Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Abstract

Background Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time. Objective The aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data. Methods We constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients’ Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. Results A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). Conclusions Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial Registration Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

“…44 In recent studies, diagnostic measures [eg, sensitivity, specificity, area under the receiver operating characteristic curve (AUC)] are implemented in machine learning by extending the existing statistical approach for the selection criteria and prediction of PHQ-9. 13,47,54,[57][58][59] Machine learning is used in psychiatry to increase the accuracy of diagnosis and prognosis and make treatment and prevention decisions. 60,61 It is particularly useful for predicting human behavior, including high-risk behaviors, and it is effective for discriminating psychopathology.…”

“…For the diagnosis of depression and mood disorders, machine learning with excellent predictive suitability has been introduced. 57,110,113,116,117 Datasets collected in mobile settings are large; machine learning-based predictive models can analyze a large amount of data. This technique is useful for analyzing and conceptualizing multiple predictors.…”

Digital phenotype of mood disorders: A conceptual and critical review

“…They observed changes in GPS features, exercise duration, and use of active apps before the rise of depressive symptoms, suggesting a directional correlation between changes in behaviors and subsequent changes in depressive symptoms. Bai R. et al ( 42 ) recruited 334 patients with a major depressive disorder to a study using an app called “Mood Mirror” that allows active data and passive data collection (phone and wearable wristband) in order to classify patients between several mood states: steady remission, mood Swing (drastic or moderate), and steady depressed. They tested several combinations of data in order to achieve the best classification.…”

End-to-end design of wearable sensors

“…a Refs. Glucose-level prediction Dexcom G6+ Interstitial glucose concentration, electrodermal activity, skin temperature, activity DT 16 Yes, at home 271 Dexcom C4, Dexcom C7 plus, Medtronic iPro2 Glucose concentration NN 278 Yes, with follow-up visits 272 Abbott FreeStyle Libre Glucose concentration ARIMA, RF, SVM 25 Yes 273 Epilepsy management Empatica E4 Motor seizures DNN-LSTM 38 No, in controlled environment 274 Face action, fatigue and drowsiness monitoring Eyeglass platform with accelerometer, gyroscope and electrooculography sensors Facial action detection, blinks, percentage of eye closure CNN, LR 17 No, in controlled environment 275 , 276 Parkinson disease Six Opal IMU sensors Balance and gait features NN, SVM, kNN, DT, RF, GB, LR 524 patients with Parkinson disease and 43 patients with essential tremor No, in controlled environment 277 Great Lakes NeuroTechnologies wrist and ankle accelerometers Free movement gyroscope data Ensemble methods (LSTM, 1D CNN-LSTM, 2D CNN-LSTM) 24 No, in controlled environment 278 Mood disorder Mi Band 2 supported with clinician report, self-report and smartphone use log through app Daily phone usage, sleep data, step count data, self-evaluated mood scores of the user SVM, RF, kNN 334 Yes, with follow-up visits 279 Respiratory disorders and diseases Two wireless wearables attached to the chest (non-commercial) Respiratory behaviours RF 11 No, in controlled ...…”