University of Oulu

Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021). Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data. Frontiers in Psychiatry, 12.

Predicting symptoms of depression and anxiety using smartphone and wearable data

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Author: Moshe, Isaac1; Terhorst, Yannik2,3; Opoku Asare, Kennedy4;
Organizations: 1Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
2Department of Research Methods, Ulm University, Ulm, Germany
3Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany
4Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
5Department of Rehabilitation Psychology and Psychotherapy, Institute of Psychology, University of Freiburg, Freiburg, Germany
6Department of Preventive Medicine, Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, United States
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: Frontiers Media, 2021
Publish Date: 2021-04-14


Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.

Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.

Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants’ location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.

Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.

Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

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Series: Frontiers in psychiatry
ISSN: 1664-0640
ISSN-E: 1664-0640
ISSN-L: 1664-0640
Volume: 12
Article number: 625247
DOI: 10.3389/fpsyt.2021.625247
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
3124 Neurology and psychiatry
Funding: DF and KO are partly funded by the Academy of Finland (Grants 316253 - SENSATE, 320089 - SENSATE, and 318927 - 6Genesis Flagship) and the Infotech Institute at the university of University of Oulu. DM was supported by National Institute of Mental Health grants P50 MH119029 and R01 MH111610. LP-R was supported by grants from The Jenny and Antti Wihuri Foundation and the Yrjö Jahnsson Foundation.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
Copyright information: © 2021 Moshe, Terhorst, Opoku Asare, Sander, Ferreira, Baumeister, Mohr and Pulkki-Råback. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.