Physical activity and diabetes: An application of the theory of planned behaviour to explain physical activity for Type 1 and Type 2 diabetes in an adult population sample
Bibliographic record
Abstract
Physical activity (PA) plays a key role in the management of Type 1 (T1D) and Type 2 diabetes (T2D) but there are few theory-based, effective programs to promote PA for individuals with diabetes. The purpose of this study was to investigate the utility of the Theory of Planned Behaviour (TPB) in understanding PA in an adult population with T1D or T2D. A total of 2311 individuals (691 T1D; 1614 T2D) completed self-report TPB constructs of attitude, subjective norms, perceived behavioural control (PBC), intention and PA at baseline and 1717 (524 T1D; 1123 T2D) completed the PA measure again at 6-month follow-up. Multi-group Structural Equation Modelling was conducted to: (1) test the fit of the TPB structure (2) determine the TPB structural invariance between the two types of diabetes and (3) to examine the explained variances in PA and compare the strength of associations of the TPB constructs in the two types of diabetes. The TPB constructs explained > or =40% of the variance in intentions for both diabetes groups. In cross-sectional models, the TPB accounted for 23 and 19% of the variance in PA for T1D and T2D, respectively. In prospective models, the TPB explained 13 and 8% of the variance in PA for T1D and T2D, respectively. When adjusting for past PA behaviour, the impact of PBC and intention on behaviour was reduced in both groups. The findings provide evidence for the utility of the TPB for the design of PA promotion interventions for adults with either T1D or T2D.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".