  The Bluetooth sensor embedded in mobile phones provides an unobtrusive,
continuous, and cost-efficient means to capture individuals' proximity
information, such as the nearby Bluetooth devices count (NBDC). The continuous
NBDC data can partially reflect individuals' behaviors and status, such as
social connections and interactions, working status, mobility, and social
isolation and loneliness, which were found to be significantly associated with
depression by previous survey-based studies. This paper aims to explore the
NBDC data's value in predicting depressive symptom severity as measured via the
8-item Patient Health Questionnaire (PHQ-8). The data used in this paper
included 2,886 bi-weekly PHQ-8 records collected from 316 participants
recruited from three study sites in the Netherlands, Spain, and the UK as part
of the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8
score, we extracted 49 Bluetooth features, including statistical features and
nonlinear features for measuring periodicity and regularity of individuals'
life rhythms. Linear mixed-effect models were used to explore associations
between Bluetooth features and the PHQ-8 score. We then applied hierarchical
Bayesian linear regression models to predict the PHQ-8 score from the extracted
Bluetooth features. A number of significant associations were found between
Bluetooth features and depressive symptom severity. Compared with commonly used
machine learning models, the proposed hierarchical Bayesian linear regression
model achieved the best prediction metrics, R2= 0.526, and root mean squared
error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the
variance in the PHQ-8 score relative to the baseline model without Bluetooth
features (R2=0.338, RMSE = 4.547).
