The Last JITAI? The Unreasonable Effectiveness of Large Language Models in Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Prospective Cardiac Rehabilitation Setting

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Detalles Bibliográficos
Publicado en:	arXiv.org (Apr 15, 2024), p. n/a
Autor principal:	Haag, David
Otros Autores:	Kumar, Devender, Gruber, Sebastian, Sareban, Mahdi, Treff, Gunnar, Niebauer, Josef, Bull, Christopher, Smeddinck, Jan David
Publicado:	Cornell University Library, arXiv.org
Materias:	Exercise Just in time Large language models Machine learning Rehabilitation Context Customization Effectiveness
Acceso en línea:	Citation/Abstract Full text outside of ProQuest
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Descripción
Resumen:	We investigated the viability of using Large Language Models (LLMs) for triggering and personalizing content for Just-in-Time Adaptive Interventions (JITAIs) in digital health. JITAIs are being explored as a key mechanism for sustainable behavior change, adapting interventions to an individual's current context and needs. However, traditional rule-based and machine learning models for JITAI implementation face scalability and flexibility limitations, such as lack of personalization, difficulty in managing multi-parametric systems, and issues with data sparsity. To investigate JITAI implementation via LLMs, we tested the contemporary overall performance-leading model 'GPT-4' with examples grounded in the use case of fostering heart-healthy physical activity in outpatient cardiac rehabilitation. Three personas and five sets of context information per persona were used as a basis of triggering and personalizing JITAIs. Subsequently, we generated a total of 450 proposed JITAI decisions and message content, divided equally into JITAIs generated by 10 iterations with GPT-4, a baseline provided by 10 laypersons (LayPs), and a gold standard set by 10 healthcare professionals (HCPs). Ratings from 27 LayPs and 11 HCPs indicated that JITAIs generated by GPT-4 were superior to those by HCPs and LayPs over all assessed scales: i.e., appropriateness, engagement, effectiveness, and professionality. This study indicates that LLMs have significant potential for implementing JITAIs as a building block of personalized or "precision" health, offering scalability, effective personalization based on opportunistically sampled information, and good acceptability.
ISSN:	2331-8422
Fuente:	Engineering Database