Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Assessing the Real-World, Long-Term Impact of Lemborexant on Sleep Quality in a Home-Based Clinical Study

Purpose

Both subjective and objective evaluations are essential for the treatment of insomnia. Lemborexant has been shown to be effective in the long-term based solely on a subjective basis, and no long-term objective measures have been evaluated under natural sleep conditions. Small, lightweight sleep electroencephalogram (EEG) monitor was used, instead of polysomnography, to objectively evaluate sleep at home 4 and 12 weeks after lemborexant treatment.

Patients and Methods

Adults and elderly subjects with insomnia disorder, per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, were enrolled in this open-label, single-arm, single-center trial. Objective and subjective measures of sleep were prospectively assessed. Sleep disturbance, excessive sleepiness, and depressive symptoms were assessed using questionnaires.

Results

A total of 45 subjects were screened, of which 33 were enrolled. Paired t-tests were conducted to evaluate changes in sleep variables and compared with the baseline; subjects showed significant improvements in objective sleep efficiency (SE) and subjective sleep parameters at weeks 4 and 12 following treatment with lemborexant. When baseline values were taken into account, a repeated-multivariate analysis of variance (MANOVA) revealed statistically significant changes in the objective measures. Sleep disturbance, excessive sleepiness, and depressive symptoms improved after three months of lemborexant treatment.

Conclusion

Furthermore, lemborexant therapy improved nocturnal sleep, when measured objectively using sleep EEG monitoring at home, and improved daytime sleepiness and depressive symptoms in older adults with insomnia disorder.

Remote collection of electrophysiological data with brain wearables: opportunities and challenges

Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.

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.

Regularity and Timing of Sleep Patterns and Behavioral Health Among Adolescents

OBJECTIVE

Sleep is vital to supporting adolescent behavioral health and functioning; however, sleep disturbances remain under-recognized and undertreated in many health care settings. One barrier is the complexity of sleep, which makes it difficult for providers to determine which aspects-beyond sleep duration-may be most important to assess and treat to support adolescent health. This study examined associations between 2 sleep indices (regularity and timing) and adolescent behavioral health and functioning over and above the impact of shortened/fragmented sleep.

METHOD

Eighty-nine adolescents recruited from the community (mean age = 14.04, 45% female participants) completed 7 days/nights of actigraphy and, along with a parent/guardian, reported on behavioral health (internalizing and externalizing symptoms) and psychosocial functioning. Stepwise linear regressions examined associations between sleep timing and regularity and behavioral/functional outcomes after accounting for shortened/fragmented sleep.

RESULTS

Delayed sleep timing was associated with greater self-reported internalizing (F[6,82] = 11.57, p = 0.001) and externalizing (F[6,82] = 11.12, p = 0.001) symptoms after accounting for shortened/fragmented sleep. Irregular sleep was associated with greater self-reported and parent-reported externalizing symptoms (self: F[7,81] = 6.55, p = 0.01; parent: F[7,80] = 6.20, p = 0.01) and lower psychosocial functioning (self: F[7,81] = 6.03, p = 0.02; parent: F[7,78] = 3.99, p < 0.05) after accounting for both shortened/fragmented sleep and delayed sleep timing.

CONCLUSION

Sleep regularity and timing may be critical for understanding the risk of poor behavioral health and functional deficits among adolescents and as prevention and intervention targets. Future work should focus on developing and evaluating convenient, low-cost, and effective methods for addressing delayed and/or irregular adolescent sleep patterns in real-world health care settings.

Impact of daily caffeine intake and timing on electroencephalogram-measured sleep in adolescents

STUDY OBJECTIVES

Caffeine use is ubiquitous among adolescents and may be harmful to sleep, with downstream implications for health and development. Research has been limited by self-reported and/or aggregated measures of sleep and caffeine collected at a single time point. This study examines bidirectional associations between daily caffeine consumption and electroencephalogram-measured sleep among adolescents and explores whether these relationships depend on timing of caffeine use.

METHODS

Ninety-eight adolescents aged 11-17 (mean =14.38, standard deviation = 1.77; 50% female) participated in 7 consecutive nights of at-home sleep electroencephalography and completed a daily diary querying morning, afternoon, and evening caffeine use. Linear mixed-effects regressions examined relationships between caffeine consumption and total sleep time, sleep-onset latency, sleep efficiency, wake after sleep onset, and time spent in sleep stages. Impact of sleep indices on next-day caffeine use was also examined.

RESULTS

Increased total caffeine consumption was associated was increased sleep-onset latency (β = .13; 95% CI = .06, .21; P < .001) and reduced total sleep time (β = -.17; 95% confidence interval [CI] = -.31, -.02; P = .02), sleep efficiency (β = -1.59; 95% CI = -2.51, -.67; P < .001), and rapid eye movement sleep (β = -.12; 95% CI = -.19, -.05; P < .001). Findings were driven by afternoon and evening caffeine consumption. Reduced sleep efficiency was associated with increased afternoon caffeine intake the following day (β = -.006; 95% CI = -.012, -.001; P = .01).

CONCLUSIONS

Caffeine consumption, especially afternoon and evening use, impacts several aspects of adolescent sleep health. In contrast, most sleep indicators did not affect next-day caffeine use, suggesting multiple drivers of adolescent caffeine consumption. Federal mandates requiring caffeine content labeling and behavioral interventions focused on reducing caffeine intake may support adolescent sleep health.

CITATION

Lunsford-Avery JR, Kollins SH, Kansagra S, Wang KW, Engelhard MM. Impact of daily caffeine intake and timing on electroencephalogram-measured sleep in adolescents. J Clin Sleep Med. 2022;18(3):877-884.

Exploring the feasibility and acceptability of a sleep wearable headband among a community sample of chronic pain individuals: An at-home observational study

Background

Chronic pain conditions affect up to one third of the adult population in the United Kingdom. Sleep problems are prevalent and negatively impact quality of life. Lack of standardised tools for routine screening and assessment of sleep changes have been a barrier for sleep management. Novel sleep wearables offer an exciting and accessible way to measure sleep but have not been tested outside of the consumer-led landscape and are not commonly used in research and clinical settings.

Aims

The study aimed to explore the feasibility and acceptability of a sleep monitoring headband (Dreem 2) utilising EEG technology and accompanying smartphone application among a cohort of adults with chronic pain.

Results

Twenty-one adults (81% women) completed a one-week home sleep study using a sleep headband and accompanying app. Ninety per cent of participants met the pre-defined requirement of two-night's sleep recording. All participants recorded one night of sleep data via the sleep headband. The majority (76%) of participants were satisfied with the sleep study, and 86% of participants were willing to wear the headband longer than the 2-night minimum requirement. Finally, 76% reported the headband as ‘somewhat’ or ‘extremely’ comfortable whist awake; 57% rated the headband as comfortable during sleep.

Conclusion

The Dreem 2 headband appears to be a feasible and acceptable means of collecting sleep measurements among individuals with chronic pain, despite common sleep disturbances. These devices may have utility for screening, assessment and monitoring in research and practice. Further research is needed to provide guidelines and training for integration.

Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework

Clarifying whether physiological sleep measures predict mortality could inform risk screening; however, such investigations should account for complex and potentially non-linear relationships among health risk factors. We aimed to establish the predictive utility of polysomnography (PSG)-assessed sleep measures for mortality using a novel permutation random forest (PRF) machine learning framework. Data collected from the years 1995 to present are from the Sleep Heart Health Study (SHHS; n = 5,734) and the Wisconsin Sleep Cohort Study (WSCS; n = 1,015), and include initial assessments of sleep and health, and up to 15 years of follow-up for all-cause mortality. We applied PRF models to quantify the predictive abilities of 24 measures grouped into five domains: PSG-assessed sleep (four measures), self-reported sleep (three), health (eight), health behaviours (four), and sociodemographic factors (five). A 10-fold repeated internal validation (WSCS and SHHS combined) and external validation (training in SHHS; testing in WSCS) were used to compute unbiased variable importance metrics and associated p values. We observed that health, sociodemographic factors, and PSG-assessed sleep domains predicted mortality using both external validation and repeated internal validation. The PSG-assessed sleep efficiency and the percentage of sleep time with oxygen saturation <90% were among the most predictive individual measures. Multivariable Cox regression also revealed the PSG-assessed sleep domain to be predictive, with very low sleep efficiency and high hypoxaemia conferring the highest risk. These findings, coupled with the emergence of new low-burden technologies for objectively assessing sleep and overnight oxygen saturation, suggest that consideration of physiological sleep measures may improve risk screening.

Self-administered electroencephalography-based sleep assessment: compliance and perceived feasibility in children and adults

Background

Sleep is a crucial part of our lives and insufficient sleep has been linked to several health disorders in both children and adults. However, most studies are based on single night laboratory polysomnography, actigraphy, or sleep diaries. The primary aim of this study was to evaluate compliance to and perceived feasibility of the Zmachine insight+ for assessment of habitual sleep parameters in a sample of children and adults for six nights. The secondary aim was to report sleep parameters derived from the Zmachine.

Methods

We analyzed data from 12 families who participated in the SCREENS pilot trial (2018–2019). Children (n=14) and adults (n=19) had to undergo three nights of EEG-based sleep assessment at baseline and follow-up. We assessed compliance to the sleep assessment protocol and summarized perceived feasibility in children and adults. Summary estimates were computed for total sleep time, sleep onset latency, wake after sleep onset, light sleep, deep sleep, and rapid eye movement sleep.

Results

Compliance to the sleep assessment protocol was high with 92.9 and 89.4% of children and adults meeting the a priori specified compliance goal of at least two out of three nights of complete sleep data at both baseline and follow-up. In general, the protocol was perceived as feasible, with low prevalence of sleep disruption and only minor issues, e.g. difficulties with removing sensors. Results on sleep parameters indicate large within group variation.

Conclusions

Our findings support the use of a self-administered EEG-based habitual sleep assessment protocol, including multiple days of measurement, in children and adults.

Trial registration

Cilinicaltrials.gov: NCT03788525 [Secondary outcome measures; Retrospectively registered; 27th December, 2018].