioral Therapy for Insomnia: A Randomized Waitlist Controlled Trial",
journal="J Med Internet Res",
year="2017",
month="Apr",
day="11",
volume="19",
number="4",
pages="e70",
keywords="insomnia",
keywords="smartphone app",
keywords="cognitive behavioral therapy",
keywords="eHealth",
abstract="Background: This study is one of the first randomized controlled trials investigating cognitive behavioral therapy for insomnia (CBT-I) delivered by a fully automated mobile phone app. Such an app can potentially increase the accessibility of insomnia treatment for the 10\% of people who have insomnia. Objective: The objective of our study was to investigate the efficacy of CBT-I delivered via the Sleepcare mobile phone app, compared with a waitlist control group, in a randomized controlled trial. Methods: We recruited participants in the Netherlands with relatively mild insomnia disorder. After answering an online pretest questionnaire, they were randomly assigned to the app (n=74) or the waitlist condition (n=77). The app packaged a sleep diary, a relaxation exercise, sleep restriction exercise, and sleep hygiene and education. The app was fully automated and adjusted itself to a participant's progress. Program duration was 6 to 7 weeks, after which participants received posttest measurements and a 3-month follow-up. The participants in the waitlist condition received the app after they completed the posttest questionnaire. The measurements consisted of questionnaires and 7-day online diaries. The questionnaires measured insomnia severity, dysfunctional beliefs about sleep, and anxiety and depression symptoms. The diary measured sleep variables such as sleep efficiency. We performed multilevel analyses to study the interaction effects between time and condition. Results: The results showed significant interaction effects (P<.01) favoring the app condition on the primary outcome measures of insomnia severity (d=--0.66) and sleep efficiency (d=0.71). Overall, these improvements were also retained in a 3-month follow-up. Conclusions: This study demonstrated the efficacy of a fully automated mobile phone app in the treatment of relatively mild insomnia. The effects were in the range of what is found for Web-based treatment in general. This supports the applicability of such technical tools in the treatment of insomnia. Future work should examine the generalizability to a more diverse population. Furthermore, the separate components of such an app should be investigated. It remains to be seen how this app can best be integrated into the current health regimens. Trial Registration: Netherlands Trial Register: NTR5560; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5560 (Archived by WebCite at http://www.webcitation.org/6noLaUdJ4) ",
doi="10.2196/jmir.6524",
url="http://www.jmir.org/2017/4/e70/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28400355"
}


@Article{info:doi/10.2196/jmir.5639,
author="Chan, Charles
and West, Stacey
and Glozier, Nick",
title="Commencing and Persisting With a Web-Based Cognitive Behavioral Intervention for Insomnia: A Qualitative Study of Treatment Completers",
journal="J Med Internet Res",
year="2017",
month="Feb",
day="10",
volume="19",
number="2",
pages="e37",
keywords="adherence",
keywords="persistence",
keywords="eHealth",
keywords="online intervention",
keywords="Web-based intervention",
keywords="motivations",
keywords="barriers",
keywords="insomnia",
keywords="depression",
keywords="men",
abstract="Background: Computerized cognitive behavioral therapy for insomnia (CCBT-I) has a growing evidence base as a stand-alone intervention, but it is less clear what factors may limit its acceptability and feasibility when combined with clinical care. Objective: The purpose of this study was to explore barriers and facilitators to use of an adjunctive CCBT-I program among depressed patients in a psychiatric clinic by using both quantitative and qualitative approaches. Methods: We conducted the qualitative component of the study using face-to-face or telephone interviews with participants who had enrolled in a clinical trial of a CCBT-I program as an adjunctive treatment in a psychiatric clinical setting. In line with the grounded theory approach, we used a semistructured interview guide with new thematic questions being formulated during the transcription and data analysis, as well as being added to the interview schedule. A range of open and closed questions addressing user experience were asked of all study participants who completed the 12-week trial in an online survey. Results: Three themes emerged from the interviews and open questions, consistent with nonadjunctive CCBT-I implementation. Identification with the adjunctive intervention's target symptom of insomnia and the clinical setting were seen as key reasons to engage initially. Persistence was related to factors to do with the program, its structure, and its content, rather than any nonclinical factors. The survey results showed that only the key active behavioral intervention, sleep restriction, was rated as a major problem by more than 15\% of the sample. In this clinical setting, the support of the clinician in completing the unsupported program was highlighted, as was the need for the program and clinical treatment to be coordinated. Conclusions: The use of a normally unsupported CCBT-I program as an adjunctive treatment can be aided by the clinician's approach. A key behavioral component of the intervention, specific to insomnia treatment, was identified as a major problem for persistence. As such, clinicians need to be aware of when such components are delivered in the program and coordinate their care accordingly, if the use of the program is to be optimized. ClinicalTrial: Australian and New Zealand Clinical Trials Registry ACTRN12612000985886; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=362875\&isReview=true (Archived by WebCite at http://www.webcitation.org/6njjhl42X) ",
doi="10.2196/jmir.5639",
url="http://www.jmir.org/2017/2/e37/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28188124"
}


@Article{info:doi/10.2196/jmir.5654,
author="Coulson, S. Neil
and Smedley, Richard
and Bostock, Sophie
and Kyle, D. Simon
and Gollancz, Rosie
and Luik, I. Annemarie
and Hames, Peter
and Espie, A. Colin",
title="The Pros and Cons of Getting Engaged in an Online Social Community Embedded Within Digital Cognitive Behavioral Therapy for Insomnia: Survey Among Users",
journal="J Med Internet Res",
year="2016",
month="Apr",
day="25",
volume="18",
number="4",
pages="e88",
keywords="engagement",
keywords="sleep",
keywords="online community",
keywords="discussion forum",
keywords="insomnia",
keywords="cognitive behavioral therapy",
abstract="Background: Sleepio is a proven digital sleep improvement program based on cognitive behavioral therapy techniques. Users have the option to join an online community that includes weekly expert discussions, peer-to-peer discussion forums, and personal message walls. Objective: The aim of this study was to conduct an online survey to (1) explore the reasons for deciding to engage with the Sleepio online community, (2) explore the potential benefits arising from engagement with the online community, and (3) identify and describe any problematic issues related to use of the online community. Methods: We developed an online survey and posted an invitation to the community discussion forum inviting users to participate. In addition, we sent an email invitation to 970 individuals who had previously or were currently working through the Sleepio program to participate in this study. Results: In total, 100 respondents (70/100, 70\% female; mean age 51 years, range 26--82 years) completed the online survey. Most respondents had started Sleepio with chronic sleep problems (59/100, 59\% up to 10 years; 35/100, 35\% >10 years) and had actively engaged with the online community (85/100, 85\%) had made a discussion or wall post). At the time of the survey, respondents had used Sleepio for a median of 12 weeks (range from 3 weeks to 2 years). We analyzed responses to the open-ended questions using thematic analysis. This analysis revealed 5 initial drivers for engagement: (1) the desire to connect with people facing similar issues, (2) seeking personalized advice, (3) curiosity, (4) being invited by other members, and (5) wanting to use all available sleep improvement tools. Advantages of engagement included access to continuous support, a reduced sense of isolation, being part of a nonjudgmental community, personalized advice, positive comparisons with others, encouragement to keep going, and altruism. We found 5 potential disadvantages: design and navigation issues, uncertain quality of user-generated content, negative comparisons with others, excessive time commitments, and data privacy concerns. Participants related their community experiences to engagement with the Sleepio program, with many stating it had supported their efforts to improve their sleep, as well as helping with adherence and commitment to the program. Despite some concerns, members regarded the Sleepio community as a valuable resource. Conclusions: Online communities may be a useful means through which to support long-term engagement with Web-based therapy for insomnia. ",
doi="10.2196/jmir.5654",
url="http://www.jmir.org/2016/4/e88/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27113540"
}


@Article{info:doi/10.2196/jmir.4115,
author="Horsch, Corine
and Lancee, Jaap
and Beun, Jan Robbert
and Neerincx, A. Mark
and Brinkman, Willem-Paul",
title="Adherence to Technology-Mediated Insomnia Treatment: A Meta-Analysis, Interviews, and Focus Groups",
journal="J Med Internet Res",
year="2015",
month="Sep",
day="04",
volume="17",
number="9",
pages="e214",
keywords="sleep initiation and maintenance disorders",
keywords="patient compliance",
keywords="meta-analysis",
keywords="interview",
keywords="focus groups",
keywords="mobile apps",
keywords="user-computer interface",
abstract="Background: Several technologies have been proposed to support the reduction of insomnia complaints. A user-centered assessment of these technologies could provide insight into underlying factors related to treatment adherence. Objective: Gaining insight into adherence to technology-mediated insomnia treatment as a solid base for improving those adherence rates by applying adherence-enhancing strategies. Methods: Adherence to technology-mediated sleep products was studied in three ways. First, a meta-analysis was performed to investigate adherence rates in technology-mediated insomnia therapy. Several databases were queried for technology-mediated insomnia treatments. After inclusion and exclusion steps, data from 18 studies were retrieved and aggregated to find an average adherence rate. Next, 15 semistructured interviews about sleep-support technologies were conducted to investigate perceived adherence. Lastly, several scenarios were written about the usage of a virtual sleep coach that could support adherence rates. The scenarios were discussed in six different focus groups consisting of potential users (n=15), sleep experts (n=7), and coaches (n=9). Results: From the meta-analysis, average treatment adherence appeared to be approximately 52\% (95\% CI 43\%-61\%) for technology-mediated insomnia treatments. This means that, on average, half of the treatment exercises were not executed, suggesting there is a substantial need for adherence and room for improvement in this area. However, the users in the interviews believed they adhered quite well to their sleep products. Users mentioned relying on personal commitment (ie, willpower) for therapy adherence. Participants of the focus groups reconfirmed their belief in the effectiveness of personal commitment, which they regarded as more effective than adherence-enhancing strategies. Conclusions: Although adherence rates for insomnia interventions indicate extensive room for improvement, users might not consider adherence to be a problem; they believe willpower to be an effective adherence strategy. A virtual coach should be able to cope with this ``adherence bias'' and persuade users to accept adherence-enhancing strategies, such as reminders, compliments, and community building. ",
doi="10.2196/jmir.4115",
url="http://www.jmir.org/2015/9/e214/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26341671"
}


@Article{info:doi/10.2196/resprot.3852,
author="Babson, A. Kimberly
and Ramo, E. Danielle
and Baldini, Lisa
and Vandrey, Ryan
and Bonn-Miller, O. Marcel",
title="Mobile App-Delivered Cognitive Behavioral Therapy for Insomnia: Feasibility and Initial Efficacy Among Veterans With Cannabis Use Disorders",
journal="JMIR Res Protoc",
year="2015",
month="Jul",
day="17",
volume="4",
number="3",
pages="e87",
keywords="cannabis",
keywords="marijuana",
keywords="sleep",
keywords="CBT-I",
keywords="intervention",
abstract="Background: Cannabis is the most frequently used illicit substance in the United States resulting in high rates of cannabis use disorders. Current treatments for cannabis use are often met with high rates of lapse/relapse, tied to (1) behavioral health factors that impact cannabis use such as poor sleep, and (2) access, stigma, supply, and cost of receiving a substance use intervention. Objective: This pilot study examined the feasibility, usability, and changes in cannabis use and sleep difficulties following mobile phone--delivered Cognitive Behavioral Therapy for Insomnia (CBT-I) in the context of a cannabis cessation attempt. Methods: Four male veterans with DSM-5 cannabis use disorder and sleep problems were randomized to receive a 2-week intervention: CBT-I Coach mobile app (n=2) or a placebo control (mood-tracking app) (n=2). Cannabis and sleep measures were assessed pre- and post-treatment. Participants also reported use and helpfulness of each app. Changes in sleep and cannabis use were evaluated for each participant individually. Results: Both participants receiving CBT-I used the app daily over 2 weeks and found the app user-friendly, helpful, and would use it in the future. In addition, they reported decreased cannabis use and improved sleep efficiency; one also reported increased sleep quality. In contrast, one participant in the control group dropped out of the study, and the other used the app minimally and reported increased sleep quality but also increased cannabis use. The mood app was rated as not helpful, and there was low likelihood of future participation. Conclusions: This pilot study examined the feasibility and initial patient acceptance of mobile phone delivery of CBT-I for cannabis dependence. Positive ratings of the app and preliminary reports of reductions in cannabis use and improvements in sleep are both encouraging and support additional evaluation of this intervention. ",
doi="10.2196/resprot.3852",
url="http://www.researchprotocols.org/2015/3/e87/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26187404"
}


@Article{info:doi/10.2196/jmir.4476,
author="McIver, J. David
and Hawkins, B. Jared
and Chunara, Rumi
and Chatterjee, K. Arnaub
and Bhandari, Aman
and Fitzgerald, P. Timothy
and Jain, H. Sachin
and Brownstein, S. John",
title="Characterizing Sleep Issues Using Twitter",
journal="J Med Internet Res",
year="2015",
month="Jun",
day="08",
volume="17",
number="6",
pages="e140",
keywords="sleep issues",
keywords="social media",
keywords="insomnia",
keywords="novel methods",
keywords="sentiment",
keywords="depression",
abstract="Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, ``can't sleep'', Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered. ",
doi="10.2196/jmir.4476",
url="http://www.jmir.org/2015/6/e140/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26054530"
}


@Article{info:doi/10.2196/jmir.2818,
author="Hedman, Erik
and Lj{\'o}tsson, Brj{\'a}nn
and Blom, Kerstin
and El Alaoui, Samir
and Kraepelien, Martin
and R{\"u}ck, Christian
and Andersson, Gerhard
and Svanborg, Cecilia
and Lindefors, Nils
and Kaldo, Viktor",
title="Telephone Versus Internet Administration of Self-Report Measures of Social Anxiety, Depressive Symptoms, and Insomnia: Psychometric Evaluation of a Method to Reduce the Impact of Missing Data",
journal="J Med Internet Res",
year="2013",
month="Oct",
day="18",
volume="15",
number="10",
pages="e229",
keywords="Internet",
keywords="telephone",
keywords="self-report measures",
keywords="missing data",
keywords="method validation",
abstract="Background: Internet-administered self-report measures of social anxiety, depressive symptoms, and sleep difficulties are widely used in clinical trials and in clinical routine care, but data loss is a common problem that could render skewed estimates of symptom levels and treatment effects. One way of reducing the negative impact of missing data could be to use telephone administration of self-report measures as a means to complete the data missing from the online data collection. Objective: The aim of the study was to compare the convergence of telephone and Internet administration of self-report measures of social anxiety, depressive symptoms, and sleep difficulties. Methods: The Liebowitz Social Anxiety Scale-Self-Report (LSAS-SR), Montgomery-{\AA}sberg Depression Rating Scale-Self-Rated (MADRS-S), and the Insomnia Severity Index (ISI) were administered over the telephone and via the Internet to a clinical sample (N=82) of psychiatric patients at a clinic specializing in Internet-delivered treatment. Shortened versions of the LSAS-SR and the ISI were used when administered via telephone. Results: As predicted, the results showed that the estimates produced by the two administration formats were highly correlated (r=.82-.91; P<.001) and internal consistencies were high in both administration formats (telephone: Cronbach alpha=.76-.86 and Internet: Cronbach alpha=.79-.93). The correlation coefficients were similar across questionnaires and the shorter versions of the questionnaires used in the telephone administration of the LSAS-SR and ISI performed in general equally well compared to when the full scale was used, as was the case with the MADRS-S. Conclusions: Telephone administration of self-report questionnaires is a valid method that can be used to reduce data loss in routine psychiatric practice as well as in clinical trials, thereby contributing to more accurate symptom estimates. ",
doi="10.2196/jmir.2818",
url="http://www.jmir.org/2013/10/e229/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24140566"
}