Mental health monitoring in the workplace using artificial intelligence: A scoping review
- 1Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
- 2Geoffrey and Robyn Sperber Health Sciences Library, University of Alberta, Edmonton, Alberta, Canada
- 3Department of Emergency Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
Abstract
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. Author summary: Workplace mental health is an important concern for both employees and employers. Remote health monitoring with artificial intelligence (AI) can help to detect issues and prompt intervention. This review mapped existing evidence sourced from a systematic search of online databases until March 2024. We included studies that evaluated AI for monitoring of a common workplace mental health concern in an actual workplace setting. Thirteen studies were included. The most common workplaces were an office (8/13, 62%) and healthcare (4/13, 31%). Stress was the most frequently monitored mental health concern (10/13, 77%). The most common method was wearable sensors (8/13, 62%). A wide variety of AI methods were applied. Model accuracy ranged from 62%-99%. Overall, we found heterogeneity across studies and methods, which limits generalizability. Our findings suggest potential for AI-based workplace mental health monitoring which could improve workplace mental health. Standardization of methods and definitions is a priority for future work, as well comprehensively describing practical challenges and methodological limitations associated with the implementation of this technology in the workplace setting.
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BibTeX
@article{jack2026mental,
title = {{Mental health monitoring in the workplace using artificial intelligence: A scoping review}},
author = {Kalee Jack and Julie Tian and Viktoriia Kurkova and Setayesh Modanloo and Liz Dennett and Jasmine Noble and Andrew Greenshaw and Jake Hayward},
journal = {PLOS Digital Health},
year = {2026},
month = sep,
doi = {10.1371/journal.pdig.0001743},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001743},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Mental health monitoring in the workplace using artificial intelligence: A scoping review
AU - Kalee Jack
AU - Julie Tian
AU - Viktoriia Kurkova
AU - Setayesh Modanloo
AU - Liz Dennett
AU - Jasmine Noble
AU - Andrew Greenshaw
AU - Jake Hayward
PY - 2026
DA - 2026/09/21
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001743
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001743
AB - 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. Author summary: Workplace mental health is an important concern for both employees and employers. Remote health monitoring with artificial intelligence (AI) can help to detect issues and prompt intervention. This review mapped existing evidence sourced from a systematic search of online databases until March 2024. We included studies that evaluated AI for monitoring of a common workplace mental health concern in an actual workplace setting. Thirteen studies were included. The most common workplaces were an office (8/13, 62%) and healthcare (4/13, 31%). Stress was the most frequently monitored mental health concern (10/13, 77%). The most common method was wearable sensors (8/13, 62%). A wide variety of AI methods were applied. Model accuracy ranged from 62%-99%. Overall, we found heterogeneity across studies and methods, which limits generalizability. Our findings suggest potential for AI-based workplace mental health monitoring which could improve workplace mental health. Standardization of methods and definitions is a priority for future work, as well comprehensively describing practical challenges and methodological limitations associated with the implementation of this technology in the workplace setting.
SN - 2767-3170
ER -