Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults

Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this challenge, we propose a deep learning architecture that combines a convolutional neural network and bidirectional long short-term memory to accurately classify sleep stages. By supplementing photoplethysmography (PPG) signals with respiratory sensor inputs, we demonstrated significant improvements in prediction accuracy and Cohen's kappa (k) for 2- (92.7 %; k = 0.768), 3- (80.2 %; k = 0.714), 4- (76.8 %, k = 0.550), and 5-stage (76.7 %, k = 0.616) sleep classification using raw data. This relatively translatable approach, with a less intensive AI model and leveraging only a few, inexpensive sensors, shows promise in accurately staging sleep. This has potential for diagnosing and managing sleep disorders in a more accessible and practical manner, possibly even at home.

Sleep Behavior in Royal Australian Navy Shift Workers by Shift and Exposure to the SleepTank App

INTRODUCTION

Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule.

MATERIALS AND METHODS

Royal Australian Navy volunteers agreed to wear a Fitbit Versa 2 with the SleepTank app (SleepTank condition), or without the SleepTank app (Controls), for up to 10 weeks from May to July 2023 during the trial of a novel shift rotation schedule. Participants from across 6 units worked a combination of early (6:00 AM to 2:00 PM), day (7:30 AM to 4:30 PM), late (4:00 PM to 12:00 AM), and night shifts (12:00 AM to 6:00 AM) or stable day shifts (6:00 AM to 4:00 PM). Differences in sleep behavior (time in bed, total sleep time, bedtime, wake time, sleep efficiency [SE]) between conditions and shift types were tested using Analysis of Variance. This study was approved by the Australian Departments of Defence and Veterans' Affairs Human Research Ethics Committee.

RESULTS

Thirty-four participants completed the full study (n = 17 Controls; n = 17 SleepTank). There was a significant effect of shift type on 24-hour time in bed (TIB24; F(4,9) = 8.15, P < .001, η2 = 0.15) and total sleep time (TST24; F(4,9) = 8.54, P < .001, η2 = 0.18); both were shorter in early shifts and night shifts compared to other shift types. TIB24 and TST24 were not significantly different between conditions, but there was a trend for greater SE in the SleepTank condition relative to Controls (F(1,9) = 2.99, P = .08, η2 = 0.11).

CONCLUSIONS

Sleep data collected by Fitbit Versa 2s indicated shorter sleep duration (TIB24, TST24) for Royal Australian Navy workers during early and late shifts relative to stable day shifts. Access to the SleepTank app did not greatly influence measures of sleep duration but may be protective against fatigue by affecting SE. Further research is needed to evaluate the utility of the SleepTank app as a means of improving sleep hygiene in real-world, shift-working environments.

The CCJR® Gerard A. Engh Excellence in Knee Research Award: Remote Monitoring of Sleep Disturbance Following Total Knee Arthroplasty: A Cautionary Note

BACKGROUND

Sleep disturbances are common after total knee arthroplasty (TKA). Despite the rising popularity of wearables to track sleep, little evidence exists in the arthroplasty literature regarding their efficacy. We aimed to correlate validated wearable sleep metrics with patient-reported sleep quality following TKA.

METHODS

Patients undergoing primary TKA were consecutively enrolled. Patients used a wearable device preoperatively and 90 days postoperatively to track five previously-validated measures of sleep. Each month, they rated their sleep quality. Wearable sleep data was correlated with patient-reported sleep quality using a point biserial correlation test. Categorical data were compared using Chi-square tests. A total of 110 patients were included.

RESULTS

Preoperatively, 20.8% of patients reported "fairly bad" or "very bad" sleep; this increased to 44.4% 30 days postoperatively, then decreased to 26.5% 60 days postoperatively, and to 20.2% 90 days postoperatively. At 30 days postoperatively, time in bed, time asleep, and minutes of rapid eye movement sleep weakly correlated with patient-reported sleep quality (correlations 0.356, 0.345, and 0.345, respectively; P < .001). Sleep quality did not correlate with any wearable metric collected 60 or 90 days postoperatively.

CONCLUSIONS

Patient-reported sleep quality following TKA initially worsened postoperatively, then improved to preoperative levels by 90 days. Time in bed, time asleep, and rapid eye movement sleep minutes only weakly correlated with patient-reported sleep quality at 30 days; no other correlations were detected. Surgeons that utilize remote monitoring following TKA should be aware that surrogate measures generated from these devices may correlate weakly, if at all, with the patient-reported outcome of the parameter being studied.

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

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.

Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program

Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.

Psychological endurance: how grit, resilience, and related factors contribute to sustained effort despite adversity

Many concepts describe how individuals sustain effort despite challenging circumstances. For example, scholars and practitioners may incorporate discussions of grit, hardiness, self-control, and resilience into their ideas of performance under adversity. Although there are nuanced points underlying each construct capable of generating empirically sound propositions, the shared attributes make them difficult to differentiate. As a result, substantial confusion arises when debating how these related factors concomitantly contribute to success, especially when practitioners attempt to communicate these ideas in applied settings. The model proposed here-psychological endurance-is a unified theory to explore how multiple concepts contribute to sustained goal-directed behaviors and individual success. Central to this model is the metaphor of a psychological battery, which potentiates and sustains optimal performance despite adversity. Grit and hardiness are associated with the maximum charge of the psychological battery, or how long an individual could sustain effort. Self-control modulates energy management that augments effort required to sustain endurance, whereas resilience represents the ability to recharge. These factors are constrained by both psychological and physiological stressors in the environment that drain the psychology battery. Taken together, these ideas form a novel framework to discuss related psychological concepts, and ideally, optimize intervention to enhance psychological endurance.

The Integration of Sleep Research Into Eating Disorders Research: Recommendations and Best Practices

OBJECTIVE

Sleep disturbance is common among individuals with eating disorders (EDs), with approximately 50% of patients with EDs reporting sleep disturbance. Sleep problems may promote, exacerbate, or maintain ED symptoms through a variety of hypothesized mechanisms, such as impaired executive function, increased negative affect, and disruptions to appetitive rhythms. Although research investigating the role of sleep in EDs is growing, the current literature suffers from methodological limitations and inconsistencies, which reduce our ability to translate findings to improve clinical practice. The purpose of this forum is to propose a coordinated approach to more seamlessly integrate sleep research into ED research with particular emphasis on best practices in the definition and assessment of sleep characteristics.

METHODS

In this article, we will describe the current status of sleep-related research and relevant gaps within ED research practices, define key sleep characteristics, and review common assessment strategies for these sleep characteristics. Throughout the forum, we also discuss study design considerations and recommendations for future research aiming to integrate sleep research into ED research.

RESULTS/DISCUSSION

Given the potential role of sleep in ED maintenance and treatment, it is important to build upon preliminary findings using a rigorous and systematic approach. Moving forward as a field necessitates a common lens through which future research on sleep and EDs may be conducted, communicated, and evaluated.

Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use

GOAL AND AIMS

To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement.

FOCUS TECHNOLOGY

Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X.

REFERENCE TECHNOLOGY

Polysomnography.

SAMPLE

Sixty-three participants (36 female) aged 20-68.

DESIGN

Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message.

CORE ANALYTICS

Detection and timing accuracy for the sleep onset times and wake times.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods.

CORE OUTCOMES

Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency.

IMPORTANT ADDITIONAL OUTCOMES

With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit.

CORE CONCLUSIONS

Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

Early adverse physiological event detection using commercial wearables: challenges and opportunities

Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

Advancing a U.S. navy shipboard infrastructure for sleep monitoring with wearable technology

Development of fatigue management solutions is critical to U.S. Navy populations. This study explored the operational feasibility and acceptability of commercial wearable devices (Oura Ring and ReadiBand) in a warship environment with 845 Sailors across five ship cohorts during at-sea operations ranging from 10 to 31 days. Participants were required to wear both devices and check-in daily with research staff. Both devices functioned as designed in the environment and reliably collected sleep-wake data. Over 10,000 person-days at-sea, overall prevalence of Oura and ReadiBand use was 69% and 71%, respectively. Individual use rates were 71 ± 38% of days underway for Oura and 59 ± 34% for ReadiBand. Analysis of individual factors showed increasing device use and less device interference with age, and more men than women found the devices comfortable. This study provides initial support that commercial wearables can contribute to infrastructures for operational fatigue management in naval environments.

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

LSD increases sleep duration the night after microdosing

Microdosing psychedelic drugs at a level below the threshold to induce hallucinations is an increasingly common lifestyle practice. However, the effects of microdosing on sleep have not been previously reported. Here, we report results from a Phase 1 randomized controlled trial in which 80 healthy adult male volunteers received a 6-week course of either LSD (10 µg) or placebo with doses self-administered every third day. Participants used a commercially available sleep/activity tracker for the duration of the trial. Data from 3231 nights of sleep showed that on the night after microdosing, participants in the LSD group slept an extra 24.3 min per night (95% Confidence Interval 10.3-38.3 min) compared to placebo-with no reductions of sleep observed on the dosing day itself. There were no changes in the proportion of time spent in various sleep stages or in participant physical activity. These results show a clear modification of the physiological sleep requirements in healthy male volunteers who microdose LSD. The clear, clinically significant changes in objective measurements of sleep observed are difficult to explain as a placebo effect. Trial registration: Australian New Zealand Clinical Trials Registry: A randomized, double-blind, placebo-controlled trial of repeated microdoses of lysergic acid diethylamide (LSD) in healthy volunteers; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381476 ; ACTRN12621000436875.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Harnessing technology to improve sleep in frontline healthcare workers: A pilot study of electronic noise-masking earbuds on subjective and objective sleep measures

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen's d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen's d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

Sleep Patterns of Premedical Undergraduate Students: Pilot Study and Protocol Evaluation

BACKGROUND

Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns.

OBJECTIVE

This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students?

METHODS

An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments.

RESULTS

According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI≥5) across cohorts. When students perceived no problem at all in their sleep quality, 50% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app's feature description was vague and could lead to confusion.

CONCLUSIONS

Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors.

The Negative Effects of Travel on Student Athletes Through Sleep and Circadian Disruption

Collegiate athletes must satisfy the academic obligations common to all undergraduates, but they have the additional structural and social stressors of extensive practice time, competition schedules, and frequent travel away from their home campus. Clearly such stressors can have negative impacts on both their academic and athletic performances as well as on their health. These concerns are made more acute by recent proposals and decisions to reorganize major collegiate athletic conferences. These rearrangements will require more multi-day travel that interferes with the academic work and personal schedules of athletes. Of particular concern is additional east-west travel that results in circadian rhythm disruptions commonly called jet lag that contribute to the loss of amount as well as quality of sleep. Circadian misalignment and sleep deprivation and/or sleep disturbances have profound effects on physical and mental health and performance. We, as concerned scientists and physicians with relevant expertise, developed this white paper to raise awareness of these challenges to the wellbeing of our student-athletes and their co-travelers. We also offer practical steps to mitigate the negative consequences of collegiate travel schedules. We discuss the importance of bedtime protocols, the availability of early afternoon naps, and adherence to scheduled lighting exposure protocols before, during, and after travel, with support from wearables and apps. We call upon departments of athletics to engage with sleep and circadian experts to advise and help design tailored implementation of these mitigating practices that could contribute to the current and long-term health and wellbeing of their students and their staff members.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices

AIMS

Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker).

FOCUS TECHNOLOGY

Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3.

REFERENCE TECHNOLOGY

In-lab polysomnography with 3-reader, consensus sleep scoring.

SAMPLE

Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years).

DESIGN

Overnight in a sleep laboratory from habitual sleep time to wake time.

CORE ANALYTICS

Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography).

CORE OUTCOMES

EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33).

IMPORTANT ADDITIONAL OUTCOMES

Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset).

CORE CONCLUSION

EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography

The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.

Improving the design of patient-generated health data visualizations: design considerations from a Fitbit sleep study

Interactive data visualization can be a viable way to discover patterns in patient-generated health data and enable health behavior changes. However, very few studies have investigated the design and usability of such data visualization. The present study aimed to (1) explore user experiences with sleep data visualizations in the Fitbit app, and (2) focus on end users' perspectives to identify areas of improvement and potential solutions. The study recruited eighteen pre-medicine college students, who wore Fitbit watches for a two-week sleep data collection period and participated in an exit semi-structured interview to share their experience. A focus group was conducted subsequently to ideate potential solutions. The qualitative analysis identified six pain points (PPs) from the interview data using affinity mapping. Four design solutions were proposed by the focus group to address these PPs and illustrated by a set of mock-ups. The study findings informed four design considerations: (1) usability, (2) transparency and explainability, (3) understandability and actionability, and (4) individualized benchmarking. Further research is needed to examine the design guidelines and best practices of sleep data visualization, to create well-designed visualizations for the general population that enables health behavior changes.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high-resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

iSleepFirst: burnout, fatigue, and wearable-tracked sleep deprivation among residents staffing the medical intensive care unit

PURPOSE

This study aimed to evaluate the relationship between sleep, burnout, and psychomotor vigilance in residents working in the medical intensive care unit (ICU).

METHODS

A prospective cohort study of residents was implemented during a consecutive 4-week. Residents were recruited to wear a sleep tracker for 2 weeks before and 2 weeks during their medical ICU rotation. Data collected included wearable-tracked sleep minutes, Oldenburg burnout inventory (OBI) score, Epworth sleepiness scale (ESS), psychomotor vigilance testing, and American Academy of Sleep Medicine sleep diary. The primary outcome was sleep duration tracked by the wearable. The secondary outcomes were burnout, psychomotor vigilance (PVT), and perceived sleepiness.

RESULTS

A total of 40 residents completed the study. The age range was 26-34 years with 19 males. Total sleep minutes measured by the wearable decreased from 402 min (95% CI: 377-427) before ICU to 389 (95% CI: 360-418) during ICU (p < 0.05). Residents overestimated sleep, logging 464 min (95% CI: 452-476) before and 442 (95% CI: 430-454) during ICU. ESS scores increased from 5.93 (95% CI: 4.89, 7.07) to 8.33 (95% CI: 7.09,9.58) during ICU (p < 0.001). OBI scores increased from 34.5 (95% CI: 32.9-36.2) to 42.8 (95% CI: 40.7-45.0) (p < 0.001). PVT scores worsened with increased reaction time while on ICU rotation (348.5 ms pre-ICU, 370.9 ms post-ICU, p < 0.001).

CONCLUSIONS

Resident ICU rotations are associated with decreased objective sleep and self-reported sleep. Residents overestimate sleep duration. Burnout and sleepiness increase and associated PVT scores worsen while working in the ICU. Institutions should ensure resident sleep and wellness checks during ICU rotation.

Validation of Polar Grit X Pro for Estimating Energy Expenditure during Military Field Training: A Pilot Study

Wearables are lightweight, portable technology devices that are traditionally used to monitor physical activity and workload as well as basic physiological parameters such as heart rate. However recent advances in monitors have enabled better algorithms for estimation of caloric expenditure from heart rate for use in weight loss as well as sport performance. can be used for estimating energy expenditure and nutritional demand. Recently, the military has adopted the use of personal wearables for utilization in field studies for ecological validity of training. With popularity of use, the need for validation of these devices for caloric estimates is needed to assist in work-rest cycles. Thus the purpose of this effort was to evaluate the Polar Grit X for energy expenditure (EE) for use in military training exercises. Polar Grit X Pro watches were worn by active-duty elite male operators (N = 16; age: 31.7 ± 5.0 years, height: 180.1 ± 6.2 cm, weight: 91.7 ± 9.4 kg). Metrics were measured against indirect calorimetry of a metabolic cart and heart rate via a Polar heart rate monitor chest strap while exercising on a treadmill. Participants each performed five 10-minute bouts of running at a self-selected speed and incline to maintain a heart rate within one of five heart rate zones, as ordered and defined by Polar. Polar Grit X Pro watch had a good to excellent interrater reliability to indirect calorimetry at estimating energy expenditure (ICC = 0.8, 95% CI = 0.61-0.89, F (74,17.3) = 11.76, p < 0.0001) and a fair to good interrater reliability in estimating macronutrient partitioning (ICC = 0.49, 95% CI = 0.3-0.65, F (74,74.54) = 2.98, p < 0.0001). There is a strong relationship between energy expenditure as estimated from the Polar Grit X Pro and measured through indirect calorimetry. The Polar Grit X Pro watch is a suitable tool for estimating energy expenditure in free-living participants in a field setting and at a range of exercise intensities.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study

BACKGROUND

Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography.

OBJECTIVE

This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment.

METHODS

The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography.

RESULTS

The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal.

CONCLUSIONS

Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study

BACKGROUND

Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people.

OBJECTIVE

This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population.

METHODS

Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard.

RESULTS

The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21).

CONCLUSIONS

These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.

A useful tool or a new challenge? Hand-wrist-worn sleep trackers in patients with insomnia

Consumer sleep wearables are increasingly popular, even among patients with sleep problems. However, the daily feedback provided by these devices could exacerbate sleep-related worry. To investigate this issue, 14 patients received a self-help guide booklet to improve sleep and wore the sleep tracker Fitbit Inspire 2 on their non-dominant hand for 4 weeks, while a control group of 12 patients only kept a handwritten sleep diary. All patients completed questionnaires at a primary care centre's first and final visit to assess general anxiety, sleep quality, sleep reactivity to stress, and quality of life. Our analysis showed that sleep quality, sleep reactivity to stress, and quality of life improved significantly for all patients between the first and final visit (p < 0.05). However, there were no significant differences between the Fitbit and control groups. Using sleep diary-derived estimates from the first and last week, we found that the control group but not the Fitbit group, increased their average time asleep each night and sleep efficiency (p < 0.05). However, these differences were primarily driven by baseline differences between the two groups. Our findings suggest that using wearables does not necessarily exacerbate sleep worries among people with insomnia.

Country differences in nocturnal sleep variability: Observations from a large-scale, long-term sleep wearable study

STUDY OBJECTIVES

Country or regional differences in sleep duration are well-known, but few large-scale studies have specifically evaluated sleep variability, either across the work week, or in terms of differences in weekday and weekend sleep.

METHODS

Sleep measures, obtained over 50 million night's sleep from ∼220,000 wearable device users in 35 countries, were analysed. Each person contributed an average of ∼242 nights of data. Multiple regression was used to assess the impact country of residence had on sleep duration, timing, efficiency, weekday sleep variability, weekend sleep extension and social jetlag.

RESULTS

Nocturnal sleep was shorter and had a later onset in Asia than other regions. Despite this, sleep efficiency was lower and weekday sleep variability was higher. Weekend sleep extension was longer in Europe and the USA than in Asia, and was only partially related to weekday sleep duration. There were also cross-country differences in social jetlag although the regional differences were less distinct than for weekend sleep extension.

CONCLUSIONS

In addition to regional differences in sleep duration, cross-country differences in sleep variability and weekend sleep extension suggest that using the latter as an indicator of sleep debt may need to be reconsidered. In countries exhibiting both short sleep and high weekday sleep variability, a culturally different means of coping with inadequate sleep is likely. Country or region differences in culture, particularly those related to work, merit closer examination as factors influencing the variability in normative sleep patterns around the world.

Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use?

As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.

Dawn of a New Dawn: Advances in Sleep Health to Optimize Performance

Optimal sleep health is a critical component to high-level performance. In populations such as the military, public service (eg, firefighters), and health care, achieving optimal sleep health is difficult and subsequently deficiencies in sleep health may lead to performance decrements. However, advances in sleep monitoring technologies and mitigation strategies for poor sleep health show promise for further ecological scientific investigation within these populations. The current review briefly outlines the relationship between sleep health and performance as well as current advances in behavioral and technological approaches to improving sleep health for performance.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights

GOAL AND AIMS

Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography.

SAMPLE

Twenty-one university students (10 females).

DESIGN

Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes.

CORE ANALYTICS

Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Variability of specificity and negative predictive value across subjects and across nights.

CORE OUTCOMES

Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively).

IMPORTANT ADDITIONAL OUTCOMES

Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights.

CORE CONCLUSION

This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea

GOAL AND AIMS

Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.

FOCUS TECHNOLOGY

Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30.

DESIGN

Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule.

CORE ANALYTICS

Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix.

CORE OUTCOMES

The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep.

CORE CONCLUSION

Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.

Classification of blood pressure during sleep impacts designation of nocturnal nondipping

The identification of nocturnal nondipping blood pressure (< 10% drop in mean systolic blood pressure from awake to sleep periods), as captured by ambulatory blood pressure monitoring, is a valuable element of risk prediction for cardiovascular disease, independent of daytime or clinic blood pressure measurements. However, capturing measurements, including determination of wake/sleep periods, is challenging. Accordingly, we sought to evaluate the impact of different definitions and algorithms for defining sleep onset on the classification of nocturnal nondipping. Using approaches based upon participant self-reports, applied definition of a common sleep period (12 am -6 am), manual actigraphy, and automated actigraphy we identified changes to the classification of nocturnal nondipping, and conducted a secondary analysis on the potential impact of an ambulatory blood pressure monitor on sleep. Among 61 participants in the Eastern Caribbean Health Outcomes Research Network hypertension study with complete ambulatory blood pressure monitor and sleep data, the concordance for nocturnal nondipping across methods was 0.54 by Fleiss' Kappa (depending on the method, 36 to 51 participants classified as having nocturnal nondipping). Sleep quality for participants with dipping versus nondipping was significantly different for total sleep length when wearing the ambulatory blood pressure monitor (shorter sleep duration) versus not (longer sleep duration), although there were no differences in sleep efficiency or disturbances. These findings indicate that consideration of sleep time measurements is critical for interpreting ambulatory blood pressure. As technology advances to detect blood pressure and sleep patterns, further investigation is needed to determine which method should be used for diagnosis, treatment, and future cardiovascular risk.

Sleep Disturbances Following Total Knee Arthroplasty

BACKGROUND

Sleep disturbances are common after total knee arthroplasty (TKA), yet literature examining sleep and postoperative pain remains sparse. With the use of wearable devices, convenient objective remote sleep monitoring is now possible. We aimed to measure patient sleep following TKA using validated questionnaires and wearable devices to compare sleep patterns to pain scores 90 days postoperatively.

METHODS

Adult patients with body mass index < 45 undergoing unilateral primary TKA were enrolled. Patients wore a monitor, which tracked sleep duration and disturbances (getting up at least once during the night). They completed weekly Pittsburgh Sleep Quality Index (PSQI) questionnaires and visual analog scale (VAS) pain scores. Sleep patterns were compared with pain scores and sleep duration was compared with PSQI responses.

RESULTS

There were 110 patients included with 54.5% women; average age was 64 years (range, 43-80). VAS scores decreased postoperatively. PSQI overall sleep scores, sleep quantity, and sleep quality worsened for the first 30 days then improved past baseline levels by 90 days. Recorded sleep duration did not change, and recordings did not correlate at any point with VAS scores. PSQI overall score and sleep quantity did not correlate with VAS. At 30 days postoperatively, patients reporting "very bad" sleep had significantly worse VAS scores than those reporting "bad" sleep.

CONCLUSION

Patient-reported sleep quality (very bad sleep) correlated well with VAS pain score at 30 days, while sleep duration (monitored or patient-reported) did not correlate with any clinical measure and does not seem to be a useful metric in assessing TKA outcome.

Sleep Patterns Fluctuate Following Training and Games across the Season in a Semi-Professional, Female Basketball Team

Quantifying athlete sleep patterns may inform development of optimal training schedules and sleep strategies, considering the competitive challenges faced across the season. Therefore, this study comprehensively quantified the sleep patterns of a female basketball team and examined variations in sleep between nights. Seven semi-professional, female basketball players had their sleep monitored using wrist-worn activity monitors and perceptual ratings during a 13-week in-season. Sleep variables were compared between different nights (control nights, training nights, training nights before games, nights before games, non-congested game nights, and congested game nights), using generalized linear mixed models, as well as Cohen's d and odds ratios as effect sizes. Players experienced less sleep on training nights before games compared to control nights, training nights, nights before games, and congested game nights (p < 0.05, d = 0.43-0.69). Players also exhibited later sleep onset times on non-congested game nights compared to control nights (p = 0.01, d = 0.68), and earlier sleep offset times following training nights before games compared to all other nights (p < 0.01, d = 0.74-0.79). Moreover, the odds of players attaining better perceived sleep quality was 88% lower on congested game nights than on nights before games (p < 0.001). While players in this study attained an adequate sleep duration (7.3 ± 0.3 h) and efficiency (85 ± 2%) on average across the in-season, they were susceptible to poor sleep on training nights before games and following games. Although limited to a team-based case series design, these findings suggest basketball coaches may need to reconsider scheduling team-based, on-court training sessions on nights prior to games and consider implementing suitable psychological and recovery strategies around games to optimize player sleep.

Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport

Technological advances in sleep monitoring have seen an explosion of devices used to gather important sleep metrics. These devices range from instrumented 'smart pyjamas' through to at-home polysomnography devices. Alongside these developments in sleep technologies, there have been concomitant increases in sleep monitoring in athletic populations, both in the research and in practical settings. The increase in sleep monitoring in sport is likely due to the increased knowledge of the importance of sleep in the recovery process and performance of an athlete, as well as the well-reported challenges that athletes can face with their sleep. This narrative review will discuss: (1) the importance of sleep to athletes; (2) the various wearable tools and technologies being used to monitor sleep in the sport setting; (3) the role that sleep tracking devices may play in gathering information about sleep; (4) the reliability and validity of sleep tracking devices; (5) the limitations and cautions associated with sleep trackers; and, (6) the use of sleep trackers to guide behaviour change in athletes. We also provide some practical recommendations for practitioners working with athletes to ensure that the selection of such devices and technology will meet the goals and requirements of the athlete.

Exploratory Validation of Sleep-Tracking Devices in Patients with Psychiatric Disorders

Purpose

Sleep-tracking devices have performed well in recent studies that evaluated their use in healthy adults by comparing them with the gold standard sleep assessment technique, polysomnography (PSG). These devices have not been validated for use in patients with psychiatric disorders. Therefore, we tested the performance of three sleep-tracking devices against PSG in patients with psychiatric disorders.

Patients and methods

In total, 52 patients (32 women; 48.1 ± 17.2 years, mean ± SD; 18 patients diagnosed with schizophrenia, 19 with depressive disorder, 3 with bipolar disorder, and 12 with sleep disorder cases) were tested in a sleep laboratory with PSG, along with portable electroencephalography (EEG) device (Sleepgraph), actigraphy (MTN-220/221) and consumer sleep-tracking device (Fitbit Sense).

Results

Epoch-by-epoch sensitivity (for sleep) and specificity (for wake), respectively, were as follows: Sleepgraph (0.95, 0.76), Fitbit Sense (0.95, 0.45) and MTN-220/221 (0.93, 0.40). Portable EEG (Sleepgraph) had the best sleep stage-tracking performance. Sleep-wake summary metrics demonstrated lower performance on poor sleep (ice, shorter total sleep time, lower sleep efficiency, longer sleep latency, longer wake after sleep onset).

Conclusion

Devices demonstrated similar sleep-wake detecting performance as compared with previous studies that evaluated sleep in healthy adults. Consumer sleep device may exhibit poor sleep stage-tracking performance in patients with psychiatric disorders due to factors that affect the sleep determination algorithm, such as changes in autonomic nervous system activity. However, Sleepgraph, a portable EEG device, demonstrated higher performance in mental disorders than the Fitbit Sense and actigraphy.

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

Purpose

Previous studies have found that many commercial wearable devices can accurately track sleep-wake patterns in laboratory or home settings. However, nearly all previous studies tested devices under conditions with fixed time in bed (TIB) and during nighttime sleep episodes only. Despite its relevance to shift workers and others with irregular sleep schedules, it is largely unknown how devices track daytime sleep. Therefore, we tested the sleep-tracking performance of four commercial wearable devices during unrestricted home daytime sleep.

Participants and Methods

Participants were 16 healthy young adults (6 men, 10 women; 26.6 ± 4.6 years, mean ± SD) with habitual daytime sleep schedules. Participants slept at home for 1 week under unrestricted conditions (ie, self-selecting TIB) using a set of four commercial wearable devices and completed reference sleep logs. Wearables included the Fatigue Science ReadiBand, Fitbit Inspire HR, Oura Ring, and Polar Vantage V Titan. Daytime sleep episode TIB biases and frequencies of missed and false-positive daytime sleep episodes were examined.

Results

TIB bias was low in general for all devices on most daytime sleep episodes, but some exhibited large biases (eg, >1 h). Total missed daytime sleep episodes were as follows: Fatigue Science: 3.6%; Fitbit: 4.8%; Oura: 6.0%; Polar: 37.3%. Missed episodes occurred most often when TIB was short (eg, naps <4 h).

Conclusion

When daytime sleep episodes were recorded, the devices generally exhibited similar performance for tracking TIB (ie, most episodes had low bias). However, the devices failed to detect some daytime episodes, which occurred most often when TIB was short, but varied across devices (especially Polar, which missed over one-third of episodes). Findings suggest that accurate daytime sleep tracking is largely achievable with commercial wearable devices. However, performance differences for missed recordings suggest that some devices vary in reliability (especially for naps), but improvements could likely be made with changes to algorithm sensitivities.

Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview

Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep's functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG-electroencephalography, ECG-electrocardiography, EMG-electromyography, EOG-electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.

Perceptions of and Experiences with Consumer Sleep Technologies That Use Artificial Intelligence

This study aims to assess the perspectives and usability of different consumer sleep technologies (CSTs) that leverage artificial intelligence (AI). We answer the following research questions: (1) what are user perceptions and ideations of CSTs (phase 1), (2) what are the users' actual experiences with CSTs (phase 2), (3) and what are the design recommendations from participants (phases 1 and 2)? In this two-phase qualitative study, we conducted focus groups and usability testing to describe user ideations of desires and experiences with different AI sleep technologies and identify ways to improve the technologies. Results showed that focus group participants prioritized comfort, actionable feedback, and ease of use. Participants desired customized suggestions about their habitual sleeping environments and were interested in CSTs+AI that could integrate with tools and CSTs they already use. Usability study participants felt CSTs+AI provided an accurate picture of the quantity and quality of sleep. Participants identified room for improvement in usability, accuracy, and design of the technologies. We conclude that CSTs can be a valuable, affordable, and convenient tool for people who have issues or concerns with sleep and want more information. They provide objective data that can be discussed with clinicians.