Environmental and individual correlates of leisure-time physical activity in Ghana: a cross-sectional study
Bibliographic record
Abstract
Background: Leisure-time physical activity (LTPA) offers significant health benefits yet remains the least engaged domain of overall physical activity in sub-Saharan Africa. Limited evidence exists regarding how environmental and individual factors impact LTPA, particularly among healthcare professionals. This study explored the correlates of LTPA among Physician Assistants in Ghana, with a major focus on natural environmental conditions. Methods: Data from a cross-sectional survey conducted among 439 Physician Assistants in Ghana between October and December 2024 was used. Participants reported their levels of LTPA using a revised Global Physical Activity Questionnaire. Climate data, including temperature, wind direction, rainfall, and humidity, from the Ghana Meteorological Agency were matched to participants’ practice regions. An adjusted linear regression was used to examine the relationship between LTPA and individual/environmental correlates. Results: The mean weekly moderate-to-vigorous-intensity physical activity was 179.3±18.3 minutes. Overall, 69% of respondents did not meet the recommended 150 minutes per week of activity levels by the World Health Organization. In adjusted models, each one-percentage-point increase in relative humidity was associated with an additional 10 minutes of LTPA per week (β =10.4, 95% CI: 1.9 to 18.9). Conversely, wind direction (β =-35.4, 95% CI: -57.3 to -13.5) and male gender (β =-112.9, 95% CI: -190.8 to -35.0) were associated with lower LTPA. Conclusions: Climatic factors significantly influence LTPA in the Ghanaian setting. The findings underscore the need for context-specific interventions that consider climate variability and gender disparities in promoting active lifestyles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".