Investigating Motivational Regulations and Physical Activity Over 25 Weeks
Bibliographic record
Abstract
BACKGROUND: Because motivation has been deemed a key barrier to physical activity, it is imperative that we know how motivational levels change over time and how that change relates to physical activity. Based in Self-Determination Theory, this study investigated fluctuations in physical activity and motivational regulations over 25 weeks and tested the relationship between these 2 variables. METHODS: Data from the Physical Activity Counseling trial were examined. Inactive adults recruited from a primary care center (N = 120) answered motivation and physical activity questionnaires during the intervention and postintervention phases. Hierarchical linear modeling was used to test the hypotheses. RESULTS: Quadratic changes were found for external regulation (γ20= 0.02, P < .05) and physical activity (γ20 = -2.64, P < .001), while identified (γ10= 0.04, P = .03) and intrinsic (γ10= 0.04, P = .01) regulations increased linearly over the course of the 25 weeks. Only identified regulation (γ30= 3.15, P = .01) and intrinsic motivation (γ30= 4.68, P < .001) were significantly and positively related with physical activity. CONCLUSION: Physical activity, external and identified regulations and intrinsic motivation changed over the 25 weeks. Intervention should aim at fostering identified regulation and intrinsic motivation as greater levels of these regulations were related with 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".