Just-in-time adaptive interventions (JITAIs) can promote behavior change in patients. It was the aim of our study to make JITAIs adaptable, i.e., to configure JITAIs for different purposes and to personalize them for different participants, whilst enabling central maintenance and integrated data analysis across deployments and individuals. We present a concept for adaptable JITAIs that was created following a design science approach. It builds on multi-level conceptual modeling and knowledge graphs and will be evaluated in user studies.
Keywords: Artificial Intelligence; Data Aggregation; Data Analysis; Semantic Web.