  Background: Loneliness among students is increasing across the world, with
potential consequences for mental health and academic success. To address this
growing problem, accurate methods of detection are needed to identify
loneliness and to differentiate social and emotional loneliness so that
intervention can be personalized to individual need. Passive sensing technology
provides a unique technique to capture behavioral patterns linked with distinct
loneliness forms, allowing for more nuanced understanding and interventions for
loneliness.
  Methods: To differentiate between social and emotional loneliness using
digital biomarkers, our study included statistical tests, machine learning for
predictive modeling, and SHAP values for feature importance analysis, revealing
important factors in loneliness classification.
  Results: Our analysis revealed significant behavioral differences between
socially and emotionally lonely groups, particularly in terms of phone usage
and location-based features , with machine learning models demonstrating
substantial predictive power in classifying loneliness levels. The XGBoost
model, in particular, showed high accuracy and was effective in identifying key
digital biomarkers, including phone usage duration and location-based features,
as significant predictors of loneliness categories.
  Conclusion: This study underscores the potential of passive sensing data,
combined with machine learning techniques, to provide insights into the
behavioral manifestations of social and emotional loneliness among students.
The identification of key digital biomarkers paves the way for targeted
interventions aimed at mitigating loneliness in this population.
