Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
Abstract
A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. From 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.
Keywords
Cite
@article{arxiv.2212.10540,
title = {Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis},
author = {Shaoxiong Sun and Amos A. Folarin and Yuezhou Zhang and Nicholas Cummins and Rafael Garcia-Dias and Callum Stewart and Yatharth Ranjan and Zulqarnain Rashid and Pauline Conde and Petroula Laiou and Heet Sankesara and Faith Matcham and Daniel Leightley and Katie M. White and Carolin Oetzmann and Alina Ivan and Femke Lamers and Sara Siddi and Sara Simblett and Raluca Nica and Aki Rintala and David C. Mohr and Inez Myin-Germeys and Til Wykes and Josep Maria Haro and Brenda W. J. H. Penninx and Srinivasan Vairavan and Vaibhav A. Narayan and Peter Annas and Matthew Hotopf and Richard J. B. Dobson},
journal= {arXiv preprint arXiv:2212.10540},
year = {2023}
}