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
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".