AI-Derived Emotional States and Trajectories in Real-World Mental Health Sessions: Large-Scale Observational Study

Background AI-powered digital mental health interventions (DMHIs) are increasingly deployed to support employee mental health, yet evaluations have focused predominantly on pretest-posttest symptom outcomes. The emotional states that unfold within real-world AI-assisted sessions remain largely uncharacterized, limiting understanding of how and when these tools may support users in distress. Objective This study aimed to (1) characterize AI-derived emotional states and patterns that emerge during AI-supported mental health sessions, (2) characterize and compare reduced distress and persistent distress sessions among sessions beginning in AI-derived distress states, and (3) identify conversational features associated with reduced distress trajectories. Methods This retrospective study analyzed message-level AI-derived emotional state data from 28,870 sessions contributed by 4463 unique users of Yuna, an AI-powered DMHI. Yuna’s emotion detection system, grounded in semantic space theory, generated continuous scores across 36 fine-grained emotional states at the message level. Fine-grained states were aggregated into 4 categories: distress associated, cognitive processing, regulated positive, and social-interpersonal. Session-level trajectory types were operationalized from start-to-end emotional category transitions. Linear mixed-effects models and mixed-effects logistic regression were used to identify characteristics and conversational predictors of reduced distress trajectories among sessions beginning in distress-associated states. Results Five trajectory types were identified: stable (7679/28,870, 26.6%), reduced distress (7042/28,870, 24.4%), transitional processing (6195/28,870, 21.5%), persistent distress (4568/28,870, 15.8%), and escalating distress (3386/28,870, 11.7%). Cognitive processing states were the most frequently observed at the message level (130,611/365,088, 35.8%), followed by distress-associated states (117,485/365,088, 32.2%). Among sessions beginning in distress-associated states with available session duration data (n=1597), 73.3% (1170/1597) demonstrated reduced distress trajectories by session end. Reduced distress sessions were characterized by more messages (B=1.67; P=.02), longer duration (B=3.61; P=.001), AI-derived emotional switches (B=1.57; P=.01), unique AI-derived emotional states (B=0.94; P<.001), and higher proportions of cognitive processing (B=0.09; P<.001) and regulated positive (B=0.10; P<.001) states relative to persistent distress sessions. In the mixed-effects logistic regression, a greater number of unique AI-derived emotional states (odds ratio [OR] 1.16, 95% CI 1.09-1.24; P<.001) and longer session duration (OR 1.01, 95% CI 1.00-1.02; P=.04) were positively associated with reduced distress trajectories, while more AI-derived emotional switches were negatively associated with reduced distress trajectories (OR 0.97, 95% CI 0.95-0.99; P=.003). Conclusions AI-derived emotional trajectory data may offer a scalable, low-burden signal for understanding engagement quality in real-world mental health sessions, laying an empirical foundation for process-level evaluation of AI-powered DMHIs. Future research incorporating self-report validation, longitudinal designs, and experimental methods is needed to determine whether these trajectory patterns carry a meaningful signal for user outcomes.