Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study

Abstract

Background Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. Objective This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. Methods Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. Results The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.21; Emfit: r2=0.11, P=.58). Conclusions These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.

P-tau217 and other blood biomarkers of dementia: variation with time of day

“…The cognitively intact adults included 17 from study one (72.0±4.5 years; 11M: 6F; 28.9±1. Comorbidities included hypertension, Type-2 diabetes, arthritis, hyperthyroidism, and asthma [12,13] For both studies combined, 90% of scheduled samples were obtained. The plasma levels for each biomarker at each timepoint (mean ± SD) are given in Table 1 for all participants combined as well as separately for each group for the nine timepoint comparison (Study Two).…”

“…The full study protocols have been reported in detail elsewhere [12,13]. Briefly, following a screening visit to assess eligibility, participants were monitored for up to 14 days at-home using a variety of technologies to assess their sleep-wake patterns, environment, and cognitive function.…”

Reliable Contactless Monitoring of Heart Rate, Breathing Rate and Breathing Disturbance During Sleep in Aging: A Digital Health Technology Evaluation Study

“…The accuracy of both the vital signs estimates was higher during sleep than wake primarily due to reduced body movements. Somnofy, the best performing device in estimating breathing rate was also the best performing device in our evaluation of sleep stage classification performance [36].…”

“…The study received a favourable opinion from the University of Surrey Ethics committee (UEC-2019-065-FHMS) and was conducted in line with the Declaration of Helsinki and the principles of Good Clinical Practice. A detailed description of the inclusion exclusion criteria can be found in our previous publications in which we evaluate the ability of these technologies to estimate sleep timing in the home environment and sleep stages as defined by polysomnography [35, 36].…”

“…MDC is the smallest change in the estimate that can be detected by the device that exceeds the measurement error. We have used intraclass correlation with two-way random effects (ICC) to measure the reliability of measurement and standardised absolute difference (SAD), a directionless Cohen’s D described in [36, 47], for measuring the dispersion in the bias. Apart from the above metrics, coefficient of determination (r 2 , measure of how close the measured estimates are to the reference computed using simple linear regression) was also used for the concordance analysis.…”