Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep

Chinoy, Evan D.; Cuellar, Joseph A; Jameson, Jason; Markwald, Rachel R.

doi:10.2147/nss.s395732

Cited by 8 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, our study focused on remote assessment and personalized sleep education and trainings only. In terms of remote assessment, incorporation of commercial wearable data adds an important objective measure of sleep (Kang et al, 2017;Kubala et al, 2020;Stone et al, 2020;Chinoy et al, 2023). However, commercial wearable data has not yet been validated for clinical use, and a number of participants reported dissatisfaction with having to wear a commercial sleep tracker device as part of this study.…”

Section: Perceptions Of Preliminary Research Resultsmentioning

confidence: 99%

Virtual first: implementation of a novel sleep telehealth platform in the United States military

Wickwire,

Collen,

Capaldi

et al. 2024

Front. Sleep

View full text Add to dashboard Cite

BackgroundThere is a gross shortage of sleep specialist providers within the military health system. Telehealth and mobile health represent promising approaches to increase access to high quality, cost-effective care in the U.S military.ObjectivesThis paper reports findings from a mixed-methods clinical implementation study of a novel sleep telehealth platform at two military treatment facilities in the National Capitol Region. The platform includes a mobile app and integrated wearable sensors (i.e., a commercial off-the-shelf sleep tracker [Fitbit]). The primary purpose was to evaluate the implementation of a 10-day remote monitoring assessment and provision of evidence-based sleep treatment recommendations to patients and providers. In addition, we sought to observe, in an exploratory manner, subsequent engagement with the app during 5 days of personalized sleep education and training.MethodsPatients with sleep problems completed an intensive 10-day remote monitoring assessment that included a baseline intake questionnaire, daily sleep diaries, twice daily symptom surveys, and Fitbit. Based on this assessment, patients received personalized assessment results. Concurrently, a provider report was generated that included provisional diagnoses and evidence-based treatment recommendations. Next, participants gained access to personalized sleep education and trainings within the mobile app. Within an established implementation science framework, outcomes were assessed via behavioral adherence (engagement with the app) and separate questionnaires for patients and providers. Last, we conducted four focus groups with patients and 12 key informant interviews with primary care managers (PCMs) and economic stakeholders to seek feedback and recommendations for future directions.ResultsTwo hundred and seventy patients participated in the study. Using validated research questionnaires, participants reported high-risk for obstructive sleep apnea (65.6%), moderate to severe insomnia (38.2%), and moderate to severe daytime sleepiness (38.5%), and moderate to severe anxiety (14.1%) and depressive (20.4%) symptoms. Total sleep time was 6.6 (SD = 1.8) h based on sleep diaries and 6.1 (SD = 1.8) h based on Fitbit. Regarding implementation, reach, effectiveness, adoption, implementation, and maintenance were all notably high, based on quantitative and qualitative data from participants and PCMs.ConclusionsSleep telehealth and mobile health represent promising approaches to increase access to cost-effective, evidence-based care for sleep disorders in the U.S. military.

show abstract

Section: Perceptions Of Preliminary Research Resultsmentioning

confidence: 99%

Virtual first: implementation of a novel sleep telehealth platform in the United States military

Wickwire,

Collen,

Capaldi

et al. 2024

Front. Sleep

View full text Add to dashboard Cite

show abstract

“…Studies employing Fitbit devices in general healthy adults indicates similar agreement between sleep duration reported in sleep logs and that derived from the Fitbit Inspire 2 [ 28 ] and Fitbit Flex [ 29 ] devices. In other studies of healthy young adults, Fitbit was able to estimate TIB, TST, SE%, sleep onset latency, and wake after sleep onset with minimal bias during the nocturnal sleep episodes [ 30 ] but mishandled daytime sleep episodes [ 31 ]. This aligns with our observation of low CCC values related to data variability from daytime sleep episodes.…”

Section: Discussionmentioning

confidence: 99%

Pilot study comparing sleep logs to a commercial wearable device in describing the sleep patterns of physicians-in-training

Hassinger,

Kwon,

Wang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

With the increasing burden of professional burnout in physicians, attention is being paid to optimizing sleep health, starting in training. The multiple dimensions of physicians’ sleep are not well described due to obstacles to easily and reliably measuring sleep. This pilot study tested the feasibility of using commercial wearable devices and completing manual sleep logs to describe sleep patterns of medical students and residents. Prospective pilot study of 50 resident physicians and medical students during a single year of training. Participants completed a manual sleep log while concurrently wearing the Fitbit Inspire device for 14-consecutive days over three clinical rotations of varying work schedules: light, medium, and heavy clinical rotations. Study completion was achieved in 24/50 (48%) participants. Overall correlation coefficients between the sleep log and Fitbit were statistically low; however, the discrepancies were acceptable, i.e., Fitbit underestimated time in bed and total sleep time by 4.3 and 2.7 minutes, respectively. Sleep onset time and waketime were within 8 minutes, with good agreement. Treatment of sleep episodes during the day led to variance in the data. Average missingness of collected data did not vary between medical students or residents or by rotation type. When comparing the light to heavy rotations, hours slept went from 7.7 (±0.64) to 6.7 (±0.88), quality-of-life and sleep health decreased and stress, burnout, and medical errors increased. Burnout was significantly associated with worse sleep health, hours worked, and quality-of-life. Prospective data collection of sleep patterns using both sleep logs and commercial wearable devices is burdensome for physicians-in-training. Using commercial wearable devices may increase study success as long as attention is paid to daytime sleep. In future studies investigating the sleep of physicians, the timing of data collection should account for rotation type.

show abstract

“…To guide our determination of sample size, we drew insights from pertinent studies in related areas [19][20][21][22]. These studies explored the daytime sleep-tracking performance of commercial wearable devices at home [19], the reproducibility of a standardized actigraphy scoring algorithm for sleep in a US Hispanic or Latino population [22], the validation of consumer sleep wearable devices with actigraphy and polysomnography in adolescents [20], and the comparison of consumer sleep-tracking devices with polysomnography [21]. The participant counts in these studies ranged from 16 to 58, and the data collection spanned from 112 to 350 person-days [19][20][21][22].…”

Section: Methodsmentioning

confidence: 99%

Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study

Lin,

Chen,

Chuang

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background The sleep and circadian rhythm patterns associated with smartphone use, which are influenced by mental activities, might be closely linked to sleep quality and depressive symptoms, similar to the conventional actigraphy-based assessments of physical activity. Objective The primary objective of this study was to develop app-defined circadian rhythm and sleep indicators and compare them with actigraphy-derived measures. Additionally, we aimed to explore the clinical correlations of these indicators in individuals with insomnia and healthy controls. Methods The mobile app “Rhythm” was developed to record smartphone use time stamps and calculate circadian rhythms in 33 patients with insomnia and 33 age- and gender-matched healthy controls, totaling 2097 person-days. Simultaneously, we used standard actigraphy to quantify participants’ sleep-wake cycles. Sleep indicators included sleep onset, wake time (WT), wake after sleep onset (WASO), and the number of awakenings (NAWK). Circadian rhythm metrics quantified the relative amplitude, interdaily stability, and intradaily variability based on either smartphone use or physical activity data. Results Comparisons between app-defined and actigraphy-defined sleep onsets, WTs, total sleep times, and NAWK did not reveal any significant differences (all P>.05). Both app-defined and actigraphy-defined sleep indicators successfully captured clinical features of insomnia, indicating prolonged WASO, increased NAWK, and delayed sleep onset and WT in patients with insomnia compared with healthy controls. The Pittsburgh Sleep Quality Index scores were positively correlated with WASO and NAWK, regardless of whether they were measured by the app or actigraphy. Depressive symptom scores were positively correlated with app-defined intradaily variability (β=9.786, SD 3.756; P=.01) and negatively correlated with actigraphy-based relative amplitude (β=–21.693, SD 8.214; P=.01), indicating disrupted circadian rhythmicity in individuals with depression. However, depressive symptom scores were negatively correlated with actigraphy-based intradaily variability (β=–7.877, SD 3.110; P=.01) and not significantly correlated with app-defined relative amplitude (β=–3.859, SD 12.352; P=.76). Conclusions This study highlights the potential of smartphone-derived sleep and circadian rhythms as digital biomarkers, complementing standard actigraphy indicators. Although significant correlations with clinical manifestations of insomnia were observed, limitations in the evidence and the need for further research on predictive utility should be considered. Nonetheless, smartphone data hold promise for enhancing sleep monitoring and mental health assessments in digital health research.

show abstract

Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep

Cited by 8 publications

References 43 publications

Virtual first: implementation of a novel sleep telehealth platform in the United States military

Virtual first: implementation of a novel sleep telehealth platform in the United States military

Pilot study comparing sleep logs to a commercial wearable device in describing the sleep patterns of physicians-in-training

Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study

Contact Info

Product

Resources

About