TY - JOUR
T1 - Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
AU - Moshe, Isaac
AU - Terhorst, Yannik
AU - Opoku Asare, Kennedy
AU - Sander, Lasse Bosse
AU - Ferreira, Denzil
AU - Baumeister, Harald
AU - Mohr, David C.
AU - Pulkki-Råback, Laura
N1 - Funding Information:
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.
Publisher Copyright:
© Copyright © 2021 Moshe, Terhorst, Opoku Asare, Sander, Ferreira, Baumeister, Mohr and Pulkki-Råback.
PY - 2021/1/28
Y1 - 2021/1/28
N2 - 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.
AB - 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.
KW - anxiety
KW - depression
KW - digital phenotyping
KW - mobile sensing
KW - predicting symptoms
UR - http://www.scopus.com/inward/record.url?scp=85101009263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101009263&partnerID=8YFLogxK
U2 - 10.3389/fpsyt.2021.625247
DO - 10.3389/fpsyt.2021.625247
M3 - Article
C2 - 33584388
AN - SCOPUS:85101009263
SN - 1664-0640
VL - 12
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
M1 - 625247
ER -
