Using Narrative Game Design to Increase Children’s Physical Activity: Exploratory Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Physical activity is crucial for child obesity prevention and intervention. Narratives embedded in active games can increase children's physical activity. OBJECTIVE: Little is known about the narrative characteristics that would motivate children to exercise. We attempted to fill the gaps in understanding regarding narrative design for active video games. METHODS: In this exploratory study, four animated narratives of different genres were professionally generated. Children (N=41) between the ages of 8 and 12 years were interviewed to identify their preferences. Sessions were digitally recorded, transcribed, and analyzed using exploratory thematic analysis. RESULTS: Findings revealed that the children rated the dystopian science fiction story as their favorite across all weight, race, and gender groups. The physical activity-friendly narrative strategies included virtuous characters, extraordinary character actions, interesting plots, super powers, and engaging cliffhangers. Alternatively, information not related to physical activity, difficult-to-follow plot lines, passive protagonists, and repetitive narrative tropes were less appealing for physical activity. CONCLUSIONS: This research provides preliminary evidence that narratives have characteristics that may increase child physical activity when playing active games. Future empirical studies should verify and test these design principles.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".