Promoting physical activity: development and testing of self-determination theory-based interventions
Bibliographic record
Abstract
A growing number of studies have pulled from Deci and Ryan's Self-Determination Theory to design interventions targeting health behavior change. More recently, researchers have begun using SDT to promote the adoption and maintenance of an active lifestyle. In this review, we aim to highlight how researchers and practitioners can draw from the SDT framework to develop, implement, and evaluate intervention efforts centered on increasing physical activity levels in different contexts and different populations. In the present paper, the rationale for using SDT to foster physical activity engagement is briefly reviewed before particular attention is given to three recent randomized controlled trials, the Canadian Physical Activity Counseling (PAC) Trial, the Empower trial from the UK, and the Portuguese PESO (Promotion of Health and Exercise in Obesity) trial, each of which focused on promoting physical activity behavior. The SDT-based intervention components, procedures, and participants are highlighted, and the key findings that have emanated from these three trials are presented. Lastly, we outline some of the limitations of the work conducted to date in this area and we acknowledge the challenges that arise when attempting to design, deliver, and test SDT-grounded interventions in the context of physical activity promotion.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".