Loneliness and depression are interrelated mental health issues affecting students well-being. Using passive sensing data provides a novel approach to examine the granular behavioural indicators differentiating loneliness and depression, and the mediators in their relationship. This study aimed to investigate associations between behavioural features and loneliness and depression among students, exploring the complex relationships between these mental health conditions and associated behaviours. This study combined regression analysis, mediation analysis, and machine learning analysis to explore relationships between behavioural features, loneliness, and depression using passive sensing data, capturing daily life behaviours such as physical activity, phone usage, sleep patterns, and social interactions. Results revealed significant associations between behavioural features and loneliness and depression, emphasizing their interconnected nature. Increased activity and sleep duration were identified as protective factors. Distinct behavioural features for each condition were also found. Mediation analysis highlighted significant indirect effects in the relationship between loneliness and depression. The XGBoost model achieved the highest accuracy in predicting these conditions. This study demonstrated the importance of using passive sensing data and a multi-method approach to understand the complex relationship between loneliness, depression, and associated behaviours. Identifying specific behavioural features and mediators contributes to a deeper understanding of factors influencing loneliness and depression among students. This comprehensive perspective emphasizes the importance of interdisciplinary collaboration for a more nuanced understanding of complex human experiences.

翻译：孤独与抑郁是相互关联的心理健康问题，影响学生的幸福感。利用被动感知数据为探究区分孤独与抑郁的细粒度行为指标及其关系中的中介因素提供了新途径。本研究旨在考察大学生行为特征与孤独感、抑郁情绪之间的关联，探索这些心理健康状况与相关行为之间的复杂关系。本研究结合回归分析、中介效应分析与机器学习分析，利用被动感知数据（记录日常行为如身体活动、手机使用、睡眠模式及社交互动）探究行为特征、孤独感与抑郁情绪的关系。结果显示，行为特征与孤独感、抑郁情绪存在显著关联，凸显其相互关联性：增加活动量和睡眠时长被识别为保护因素，同时发现两种心理状态各自对应的独特行为特征。中介效应分析揭示了孤独与抑郁关系中的显著间接效应。XGBoost模型在预测这两种心理状态时达到最高准确率。本研究论证了利用被动感知数据及多方法路径理解孤独、抑郁与相关行为复杂关联的重要性。识别特定行为特征及中介因素有助于深化对学生孤独感与抑郁情绪影响机制的认识。这一综合性视角凸显了跨学科合作对细致理解人类复杂体验的重要意义。