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. 2023 Oct 11;46(10):zsad194.
doi: 10.1093/sleep/zsad194.

Contactless and longitudinal monitoring of nocturnal sleep and daytime naps in older men and women: a digital health technology evaluation study

Kiran K G Ravindran  1   2 , Ciro Della Monica  1   2 , Giuseppe Atzori  1   2 , Damion Lambert  1   2 , Hana Hassanin  2   3 , Victoria Revell  1 , Derk-Jan Dijk  1   2
Affiliations

Contactless and longitudinal monitoring of nocturnal sleep and daytime naps in older men and women: a digital health technology evaluation study

Kiran K G Ravindran et al. Sleep. .

Abstract

Study objectives: To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults.

Methods: We collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD).

Results: Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary.

Conclusions: CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.

Keywords: 24-hour sleep assessment; Actiwatch; Emfit QS; Withings Sleep Analyser; actigraphy; bed sensor; contactless sleep technologies; evaluation; home care; nearables; older adults; sleep; sleep diary.

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Figures

Graphical Abstract
Graphical Abstract
Figure 1.
Figure 1.
Sleep behavior patterns over 14 days (D-14 to D-1) at home in a male participant aged 72. (A) The raw epoch-by-epoch sleep–wake time-series data and the associated sleep diary information. (B) The 24-hour total sleep time estimates for the four methodologies used. See Results for more detailed explanation.
Figure 2.
Figure 2.
Comparison of timings of bed entries and exits for the Actiwatch, Withings Sleep Analyser, and Emfit. Scatterplots represent the agreement between the device and the sleep diary estimates. AWS-A represents the automatic Actiwatch analysis. The number of nights is 379 for AWS-A, 306 for WSA and 205 for Emfit.
Figure 3.
Figure 3.
Differences in the all-night sleep summary measure estimations of CSTs and AWS devices against AWS|SD (Device (-) AWS|SD). The violins with gray outline show the device estimates based on Analysis period—Automatic (AP-A, analysis period determined by the device), and the violins with red outline depict the Analysis period set with the aid of sleep diary information (AP-SD, analysis period set from sleep diary lights off to lights on). The number of participants (days) used for each of the devices is AWS—34 (379), WSA—27 (306), and Emfit—16 (205).
Figure 4.
Figure 4.
Pooled confusion matrices. The pooled confusion matrices are derived by summing participant wise EBE concordance confusion matrices. The panels on the top indicate the matrices computed over the sleep diary lights-off period (AP-SD) and the panel on the bottom indicate the period between 18:00 and 12:00 (hh:mm). The percentage in the confusion matrices depicts the percentages of true and false positive and negatives with respect to the total data. Total number of epochs for each device for the AP-SD (WSA—30571; Emfit—174779) and period between 18:00 and 12:00 hours (WSA—154024; Emfit—207222). The number of participants used in each of the devices is WSA [n = 27] and Emfit [n = 16].
Figure 5.
Figure 5.
Nap estimation agreement between the device and the ground truth sleep diary. (A) The plots on the top depict the nap durations commonly available between the compared device and the sleep diary (XY quadrant). The data points depicted on the horizontal axis to the left of zero indicate the sleep diary nap events that were missed by the device. For WSA, naps detected automatically are indicated by circles while the naps detected via bed presence are indicated by squares. (B) The Venn diagrams on the bottom depict the portion of naps detected by the different devices compared to sleep diary. The overlapping regions are the accurately detected naps and the nonoverlapping naps regions on side of the device indicates unreported naps and those on the side of the sleep diary indicate naps missed by the device.
Figure 6.
Figure 6.
The 24-hour estimates of sleep and naps. (A) Number of naps recorded, unreported naps, and naps accurately detected by the device. For cohort 2, sleep diary (SD): blue—naps in bed and green—naps not in bed; WSA-A naps automatically recorded; WSA-BP naps detected using bed presence. (B, C) Nap duration (B) followed by TST (C). The blue horizontal line depicts the mean value, and(D) depicts the differences in the 24-hour TST estimates obtained from the devices compared to the sleep diary information.
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