Integration of AI-Assisted Mental Well-Being Metrics (MWI and PES) into Corporate Practice: Methodological Development and Results of a Pilot Study - Chaban, Oleg; Shevchenko, Oleg (Psychosomatic Medicine and General Practice, 2026)
Background. With growing demand for scalable mental wellbeing solutions in corporate settings, there is a need for objective metrics that monitor psychological state without privacy intrusion. Traditional questionnaires have limitations due to social desirability bias and low compliance. Objective. To develop and validate a system of AI-assisted metrics for daily mental wellbeing monitoring (Mental Wellbeing Index, MWI) and platform engagement (Platform Engagement Score, PES) in corporate context, and to investigate the relationship between these indicators. Methods. A pilot study was conducted over 6 weeks (November 10 - December 22, 2025) with 12 employees of a Ukrainian company. MWI was calculated daily based on AI analysis of dialogues across 5 dimensions (energy, clarity, balance, control, connection). PES was assessed weekly across 5 components (frequency, quality, completion, initiative, response speed). Pearson correlation analysis and quadrant matrix were used for user stratification. Results. Mean MWI was 4.93±0.52 (range 0-10), mean PES was 7.78±0.89 (range 0-10). A paradoxical pattern emerged: 58% of users with lower MWI (<5.5) showed high PES (>7.0), indicating active help-seeking behavior. The strongest correlation was found between dialogue quality and MWI (r=0.35, p<0.05), 3 times stronger than frequency impact (r=0.12). Highly engaged users (PES≥7) had MWI 0.41 points higher (+9%) than low-engagement users. Conclusions. The MWI/PES system shows potential for early burnout detection and wellbeing program effectiveness monitoring. Key factor is dialogue quality rather than mere quantity. The "help-seeking" pattern (low MWI + high PES) suggests the platform effectively engages users who need support most. Further longitudinal studies with larger samples are needed to validate long-term effectiveness. Keywords: digital mental health, artificial intelligence, workplace wellbeing, cognitive-behavioral therapy, micro-interventions, burnout prevention, conversational AI