Regional Difference in the Impact of COVID-19 Pandemic on Domain-Specific Physical Activity, Sedentary Behavior, Sleeping Time, and Step Count: Web-Based Cross-sectional Nationwide Survey and Accelerometer-Based Observational Study

Abstract

Background Physical activity (PA) and sedentary behavior (SB) have been affected by the COVID-19 pandemic and its restrictive environments, such as social distancing and lockdown measures. However, regional differences in the changes in domain-specific PA and SB in response to the COVID-19 pandemic are not clearly understood. Objective This study aimed to examine regional differences in domain-specific PA and SB, as well as sleeping time in response to the COVID-19 pandemic in Japan. Methods A web-based cross-sectional nationwide survey and an accelerometer-based longitudinal observation were conducted. In the web-based survey, we recruited 150 Japanese men and 150 Japanese women for each of the following age groups: 20s, 30s, 40s, 50s, 60s, and 70s (n=1800). A total of 1627 adults provided valid responses to web-based surveillance from June to July 2020. Participants were recruited from urban (Greater Tokyo Area, n=1028), urban-rural (regional core cities, n=459), or rural (regional small and medium cities, n=140) areas. They answered sociodemographic and health-related questions and retrospectively registered the PA data of their average day before and during the COVID-19 pandemic in a web-based PA record system. In the accelerometer-based observation, PA and step count data were obtained using a triaxial accelerometer on people living in urban (n=370) and rural (n=308) areas. Results Before the COVID-19 pandemic, there were no significant differences between these 3 regions in the time spent sleeping, staying at home, working or studying, and exercising (P>.05). By contrast, people living in urban areas had a longer duration of SB and transportation and a shorter duration of moderate-to-vigorous PA and lying or napping time compared with people living in rural areas (P>.05). During the COVID-19 pandemic, a significant decrease was observed in transportation time in urban (–7.2 min/day, P<.001) and urban-rural (–2.0 min/day, P=.009) areas but not in rural (–0.4 min/day, P=.52) areas. The moderate-to-vigorous PA was decreased in urban (–31.3 min/day, P<.001) and urban-rural (–30.0 min/day, P<.001) areas but not in rural areas (–17.3 min/day, P=.08). A significant increase was observed in time spent sleeping in urban (+22.4 min/day, P<.001) and urban-rural (+24.2 min/day, P<.001) but not in rural areas (+3.9 min/day, P=.74). Lying or napping was increased in urban (+14.9 min/day, P<.001) but not in rural areas (−6.9 min/day, P=.68). PA and step count obtained using an accelerometer significantly decreased in urban (P<.05) but not in rural areas (P>.05). Conclusions The effect of the COVID-19 pandemic on PA and SB was significantly dependent on living area, even in a single country. The effects of PA and SB were greater in the Greater Tokyo Area and regional core cities but were not observed in regional small and medium cities in Japan.

The COVID-19 Pandemic and Daily Steps in the General Population: Meta-analysis of Observational Studies

“…The detailed duration of observation for each study is shown in Table 1 . Nine studies measured daily steps with smartphones [ 19 , 37 , 40 , 41 , 43 , 45 , 46 , 50 , 54 ], 8 with wearable activity trackers [ 20 , 38 , 39 , 47 , 48 , 51 - 53 ], and 2 with both wearable activity trackers and smartphones [ 44 , 49 ]; 1 study did not specify the measurement device used in the survey [ 42 ]. One study provided 4 groups of data from 4 countries separately [ 38 ].…”

“…Of these, 151 studies did not report relevant outcomes (ie, daily steps), 14 did not provide necessary data for meta-analysis, 13 focused on irrelevant populations, 5 were not observational studies, 3 were conducted with financial incentives, 2 reported duplicated data, and 1 was a conference abstract. The remaining 20 studies were included in the meta-analysis [19,20,[37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Figure 1 shows the PRISMA flow diagram.…”

“…The included studies were conducted in Australia [38], Canada [39], China [19,[40][41][42][43], France [38], Italy [44], Japan [45][46][47][48], Norway [49], Singapore [20], South Korea [50], Spain [51], the United Kingdom [38,52], and the United States [37,38,53,54], and all of them investigated the change in daily steps in the early stage of COVID-19 (from January to September 2020). The reference pre-COVID-19 period was from January 2019 to March 2020.…”

ICT and Physical Activity

“…Using such a system, it is possible to assess the physical activity of a large number of people simultaneously and at the same time with high accuracy at low cost [17] and to assess each activity intensity (Figure 2) [18]. It is also possible to evaluate the physical activity of a large population with COVID-19 online, and the effects of COVID-19 on physical activity have been clarified [19,20]. The 24-hour activity recording method suggests the possibility of physical activity assessment by voice input (Figures 3 and 4) by introducing a voice recognition application programming interface (API) [21].…”

Regional Differences in the Influence of COVID-19 Pandemic on Physical Activity Among Chinese Adults: A Social-Ecological Perspective

“…Previous research Vol.32, No. 4, November 2023: 419-427 has demonstrated the effectiveness of the socio-ecological model in identifying multiple factors influencing PA in COVID-19 pandemic [12][13][14]. For example, one study reported that high levels of social support were linked to a 64% increased likelihood of sustaining PA [12].…”