Measuring the Quality of Mobile Apps for the Management of Pain: Systematic Search and Evaluation Using the Mobile App Rating Scale
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a major health issue requiring an approach that not only considers medication, but also many other factors included in the biopsychosocial model of pain. New technologies, such as mobile apps, are tools to address these factors, although in many cases they lack proven quality or are not based on scientific evidence, so it is necessary to review and measure their quality. OBJECTIVE: The aim is to evaluate and measure the quality of mobile apps for the management of pain using the Mobile App Rating Scale (MARS). METHODS: This study included 18 pain-related mobile apps from the App Store and Play Store. The MARS was administered to measure their quality. We list the scores (of each section and the final score) of every app and we report the mean score (and standard deviation) for an overall vision of the quality of the pain-related apps. We compare the section scores between the groups defined according to the tertiles via analysis of variance (ANOVA) or Kruskal-Wallis test, depending on the normality of the distribution (Shapiro-Wilk test). RESULTS: The global quality ranged from 1.74 (worst app) to 4.35 (best app). Overall, the 18 apps obtained a mean score of 3.17 (SD 0.75). The best-rated sections were functionality (mean 3.92, SD 0.72), esthetics (mean 3.29, SD 1.05), and engagement (mean 2.87, SD 1.14), whereas the worst rated were app specific (mean 2.48, SD 1.00), information (mean 2.52, SD 0.82), and app subjective quality (mean 2.68, SD 1.22). The main differences between tertiles were found on app subjective quality, engagement, esthetics, and app specific. CONCLUSIONS: Current pain-related apps are of a certain quality mainly regarding their technical aspects, although they fail to offer information and have an impact on the user. Most apps are not based on scientific evidence, have not been rigorously tested, and the confidentiality of the information collected is not guaranteed. Future apps would need to improve these aspects and exploit the capabilities of current devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".