Effect of wearables on sleep in healthy individuals: a randomized crossover trial and validation study

Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Wearable devices have the potential to advance health care by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep outcomes for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may affect those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment.

Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study

BACKGROUND

Although wearable technology has become increasingly common, comprehensive studies examining its ownership across different sociodemographic groups are limited.

OBJECTIVE

The aims of this study were to (1) measure wearable device ownership by sociodemographic characteristics in a cohort of US consumers and (2) investigate how these devices are acquired and used for health-related purposes.

METHODS

Data from the Rock Health Digital Health Consumer Adoption Survey collected from 2020 to 2022 with 23,974 US participants were analyzed. The sample was US Census-matched for demographics, including age, race/ethnicity, gender, and income. The relationship between sociodemographic factors and wearable ownership was explored using descriptive analysis and multivariate logistic regression.

RESULTS

Of the 23,974 respondents, 10,679 (44.5%) owned wearables. Ownership was higher among younger individuals, those with higher incomes and education levels, and respondents living in urban areas. Compared to those aged 18-24 years, respondents 65 years and older had significantly lower odds of wearable ownership (odds ratio [OR] 0.18, 95% CI 0.16-0.21). Higher annual income (≥US $200,000; OR 2.27, 95% CI 2.01-2.57) and advanced degrees (OR 2.23, 95% CI 2.01-2.48) were strong predictors of ownership. Living in rural areas reduced ownership odds (OR 0.65, 95% CI 0.60-0.72). There was a notable difference in ownership based on gender and health insurance status. Women had slightly higher ownership odds than men (OR 1.10, 95% CI 1.04-1.17). Private insurance increased ownership odds (OR 1.28, 95% CI 1.17-1.40), whereas being uninsured (OR 0.41, 95% CI 0.36-0.47) or on Medicaid (OR 0.75, 95% CI 0.68-0.82) decreased the odds of ownership. Interestingly, minority groups such as non-Hispanic Black (OR 1.14, 95% CI 1.03-1.25) and Hispanic/Latine (OR 1.20, 95% CI 1.10-1.31) respondents showed slightly higher ownership odds than other racial/ethnic groups.

CONCLUSIONS

Our findings suggest that despite overall growth in wearable ownership, sociodemographic divides persist. The data indicate a need for equitable access strategies as wearables become integral to clinical and public health domains.

Baseline Sleep Characteristics in NCAA Division I Collegiate Athletes

OBJECTIVE

The authors report no conflicts of interest.To determine baseline sleep characteristics of male/female student-athletes across multiple sports using objective and subjective measures.

DESIGN

Prospective study.

SETTING

Division I college.

PARTICIPANTS

Eighty-two male and female Division I student-athletes.

INTERVENTIONS

Participants completed 2 validated sleep questionnaires (Epworth Sleepiness Scale [ESS] and Single-Item Sleep Quality Scale [SISQS]) to assess subjective sleep. They also wore a validated sleep monitoring device (WHOOP 4.0 band) for at least 14 nights to collect objective data on total sleep time (TST) and sleep architecture.

MAIN OUTCOME MEASURES

Overnight sleep variables, including TST, time spent awake in bed after falling asleep, time spent in light sleep, rapid eye movement (REM) sleep, and slow-wave sleep (SWS) cycles. Sleep quality and daytime sleepiness were also assessed.

RESULTS

There were no statistical differences between male and female student-athletes in average TST, sleep architecture, sleep consistency, SISQS, and ESS scores. The average TST was 409.2 ± 36.3 minutes. Sleep architecture consisted of 25.6% REM, 19.9% SWS, and 54.4% light sleep. The average sleep consistency was 61.6% ± 8.9%. The average SISQS score was 6.48 ± 1.71, and the average ESS score was 7.57 ± 3.82. A significant difference was found in average wake time between males and females (55.0 vs 43.7 min, P = 0.020), with an overall average of 50.2 ± 16.2 minutes.

CONCLUSIONS

College student-athletes do not typically obtain the recommended amount of sleep. Optimizing sleep can positively affect academic and athletic performance.

A systematic review of ambient heat and sleep in a warming climate

Climate change is elevating nighttime and daytime temperatures worldwide, affecting a broad continuum of behavioral and health outcomes. Disturbed sleep is a plausible pathway linking rising ambient temperatures with several observed adverse human responses shown to increase during hot weather. This systematic review aims to provide a comprehensive overview of the literature investigating the relationship between ambient temperature and valid sleep outcomes measured in real-world settings, globally. We show that higher outdoor or indoor temperatures are generally associated with degraded sleep quality and quantity worldwide. The negative effect of heat persists across sleep measures, and is stronger during the hottest months and days, in vulnerable populations, and the warmest regions. Although we identify opportunities to strengthen the state of the science, limited evidence of fast sleep adaptation to heat suggests rising temperatures induced by climate change and urbanization pose a planetary threat to human sleep, and therefore health, performance, and wellbeing.

Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer

Introduction

Cancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects.

Methods

This prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes.

Analysis

Machine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics.

Conclusion

The individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

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.

Sleep architecture of elite soccer players surrounding match days as measured by WHOOP straps

This study aimed to quantify and compare sleep architecture before and after home and away matches in elite soccer players from the English Premier League. Across two seasons, 6 male players (age 28 ± 5 y; body mass 85.1 ± 9.5 kg; height 1.86 ± 0.09 m) wore WHOOP straps to monitor sleep across 13 matches that kicked off before 17:00 h. For each, sleep was recorded the night before (MD-1), after (MD) and following the match (MD +1). Across these 3 days total sleep time (TST), sleep efficiency (SE), sleep disturbances, wake time, light sleep, deep sleep, REM sleep, sleep and wake onsets, alongside external load, were compared. TST was reduced after MD versus MD +1 (392.9 ± 76.4 vs 459.1 ± 66.7 min, p = 0.003) but no differences existed in any other sleep variables between days (p > 0.05). TST did not differ after home (386.9 ± 75.7 min) vs. away matches (401.0 ± 78.3 min) (p = 0.475), nor did other sleep variables (p > 0.05). GPS-derived external load peaked on MD (p < 0.05). In conclusion, despite reduced TST on MD, sleep architecture was unaffected after matches played before 17:00 h, suggesting sleep quality was not significantly compromised.

Imagery rescripting and cognitive restructuring for inpatients with moderate and severe depression - a controlled pilot study

BACKGROUND

This controlled pilot study investigates the effect of the combined use of cognitive restructuring (CR) and imagery rescripting (IR) compared to treatment as usual among inpatients with moderate and severe depression. Alongside expert ratings and self-report tools, fitness wristbands were used as an assessment tool.

METHODS

In addition to the standard inpatient care (SIC) program, 33 inpatients with moderate and severe depression were randomly assigned to an intervention group (two sessions of IR and CR) or an active treatment-as-usual (TAU) control group (two sessions of problem-solving and build-up of positive activity). Depression severity was assessed by the Hamilton Depression Rating Scale-21 (HDRS-21), the Beck Depression Inventory-II (BDI-II), and as a diagnostic adjunct daily step count via the Fitbit Charge 3™. We applied for analyses of HDRS-21 and BDI-II, 2 × 2 repeated-measures analysis of variance (ANOVA), and an asymptotic Wilcoxon test for step count.

RESULTS

The main effect of time on both treatments was η2 = .402. Based on the data from the HDRS-21, patients in the intervention group achieved significantly greater improvements over time than the TAU group (η2 = .34). The BDI-II data did not demonstrate a significant interaction effect by group (η2 = .067). The daily hourly step count for participants of the intervention group was significantly higher (r = .67) than the step count for the control group.

CONCLUSIONS

The findings support the utilization of imagery-based interventions for treating depression. They also provide insights into using fitness trackers as psychopathological assessment tools for depressed patients.

TRIAL REGISTRATION

The trial is registered at the German Clinical Trials Register (Deutsches Register Klinischer Studien) under the registration number: DRKS00030809.

Regular snoring is associated with uncontrolled hypertension

Snoring may be a risk factor for cardiovascular disease independent of other co-morbidities. However, most prior studies have relied on subjective, self-report, snoring evaluation. This study assessed snoring prevalence objectively over multiple months using in-home monitoring technology, and its association with hypertension prevalence. In this study, 12,287 participants were monitored nightly for approximately six months using under-the-mattress sensor technology to estimate the average percentage of sleep time spent snoring per night and the estimated apnea-hypopnea index (eAHI). Blood pressure cuff measurements from multiple daytime assessments were averaged to define uncontrolled hypertension based on mean systolic blood pressure≥140 mmHg and/or a mean diastolic blood pressure ≥90 mmHg. Associations between snoring and uncontrolled hypertension were examined using logistic regressions controlled for age, body mass index, sex, and eAHI. Participants were middle-aged (mean ± SD; 50 ± 12 y) and most were male (88%). There were 2467 cases (20%) with uncontrolled hypertension. Approximately 29, 14 and 7% of the study population snored for an average of >10, 20, and 30% per night, respectively. A higher proportion of time spent snoring (75th vs. 5th; 12% vs. 0.04%) was associated with a ~1.9-fold increase (OR [95%CI]; 1.87 [1.63, 2.15]) in uncontrolled hypertension independent of sleep apnea. Multi-night objective snoring assessments and repeat daytime blood pressure recordings in a large global consumer sample, indicate that snoring is common and positively associated with hypertension. These findings highlight the potential clinical utility of simple, objective, and noninvasive methods to detect snoring and its potential adverse health consequences.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data

OBJECTIVES

Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range.

METHODS

Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated.

RESULTS

The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants.

CONCLUSIONS

These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Wearable-derived maternal heart rate variability as a novel digital biomarker of preterm birth

Despite considerable health consequences from preterm births, their incidence remains unchanged over recent decades, due partially to limited screening methods and limited use of extant methods. Wearable technology offers a novel, noninvasive, and acceptable way to track vital signs, such as maternal heart rate variability (mHRV). Previous research observed that mHRV declines throughout the first 33 weeks of gestation in term, singleton pregnancies, after which it improves. The aim of this study was to explore whether mHRV inflection is a feature of gestational age or an indication of time to delivery. This retrospective case-control study considered term and preterm deliveries. Remote data collection via non-invasive wearable technology enabled diverse participation with subjects representing 42 US states and 16 countries. Participants (N = 241) were retroactively identified from the WHOOP (Whoop, Inc.) userbase and wore WHOOP straps during singleton pregnancies between March 2021 and October 2022. Mixed effect spline models by gestational age and time until birth were fit for within-person mHRV, grouped into preterm and term births. For term pregnancies, gestational age (Akaike information criterion (AIC) = 26627.6, R2m = 0.0109, R2c = 0.8571) and weeks until birth (AIC = 26616.3, R2m = 0.0112, R2c = 0.8576) were representative of mHRV trends, with significantly stronger fit for weeks until birth (relative log-likelihood ratio = 279.5). For preterm pregnancies, gestational age (AIC = 1861.9, R2m = 0.0016, R2c = 0.8582) and time until birth (AIC = 1848.0, R2m = 0.0100, R2c = 0.8676) were representative of mHRV trends, with significantly stronger fit for weeks until birth (relative log-likelihood ratio = 859.4). This study suggests that wearable technology, such as the WHOOP strap, may provide a digital biomarker for preterm delivery by screening for changes in nighttime mHRV throughout pregnancy that could in turn alert to the need for further evaluation and intervention.

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.

A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives

Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training.

The Stress Phenotyping Framework: A multidisciplinary biobehavioral approach for assessing and therapeutically targeting maladaptive stress physiology

Although dysregulated stress biology is becoming increasingly recognized as a key driver of lifelong disparities in chronic disease, we presently have no validated biomarkers of toxic stress physiology; no biological, behavioral, or cognitive treatments specifically focused on normalizing toxic stress processes; and no agreed-upon guidelines for treating stress in the clinic or evaluating the efficacy of interventions that seek to reduce toxic stress and improve human functioning. We address these critical issues by (a) systematically describing key systems and mechanisms that are dysregulated by stress; (b) summarizing indicators, biomarkers, and instruments for assessing stress response systems; and (c) highlighting therapeutic approaches that can be used to normalize stress-related biopsychosocial functioning. We also present a novel multidisciplinary Stress Phenotyping Framework that can bring stress researchers and clinicians one step closer to realizing the goal of using precision medicine-based approaches to prevent and treat stress-associated health problems.

Pre-sleep feeding, sleep quality, and markers of recovery in division I NCAA female soccer players

Pre-sleep nutrition habits in elite female athletes have yet to be evaluated. A retrospective analysis was performed with 14 NCAA Division I female soccer players who wore a WHOOP, Inc. band - a wearable device that quantifies recovery by measuring sleep, activity, and heart rate metrics through actigraphy and photoplethysmography, respectively - 24 h a day for an entire competitive season to measure sleep and recovery. Pre-sleep food consumption data were collected via surveys every 3 days. Average pre-sleep nutritional intake (mean ± sd: kcals 330 ± 284; cho 46.2 ± 40.5 g; pro 7.6 ± 7.3 g; fat 12 ± 10.5 g) was recorded. Macronutrients and kcals were grouped into high and low categories based upon the 50^th percentile of the mean to compare the impact of a high versus low pre-sleep intake on sleep and recovery variables. Sleep duration (p = 0.10, 0.69, 0.16, 0.17) and sleep disturbances (p = 0.42, 0.65, 0.81, 0.81) were not affected by high versus low kcal, PRO, fat, CHO intake, respectively. Recovery (p = 0.81, 0.06, 0.81, 0.92), RHR (p = 0.84, 0.64, 0.26, 0.66), or HRV (p = 0.84, 0.70, 0.76, 0.93) were also not affected by high versus low kcal, PRO, fat, or CHO consumption, respectively. Consuming a small meal before bed may have no impact on sleep or recovery.

Validated Wearable Device Shows Acute Postoperative Changes in Sleep Patterns Consistent With Patient-Reported Outcomes and Progressive Decreases in Device Compliance After Shoulder Surgery

Purpose

To assess the utility of a validated wearable device (VWD) in examining preoperative and postoperative sleep patterns and how these data compare to patient-reported outcomes (PROs) after rotator cuff repair (RCR) or total shoulder arthroplasty (TSA).

Methods

Male and female adult patients undergoing either RCR or TSA were followed up from 34 days preoperatively to 6 weeks postoperatively. Sleep metrics were collected using a VWD in an unsupervised setting. PROs were assessed using the following validated outcome measures: Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function questionnaire; American Shoulder and Elbow Surgeons self-evaluation questionnaire; visual analog scale assessing pain; and Disabilities of the Arm, Shoulder and Hand questionnaire. Data were analyzed preoperatively and at 2-week intervals postoperatively with χ2 analysis to evaluate device compliance. Sleep metrics and PROs were evaluated at each interval relative to preoperative values within each surgery type with an analysis of variance repeated on time point. The relation between sleep metrics and PROs was assessed with correlation analysis.

Results

A total of 57 patients were included, 37 in the RCR group and 20 in the TSA group. The rate of device compliance in the RCR group decreased from 84% at surgery to 46% by 6 weeks postoperatively (P < .001). Similarly, the rate of device compliance in the TSA group decreased from 81% to 52% (P < .001). Deep sleep decreased in RCR patients at 2 to 4 weeks (decrease by 10.99 ± 3.96 minutes, P = .021) and 4 to 6 weeks postoperatively (decrease by 13.37 ± 4.08 minutes, P = .008). TSA patients showed decreased deep sleep at 0 to 2 weeks postoperatively (decrease by 12.91 ± 5.62 minutes, P = .045) and increased rapid eye movement sleep at 2 to 4 weeks postoperatively (increase by 26.91 ± 10.70 minutes, P = .031). Rapid eye movement sleep in the RCR group and total sleep in the TSA group were positively correlated with more favorable PROs (P < .05).

Conclusions

VWDs allow for monitoring components of sleep that offer insight into potential targets for improving postoperative fatigue, pain, and overall recovery after shoulder surgery. However, population demographic factors and ease of device use are barriers to optimized patient compliance during data collection.

Level of Evidence

Level IV, diagnostic case series.

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.

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.

Biomedical Applications of Electrets: Recent Advance and Future Perspectives

Recently, electrical stimulation, as a non-pharmacological physical stimulus, has been widely exploited in biomedical and clinical applications due to its ability to significantly enhance cell proliferation and differentiation. As a kind of dielectric material with permanent polarization characteristics, electrets have demonstrated tremendous potential in this field owing to their merits of low cost, stable performance, and excellent biocompatibility. This review provides a comprehensive summary of the recent advances in electrets and their biomedical applications. We first provide a brief introduction to the development of electrets, as well as typical materials and fabrication methods. Subsequently, we systematically describe the recent advances of electrets in biomedical applications, including bone regeneration, wound healing, nerve regeneration, drug delivery, and wearable electronics. Finally, the present challenges and opportunities have also been discussed in this emerging field. This review is anticipated to provide state-of-the-art insights on the electrical stimulation-related applications of electrets.

A Narrative Review of Commercial Platforms Offering Tracking of Heart Rate Variability in Corporate Employees to Detect and Manage Stress

The COVID-19 pandemic has resulted in employees being at risk of significant stress. There is increased interest by employers to offer employees stress monitoring via third party commercial sensor-based devices. These devices assess physiological parameters such as heart rate variability and are marketed as an indirect measure of the cardiac autonomic nervous system. Stress is correlated with an increase in sympathetic nervous activity that may be associated with an acute or chronic stress response. Interestingly, recent studies have shown that individuals affected with COVID will have some residual autonomic dysfunction that will likely render it difficult to track both stress and stress reduction using heart rate variability. The aims of the present study are to explore web and blog information using five operational commercial technology solution platforms that offer heart rate variability for stress detection. Across five platforms we found a number that combined HRV with other biometrics to assess stress. The type of stress being measured was not defined. Importantly, no company considered cardiac autonomic dysfunction because of post-COVID infection and only one other company mentioned other factors affecting the cardiac autonomic nervous system and how this may impact HRV accuracy. All companies suggested they could only assess associations with stress and were careful not to claim HRV could diagnosis stress. We recommend that managers think carefully about whether HRV is accurate enough for their employees to manage their stress during COVID.

The long-term and short-term effects of ambient air pollutants on sleep characteristics in the Chinese population: big data analysis from real world by sleep records of consumer wearable devices

Several studies on long-term air pollution exposure and sleep have reported inconsistent results. Large-scale studies on short-term air pollution exposures and sleep have not been conducted. We investigated the associations of long- and short-term exposure to ambient air pollutants with sleep in a Chinese population based on over 1 million nights of sleep data from consumer wearable devices. Air pollution data including particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) were collected from the Ministry of Ecology and Environment. Short-term exposure was defined as a moving average of the exposure level for different lag days from Lag0 to Lag0-6. A 365-day moving average of air pollution was regarded as long-term exposure. Sleep data were recorded using wearable devices from 2017 to 2019. The mixed-effects model was used to evaluate the associations. We observed that sleep parameters were associated with long-term exposure to all air pollutants. Higher levels of air pollutant concentrations were associated with longer total sleep and light sleep duration, shorter deep sleep duration, and decreases in wake after sleep onset (WASO), with stronger associations of exposures to NO2 and CO [a 1-interquartile range (IQR) increased NO2 (10.3 μg/m3) was associated with 8.7 min (95% CI: 8.08 to 9.32) longer sleep duration, a 1-IQR increased CO (0.3 mg/m3) was associated with 5.0 min (95% CI: - 5.13 to - 4.89) shorter deep sleep duration, 7.7 min (95% CI: 7.46 to 7.85) longer light sleep duration, and 0.5% (95% CI: - 0.5 to - 0.4%) lower proportion of WASO duration to total sleep]. The cumulative effect of short-term exposure on Lag0-6 is similar to long-term exposure but relatively less. Subgroup analyses indicated generally greater effects on individuals who were female, younger (< 45 years), slept longer (≥ 7 h), and during cold seasons, but the pattern of effects was mixed. We supplemented two additional types of stratified analyses to reduce repeated measures of outcomes and exposures while accounting for individual variation. The results were consistent with the overall results, proving the robustness of the overall results. In summary, both short- and long-term exposure to air pollution affect sleep, and the effects are comparable. Although people tend to have prolonged total sleep duration with increasing air pollutant concentrations, their sleep quality might remain poor because of the reduction in deep sleep.

Effects of a feedback intervention on antibiotic prescription control in primary care institutions based on a Health Information System: a cluster randomized cross-over controlled trial

OBJECTIVES

Overuse and misuse of antibiotics are major factors in the development of antibiotic resistance in primary care institutions of rural China. In this study, the effectiveness of a Health Information System-based, automatic, and confidential antibiotic feedback intervention was evaluated.

METHODS

A randomized, cross-over, cluster-controlled trial was conducted in primary care institutions. All institutions were randomly divided into two groups and given either a three-month intervention followed by a three-month period without any intervention or vice versa. The intervention consisted of three feedback measures: a real-time pop-up warning message of inappropriate antibiotic prescriptions on the prescribing physician's computer screen, a 10-day antibiotic prescription summary, and distribution of educational manuals. The primary outcome was the 10-day inappropriate antibiotic prescription rate.

RESULTS

There were no significant differences in inappropriate antibiotic prescription rates (69.1% vs. 72.0%) between two groups at baseline (P = 0.072). After three months (cross-over point), inappropriate antibiotic prescription rates decreased significantly faster in group A (12.3%, P < 0.001) compared to group B (4.4%, P < 0.001). At the end point, the inappropriate antibiotic prescription rates decreased in group B (15.1%, P < 0.001) while the rates increased in group A (7.2%, P < 0.001). The characteristics of physicians did not significantly affect the rate of antibiotic or inappropriate antibiotic prescription rates.

CONCLUSION

A Health Information System-based, real-time pop-up warnings, a 10-day prescription summary, and the distribution of educational manuals, can effectively reduce the rates of antibiotic and inappropriate antibiotic prescriptions.

Sleep Technology

Pediatric sleep providers frequently encounter issues related to sleep technology in clinical settings. In this review article, we discuss technical issues related to standard polysomnography, research on putative complementary novel metrics derived from polysomnographic signals as well as research on home sleep apnea testing in children and consumer sleep devices. Although developments across several of these domains are exciting, it remains a rapidly evolving area. When evaluating innovative devices and home sleep testing approaches, clinicians should be mindful of accurately interpreting diagnostic agreement statistics to apply these technologies appropriately.

Performance of a multisensor smart ring to evaluate sleep: in-lab and home-based evaluation of generalized and personalized algorithms

STUDY OBJECTIVES

Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and at-home electroencephalography (EEG)-derived sleep monitoring device, the Dreem 2 Headband.

METHODS

Thirty-six healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep patterns were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and two nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects' data. Epoch-by-epoch analyses compared the wearable devices to in-lab PSG and to at-home EEG Headband.

RESULTS

Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures.

CONCLUSION

The Happy Ring performed well at home and in the lab, especially regarding sleep/wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.

Sex differences and indications of metabolic compensation in within-day energy balance in elite Division 1 swimmers

To determine whether mismatched energy intake and expenditure across the day and associated sex differences may be related with metabolic compensation and/or negative health outcomes, we assessed total-day and hourly energy balance (TDEB and EB), total-day and hourly energy intake (TDEI and EI), total-day and hourly energy expenditure (TDEE and EE) and within-day energy balance (WDEB) in elite male and female swimmers (n = 25; 18-22 years). Total triiodothyronine (TT₃), resting metabolic rate (RMR), and the ratio of actual-to-predicted RMR were determined. Males exhibited higher TDEB (+758 ± 702 kcal vs +52 ± 505 kcal, t-test; p = 0.007) than females. Males exhibited a more positive hourly EB, driven by greater hourly EI at 11:00, 13:00, 16:00, and 19:00 h (ANOVA, p < 0.05), while EE did not differ. TT₃ was negatively correlated with consecutive hours of negative EB (R = -0.604, p = 0.049) and positively correlated to hours in EB (R = 0.740, p = 0.009) in those exhibiting metabolic suppression (n = 12). In individuals in TDEB (n = 21), "backloaders" (consumption of ≥50% daily kcals at or after 1700 h) had lower TT₃ (79.3 ng/dL vs 92.9 ng/dL, p = 0.009) than "nonbackloaders" (n = 12). WDEB analyses indicate a greater risk of energy deficiency in females and may capture indices of metabolic compensation not evident with EB analyses alone.

The effect of wearable-delivered sleep interventions on sleep outcomes among adults: A systematic review and meta-analysis of randomized controlled trials

The aims of the review were to (i) evaluate the effectiveness of wearable-delivered sleep interventions on sleep outcomes among adults, and (ii) explore the effect of factors affecting total sleep time. Eight databases were searched to identify relevant studies in English from inception until December 23, 2021. The Cochrane Risk of Bias tool version 2.0 and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria were used to assess the risk of bias and certainty of the evidence, respectively. Twenty randomized controlled trials (RCTs) were included, involving 1608 adults across nine countries. Wearable-delivered sleep interventions elicited significant improvement of 1.96 events/h for the oxygen desaturation index and 3.13 events/h for the respiratory distress index. Meta-analyses found that wearable-delivered sleep interventions significantly decreased sleep disturbance (Hedges' g [g] = -0.37, 95% confidence interval [CI]: -0.59, -0.15) and sleep-related impairment (g = -1.06, 95% CI: -1.99, -0.13) versus the comparators. The wearable-delivered sleep interventions may complement usual care to improve sleep outcomes. More rigorous RCTs with a long-term assessment in a wide range of populations are warranted.

Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients

Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.

Monitoring one heart to help two: heart rate variability and resting heart rate using wearable technology in active women across the perinatal period

BACKGROUND

Characterizing normal heart rate variability (HRV) and resting heart rate (RHR) in healthy women over the course of a pregnancy allows for further investigation into disease states, as pregnancy is the ideal time period for these explorations due to known decreases in cardiovascular health. To our knowledge, this is the first study to continuously monitor HRV and RHR using wearable technology in healthy pregnant women.

METHODS

A total of 18 healthy women participated in a prospective cohort study of HRV and RHR while wearing a WHOOP® strap prior to conception, throughout pregnancy, and into postpartum. The study lasted from March 2019 to July 2021; data were analyzed using linear mixed models with splines for non-linear trends.

RESULTS

Eighteen women were followed for an average of 405.8 days (SD = 153). Minutes of logged daily activity decreased from 28 minutes pre-pregnancy to 14 minutes by third trimester. A steady decrease in daily HRV and increase in daily RHR were generally seen during pregnancy (HRV Est. = - 0.10, P < 0.0001; RHR Est. = 0.05, P < 0.0001). The effect was moderated by activity minutes for both HRV and RHR. However, at 49 days prior to birth there was a reversal of these indices with a steady increase in daily HRV (Est. = 0.38, P < 0.0001) and decrease in daily RHR (Est. = - 0.23, P < 0.0001), regardless of activity level, that continued into the postpartum period.

CONCLUSIONS

In healthy women, there were significant changes to HRV and RHR throughout pregnancy, including a rapid improvement in cardiovascular health prior to birth that was not otherwise known. Physical activity minutes of any type moderated the known negative consequences of pregnancy on cardiovascular health. By establishing normal changes using daily data, future research can now evaluate disease states as well as physical activity interventions during pregnancy and their impact on cardiovascular fitness.

Effect of age, sex, and morbidity count on trial attrition: meta-analysis of individual participant level data from phase 3/4 industry funded clinical trials

Objectives

To estimate the association between individual participant characteristics and attrition from randomised controlled trials.

Design

Meta-analysis of individual participant level data (IPD).

Data sources

Clinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).

Eligibility criteria for selecting studies

Eligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).

Main outcome measures

Association between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.

Results

In 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.

Conclusions

Increased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

The Effectiveness of Digital Insomnia Treatment with Adjunctive Wearable Technology: A Pilot Randomized Controlled Trial

OBJECTIVE

This pilot trial aimed to provide evidence for whether the integration of a wearable device with digital behavioral therapy for insomnia (dBTi) improves treatment outcomes and engagement.

PARTICIPANTS AND METHODS

One hundred and twenty-eight participants with insomnia symptoms were randomized to a 3-week dBTi program (SleepFix®) with a wearable device enabling sleep data synchronization (dBTi+wearable group; n = 62) or dBTi alone (n = 66). Participants completed the Insomnia Severity Index (ISI) and modified Pittsburgh Sleep Quality Index (PSQI) parameters: wake-after-sleep-onset (WASO), sleep-onset-latency (SOL), and total sleep time (TST) at baseline and weeks 1, 2, 3, and primary endpoint of week 6 and follow-up at 12 weeks. Engagement was measured by the number of daily sleep diaries logged in the app.

RESULTS

There was no difference in ISI change scores between the groups from pre- to post-treatment (Cohen's d= 0.7, p= .061). The dBTi+wearable group showed greater improvements in WASO (d= 0.8, p = .005) and TST (d= 0.3, p= .049) compared to the dBTi group. Significantly greater engagement (sleep diary entries) was observed in the dBTi+wearable group (mean = 22.4, SD = 10.0) compared to the dBTi group (mean = 14.1, SD = 14.2) (p = .010).

CONCLUSIONS

This pilot trial found that integration of wearable device with a digital insomnia therapy enhanced user engagement and led to improvements in sleep parameters compared to dBTi alone. These findings suggest that adjunctive wearable technologies may improve digital insomnia therapy effectiveness.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Agreement of Sleep Measures-A Comparison between a Sleep Diary and Three Consumer Wearable Devices

Nowadays, self-tracking and optimization are widely spread. As sleep is essential for well-being, health, and peak performance, the number of available consumer technologies to assess individual sleep behavior is increasing rapidly. However, little is known about the consumer wearables' usability and reliability for sleep tracking. Therefore, the aim of the present study was to compare the sleep measures of wearable devices with a standardized sleep diary in young healthy adults in free-living conditions. We tracked night sleep from 30 participants (19 females, 11 males; 24.3 ± 4.2 years old). Each wore three wearables and simultaneously assessed individual sleep patterns for four consecutive nights. Wearables and diaries correlated substantially regarding time in bed (Range CCC_Lin: 0.74-0.84) and total sleep time (Range CCC_Lin: 0.76-0.85). There was no sufficient agreement regarding the measures of sleep efficiency (Range CCC_Lin: 0.05-0.34) and sleep interruptions (Range CCC_Lin: -0.02-0.10). Finally, these results show wearables to be an easy-to-handle, time- and cost-efficient alternative to tracking sleep in healthy populations. Future research should develop and empirically test the usability of such consumer sleep technologies.

Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study

BACKGROUND

Respiratory rate (RR) is arguably the most important vital sign to detect clinical deterioration. Change in RR can also, for example, be associated with the onset of different diseases, opioid overdoses, intense workouts, or mood. However, unlike for most other vital parameters, an easy and accurate measuring method is lacking.

OBJECTIVE

This study aims to validate the radar-based sleep monitor, Somnofy, for measuring RRs and investigate whether events affecting RR can be detected from personalized baselines calculated from nightly averages.

METHODS

First, RRs from Somnofy for 37 healthy adults during full nights of sleep were extensively validated against respiratory inductance plethysmography. Then, the night-to-night consistency of a proposed filtered average RR was analyzed for 6 healthy participants in a pilot study in which they used Somnofy at home for 3 months.

RESULTS

Somnofy measured RR 84% of the time, with mean absolute error of 0.18 (SD 0.05) respirations per minute, and Bland-Altman 95% limits of agreement adjusted for repeated measurements ranged from -0.99 to 0.85. The accuracy and coverage were substantially higher in deep and light sleep than in rapid eye movement sleep and wake. The results were independent of age, sex, and BMI, but dependent on supine sleeping position for some radar orientations. For nightly filtered averages, the 95% limits of agreement ranged from -0.07 to -0.04 respirations per minute. In the longitudinal part of the study, the nightly average was consistent from night to night, and all substantial deviations coincided with self-reported illnesses.

CONCLUSIONS

RRs from Somnofy were more accurate than those from any other alternative method suitable for longitudinal measurements. Moreover, the nightly averages were consistent from night to night. Thus, several factors affecting RR should be detectable as anomalies from personalized baselines, enabling a range of applications. More studies are necessary to investigate its potential in children and older adults or in a clinical setting.

Sleep and Alcohol Use Patterns During Federal Holidays and Daylight Saving Time Transitions in the United States

We conducted a retrospective observational study using remote wearable and mobile application data to evaluate whether US public holidays or Daylight Saving Time transitions were associated with significant changes in sleep behaviors, including sleep duration, sleep onset and offset, and the consistency of sleep timing, as well as changes in the point prevalence of alcohol use. These metrics were analyzed using objective, high resolution sleep-wake data (10,350,760 sleep episodes) and 5,777,008 survey responses of 24,250 US subscribers (74.5% male; mean age of 37.6 ± 9.8 years) to the wrist-worn biometric device platform, WHOOP (Boston, Massachusetts, United States), who were active users during 1 May 2020, through 1 May 2021. Compared to baseline, statistically significant differences in sleep and alcohol measures were found on most DST transitions, US public holidays, and their eves. For example, New Year's Eve corresponded with a sleep consistency decrease of 13.8 ± 0.3%, a sleep onset delay of 88.9 ± 3.2 min (00:01 vs. 22:33 baseline) later, a sleep offset delay of 78.1 ± 3.1 min (07:56 vs. 06:39), and an increase in the prevalence of alcohol consumption, with more than twice as many participants having reported alcohol consumption [+138.0% ± 6.7 (74.2% vs. 31.2%)] compared to baseline. In this analysis of a non-random sample of mostly male subscribers conducted during the COVID-19 pandemic, the majority of US public holidays and holiday eves were associated with sample-level increases in sleep duration, decreases in sleep consistency, later sleep onset and offset, and increases in the prevalence of alcohol consumption. Future work would be warranted to explore the generalizability of these findings and their public health implications, including in more representative samples and over longer time intervals.

Stress Resilience Program for Health Care Professionals During a Pandemic: A Pilot Program

BACKGROUND

The COVID-19 pandemic has led to increased burnout and staff turnover for health care providers (HCPs). The purpose of this pilot study was to evaluate the safety and acceptability of a Stress Resilience Program (SRP) for reducing perceived stress and improving resilience among HCPs during a pandemic.

METHOD

Of the 12 HCPs expressing interest in the study, 10 were enrolled in this study. Participants attended three in-person visits (consent/screen, baseline, and end-of-study). The SRP consisted of education related to resilience enhancement and a breathing device (BreatherFit®) for combined respiratory muscle training (cRMT). Participants completed 4 weeks of cRMT and applied situational breathing strategies as needed. Outcomes measured were changes in stress (PSS-10), resilience (BRS), depression (PRIME-MD), and sleep (PSQI and Ōura Ring®).

FINDINGS

The majority of participants were male (60%) and White (60%) with an average age of 39.7 years. Changes from baseline to end-of-treatment indicated a positive trend with significant stress reduction (-3.2 ± 3.9, p = .028) and nonsignificant depression reduction (-0.5 ± 0.7, p = .05). Resilience was high at baseline and continued to stay high during the study with a nonsignificant increase at end-of-study (+0.07 ± 0.7, p = .77). No changes in overall sleep scores were noted. All participants agreed the study was worthwhile, 80% indicated they would repeat the experience, while 90% indicated they would recommend the study to others.

CONCLUSION/APPLICATION TO PRACTICE

Because of its size and portability, SRP is an easily applicable and promising option for reducing stress among HCPs during a high-stress period, such as a pandemic. Larger studies are needed.

Physiological markers of sleep quality: A scoping review

Understanding the associations between adequate sleep, performance and health outcomes is vital, yet a major limitation in the design and interpretation of studies of sleep and performance is the variability of subjective and objective markers used to assess sleep quality. The aim of this scoping review is to investigate how various physiological signals recorded during sleep or wakefulness relate to objective measures of cognitive or physical performance and subjectively perceived sleep quality to inform conceptual understanding of the elusive, amorphous, and multi-dimensional construct of sleep quality. We also aimed to suggest priorities for future areas of research in sleep quality and performance. We searched six databases ultimately yielding 439 studies after duplicate removal. Sixty-five studies were selected for full review. In general, correlations between objectively measured sleep and objective performance or subjectively assessed sleep quality were weak to moderate. Slow wave sleep was moderately correlated with better performance on tasks of vigilance, motor speed, and executive function as well as better subjective sleep quality and feeling well-rested, suggesting that slow wave sleep may be important for sleep quality and optimal daytime performance. However, these findings were inconsistent across studies. Increased sleep fragmentation was associated with poorer subjective sleep quality in both polysomnographic and actigraphic studies. Studies which simultaneously assessed physiologic sleep measures, performance measures and subjective sleep perception were few, limiting the ability to evaluate correlations between subjective and objective outcomes concurrently in the same individuals. Factors influencing the relationship between sleep quality and performance include circadian variability, sleep inertia, and mismatch between sleep stages studied and outcome measures of choice. Ultimately, the determination of "quality sleep" remains largely subjective and inconsistently quantifiable by current measures. Methods evaluating sleep as a continuous measure rather than traditional sleep stages may provide an intriguing approach to future studies of sleep and performance. Future well-designed studies using novel measures of sleep or multimodal ambulatory wearables assessing the three domains of sleep and performance (objective sleep physiology, objective performance, and subjective sleep quality) are needed to better define quality sleep.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Prior sleep-wake behaviors are associated with mental health outcomes during the COVID-19 pandemic among adult users of a wearable device in the United States

Objectives

To characterize objective sleep patterns among U.S. adults before and during the COVID-19 pandemic, and to assess for associations between adverse mental health symptoms and (1) sleep duration and (2) the consistency of sleep timing before and during the pandemic.

Design

Longitudinal objective sleep-wake data during January-June 2020 were linked with mental health and substance use assessments conducted during June 2020 for The COVID-19 Outbreak Public Evaluation (COPE) Initiative.

Setting

Adult users of WHOOP—a commercial, digital sleep wearable.

Participants

Adults residing in the U.S. and actively using WHOOP wearable devices, recruited by WHOOP, Inc.

Intervention

The COVID-19 pandemic and its mitigation.

Measurements

Anxiety or depression symptoms, burnout symptoms, and new or increased substance use to cope with stress or emotions.

Results

Of 4912 participants in the primary analytic sample (response rate, 14.9%), we observed acutely increased sleep duration (0.25 h or 15 m) and sleep consistency (3.51 points out of 100) and delayed sleep timing (onset, 18.7 m; offset, 36.6 m) during mid-March through mid-April 2020. Adjusting for demographic and lifestyle variables, participants with persistently insufficient sleep duration and inconsistent sleep timing had higher odds of adverse mental health symptoms and substance use in June 2020.

Conclusions

U.S. adult wearable users displayed increased sleep duration, more consistent sleep timing, and delayed sleep onset and offset times after the COVID-19 pandemic onset, with subsample heterogeneity. Associations between adverse mental health symptoms and pre- and mid-pandemic short sleep duration and inconsistent sleep timing suggest that these characteristics warrant further investigation as potential modifiable mental health and substance use risk factors.

Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Background

The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used.

Objective

The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary.

Methods

Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels.

Results

Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time.

Conclusions

We demonstrate that >2 months’ worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health.

Biometrics from a Wearable Device Reveals Temporary Effects of COVID-19 Vaccines on Cardiovascular, Respiratory, and Sleep Physiology

Although vaccines against SARS-CoV-2 have been proven safe and effective, transient side-effects lasting 24-48 h postvaccination have been reported. To better understand the subjective and objective response to COVID-19 vaccination, we conducted a retrospective analysis on 69,619 subscribers to a wrist-worn biometric device (WHOOP Inc., Boston, MA) who received either the AstraZeneca, Janssen/Johnson & Johnson, Moderna, or Pfizer/BioNTech vaccine. The WHOOP device measures resting heart rate (RHR), heart rate variability (HRV), respiratory rate (RR), and sleep architecture, and these physiological measures were normalized to the same day of the week, 1 wk before vaccination. Averaging across vaccines, RHR, RR, and percent sleep derived from light sleep were elevated on the first night following vaccination and returned to baseline within 4 nights postvaccination. When statistical differences were observed between doses on the first night postvaccination, larger deviations in physiological measures were observed following the first dose of AstraZeneca and the second dose of Moderna and Pfizer/BioNTech. When statistical differences were observed between age groups or gender on the first night postvaccination, larger deviations in physiological measures were observed in younger populations and in females (compared with males). When combining self-reported symptoms (fatigue, muscle aches, headache, chills, or fever) with the objectively measured physiological parameters, we found that self-reporting fever or chills had the strongest association with deviations in physiological measures following vaccination. In summary, these results suggest that COVID-19 vaccines temporarily affect cardiovascular, respiratory, and sleep physiology and that dose, gender, and age affect the physiological response to vaccination.

NEW & NOTEWORTHY Here we report the first large-scale study investigating the effect of COVID-19 vaccines on cardiovascular, respiratory, and sleep physiology. We find that vaccines temporarily impact measures of cardiovascular, respiratory, and sleep physiology and that the degree of change in physiology is influenced by the manufacturer and dose of the vaccine and the gender and age of the vaccine recipient. These results provide insights into physiological changes that occur with COVID-19 vaccination and indicate that the unique responses that occur postvaccination may depend on manufacturer, dose, gender, and age.

Orthopaedic Surgeon Physiological Indicators of Strain as Measured by a Wearable Fitness Device

INTRODUCTION

Symptoms of stress, depression, and burnout are prevalent in medicine, adversely affecting physician performance. We investigated real-time measurements of physiological strain in orthopaedic resident and faculty surgeon volunteers and identified potential daily stressors.

METHODS

We performed a prospective blinded cohort pilot study in our academic orthopaedic department. Physicians used a wearable fitness device for 12 weeks to objectively measure heart rate variability (HRV), a documented parameter of overall well-being. Baseline burnout levels were assessed using the Maslach Burnout Inventory questionnaire. Daily surveys inquiring on work responsibilities (clinic, operating room [OR], or "other") were correlated with physiological parameters of strain. Descriptive statistics and linear mixed effects modeling were used to evaluate bivariate relationships.

RESULTS

Of the 21 participating surgeons, 9 faculty and 12 residents, there was a response rate of 95.2% for the initial burnout survey. Daily surveys were completed for 63.8% (54.9 ± 22.3 days) of the total collection window, and surgeons wore the device for 83.2% of the study (71.6 ± 25.0 days). Residents trended toward lower personal accomplishment and greater psychological detachment on the Maslach Burnout Inventory, with 5 surgeons including 1 faculty surgeon (11.1%) and 4 resident surgeons (33.3%) found to have negatively trending HRV throughout the study period demonstrating higher physiological strain. Time in the OR led to increased next-day HRV (y-intercept = 47.39; B = 4.90; 95% confidence interval, 2.14-7.66; P < 0.001), indicative of lower physiological strain. An increase in device-reported sleep from a surgeon's baseline resulted in a significant increase in next-day HRV (y-intercept = 50.46; B = 0.64; 95% confidence interval, 0.11-1.17; P = 0.02).

DISCUSSION

Orthopaedic residents, more than faculty, had physiologic findings suggestive of burnout. Time in the OR and increased sleep improved physiological strain parameters. Real-time biometric measurements can identify those at risk of burnout and in need of well-being interventions.

LEVEL OF EVIDENCE

Level III.

Delivering epilepsy care in low-resource settings: the role of technology

INTRODUCTION

The implementation of technology in the field of epileptology has traditionally focused on its use for diagnosis and treatment and has, unsurprisingly, been capital-intensive, making it therefore mainly implementable in advanced high-income countries. Because of technological innovations over the past 20 years there has been almost a paradigm shift, particularly in access to and the potential for implementing relevant technology in lesser developed environments. Nearly 80% of people living with epilepsy live in low and middle-income countries.

AREAS COVERED

The challenge and the purpose of this paper is to discuss how technology can be implemented into lesser-resourced contexts not only cost-effectively but in a cost-saving way while also building capacity and thus sustainability.

EXPERT OPINION

The rate of technological advancement presents the risk of progressive widening of the technology and care gaps between advanced and lesser developed regions. Implementing technology is both about finding relevant appropriate technologies for the individual contexts of a diverse range of countries but also about repurposing low-tech technologies for application in epilepsy care in these areas. Finally exciting advances such as autonomous driving, digital twinning and robotic surgery will likely transform epilepsy care in several lower-resourced settings in the next 5-10 years.

Sleep and Physical Performance: A Case Study of Collegiate Women's Division 1 Basketball Players

In this work, we present a case study to evaluate the connections between sleep, training load, and the perceptions of physical/emotional state of a collegiate, division 1 Women's basketball team. The study took place during the off- (3 weeks) and pre-season (6 weeks) while sleep was tracked using WHOOP wearable straps. Training load was recorded by the strength coach and athletes. Short Recovery and Short Stress (SRSS) questionnaire was used to evaluate the perceptions of athletes on their own emotional and physical states. Our results showed that heart rate measurements are associated with stress levels and recovery perception. We also discovered that the training load was not linked to the sleep variables without the considerations of athletic performance. However, training load may alter perceived stress and recovery which requires further exploration.

Guiding principles for determining work shift duration and addressing the effects of work shift duration on performance, safety, and health: guidance from the American Academy of Sleep Medicine and the Sleep Research Society

CITATION

Risks associated with fatigue that accumulates during work shifts have historically been managed through working time arrangements that specify fixed maximum durations of work shifts and minimum durations of time off. By themselves, such arrangements are not sufficient to curb risks to performance, safety, and health caused by misalignment between work schedules and the biological regulation of waking alertness and sleep. Science-based approaches for determining shift duration and mitigating associated risks, while addressing operational needs, require: (1) a recognition of the factors contributing to fatigue and fatigue-related risks; (2) an understanding of evidence-based countermeasures that may reduce fatigue and/or fatigue-related risks; and (3) an informed approach to selecting workplace-specific strategies for managing work hours. We propose a series of guiding principles to assist stakeholders with designing a shift duration decision-making process that effectively balances the need to meet operational demands with the need to manage fatigue-related risks.

Establishing a Global Standard for Wearable Devices in Sport and Exercise Medicine: Perspectives from Academic and Industry Stakeholders

Millions of consumer sport and fitness wearables (CSFWs) are used worldwide, and millions of datapoints are generated by each device. Moreover, these numbers are rapidly growing, and they contain a heterogeneity of devices, data types, and contexts for data collection. Companies and consumers would benefit from guiding standards on device quality and data formats. To address this growing need, we convened a virtual panel of industry and academic stakeholders, and this manuscript summarizes the outcomes of the discussion. Our objectives were to identify (1) key facilitators of and barriers to participation by CSFW manufacturers in guiding standards and (2) stakeholder priorities. The venues were the Yale Center for Biomedical Data Science Digital Health Monthly Seminar Series (62 participants) and the New England Chapter of the American College of Sports Medicine Annual Meeting (59 participants). In the discussion, stakeholders outlined both facilitators of (e.g., commercial return on investment in device quality, lucrative research partnerships, and transparent and multilevel evaluation of device quality) and barriers (e.g., competitive advantage conflict, lack of flexibility in previously developed devices) to participation in guiding standards. There was general agreement to adopt Keadle et al.'s standard pathway for testing devices (i.e., benchtop, laboratory, field-based, implementation) without consensus on the prioritization of these steps. Overall, there was enthusiasm not to add prescriptive or regulatory steps, but instead create a networking hub that connects companies to consumers and researchers for flexible guidance navigating the heterogeneity, multi-tiered development, dynamicity, and nebulousness of the CSFW field.