Comparing Motives for Lifestyle and Exercise Activities Using the Theory of Planned Behavior
Bibliographic record
Abstract
PURPOSE: Regular exercise (EX) and lifestyle physical activity (LPA) are often used interchangeably or combined in promotional messaging based on evidence for their relatively equivocal health outcomes. Still, differences between their motivational correlates are relatively unexplored. Thus, the purpose of this study was to compare the motives towards LPA and EX and their relationship with behaviour using the well-validated theory of planned behaviour (TPB). It was hypothesized that the TPB would be a more efficacious model for predicting EX compared to LPA based on the planned nature exercise. METHODS: Participants were a voluntary sample of post-degree students (N = 150) who completed measures of the TPB framed in terms of EX and LPA. Physical activity was assessed using the Godin Leisure Time Exercise Questionnaire framed for both EX and LPA activities. RESULTS: Results identified marked differences between the instrumental attitudes of the two activities favouring EX (p < .01; d = .68). Most important, EX had larger TPB-behaviour correlations (p<01;q=.15- .20) in comparison to LPAand this did not appear to be a function of intensity level. CONCLUSIONS: Our results suggest that the correlates for these activities may differ. Differences in subjective norm and instrumental attitude measurements between EX and LPA have important implications for health promotion initiatives and physical activity intervention development. Although more research is necessary, this may affect the efficacy of promotion campaigns that do not tailor content exclusively for EX and LPA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".