The impact of the COVID-19 pandemic on daily rhythms

TL;DR

This study addresses how the COVID-19 pandemic reshaped daily movement rhythms and whether effects differed by demographics. It combines high-resolution wearable step data with monthly questionnaires in a year-long longitudinal design, quantifying daily movement distributions across four segments and assessing consistency using the inverse Earth mover's distance, $c^s_t = 1 / D({\bf d}_t, {\bf d}_{t+1})$, with monthly and long-term baselines $\bar{\bf d}^m$ and $\bar{\bf d}^l$. Using three linear mixed-effects analyses, the authors relate movement consistency to socio-demographic factors and on-site attendance, finding that non-walking activity decreases while walking remains stable, and that migrants and individuals living alone show lower long-term movement consistency; on-site workers exhibit more consistent rhythms. The results highlight unequal impacts of pandemic policies on daily routines and provide actionable insights for organizations and policymakers to tailor support in the post-pandemic era, leveraging $D$-based rhythm metrics and policy context via the stringency index $\bar{\mathrm{SI}}$.

Abstract

The COVID-19 pandemic has significantly impacted daily activity rhythms and life routines. Understanding the dynamics of these impacts on different groups of people is essential for creating environments where people's lives and well-being are least disturbed during such circumstances. Starting in June 2021, we conducted a year-long study to collect high-resolution data from fitness trackers as well as answers to monthly questionnaires from 128 working adults. Using questionnaires, we investigate how routines of exercising and working have changed throughout the pandemic for different people. In addition to that, for each person in the study, we build temporal distributions of daily step counts to quantify their daily movement rhythms and use the inverse of the Earth mover's distance between different movement rhythms to quantify the movement consistency over time. Throughout the pandemic, our cohort shows a shift in exercise routines, manifested in a decrease in time spent on non-walking physical exercises as opposed to the unchanged amount of time spent on walking. In terms of daily rhythms of movement, we show that migrants and those who live alone demonstrate a lower level of consistency of daily rhythms of movement compared to their counterparts. We also observe a relationship between movement and on-site work attendance, as participants who go to work (as opposed to working remotely) also tend to maintain more consistent daily rhythms of movement. Men and migrants show a faster pace in going back to work after the decrease in restriction measures that were set in place due to the pandemic. Our results quantitatively demonstrate the unequal effect of the pandemic among different sub-populations and inform organizations and policymakers to provide more adequate support and adapt to the different needs of different groups in the post-pandemic era.

Figures (8)

• Figure 1: Step count values of all participants over the course of the study.
• Figure 2: Visualization of movement consistency. A sample of 4 individual-level daily step allocations to demonstrate the level of movement consistency over time. The red line illustrates the step count distribution of ${\bf d}_t$ and the blue line illustrates the distribution of $d_{t+1}$. The markers indicate the distribution of steps in a single time segment (N: night midnight-6am, M: morning 6am-noon, A: afternoon noon-6pm, E: evening 6pm-midnight). The purple area indicates the distribution dissimilarity between two consecutive days. In panels (a) and (b), this area is considerably larger than that of panels (c) and (d), highlighting higher variability in day-to-day rhythms of movement.
• Figure 3: Short-term movement consistency computation. The short-term movement consistency is denoted as $c^s_t=1/D({\bf d}_t,{\bf d}_{t+1})$ and quantified as the inverse of the distance in step count distribution between ${\bf d}_t$ and ${\bf d}_{t+1}$ of an individual.
• Figure 4: lmc calculation. The long-term movement consistency is denoted as $c^l_t=1/D({\bf d}_t,\bar{{\bf d}}^l)$ and quantified as the inverse of the distance in step count distribution between ${\bf d}_t$ and the average distribution $\bar{{ \bf d}}^l$ of an individual.
• Figure 5: Time allocation for different activities of sub-populations during each stage of the pandemic. Comparisons are made using Mann-Whitney U test. Asterisks denote the significance of the results. *$p<0.05$, **$p<0.01$, ***$p<0.001$.