COVID-19-related mobility reduction: heterogenous effects on sleep and physical activity rhythms

Ju Lynn Ong, TeYang Lau, Stijn A A Massar, Zhi Ting Chong, Ben K L Ng, Daphne Koek, Wanting Zhao, B T Thomas Yeo, Karen Cheong, Michael W L Chee, Ju Lynn Ong, TeYang Lau, Stijn A A Massar, Zhi Ting Chong, Ben K L Ng, Daphne Koek, Wanting Zhao, B T Thomas Yeo, Karen Cheong, Michael W L Chee

Abstract

Study objectives: Mobility restrictions imposed to suppress transmission of COVID-19 can alter physical activity (PA) and sleep patterns that are important for health and well-being. Characterization of response heterogeneity and their underlying associations may assist in stratifying the health impact of the pandemic.

Methods: We obtained wearable data covering baseline, incremental mobility restriction, and lockdown periods from 1,824 city-dwelling, working adults aged 21-40 years, incorporating 206,381 nights of sleep and 334,038 days of PA. Distinct rest-activity rhythm (RAR) profiles were identified using k-means clustering, indicating participants' temporal distribution of step counts over the day. Hierarchical clustering of the proportion of days spent in each of these RAR profiles revealed four groups who expressed different mixtures of RAR profiles before and during the lockdown.

Results: Time in bed increased by 20 min during the lockdown without loss of sleep efficiency, while social jetlag measures decreased by 15 min. Resting heart rate declined by ~2 bpm. PA dropped an average of 42%. Four groups with different compositions of RAR profiles were found. Three were better able to maintain PA and weekday/weekend differentiation during lockdown. The least active group comprising ~51% of the sample, were younger and predominantly singles. Habitually less active already, this group showed the greatest reduction in PA during lockdown with little weekday/weekend differences.

Conclusion: In the early aftermath of COVID-19 mobility restriction, PA appears to be more severely affected than sleep. RAR evaluation uncovered heterogeneity of responses to lockdown that could associate with different outcomes should the resolution of COVID-19 be protracted.

Keywords: COVID-19; machine learning; mobility restrictions; rest-activity rhythms; sleep; wearables.

© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.

Figures

Figure 1.
Figure 1.
Time-series plots between January 2 and April 27, 2020 (blue curves) and January 3 and April 29, 2019 (red curves) for sleep (top panel) and PA/heart rate (bottom panel) parameters. (A) Bedtime, (B) Waketime, (C) TIB, (D) TST, (E) Sleep efficiency, (F) Step counts, (G) Time spent in MVPA, and (H) Resting heart rate. Weekends (gray-shaded regions) and public holidays (light blue- and pink-shaded regions) are also delineated. Dates reflect the “morning” of each record, such that sleep records always preceded PA. Dates in 2019 were shifted by 1 day in order to ensure a matching by day of the week. Key events during the COVID-19 pandemic period (“Baseline,” “Increased restrictions,” and “Lockdown”) are also indicated.
Figure 2.
Figure 2.
RAR profiles. (A) Centroids for the four key RAR profiles determined by k-means clustering of intraday step counts across 125,851 days from January to April 2020 from all participants for the “Active 3-Peak Early” (green curve, n = 28,723 days), “3-Peak Middle” (blue curve, n = 32,981 days), “Active 2-Peak Later” (brown curve, n = 30,635 days), and “Inactive 3-Peak” (red curve, n = 33,512 days) clusters. Clusters were differentiated by timing of morning rise and evening drop as well as magnitude of steps across the 24 h day. (B) Proportion of time spent in the four RAR profiles from January to April 2020. Pre-lockdown, weekday rhythms primarily consisted of the “Active 3-Peak Early” and “3-Peak Middle” profiles, while weekend rhythms mainly consisted of the “Active 2-Peak Later” and “Inactive 3-Peak” profiles. During the lockdown, a clear increase in the proportion of time spent in the “Active2-Peak Later” and “Inactive 3-Peak” profiles was observed, together with an attenuation of weekday–weekend rhythms.
Figure 3.
Figure 3.
Identification of groups with similar RAR changes pre- and during the lockdown. (A) Hierarchical clustering of participants based on proportion of time spent in the four RAR profiles pre- and during the lockdown. Visual inspection of the dendrogram identified four groups of participants with similar changes in the patterns of RAR profiles pre- and during the lockdown. (B) Proportion of days spent in the dominant RAR profiles on weekdays and weekends for each group, pre- and during the lockdown. (C) RAR profiles across days (columns) by participant (rows) ordered by groups partitioned from the hierarchical clustering. Groups are colored by their dominant RAR profile color for ease of reference.
Figure 4.
Figure 4.
Boxplots for comparisons between groups identified by the hierarchical clustering, during the “Baseline” and “Lockdown” period. (A) Bedtime, (B) Waketime, (C) TIB, (D) TST, (E) SJL, (F) Step counts, (G) Time spent in MVPA, and (H) Resting heart rate. Asterisks denote significant pairwise comparisons between “Baseline” and “Lockdown” periods for each group. *p < 0.05, **p < 0.01, ***p < 0.001.

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