Protection motivation theory and the prediction of physical activity among adults with type 1 or type 2 diabetes in a large population sample
Bibliographic record
Abstract
OBJECTIVES: To investigate the utility of the protection motivation theory (PMT) for explaining physical activity (PA) in an adult population with type 1 diabetes (T1D) and type 2 diabetes (T2D). DESIGN: Cross-sectional and 6-month longitudinal analysis using PMT. METHODS: Two thousand three hundred and eleven individuals with T1D (N=697) and T2D (N=1,614) completed self-report PMT constructs of vulnerability, severity, response efficacy, self-efficacy, and intention, and PA behaviour at baseline and 6-month follow-up. Multi-group structural equation modelling was conducted to: (1) test the fit of the PMT structure; (2) determine the similarities and differences in the PMT structure between the two types of diabetes; and (3) examine the explained variance and compare the strength of association of the PMT constructs in predicting PA intention and behaviour. RESULTS: The findings provide evidence for the utility of the PMT in both diabetes samples (chi(2)/df=1.27-4.08, RMSEA=.02-.05). Self-efficacy was a stronger predictor of intention (beta=0.64-0.68) than response efficacy (beta=0.14-0.16) in individuals with T1D or T2D. Severity was significantly related to intention (beta=0.06) in T2D individuals only, whereas vulnerability was not significantly related to intention or PA behaviour. Self-efficacy (beta's=0.20-0.28) and intention (beta's=0.12-0.30) were significantly associated with PA behaviour. CONCLUSIONS: Promotion of PA behaviour should primarily target self-efficacy to form intentions and to change behaviour. In addition, for individuals with T2D, severity information should be incorporated into PA intervention materials in this population.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".