Gamification in Child and Adolescent Health: Trends, Applications, and Policy Relevance for Mental Well-Being, Physical Activity, and Nutrition
Bibliographic record
Abstract
Aim: This study presents a bibliometric analysis of the use of gamification in mental health research (GMHR), aiming to provide a comprehensive quantitative evaluation of publication trends, key contributors, collaboration networks, thematic development, and emerging research themes in this field. Methods: Relevant literature was retrieved from the Scopus database and analyzed using bibliometric tools, including VOSviewer and RStudio. The analysis focused on identifying publication outputs, author collaborations, co-word networks, and thematic evolution from 2013 to 2024. Results: A total of 276 documents were identified, with an annual growth rate of 32.98%. The average number of co-authors per document was 4.99, indicating robust collaborative activity, including international partnerships. Leading contributors included Cheng VWS, Hickie IB, and Fleisch E, who are affiliated with prominent research institutions. Key research themes included gamification, mHealth, anxiety, and ADHD. The findings revealed a dynamic and expanding field responding to contemporary societal mental health needs. Conclusion: Gamification is increasingly being integrated into mental health interventions, showing promise in addressing psychological well-being, particularly among children, adolescents, and individuals with ADHD. Interventions utilizing serious games and mobile health apps have been shown to enhance user engagement, adherence, and cognitive outcomes, especially when targeting anxiety and attention disorders. Empirical studies within the most cited GMHR publications demonstrate effectiveness in areas such as cognitive-behavioral therapy (CBT) delivery, biofeedback-based regulation, and emotion recognition training through game mechanics.
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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 | 0.029 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.054 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".