Intensity-Specific Leisure-Time Physical Activity and The Built Environment Among Brazilian Adults: A Best-Fit Model
Bibliographic record
Abstract
BACKGROUND: There is little understanding about which sets of environmental features could simultaneously predict intensity-specific leisure-time physical activity (LTPA) among Brazilians. The objectives were to identify the environmental correlates for intensity-specific LTPA, and to build the best-fit linear models to predict intensity-specific LTPA among adults of Curitiba, Brazil. METHODS: Cross sectional study in Curitiba, Brazil (2009, n = 1461). The International Physical Activity Questionnaire and Abbreviated Neighborhood Environment Assessment Scale were used. Ninety-two perceived environment variables were categorized in 10 domains. LTPA was classified as walking for leisure (LWLK), moderate-intensity leisure-time PA (MLPA), vigorous-intensity leisure-time PA (VLPA), and moderate-to-vigorous intensity leisure-time PA (MVLPA). Best fitting linear predictive models were built. RESULTS: Forty environmental variables were correlated to at least 1 LTPA outcome. The variability explained by the 4 best-fit models ranged from 17% (MLPA) to 46% (MVLPA). All models contained recreation areas and aesthetics variables; none included residential density predictors. At least 1 neighborhood satisfaction variable was present in each of the intensity-specific models, but not for overall MVLPA. CONCLUSIONS: This study demonstrates the simultaneous effect of sets of perceived environmental features on intensity-specific LTPA among Brazilian adults. The differences found compared with high-income countries suggest caution in generalizing results across settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".