The use of the behaviour change wheel in the development of ParticipACTION’s physical activity app
Bibliographic record
Abstract
The purpose of this study was to provide a detailed and systematic outline of how a theoretical behaviour change framework was applied in the development of ParticipACTION's app to support a more active Canada. The app development process was guided by the Behaviour Change Wheel (BCW) framework, a theoretically-based approach for intervention development, in collaboration with the commercial app industry. Specifically, a behavioural diagnosis was used to understand what needs to change for the targeted behaviour to occur. Current literature, along with a series of surveys, and market research informed app development. Additionally, a validated app behaviour change scale, was consulted throughout development to help ensure app features maximized behaviour change potential. The behavioural diagnosis revealed that the app needed to target individuals' physical and psychological capabilities, physical and social opportunities, and reflective and automatic motivations in order to increase physical activity levels. To accomplish this, 6 of a possible 9 intervention functions and 2 of 7 policy categories were selected from the BCW to be included in the app. Goals and planning, feedback and monitoring, behaviour identification, action planning and knowledge shaping were selected as the main behaviour change techniques for the app. Collaboration with a mobile app development firm helped to embed the selected behaviour change techniques, policy categories, intervention functions, and sources of behaviour within the app. Using a systematic approach, this study used the BCW to ensure the health promotion app was theoretically informed. Future research will evaluate its effectiveness in increasing the physical activity of Canadians.
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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 | 0.042 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".