  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.
