Applying the health action process approach to predict physical activity among adults with spinal cord injury
Bibliographic record
Abstract
Physical activity participation rates among adults with spinal cord injury (SCI) are extremely low. The health action process approach (HAPA) is one framework that can help us understand physical activity participation among adults with SCI, but this framework has seldom been evaluated in this population. The purpose of this study was to test the HAPA model among adults with SCI. Adults with SCI (n = 73; mean age = 53 years; 74% male; 47% completed high school or lower; 56% paraplegia) consented to the study and completed a survey assessing leisure time physical activity and HAPA constructs (i.e., task self-efficacy, outcome expectations, risk perceptions, intentions, barrier self-efficacy, and action planning). A HAPA model was tested via a path analysis with 5000 bootstraps using MPlus. The HAPA model had good model fit (chi-square (10) = 13.09, p = .22; CFI = .98; RMSEA = .07; SRMR = .07). Task self-efficacy (ß = .43) and outcome expectations (ß = .29) significantly predicted intentions, while only intentions was directly related to action planning (ß = .59). Both barrier self-efficacy (ß = .31) and action planning (ß = .32) were significantly linked with moderate to vigorous leisure time physical activity. Intention was indirectly and significantly related with moderate to vigorous leisure time physical activity through action planning (ß= .19). Results from this study provide confirmation that HAPA can be used to understand physical activity participation among adults with SCI. This study helps to extend the generalizability of HAPA to adults with SCI and provides support for the use of these constructs for physical activity intervention development.Acknowledgments: We'd like to thank the Ontario Neurotrauma Foundation and Réseau Provincial de Recherche en Adaptation-Réadaptation for the financial support of this study.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".