Exploring the categorization of barriers to physical activity and their associations with leisure time physical activity and body mass index in a sample of Jamaican adults
Bibliographic record
Abstract
BACKGROUND: Over 80% of Jamaicans are not meeting World Health Organization physical activity guidelines, and over 50% are overweight or obese. Research on barriers to physical activity and their associations with physical activity engagement among Jamaican adults is limited. This study explores the measurement and categorization of barriers to physical activity in Jamaican adults to inform the design and implementation of behavioral physical activity-promoting interventions. PURPOSE: This study applied a psychometric approach to explore the perception of barriers to physical activity and their associations with leisure time physical activity (LTPA) and body mass index (BMI) in Jamaican adults. METHODS: A total of 506 Jamaican adults completed self-report measures of demographic data, height, and weight (to calculate BMI), barriers to physical activity, and LTPA in a cross-sectional survey design. Exploratory structural equation modeling was used to determine the best-fitting model for responses to the barriers to physical activity (BPA) scale. Structural equation modeling was used to examine the associations among BPA, LTPA, and BMI. RESULTS: A 15-item BPA scale with 2 categories of barriers was identified: (1) psychosocial (PS-BPA; 7 items; eg, lack of self-motivation or confidence) and (2) environmental/demographic (ED-BPA; 8 items; safety or financial/cost issues) was identified. PS-BPA but not ED-BPA meaningfully associated with LTPA. LTPA was negatively associated with BMI. CONCLUSIONS: Psychosocial barriers may at least partially explain why most Jamaican adults do not meet global physical activity guidelines. Psychosocial correlates of physical activity should be emphasized when investigating and targeting physical activity barriers in behavioral interventions among Jamaican adults.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".