Student Mental Health: Screening for Stress, Anxiety and Depression Using Fitbit Data

Authors

• Rebecca Lopez, Worcester Polytechnic Institute
• Avantika Shrestha, Worcester Polytechnic Institute
• ML Tlachac, Bryant UniversityFollow
• Kevin Hickey, Worcester Polytechnic Institute
• Xingting Guo, Worcester Polytechnic Institute
• Shichao Liu, Worcester Polytechnic Institute
• Elke Rundensteiner, Worcester Polytechnic Institute

Document Type

Conference Proceeding

Comments

Conference paper for the 19th International Conference on Health Informatics. Published in Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2026) - Volume 4: HEALTHINF, pages 820-828.

Keywords

digital health; digital biomarkers; digital phenotyping; wearable technology

Identifier Data

10.5220/0014638300004070

Publisher

SCITEPRESS – Science and Technology Publications, Lda.

Rights Management

(CC BY-NC-ND 4.0) Proceedings Copyright © 2026 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract

College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited concerning the variety of psychological instruments administered, physiological modalities, and time series parameters. In this research, we collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic. We assess the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities. Our findings indicate potential in physiological modalities such as heart rate and sleep to screen for mental illness with the F1 scores as high as 0.79 for anxiety, the former modality reaching 0.77 for stress screening, and the latter modality achieving 0.78 for depression. This research highlights the potential of wearable devices to support continuous mental health monitoring, the importance of identifying best data aggregation levels, and appropriate modalities for screening for different mental ailments.