Health behaviour change: Theories, progress, and recommendations for the next generation of physical activity research
Bibliographic record
Abstract
Adaptive behaviour change is central to improving population health, yet poor adoption of health-enhancing behaviours contributes to noncommunicable diseases and so remains a global concern. Research on physical activity behaviour change has continued to expand and evolve since the turn of the millennium, guided by diverse theoretical approaches-from social cognitive theories, organismic dialectical approaches such as Self-Determination Theory, dual-process frameworks, and integrated practical models and taxonomies. Key challenges and opportunities remain, however, and in this paper we offer several calls to action for those working to advance physical activity behaviour change theory, research, and practice. First, we advocate for more precise examination of behaviour change itself, moving beyond static models to incorporate dynamic theories and methodologies (including data analysis) that better capture how behaviours evolve and change over time. Second, we emphasise the need to prioritise behaviour maintenance, recognising that many interventions succeed in initiating change but fail to support long-term adherence. Third, we call for a concerted effort to broaden our target populations in behaviour change research, ensuring that interventions (and the theories that inform them) are more inclusive, widely applicable, contextually relevant, and equitable. Finally, we highlight the growing recognition of automatic processes in shaping physical activity behaviours and outline the importance of refining measurement tools and intervention strategies to account for these non-conscious influences. These considerations are articulated with a view to supporting the next generation of physical activity behaviour change research and practice, and in doing so contribute to improved population health equity and outcomes.
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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 | 0.079 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".