Health Promotion in Popular Web-Based Community Games Among Young People: Proposals, Recommendations, and Applications
Bibliographic record
Abstract
BACKGROUND: Young people use digital technology on a daily basis and enjoy web-based games that promote social interactions among peers. These interactions in web-based communities can develop social knowledge and life skills. Intervening via existing web-based community games represents an innovative opportunity for health promotion interventions. OBJECTIVE: The aim of this study was to collect and describe players' proposals for delivering health promotion through existing web-based community games among young people, elaborate on related recommendations adapted from a concrete experience of intervention research, and describe the application of these recommendations in new interventions. METHODS: We implemented a health promotion and prevention intervention via a web-based community game (Habbo; Sulake Oy). During the implementation of the intervention, we conducted an observational qualitative study on young people's proposals via an intercept web-based focus group. We asked 22 young participants (3 groups in total) for their proposals about the best ways to carry out a health intervention in this context. First, using verbatim transcriptions of the players' proposals, we conducted a qualitative thematic analysis. Second, we elaborated on recommendations for action development and implementation based on our experiences and work with a multidisciplinary consortium of experts. Third, we applied these recommendations in new interventions and described their application. RESULTS: A thematic analysis of the participants' proposals revealed 3 main themes and 14 subthemes related to their proposals and process elements: the conditions for developing an attractive intervention within a game, the value of involving peers in developing the intervention, and the ways to mobilize and monitor gamers' participation. These proposals emphasized the importance of interventions involving and moderating a small group of players in a playful manner but with professional aspects. We established 16 domains with 27 recommendations for preparing an intervention and implementing it in web-based games by adopting the codes of game culture. The application of the recommendations showed their usefulness and that it was possible to make adapted and diverse interventions in the game. CONCLUSIONS: Integrated health promotion interventions in existing web-based community games have the potential for promoting the health and well-being of young people. There is a need to incorporate specific key aspects of the games and gaming community recommendations, from conception to implementation, to maximize the relevance, acceptability, and feasibility of the interventions integrated in current digital practices. TRIAL REGISTRATION: ClinicalTrials.gov NCT04888208; https://clinicaltrials.gov/ct2/show/NCT04888208.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".