Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D, Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study, JMIR Mhealth Uhealth 2021;9(7):e26540
Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis : exploratory study
|Author:||Opoku Asare, Kennedy1; Terhorst, Yannik2; Vega, Julio3;|
1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
2Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany
3Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
4Department of Computer Science, University of Helsinki, Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021092146687
|Publish Date:|| 2021-09-21
Background: Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression.
Objective: The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression.
Methods: Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8–86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression.
Results: Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants‘ age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status–normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status–normalized entropy and depression (β=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%–92.51%; recall, 92.19%–95.56%; F1, 88.73%–94.00%; area under the curve receiver operating characteristic, 94.69%–99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%–98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression.
Conclusions: Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.
JMIR mHealth and uHealth
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research is supported by the Academy of Finland 6Genesis Flagship (Grant No. 318927), SENSATE (Grant Nos 316253, 320089), Infotech Institute University of Oulu Emerging Project, and Nokia Foundation (Jorma Ollila Grant for EP). We thank the Carat Project for making their data set available for this study, and all participants who contributed to the Carat Project.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
©Kennedy Opoku Asare, Yannik Terhorst, Julio Vega, Ella Peltonen, Eemil Lagerspetz, Denzil Ferreira. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 12.07.2021This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.