Sleep Tracking Research Articles

The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study.

BackgroundObstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown.ObjectiveWe aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy.MethodsThirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed.ResultsIn total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t667=28.28; P<.001; 95% limits of agreement [LoA] –2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t1516=31.99; P<.001; 95% LoA –2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively.Conclusionsarm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness.

Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update

Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update

A longitudinal analysis of COVID-19 lockdown stringency on sleep and resting heart rate measures across 20 countries

Lockdowns imposed to stem the spread of COVID-19 massively disrupted the daily routines of many worldwide, but studies to date have been mostly confined to observations within a limited number of countries, based on subjective reports and surveys from specific time periods during the pandemic. We investigated associations between lockdown stringency and objective sleep and resting-heart rate measures in ~ 113,000 users of a consumer sleep tracker across 20 countries from Jan to Jul 2020, compared to an equivalent period in 2019. With stricter lockdown measures, midsleep times were universally delayed, particularly on weekdays, while midsleep variability and resting heart rate declined. These shifts (midsleep: + 0.09 to + 0.58 h; midsleep variability: − 0.12 to − 0.26 h; resting heart rate: − 0.35 to − 2.08 bpm) correlated with the severity of lockdown across different countries (all Ps < 0.001) and highlight the graded influence of stringency lockdowns on human physiology.

«Можно ли это изменить?» Ролевое экспериментирование подростков в процессе дигитально опосредованного социально-эмоционального обучения

Schools are increasingly focusing on ways to increase adolescents’ self-efficacy in areas of their life from relationships to sleep. Commercially available apps such as mood and sleep trackers may be able to sup-port this process. This paper draws on Rubtsova’s explication of “role experimentation” and on Vygotsky’s reading of Freud to delineate dynamics within adolescent personality development. This theoretical back-ground is utilized in a study of seven 12—13 year-olds in a secondary school in London. Participants used an app designed by other students to track their sleep for two weeks. Their data mediated dramatic situations while discussing their experiences with their peers in the study. This process supported self-reflection and helped participants develop concepts for talking about their everyday life in subsequent one-on-one interviews. In negotiating peer and student roles, participants experimented with scientific and everyday concepts, allowing them to see their own data and the experiences it signified from a new angle.

The Neoliberalization of Sleep

This article explores the implications of sleep apps which are sociologically significant in that they represent an attempt to colonize, exploit, and make profitable one of the last vestiges of the human lifeworld through discourses of self-subjectification, authenticity, and self-improvement. I assess the websites of two sleep tracking apps (Pillow and Sleep Score) using critical discourse analysis (CDA), new materialism, and autoethnography. I make the case that the neoliberal values associated with the use of these apps perpetuate the logic that a better sleep makes for a more productive worker, better citizen, and ideal consumer subject. I also demonstrate how these apps function to open new sites of potential profit and reproduce a form of embodied neoliberal subjectivity generated by intra-active entanglements between identities, technologies, and discourses. Finally, I take up the issue of marginalization and intersecting subject positions as it relates to inequalities that these sleep trackers might exacerbate.

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring.

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.

Effects of window opening on the bedroom environment and resulting sleep quality

The effects of two window states (closed or open) on the bedroom environment and on sleep quality were investigated. Twenty-seven subjects (14 males and 13 females, 20–33 years old) without sleep disorders and chronic diseases participated. The subjects slept for two consecutive nights with windows open and two consecutive nights with windows closed in four dormitory rooms adapted for the purpose of this study, one person at a time. The order of exposure was balanced among participants. Bed temperature, room temperature, relative humidity (RH), carbon dioxide (CO2), particles (PM2.5), and noise were monitored during sleep. Sleep quality was measured using subjective ratings, a wrist-worn sleep tracker, and (for one group of 14 subjects only) polysomnography (PSG) for home use; snoring in this sub-group and awakenings were also registered. Higher PM2.5 and noise levels were found with windows open, while higher room temperature, RH, and CO2 levels were measured with windows closed. There were no differences between conditions in terms of objectively measured sleep stages but the subjects with the PSG attached snored significantly less and woke up significantly less often when sleeping with windows open. Start sleep time, end sleep time, total sleep time (TST) and time in bed (TIB) measured with the sleep tracker were confirmed by the measurements made using PSG, light sleep (N1 + N2) and sleep latency were in moderate agreement but there was no significant agreement for REM and deep sleep (N3). When sleeping with windows open, the subjects rated the air as fresher but reported higher noise levels, feeling less rested, a worse mental state and well-being, and their replies on the Groningen sleep quality scale indicated poorer sleep quality. There was no clear association between the performance test score and sleep quality. These results suggest that sleeping with windows open can provide some benefits by increasing ventilation with outdoor air, reducing CO2 concentrations, improving air quality as indicated by the subjectively rated air freshness and some of the parameters defining sleep quality, but it may also result in some discomfort if there are episodic loud noise events outdoors. Further studies are required to clarify the role of open windows in achieving good sleep quality.

As Consumer Sleep Trackers Gain in Popularity, Sleep Neurologists Seek More Data to Assess How to Use Them in Practice

As Consumer Sleep Trackers Gain in Popularity, Sleep Neurologists Seek More Data to Assess How to Use Them in Practice

278 Understanding Perspectives on Artificial Intelligence Technologies for Sleep Self-Management

Abstract Introduction Until recently, understanding one’s sleep activity relied on technology only available in sleep labs with data analyzed by experts. Transitioning this technology from the lab to natural environments results in noisy data. Fortunately, advances in signal processing through Artificial Intelligence (AI) have made these technologies accessible to consumers. This study seeks to provide recommendations that address user preferences and concerns related to sleep self-management devices and software that leverage AI, as they have the potential to increase both the quantity and quality of sleep data available to researchers. Methods We assigned adult participants (N=25) with Pittsburgh Sleep Quality Index scores ≥ 5 (indicating low sleep quality) to one of four focus group sessions based on their self-reported prior use of sleep technologies. After a short demonstration, the moderator solicited participant feedback on devices and software in each of the following four categories: • headbands (Beddr, Dreem 2, Muse S) • sleep tracking mats (Withings) • snoring detectors (Smart Nora) • mobile applications (Sleep Cycle Alarm Clock, Sleep Score, Do I Snore, Sleep Rate) Results Participants anticipated discomfort from wearing headbands and placing snoring detectors under their pillow, although a subset of participants indicated that they would be willing to sacrifice comfort in exchange for improved accuracy. Conversely, participants were interested in sleep tracking pads since they could passively collect sleep data without additional burden. Similarly, participants viewed mobile applications positively due to their ability to collect sleep data from a nightstand rather than being attached to the participant; however, there were concerns about remembering to activate these applications. Conclusion Based on these results, we recommend using sleep tracking mats to collect patient-generated sleep data due to their ease of use and relative comfort, the main concerns related to lab-based sleep study participation. As a passive sensor, these require the least setup and support consistent data collection. Other devices run the risk of participants forgetting to use the device or becoming removed during the night resulting in missing data. By leveraging these existing technologies for remote sleep studies, researchers can increase recruitment and accessibility to promote sleep research participant diversity. Support (if any):

274 Multimodal longitudinal sleep tracking combining wearable, smartphone tap analysis and electronic questionnaires

Abstract Introduction The proliferation of wearable and smartphone technologies has enabled continuous monitoring of sleep using data from different channels (physiological [wearables], behavioural [phone usage] and ecological momentary assessment [EMA self-report]). As these modalities use different methods to assess sleep, information gaps suggested by discrepancies between estimates may be filled in through cross-referencing among the modalities to produce a more accurate sleep measurement. Moreover, the pattern of discrepancies could inform about specific sleep and peri-sleep behaviors (e.g. phone use before bedtime). Methods 198 staff and students from the National University of Singapore (61 male, mean age 26.15±5.83 years) were recruited for an 8-week study. Sleep timings were assessed daily from three modalities: a wearable sleep and activity tracker (Oura ring), estimations from smartphone touchscreen interactions (tappigraphy) and smartphone derived EMA self-reports. Sleep estimates from the different modalities were compared for agreement (bivariate correlation) and discrepancies (t-test). Additionally, clustering analysis of high-discrepancy nights (&amp;gt;1h discrepancy between modalities) was performed to identify pattens of sleep behaviors that could lead to specific discrepancies. Results Adherence throughout the 8-week monitoring period (total 11,088 nights) was = high for the Oura ring; 9826 nights [80%]), Tappigraphy; 9740 nights [88%)), and EMA; 9166 nights [83%]). Sleep estimates across the three modalities showed high agreement (r=0.79-.91), with some discrepancies: Relative to self-report data, Oura wake time tended to be a later (Mean diff=9mins, t=18.58, p&amp;lt;.001), while tappigraphy estimates of bedtime tended to be early (Mean diff=15mins, t=26.48, p&amp;lt;.001). On 23% of nights (1755 nights), however, large discrepancies were detected (&amp;gt;1h). K-means clustering identified three distinct patterns of discrepancy, which were dominantly expressed in different individuals. Group comparison revealed that these individuals differed in demographic variables (age, student/work status), sleep variables (sleep timing, duration, subjective sleepiness), and phone usage characteristics (overall and pre-bedtime phone usage). Conclusion These data show that the combined use of three streams of data concerning sleep is complementary. Moreover, discrepancy patterns provide specific insights into sleep and peri-sleep behaviors facilitating digital phenotyping. Support (if any) This research was supported by the National Medical Research Council Singapore (NMRC/STaR/015/2013 and NMRC/STaR19may-0001).

Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth.

The rapid growth of point-of-care polysomnographic alternatives has necessitated standardized evaluation and validation frameworks. The current average across participant validation methods may overestimate the agreement between wearable sleep tracker devices and polysomnography (PSG) systems because of the high base rate of sleep during the night and the interindividual difference across the sampling population. This study proposes an evaluation framework to assess the aggregating differences of the sleep architecture features and the chronologically epoch-by-epoch mismatch of the wearable sleep tracker devices and the PSG ground truth. An AASM-based sleep stage categorizing method was proposed to standardize the sleep stages scored by different types of wearable trackers. Sleep features and sleep stage architecture were extracted from the PSG and the wearable device’s hypnograms. Therefrom, a localized quantifier index was developed to characterize the local mismatch of sleep scoring. We evaluated different commonly used wearable sleep tracking devices with the data collected from 22 different subjects over 30 nights of 8-h sleeping. The proposed localization quantifiers can characterize the chronologically localized mismatches over the sleeping time. The outperformance of the proposed method over existing evaluation methods was reported. The proposed evaluation method can be utilized for the improvement of the sensor design and scoring algorithm.

231 COVID-19 Lockdown Policies Across 20 Countries Modulate Sleep and Resting Heart Rate Measures

IntroductionLockdowns imposed to stem the spread of COVID-19 have disrupted the lifestyles of many worldwide, but studies to date are mostly confined to observations within a limited number of countries, based on subjective reports and survey from a narrow time window. In the present study, we investigate associations between the severity of lockdown policies and objective sleep and resting-heart rate measures.MethodsData from 113,000 users of a consumer sleep tracker across 20 countries were gathered between Jan–Jul 2020 and compared with an equivalent period in 2019 as a control for naturally occurring seasonal fluctuations. Lockdown stringency was derived using scores from the Oxford Government Response Tracker. Multilevel growth curve models were used to quantify the effect of lockdown stringency on changes to sleep patterns (midsleep time and midsleep variability) and resting heart rate changes, and to predict changes in resting heart rate from changes to sleep patterns.ResultsLockdown severity modulated the size of shifts in sleep midpoint and regularity during this period. Midsleep times were delayed in all countries during strict lockdowns, particularly on weekdays, while midsleep variability reduced. The largest shifts in midsleep time (+0.09 to +0.58 hours), midsleep variability (–0.12 to –0.26 hours) and resting heart rate (–0.35 to –2.08 bpm) occurred in April and May - when most countries imposed their strictest lockdown measures. In addition, multilevel modelling revealed that for each unit increase in stringency index, midsleep time was delayed by 0.96 min, midsleep variability decreased by 0.46 min and resting heart rate decreased by 0.06 bpm. Finally, in models predicting changes in resting heart rate from changes to sleep patterns, midsleep variability was shown to be the strongest predictor of resting heart rate, wherein an hour increase in the standard deviation of midsleep variability predicted a 5.12 increase in bpm, while an hour increase in midsleep time only predicted a 1.25 decrease in bpm.ConclusionOur findings demonstrate the utility of large-scale data from consumer wearables in providing population-level insights into how lockdown severity directly impacts sleep health during this pandemic period.Support (if any)Work conducted at NUS is supported by a grant awarded to Michael Chee (NMRC/STAR19may-0001).

254 Validation of a Non-Wearable Sleep Tracking Device in Healthy Adults Under Normal and Restricted Sleep Conditions

Abstract Introduction Polysomnography (PSG) is the gold standard for measuring sleep, but this method is cumbersome, costly, and sometimes does not reflect naturalistic sleep patterns. Leading technology companies have developed non-wearable sleep tracking devices that have attracted public interest. However, the accuracy of these devices has either been shown to be poor or the validation tests have not been conducted by independent laboratories without potential conflicts of interest. Relative to PSG and actigraphy, and under conditions of both normal and restricted sleep, we assessed the accuracy of early and newer versions of a non-wearable sleep tracking device (Beddit, Apple Inc.). Methods Participants were 35 healthy young adults (Mage=18.97, SD=0.95 years; 77.14% female; 42.86% Caucasian). We randomly assigned them to go to bed at 10:30pm (normal sleep) or 1:30am (restricted sleep) in a controlled sleep laboratory environment. Lights-on was 7:00am for all participants. Sleep was measured by the early version (3.0) or newer version (3.5) of a non-wearable device that uses a sensor strip to measure movement, heart rate, and breathing. We also measured PSG, wristband actigraphy, and self-report. For each device, we tested accuracy against PSG for total sleep time (TST), sleep efficiency (SE%), sleep onset latency (SOL), and wake after sleep onset (WASO). Results While the early version displayed poor reliability (ICCs&amp;lt;0.30), the newer version of the non-wearable device yielded excellent reliability with PSG under both normal and restricted sleep conditions. Not only was agreement excellent for TST (ICC=0.96) and SE% (ICC=0.98), but agreement was also excellent for the notoriously difficult metrics of SOL (ICC=0.92) and WASO (ICC=0.92). This newer version significantly outperformed clinical grade actigraphy (ICCs often in the 0.40 to 0.75 range), and self-reported sleep (ICCs often below 0.40). Conclusion Surprisingly, a non-wearable device demonstrated greater agreement with PSG than clinical grade actigraphy. Though the field has generally been skeptical of commercial non-wearable devices, this independent validation provides optimism that some such devices would be efficacious for research in healthy adults. Future work is needed to test the validity of this device in older adults and clinical populations. Support (if any) National Science Foundation (1920730 and 1943323)

269 Sleep Enhancement Technology: A Survey of Apps

Abstract Introduction The ever-evolving market for sleep technologies far outpaces the ability of providers to understand and counsel patients about developments in this area. Although significant literature has validated the performance of sleep tracking technologies, there is little evidence regarding sleep enhancement technologies. Our study systematically surveys currently available commercial sleep enhancement smartphone applications to empower both providers and patients alike. Methods We systematically searched the App Store (Apple) and Google Play Store (Android) in the US on 26 MAY 2020 using the keyword “sleep.” This survey is inclusive of all smartphone applications found. Results We identified 342 apps: 70.2% were found on Android (N=240) and 29.8% on Apple (N=102). Ninety-five percent of apps offer a free version. The majority of sleep apps are intended for use during wake (65.8% exclusively during wake; 28.7% during both wake and sleep), with only 5.6% intended to be used during sleep alone. Most apps purport to enhance rather than measure sleep (78.7% versus 1.8%). The vast majority of apps claim to enhance sleep via reductions in sleep latency (65.8%). Reduced sleep latency is primarily achieved using a combination of non-verbal auditory stimuli such as nature sounds (84.4%), artificial stimuli (64.5%), and instrumental music (77.1%). Conclusion Interestingly, most sleep apps are designed to be used while awake, prior to sleep, and focus on the enhancement of sleep, rather than measurement, by targeting sleep latency. Given the multitude of available sleep enhancement apps, many of which are free to try, these should be considered a reasonable strategy for providers and consumers to consider for empowering patients to improve sleep! Support (if any) Department of Defense Military Operational Medicine Research Program (MOMRP)

260 Beyond Validation Testing: A Sleep Tracker for Longitudinal Data Collection in Operational Environments

Abstract Introduction Sleep tracking wearables are increasingly being validation tested against polysomnography (PSG) and actigraphy, but they may not be ideal for long-term epidemiological sleep studies, or for use in operational environments. A given device’s short battery life, limited data storage capacity, inability to detect naps or estimate sleep architecture, or privacy concerns may discourage researchers from using wearables to collect objective sleep data in real-world settings. The Zulu watch (Institutes for Behavior Resources) is designed to collect longitudinal sleep data in operational populations with irregular sleep patterns, such as long-haul pilots or shift workers. It is capable of on-wrist sleep-wake determination, nap detection, on-wrist sleep depth scoring (i.e., interrupted sleep, light sleep, or deep sleep), on-wrist data storage up to 80 sleep intervals, and year-long battery life. Laboratory testing is an important initial step toward establishing the performance of a device for longitudinal real-world sleep evaluation; therefore, the Zulu watch sleep tracking was subjected to testing against gold-standard PSG and actigraphy. Methods Eight healthy young adult participants (30.4±3.2 years; mean±SD) wore a Zulu watch and Philips Respironics Actiwatch 2 simultaneously over a 3-day laboratory PSG sleep study, with 8 hours time-in-bed each night. Overall epoch-by-epoch agreement of sensitivity (for sleep), specificity (for wake), and accuracy of Zulu watch data were tested against PSG and Actiwatch 2. Results Compared with either PSG or actigraphy, both accuracy and sensitivity for Zulu watch sleep-wake determination were &amp;gt;90% while specificity was low (~26% vs. PSG, ~33% vs. actigraphy). Accuracy for sleep scoring vs. PSG was ~87% for interrupted sleep, ~52% for light sleep, and ~49% for deep sleep. Conclusion The Zulu watch showed mixed results but may be a viable candidate for sleep evaluation based on initial laboratory performance testing in healthy adults. The next steps will be to compare the Zulu watch against self-report of sleep in operational and substance use disorder populations. Longitudinal epidemiological sleep studies can become more feasible if technology is tailored to the specific needs of the real-world environment. Support (if any) Medical Technology Enterprise Consortium award MTEC-17-08-Multi-Topic-0104; Office of Naval Research, Code 34.

How are Consumer Sleep Technology Data Being Used to Deliver Behavioral Sleep Medicine Interventions? A Systematic Review

ABSTRACT Background: The rapid growth of consumer sleep technology demonstrates the population’s interest in measuring sleep. However, the extent to which these devices can be used in the delivery of behavioral sleep interventions is currently unknown. The objectives of this systematic review were to evaluate the use of consumer sleep technology (wearable and mobile) in behavioral sleep medicine interventions, identify gaps in the literature and potential future directions. Methods: We completed a scoping review of studies conducted in adult populations that used consumer sleep tracking technology to deliver sleep-related interventions. Results: Our initial search returned 4,538 articles and 14 articles met our inclusion/exclusion criteria. Results demonstrated that wearable devices are being used for two main purposes: 1. To deliver treatment for insomnia and 2. Sleep monitoring as part of overall wellness programs. Half of the articles reviewed (n = 7) used consumer sleep technology in a cognitive behavioral therapy for insomnia. The majority of the studies reviewed (n = 10) were fully digital, without human intervention, and only two small studies evaluated interventions delivered with and without a sleep tracking device. Conclusions: These studies demonstrate opportunities to utilize consumer sleep trackers in insomnia treatment and wellness programs, but most new and innovative interventions are in the early, feasibility stages. Future research is needed to determine how to leverage wearables to improve existing behavioral sleep treatments and determine how this technology can engage patients and reduce barriers to behavioral sleep medicine interventions.

Impact of COVID-19 social-distancing on sleep timing and duration during a university semester.

Social-distancing directives to contain community transmission of the COVID-19 virus can be expected to affect sleep timing, duration or quality. Remote work or school may increase time available for sleep, with benefits for immune function and mental health, particularly in those individuals who obtain less sleep than age-adjusted recommendations. Young adults are thought to regularly carry significant sleep debt related in part to misalignment between endogenous circadian clock time and social time. We examined the impact of social-distancing measures on sleep in young adults by comparing sleep self-studies submitted by students enrolled in a university course during the 2020 summer session (entirely remote instruction, N = 80) with self-studies submitted by students enrolled in the same course during previous summer semesters (on-campus instruction, N = 452; cross-sectional study design). Self-studies included 2–8 week sleep diaries, two chronotype questionnaires, written reports, and sleep tracker (Fitbit) data from a subsample. Students in the 2020 remote instruction semester slept later, less efficiently, less at night and more in the day, but did not sleep more overall despite online, asynchronous classes and ~44% fewer work days compared to students in previous summers. Subjectively, the net impact on sleep was judged as positive or negative in equal numbers of students, with students identifying as evening types significantly more likely to report a positive impact, and morning types a negative impact. Several features of the data suggest that the average amount of sleep reported by students in this summer course, historically and during the 2020 remote school semester, represents a homeostatic balance, rather than a chronic deficit. Regardless of the interpretation, the results provide additional evidence that social-distancing measures affect sleep in heterogeneous ways.

Internet of Things for Sleep Monitoring

The Internet of Things (IoT) technologies, including wearables and non-wearables, enable and facilitate continuous sleep monitoring in a completely unobtrusive manner. This paper presents an overview of such technologies with a prototype IoT sleep tracking platform as an experimental demonstration. Such IoT sleep devices, which measure physiological and/or environmental parameters non-intrusively, may help individuals optimize their sleep location and environment to get the most rest and improve their health and quality of life. Overall, this paper demonstrates the feasibility of conducting an IoT-based at-home sleep study and assists in planning for future IoT-based sleep studies.

Engineering a mobile platform to promote sleep in the pediatric primary care setting.

Study ObjectivesPediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion. The objective of this study is to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep.MethodsChildren aged 10–12 years were enrolled (Study #1: N = 30; Study #2: N = 43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms.ResultsIn study #1, 1514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: −8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms.ConclusionsWe have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework.Clinical TrialsBoth studies included in this manuscript were registered at clinicaltrials.gov:-Study #1: NCT03263338-Study #2: NCT03426644

Multi-Night Validation of a Sleep Tracking Ring in Adolescents Compared with a Research Actigraph and Polysomnography.

BackgroundWearable devices have tremendous potential for large-scale longitudinal measurement of sleep, but their accuracy needs to be validated. We compared the performance of the multisensor Oura ring (Oura Health Oy, Oulu, Finland) to polysomnography (PSG) and a research actigraph in healthy adolescents.MethodsFifty-three adolescents (28 females; aged 15–19 years) underwent overnight PSG monitoring while wearing both an Oura ring and Actiwatch 2 (Philips Respironics, USA). Measurements were made over multiple nights and across three levels of sleep opportunity (5 nights with either 6.5 or 8h, and 3 nights with 9h). Actiwatch data at two sensitivity settings were analyzed. Discrepancies in estimated sleep measures as well as sleep-wake, and sleep stage agreements were evaluated using Bland–Altman plots and epoch-by-epoch (EBE) analyses.ResultsCompared with PSG, Oura consistently underestimated TST by an average of 32.8 to 47.3 minutes (Ps < 0.001) across the different TIB conditions; Actiwatch 2 at its default setting underestimated TST by 25.8 to 33.9 minutes. Oura significantly overestimated WASO by an average of 30.7 to 46.3 minutes. It was comparable to Actiwatch 2 at default sensitivity in the 6.5, and 8h TIB conditions. Relative to PSG, Oura significantly underestimated REM sleep (12.8 to 19.5 minutes) and light sleep (51.1 to 81.2 minutes) but overestimated N3 by 31.5 to 46.8 minutes (Ps < 0.01). EBE analyses demonstrated excellent sleep-wake accuracies, specificities, and sensitivities – between 0.88 and 0.89 across all TIBs.ConclusionThe Oura ring yielded comparable sleep measurement to research grade actigraphy at the latter’s default settings. Sleep staging needs improvement. However, the device appears adequate for characterizing the effect of sleep duration manipulation on adolescent sleep macro-architecture.