Patterns of Engagement With an AI Conversational Agent for Mental Health and Associations With Anxiety and Depression: Cross-Sectional Study.

Abstract

Background: Digital mental health interventions using conversational AI agents are increasingly being adopted as scalable alternatives to traditional care. Engagement is typically measured using volume-based metrics (eg, session counts and total time on a platform). However, these metrics overlook engagement patterns over time, which are not well understood in relation to mental health outcomes.

Objective: The purpose of this cross-sectional study was to explore how different patterns of engagement with Mental's AI conversational agent relate to self-reported depression and anxiety. We aimed to (1) identify and describe engagement profiles based on patterns of interaction depth and temporal consistency, (2) compare depression and anxiety symptoms across engagement profiles, and (3) explore whether engagement profiles were associated with mental health symptom severity.

Methods: This cross-sectional observational study linked survey responses to back-end app usage data from 112 Mental app users who completed at least 5 sessions with the conversational AI agent. Engagement profiles were derived using median splits on interaction depth (α parameter) and temporal consistency (Gini coefficient). Depression was assessed using the Patient Health Questionnaire-8 (PHQ-8), and anxiety was assessed using the Generalized Anxiety Disorder-7 (GAD-7). One-way ANOVAs compared symptoms across profiles. Linear regression models examined associations between profiles and symptom severity, adjusting for age, gender, and total duration of use.

Results: We identified 4 distinct engagement profiles based on interaction depth and temporal consistency: extended and episodic (profile 1; n=25), extended and consistent (profile 2; n=31), brief and episodic (profile 3; n=31), and brief and consistent (profile 4; n=25). Users in profile 1 (extended and episodic) reported the lowest anxiety (mean 2.68, SD 2.43) and depression (mean 3.48, SD 4.06), while profile 4 (brief and consistent) reported the highest anxiety (mean 10.00, SD 7.03) and depression (mean 10.60, SD 8.75). Significant differences were observed for anxiety (F3,108=8.07, P<.001, η²=0.18) and depression (F3,108=5.47, P=.002, η²=0.13). In adjusted models, engagement profile was significantly associated with depression (R²=0.16, F7,104=2.87, and P=.009) and anxiety (R²=0.21, F7,104=4.04, and P<.001). Compared to profile 1, users in profiles 2 and 4 reported significantly higher depression and anxiety. Profile 3 differed from profile 1 for anxiety only (β=3.11, P=.047).

Conclusions: Users with longer, clustered sessions reported the lowest symptoms, whereas those with brief, evenly distributed use reported the highest symptom levels, suggesting that the structure of engagement may be associated with symptom levels in ways that aggregate usage metrics do not capture. These findings are preliminary and hypothesis-generating, highlighting the importance of considering how engagement unfolds over time and suggesting that pattern-based measurement may improve the understanding of user outcomes in AI-powered mental health care. Future work should examine the directionality of these associations and whether distinct engagement patterns reflect meaningfully different modes of interacting with AI-powered care.