The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

TL;DR

This study demonstrates that GPT-4 can effectively generate personalized, context-aware health interventions in cardiac rehabilitation, outperforming humans in key metrics and offering a scalable solution for digital health behavior support.

Contribution

The paper introduces the novel application of GPT-4 for generating JITAI content, showing its superiority over human suggestions in a cardiac health context.

Findings

GPT-4-generated JITAI decisions outperformed humans in all metrics.

Large language models can personalize health interventions effectively.

LLMs offer scalable solutions for context-aware digital health interventions.

Abstract

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual's current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.