Employing environmental data and machine learning to improve mobile health receptivity - Theilig, Max-Marcel; Korbel, Jakob J.; Mayer, Gwendolyn; Hoffmann, Christian; Zarnekow, Rüdiger (IEEE access, 2019)
Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment.