Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices

AIMS

Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker).

FOCUS TECHNOLOGY

Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3.

REFERENCE TECHNOLOGY

In-lab polysomnography with 3-reader, consensus sleep scoring.

SAMPLE

Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years).

DESIGN

Overnight in a sleep laboratory from habitual sleep time to wake time.

CORE ANALYTICS

Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography).

CORE OUTCOMES

EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33).

IMPORTANT ADDITIONAL OUTCOMES

Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset).

CORE CONCLUSION

EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Novel and noninvasive methods for in-home sleep measurement and subsequent state coding in 12-month-old infants

Research studying the role of sleep in development abounds but focuses on global aspects of sleep like quality or timing. Far fewer studies include the ultradian cycle, or patterns of REM and non-REM (NREM), because doing so is costly in time and resources. In a complete, lab-based sleep study, individuals are monitored by a technician overnight while wearing a host of sensors to capture brain activity, eye and limb movement, and cardiorespiratory rates. There is a need for creative, minimally disruptive solutions to study sleep that do not compromise the richness and accuracy of the measurements. The current pilot study parsed down the physiological measures to only movement and cardiorespiratory rates, creating a protocol simple enough for caregivers to incorporate into bedtime. Ten 12-month-old infants (+/- 3 weeks) wore an actigraph and wireless cardiorespiratory sensor for five nights of data collection. Of this, 92% were useable data. Actigraphy was analyzed with the Sadeh algorithm to delineate sleep from wake. Heart rate and respiration were then used to state score visually or via an algorithm; greater variability demarcated REM from NREM. Time spent in each state was compared between scoring methods as well as to published results from age matched infants who underwent polysomnography (PSG). Visually scored data, using a 1-hour viewing window, was in line with peer's PSG values. To automatically state score, epoch-by-epoch cardiorespiratory and actigraphy files were produced for each minute of data collection. Heart and respiratory rates were transformed into z-scores and iterations of scoring, using increasingly greater z score thresholds, were compared to determine which identified state proportions most similar to data collected with PSG. Based on these results, our novel method appears to be a feasible choice for studying the ultradian cycle. The combination of actigraphy and cardiorespiratory monitoring is uniquely advantageous because it is less resource intensive and more naturalistic, being put on by caregivers while still resulting in high rates of good data. Taken together, it is a quality option for infant researchers interested in incorporating sleep into their paradigms.

How reliable is measurement of posture during sleep: real-world measurement of body posture and movement during sleep using accelerometers

Objective Understanding sleeping behaviours could improve prevention and treatment of sleep problems and associated health conditions. This study aimed to evaluate a method to assess body posture and movement during sleep using trunk-worn accelerometers for 28 days. Approach Participants (50 adults with low back pain (66% female); aged 32(±9) years) wore two activPAL-micro sensors (thigh, trunk) during their normal daily life for 28 consecutive days. Parameters related to body posture (e.g., time spent lying supine or prone) and movement (e.g., number of turns) during sleep were calculated for each night. Average values for each parameter were identified for different periods, the Spearman-Brown Prophecy Formula was used to estimate the minimum number of nights required to obtain a reliable estimate of each parameter, and repeatability of measures between different weeks was calculated. Main Results Participants spent 8.1(±0.8) hours asleep and most time (44%) was spent in a supine posture. The minimum number of nights required for reliable estimates varied between sleep parameters, range 4-21 nights. The most stable parameters (i.e., requiring less than seven nights) were "average activity", "no. of turns", "time spent prone", and "posture changes in the first hour". Some measures differed substantially between weeks. Significance Most sleep parameters related to body posture and movement require a week or more of monitoring to provide reliable estimates of behaviour over one month. Notably, one week may not reflect behaviour in another week, and the time varying nature of sleep needs to be considered.

Sleep Measurement in Children-Are We on the Right Track?

Sleep plays a critical role in the development of healthy children. Detecting sleep and sleep disorders and the effectiveness of interventions for improving sleep in children require valid sleep measures. Assessment of sleep in children, in particular infants and young children, can be a quite challenging task. Many subjective and objective methods are available to evaluate various aspects of sleep in childhood, each with their strengths and limitations. None can, however, replace the importance of thorough clinical interview with detailed history and clinical examination by a sleep specialist.

Influence of socioeconomic status on objective sleep measurement: A systematic review and meta-analysis of actigraphy studies

Social inequities have many health effects; one of these is a potential relationship to sleep disturbances. Socioeconomic status (SES) is an important factor that contributes to social inequities. SES is a marker of living conditions and habits that influence health by way of different processes, including stress-related mechanisms. However, a systematic review of the relationship between SES and objectively measured sleep parameters has not been conducted. Therefore, a systematic review and meta-analysis were performed to examine the relationship between SES and sleep parameters measured with actigraphy in the general population. Nineteen articles were identified and included from a keyword search in Medline/PubMed, Web of Science, and SCOPUS, following PRISMA guidelines. For an article to be included, it had to have a measure of SES and also, an actigraphy-based measure of sleep. For, included studies, qualitative and quantitative data were extracted, and study quality was assessed with The National Institute of Health's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Bivariate correlations were extracted and entered into a meta-analysis, along with a qualitative review of articles. These analyses revealed that SES was associated with sleep parameters in the predicted direction, with lower SES associated with worse sleep parameters. Specifically, lower SES was associated with lower total sleep time, longer sleep latency, greater sleep fragmentation, and higher variability in sleep onset and sleep latency. Higher education, higher perceived economic well-being, and higher income were significantly associated with improved sleep efficiency and longer sleep duration. For the 19 articles included, 10 were rated as fair or poor in study quality. Thus, higher quality studies in this area are needed. This meta-analysis and systematic review demonstrated that social inequities of sleep can be measured objectively, opening the path to the development and integration of methodologies combining actigraphy with current subjective measures for utilization in clinical practice.

The Future of Sleep Measurements: A Review and Perspective

This article provides an overview of the current use, limitations, and future directions of the variety of subjective and objective sleep assessments available. This article argues for various ways and sources of collecting, combining, and using data to enlighten clinical practice and the sleep research of the future. It highlights the prospects of digital management platforms to store and present the data, and the importance of codesign when developing such platforms and other new instruments. It also discusses the abundance of opportunities that data science and machine learning open for the analysis of data.

Validity of an under-mattress sensor for objective sleep measurement in critically ill patients: a prospective observational study

Background

Considering the adverse effects of sleep disturbance in critical care settings, accurate assessment could aid therapy; however, methodological inadequacies mean that no viable option is currently available. Research in healthy population has recently shown that a non-wearable sleep measurement device placed under the mattress of the bed could be beneficial in intensive care settings. Therefore, we aimed to validate this device compared with polysomnography (PSG) and to assess how it related to subjective sleep evaluations.

Methods

This observational study measured the sleep of critically ill adult patients. The primary goal was to validate the Nemuri SCAN (NSCAN; Paramount Bed Co., Ltd., Tokyo, Japan) against the reference standard PSG for 24 h. The secondary goal was to evaluate the association between the objective parameters obtained from NSCAN and PSG and the subjective report data obtained using the Richards–Campbell Sleep Questionnaire (RCSQ) for the nighttime.

Results

Eleven participants were evaluated. The median of the total sleep time scored by PSG was 456.0 (353.0–517.5) min during the nighttime and 305.0 (186.2–542.5) min during the daytime. PSG over 24 h revealed significant decreases in restorative sleep, with excessive daytime sleep, but with a normal quantity of nighttime sleep. The agreement, sensitivity, and specificity rates (with 95% confidence intervals) for the NSCAN compared with PSG were 68.4% (67.9–69.0%), 90.1% (89.7–90.6%), and 38.7% (37.9–39.7%), respectively. The median RCSQ value when subjectively evaluating nighttime sleep was 68.0 (26.3–83.5); this showed no correlation with the NSCAN sleep parameters, despite a positive correlation with the ratio of the stage N2 isolated or combined with restorative sleep in the PSG assessment.

Conclusions

NSCAN had moderate agreement, high sensitivity, and poor specificity in intensive care settings, which is most likely due to its inability to identify immobile wakefulness often observed in critically ill patients or sleep depth. This remains a barrier to its use in the assessment of subjective sleep quality.

Trial registration

This investigation was part of an interventional trial registered with the University Hospital Medical Information Network Individual Clinical Trials Registry (UMIN000026350, http://www.umin.ac.jp/icdr/index-j.html) on March 1, 2017.

Accuracy of a smartphone application in estimating sleep in children

PURPOSE

Chronic sleep problems can lead to difficulties for both the individual and society at large, making it important to effectively measure sleep. This study assessed the accuracy of an iPhone application (app) that could potentially be used as a simple, inexpensive means to measure sleep over an extended period of time in the home.

METHODS

Twenty-five subjects from the ages of 2-14 who were undergoing overnight polysomnography (PSG) were recruited. The phone was placed on the mattress, near their pillow, and recorded data simultaneously with the PSG. The data were then downloaded and certain parameters were compared between the app and PSG, including total sleep time, sleep latency, and time spent in various defined "stages."

RESULTS

Although there seemed to be a visual relationship between the graphs generated by the app and PSG, this was not confirmed on numerical analysis. There was no correlation between total sleep time or sleep latency between the app and PSG. Sleep latency from the PSG and latency to "deep sleep" from the app had a significant relationship (p = 0.03). No combination of PSG sleep stages corresponded with app "stages" in a meaningful way.

CONCLUSIONS

The Sleep Cycle App may have value in increasing the user's awareness of sleep issues, but it is not yet accurate enough to be used as a clinical tool.

Sleep assessment of hospitalised patients: a literature review

BACKGROUND

Sleep is a dynamic and essential part of human life and health. In healthcare settings, nurses are strategically placed to promote sleep and sleep health. In this regard, nursing actions should be based upon effective methods of assessment of patient sleep. Standardised sleep assessment does not currently occur in the care of acute hospitalised patients. Use of an appropriate measurement tool would help evaluate inpatient sleep. An effective, efficient sleep assessment tool is needed to aid clinicians. Such assessment would enable specific nursing intervention to be tailored to individual patients.

OBJECTIVE

The objective of this paper was to examine the literature on sleep measurement to identify subjective sleep assessment tools that may be suitable for routine use with hospitalised patients, and to evaluate their reliability and validity.

METHOD

A review of existing literature was undertaken to identify and evaluate subjective sleep measurement tools.

RESULTS

The initial literature searches identified 402 articles, of which ten met the criteria for review. These reported on three subjective sleep measurement scales: the Richards-Campbell Sleep Questionnaire; the St Mary's Hospital Sleep Questionnaire; and the Verran Snyder-Halpern Sleep Scale. The Richards-Campbell Sleep Questionnaire is brief and easy to use. In specific samples, its items correlate with domains reflecting sleep quality and has shown excellent internal consistency. Equivocal results and scoring challenges were found with the St Mary's Hospital Sleep Questionnaire. The Verran Snyder-Halpern Sleep Scale captured sleep disturbance and total sleep time, but time-to-complete is more burdensome than the Richards-Campbell Sleep Questionnaire.

CONCLUSIONS

The current use of sleep assessment instruments in the acute hospital setting is restricted mainly to research activities. Of the three tools identified that could be used clinically to measure inpatient sleep, and although it was developed for use in the intensive care setting, the Richards-Campbell Sleep Questionnaire held greatest potential due to its ease and rapidity of use. However, it has yet to be validated for use with general hospital inpatients, and further research is required in this area.