Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Abstract

Background: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening.

Objective: The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population.

Methods: This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds.

Results: A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression.

Conclusions: Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

Keywords: circadian rhythm; depression; digital biomarkers; fitness tracker; heart rate; machine learning; rest-activity rhythm; screening; wearable electronic device.

Figure 1

Average 24-hour profiles of heart rate and steps measured by the wearable activity tracker. The purple and orange lines represent the means and the gray shaded areas represent the SDs. bpm: beats per minute.

Figure 2

Scatterplot of 9-item Patient Health Questionnaire (PHQ-9) scores at two assessments (T1 and T2). The blue line is the linear projection of the relationship between two scores with CIs and the dashed diagonal line represents no change in scores between two assessments.

Figure 3

A and B. Performance evaluation of model A based on the contrasted subsample with default cutoff point (ie, either baseline or follow-up PHQ-9 score of ≥10). A. Confusion matrix of predicted and observed outcomes. B. Area under the curve (AUC) with 95% CI. C and D. Performance evaluation of model A based on the contrasted subsample comprised of the top 20% and bottom 20% of participants by average PHQ-9 score. C. Confusion matrix of predicted and observed outcomes. D. AUC with 95% CI. PHQ-9: 9-item Patient Health Questionnaire.

Figure 4

Relative importance of digital biomarkers averaged from four models. 15m, 30m, and 60m: 15- , 30-, and 60-minute time interval in which raw data were aggregated; AC: autocorrelation; cv: coefficient of variation; hr: heart rate based; ICV: interdaily coefficient of variation; IS: interdaily stability; NHR: nighttime heart rate in a specified 2-hour time interval (0204: 2 AM-4 AM; 0406: 4 AM-6 AM); sd: standard deviation; st: steps based; wd: weekdays based.

Figure 5

Digital biomarkers of depressed and healthy participants. All scatterplots show the contrasted subsample with the default cutoff point. Red dots represent depressed participants and blue dots represent healthy participants; the background coloring represents a decision boundary of linear discriminant analysis. A. IS.st.wd: weekday steps–based interdaily stability; NHR.0406.cv: variation of heart rate between 4 AM and 6 AM. B. peaks.st: daily steps–based peaks; NHR.0406.cv: variation of heart rate between 4 AM and 6 AM. C. ICV.hr.wd: interdaily coefficient of variation of heart rate on weekdays; AC.st.15m.wd: autocorrelation of weekday steps–based rhythm (steps aggregated in 15-minute intervals). D. ICV.hr.wd/peaks.st: interdaily coefficient of variation of heart rate on weekdays divided by daily steps–based peaks; IS.st.wd/NHR.0406.cv: weekday steps–based interdaily stability divided by variation of heart rate between 4 AM and 6AM.