Building and strengthening physical activity identity: a theory-informed user-guide
Bibliographic record
Abstract
Physical activity identity, or viewing oneself as a physically active person, reliably predicts physical activity. Yet, little is known about how physical activity identity can be developed or strengthened. In this critical narrative review, we conducted a comprehensive literature search to identify models of physical activity identity, health psychology, behaviour change, identity or self-related constructs in search of explanations, constructs, or insights important for physical activity identity building and strengthening. Identified models included: the physical activity self-definition model, maintain IT, M-PAC, PRIME, possible selves, and self-determination theory. Using content analysis, we identified themes around candidate antecedents of physical activity identity. Nine common physical activity identity inputs were identified that we categorised as behavioural (physical activity; self-regulation; investment), cognitive (perceived ability; imaginal experiences, rules/standards; alignment with goals or values) or social (attachment ties; social appraisals). For each candidate input, we identify which models include the input, consider relevant research, discuss how and why the input may be related to physical activity identity, and offer practical strategies for building or strengthening physical activity identity. We offer a list of theory-informed physical activity identity inputs, a working figure which represents these identity inputs, and suggestions about how they may relate to physical activity identity (directly; indirectly). We aim to support future researchers in advancing the physical activity identity literature, and help practitioners support physical activity behaviour change.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.018 |
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".