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Background Physical inactivity is a major contributor to the development and persistence of chronic diseases. Mobile health apps that foster physical activity have the potential to assist in behavior change. However, the quality of the mobile health apps available in app stores is hard to assess for making informed decisions by end users and health care providers. Objective This study aimed at systematically reviewing and analyzing the content and quality of physical activity apps available in the 2 major app stores (Google Play and App Store) by using the German version of the Mobile App Rating Scale (MARS-G). Moreover, the privacy and security measures were assessed. Methods A web crawler was used to systematically search for apps promoting physical activity in the Google Play store and App Store. Two independent raters used the MARS-G to assess app quality. Further, app characteristics, content and functions, and privacy and security measures were assessed. The correlation between user star ratings and MARS was calculated. Exploratory regression analysis was conducted to determine relevant predictors for the overall quality of physical activity apps. Results Of the 2231 identified apps, 312 met the inclusion criteria. The results indicated that the overall quality was moderate (mean 3.60 [SD 0.59], range 1-4.75). The scores of the subscales, that is, information (mean 3.24 [SD 0.56], range 1.17-4.4), engagement (mean 3.19 [SD 0.82], range 1.2-5), aesthetics (mean 3.65 [SD 0.79], range 1-5), and functionality (mean 4.35 [SD 0.58], range 1.88-5) were obtained. An efficacy study could not be identified for any of the included apps. The features of data security and privacy were mainly not applied. Average user ratings showed significant small correlations with the MARS ratings (r=0.22, 95% CI 0.08-0.35; P<.001). The amount of content and number of functions were predictive of the overall quality of these physical activity apps, whereas app store and price were not. Conclusions Apps for physical activity showed a broad range of quality ratings, with moderate overall quality ratings. Given the present privacy, security, and evidence concerns inherent to most rated apps, their medical use is questionable. There is a need for open-source databases of expert quality ratings to foster informed health care decisions by users and health care providers.

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BACKGROUND Physical inactivity is a major contributor to the development and maintenance of chronic diseases. Mobile applications (apps) for physical activity have the potential to foster health and well-being. OBJECTIVE The present study aimed at systematically reviewing and analyzing the content and quality of physical activity apps. METHODS A web crawler was used to systematically search for apps promoting physical activity in the Google Play and Apple App Store. Two independent raters used the German Mobile Application Rating Scale (MARS) to assess app quality. Furthermore, app characteristics, content and functions, privacy and security measures were assessed. Correlation between user star ratings and the MARS was calculated. Exploratory regression analysis was conducted to determine relevant predictors for overall quality. RESULTS 312 of 2,231 identified apps met inclusion criteria. Results indicate that overall quality was moderate (M = 3.60, SD = 0.59, range = 1-4.75). For none of the included apps, a scientific evaluation could be identified in the app description. Only 21% of the apps had an imprint or contact information. Average user ratings showed significant small correlations with the MARS rating (r =.22; 95% CI: 0.08-0.35; P < .001). The number of content and function was predictive for overall quality. CONCLUSIONS There is a large quality range within apps for physical activity with moderate overall quality ratings. Given the present privacy, security and evidence concerns inherent to most rated apps, recommendations for the use of physical activity apps can only be given with major limitations. There is a need for central databases that identify high-quality apps and new evaluation frameworks.

Having a hand tremor often complicates interactions with touchscreens on mobile devices. Due to the uncontrollable oscillations of both hands, hitting targets can be hard, and interaction can be slow. Correcting input needs additional time and mental effort. We propose a method for automatically correcting such inputs based on motion data, gathered both with the devices' sensors and a small wearable sensor on the finger used for tapping. The development was informed by interviews with persons with tremor. Two empirical studies showed that our method, involving both smartphone and finger motion sensors without changing the user interface, allows users with tremor to select objects with up to 40 % fewer misses.