Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach
Journal:
arXiv
Published Date:
May 21, 2025
Abstract
College students are increasingly affected by stress, anxiety, and
depression, 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 a smartwatch sensor
and machine learning (ML) 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 full suite of
interventions or a control group using the app solely for real-time stress
logging and weekly 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. Similarly,
secondary outcomes of psychological assessments, including the Generalized
Anxiety Disorder-7 (GAD-7) for anxiety, the Patient Health Questionnaire
(PHQ-8) for depression, and the Perceived Stress Scale (PSS), were also
analyzed via GLMM. The finding of the objective measure, MS, indicates a
substantial decrease in MS among the treatment group compared to the control
group, while no notable between-group differences were observed in subjective
scores of anxiety (GAD-7), depression (PHQ-8), or stress (PSS). However, the
treatment group exhibited a clinically meaningful decline in GAD-7 and PSS
scores. 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 like
depression.
