The Influence of Social Distancing Behaviors and Psychosocial Factors on Physical Activity During the COVID-19 Pandemic: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has arguably facilitated a shift toward increased sedentariness and reduced physical activity. Moreover, there is mounting evidence that mental health has also declined during the pandemic. However, it remains unknown to what extent social distancing (SD) behaviors and mental health have affected the physical activity levels of the general population. OBJECTIVE: The purpose of this study was to determine the influence of SD behaviors and prevailing mental health on the odds of being physically active during the early COVID-19 pandemic response. METHODS: A total of 4819 adults (2474/4819, 51.3%, female) from the US population with a median age of 46 (IQR 35-59) completed an online survey during the early pandemic response (April-June 2020). The survey included questions on adherence to 11 SD behaviors, and validated questionnaires which assessed self-reported physical activity, depression, anxiety, and mental well-being. Respondents were categorized into 2 physical activity groups: inactive (0-599 metabolic equivalent of task [MET]-minutes/week) and active (≥600 MET-minutes/week). A logistic generalized additive model (GAM) was used to determine which SD factors and mental health outcomes were associated with physical activity level. RESULTS: The GAM analysis revealed that wearing a facemask in public (odds ratio [OR] 1.46, 95% CI 1.14-1.79; P=.003), limiting the use of public transport (OR 1.47, 95% CI 1.19-1.83; P=.001), and restricting travel outside the house (OR 1.56, 95% CI 1.19-2.05; P=.002) were SD behaviors associated with higher odds of being more physically active. Conversely, avoiding physical activity outside the house was associated with higher odds of being inactive (OR 0.52, 95% CI 0.46-0.63; P<.001). Leaving the house more frequently, and a higher mental well-being were associated with increasing odds of being physically active (P<.001). Engaging with a moderate number of SD behaviors (3-7 total) was positively associated with physical activity, whereas a very high SD vigilance (ie, engaging with ≥10 total behaviors) decreased the odds of being active during the early pandemic response. CONCLUSIONS: Based on the findings of our study, we suggest that future public health messaging of SD guidelines should include (1) a clear portrayal of the benefits of regular exercise on mental health; and (2) a specific focus on how to be physically active outdoors in a COVID-safe manner.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".