Validation of the Sleep-Wake Scoring of a New Wrist-Worn Sleep Monitoring Device

Relationship of sleep with diurnal cortisol rhythm considering sleep measurement and cortisol sampling schemes

Information on the relationships between the previous night's sleep and the next-day diurnal cortisol rhythm is inconsistent due to confounding factors such as sleep measurements (trait/state sleep and objective/subjective sleep) and cortisol sampling schemes. Therefore, this study aimed to investigate these relationships, considering the confounding factors. College students (n = 79) wore actigraphy for 3 days to undergo an evaluation of previous night-time sleep objectively and reported their subjective sleep parameters in a sleep diary. In addition, participants provided six salivary cortisol samples daily. Furthermore, six cortisol sampling schemes were created to reflect diurnal cortisol rhythms, and two different methods were used to calculate the index of diurnal cortisol slope (DCS). A multilevel model was created to examine the impact of both trait and state sleep on next-day diurnal cortisol rhythm. The results revealed that higher objective state sleep efficiency and longer objective state total sleep time were associated with a higher cortisol awakening response (CAR). Moreover, higher objective trait sleep efficiency and longer objective trait total sleep time were associated with higher waking cortisol levels and steeper DCS. In addition, a minimum of four saliva samples were required at different time points, including upon waking, 30 min after waking, 1 h after waking, and at bedtime, to explore the relationship of sleep efficiency/total sleep time with waking cortisol, CAR, and DCS. Furthermore, the index of the peak-to-bed slope was appropriately employed to examine the relationship between sleep efficiency and DCS, whereas the wake-to-bed slope was effective for examining the relationship between total sleep time and DCS. In summary, this study clarified the relationship between sleep and next-day diurnal cortisol rhythm and suggested a cost-effective cortisol sampling schedule and calculation methods.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

Clinical and neuroradiological correlates of sleep in myotonic dystrophy type 1

Abnormalities of sleep are common in myotonic dystrophy type 1 (DM1), but few previous studies have combined polysomnography with detailed clinical measures and brain imaging. In the present study, domiciliary polysomnography, symptom questionnaires and cognitive evaluation were undertaken in 39 DM1-affected individuals. Structural brain MRI was completed in those without contra-indication (n = 32). Polysomnograms were adequate for analysis in 36 participants. Sleep efficiency was reduced, and sleep architecture altered in keeping with previous studies. Twenty participants (56%) had moderate or severe sleep-disordered breathing (apnoea-hypopnoea index [AHI] ≥ 15). In linear modelling, apnoeas were positively associated with increasing age and male sex. AHI ≥ 15 was further associated with greater daytime pCO₂ and self-reported physical impairment, somnolence and fatigue. Percentage REM sleep was inversely associated with cerebral grey matter volume, stage 1 sleep was positively associated with occipital lobe volume and stage 2 sleep with amygdala volume. Hippocampus volume was positively correlated with self-reported fatigue and somnolence. Linear relationships were also observed between measures of sleep architecture and cognitive performance. Findings broadly support the hypothesis that changes in sleep architecture and excessive somnolence in DM1 reflect the primary disease process in the central nervous system.

Measurement Methods of Fatigue, Sleepiness, and Sleep Behaviour Aboard Ships: A Systematic Review

Since seafarers are known to be exposed to numerous job-related stress factors that can cause fatigue, sleepiness, and disturbed sleep behaviour, the aim of this review was to provide an overview of the subjective and objective measurement methods of these strains. Using a systematic review, 166 studies were identified within the period of January 2010 to December 2020 using the PubMed database. Of the 21 studies selected, 13 used both subjective and objective measurement methods. Six studies used only subjective and two studies only objective methods. For subjective assessment, 12 different questionnaires could be identified as well as activity and sleeping logs. Actigraphy and reaction time tests (RTT) were the most common objective methods. In single cases, electrooculography (EOG), pupillometry and ambulatory polysomnography (PSG) were used. Measurement-related limitations due to vessel-related impacts were less often reported than expected. No restrictions of daily routines on board were described, and only single-measurement disturbances due to ship movements were mentioned. The present literature review reveals that there are various routines to measure fatigue, sleepiness, and sleep behaviour on board. A combination of subjective and objective methods often appears to be beneficial. The frequent use of actigraphy and RTT on board suggests good feasibility and reliable measurements with these methods. The use of ambulatory PSG in maritime-like contexts suggests that this method would also be feasible on board.

Wearable Sensors for Measurement of Viewing Behavior, Light Exposure, and Sleep

The purpose of this study was to compare two wearable sensors to each other and to a questionnaire in an adult population. For one week, participants aged 29.2 ± 5.5 years (n = 25) simultaneously wore a Clouclip, a spectacle-mounted device that records viewing distance and illuminance, and an Actiwatch, a wrist-worn device that measures illuminance and activity. Participants maintained a daily log of activities and completed an activity questionnaire. Objective measures of time outdoors, near (10–< 60 cm) and intermediate (60–100 cm) viewing, and sleep duration were assessed with respect to the daily log and questionnaire. Findings showed that time outdoors per day from the questionnaire (3.2 ± 0.3 h) was significantly greater than the Clouclip (0.9 ± 0.8 h) and Actiwatch (0.7 ± 0.1 h, p < 0.001 for both). Illuminance from the Actiwatch was systematically lower than the Clouclip. Daily near viewing duration was similar between the questionnaire (5.7 ± 0.6 h) and Clouclip (6.1 ± 0.4 h, p = 0.76), while duration of intermediate viewing was significantly different between methods (p < 0.001). In conclusion, self-reported time outdoors and viewing behaviors were different than objective measures. The Actiwatch and Clouclip are valuable tools for studying temporal patterns of behavioral factors such as near work, light exposure, and sleep.

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

Light in the Senior Home: Effects of Dynamic and Individual Light Exposure on Sleep, Cognition, and Well-Being

Disrupted sleep is common among nursing home patients and is associated with cognitive decline and reduced well-being. Sleep disruptions may in part be a result of insufficient daytime light exposure. This pilot study examined the effects of dynamic “circadian” lighting and individual light exposure on sleep, cognitive performance, and well-being in a sample of 14 senior home residents. The study was conducted as a within-subject study design over five weeks of circadian lighting and five weeks of conventional lighting, in a counterbalanced order. Participants wore wrist accelerometers to track rest–activity and light profiles and completed cognitive batteries (National Institute of Health (NIH) toolbox) and questionnaires (depression, fatigue, sleep quality, lighting appraisal) in each condition. We found no significant differences in outcome variables between the two lighting conditions. Individual differences in overall (indoors and outdoors) light exposure levels varied greatly between participants but did not differ between lighting conditions, except at night (22:00–6:00), with maximum light exposure being greater in the conventional lighting condition. Pooled data from both conditions showed that participants with higher overall morning light exposure (6:00–12:00) had less fragmented and more stable rest–activity rhythms with higher relative amplitude. Rest–activity rhythm fragmentation and long sleep duration both uniquely predicted lower cognitive performance.

Sleep in Myopic and Non-Myopic Children

Purpose

To examine differences in sleep between myopic and non-myopic children.

Methods

Objective measurements of sleep, light exposure, and physical activity were collected from 91 children, aged 10 to 15 years, for two 14-day periods approximately 6 months apart. Sleep parameters were analyzed with respect to refractive error, season, day of the week, age, and sex.

Results

Myopic children exhibited differences in sleep duration by day of the week (P < 0.001) and season (P = 0.007). Additionally, myopic children exhibited shorter sleep latency than non-myopic children (P = 0.04). For all children, wake time was later (P < 0.001) and sleep duration was longer (P = 0.03) during the cooler season compared with the warmer season. On weekends, children went to bed later (P < 0.001), woke up later (P < 0.001), and had increased sleep duration (P < 0.001) than on weekdays. Younger children exhibited earlier bedtime (P = 0.005) and wake time (P = 0.01) than older children. Time spent outdoors was positively associated with sleep duration (P = 0.03), and daily physical activity was negatively associated with wake time (P < 0.001).

Conclusions

Myopic children tended to have more variable sleep duration and shorter latency than non-myopic children. Sleep patterns were influenced by season, day of the week, age, time outdoors, and activity.

Translational Relevance

Myopic children tended to have more variable sleep duration and shorter latency than non-myopic children, which may reflect previously reported differences in environmental and behavioral factors between refractive error groups.

Single-layered ultra-soft washable smart textiles for all-around ballistocardiograph, respiration, and posture monitoring during sleep

Good sleep is considered to be the cornerstone for maintaining both physical and mental health. However, nearly one billion people worldwide suffer from various sleep disorders. To date, polysomnography (PSG) is the most commonly used sleep-monitoring technology,however, it is complex, intrusive, expensive and uncomfortable. Unfortunately, present noninvasive monitoring technologies cannot simultaneously achieve high sensitivity, multi-parameter monitoring and comfort. Here, we present a single-layered, ultra-soft, smart textile for all-around physiological parameters monitoring and healthcare during sleep. With a high-pressure sensitivity of 10.79 mV/Pa, a wide working frequency bandwidth from 0 Hz to 40 Hz, good stability, and decent washability, the single-layered ultra-soft smart textile is simultaneously capable of real-time detection and tracking of dynamic changes in sleep posture, and subtle respiration and ballistocardiograph (BCG) monitoring. Using the set of patient generated health data, an obstructive sleep apnea-hypopnea syndrome (OSAHS) monitoring and intervention system was also developed to improve the sleep quality and prevent sudden death during sleep. This work is expected to pave a new and practical pathway for physiological monitoring during sleep.

Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation

Introduction: Sleep assessment devices are essential for the detection, diagnosis, and monitoring of sleep disorders. This paper provides a state-of-the-art review and comparison of sleep assessment devices and a market analysis.Areas covered: Hardware devices are classified into contact and contactless devices. For each group, the underlying technologies are presented, paying special attention to their limitations. A systematic literature review has been carried out by comparing the most important validation studies of sleep tracking devices in terms of sensitivity and specificity. A market analysis has also been carried out in order to list the most used, best-selling, and most highly-valued devices. Software apps have also been compared with regards to the market.Expert opinion: Thanks to technological advances, the reliability and accuracy of sensors has been significantly increased in recent years. According to validation studies, some actigraphs present a sensibility higher than 90%. However, the market analysis reveals that many hardware devices have not been validated, and especially software devices should be studied before their clinical use.

A systematic review of the accuracy of sleep wearable devices for estimating sleep onset

The accurate estimation of sleep onset is required for many purposes, including the administration of a behavioural treatment for insomnia called Intensive Sleep Retraining, facilitating power naps, and conducting objective daytime sleepiness tests. Specialised equipment and trained individuals are presently required to administer these applications in the laboratory: a costly and impractical procedure which limits their utility in practice. A wearable device could be used to administer these applications outside the laboratory, increasing accessibility. This systematic review aimed to identify practical wearable devices that accurately estimate sleep onset. The search strategy identified seventy-one articles which compared estimations of sleep onset latency from wearable devices against polysomnography (PSG). Actigraphy devices produced average estimations of sleep onset latency that were often not significantly different from PSG, but there was large inter-individual variability depending on participant characteristics. As expected, electroencephalography-based devices produced more accurate and less variable estimates. Devices that measured behavioural aspects of sleep onset consistently overestimated PSG-determined sleep onset latency, but to a comparatively low degree. This sleep measurement method could be deployed in a simple wearable device to accurately estimate sleep onset and administer Intensive Sleep Retraining, power naps, and objective daytime sleepiness tests outside the laboratory setting.

Agreement between actigraphic and polysomnographic measures of sleep in adults with and without chronic conditions: A systematic review and meta-analysis

Wrist actigraphy (ACT) may overestimate sleep and underestimate wake, and the agreement may be lower in people with chronic conditions who often have poor sleep and low activity levels. The purpose of this systematic review is to compare the agreement between ACT and polysomnographic (PSG) measures of sleep in adults without chronic conditions and sleep complaints (healthy) and with chronic conditions. We conducted a systematic review and meta-analysis using PRISMA guidelines. We searched PubMed, OVIDEMBASE, OVIDMEDLINE, OVIDPsycINFO, CENTRAL, CINAHL, ClinicalTrials.gov, International Clinical Trials Registry, and Open Grey. We included 96 studies with a total of 4134 participants, of whom 762 (18.4) were healthy adults and 724 (17.5%) were adults with chronic conditions. Among adults with chronic conditions, ACT overestimated TST, compared to PSG [M = 22.42 min (CI 95%: 11.92, 32.91 min)] and SE [M = 5.21% (CI 95%: 1.41%-9.00%)]. ACT underestimated SOL [M = -7.70 min (CI 95%: -15.22, -0.18 min)], and WASO [M = -10.90 min (CI 95%: -26.01, 4.22 min)]. These differences were consistently larger between ACT and PSG sleep measures compared to healthy adults. Research is needed to better understand factors that influence the agreement between ACT and PSG among people with chronic conditions.