Gamification in Mobile Apps for Children With Disabilities: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Children with disabilities face numerous challenges in accessing health services. Mobile health is an emerging field that could significantly reduce health inequities by providing more accessible services. Many mobile apps incorporate gamification elements such as feedback, points, and stories to increase engagement and motivation; however, little is known about how gamification has been incorporated in mobile apps for children with disabilities. OBJECTIVE: This scoping review aims to identify and synthesize the existing research evidence on the use of gamification in mobile apps for children with disabilities. Specifically, the objectives were to (1) identify the categories of these mobile apps (eg, treatment and educational) (2), describe the health-related outcomes they target, (3) assess the types and levels of gamification elements used within these apps, and (4) determine the reasons for incorporating gamification elements into mobile apps. METHODS: We searched MEDLINE, PsycINFO, CINAHL, Embase, the ACM Digital Library, and IEEE Xplore databases to identify papers published between 2008 and 2023. Original empirical research studies reporting on gamified mobile apps for children with disabilities that implemented at least 1 gamification strategy or tactic were included. Studies investigating serious games or full-fledged games were excluded. RESULTS: A total of 38 studies reporting on 32 unique gamified mobile apps were included. Findings showed that gamified apps focus on communication skills and oral health in children with autism spectrum disorder while also addressing self-management and academic skills for other disability groups. Gamified mobile apps have demonstrated potential benefits across different populations and conditions; however, there were mixed results regarding their impact. The gamification strategies included fun and playfulness (23/32, 72%), feedback on performance (17/32, 53%), and reinforcement (17/32, 53%) in more than half of apps, whereas social connectivity was used as a gamification strategy in only 4 (12%) mobile apps. There were 2 main reasons for integrating gamification elements into mobile apps described in 16 (42%) studies: increasing user engagement and motivation and enhancing intervention effects. CONCLUSIONS: This scoping review offers researchers a comprehensive review of the gamification elements currently used in mobile apps for the purposes of treatment, education, symptom management, and assessment for children with disabilities. In addition, it indicates that studies on certain disability groups and examinations of health-related outcomes have been neglected, highlighting the need for further investigations in these areas. Furthermore, research is needed to investigate the effectiveness of mobile-based gamification elements on health and health behavior outcomes, as well as the healthy development of children with disabilities.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".