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A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual. To validate the device ten healthy subjects were recruited for a 12-night sleep study. The study was divided into two parts; part A comprised two nights with both ear-EEG and polysomnography (PSG), and part B comprised 10 nights using only ear-EEG. In addition to the electrophysiological measurements, subjects filled out a questionnaire after each night of sleep. The subjects reported that the ear-EEG system was easy to use, and that the comfort was better in part B. The performance of the system was validated by comparing automatic sleep scoring based on ear-EEG with PSG-based sleep scoring performed by a professional trained sleep scorer. Cohen's kappa was used to assess the agreement between the manual and automatic sleep scorings, and the study showed an average kappa value of 0.71. The majority of the 20 recordings from part A yielded a kappa value above 0.7. The study was compared to a companioned study conducted with individualized earpieces. To compare the sleep across the two studies and two parts, 7 different sleeps metrics were calculated based on the automatic sleep scorings. The ear-EEG nights were validated through linear mixed model analysis in which the effects of equipment (individualized vs. generic earpieces), part (PSG and ear-EEG vs. only ear-EEG) and subject were investigated. We found that the subject effect was significant for all computed sleep metrics. Furthermore, the equipment did not show any statistical significant effect on any of the sleep metrics. These results corroborate that generic ear-EEG is a promising alternative to the gold standard PSG for sleep stage monitoring. This will allow sleep stage monitoring to be performed in a less obtrusive way and over longer periods of time, thereby enabling diagnosis and treatment of diseases with associated sleep disorders.

PurposeTo assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings.MethodsAutomatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. The automatic sleep staging was tested on 80 full-night recordings from 20 healthy subjects. The scoring performance and sleep metrics were determined for all ear-EEG setups and the actigraphy device.ResultsThe single-ear, the single-ear with ipsilateral mastoid setup, and the cross-ear setup performed five class sleep staging with kappa values 0.36, 0.63, and 0.72, respectively. For the single-ear with mastoid electrode and the cross-ear setup, the performance of the sleep metrics, in terms of mean absolute error, was better than the sleep metrics estimated from the actigraphy device in the current study, and also better than current state-of-the-art actigraphy studies.ConclusionA statistically significant improvement in both accuracy and kappa was observed from single-ear to single-ear with ipsilateral mastoid, and from single-ear with ipsilateral mastoid to cross-ear configurations for both two and five-sleep stage classification. In terms of sleep metrics, the results were more heterogeneous, but in general, actigraphy and single-ear with ipsilateral mastoid configuration were better than the single-ear configuration; and the cross-ear configuration was consistently better than both the actigraphy device and the single-ear configuration.

Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Internet gaming disorder (IGD) has been associated with impairments in executive functioning, particularly inattention and impulsivity. Sleep quality has separately been linked to both gaming behavior and cognitive performance, yet its role as a mediating factor in this relationship is underexplored. This study aimed to determine whether sleep quality mediates the relationship between IGD symptoms and executive dysfunction in adolescents, specifically focusing on the domains of inattention and hyperactivity or impulsivity. A reverse mediation model was also tested to explore the bidirectional nature of these relationships. A representative sample of 1000 adolescents (539/1000, 53.9% males), aged between 12 and 17 years (mean 14.52, SD 1.64), completed validated self-report measures of IGD symptoms, executive dysfunction, and sleep quality. Structural equation modeling was used to test direct and indirect effects with age and gender included as covariates. Of the sample, 2.4% (24/1000) met criteria for IGD (875/1000, 87.5% males), and 22.6% (226/1000) met criteria for chronic sleep reduction. Among those with IGD, 54.2% (542/1000) also experienced chronic sleep reduction. In model A (IGD → Sleep → Executive Dysfunction), IGD symptoms were associated with poorer sleep quality (a=0.32, 95% CI 0.19-0.44), which in turn were associated with greater executive dysfunction (b=0.05, 95% CI 0.01-0.10). The indirect effect was significant (a×b=0.02, 95% CI 0.01-0.04), and sleep quality was a partial mediator. In the reverse model (model B), executive dysfunction was associated with poorer sleep quality (a=0.15, 95% CI 0.06-0.25), which subsequently was associated with higher IGD symptoms (b=0.11, 95% CI 0.07-0.16); indirect effect a×b=0.02, 95% CI 0.01-0.04. Simple slope analysis showed that IGD symptoms were associated only with executive dysfunction at average or poor levels of sleep quality. At higher levels of sleep quality, this relationship was no longer significant. The results of this study suggest that sleep quality may be an important intermediary mechanism by which IGD might contribute to executive dysfunction and provide a basis for the development and implementation of strategies that target sleep issues in IGD. Prospective longitudinal research is needed to examine the directionality of the relationships between IGD, sleep quality, and executive dysfunction longitudinally.

As digital technology becomes increasingly embedded in daily life, digital isolation among older adults has become more pronounced. This isolation may restrict access to health information and social support, potentially leading to poorer sleep quality. However, most existing studies on digital isolation and sleep disorders were cross-sectional, lacking longitudinal evidence to establish causality. This study aims to investigate the association between digital isolation and sleep disorders in older adults using both cross-sectional and longitudinal designs and to assess the impact of specific components of digital isolation on the risk of sleep disorders. We analyzed data from the National Health and Aging Trends Study (NHATS) collected from 2011 to 2022, including a discovery sample of 5989 older adults and a validation sample of 3443. Digital isolation was measured by the use of mobile phones, computers, email, and the internet, while sleep disorders were identified based on difficulties initiating or maintaining sleep and the use of sleep medication. Multivariable logistic regression and Cox proportional hazards models were used for cross-sectional and longitudinal analyses, respectively. Cross-sectional analyses revealed a higher prevalence of sleep disorders among those with high digital isolation (discovery: 1452/2166, 67.03% vs 2259/3823, 59.06%; odds ratio [OR] 1.23, 95% CI 1.09-1.39; P<.001 and validation: 673/960, 70.10% vs 1524/2483, 61.38%; OR 1.22, 95% CI 1.02-1.47; P=.03). In longitudinal analyses, high digital isolation was associated with an increased risk of sleep disorders in the discovery (hazard ratio [HR] 1.21, 95% CI 1.05-1.38; P=.006) and pooled samples (HR 1.17, 95% CI 1.05-1.31; P=.005), but the association was not statistically significant in the validation sample after adjustment (HR 1.11, 95% CI 0.91-1.36; P=.30). Digital isolation is significantly associated with sleep disorders among older adults, particularly in cross-sectional analyses, while longitudinal findings provide partial support for this association. The nonsignificant result observed in the validation sample may reflect sample heterogeneity and suggests that mental health may mediate this relationship. Future interventions should address mental health to help mitigate the negative impact of digital isolation on sleep.

Despite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. A systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. Per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. The search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP. The findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.

More than 1 in 8 people potentially live with a mental health disorder, yet fewer than half receive treatment. Poor mental health awareness may contribute to this treatment gap, and digital health technologies, like wearables and their associated phone- and web-based applications, have the potential to reduce the mental health awareness gap due to their ease of adoption, objective feedback, and high rate of engagement. This study aimed to better understand the relationships between mental health and objective wearable-derived metrics. We examined the longitudinal results of monthly mental health surveys (Patient Health Questionnaire-2, Generalized Anxiety Disorder 2-item, and Perceived Stress Scale) delivered over 13 months to 181,574 individuals wearing a device (WHOOP, Inc.) that measures sleep, cardiorespiratory parameters, and physical activity (up to 307,860 survey responses and 7,942,176 days of total wear time). Generalized linear mixed models, cross-lag analyses, and intrapersonal scaling were used to assess interpersonal and intrapersonal associations between wearable-derived metrics and mental health outcomes. Age, gender, BMI, and time of year were used as covariates in the models. Interpersonal associations between wearable-derived metrics and mental health outcomes indicate that individuals with better sleep characteristics (ie, longer sleep durations and more consistent wake and sleep times), higher heart rate variabilities (HRV), lower resting heart rates (RHR), and higher levels of physical activity report lower levels of depression, anxiety, and stress. Intrapersonal associations between wearable-derived metrics and mental health outcomes displayed similar results as the between-person analyses, with higher HRVs, lower RHRs, and more physical activity generally coinciding with improved mental health outcomes. However, intrapersonal wearable-derived sleep metric associations diverged from the interpersonal association findings when specifically looking at sleep duration and depression, whereby increased sleep durations within an individual were associated with higher levels of depression. In interpersonal analyses, the largest association observed was between the Perceived Stress Scale scores and RHR, with a standardized coefficient of 0.09 (P<.001); in intrapersonal analyses, the largest association observed was between the Patient Health Questionnaire-2 scores and summated heart rate zones-a proxy for physical activity-with a standardized coefficient of -0.04 (P<.001). Cross-lagged models demonstrated that higher levels of reported stress preceded higher RHRs, respiratory rates, and sleep duration variabilities, as well as lower HRVs. Overall, this investigation reveals that numerous physiological variables measured by wearables are associated with mental state in free-living environments. These findings underscore the potential of wearable-derived physiological and behavioral monitoring to serve as objective complements to traditional subjective assessments in mental health research and care. However, given the complex nature of mental health disorders, further research is needed to determine how these metrics can be effectively integrated into clinical practice.

Disturbed sleep patterns are common among older adults and may contribute to cognitive and physical declines. However, evidence for the relationship between sleep duration and cognitive frailty, a concept combining physical frailty and cognitive impairment in older adults is lacking. The objective of our study was to examine the associations of sleep duration and its changes with cognitive frailty. We analyzed data from the 2008-2018 waves of the Chinese Longitudinal Healthy Longevity Survey. Cognitive frailty was rendered based on the modified Fried frailty phenotype and Mini-Mental State Examination. Sleep duration was categorized as short (<6 h), moderate (6-9 h), and long (>9 h). We examined the association of sleep duration with cognitive frailty status at baseline using logistic regressions and with future incidence of cognitive frailty using Cox proportional hazards models. Restricted cubic splines were employed to explore potential non-linear associations. Among 11,303 participants, 1,298 (11.5%) had cognitive frailty at baseline. Compared to participants who had moderate sleep duration, the odds of having cognitive frailty were higher in those with long sleep duration (odds ratio [OR] =1.71, 95% confidence interval [CI] =1.48-1.97, p<0.001). A J-shaped association between sleep duration and cognitive frailty was also observed (p<0.001). Additionally, during a median follow-up of 6.7 years among 5,201 participants who were not cognitively frail at baseline, 521 (10.0%) developed cognitive frailty. A higher risk of cognitive frailty was observed in participants with long sleep duration (hazard ratio [HR] =1.32, 95% CI =1.07-1.62, p=0.008). Long sleep duration was associated with cognitive frailly in older Chinese adults. These findings provide insights into the relationship between sleep duration and cognitive frailty, with potential implications for public health policies and clinical practice.

Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established. We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests. We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes. A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores. Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.