Machine-learning enabled real-time stress monitoring, detection, and management in college students: a wearable technology approach

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. Here, ML refers to the app’s embedded stress detection algorithm. In a 12-week randomized controlled trial, 117 university student iPhone users with moderate symptoms of anxiety were assigned via computer-generated sequence 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 using both physiological and self-reported indicators and analyzed via Generalized Linear Mixed Models (GLMM) approaches. 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 significant decrease in MS among the treatment group compared to the control group (βStd = −0.10, p < .001). While no notable between-group differences were observed in subjective scores of anxiety (GAD-7), depression (PHQ-8), or stress (PSS), the treatment group exhibited 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.