Reliable data collection in participatory trials to assess digital healthcare apps.

Digital healthcare mobile apps are experiencing exponential growth due to the development of the mobile network and widespread usage of smartphones. Unlike high growth rates, sufficient validated apps are few. Because mobile health apps are low-risk technology, and it tends to reduce regulatory oversight. With this tendency, the apps have a direct trade characteristic between developers and end-users. The existing platforms are not suitable to collect reliable data for evaluating the effectiveness of the apps. Moreover, these platforms only reflect the perspectives of developers and experts, not end-users. For instance, the methods of data collection in clinical trials are not appropriate for participant-oriented assessment of healthcare apps since its complexity and high cost. Thus, we identified a need for a participant-oriented data collection platform for end-users as an interpretable, systematic, and sustainable tool---as a first step to validate the effectiveness of the apps. To collect reliable data in the participatory trial format, we have defined data preparation, storage, and sharing stage. Interpretable data preparation consists of a protocol database system and semantic feature retrieval method to create a protocol without professional knowledge. Collected data reliability weight calculation belongs to the systematic data storage stage. To sustainable data collection, we integrate the weight method and reward distribution function. We validate the methods with 718 of human participants to conduct statistical tests. The validation results showed that the methods have significant differences in the comparative experiment, and we convinced the methods are essential to reliable data collection. Furthermore, we created a web-based system for our pilot platform to collect reliable data in an integrated pipeline. We validate the platform features with existing clinical and pragmatic trial data collection platforms. In conclusion, we show that the method and platform support reliable data collection, forging a path to effectiveness validation of digital healthcare apps.