Physical activity identity as an axis of dual process motivation and self-regulation processes: Current evidence and future research directions
Bibliographic record
Abstract
Regular physical activity (PA) has considerable health, social, and environmental benefits, yet many people participate in less PA than recommended. Understanding the factors driving PA participation is critical for promotion. Many theoretical approaches, across several traditions, have been applied to understand and change PA with modest success, demonstrating room for theoretical innovation. The purpose of this critical review was to overview the application of identity theory, as one potential domain of theoretical innovation for understanding and changing PA, while emphasizing several pertinent areas to advance theoretical and applied research. Contemporary evidence shows that the relationship between PA identity and behavior is sizeable, and comparable to many of the most well-researched PA correlates (e.g., intention, self-efficacy, habit). PA identity and behavior also covary over time, and the dual-process and self-regulatory control system often proposed for how identity may drive PA has preliminary support. Despite this evidence, we suggest that refined testing of the identity control system with a particular emphasis on the affect-behavior connection is needed. Research on the forms of motivation (automatic, reflective) and self-regulation processes that drive this system also require more research, which may benefit from refinement of measurement of identity and its associated processes, including applications in real time (e.g., ecological momentary assessment). Integration of PA identity into a cohesive model and/or within well-established PA theories is still needed, and advancing our understanding and effectiveness of PA identity change through interventions should be an ongoing research direction.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".