A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Abstract

Mental health disorders are ambiguously defined and diagnosed. The established diagnosis technique, which is based on structured interviews, questionnaires and data subjectively reported by the patients themselves, leaves the mental health field behind other medical areas. We support these statements with examples from major depressive disorder (MDD). The National Institute of Mental Health (NIMH) launched the Research Domain Criteria (RDoC) project in 2009 as a new framework to investigate psychiatric pathologies from a multidisciplinary point of view. This is a good step in the right direction. Contemporary psychiatry considers mental illnesses as diseases that manifest in the mind and arise from the brain, expressed as a behavioral condition; therefore, we claim that these syndromes should be characterized primarily using behavioral characteristics. We suggest the use of smartphones and wearable devices to passively collect quantified behavioral data from patients by utilizing digital biomarkers of mental disorder symptoms. Various digital biomarkers of MDD symptoms have already been detected, and apps for collecting this longitudinal behavioral data have already been developed. This quantified data can be used to determine a patient’s diagnosis and personalized treatment, and thereby minimize the diagnosis rate of comorbidities. As there is a wide spectrum of human behavior, such a fluidic and personalized approach is essential.

Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation

“…These methods can be prone to bias and subjectivity. The use of digital markers of continuous daily behavioral monitoring as an objective indicator to detect depression represents a promising supplementary approach [ 13 ].…”

“…However, using multiple scales can lead to participants taking too long to complete the questionnaire, reducing its validity. Furthermore, understanding daily behavior features requires repeated behavioral monitoring over an extended period, while retrospective reports from a single point in time may not accurately reflect true behavior [ 13 ].…”

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

“…As an example, there is a correlation between circadian rhythm, step counts, or heart rate variability and the diagnosis of a mood disorder or mood episode [45][46][47][48]. Other correlations have been found between data and symptoms of schizophrenia [49][50][51], major depression [52][53][54][55][56][57], mood disorders [46,58,59], posttraumatic stress disorder [60,61], generalized anxiety disorder [62], suicidal thoughts [63,64], sleep disorders [65], addiction [66], stress [53,67], postpartum [68,69], autism [70], and child and adolescent psychiatry [71,72]. Among other examples of the efficiency of DP for prediction or diagnosis in mental health, Instagram photos or Facebook language have been found to be predictors of depression [73,74]; suicidal risk could be assessed from social media [75,76] with increasing precision if DP would integrate electronic health records data [77,78]; automated analysis of free speech can measure relevant mental health changes in emergent psychosis [79] or incoherence in speech in schizophrenia [80].…”

AI-Enabled Clinical Decision Support Tools for Mental Healthcare: A Product Review

“…Both tools employ AI algorithms to generate a personalized patient report for practitioners that ranks psychiatric drugs according to the predicted likelihood of a patient's response. The report also provides insights into how patients might metabolize certain medications and predicts associated side effects and recommended dosages (Taliaz, 2023;Taliaz & Souery, 2021).…”