Reference and Solution Architecture for GenAI- and GIS-Enhanced Physical Activity Interventions: Towards Implementing the AI4Motion Platform

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Authors	DOLEŽEL Michal LÍSKOVEC Radim
Year of publication	2025
Type	Article in Periodical
Magazine / Source	JOURNAL OF MEDICAL SYSTEMS
MU Faculty or unit	Faculty of Science
Citation
web	https://link.springer.com/article/10.1007/s10916-025-02269-x
Doi	https://doi.org/10.1007/s10916-025-02269-x
Keywords	LLM; LBS; API; LLaMA; Digital Health Intervention; Geofencing
Description	Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and wearables. Participant intervention fatigue may happen when DBCIs become too frequent, repetitive, demanding, or lack perceived relevance, and this may result in participants’ reduced motivation and adherence over time. Advancing technology-supported engagement mechanisms is therefore of utmost importance. To address this problem, we present a reference and solution architecture based on open-source technologies and open Application Programming Interfaces (Open APIs). First, we integrated a Large Language Model (LLM) component into the DBCI design. Second, to support context-awareness, we enhanced this integration by adding a Geographic Information Systems (GIS) element. Our pilot implemented AI4Motion platform targets both personalization and contextualization aspects of DBCIs. Our work contributes to the emerging discussion on LLM/GIS-related system design patterns for digital platforms supporting Ecological Momentary Assessment (EMA), Experience Sampling Method (ESM), and Just-in-Time Adaptive Interventions (JITAIs).