[论文解读] Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach

Alan Ta, Nilsu Salgin+6 more|ArXiv.org|May 21, 2025

Emotion and Mood Recognition被引用 3

一句话总结

评估用于实时压力检测与自我管理的可穿戴与机器学习驱动的移动健康干预（mHELP），在12周随机试验（n=117）中对大学生的效果。

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.

研究动机与目标

• 研究可穿戴设备驱动的mHealth干预是否能够在大学生中实现实时压力检测。
• 评估mHELP在降低 Moments of Stress (MS) 方面的有效性，相对于仅记录的对照。
• 使用既定量表评估次级心理结果（焦虑、抑郁、感知压力）。
• 探讨实时监测是否转化为自我报告心理健康的改善。

提出的方法

• 12周随机对照试验，参与者117名。
• 将参与者分配至治疗组（完整的mHELP套件）或对照组（实时日志记录与每周评估）。
• 使用可穿戴智能手表传感器检测压力；ML算法进行实时压力检测。
• 使用广义线性混合模型（GLMM）分析主要和次要结果。
• 主要结局：从生理与自我报告指标推导的 Moments of Stress (MS)。
• 次要结局：GAD-7（焦虑）、PHQ-8（抑郁）、PSS（感知压力）。

实验结果

研究问题

• RQ1 与仅日志对照相比，完整的mHELP干预是否在实时降低 Moments of Stress（MS）？
• RQ2 mHELP对焦虑（GAD-7）、抑郁（PHQ-8）及感知压力（PSS）的影响如何？
• RQ3 可穿戴驱动的mHealth工具是否能有效支持大学生的压力自我管理？
• RQ4 在12周内两组在急性与慢性心理健康症状方面是否存在差异？

主要发现

• 治疗组相对于对照组在 Moments of Stress 上有显著降低。
• 在GAD-7、PHQ-8或PSS分数上未观察到显著的组间差异。
• 治疗组在GAD-7和PSS分数上展现出临床意义的下降。
• 结果表明可穿戴驱动的mHealth工具在降低大学生急性压力方面具有潜力。
• 结果提示需要延长干预时间并为慢性症状如抑郁定制功能。