Integrating digital phenotyping in clinical characterization of individuals with mood disorders

• Published in: Neuroscience and biobehavioral reviews Vol. 104; pp. 223 - 230
• Main Authors: Brietzke, Elisa, Hawken, Emily R., Idzikowski, Maia, Pong, Janice, Kennedy, Sidney H., Soares, Claudio N.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2019
• Subjects: Apps; Biomarkers; Bipolar disorder; Bipolar Disorder - diagnosis; Cognition; Depressive Disorder, Major - diagnosis; Digital phenotyping; Ecological Momentary Assessment; Functioning; Humans; Individuality; Major depressive disorder; Medical Informatics Applications; Mood disorders; Phenotype; Prediction; Digital phenotyping; Ecological momentary assessment; Prediction; Mood disorders; Bipolar disorder; Cognition; Apps; Major depressive disorder; Functioning
• ISSN: 0149-7634; 1873-7528
• DOI: 10.1016/j.neubiorev.2019.07.009

•Digital phenotyping is the continuous acquisition of data on human‐machine interactions and behaviors via smartphones/personal devices.•Using digital phenotypes for the assessment of mood disorders may help identify clinically meaningful inter/intra-individual variabilities.•Integration of digital phenotypes with other traditional biomarkers may increase the predictive power of critical outcomes in mood disorders. Major Depressive Disorder (MDD) and bipolar disorder (BD) are still under recognized and undertreated, especially in primary care settings. One of the challenges faced by clinicians is the remarkable inter-individual variability among patients with these conditions. In addition, each patient with MDD and BD experiences a unique pattern of longitudinal changes across time, i.e., intra-individual variability can also be problematic. The immense amount of data generated and collected through the use of smartphones or personal devices offers an opportunity to obtain continuous and reliable information on each individual’s behavior, a less burdensome way to capture both intra and inter-individual variability over time. Digital phenotypes (DP) are a promising strategy to be integrated with other “Omics” platforms for prediction of relevant outcomes in mood disorders, including but not restricted to, relapse, recurrence, cognitive decline and functional impairment. Despite existing limitations and some skepticism, digital phenotyping represents a field in great expansion and might eventually constitute a feasible strategy in biomarkers research for mood disorders.