Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease

STUDY OBJECTIVES

Wearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD.

METHODS

Ten participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data.

RESULTS

FB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79-0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5% sensitivity and 93.7 ± 2.5% specificity, rapid eye movement sleep with high sensitivity and specificity (78.7 ± 31.9%, 95.6 ± 2.3%), and deep sleep with lower sensitivity but high specificity (56.4 ± 28.8%, 95.0 ± 4.8%). FB heart rate was strongly correlated with PSG, and the mean absolute error between FB and PSG heart rate data was 1.16 ± 0.42 beats/min. At home, longer sleep and shorter wake times were observed compared with hospital data, whereas percentage sleep stage times were consistent with hospital data.

CONCLUSIONS

Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD.

CITATION

Doheny EP, Renerts K, Braun A, et al. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease. J Clin Sleep Med. 2024;20(7):1163-1171.

Metrology of two wearable sleep trackers against polysomnography in patients with sleep complaints

Sleep trackers are used widely by patients with sleep complaints, however their metrological validation is often poor and relies on healthy subjects. We assessed the metrological validity of two commercially available sleep trackers (Withings Activité/Fitbit Alta HR) through a prospective observational monocentric study, in adult patients referred for polysomnography (PSG). We compared the total sleep time (TST), REM time, REM latency, nonREM1 + 2 time, nonREM3 time, and wake after sleep onset (WASO). We report absolute and relative errors, Bland-Altman representations, and a contingency table of times spent in sleep stages with respect to PSG. Sixty-five patients were included (final sample size 58 for Withings and 52 for Fitbit). Both devices gave a relatively accurate sleep start time with a median absolute error of 5 (IQR -43; 27) min for Withings and -2.0 (-12.5; 4.2) min for Fitbit but both overestimated TST. Withings tended to underestimate WASO with a median absolute error of -25.0 (-61.5; -8.5) min, while Fitbit tended to overestimate it (median absolute error 10 (-18; 43) min. Withings underestimated light sleep and overestimated deep sleep, while Fitbit overestimated light and REM sleep and underestimated deep sleep. The overall kappas for concordance of each epoch between PSG and devices were low: 0.12 (95%CI 0.117-0.121) for Withings and VPSG indications 0.07 (95%CI 0.067-0.071) for Fitbit, as well as kappas for each VPSG indication 0.07 (95%CI 0.067-0.071). Thus, commercially available sleep trackers are not reliable for sleep architecture in patients with sleep complaints/pathologies and should not replace actigraphy and/or PSG.

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.

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Smart Tattoo Sensors 2.0: A Ten-Year Progress Report through a Narrative Review

The increased interest in sensing tattoos reflects a shift in wearable technology, emphasizing their flexible, skin-adherent nature. These devices, driven by advancements in nanotechnology and materials science, offer highly sensitive and customizable sensors. The growing body of research in this area indicates a rising curiosity in their design and applications, with potential uses ranging from vital sign monitoring to biomarker detection. Sensing tattoos present a promising avenue in wearable healthcare technology, attracting attention from researchers, clinicians, and technology enthusiasts. The objective of this study is to analyze the development, application, and integration of the sensing tattoos in the health domain. A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 37 studies. Sensing tattoos hold transformative potential in health monitoring and physiological sensing, driven by their focus on affordability, user-friendly design, and versatile sensorization solutions. Despite their promise, ongoing refinement is essential, addressing limitations in adhesion, signal quality, biocompatibility, and regulatory complexities. Identified opportunities, including non-invasive health monitoring, multiplexed detection, and cost-effective fabrication methods, open avenues for personalized healthcare applications. However, bridging gaps in medical device standards, cybersecurity, and regulatory compliance is imperative for seamless integration. A key theme calls for a holistic, user-centric approach, emphasizing interdisciplinary collaboration. Balancing innovation with practicality, prioritizing ethics, and fostering collaboration are crucial for the evolution of these technologies. The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the responsible integration of sensing tattoos into daily life.

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.

Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study

STUDY OBJECTIVES

Reduced sleep duration and work hour variability contribute to medical error and physician burnout. This study assesses the relationships between physician performance, burnout, and the dimensions of sleep beyond hours slept.

METHODS

This was an ancillary analysis of 3 years of data from an international prospective cohort study: the Intern Health Study. Actigraphy data from 3,654 intern physicians capturing sleep timing, regularity, efficiency, and duration were used individually and combined as a composite sleep health index to measure the association of multidimensional sleep patterns on self-reported medical errors and burnout.

RESULTS

From 2017-2019, interns' work hours decreased by 4 hours per week and total sleep time also decreased (6.7 to 5.99 hours), and sleep efficiency, timing, and regularity all worsened (all P < .05). In the 21.2% of participants who committed an error, there was no difference in sleep duration, timing, or regularity. Lower sleep efficiency was associated with higher odds of committing an error (P = .003) and higher burnout scores (P < .001). Although overall sleep quality was poor in the entire cohort, interns in the lowest quintile of sleep duration, regularity, and efficiency had higher burnout scores than those in the best quintile.

CONCLUSIONS

Sleep efficiency, not duration, was associated with increased self-reported medical errors and burnout in intern physicians. Overall sleep quality and duration worsened despite fewer hours worked. Future studies on physician burnout should measure all aspects of sleep health.

CITATION

Hassinger AB, Velez C, Wang J, Mador MJ, Wilding GE, Mishra A. Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study. J Clin Sleep Med. 2024;20(2):221-227.

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.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

BACKGROUND

Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive.

OBJECTIVE

This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders.

METHODS

We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs.

RESULTS

The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments.

CONCLUSIONS

Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.

Clinical utility of smartphone applications for sleep physicians

PURPOSE

To review various smartphone applications (apps) for sleep architecture and screening of obstructive sleep apnea (OSA) and to outline their utility for sleep physicians.

METHODS

Mobile application stores (Google Play and Apple iOS App Store) were searched for sleep analysis applications (apps) that are targeted for consumer use. Apps were identified by two independent investigators for apps published through July 2022. App information including parameters obtained for sleep analysis were extracted from each app.

RESULTS

The search identified 50 apps that reported sufficient outcome measures to be considered for assessment. Half of the apps tracked sleep with phone-only technology, while 19 utilized sleep and fitness trackers, three utilized sleep-only wearable devices, and three utilized nearable devices. Seven apps provided data useful for tracking users for signs and symptoms of obstructive sleep apnea.

CONCLUSION

There are a variety of sleep analysis apps available on the market to consumers currently. Though the sleep analysis of these apps may not be validated, sleep physicians should be aware of these apps to improve understanding and education of their patients.

Association between sleep quality and nocturnal erection monitor by RigiScan in erectile dysfunction patients: a prospective study using fitbit charge 2

BACKGROUND

Few studies were conducted to explore the association between sleep quality and nocturnal erection. Here, we intended to explore the association between sleep quality and nocturnal erection monitor when conducting nocturnal erection monitor. All erectile dysfunction (ED) patients underwent sleep monitors using Fitbit Charge 2™ (Fitbit Inc.) and nocturnal penile tumescence and rigidity (NPTR) monitors using RigiScan® (GOTOP medical, Inc., USA) for two nights. Subsequently, the patients were divided into two groups: Group A included patients who experienced effective erections only on the second night, while Group B included patients who had effective erections on both nights. To explore the associations between NPTR parameters and sleep parameters, a comparative analysis was performed between Group A and Group B for both nights.

RESULTS

Finally, our study included 103 participants, with 47 patients in Group A and 56 patients in Group B. Notably, the Group A patients showed significant improvements in NPTR parameters on the second night compared to the first night. Conversely, the NPTR parameters on Group B of the second night did not demonstrate a superior outcome when compared to the second night of Group A. Interestingly, it was found that only the disparities in sleep parameters accounted for the variation in NPTR parameters between the two groups on the first night. After correlation and ROC analysis, we identified the rapid eye movement (REM) sleep time and wake after sleep onset (WASO) time monitoring by the Fitbit Charge 2 as the primary parameters for predicting abnormal NPTR results in the first night.

CONCLUSIONS

Therefore, our study strongly suggests a close association between sleep parameters and NPTR parameters. It emphasizes the importance of incorporating sleep monitoring alongside nocturnal erection monitoring to enhance the reliability of the NPTR results.

Chronotype-Dependent Sleep Loss Is Associated with a Lower Amplitude in Circadian Rhythm and a Higher Fragmentation of REM Sleep in Young Healthy Adults

In modern society, the time and duration of sleep on workdays are primarily determined by external factors, e.g., the alarm clock. This can lead to a misalignment of the intrinsically determined sleep timing, which is dependent on the individual chronotype, resulting in reduced sleep quality. Although this is highly relevant given the high incidence of sleep disorders, little is known about the effect of this misalignment on sleep architecture. Using Fitbit trackers and questionnaire surveys, our study aims to elucidate sleep timing, sleep architecture, and subjective sleep quality in young healthy adults (n = 59) under real-life conditions (average of 82.4 ± 9.7 days). Correlations between variables were calculated to identify the direction of relationships. On workdays, the midpoint of sleep was earlier, the sleep duration was shorter, and tiredness upon waking was higher than on free days. A higher discrepancy between sleep duration on workdays and free days was associated with a lower stability of the circadian rhythm of REM sleep and also with a higher fragmentation of REM sleep. Similarly, a higher tiredness upon waking on free days, thus under intrinsically determined sleep timing conditions, was associated with a lower proportion and a higher fragmentation of REM sleep. This suggests that the misalignment between extrinsically and intrinsically determined sleep timing affects the architecture of sleep stages, particularly REM sleep, which is closely connected to sleep quality.

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.

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

GOAL AND AIMS

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

SAMPLE

Twenty-one university students (10 females).

DESIGN

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

CORE ANALYTICS

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

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

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

CORE OUTCOMES

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

IMPORTANT ADDITIONAL OUTCOMES

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

CORE CONCLUSION

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

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

GOAL AND AIMS

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

FOCUS TECHNOLOGY

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

DESIGN

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

CORE ANALYTICS

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

CORE OUTCOMES

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

CORE CONCLUSION

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

Resident Sleep During Traditional Home Call Compared to Night Float

Purpose  This article aims to compare resident sleep while on night float with a traditional home call. Methods  We conducted a crossover observational study assessing sleep patterns of seven postgraduate year-2 ophthalmology residents at the University of Washington from 2019 to 2021 using the Fitbit Alta HR device. Overnight call was scheduled from 5 p.m. to 8 a.m. on weekdays, and 8 a.m. to 8 a.m. on weekends. The residency program implemented a partial night float rotation, during which two to three nights of consecutive call were assigned to a resident without other clinical duties. Sleep was recorded using the Fitbit Alta HR for residents while on a 5-week partial night float rotation, on 10-week home call rotations, with postcall relief, and for stretches of seven or more days without call responsibilities. Mixed model regression analysis was used to compare average sleep on home call, night float, and periods without call. Results  Sleep data were recorded for a total of 1,015 nights, including 503 nights on home call rotation and 230 nights on night float rotation. Residents slept more during periods away from call compared to either night float or home call rotations ( p  < 0.001). Residents experienced increased average overall sleep during 10-week rotations on night float compared to home call ( p  = 0.008). While there was no difference in overnight sleep on call between night float and home call ( p  = 0.701), residents experienced more sleep overall while on call on night float compared to home call due to more sleep being recorded during postcall naps ( p  = 0.016). Conclusion  Implementing a night float system can increase resident sleep by allowing for more sleep recovery during time away from clinical duties.

The Effect of Night Float Rotation on Resident Sleep, Activity, and Well-Being

BACKGROUND

Night float call systems are becoming increasingly common at training programs with the goal of reducing fatigue related to sleep deprivation and sleep disturbance. Previous studies have shown that trainees obtain less sleep during the night float rotation and have decreased sleep efficiency for several days after the rotation. The impact on physical and emotional well-being has not been documented.

METHODS

Twenty-seven anesthesia residents were enrolled in a study using wearable sleep and activity trackers and National Institutes of Health Patient-Reported Outcome Measurement Information System (NIH PROMIS) surveys for sleep disturbance, fatigue, and positive affect to record data the week before ("baseline"), during ("night float"), and 1 week after ("recovery") their night float rotation. Each subject's data during the night float week and recovery week were compared to his or her own baseline week data using a paired, nonparametric analysis. The primary outcome variable was the change in average daily sleep hours during the night float week compared to the baseline week. Average daily rapid eye movement (REM) sleep, daily steps, and NIH PROMIS scores comparing night float and recovery weeks to baseline week were prespecified secondary outcomes. NIH PROMIS scores range from 0 to 100 with 50 as the national mean and more of the construct having a higher score.

RESULTS

There was no difference in average daily sleep hours between the night float and the baseline weeks (6.7 [5.9-7.8] vs 6.7 [5.5-7.7] hours, median [interquartile range]; P = .20). Residents had less REM sleep during the night float compared to the baseline weeks (1.1 [0.7-1.5] vs 1.4 [1.1-1.9] hours, P = .002). NIH PROMIS fatigue scores were higher during the night float than the baseline week (58.8 [54.6-65.1] vs 48.6 [46.0-55.1], P = .0004) and did not return to baseline during the recovery week (51.0 [48.6-58.8], P = .029 compared to baseline). Sleep disturbance was not different among the weeks. Positive affect was reduced after night float compared to baseline (39.6 [35.0-43.5] vs 44.8 [40.1-49.6], P = .0009), but returned to baseline during the recovery week (43.6 [39.6-48.2], P = .38).

CONCLUSIONS

The residents slept the same number of total hours during their night float week but had less REM sleep, were more fatigued, and had less positive affect. All of these resolved to baseline except fatigue, that was still greater than the baseline week. This methodology appears to robustly capture psychophysiological data that might be useful for quality initiatives.

Sleep Disturbances Following Total Knee Arthroplasty

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies

The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed.

Understanding the Need for Sleep to Improve Cognition

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

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.

Validation of Fitbit Inspire 2TM Against Polysomnography in Adults Considering Adaptation for Use

PURPOSE

The commercialization of sleep activity tracking devices has made it possible to manage sleep quality at home. However, it is necessary to verify the reliability and accuracy of wearable devices through comparison with polysomnography (PSG), which is the standard for tracking sleep activity. This study aimed to monitor overall sleep activity using Fitbit Inspire 2™ (FBI2) and to evaluate its performance and effectiveness through PSG under the same conditions.

PATIENTS AND METHODS

We compared the FBI2 and PSG data of nine participants (four male and five female participants; average age, 39 years) without severe sleeping problems. The participants wore FBI2 continuously for 14 days, considering the period of adaptation to the device. FBI2 and PSG sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch analysis for 18 samples by pooling data from two replicates.

RESULTS

The average values for each sleep stage obtained from FBI2 and PSG showed significant differences in the total sleep time (TST), deep sleep, and rapid eye motion (REM). In the Bland-Altman analysis, TST (P = 0.02), deep sleep (P = 0.05), and REM (P = 0.03) were significantly overstated in FBI2 compared to PSG. In addition, time in bed, sleep efficiency, and wake after sleep onset were overestimated, while light sleep was underestimated. However, these differences were not statistically significant. FBI2 showed a high sensitivity (93.9%) and low specificity (13.1%), with an accuracy of 76%. The sensitivity and specificity of each sleep stage was 54.3% and 62.3%, respectively, for light sleep, 84.8% and 50.1%, respectively, for deep sleep, and 86.4% and 59.1%, respectively for REM sleep.

CONCLUSION

The use of FBI2 as an objective tool for measuring sleep in daily life can be considered appropriate. However, further research is warranted on its application in participants with sleep-wake problems.

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.

Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy

Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4^TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland–Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p < 0.0001) and SE (-4.9%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9% vs. 86.5%) and sensitivity (detecting sleep; 92.6% vs. 89.9%), but comparatively poor specificity (detecting wake; 35.7% vs. 62.2%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.

Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: Transforming the healthcare applications

During the past two decades, the era of digitalization in pharmaceutical device manufacturing has gained significant momentum for maintaining human health. From various available technologies, internet of things (IoT) sensors are being increasingly used as wearable devices (e.g., smart watches, wrist bands, mobile phones, tablets, implantable pumps, etc.) that enable real-time monitoring of data. Such devices are integrated with smart materials that typically monitor the real-time data (blood pressure, blood sugar, heart and pulse rate, cytokine levels, etc.) to advise patients and physicians. Hence, there has been a great demand for wearable devices as potential tools for remote clinical trial monitoring in cancers and other diseases and they are proving to be very cost-effective.

Smartphone-Based Ecological Momentary Assessment for the Measurement of the Performance Status and Health-Related Quality of Life in Cancer Patients Under Systemic Anticancer Therapies: Development and Acceptability of a Mobile App

Background

We have defined a project to develop a mobile app that continually records smartphone parameters which may help define the Eastern Cooperative Oncology Group performance status (ECOG-PS) and the health-related quality of life (HRQoL), without interaction with patients or professionals. This project is divided into 3 phases. Here we describe phase 1. The objective of this phase was to develop the app and assess its usability concerning patient characteristics, acceptability, and satisfaction.

Methods

The app eB2-ECOG was developed and installed in the smartphone of cancer patients who will be followed for six months. Criteria inclusion were: age over 18-year-old; diagnosed with unresectable or metastatic lung cancer, gastrointestinal stromal tumor, sarcoma, or head and neck cancer; under systemic anticancer therapies; and possession of a Smartphone. The app will collect passive and active data from the patients while healthcare professionals will evaluate the ECOG-PS and HRQoL through conventional tools. Acceptability was assessed during the follow-up. Patients answered a satisfaction survey in the app between 3-6 months from their inclusion.

Results

The app developed provides a system for continuously collecting, merging, and processing data related to patient's health and physical activity. It provides a transparent capture service based on all the available data of a patient. Currently, 106 patients have been recruited. A total of 36 patients were excluded, most of them (21/36) due to technological reasons. We assessed 69 patients (53 lung cancer, 8 gastrointestinal stromal tumors, 5 sarcomas, and 3 head and neck cancer). Concerning app satisfaction, 70.4% (20/27) of patients found the app intuitive and easy to use, and 51.9% (17/27) of them said that the app helped them to improve and handle their problems better. Overall, 17 out of 27 patients [62.9%] were satisfied with the app, and 14 of them [51.8%] would recommend the app to other patients.

Conclusions

We observed that the app's acceptability and satisfaction were good, which is essential for the continuity of the project. In the subsequent phases, we will develop predictive models based on the collected information during this phase. We will validate the method and analyze the sensitivity of the automated results.

Targeting Anxiety to Improve Sleep Disturbance: A Randomized Clinical Trial of App-Based Mindfulness Training

OBJECTIVE

Sleep disturbance is experienced by nearly 20% of Americans and is highly comorbid with anxiety. Sleep disturbances may predict the development of anxiety disorders. Mindfulness training (MT) has shown efficacy for anxiety yet remains limited by in-person-based delivery. Digitally delivered MT may target habitual worry processes, yet its effects on sleep have not been studied. This study tested if app-based MT for anxiety could reduce worry and improve sleep and examined the underlying mechanisms.

METHODS

Individuals reporting worry interfering with sleep were randomized to treatment as usual (TAU; n = 40) or TAU + app-based MT (n = 40). Treatment-related changes in worry-related sleep disturbances (WRSDs), worry, nonreactivity, and anxiety were evaluated via self-report questionnaires at 1 and 2 months after treatment initiation. Fitbit devices were used to record total sleep time and estimate sleep efficiency. At 2 months, TAU received access to app-based MT, and both groups were reassessed at 4 months.

RESULTS

In a modified intent-to-treat analysis, WRSD scores decreased by 27% in TAU + MT (n = 36) and 6% in TAU (n = 35) at 2 months (median [IQR] change = 11 [4.3] versus 15 [5.0], p = .001). These WRSD reductions were mediated by decreased worry, particularly improved nonreactivity (p values < .001). At 4 months, TAU reported a significant 29% reduction after beginning app-based MT at 2 months and TAU + MT maintained its gains. No significant between-group differences in average estimated total sleep time or sleep efficiency were found after 2 months of using the app.

CONCLUSIONS

Few mindfulness-related apps have been evaluated for clinical efficacy and/or mechanism. Results from this study demonstrate a mechanistic link between MT and increased emotional nonreactivity, decreased worry, and reduction in reported sleep disturbances, suggesting that app-based MT may be a viable option to help individuals who report that worry interferes with their sleep.Trial Registration: ClinicalTrials.gov identifier: NCT03684057.

Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device

Purpose

Sleep is an essential factor for athletes, and it is important to intervene in sleep to manage it. We need a device that can evaluate sleep easily and constantly. Consumer wearable devices can be useful tools for athletes. In order to use consumer wearable devices in clinical research, it is essential to conduct a validation study. Thus, we conducted a validation study to assess the Fitbit Alta HRTM (FBA)- a consumer wearable device with an accelerometer and a heart rate monitor to detect sleep stages and quality against electroencephalographic (EEG) studies in athletes.

Patients and Methods

Forty college athletes participated in the study. EEG was applied to participants simultaneously while wearing FBA.

Results

Regarding sleep parameters, there was a strong correlation between the total sleep time (TST)-EEG and the TST-Fitbit (r = 0.83; p < 0.001). Regarding the sleep stages, there was a modest correlation between the N3 sleep-EEG and the N3 sleep-Fitbit (r = 0.68; p < 0.001). In addition, there was a strong correlation between the percentage of N3 sleep in between sleep onset and initial rapid eye movement sleep-EEG and those on Fitbit (r = 0.73; p < 0.001).

Conclusion

These results demonstrate that FBA facilitates sleep monitoring and exhibits acceptable agreement with EEG. Therefore, FBA is a useful tool in athletes' sleep management.

NExUS-Heart: Novel examinations using smart technologies for heart health-Data sharing from commercial wearable devices and telehealth engagement in participants with or at risk of atrial fibrillation

Background

Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors.

Objective

The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health.

Methods

We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA₂DS₂-VASc stroke risk score of >2) in the University of Massachusetts Memorial Health Care system.

Results

The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02-3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status.

Conclusion

Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.

Toward a Digital Future in Bipolar Disorder Assessment: A Systematic Review of Disruptions in the Rest-Activity Cycle as Measured by Actigraphy

BACKGROUND

Disruptions in rest and activity patterns are core features of bipolar disorder (BD). However, previous methods have been limited in fully characterizing the patterns. There is still a need to capture dysfunction in daily activity as well as rest patterns in order to more holistically understand the nature of 24-h rhythms in BD. Recent developments in the standardization, processing, and analyses of wearable digital actigraphy devices are advancing longitudinal investigation of rest-activity patterns in real time. The current systematic review aimed to summarize the literature on actigraphy measures of rest-activity patterns in BD to inform the future use of this technology.

METHODS

A comprehensive systematic review using PRISMA guidelines was conducted through PubMed, MEDLINE, PsycINFO, and EMBASE databases, for papers published up to February 2021. Relevant articles utilizing actigraphy measures were extracted and summarized. These papers contributed to three research areas addressed, pertaining to the nature of rest-activity patterns in BD, and the effects of therapeutic interventions on these patterns.

RESULTS

Seventy articles were included. BD was associated with longer sleep onset latency and duration, particularly during depressive episodes and with predictive value for worsening of future manic symptoms. Lower overall daily activity was also associated with BD, especially during depressive episodes, while more variable activity patterns within a day were seen in mania. A small number of studies linked these disruptions with differential patterns of brain functioning and cognitive impairments, as well as more adverse outcomes including increased suicide risk. The stabilizing effect of therapeutic options, including pharmacotherapies and chronotherapies, on activity patterns was supported.

CONCLUSION

The use of actigraphy provides valuable information about rest-activity patterns in BD. Although results suggest that variability in rhythms over time may be a specific feature of BD, definitive conclusions are limited by the small number of studies assessing longitudinal changes over days. Thus, there is an urgent need to extend this work to examine patterns of rhythmicity and regularity in BD. Actigraphy research holds great promise to identify a much-needed specific phenotypic marker for BD that will aid in the development of improved detection, treatment, and prevention options.

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

OBJECTIVE

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients.

DATA SOURCES

PubMed.

REVIEW METHODS

A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review.

CONCLUSIONS

In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care.

IMPLICATIONS FOR PRACTICE

Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms

Movement-based sleep-wake detection devices (i.e., actigraphy devices) were first developed in the early 1970s and have repeatedly been validated against polysomnography, which is considered the “gold-standard” of sleep measurement. Indeed, they have become important tools for objectively inferring sleep in free-living conditions. Standard actigraphy devices are rooted in accelerometry to measure movement and make predictions, via scoring algorithms, as to whether the wearer is in a state of wakefulness or sleep. Two important developments have become incorporated in newer devices. First, additional sensors, including measures of heart rate and heart rate variability and higher resolution movement sensing through triaxial accelerometers, have been introduced to improve upon traditional, movement-based scoring algorithms. Second, these devices have transcended scientific utility and are now being manufactured and distributed to the general public. This review will provide an overview of: (1) the history of actigraphic sleep measurement, (2) the physiological underpinnings of heart rate and heart rate variability measurement in wearables, (3) the refinement and validation of both standard actigraphy and newer, multisensory devices for real-world sleep-wake detection, (4) the practical applications of actigraphy, (5) important limitations of actigraphic measurement, and lastly (6) future directions within the field.

Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study

BACKGROUND

Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation.

OBJECTIVE

This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work.

METHODS

A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement.

RESULTS

Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non-rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm.

CONCLUSIONS

We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

HRS White Paper on Clinical Utilization of Digital Health Technology

This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.

Respiratory Event Detection during Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data

This paper presents an automatic algorithm for the detection of respiratory events in patients using electrocardiogram (ECG) and respiratory signals. The proposed method was developed using data of polysomnogram (PSG) and those recorded from a patch-type device. In total, data of 1,285 subjects were used for algorithm development and evaluation. The proposed method involved respiratory event detection and apnea-hypopnea index (AHI) estimation. Handcrafted features from the ECG and respiratory signals were applied to machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, random forest, multi-layer perceptron, and the support vector machine (SVM). High performance was demonstrated when using SVM, where the overall accuracy achieved was 83% and the Cohens kappa was 0.53 for the minute-by-minute respiratory event detection. The correlation coefficient between the reference AHI obtained using the PSG and estimated AHI as per the proposed method was 0.87. Furthermore, patient classification based on an AHI cutoff of 15 showed an accuracy of 87% and a Cohens kappa of 0.72. The proposed method increases performance result, as it records the ECG and respiratory signals simultaneously. Overall, it can be used to lower the development cost of commercial software owing to the use of open datasets.

Performance of seven consumer sleep-tracking devices compared with polysomnography

STUDY OBJECTIVES

Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG).

METHODS

In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG.

RESULTS

Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18-0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep.

CONCLUSIONS

Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.

Preoperative REM sleep is associated with complication development after colorectal surgery

BACKGROUND

While total sleep duration and rapid eye movement (REM) sleep duration have been associated with long-term mortality in non-surgical cohorts, the impact of preoperative sleep on postoperative outcomes has not been well studied.

METHODS

In this secondary analysis of a prospective observational cohort study, patients who recorded at least 1 sleep episode using a consumer wearable device in the 7 days before elective colorectal surgery were included. 30-day postoperative outcomes among those who did and did not receive at least 6 h of total sleep, as well as those who did and did not receive at least 1 h of rapid eye movement (REM) sleep, were compared.

RESULTS

34 out of 95 (35.8%) patients averaged at least 6 h of sleep per night, while 44 out of 82 (53.7%) averaged 1 h or more of REM sleep. Patients who slept less than 6 h had similar postoperative outcomes compared to those who slept 6 h or more. Patients who averaged less than 1 h of REM sleep, compared to those who achieved 1 h or more of REM sleep, had significantly higher rates of complication development (29.0% vs. 9.1%, P = 0.02), and return to the OR (10.5% vs. 0%, P = 0.04). After adjustment for confounding factors, increased REM sleep duration remained significantly associated with decreased complication development (increase in REM sleep from 50 to 60 min: OR 0.72, P = 0.009; REM sleep ≥ 1 h: OR 0.22, P = 0.03).

CONCLUSION

In this cohort of patients undergoing elective colorectal surgery, those who developed a complication within 30 days were less likely to average at least 1 h of REM sleep in the week before surgery than those who did not develop a complication. Preoperative REM sleep duration may represent a risk factor for surgical complications; however additional research is necessary to confirm this relationship.

Relationships Between Sleep, Activity, and Burnout in Ophthalmology Residents

OBJECTIVE

To objectively measure sleep and activity levels and examine their relationship to burnout, depression, anxiety, and stress in ophthalmology residents.

DESIGN

A cohort study of ophthalmology residents at the University of Washington from July 1, 2017 to June 30, 2018.

SETTING

Single-center academic institution.

PARTICIPANTS

Fourteen ophthalmology residents at the University of Washington enrolled between July 1, 2017 and June 30, 2018.

RESULTS

Data were collected from 14 residents, ages 27 to 34. Wrist actigraphy allowed for objective measurement of resident sleep and activity, though adherence to wrist actigraphy usage dropped significantly over time. Residents recorded significantly less sleep on call compared to when they were off call, with mean (SD) 3.6 (2.0) hours on primary call, 5.6 (1.8) hours on secondary call, and 6.7 (1.4) hours off call. Lower average sleep on call was associated with higher emotional exhaustion (r = -0.69, p = 0.04), lower personal accomplishment (r = 0.82, p = 0.007), higher anxiety (r = -0.90, p = 0.001), and higher stress (r = -0.75, p = 0.02). Higher daily activity was associated with higher sense of personal accomplishment (r = 0.57, p = 0.04). Average nightly sleep, average sleep while not on call, and daily sedentary time was not associated with any subset of burnout.

CONCLUSIONS

The association between objectively measured sleep while on call and burnout, depression, and anxiety are consistent with findings from prior studies which relied on subjective measures of sleep. The direction of causality - whether poor sleep caused burnout, burnout caused poor sleep, or both - could not be assessed in the present study. However, these results are consistent with the hypothesis that poor sleep on call contributes to resident burnout and that physical activity may reduce aspects of burnout. The use of wrist actigraphy to objectively measure sleep and activity patterns may help focus and evaluate interventions aimed at decreasing resident burnout.

A novel digital approach to describe real world outcomes among patients with constipation

Understanding day-to-day variations in symptoms and medication management can be important in describing patient centered outcomes for people with constipation. Patient Generated Health Data (PGHD) from digital devices is a potential solution, but its utility as a tool for describing experiences of people with frequent constipation is unknown. We conducted a virtual, 16-week prospective study of individuals with frequent constipation from an online wellness platform that connects mobile consumer digital devices including wearable monitors capable of passively collecting steps, sleep, and heart rate data. Participants wore a Fitbit monitoring device for the study duration and were administered daily and monthly surveys assessing constipation symptom severity and medication usage. A set of 38 predetermined day-level behavioral activity metrics were computed from minute-level data streams for steps, sleep and heart rate. Mixed effects regression models were used to compare activity metrics between constipation status (irregular or constipated vs. regular day), medication use (medication day vs. non-medication day) and the interaction of medication day with irregular or constipation days, as well as to model likelihood to treat with constipation medications based on daily self-reported symptom severity. Correction for multiple comparisons was performed with the Benjamini-Hochberg procedure for false discovery rate. This study analyzed 1540 enrolled participants with completed daily surveys (mean age 36.6 sd 10.0, 72.8% female, 88.8% Caucasian). Of those, 1293 completed all monthly surveys and 756 had sufficient Fitbit data density for analysis of activity metrics. At a daily-level, 22 of the 38 activity metrics were significantly associated with bowel movement or medication treatment patterns for constipation. Participants were measured to have fewer steps on irregular days compared to regular days (-200 steps, 95% CI [-280, -120]), longer periods of inactivity on constipated days (9.1 min, 95% CI [5.2, 12.9]), reduced total sleep time on irregular and constipated days (-2.4 min, 95% CI [-4.3, -0.4] and -4.0 min, 95% CI [-6.5, -1.4], respectively). Participants reported greater severity of symptoms for bloating, hard stool, difficulty passing, and painful bowel movements on irregular, constipation and medication days compared to regular days with no medication. Interaction analysis of medication days with irregular or constipation days observed small increases in severity compared to non-medication days. Participants were 4.3% (95% CI 3.2, 5.3) more likely to treat with medication on constipated days versus regular. No significant increase in likelihood was observed for irregular days. Daily likelihood to treat increased for each 1-point change in symptom severity of bloating (2.4%, 95% CI [2.0, 2.7]), inability to pass (2.2%, 95% CI [1.4, 3.0]) and incomplete bowel movements (1.3%, 95% CI [0.9, 1.7]). This is the first large scale virtual prospective study describing the association between passively collected PGHD and constipation symptoms and severity at a day-to-day granularity level. Constipation status, irregular or constipated, was associated with a number of activity metrics in steps and sleep, and likelihood to treat with medication increased with increasing severity for a number of constipation symptoms. Given the small magnitude of effect, further research is needed to understand the clinical relevance of these results. PGHD may be useful as a tool for describing real world patient centered experiences for people with constipation.

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

PURPOSE

There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.

METHODS

We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.

RESULTS

The classifier achieved substantial agreement on four-class sleep staging with an average Cohen's kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa (ρ = -0.30, p<0.001) and age and accuracy (ρ = -0.22, p<0.001).

CONCLUSION

This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.

Sleep Tracking of a Commercially Available Smart Ring and Smartwatch Against Medical-Grade Actigraphy in Everyday Settings: Instrument Validation Study

BACKGROUND

Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines.

OBJECTIVE

This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines.

METHODS

We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable.

RESULTS

We found significant correlations between the ring's and actigraphy's TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups.

CONCLUSIONS

In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.