Evaluating the Quality and Features of Visual Acuity Apps Using the Mobile App Rating Scale: Systematic Review
Bibliographic record
Abstract
Background: Mobile visual acuity (VA) apps have emerged as valuable tools in both clinical and home settings, particularly in the context of expanding teleophthalmology. Despite the growing number of apps available to measure visual acuity, studies evaluating their overall quality, functionality, and clinical relevance are limited. Objective: This study aimed to systematically evaluate the quality and features of mobile VA apps available on iOS and Android platforms using the clinically validated Mobile App Rating Scale (MARS). Methods: A comprehensive search of the Google Play Store and Apple App Store was conducted between January 2024 and March 2024 using standardized search terms. Eligible apps included free, English-language VA testing tools not requiring external devices. App characteristics and features were extracted. Each app was independently evaluated by 2 trained reviewers using MARS, which rates engagement, functionality, aesthetics, information quality, and subjective quality on a 5-point scale. Results: Of the 725 apps initially identified, 44 met the inclusion criteria, with 23 from the Google Play Store and 21 from the Apple App Store. The most common VA test optotypes used were Tumbling E (n=21; 48%), Snellen Chart (18/44; 41%), and Landolt C (n=14; 32%). Common supplemental features included color vision testing (n=20; 46%), astigmatism tests (n=13; 30%), Amsler grid (n=13; 30%), and contrast testing (n=12; 28%). The average MARS scores were comparable across platforms: 3.04 (SD 0.80) for Android and 3.02 (SD 0.84) for iOS. Functionality received the highest ratings (mean 3.65, SD 0.75 for Android; mean 3.71, SD 0.82 for iOS), while subjective quality received the lowest (mean 2.09, SD 1.01 for Android; mean 2.21, SD 1.01 for iOS). Few apps had undergone clinical validation. Only Apple App Store apps demonstrated significant correlations between MARS scores and app store star ratings. Conclusions: VA apps exhibited considerable heterogeneity in quality, functionality, and clinical use. Total mean MARS scores were similar between the Google Play Store and the Apple App Store, suggesting that neither platform consistently offers superior app quality. While many apps are technically sound, low subjective-quality scores and a lack of clinical validation limit their current use in professional practice. These findings underscore the need for more rigorous app development and validation standards to improve their relevance and reliability in teleophthalmology.
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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 | 0.017 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".