Measuring the Quality of Mobile Apps for the Management of Pain: Systematic Search and Evaluation Using the Mobile App Rating Scale

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.