2026-10-12T22:13:01Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33957196 2026-09-21T17:41:04Z category_21 category_272 category_734 category_106 portal_5 item_type_3 month_year_09_2026

<p>Summary table of the 13 included studies.</p> Kalee Jack (25084741) Julie Tian (19469783) Viktoriia Kurkova (25084744) Setayesh Modanloo (16682175) Liz Dennett (8546175) Jasmine Noble (25084747) Andrew Greenshaw (6197054) Jake Hayward (2618986) Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Science Policy support vector machines monte carlo ). model accuracy ranged identify standard metrics heart rate variability advanced optimization techniques actual workplace setting remote health monitoring mental health monitoring 46 %) surveys studies monitored stress studies monitored report surveys 62 %) 54 %) 31 %) wide variety wearable sensors use larger thirteen studies scoping review random forest quality datasets prevalent concern potential solution physical activity office environment markov chain march 2024 map evidence future work enriched training enable cross artificial intelligence 77 %). 62 %). 54 %). 38 %). 31 %), <div><p>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.</p></div> 2026-09-21T17:40:57Z Dataset Dataset 10.1371/journal.pdig.0001743.t002 https://figshare.com/articles/dataset/_p_Summary_table_of_the_13_included_studies_p_/33957196 CC BY 4.0