  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.
