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

TL;DR

This paper introduces an AI and GIS-based platform to improve digital health interventions by making them more personalized and context-aware.

Contribution

The paper presents a novel reference architecture integrating LLMs and GIS for adaptive digital health interventions.

Findings

A pilot AI4Motion platform was implemented using open-source technologies and Open APIs.

The integration of LLM and GIS components enhances personalization and context-awareness in DBCIs.

The work contributes to system design patterns for platforms supporting EMA, ESM, and JITAI.

Abstract

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).

The online version contains supplementary material available at 10.1007/s10916-025-02269-x.

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