Engaging Adolescents to Inform the Development of a Mobile Gaming App to Incentivize Physical Activity
Bibliographic record
Abstract
BACKGROUND: Involving youth in the development of a mobile game designed to increase physical activity may increase relevancy and adoption. OBJECTIVE: To share the development process used to create a gaming app aimed at incentivizing physical activity in high school students. METHODS: Five focus groups were conducted with high school students (N=50) to understand gaming behaviors. A subset of students from the focus groups chose to complete a Web-based survey (N=10). Four different versions of gaming artwork and concept design based on student input were pilot tested (N=35), and group consensus building determined the direction of the game. The 4 game versions differed in their artwork style and gaming concept with some requiring competition versus cooperation, or being more individual versus team based. Group consensus building meant that all artwork and game concept options were displayed at the front of a classroom. Students could then vote for their top artwork and concept choices by putting stickers on the top 1 or 2 artwork and concept options that they liked best. Once all votes were cast, investigators discussed the voting results with students, and brainstormed ways to incorporate popular aspects of the 3 "losing" artwork and game concepts into the winning ideas. RESULTS: Focus group transcripts were analyzed for common themes. Artwork and gaming concept-voting data was tallied at the time of voting to share with students in real time. Focus groups and survey results revealed important themes for a successful gaming app: (1) competition, (2) balanced in-game rewards, (3) accessibility, and (4) aesthetic features. Consensus voting indicated the popularity of a collaborative competitive content design (35/66, 53%) and playful art (27/71, 38%). CONCLUSIONS: To ensure saliency and effectiveness of game-based physical activity interventions, youth need to be included in design and implementation. Furthermore, the unique preferences and social constructs of high school students need to be considered during intervention development.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".