Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

作     者：Sun, Shaoxiong Folarin, Amos A. Zhang, Yuezhou Cummins, Nicholas Garcia-Dias, Rafael Stewart, Callum Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Laiou, Petroula Sankesara, Heet Matcham, Faith Leightley, Daniel White, Katie M. Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Nica, Raluca Rintala, Aki Mohr, David C. Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W.J.H. Vairavan, Srinivasan Narayan, Vaibhav A. Annas, Peter Hotopf, Matthew Dobson, Richard J.B. 

作者机构：Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Faculty of Medicine and Health Sciences Universitat de Barcelona Barcelona Spain Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom RADAR-CNS Patient Advisory Board King's College London London United Kingdom The Romanian League for Mental Health Bucharest Romania Department of Neurosciences Center for Contextual Psychiatry Katholieke Universiteit Leuven Leuven Belgium Physical Activity and Functional Capacity Research Group Faculty of Health Care and Social Services LAB University of Applied Sciences Lahti Finland Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States South London and Maudsley NHS Foundation Trust London United Kingdom Janssen Research and Development LLC TitusvilleNJ United States Davos Alzheimer's Collaborative WaynePA United States H Lundbeck A/S Copenhagen Denmark 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Smartphones 

摘      要：Background: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include 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. Objective: We aimed to address these 3 challenges to inform future work in stratified analyses. Methods: Using smartphone and wearable data collected 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