Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

TY - UNPB

T1 - Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones

T2 - A Preliminary Longitudinal Study

AU - Zhang, Yuezhou

AU - Folarin, Amos A

AU - Sun, Shaoxiong

AU - Cummins, Nicholas

AU - Ranjan, Yatharth

AU - Rashid, Zulqarnain

AU - Conde, Pauline

AU - Stewart, Callum

AU - Laiou, Petroula

AU - Matcham, Faith

AU - Oetzmann, Carolin

AU - Lamers, Femke

AU - Siddi, Sara

AU - Simblett, Sara

AU - Rintala, Aki

AU - Mohr, David C

AU - Myin-Germeys, Inez

AU - Wykes, Til

AU - Haro, Josep Maria

AU - Pennix, Brenda WJH

AU - Narayan, Vaibhav A

AU - Annas, Peter

AU - Hotopf, Matthew

AU - Dobson, Richard JB

PY - 2021/4/26

Y1 - 2021/4/26

N2 - 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).

AB - 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).

KW - stat.ML

KW - cs.LG

M3 - Preprint

BT - Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones

ER -