Presentation
Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach
DescriptionCollege students are increasingly affected by stress, yet face barriers to traditional mental health care. This study evaluated the efficacy of a mobile health (mHealth) intervention, Mental Health Evaluation and Lookout Program (mHelp), which integrates smartwatch sensor and machine-learning algorithms for real-time stress detection and self-management. In a 12-week randomized controlled trial (n=117), participants were assigned to a treatment group using mHelp’s suite of interventions or a control group using the app solely for stress logging and psychological assessments. The primary outcome, “Moments of Stress” (MS) was assessed via physiological and self-reported indicators and analyzed using Generalized Linear Mixed Models (GLMM) approaches. Secondary assessments: Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire (PHQ-8), and Perceived Stress Scale (PSS), were also analyzed via GLMM. The findings indicate a substantial decrease in MS among the treatment group compared to the control group, while no notable between-group differences were observed in subjective measures of anxiety (GAD-7), depression (PHQ-8), or stress (PSS). However, the treatment group exhibited a clinically meaningful decline in GAD-7 and PSS. These findings underscore the potential of wearable-enabled mHealth tools to reduce acute stress in college populations and highlight the need for extended interventions and tailored features to address chronic symptoms.
Event Type
Lecture
TimeTuesday, October 14th3:40pm - 4pm CDT
LocationGrand Hall K
Student Forum
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