Background In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations’ adherence to home confinement to adapt policies and control measures accordingly. Objective The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals’ natural activity patterns regardless of their location. Methods We analyzed big data from individuals using activity trackers (Withings) that count the wearer’s average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens’ activities. Results Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people’s activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. Conclusions Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.

Study Objectives: To assess the diagnostic performance of a nonintrusive device placed under the mattress to detect sleep apnea syndrome. Methods: One hundred eighteen patients suspected to have obstructive sleep apnea syndrome completed a night at a sleep clinic with a simultaneous polysomnography (PSG) and recording with the Withings Sleep Analyzers. PSG nights were scored twice: first as simple polygraphy, then as PSG. Results: Average (standard deviation) apnea-hypopnea index from PSG was 31.2 events/h (25.0) and 32.8 events/h (29.9) according to the Withings Sleep Analyzers. The mean absolute error was 9.5 events/h. The sensitivity, specificity, and area under the receiver operating characteristic curve at thresholds of apnea-hypopnea index ≥ 15 events/h were, respectively, sensitivity (Se) 15 = 88.0%, specificity (Sp) 15 = 88.6%, and area under the receiver operating characteristic curve (AUROC) 15 = 0.926. At the threshold of apnea-hypopnea index ≥ 30 events/h, results included Se 30 = 86.0%, Sp 30 = 91.2%, AUROC 30 = 0.954. The average total sleep time from PSG and the Withings Sleep Analyzers was 366.6 (61.2) and 392.4 (67.2) minutes, sleep efficiency was 82.5% (11.6) and 82.6% (11.6), and wake after sleep onset was 62.7 (48.0) and 45.2 (37.3) minutes, respectively. Conclusions: Withings Sleep Analyzers accurately detect moderate-severe sleep apnea syndrome in patients suspected of sleep apnea syndrome. This simple and automated approach could be of great clinical value given the high prevalence of sleep apnea syndrome in the general population.

We show that a non-intrusive pneumatic sensor can be used to measure respiratory rate, heart rate, and their variability during sleep. The pneumatic sensor was included in a polysomnography (PSG) study involving 42 participants in a sleep laboratory. The agreement between the pneumatic sensor and the PSG for respiratory rate, heart rate, and their variability was quantified by Bland-Altman analysis. The respiratory rate has a mean value of 15.4 breaths per minute for a bias of −0.06 and 95% limits of agreement (LOA) of [−0.6; 0.5] breath per minute. The respiratory rate variability root mean square of successive differences (RMSSD) has a mean value of 459.51 ms, a bias of 9.2 and a 95% LOA of [−43.5; 61.9] ms. The heart rate has a mean value of 60.6 beats per minute for a bias of −0.8 and a 95% LOA of [−4.3; 2.7] beat per minute. The heart rate variability RMSSD has a mean value of 44.1 ms, a bias of 14.7 and a 95% LOA of [−19; 48.4] ms. These results show that a non-intrusive pneumatic sensor can accurately estimate cardiorespiratory metrics overnight.