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.
Date of current version December 23, 2019
Gesehen am 01.04.2020