Using the Behaviour Change Wheel Program Planning Model to Design Games for Health: Development Study
Bibliographic record
Abstract
BACKGROUND: Games for health are a promising approach to health promotion. Their success depends on achieving both experiential (game) and instrumental (health) objectives. There is little to guide game for health (G4H) designers in integrating the science of behavior change with the art of game design. OBJECTIVE: The aim of this study is to extend the Behaviour Change Wheel program planning model to develop Challenges for Healthy Aging: Leveraging Limits for Engaging Networked Game-Based Exercise (CHALLENGE), a G4H centered on increasing physical activity in insufficiently active older women. METHODS: We present and apply the G4H Mechanics, Experiences, and Change (MECHA) process, which supplements the Behaviour Change Wheel program planning model. The additional steps are centered on identifying target G4H player experiences and corresponding game mechanics to help game designers integrate design elements and G4H objectives into behavioral interventions. RESULTS: We identified a target behavior of increasing moderate-intensity walking among insufficiently active older women and key psychosocial determinants of this behavior from self-determination theory (eg, autonomy). We used MECHA to map these constructs to intervention functions (eg, persuasion) and G4H target player experiences (eg, captivation). Next, we identified behavior change techniques (eg, framing or reframing) and specific game mechanics (eg, transforming) to help realize intervention functions and elicit targeted player experiences. CONCLUSIONS: MECHA can help researchers map specific linkages between distal intervention objectives and more proximal game design mechanics in games for health. This can facilitate G4H program planning, evaluation, and clearer scientific communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".