Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study.

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 K 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 James Butler Dobson

Published in: JMIR mHealth and uHealth (2021)

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.

Keyphrases

mental health
electronic health record
bipolar disorder
big data
stress induced
healthcare
primary care
machine learning
radiation therapy
quality improvement
artificial intelligence