%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e24607 %T Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review %A Aji,Melissa %A Gordon,Christopher %A Stratton,Elizabeth %A Calvo,Rafael A %A Bartlett,Delwyn %A Grunstein,Ronald %A Glozier,Nick %+ Brain and Mind Center, The University of Sydney, Level 5, Professor Marie Bashir Centre, Missenden Road, Camperdown, 2050, Australia, 61 29515 1596, nick.glozier@sydney.edu.au %K mobile applications %K sleep %K insomnia %K internet-based intervention %K mHealth %K mobile health %K systematic review %D 2021 %7 17.2.2021 %9 Review %J J Med Internet Res %G English %X 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. %M 33595441 %R 10.2196/24607 %U http://www.jmir.org/2021/2/e24607/ %U https://doi.org/10.2196/24607 %U http://www.ncbi.nlm.nih.gov/pubmed/33595441 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e20319 %T The Role of Technology and Social Media Use in Sleep-Onset Difficulties Among Italian Adolescents: Cross-sectional Study %A Varghese,Nirosha Elsem %A Santoro,Eugenio %A Lugo,Alessandra %A Madrid-Valero,Juan J %A Ghislandi,Simone %A Torbica,Aleksandra %A Gallus,Silvano %+ Department of Public Health, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan, 20156, Italy, 39 02 39014562, eugenio.santoro@marionegri.it %K sleep-onset difficulties %K adolescents %K social media %K electronic device use %D 2021 %7 21.1.2021 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 33475517 %R 10.2196/20319 %U http://www.jmir.org/2021/1/e20319/ %U https://doi.org/10.2196/20319 %U http://www.ncbi.nlm.nih.gov/pubmed/33475517 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 6 %N 4 %P e24206 %T Anxiety and Sleep Disturbances Among Health Care Workers During the COVID-19 Pandemic in India: Cross-Sectional Online Survey %A Gupta,Bhawna %A Sharma,Vyom %A Kumar,Narinder %A Mahajan,Akanksha %+ Torrens University, Public Health, 196 Flinders street, Melbourne, Australia, 61 1300 575 803, bhawna.gupta@laureate.edu.au %K occupational epidemiology %K anxiety %K GAD-7 %K sleep quality %K health care worker %K pandemic %K COVID-19 %K online survey %K sleep %K mental health, personal protective equipment %D 2020 %7 22.12.2020 %9 Original Paper %J JMIR Public Health Surveill %G English %X 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. %M 33284784 %R 10.2196/24206 %U https://publichealth.jmir.org/2020/4/e24206 %U https://doi.org/10.2196/24206 %U http://www.ncbi.nlm.nih.gov/pubmed/33284784 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 12 %P e25051 %T 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 %A Rogan,Slavko %A Luijckx,Eefje %A Taeymans,Jan %A Haas,Karin %A Baur,Heiner %+ Department of Health Professions, Bern University of Applied Sciences, Murtenstr. 10, Bern, 3008, Switzerland, 41 31 848 35 56, slavko.rogan@bfh.ch %K healthy lifestyle %K pandemic %K public health %K universities %K COVID-19 %K SARS-CoV-2 %D 2020 %7 22.12.2020 %9 Protocol %J JMIR Res Protoc %G English %X 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 %M 33296868 %R 10.2196/25051 %U http://www.researchprotocols.org/2020/12/e25051/ %U https://doi.org/10.2196/25051 %U http://www.ncbi.nlm.nih.gov/pubmed/33296868 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e20529 %T The Association Between Electronic Device Use During Family Time and Family Well-Being: Population-Based Cross-Sectional Study %A Zhao,Sheng Zhi %A Guo,Ningyuan %A Wang,Man Ping %A Fong,Daniel Yee Tak %A Lai,Agnes Yuen Kwan %A Chan,Sophia Siu-Chee %A Lam,Tai Hing %A Ho,Daniel Sai Yin %+ School of Nursing, University of Hong Kong, 21 Sassoon Road, Pokfulam, HK, Hong Kong, 000000, China (Hong Kong), 852 39176636, mpwang@hku.hk %K eDevice %K smartphone %K mobile phone %K well-being %K family dinner %K family communication %K public health %D 2020 %7 14.10.2020 %9 Original Paper %J J Med Internet Res %G English %X 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 (β) 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 β=–.25; 95% CI –0.44 to –0.05), and often use (vs never use) of eDevice during family dinner by oneself (adjusted β=–.51; 95% CI –0.91 to –0.10) and family members (adjusted β=–.54; 95% CI –0.79 to –0.29). Similarly, lower family well-being was associated with eDevice use before sleep (adjusted β=–.26; 95% CI –0.42 to –0.09), and often use during family dinner by oneself (adjusted β=–.48; 95% CI –0.83 to –0.12) and family members (adjusted β=–.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. %M 33052120 %R 10.2196/20529 %U http://www.jmir.org/2020/10/e20529/ %U https://doi.org/10.2196/20529 %U http://www.ncbi.nlm.nih.gov/pubmed/33052120 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 10 %P e20501 %T Ethnicity Differences in Sleep Changes Among Prehypertensive Adults Using a Smartphone Meditation App: Dose-Response Trial %A Sieverdes,John C %A Treiber,Frank A %A Kline,Christopher E %A Mueller,Martina %A Brunner-Jackson,Brenda %A Sox,Luke %A Cain,Mercedes %A Swem,Maria %A Diaz,Vanessa %A Chandler,Jessica %+ College of Charleston, Health and Human Performance, 24 George Street, Charleston, SC, United States, 1 843 953 6094, sieverdesjc@cofc.edu %K meditation %K sleep %K mobile phone %K prehypertension %K ethnicity %D 2020 %7 6.10.2020 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 33021484 %R 10.2196/20501 %U https://formative.jmir.org/2020/10/e20501 %U https://doi.org/10.2196/20501 %U http://www.ncbi.nlm.nih.gov/pubmed/33021484 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e20590 %T Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study %A Lunsford-Avery,Jessica R %A Keller,Casey %A Kollins,Scott H %A Krystal,Andrew D %A Jackson,Leah %A Engelhard,Matthew M %+ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 2608 Erwin Rd Suite 300, Durham, NC, , United States, 1 919 681 0035, jessica.r.avery@duke.edu %K sleep %K wearable %K mHealth %K adolescents %K EEG %K feasibility %K acceptability %K tolerability %K actigraphy %D 2020 %7 1.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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 %M 33001035 %R 10.2196/20590 %U https://mhealth.jmir.org/2020/10/e20590 %U https://doi.org/10.2196/20590 %U http://www.ncbi.nlm.nih.gov/pubmed/33001035 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 9 %P e18086 %T Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study %A Thota,Darshan %+ Madigan Army Medical Center, 9040A Jackson Ave, Joint Base Lewis-McChord, WA, 98431, United States, 1 253 968 5958, thota1@gmail.com %K Fitbit %K sleep %K healthy %K mood %K context %K waking %D 2020 %7 29.9.2020 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 32990631 %R 10.2196/18086 %U http://formative.jmir.org/2020/9/e18086/ %U https://doi.org/10.2196/18086 %U http://www.ncbi.nlm.nih.gov/pubmed/32990631 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e22181 %T Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study %A Lin,Yu-Hsuan %A Chiang,Ting-Wei %A Lin,Yu-Lun %+ Institute of Population Health Sciences, National Health Research Institutes, Room A3234, No 35 Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan, 1 886 37206166 ext 36383, yuhsuanlin@nhri.edu.tw %K internet search %K Google Trends %K infodemiology %K infoveillance %K COVID-19 %K insomnia %K mental health %D 2020 %7 21.9.2020 %9 Original Paper %J J Med Internet Res %G English %X 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 (ρ=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression (ρ=–0.12, P=.63) or suicide (ρ=–0.07, P=.79). Conclusions: Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic. %M 32924951 %R 10.2196/22181 %U http://www.jmir.org/2020/9/e22181/ %U https://doi.org/10.2196/22181 %U http://www.ncbi.nlm.nih.gov/pubmed/32924951 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 5 %N 1 %P e20921 %T Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review %A Pan,Qiang %A Brulin,Damien %A Campo,Eric %+ LAAS-CNRS, University of Toulouse, 7, avenue du Colonel Roche, Toulouse, 31400, France, 33 561 337 961, eric.campo@univ-tlse2.fr %K EEG %K ECG %K classification %K mobile phone %D 2020 %7 26.8.2020 %9 Review %J JMIR Biomed Eng %G English %X 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. %R 10.2196/20921 %U http://biomedeng.jmir.org/2020/1/e20921/ %U https://doi.org/10.2196/20921 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e17755 %T Mobile App Use for Insomnia Self-Management in Urban Community-Dwelling Older Korean Adults: Retrospective Intervention Study %A Chung,Kyungmi %A Kim,Seoyoung %A Lee,Eun %A Park,Jin Young %+ Department of Psychiatry, Yonsei University College of Medicine, Yongin Severance Hospital, Yonsei University Health System, Department of Psychiatry, Yongin Severance Hospital, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin, 16995, Republic of Korea, 82 2 2228 0972, empathy@yuhs.ac %K sleep hygiene %K cognitive behavioral therapy %K sleep initiation and maintenance disorders %K telemedicine %K mobile apps %K treatment adherence and compliance %K health education %K health services for the aged %K community mental health services %K health care quality, access, and evaluation %D 2020 %7 24.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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; ρ=.60, P<.001) and memory complaints (40/40; ρ=.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. %M 32831177 %R 10.2196/17755 %U http://mhealth.jmir.org/2020/8/e17755/ %U https://doi.org/10.2196/17755 %U http://www.ncbi.nlm.nih.gov/pubmed/32831177 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e15697 %T Viewing Trends and Users’ Perceptions of the Effect of Sleep-Aiding Music on YouTube: Quantification and Thematic Content Analysis %A Eke,Ransome %A Li,Tong %A Bond,Kiersten %A Ho,Arlene %A Graves,Lisa %+ Department of Health Science, University of Alabama, 105 Russell Building, Tuscaloosa, AL, 35487, United States, 1 205 348 2553, reke@ua.edu %K insomnia %K sleep deprivation %K YouTube %K utilization %K pattern %K perception %K content analysis %D 2020 %7 24.8.2020 %9 Original Paper %J J Med Internet Res %G English %X 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 κ 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. %M 32831182 %R 10.2196/15697 %U http://www.jmir.org/2020/8/e15697/ %U https://doi.org/10.2196/15697 %U http://www.ncbi.nlm.nih.gov/pubmed/32831182 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e17449 %T Using Web-Based Social Media to Recruit Heavy-Drinking Young Adults for Sleep Intervention: Prospective Observational Study %A Ash,Garrett I %A Robledo,David S %A Ishii,Momoko %A Pittman,Brian %A DeMartini,Kelly S %A O'Malley,Stephanie S %A Redeker,Nancy S %A Fucito,Lisa M %+ Department of Psychiatry, Yale School of Medicine, 300 George Street, #901, New Haven, CT, 06511, United States, 1 2034443079, garrett.ash@yale.edu %K substance abuse %K social media %K alcohol drinking %K sleep %K mobile phone %D 2020 %7 11.8.2020 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 32780027 %R 10.2196/17449 %U https://www.jmir.org/2020/8/e17449 %U https://doi.org/10.2196/17449 %U http://www.ncbi.nlm.nih.gov/pubmed/32780027 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e18370 %T Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation %A Liu,Jiaxing %A Zhao,Yang %A Lai,Boya %A Wang,Hailiang %A Tsui,Kwok Leung %+ Centre for Systems Informatics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, 999077, China (Hong Kong), 852 34425792, yang.zhao@my.cityu.edu.hk %K sleep/wake identification %K hidden Markov model %K personalized health %K unsupervised learning %K sleep %K physical activity %K wearables %K heart rate %D 2020 %7 5.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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. %M 32755887 %R 10.2196/18370 %U https://mhealth.jmir.org/2020/8/e18370 %U https://doi.org/10.2196/18370 %U http://www.ncbi.nlm.nih.gov/pubmed/32755887 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e17096 %T Combination of 3-Dimensional Virtual Reality and Hands-On Aromatherapy in Improving Institutionalized Older Adults’ Psychological Health: Quasi-Experimental Study %A Cheng,Vivian Ya-Wen %A Huang,Chiu-Mieh %A Liao,Jung-Yu %A Hsu,Hsiao-Pei %A Wang,Shih-Wen %A Huang,Su-Fei %A Guo,Jong-Long %+ Department of Health Promotion and Health Education, College of Education, National Taiwan Normal University, 162, Section 1, He-ping East Road, Taipei, 10610, Taiwan, 886 277493705, jonglong@ntnu.edu.tw %K three-dimensional %K virtual reality %K aromatherapy %K older adult %K happiness %K stress %K sleep quality %K meditation %K life satisfaction %D 2020 %7 23.7.2020 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 32706660 %R 10.2196/17096 %U http://www.jmir.org/2020/7/e17096/ %U https://doi.org/10.2196/17096 %U http://www.ncbi.nlm.nih.gov/pubmed/32706660 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e18095 %T Relationships Between the Usage of Televisions, Computers, and Mobile Phones and the Quality of Sleep in a Chinese Population: Community-Based Cross-Sectional Study %A Xie,Yao Jie %A Cheung,Daphne SK %A Loke,Alice Y %A Nogueira,Bernice L %A Liu,Karry M %A Leung,Angela YM %A Tsang,Alice SM %A Leong,Cindy SU %A Molassiotis,Alex %+ School of Nursing, The Hong Kong Polytechnic University, FG424, PolyU, Hong Kong, China (Hong Kong), 852 63135399, grace.yj.xie@polyu.edu.hk %K electronic device %K screen-based %K sleep %K Chinese %K digital %K mobile phone %D 2020 %7 7.7.2020 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 32369439 %R 10.2196/18095 %U https://www.jmir.org/2020/7/e18095 %U https://doi.org/10.2196/18095 %U http://www.ncbi.nlm.nih.gov/pubmed/32369439 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 6 %P e15403 %T Associations of Electronic Device Use Before and After Sleep With Psychological Distress Among Chinese Adults in Hong Kong: Cross-Sectional Study %A Lee,Jung Jae %A Wang,Man Ping %A Luk,Tzu Tsun %A Guo,Ningyuan %A Chan,Sophia Siu-Chee %A Lam,Tai Hing %+ School of Nursing, University of Hong Kong, 4/F William MW Mong Block Building,, 21 Sassoon Rd, Pokfulam,, Hong Kong, , China (Hong Kong), 852 3917 6636, mpwang@hku.hk %K addictive behavior %K anxiety %K computers %K depression %K devices %K internet %K smartphone %K withdrawal symptoms %D 2020 %7 11.6.2020 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 32525489 %R 10.2196/15403 %U http://mental.jmir.org/2020/6/e15403/ %U https://doi.org/10.2196/15403 %U http://www.ncbi.nlm.nih.gov/pubmed/32525489 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 6 %P e16880 %T Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study %A Miyashita,Hirotaka %A Nakamura,Mitsuteru %A Svensson,Akiko Kishi %A Nakamura,Masahiro %A Tokuno,Shinichi %A Chung,Ung-Il %A Svensson,Thomas %+ Precision Health, Department of Bioengineering, Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,, Tokyo, 113-8656, Japan, 81 3 5841 4737, t-svensson@umin.ac.jp %K voice analysis %K emotional status %K vitality %K sleep %K mobile phone %D 2020 %7 9.6.2020 %9 Original Paper %J JMIR Form Res %G English %X 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 (Δvitality) and specific sleep measures. Results: A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. Conclusions: Total sleep time and sleep onset latency are significantly associated with Δvitality, 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. %M 32515745 %R 10.2196/16880 %U https://formative.jmir.org/2020/6/e16880 %U https://doi.org/10.2196/16880 %U http://www.ncbi.nlm.nih.gov/pubmed/32515745 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 6 %N 1 %P e15859 %T Assessing Breast Cancer Survivors’ Perceptions of Using Voice-Activated Technology to Address Insomnia: Feasibility Study Featuring Focus Groups and In-Depth Interviews %A Arem,Hannah %A Scott,Remle %A Greenberg,Daniel %A Kaltman,Rebecca %A Lieberman,Daniel %A Lewin,Daniel %+ Department of Epidemiology, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW, Rm 514, Washington, DC, 20052, United States, 1 2029944676, hannaharem@gwu.edu %K artificial intelligence %K breast neoplasms %K survivors %K insomnia %K cognitive behavioral therapy %K mobile phones %D 2020 %7 26.5.2020 %9 Original Paper %J JMIR Cancer %G English %X 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. %M 32348274 %R 10.2196/15859 %U http://cancer.jmir.org/2020/1/e15859/ %U https://doi.org/10.2196/15859 %U http://www.ncbi.nlm.nih.gov/pubmed/32348274 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 4 %P e17071 %T Temporal Associations of Daily Changes in Sleep and Depression Core Symptoms in Patients Suffering From Major Depressive Disorder: Idiographic Time-Series Analysis %A Lorenz,Noah %A Sander,Christian %A Ivanova,Galina %A Hegerl,Ulrich %+ Research Centre of the German Depression Foundation, Goerdelerring 9, Leipzig, 04109, Germany, 49 341 2238740, noah.lorenz@medizin.uni-leipzig.de %K depression %K sleep %K time series %K idiographic %K self-management %D 2020 %7 23.4.2020 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 32324147 %R 10.2196/17071 %U http://mental.jmir.org/2020/4/e17071/ %U https://doi.org/10.2196/17071 %U http://www.ncbi.nlm.nih.gov/pubmed/32324147 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 4 %P e16749 %T Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis %A Hu,Fang %A Li,Liuhuan %A Huang,Xiaoyu %A Yan,Xingyu %A Huang,Panpan %+ College of Basic Medicine, Hubei University of Chinese Medicine, No. 16 Huangjiahu West Road, Hongshan District, Wuhan, 430065, China, 86 15327193915, panpanhuang@hbtcm.edu.cn %K insomnia %K core symptom %K symptom community %K symptom embedding representation %K spectral clustering algorithm %D 2020 %7 16.4.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Recent research in machine-learning techniques has led to significant progress in various research fields. 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 significant 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. %M 32297869 %R 10.2196/16749 %U http://medinform.jmir.org/2020/4/e16749/ %U https://doi.org/10.2196/16749 %U http://www.ncbi.nlm.nih.gov/pubmed/32297869 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 4 %P e15841 %T Efficacy of a Theory-Based Cognitive Behavioral Technique App-Based Intervention for Patients With Insomnia: Randomized Controlled Trial %A Rajabi Majd,Nilofar %A Broström,Anders %A Ulander,Martin %A Lin,Chung-Ying %A Griffiths,Mark D %A Imani,Vida %A Ahorsu,Daniel Kwasi %A Ohayon,Maurice M %A Pakpour,Amir H %+ Department of Rehabilitation Sciences, Hong Kong Polytechnic University, ST534 Department of Rehabilitation Sciences, Hung Hom, , China (Hong Kong), 852 27666755, cylin36933@gmail.com %K app-based intervention %K cognitive behavioral therapy, insomnia %K sleep hygiene %K theory of planned behavior %D 2020 %7 1.4.2020 %9 Original Paper %J J Med Internet Res %G English %X 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 %M 32234700 %R 10.2196/15841 %U http://www.jmir.org/2020/4/e15841/ %U https://doi.org/10.2196/15841 %U http://www.ncbi.nlm.nih.gov/pubmed/32234700 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e10733 %T Clinical Applications of Mobile Health Wearable–Based Sleep Monitoring: Systematic Review %A Guillodo,Elise %A Lemey,Christophe %A Simonnet,Mathieu %A Walter,Michel %A Baca-García,Enrique %A Masetti,Vincent %A Moga,Sorin %A Larsen,Mark %A , %A Ropars,Juliette %A Berrouiguet,Sofian %+ Urci Mental Health Department, Brest Medical University Hospital, Brest, 29200, France, 33 0298223333, elise.guillodo@chu-brest.fr %K sleep %K eHealth %K telemedicine %K review %K medicine %K wearable electronic devices %D 2020 %7 1.4.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X 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. %M 32234707 %R 10.2196/10733 %U https://mhealth.jmir.org/2020/4/e10733 %U https://doi.org/10.2196/10733 %U http://www.ncbi.nlm.nih.gov/pubmed/32234707 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 3 %P e14842 %T Digital Cognitive Behavioral Therapy for Insomnia for Adolescents With Mental Health Problems: Feasibility Open Trial %A Cliffe,Bethany %A Croker,Abigail %A Denne,Megan %A Smith,Jacqueline %A Stallard,Paul %+ Department of Health, University of Bath, 1 West, Claverton Down, Bath, BA2 7AY, United Kingdom, 44 01225 388388, p.stallard@bath.ac.uk %K insomnia %K internet-based intervention %K cognitive therapy %K mental health %D 2020 %7 3.3.2020 %9 Original Paper %J JMIR Ment Health %G English %X 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 %M 32134720 %R 10.2196/14842 %U https://mental.jmir.org/2020/3/e14842 %U https://doi.org/10.2196/14842 %U http://www.ncbi.nlm.nih.gov/pubmed/32134720 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 6 %N 1 %P e15750 %T A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women’s Cancer: Pre-Post Pilot Study %A Chow,Philip I %A Drago,Fabrizio %A Kennedy,Erin M %A Cohn,Wendy F %+ University of Virginia, 560 Ray C Hunt Dr, Charlottesville, VA, 22903, United States, 1 9244345401, philip.i.chow@gmail.com %K mobile apps %K mental health %K mHealth %K women %K cancer survivors %D 2020 %7 6.2.2020 %9 Original Paper %J JMIR Cancer %G English %X 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. %R 10.2196/15750 %U http://cancer.jmir.org/2020/1/e15750/ %U https://doi.org/10.2196/15750 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 1 %P e13346 %T Efficacy of a Self-Help Web-Based Recovery Training in Improving Sleep in Workers: Randomized Controlled Trial in the General Working Population %A Behrendt,Doerte %A Ebert,David Daniel %A Spiegelhalder,Kai %A Lehr,Dirk %+ Department of Health Psychology and Applied Biological Psychology, Institute of Psychology, Leuphana University of Lueneburg, Universitätsallee 1, Lueneburg, 21335, Germany, 49 41316772374, behrendt@leuphana.de %K occupational health %K e-mental-health %K insomnia %K Web-based, cognitive behavioral therapy %K mediators %D 2020 %7 7.1.2020 %9 Original Paper %J J Med Internet Res %G English %X 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 %M 31909725 %R 10.2196/13346 %U https://www.jmir.org/2020/1/e13346 %U https://doi.org/10.2196/13346 %U http://www.ncbi.nlm.nih.gov/pubmed/31909725 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 12 %P e14647 %T Effect of Cognitive Behavioral Therapy for Insomnia on Insomnia Symptoms for Individuals With Type 2 Diabetes: Protocol for a Pilot Randomized Controlled Trial %A Alshehri,Mohammed M %A Alenazi,Aqeel M %A Hoover,Jeffrey C %A Alothman,Shaima A %A Phadnis,Milind A %A Rucker,Jason L %A Befort,Christie A %A Miles,John M %A Kluding,Patricia M %A Siengsukon,Catherine F %+ University of Kansas Medical Center, 8546 Caenen Lake Court, Lenexa, KS, 66215, United States, 1 4125512333, phdalshehri@gmail.com %K insomnia %K type 2 diabetes %K cognitive behavioral therapy %K sleep variability %K self-care %K fatigue %D 2019 %7 19.12.2019 %9 Protocol %J JMIR Res Protoc %G English %X 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 %M 31855189 %R 10.2196/14647 %U https://www.researchprotocols.org/2019/12/e14647 %U https://doi.org/10.2196/14647 %U http://www.ncbi.nlm.nih.gov/pubmed/31855189 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 12 %P e13076 %T Identifying Sleep-Deprived Authors of Tweets: Prospective Study %A Melvin,Sara %A Jamal,Amanda %A Hill,Kaitlyn %A Wang,Wei %A Young,Sean D %+ Department of Medicine, University of California, Irvine, 333 City Blvd West, Suite 640, Orange, CA, United States, 1 310 456 5239, syoung5@uci.edu %K wearable electronic devices %K safety %K natural language processing %K information storage and retrieval %K sleep deprivation %K neural networks (computer) %K sleep %K social media %D 2019 %7 6.12.2019 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 31808747 %R 10.2196/13076 %U https://mental.jmir.org/2019/12/e13076 %U https://doi.org/10.2196/13076 %U http://www.ncbi.nlm.nih.gov/pubmed/31808747 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 11 %P e16273 %T Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis %A Haghayegh,Shahab %A Khoshnevis,Sepideh %A Smolensky,Michael H %A Diller,Kenneth R %A Castriotta,Richard J %+ Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, 107 W Dean Keeton St, Austin, TX, , United States, 1 5129543436, shahab@utexas.edu %K Fitbit %K polysomnography %K sleep tracker %K wearable %K actigraphy %K sleep diary %K sleep stages %K accuracy %K validation %K comparison of performance %D 2019 %7 28.11.2019 %9 Review %J J Med Internet Res %G English %X 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. %M 31778122 %R 10.2196/16273 %U http://www.jmir.org/2019/11/e16273/ %U https://doi.org/10.2196/16273 %U http://www.ncbi.nlm.nih.gov/pubmed/31778122 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 11 %P e13371 %T Adverse Events Due to Insomnia Drugs Reported in a Regulatory Database and Online Patient Reviews: Comparative Study %A Borchert,Jill S %A Wang,Bo %A Ramzanali,Muzaina %A Stein,Amy B %A Malaiyandi,Latha M %A Dineley,Kirk E %+ College of Graduate Studies, Midwestern University, 555 31st Street, Downers Grove, IL, 60515, United States, 1 6309603907, kdinel@midwestern.edu %K drug safety %K drug ineffective %K postmarketing %K pharmacovigilance %K internet %K pharmacoepidemiology %K adverse effect %K hypnotic %K insomnia %K patient-reported outcomes %D 2019 %7 8.11.2019 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 31702558 %R 10.2196/13371 %U http://www.jmir.org/2019/11/e13371/ %U https://doi.org/10.2196/13371 %U http://www.ncbi.nlm.nih.gov/pubmed/31702558 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 9 %P e12500 %T A Web-Based Photo-Alteration Intervention to Promote Sleep: Randomized Controlled Trial %A Perucho,Isabel %A Vijayakumar,Kamalakannan M %A Talamas,Sean N %A Chee,Michael Wei-Liang %A Perrett,David I %A Liu,Jean C J %+ Division of Social Sciences, Yale-NUS College, 16 College Ave West #02-221, Singapore, 138527, Singapore, 65 66013694, jeanliu@yale-nus.edu.sg %K sleep %K public health %K physical appearance %K outward appearance %D 2019 %7 26.9.2019 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 31573913 %R 10.2196/12500 %U https://www.jmir.org/2019/9/e12500 %U https://doi.org/10.2196/12500 %U http://www.ncbi.nlm.nih.gov/pubmed/31573913 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 8 %N 3 %P e12408 %T Mobile App Use for Insomnia Self-Management: Pilot Findings on Sleep Outcomes in Veterans %A Reilly,Erin D %A Robinson,Stephanie A %A Petrakis,Beth Ann %A Kuhn,Eric %A Pigeon,Wilfred R %A Wiener,Renda Soylemez %A McInnes,D Keith %A Quigley,Karen S %+ Center for Social and Community Reintegration Research, Edith Nourse Rogers Memorial VA Hospital, 200 Springs Rd, Bldg 9, Room 106, Bedford, MA, 01730, United States, 1 781 687 4191, Erin.Reilly@va.gov %K cognitive behavioral therapy %K mobile apps %K insomnia %K sleep apnea %D 2019 %7 24.07.2019 %9 Original Paper %J Interact J Med Res %G English %X 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 (χ22=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 %M 31342904 %R 10.2196/12408 %U http://www.i-jmr.org/2019/3/e12408/ %U https://doi.org/10.2196/12408 %U http://www.ncbi.nlm.nih.gov/pubmed/31342904 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 6 %P e14290 %T Associations of Social Media Use With Physical Activity and Sleep Adequacy Among Adolescents: Cross-Sectional Survey %A Shimoga,Sandhya V %A Erlyana,Erlyana %A Rebello,Vida %+ Department of Health Care Administration, California State University Long Beach, 1250 Bellflower Blvd, Long Beach, CA, 90840, United States, 1 562 985 5800, erlyana.erlyana@csulb.edu %K adolescent %K social media %K exercise %K sleep %D 2019 %7 18.06.2019 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 31215512 %R 10.2196/14290 %U http://www.jmir.org/2019/6/e14290/ %U https://doi.org/10.2196/14290 %U http://www.ncbi.nlm.nih.gov/pubmed/31215512 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 6 %P e13482 %T Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices %A Zhang,Zhongxing %A Cajochen,Christian %A Khatami,Ramin %+ Center for Sleep Medicine, Sleep Research and Epileptology, Clinic Barmelweid AG, , Barmelweid,, Switzerland, 41 62 857 22 38, zhongxing.zhang@barmelweid.ch %K chronotypes %K social jetlag %K wearable devices %K nap %K cardiopulmonary coupling %K sleep %K big data %D 2019 %7 11.5.2019 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 31199292 %R 10.2196/13482 %U https://www.jmir.org/2019/6/e13482/ %U https://doi.org/10.2196/13482 %U http://www.ncbi.nlm.nih.gov/pubmed/31199292 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 5 %P e12455 %T Feasibility of a Sleep Self-Management Intervention in Pregnancy Using a Personalized Health Monitoring Device: Protocol for a Pilot Randomized Controlled Trial %A Hawkins,Marquis %A Iradukunda,Favorite %A Paterno,Mary %+ Department of Epidemiology, University of Pittsburgh, 130 DeSoto Street, 5138 Public Health, Pittsburgh, PA,, United States, 1 412 383 1931, mah400@pitt.edu %K eHealth %K pregnancy %K personal health monitoring %K behavior %K maternal health %D 2019 %7 29.05.2019 %9 Protocol %J JMIR Res Protoc %G English %X 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 %M 31144670 %R 10.2196/12455 %U https://www.researchprotocols.org/2019/5/e12455/ %U https://doi.org/10.2196/12455 %U http://www.ncbi.nlm.nih.gov/pubmed/31144670 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 2 %P e12635 %T Treatment Preferences for Internet-Based Cognitive Behavioral Therapy for Insomnia in Japan: Online Survey %A Sato,Daisuke %A Sutoh,Chihiro %A Seki,Yoichi %A Nagai,Eiichi %A Shimizu,Eiji %+ Department of Cognitive Behavioral Physiology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8670, Japan, 81 43 226 2027, daisuke-sato@umin.ac.jp %K patient preference %K insomnia %K internet-based cognitive behavioral therapy %D 2019 %7 15.05.2019 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 31094319 %R 10.2196/12635 %U http://formative.jmir.org/2019/2/e12635/ %U https://doi.org/10.2196/12635 %U http://www.ncbi.nlm.nih.gov/pubmed/31094319 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 4 %P e12686 %T Effectiveness of Internet-Delivered Computerized Cognitive Behavioral Therapy for Patients With Insomnia Who Remain Symptomatic Following Pharmacotherapy: Randomized Controlled Exploratory Trial %A Sato,Daisuke %A Yoshinaga,Naoki %A Nagai,Eiichi %A Nagai,Kazue %A Shimizu,Eiji %+ Department of Cognitive Behavioral Physiology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba, 260-8670, Japan, 81 43 226 2027, daisuke-sato@umin.ac.jp %K insomnia %K cognitive behavioral therapy %K randomized controlled trial %K internet %K benzodiazepines %K residual symptoms %D 2019 %7 11.04.2019 %9 Original Paper %J J Med Internet Res %G English %X 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). %M 30973344 %R 10.2196/12686 %U http://www.jmir.org/2019/4/e12686/ %U https://doi.org/10.2196/12686 %U http://www.ncbi.nlm.nih.gov/pubmed/30973344 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e9929 %T Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study %A Holtz,Bree %A Vasold,Kerri %A Cotten,Shelia %A Mackert,Michael %A Zhang,Mi %+ Department of Advertising and Public Relations, College of Communication Arts & Sciences, Michigan State University, 404 Wilson Road, Room 309, East Lansing, MI, 48824, United States, 1 517 884 4537, bholtz@msu.edu %K physicians %K primary care %K APRN %K nurse practitioners %K technology %D 2019 %7 22.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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. %M 30668515 %R 10.2196/mhealth.9929 %U https://mhealth.jmir.org/2018/1/e9929/ %U https://doi.org/10.2196/mhealth.9929 %U http://www.ncbi.nlm.nih.gov/pubmed/30668515 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 1 %P e10974 %T Accelerating Research With Technology: Rapid Recruitment for a Large-Scale Web-Based Sleep Study %A Deering,Sean %A Grade,Madeline M %A Uppal,Jaspreet K %A Foschini,Luca %A Juusola,Jessie L %A Amdur,Adam M %A Stepnowsky,Carl J %+ Evidation Health, 167 2nd Avenue, San Mateo, CA, 94401, United States, 1 6502798855, jjuusola@evidation.com %K connected health %K engagement %K health %K mHealth %K mobile health %K mobile phone %K recruitment %K sleep %K sleep quality %K wearables %D 2019 %7 21.01.2019 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Participant recruitment can be a significant bottleneck in carrying out research studies. Connected health and mobile health platforms allow for the development of Web-based studies that can offer improvement in this domain. Sleep is of vital importance to the mental and physical health of all individuals, yet is understudied on a large scale or beyond the focus of sleep disorders. For this reason and owing to the availability of digital sleep tracking tools, sleep is well suited to being studied in a Web-based environment. Objective: The aim of this study was to investigate a method for speeding up the recruitment process and maximizing participant engagement using a novel approach, the Achievement Studies platform (Evidation Health, Inc, San Mateo, CA, USA), while carrying out a study that examined the relationship between participant sleep and daytime function. Methods: Participants could access the Web-based study platform at any time from any computer or Web-enabled device to complete study procedures and track study progress. Achievement community members were invited to the study and assessed for eligibility. Eligible participants completed an electronic informed consent process to enroll in the study and were subsequently invited to complete an electronic baseline questionnaire. Then, they were asked to connect a wearable device account through their study dashboard, which shared their device data with the research team. The data were used to provide objective sleep and activity metrics for the study. Participants who completed the baseline questionnaires were subsequently sent a daily single-item Sleepiness Checker activity for 7 consecutive days at baseline and every 3 months thereafter for 1 year. Results: Overall, 1156 participants enrolled in the study within a 5-day recruitment window. In the 1st hour, the enrollment rate was 6.6 participants per minute (394 per hour). In the first 24 hours, the enrollment rate was 0.8 participants per minute (47 participants per hour). Overall, 1132 participants completed the baseline questionnaires (1132/1156, 97.9%) and 1047 participants completed the initial Sleepiness Checker activity (1047/1156, 90.6%). Furthermore, 1000 participants provided activity-specific wearable data (1000/1156, 86.5%) and 982 provided sleep-specific wearable data (982/1156, 84.9%). Conclusions: The Achievement Studies platform allowed for rapid recruitment and high study engagement (survey completion and device data sharing). This approach to carrying out research appears promising. However, conducting research in this way requires that participants have internet access and own and use a wearable device. As such, our sample may not be representative of the general population. %M 30664491 %R 10.2196/10974 %U http://www.researchprotocols.org/2019/1/e10974/ %U https://doi.org/10.2196/10974 %U http://www.ncbi.nlm.nih.gov/pubmed/30664491 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 2 %N 1 %P e11331 %T Using Actigraphy to Predict the Ecological Momentary Assessment of Mood, Fatigue, and Cognition in Older Adulthood: Mixed-Methods Study %A Parsey,Carolyn M %A Schmitter-Edgecombe,Maureen %+ Department of Neurology, University of Washington School of Medicine, 325 9th Avenue, Seattle, WA, 98104, United States, 1 206 744 3532, cmparsey@uw.edu %K actigraphy %K aging %K ecological momentary assessment %K mood %K sleep %D 2019 %7 18.01.2019 %9 Original Paper %J JMIR Aging %G English %X 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. %M 31518282 %R 10.2196/11331 %U http://aging.jmir.org/2019/1/e11331/ %U https://doi.org/10.2196/11331 %U http://www.ncbi.nlm.nih.gov/pubmed/31518282 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e193 %T 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 %A Rönkkö,Kari %+ Department of Design, Faculty of Business, Kristianstad University, Elmetorpsvägen 15, Kristianstad, SE-291 88, Sweden, 46 0442503192, kari.ronkko@hkr.se %K mHealth %K social work %K youths %K activity trackers %K mobile applications %K motivation %K self-care %K sleep hygiene %K goals %D 2018 %7 21.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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. %M 30578186 %R 10.2196/mhealth.9286 %U https://mhealth.jmir.org/2018/12/e193/ %U https://doi.org/10.2196/mhealth.9286 %U http://www.ncbi.nlm.nih.gov/pubmed/30578186 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 1 %N 2 %P e11193 %T Social Media Content About Children’s Pain and Sleep: Content and Network Analysis %A Tougas,Michelle E %A Chambers,Christine T %A Corkum,Penny %A Robillard,Julie M %A Gruzd,Anatoliy %A Howard,Vivian %A Kampen,Andrea %A Boerner,Katelynn E %A Hundert,Amos S %+ Centre for Pediatric Pain Research, IWK Health Centre, 5850/5980 University Avenue, PO Box 9700, Halifax, NS, B3K 6R8, Canada, 1 902 470 7706, christine.chambers@dal.ca %K child health %K knowledge translation %K pain %K sleep %K social media %D 2018 %7 11.12.2018 %9 Original Paper %J JMIR Pediatr Parent %G English %X 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. %M 31518292 %R 10.2196/11193 %U http://pediatrics.jmir.org/2018/2/e11193/ %U https://doi.org/10.2196/11193 %U http://www.ncbi.nlm.nih.gov/pubmed/31518292 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 12 %P e10124 %T Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study %A Pulantara,I Wayan %A Parmanto,Bambang %A Germain,Anne %+ Sleep and Behavioral Neuroscience Center, Department of Psychiatry, University of Pittsburgh, Sterling Plaza 240, Pittsburgh, PA,, United States, 1 412 383 2150, germax@upmc.edu %K just-in-time adaptive intervention %K insomnia %K sleep %K mHealth %K mobile health %K interactive Resilience Enhancing Sleep Tactics (iREST) %K behavioral therapy %K brief behavioral therapy for insomnia %K cognitive behavioral therapy for insomnia %D 2018 %7 07.12.2018 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 30530452 %R 10.2196/10124 %U https://www.jmir.org/2018/12/e10124/ %U https://doi.org/10.2196/10124 %U http://www.ncbi.nlm.nih.gov/pubmed/30530452 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 5 %N 2 %P e21 %T Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study %A Pulantara,I Wayan %A Parmanto,Bambang %A Germain,Anne %+ Health and Rehabilitation Informatics Laboratory, Department of Health Information Management, University of Pittsburgh, 6026 Forbes Tower, Pittsburgh, PA, 15260, United States, 1 412 383 6649, parmanto@pitt.edu %K Just-in-Time Adaptive Intervention %K JITAI %K mobile health %K mHealth %K sleep %K insomnia %K usability %K smartphone %K iREST %D 2018 %7 17.05.2018 %9 Original Paper %J JMIR Hum Factors %G English %X 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. %M 29773529 %R 10.2196/humanfactors.8905 %U http://humanfactors.jmir.org/2018/2/e21/ %U https://doi.org/10.2196/humanfactors.8905 %U http://www.ncbi.nlm.nih.gov/pubmed/29773529 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 3 %P e76 %T 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 %A Corkum,Penny V %A Reid,Graham J %A Hall,Wendy A %A Godbout,Roger %A Stremler,Robyn %A Weiss,Shelly K %A Gruber,Reut %A Witmans,Manisha %A Chambers,Christine T %A Begum,Esmot Ara %A Andreou,Pantelis %A Rigney,Gabrielle %+ Department of Psychology & Neuroscience, Dalhousie University, PO BOX 15000, 1355 Oxford Street, Halifax, NS, B3H 4R2, Canada, 1 902 494 5177, penny.corkum@dal.ca %K sleep %K insomnia %K children %K randomized controlled trial %K eHealth %K Internet %K treatment %D 2018 %7 26.03.2018 %9 Protocol %J JMIR Res Protoc %G English %X 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) %M 29581089 %R 10.2196/resprot.8348 %U http://www.researchprotocols.org/2018/3/e76/ %U https://doi.org/10.2196/resprot.8348 %U http://www.ncbi.nlm.nih.gov/pubmed/29581089 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 1 %P e3 %T Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App %A Baron,Kelly Glazer %A Duffecy,Jennifer %A Reid,Kathryn %A Begale,Mark %A Caccamo,Lauren %+ Rush University Medical Center, 1653 W Congress Parkway, Chicago, IL, 60612, United States, 1 3129420566, kgbaron@rush.edu %K sleep duration %K wearable %K obesity %K technology %K behavioral intervention %D 2018 %7 10.01.2018 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 29321122 %R 10.2196/mental.8634 %U http://mental.jmir.org/2018/1/e3/ %U https://doi.org/10.2196/mental.8634 %U http://www.ncbi.nlm.nih.gov/pubmed/29321122 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 10 %P e363 %T An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices %A Fagherazzi,Guy %A El Fatouhi,Douae %A Bellicha,Alice %A El Gareh,Amin %A Affret,Aurélie %A Dow,Courtney %A Delrieu,Lidia %A Vegreville,Matthieu %A Normand,Alexis %A Oppert,Jean-Michel %A Severi,Gianluca %+ Inserm, Centre de Recherche en Epidémiologie et Santé des Populations U1018, 114 rue Edouard Vaillant, Villejuif,, France, 33 1 42 11 61 40, guy.fagherazzi@gustaveroussy.fr %K connected devices %K sleep %K Withings %K Nokia %K determinants %K Internet of Things %K epidemiology %K wearables %K lifestyle %K blood pressure %K steps %K heart rate %K weight %D 2017 %7 23.10.2017 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 29061551 %R 10.2196/jmir.7930 %U http://www.jmir.org/2017/10/e363/ %U https://doi.org/10.2196/jmir.7930 %U http://www.ncbi.nlm.nih.gov/pubmed/29061551 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 9 %P e131 %T Mobile Phone Interventions for Sleep Disorders and Sleep Quality: Systematic Review %A Shin,Jong Cheol %A Kim,Julia %A Grigsby-Toussaint,Diana %+ Department of Kinesiology and Community Health, University of Illinois-Urbana Champaign, 1206 S Fourth Street, Champaign, IL, 61820, United States, 1 2173339207, dgrigs1@illinois.edu %K mHealth %K apps %K mobile health %K sleep %D 2017 %7 07.09.2017 %9 Review %J JMIR Mhealth Uhealth %G English %X 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. %M 28882808 %R 10.2196/mhealth.7244 %U http://mhealth.jmir.org/2017/9/e131/ %U https://doi.org/10.2196/mhealth.7244 %U http://www.ncbi.nlm.nih.gov/pubmed/28882808 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 4 %N 3 %P e28 %T A Smartphone App for Adolescents With Sleep Disturbance: Development of the Sleep Ninja %A Werner-Seidler,Aliza %A O'Dea,Bridianne %A Shand,Fiona %A Johnston,Lara %A Frayne,Anna %A Fogarty,Andrea S %A Christensen,Helen %+ Black Dog Institute, University of New South Wales, Hospital Road, Sydney, 2031, Australia, 61 293823769, a.werner-seidler@blackdog.org.au %K insomnia %K sleep %K adolescence %K depression %K cognitive behavioral therapy %K smartphone %D 2017 %7 28.07.2017 %9 Original Paper %J JMIR Ment Health %G English %X 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. %M 28754651 %R 10.2196/mental.7614 %U http://mental.jmir.org/2017/3/e28/ %U https://doi.org/10.2196/mental.7614 %U http://www.ncbi.nlm.nih.gov/pubmed/28754651 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 7 %P e111 %T Tamper-Resistant Mobile Health Using Blockchain Technology %A Ichikawa,Daisuke %A Kashiyama,Makiko %A Ueno,Taro %+ Sustainable Medicine, Inc., Nihonbashi Life Science Bldg 2, 3-11-5, Honcho, Nihonbashi, Chuo-ku, Tokyo, 103-0023, Japan, 81 3 3527 3593, t-ueno@umin.ac.jp %K telemedicine %K electronic health records %K sleep %K cognitive therapy %K computer security %D 2017 %7 26.07.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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. %M 28747296 %R 10.2196/mhealth.7938 %U http://mhealth.jmir.org/2017/7/e111/ %U https://doi.org/10.2196/mhealth.7938 %U http://www.ncbi.nlm.nih.gov/pubmed/28747296 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 4 %P e118 %T Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles %A Saeb,Sohrab %A Cybulski,Thaddeus R %A Schueller,Stephen M %A Kording,Konrad P %A Mohr,David C %+ Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, 750 N Lake Shore Dr, Rubloff Building, 10th floor, Chicago, IL, 60611, United States, 1 312 503 4626, s-saeb@northwestern.edu %K sleep monitoring %K mobile phones %K decision trees %K classification %D 2017 %7 18.4.2017 %9 Original Paper %J J Med Internet Res %G English %X 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. %R 10.2196/jmir.6821 %U http://www.jmir.org/2017/4/e118/ %U https://doi.org/10.2196/jmir.6821 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 4 %P e70 %T Mobile Phone-Delivered Cognitive Behavioral Therapy for Insomnia: A Randomized Waitlist Controlled Trial %A Horsch,Corine HG %A Lancee,Jaap %A Griffioen-Both,Fiemke %A Spruit,Sandor %A Fitrianie,Siska %A Neerincx,Mark A %A Beun,Robbert Jan %A Brinkman,Willem-Paul %+ Department of Intelligent Systems, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, Netherlands, 31 152784145, corinehorsch@gmail.com %K insomnia %K smartphone app %K cognitive behavioral therapy %K eHealth %D 2017 %7 11.04.2017 %9 Original Paper %J J Med Internet Res %G English %X 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) %M 28400355 %R 10.2196/jmir.6524 %U http://www.jmir.org/2017/4/e70/ %U https://doi.org/10.2196/jmir.6524 %U http://www.ncbi.nlm.nih.gov/pubmed/28400355 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 2 %P e37 %T Commencing and Persisting With a Web-Based Cognitive Behavioral Intervention for Insomnia: A Qualitative Study of Treatment Completers %A Chan,Charles %A West,Stacey %A Glozier,Nick %+ Brain and Mind Centre, University of Sydney, Professor Marie Bashir Centre Level 5, Building 11 RPAH 67-73 Missenden Road, Camperdown, 2050, Australia, 61 02 9966 7408, sccchan@gmail.com %K adherence %K persistence %K eHealth %K online intervention %K Web-based intervention %K motivations %K barriers %K insomnia %K depression %K men %D 2017 %7 10.02.2017 %9 Original Paper %J J Med Internet Res %G English %X 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) %M 28188124 %R 10.2196/jmir.5639 %U http://www.jmir.org/2017/2/e37/ %U https://doi.org/10.2196/jmir.5639 %U http://www.ncbi.nlm.nih.gov/pubmed/28188124 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 18 %N 4 %P e88 %T The Pros and Cons of Getting Engaged in an Online Social Community Embedded Within Digital Cognitive Behavioral Therapy for Insomnia: Survey Among Users %A Coulson,Neil S %A Smedley,Richard %A Bostock,Sophie %A Kyle,Simon D %A Gollancz,Rosie %A Luik,Annemarie I %A Hames,Peter %A Espie,Colin A %+ Division of Rehabilitation & Ageing, School of Medicine, University of Nottingham, Medical School, Queen's Medical Centre, University of Nottingham, Nottingham, NG7 2UH, United Kingdom, 44 1158466642, neil.coulson@nottingham.ac.uk %K engagement %K sleep %K online community %K discussion forum %K insomnia %K cognitive behavioral therapy %D 2016 %7 25.04.2016 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 27113540 %R 10.2196/jmir.5654 %U http://www.jmir.org/2016/4/e88/ %U https://doi.org/10.2196/jmir.5654 %U http://www.ncbi.nlm.nih.gov/pubmed/27113540 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 17 %N 9 %P e214 %T Adherence to Technology-Mediated Insomnia Treatment: A Meta-Analysis, Interviews, and Focus Groups %A Horsch,Corine %A Lancee,Jaap %A Beun,Robbert Jan %A Neerincx,Mark A %A Brinkman,Willem-Paul %+ Interactive Intelligence, Delft University of Technology, EWI HB, 12th Floor, Mekelweg 4, Delft, 2628 CD, Netherlands, 31 152784145, c.h.g.horsch@tudelft.nl %K sleep initiation and maintenance disorders %K patient compliance %K meta-analysis %K interview %K focus groups %K mobile apps %K user-computer interface %D 2015 %7 04.09.2015 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 26341671 %R 10.2196/jmir.4115 %U http://www.jmir.org/2015/9/e214/ %U https://doi.org/10.2196/jmir.4115 %U http://www.ncbi.nlm.nih.gov/pubmed/26341671 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 3 %P e87 %T Mobile App-Delivered Cognitive Behavioral Therapy for Insomnia: Feasibility and Initial Efficacy Among Veterans With Cannabis Use Disorders %A Babson,Kimberly A %A Ramo,Danielle E %A Baldini,Lisa %A Vandrey,Ryan %A Bonn-Miller,Marcel O %+ National Center for PTSD, VA Palo Alto Health Care System, 795 Willow Road, Menlo Park, CA, 94025, United States, 1 650 493 5000, kimberly.babson@gmail.com %K cannabis %K marijuana %K sleep %K CBT-I %K intervention %D 2015 %7 17.07.2015 %9 Original Paper %J JMIR Res Protoc %G English %X 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. %M 26187404 %R 10.2196/resprot.3852 %U http://www.researchprotocols.org/2015/3/e87/ %U https://doi.org/10.2196/resprot.3852 %U http://www.ncbi.nlm.nih.gov/pubmed/26187404 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 17 %N 6 %P e140 %T Characterizing Sleep Issues Using Twitter %A McIver,David J %A Hawkins,Jared B %A Chunara,Rumi %A Chatterjee,Arnaub K %A Bhandari,Aman %A Fitzgerald,Timothy P %A Jain,Sachin H %A Brownstein,John S %+ Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave., Boston, MA, 02115, United States, 1 902 213 9005, david.mciver@childrens.harvard.edu %K sleep issues %K social media %K insomnia %K novel methods %K sentiment %K depression %D 2015 %7 08.06.2015 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 26054530 %R 10.2196/jmir.4476 %U http://www.jmir.org/2015/6/e140/ %U https://doi.org/10.2196/jmir.4476 %U http://www.ncbi.nlm.nih.gov/pubmed/26054530 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 15 %N 10 %P e229 %T 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 %A Hedman,Erik %A Ljótsson,Brjánn %A Blom,Kerstin %A El Alaoui,Samir %A Kraepelien,Martin %A Rück,Christian %A Andersson,Gerhard %A Svanborg,Cecilia %A Lindefors,Nils %A Kaldo,Viktor %+ Osher Center for Integrative Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, Stockholm, 171 77, Sweden, 46 08 524 800 00, kire.hedman@ki.se %K Internet %K telephone %K self-report measures %K missing data %K method validation %D 2013 %7 18.10.2013 %9 Original Paper %J J Med Internet Res %G English %X 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-Åsberg 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. %M 24140566 %R 10.2196/jmir.2818 %U http://www.jmir.org/2013/10/e229/ %U https://doi.org/10.2196/jmir.2818 %U http://www.ncbi.nlm.nih.gov/pubmed/24140566