Personalizing health theories in persuasive game interventions to gamer types
Bibliographic record
Abstract
Persuasive games (PGs) informed by behaviour theories are effective tools for motivating health behaviour. It has been shown that tailoring PGs to the target audience increases their effectiveness. However, most existing studies on how to tailor PGs and gameful systems are focused on people from the Western cultures. There is a paucity of research on how to personalize PGs to the African audience. To advance research in this area, we conducted a large-scale study of 360 game players from Africa to investigate their eating habits and associated determinants of health behaviour to understand how health behaviour relates to their gamer types. We developed models showing the determinants of health behaviour for the seven gamer types identified by BrainHex. Our results show that gamer types play significant roles in the impact of various determinants on the health behaviour of Africans. People high in the achiever gamer type are motivated by perceived susceptibility (what they stand to lose), while daredevils are motivated by perceived benefit (what they stand to gain) from adopting a healthy lifestyle. Self-efficacy emerged as the most effective determinant overall, it influences health behaviour positively for all gamer types. We contribute to Human-Computer Interaction (HCI) research and practice by offering design guidelines for tailoring PGs for health to Africans based on their gamer types.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".