Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Author: Isaac Moshe1, Yannik Terhorst2,3, Kennedy Opoku Asare4, Lasse Bosse Sander5, Denzil Ferreira4, Harald Baumeister3, David C Mohr6, Laura Pulkki-Råback1
1 Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
2 Department of Research Methods, Ulm University, Ulm, Germany.
3 Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
4 Center for Ubiquitous Computing, University of Oulu, Oulu, Finland.
5 Department of Rehabilitation Psychology and Psychotherapy, Institute of Psychology, University of Freiburg, Freiburg, Germany.
6 Department of Preventive Medicine, Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, United States.
Conference/Journal: Front Psychiatry
Date published: 2021 Jan 28
Other: Volume ID: 12 , Pages: 625247 , Special Notes: doi: 10.3389/fpsyt.2021.625247. , Word Count: 314

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.

Keywords: anxiety; depression; digital phenotyping; mobile sensing; predicting symptoms.

PMID: 33584388 PMCID: PMC7876288 DOI: 10.3389/fpsyt.2021.625247