Depression imposes a notable societal burden, with limited treatment success despite multiple available psychotherapy and medications choices. Potential reasons may include the heterogeneity of depression diagnoses and the presence of comorbid anxiety symptoms. Despite technological advances and the introduction of many mobile phone applications (apps) claiming to relieve depression, major gaps in knowledge still exist regarding what apps truly measure and how they correlate with psychometric questionnaires. The goal of this study was to evaluate whether mobile daily mood self-ratings may be useful in monitoring and classifying depression symptoms in a clinically depressed population compared with standard psychometric instruments including the Patient Health Questionaire-9 (PHQ-9), the Hamilton Rating Scale for Depression (HAM-D), and the Hamilton Anxiety Rating Scale (HAM-A).
For this study, 22 patients with major depressive disorder with or without comorbid anxiety disorder were recruited. The diagnosis of depression was confirmed through the Mini International Neuropsychiatric Interview (MINI). Over an 8-week period, daily moods were self-reported through the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) application, a custom-designed application that was downloaded onto patients’ mobile devices. Depression and anxiety symptoms were also measured biweekly using the HAM-D, HAM-A, and PHQ-9.
Significant correlations were observed among self-evaluated mood, daily steps taken, SMS (text) frequency, average call duration, and biweekly psychometric scores (|r|>0.5, P<0.05). The correlation coefficients were higher in individuals with more severe depressive symptoms.
Although this study, given its limited sample size, was exploratory in nature, it helps fill a significant gap in our knowledge of the concordance between ratings obtained on the Ham-D, Ham-A, and the PHQ-9 psychometric instruments and data obtained via a smartphone app. These questionnaires represent gold-standard, commonly used psychiatric research/clinical instruments, and, thus, this information can serve as a foundation for digital phenotyping for depression and pave the way for interventional studies using smartphone applications.
MOUKADDAM, TRUONG, SHAH: Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX
CAO, SABHARWAL: Department of Electrical and Computer Engineering, Rice University, Houston, TX
The authors declare no conflicts of interest.
Please send correspondence to: Nidal Moukaddam, MD, PhD, Harris Health System/Neuropsychiatric Center, 1504 Taub Loop, Houston TX 77030 (e-mail: firstname.lastname@example.org).
Online date: September 6, 2019