Mental health monitoring in the workplace using artificial intelligence: A scoping review

Workplace mental health issues are a prevalent concern. A potential solution is remote health monitoring. In this scoping review, we map evidence on artificial intelligence (AI) for workplace mental health monitoring. MEDLINE, PsycINFO, Scopus, Embase, and Web of Science were searched until March 2024. Studies that evaluated AI for monitoring of a common workplace mental health concern in an actual workplace setting were included. Thirteen studies were included; most (8/13, 62%) were conducted in an office environment, and 4/13 (31%) were in healthcare. Most studies monitored stress (10/13, 77%). The most common method was wearable sensors (8/13, 62%). Studies monitored a wide variety of parameters; the most common were physical activity (7/13, 54%), heart rate or heart rate variability (7/13, 54%). A wide variety of participant-report surveys, including validated (7/13, 54%) and non-validated (6/13, 46%) surveys, were used to measure mental health outcomes. Studies employed diverse AI methods, including supervised, semi-supervised and other (Markov chain and Monte Carlo). The most common AI models used were support vector machines (5/13, 38%), decision trees (5/13, 38%), naive Bayes (4/13, 31%), and random forest (4/13, 38%). Model accuracy ranged from 62%-99%. Future work should identify standard metrics to enable cross-comparability and use larger, high-quality datasets for enriched training, advanced optimization techniques, and careful model tuning.