Counseling Messages for Adults with Impaired Fasting Glucose in a Public Mobile Healthcare Program: A Structural Topic Model Analysis Using the IMB Framework

Background/Objectives: Mobile health (mHealth) interventions are commonly used in public healthcare systems to support chronic disease prevention and self-management. However, limited evidence exists regarding the structural composition and theoretical alignment of counseling message content delivered through such programs. This study aimed to identify the latent content structure of nutrition counseling messages for adults with impaired fasting glucose (IFG) and to evaluate their alignment with the Information–Motivation–Behavioral Skills (IMBs) model. Methods: A total of 3130 de-identified nutrition counseling messages from 532 adults with IFG enrolled in a nationwide public mobile healthcare program in South Korea were analyzed. A Structural Topic Model (STM), with program phase (months 1–6) as a prevalence covariate, was applied to identify latent topics and temporal variation in topic prevalence. The resulting topics were independently classified into the three constructs of the IMB model by two researchers, with 90% inter-rater agreement, to assess the theoretical alignment of message content. Results: STM identified ten topics, which were classified as information (seven topics, 64.3%), behavioral skills (two topics, 28.5%), and motivation (one topic, 7.2%). The program phase was a significant predictor of prevalence for 9 of the 10 topics, with information surging to 88.7% in Phase 4 due to seasonal food safety messaging. Behavioral skills topics were most prevalent at Phase 1 and Phase 6, whereas motivation remained consistently low throughout the intervention. Conclusions: Nutrition counseling messages were heavily weighted toward information delivery, with limited motivational content, suggesting an imbalance in the key components required for behavior change under the IMB framework. Temporal patterns appeared to follow operational or seasonal factors rather than a theory-driven progression. These findings highlight the need for more theory-informed message design in public mobile healthcare programs, with the potential for AI-assisted approaches to enable personalized and adaptive counseling content.