<p>Summary table of the 13 included studies.</p>

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.</p></div>