Screening Students for Stress Using Fitbit Data

Authors

• Rebecca Lopez, Worcester Polytechnic Institute
• Avantika Shrestha, Worcester Polytechnic Institute
• Kevin Hickey, Worcester Polytechnic Institute
• Xingtong Guo, Bryant University
• ML Tlachac, Bryant UniversityFollow
• Shichao Liu, Worcester Polytechnic Institute

Document Type

Conference Proceeding

Comments

Conference paper presented at the 2024 IEEE International Conference on Big Data. 

Keywords

digital health; digital biomarkers; digital phenotyping; wearable technology

Identifier Data

10.1109/BigData62323.2024.10825089

Publisher

IEEE

Rights Management

Copyright © 2024, IEEE

Abstract

The pressures faced by college students frequently lead to heightened levels of stress. Wearable devices, which collect sensor data in a non-intrusive manner, present an opportunity for early detection of stress. Nonetheless, there is a lack of diversity in current research concerning psychological assessments, physiological metrics, and time series features. In this work, we utilize a Fitbit dataset and evaluate its use in predicting stress through machine learning. Our results demonstrate that physiological data such as calories burned and sleep hold promise for stress screening, with F1 scores reaching up to 0.81. These findings illustrate the potential of wearable technology for continuous stress monitoring and emphasize the need for selecting appropriate data aggregation levels and physiological modalities for effective screening.