Sleep tracking: A systematic review of the research using commercially available technology

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

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.

Comparing sleep measures in cancer survivors: Self-reported sleep diary versus objective wearable sleep tracker

Purpose

Cancer survivors are increasingly using wearable fitness trackers, but it's unclear if they match traditional self-reported sleep diaries. We aimed to compare sleep data from Fitbit and the Consensus Sleep Diary (CSD) in this group.

Methods

We analyzed data from two randomized clinical trials, using both CSD and Fitbit to collect sleep outcomes: total sleep time (TST), wake time after sleep onset (WASO), number of awakenings (NWAK), time in bed (TIB) and sleep efficiency (SE). Insomnia severity was measured by Insomnia Severity Index (ISI). We used the Wilcoxon Singed Ranks Test, Spearman's rank correlation coefficients, and the Mann-Whitney Test to compare sleep outcomes and assess their ability to distinguish insomnia severity levels between CSD and Fitbit data.

Results

Among 62 participants, compared to CSD, Fitbit recorded longer TST by an average of 14.6 (SD = 84.9) minutes, longer WASO by an average of 28.7 (SD = 40.5) minutes, more NWAK by an average of 16.7 (SD = 6.6) times per night, and higher SE by an average of 7.1% (SD = 14.4); but shorter TIB by an average of 24.4 (SD = 71.5) minutes. All the differences were statistically significant (all p < 0.05), except for TST (p = 0.38). Moderate correlations were found for TST (r = 0.41, p = 0.001) and TIB (r = 0.44, p < 0.001). Compared to no/mild insomnia group, participants with clinical insomnia reported more NWAK (p = 0.009) and lower SE (p = 0.029) as measured by CSD, but Fitbit outcomes didn't.

Conclusions

TST was the only similar outcome between Fitbit and CSD. Our study highlights the advantages, disadvantages, and clinical utilization of sleep trackers in oncology.

Acceptability & Usability of a Wearable Device for Sleep Health Among English- and Spanish-Speaking Patients in the Safety-Net: Qualitative Analysis

BACKGROUND

Sleep disorders are common and disproportionately affect marginalized populations. Technology such as wearable devices holds the potential to improve sleep quality and reduce sleep disparities, but most devices have not been designed or tested with racially, ethnically, and socioeconomically diverse patients. Inclusion and engagement of diverse patients throughout digital health development and implementation are critical to achieving health equity.

OBJECTIVE

This study aims to evaluate the usability and acceptability of a wearable sleep monitoring device - SomnoRing® - and its accompanying mobile application among patients treated in a safety net clinic.

METHODS

The study team recruited English- and Spanish-speaking patients from a mid-sized pulmonary and sleep medicine practice serving publicly insured patients. Eligibility criteria included initial evaluation of obstructed sleep apnea which is most appropriate for limited cardiopulmonary testing. Patients with primary insomnia or other suspected sleep disorders were not included. Patients tested the SomnoRing® over a seven-night period and participated in a one-hour semi-structured virtual qualitative interview covering perceptions of the device, motivators and barriers to use, and general experiences with digital health tools. The study team used inductive/deductive processes to code interview transcripts, guided by the Technology Acceptance Model.

RESULTS

Twenty-one individuals participated in the study. All participants owned a smartphone, almost all (19/21) felt comfortable using their phone, and few already owned a wearable (6/21). Almost all participants wore the SomnoRing® for seven nights and found it comfortable. Four themes emerged from qualitative data: 1) the SomnoRing® was easy to use compared to other wearable devices or traditional home sleep testing alternatives such as the standard polysomnogram technology for sleep studies; 2) the patient's context and environment such as family and peer influence, housing status, access to insurance, and device cost affected overall acceptance of the SomnoRing®; 3) clinical champions motivated use in supporting effective onboarding, interpretation of data, and, ongoing technical support; and 4) participants desired more assistance and information to best interpret their own sleep data summarized in the companion app.

CONCLUSIONS

Racially, ethnically, and socioeconomically diverse patients with sleep disorders perceived a wearable as useful and acceptable for sleep health. Participants also uncovered external barriers related to the perceived usefulness of the technology, such as housing status, insurance coverage, and clinical support. Future studies should further examine how to best address these barriers so that wearables, such as the SomnoRing®, can be successfully implemented in the safety-net health setting.

CLINICALTRIAL

This manuscript does not report on a clinical trial.

Comparing consumer grade sleep trackers for research purposes: A field study

Sleep tracking has been rapidly developing alongside wearable technologies and digital trackers are increasingly being used in research, replacing diaries and other more laborious methods. In this work, we describe the user expectations and experiences of four different sleep tracking devices used simultaneously during week-long field deployment. The sensor-based data collection was supplemented with qualitative data from a 2-week long daily questionnaire period which overlapped with device usage for a period of 1 week. We compare the sleep data on each of the tracking nights between all four devices, and showcase that while each device has been validated with the polysomnography (PSG) gold standard, the devices show highly varying results in everyday use. Differences between devices for measuring sleep duration or sleep stages on a single night can be up to an average of 1 h 36 min. Study participants provided their expectations and experiences with the devices, and provided qualitative insights into their usage throughout the daily questionnaires. The participants assessed each device according to ease of use, functionality and reliability, and comfortability and effect on sleep disturbances. We conclude the work with lessons learned and recommendations for researchers who wish to conduct field studies using digital sleep trackers, and how to mitigate potential challenges and problems that might arise regarding data validity and technical issues.

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.

The Effect of Noise-Masking Earbuds (SleepBuds) on Reported Sleep Quality and Tension in Health Care Shift Workers: Prospective Single-Subject Design Study

Background

Shift work is associated with sleep disorders, which impair alertness and increase risk of chronic physical and mental health disease. In health care workers, shift work and its associated sleep loss decrease provider wellness and can compromise patient care. Pharmacological sleep aids or substances such as alcohol are often used to improve sleep with variable effects on health and well-being.

Objective

We tested whether use of noise-masking earbuds can improve reported sleep quality, sleepiness, and stress level in health care shift workers, and increase alertness and reaction time post night shift.

Methods

Emergency medicine resident physicians were recruited for a prospective, single-subject design study. Entrance surveys on current sleep habits were completed. For 14 days, participants completed daily surveys reporting sleep aid use and self-rated perceived sleepiness, tension level, and last nights’ sleep quality using an 8-point Likert scale. After overnight shifts, 3-minute psychomotor vigilance tests (PVT) measuring reaction time were completed. At the end of 14 days, participants were provided noise-masking earbuds, which they used in addition to their baseline sleep regimens as they were needed for sleep for the remainder of the study period. Daily sleep surveys, post–overnight shift PVT, and earbud use data were collected for an additional 14 days. A linear mixed effects regression model was used to assess changes in the pre- and postintervention outcomes with participants serving as their own controls.

Results

In total, 36 residents were recruited, of whom 26 participants who completed daily sleep surveys and used earbuds at least once during the study period were included in the final analysis. The median number of days of earbud use was 5 (IQR 2-9) days of the available 14 days. On days when residents reported earbud use, previous nights’ sleep quality increased by 0.5 points (P<.001, 95% CI 0.23-0.80), daily sleepiness decreased by 0.6 points (P<.001, 95% CI –0.90 to –0.34), and total daily tension decreased by 0.6 points (P<.001, 95% CI –0.81 to –0.32). These effects were more pronounced in participants who reported worse-than-average preintervention sleep scores.

Conclusions

Nonpharmacological noise-masking interventions such as earbuds may improve daily sleepiness, tension, and perceived sleep quality in health care shift workers. Larger-scale studies are needed to determine this interventions’ effect on other populations of shift workers’ post–night shift alertness, users’ long-term physical and mental health, and patient outcomes.

Chronic sleep deprivation in teenagers: Practical ways to help

Teenagers of today sleep less than previous generations. Technology is largely to blame for keeping people perpetually connected in the digital world which is in turn driven by changing social demands for immediacy as a form of intimacy. The consequences for teenagers are later bed times, reduced total sleep time and a degree of sleep catch up on weekends. This is termed chronic sleep deprivation or "social jetlag". The consequences of chronic sleep deprivation are underappreciated in the medical setting. They include altered mood, more somatic and psychological symptomatology, greater anxiety, more school absenteeism, reduced educational results and compromised vocational aspirations. Engagement with reluctant teenagers and their parents may be challenging and at times frustrating for all concerned. Much of the art of improving outcomes involves developing a rapport with the teenager, assisting them to gain insight into the problems associated with chronic sleep deficiency and fostering commitment from all family members to implement unpopular boundaries on the use of technology.

Sleep and health: examining the relation of sleep to burnout and well-being using a consumer fitness tracker

Sleep is an essential requirement for both physiological and psychological functioning and has an impact on various health parameters. The present study aimed to examine how quantity and quality of sleep predicts burnout and well-being by using both self-reported and objectively collected sleep data. The participants were 104 white-collar workers who wore a fitness tracker for 14 consecutive days and filled out a questionnaire about sleep, burnout, and well-being. The results showed that self-reported sleep quality predicts burnout and well-being, but neither did self-reported nor objective sleep duration. We concluded that although measuring sleep duration with a consumer fitness tracker still needs to be improved, it is a useful addition to self-reported sleep measures. The study did solidify results from previous self-reported measures and point out the prominent role of sleep quality rather than hours of sleep.