Work

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Year: 2026

Type: article

Abstract: OBJECTIVE: To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self-management and identify patterns associated with participant engagement. METHODS: We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self-management for SSc. Twenty individuals with SSc interacted with the AI health coach over four weeks. Quantitative usage metrics (number of conversations, user messages, and chat duration) were used to classify participants into high- and low-engagement groups. A qualitative inductive content analysis of chat transcripts was used to identify the purpose of interactions. Quantitative and qualitative data were integrated using a joint display approach. RESULTS: Twenty participants (90% female; mean age 55 years) used the AI health coach for goals and strategies, information seeking, companionship, and disease monitoring. Participants with high engagement (N = 8) had more coded interactions related to goals and strategies (23.6 vs 5.1), information seeking (10.8 vs 3.9), and companionship (7.9 vs 1.0) compared with those with low engagement (N = 12). More participants in the high-engagement group used the AI health coach for companionship compared with the low-engagement group (100% vs 33%). Exploratory analyses suggested greater improvements in fatigue (mean change -5.04 [95% confidence interval -9.34 to -0.73]) among high-engagement participants, with no statistically significant between-group differences. CONCLUSION: Engagement with an AI health coach may not be fully captured by quantitative usage metrics alone. Engagement in our study was characterized by more frequent, action-oriented, and companionship-oriented interactions.

Source: Arthritis Care & Research

DOI: 10.1002/acr.80109

Authors: Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy

Institutions: University of Michigan, University of Washington

Language: English

Topic: Mobile Health and mHealth Applications

Subfield: General Health Professions

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,640