Serious Game for Change in Behavioral Intention Toward Lifestyle-Related Diseases: Experimental Study With Structural Equation Modeling Using the Theory of Planned Behavior
Bibliographic record
Abstract
BACKGROUND: Health activities should be tailored to individual lifestyles and values. To raise awareness of health behaviors, various practices related to health education, such as interactive activities among individuals with different backgrounds, have been developed. Moreover, serious games have been used as a tool for facilitating communication. However, there have been few investigations that are based on the framework of the theory of planned behavior on the mechanisms of health-related behavioral intention change from playing serious games. OBJECTIVE: We aimed to investigate the mechanisms of behavioral intention change among various age groups after an intervention using a serious game to increase awareness of lifestyle-related diseases. METHODS: Adults, undergraduates, and high school students played a serious game, called Negotiation Battle, and answered a questionnaire-Gaming Event Assessment Form for Lifestyle-related Diseases-before, immediately after, and 2-4 weeks after the game. The questionnaire was composed of 16 items based on the theory of planned behavior. We used structural equation modeling to compare responses from the 3 groups. RESULTS: For all 3 age groups (adults: mean 43.4 years, range 23-67 years; undergraduates: mean 20.9 years, range 19-34 years; high school students: mean 17.9 years, 17-18 years), perceived behavior control was the key factor of behavioral intention change. Immediately after the game, causal relationships between perceived behavioral control and behavioral intention were enhanced or maintained for all groups-adults (before: path coefficient 1.030, P<.001; after: path coefficient 2.045, P=.01), undergraduates (before: path coefficient 0.568, P=.004; after: path coefficient 0.737, P=.001), and high school students (before: path coefficient 14.543, P=.97; after: path coefficient 0.791, P<.001). Analysis of free descriptions after intervention suggested that experiencing dilemma is related to learning and behavioral intention. CONCLUSIONS: The study revealed that the serious game changed the behavioral intention of adolescents and adults regarding lifestyle-related diseases, and changes in perceived behavioral control mediated the alteration mechanism.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".