Motives for lifestyle and exercise activities: A comparison using the theory of planned behaviour
Bibliographic record
Abstract
Abstract Regular exercise and lifestyle physical activity are often used interchangeably or combined in physical activity messaging based on evidence for their relatively equivalent health outcomes. However, differences between their motivational correlates are relatively unexplored. The purpose of this study was to compare the motives towards lifestyle physical activity and exercise and their relationship with behaviour using the Theory of Planned Behaviour (TPB). The participants were a sample of undergraduate students (n=150) who completed measures of the TPB framed in terms of exercise and lifestyle physical activity and self‐reported physical activity measures with similar framing. Results identified marked differences between the instrumental attitudes towards the two activities showing instrumental attitudes towards exercise to be higher (P<0.01; d=0.68). Most importantly, exercise had larger TPB–behaviour correlations (P<0.01; q=0.15–0.20) compared with lifestyle physical activity, but follow‐up analyses by intensity (strenuous, moderate, mild) showed that these differences were only present at strenuous intensity. Our results suggest that the correlates for the two types of physical activity may differ. Although more research is necessary, this may affect the efficacy of promotion campaigns that do not tailor content exclusively for either exercise or lifestyle physical activity.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".