Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study
Year: 2021
Type: article
Abstract: BACKGROUND Research in mental health has found associations between depression and individuals’ behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. OBJECTIVE This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). METHODS The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the 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. RESULTS A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a 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). CONCLUSIONS Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals’ behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.
Source: JMIR mhealth and uhealth
DOI: 10.2196/29840
Authors: Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Faith Matcham, Carolin Oetzmann, Femke Lamers, Sara Siddi, Sara Simblett, Aki Rintala, David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W J H Penninx, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard J B Dobson
Institutions: King's College London, University College London Hospitals NHS Foundation Trust, South London and Maudsley NHS Foundation Trust, UCL Biomedical Research Centre, Health Data Research UK, University College London, Amsterdam Neuroscience, Amsterdam Public Health, GGZ inGeest, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Centro de Investigación Biomédica en Red de Salud Mental, Centro de Investigación Biomédica en Red, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, LAB University of Applied Sciences, KU Leuven, Northwestern University, Lundbeck (Denmark)
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,850
Funders: South London and Maudsley NHS Foundation Trust, Wellcome Trust, King's College Hospital NHS Foundation Trust, Scottish Government, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, Alzheimer's Society, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, King's College London, University College London, European Commission, European Society of Cardiology, Public Health Agency, ZonMw, Innovative Medicines Initiative, Institut Català de la Salut, GIESKES-STRIJBIS FONDS, Chief Scientist Office, Scottish Government Health and Social Care Directorate, NIHR Maudsley Biomedical Research Centre, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division, National Institute for Health Research Applied Research Collaboration South London, National Institute of Mental Health, Health Data Research UK
Awards: G1175529158, G1216450129, G1356971313, Remote Assessment of Disease and Relapse in Central Nervous System Disorders, LifeSense: Transforming Behavioral Assessment of Depression Using Personal Sensing Technology, Technology Enabled Services for Coordinated Care of Depression in Healthcare Settings, G2259971274, Big Data 4 Better Hearts - Sofia ref.: 116074, NIHR Senior Investigator Award, G434337789, G4652242808, G7376789991
OpenAlex ID: W3172601496