Effectiveness of a Gamified Mobile App in Enhancing Treatment Adherence for Children With Amblyopia: Explorative Study
Bibliographic record
Abstract
Background: Amblyopia is the leading cause of visual impairment in children worldwide. The predominant clinical treatment, occlusion therapy, is marred by poor adherence, often attributed to the physical discomfort and social stigma associated with eye patching. Adjunct digital visual trainings have not consistently sustained patient engagement due to their repetitive nature, thereby compromising their efficacy. Objective: This study aimed to evaluate the effectiveness of a gamified mobile app designed to increase treatment adherence among children with amblyopia by making the therapeutic process more engaging and accessible within home settings. Methods: An exploratory study was conducted, commencing with qualitative interviews and questionnaires to explore the barriers to traditional treatment adherence. This formative research informed the development of a gamified mobile app, which was shaped by cognitive appraisal theory to address identified emotional and psychological needs, potentially impacting adherence. The subsequent quantitative phase utilized a randomized controlled trial involving 34 children with amblyopia who were aged 7-10 years and recruited from a local primary school. These participants were randomly assigned to either the intervention group, which used a novel gamified mobile app developed by our team, or the control group, which utilized another commercially available mobile app. Both groups engaged with their respective apps in a home environment. The 8-item Morisky Medication Adherence Scale was adapted to measure treatment adherence. Results: Over the 4-week trial, 34 children aged 7-10 years with amblyopia were enrolled and randomized into 2 groups: intervention (n=18) and control (n=16). Children in both the intervention and control groups engaged daily for 20 minutes at home, using mobile apps designed for visual rehabilitation. The intervention group (n=18) achieved a significantly higher mean adherence rate (mean 6.56, SD 1.06) on the Morisky Medication Adherence Scale compared to the control group (n=16; mean 5.01, SD 1.22; P<.001). Thematic analysis of the design process revealed that integrating cognitive appraisal theory effectively enhanced emotional engagement and adherence. Conclusions: The integration of cognitive appraisal theory into the design of a gamified mobile app for amblyopia treatment has shown to significantly improve adherence among children.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".