Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks

Abstract

We describe the design and evaluation of a sub-clinical digital assessment tool that integrates digital biomarkers of depression. Based on three standard cognitive tasks (D2 Test of Attention, Delayed Matching to Sample Task, Spatial Working Memory Task) on which people with depression have been known to perform differently than a control group, we iteratively designed a digital assessment tool that could be deployed outside of laboratory contexts, in uncontrolled home environments on computer systems with widely varying system characteristics (e.g., displays resolution, input devices). We conducted two online studies, in which participants used the assessment tool in their own homes, and completed subjective questionnaires including the Patient Health Questionnaire (PHQ-9)—a standard self-report tool for assessing depression in clinical contexts. In a first study (n = 269), we demonstrate that each task can be used in isolation to significantly predict PHQ-9 scores. In a second study (n = 90), we replicate these results and further demonstrate that when used in combination, behavioral metrics from the three tasks significantly predicted PHQ-9 scores, even when taking into account demographic factors known to influence depression such as age and gender. A multiple regression model explained 34.4% of variance in PHQ-9 scores with behavioral metrics from each task providing unique and significant contributions to the prediction.

Digital health technologies and major depressive disorder

“…70 Several studies have demonstrated the potential of using digital biomarkers (based on sensor data from wearable devices or mobile apps) for the prediction and detection of depression. [71][72][73] There are also opportunities to develop online and mobile apps for the prevention of depression. 74 However, it should be noted that unlike conditions such as diabetes, where significant adoption of digital technologies has been observed, depression is more complex due to the unique challenges associated with the clinical management of the disorder (eg, poor mental health literacy, stigma, and the lack of objective tools for assessment and diagnosis).…”

Extracting Digital Biomarkers for Unobtrusive Stress State Screening from Multimodal Wearable Data

“…Many related works exist in the literature about biomarkers for various physiological and psychological states [7,8,9,10,11,12]. In [7], the authors examined the behavioral markers to track physical activity following hospital discharge via data from wearable devices.…”

“…In [8], researchers examined biomarkers of depression state outside of the laboratory. Similar to [7], they collected data with wearable devices and stateof-the-art questionnaires, namely Patient Health Questionnaire (PHQ-9), as a ground truth.…”

“…In another study [10], the main idea was to extract the underlying effects of seasons and weather parameters on the sleep data. Similar to the previous work [8], they utilized regression-based modeling. They focused on continuous data collection via wearable devices to differentiate their study from state-ofthe-art.…”

Research on a New Intelligent and Rapid Screening Method for Depression Risk in Young People Based on Eye Tracking Technology

“…However, it is difficult to accurately and dynamically assess the attention bias of patients with depression through traditional structured clinical interviews and self-report measures. To provide an early warning for patients with depression more objectively, intelligently, and accurately, the application of "digital biomarkers" under specific tasks captured by digital equipment in the field of depression screening is attracting the attention of more and more scholars [17][18][19]. Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured through digital devices [20].…”