Factors associated with decreased physical activity levels among community-dwelling residents during the coronavirus disease 2019 pandemic: a long-term observational study
Bibliographic record
Abstract
[Purpose] The purpose of this study was to determine the factors associated with decreased physical activity levels among community residents over a long-term observation period during the coronavirus disease 2019 (COVID-19) pandemic. [Participants and Methods] We conducted a cross-sectional study using a self-administered questionnaire and daily steps as an indicator of physical activity levels. The study population consisted of 704 community-dwelling residents aged 40 years and older who participated in the health program from 2019 to 2020. We compared the daily steps from March-December 2019 to March-December 2020 and performed multivariate analysis to identify the factors associated with decreased daily steps. [Results] Of all participants, 447 (63.5%) returned the questionnaire and 309 (43.9%) were included in the analysis. During the COVID-19 pandemic, 133 (43.0%) respondents had decreased physical activity levels. The multivariate analysis showed that working (odds ratio, 2.08; 95% confidence interval, 1.10-3.94) was significantly associated with decreased daily steps during the COVID-19 pandemic. [Conclusion] There was a significant association between decreased physical activity levels and working during the COVID-19 pandemic. When restrictive measures such as teleworking are implemented, it may be necessary to take measures to prevent a decline in physical activity levels.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".