Kennedy Opoku Asare, Isaac Moshe, Yannik Terhorst, Julio Vega, Simo Hosio, Harald Baumeister, Laura Pulkki-Råback, Denzil Ferreira, Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis, Pervasive and Mobile Computing, Volume 83, 2022, 101621, ISSN 1574-1192, https://doi.org/10.1016/j.pmcj.2022.101621
Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status : a longitudinal data analysis
|Author:||Opoku Asare, Kennedy1; Moshe, Isaac2; Terhorst, Yannik3;|
1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
2Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
3Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany
4Department of Medicine, University of Pittsburgh, Pittsburgh, USA
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022061345959
|Publish Date:|| 2022-06-13
Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring.
Pervasive and mobile computing
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work is supported by the Academy of Finland [Grants 316253 - SENSATE, 320089-SENSATE] and the Infotech Institute at the University of Oulu, Finland. KOA was supported by the Nokia Foundation, Finland. LP-R was supported by The Jenny and Antti Wihuri Foundation and the Yrjö Jahnsson Foundation, Finland. IM was supported by the Finnish Foundation for Psychiatric Research, Finland.
|Academy of Finland Grant Number:||
316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).