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Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression
Lydia Sequeira1, Marco Battaglia2, Steve Perrotta3
1Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Ontario, Canada.
Abstract:
With an estimated 75% of all mental disorders beginning in the first two decades of life,1 childhood and adolescence are crucial developmental periods to identify and intercept the unfolding of mental health problems, their relationships with physical health, and the multiple, interwoven connections to the surrounding environment.2 Because an individual's mental health is best conceptualized, captured, and treated by taking into account the network of physiological and social functions that constitute the context of individual experience, accessing and analyzing data on multiple health indicators simultaneously can accelerate prediction of disease progression. With the advent of new technologies, dense and extensive amounts of biopsychosocial readouts that can be translated into clinically relevant information have become available in real time, with the potential to revolutionize the practice of medicine. However, challenges to this more ecological and comprehensive approach to mental health measurement include the actual capacity of capturing, safely storing, and analyzing dense data sets (encompassing, for example, mood, cognitions, physical activity, sleep, social interactions) from multiple synchronized sources, and identifying which among multiple indicators ultimately prove useful to improve prediction of a deterioration in symptoms and of initiating early intervention. In this Translations article, we focus on digital phenotyping (DP), which relates to the capturing of the aforementioned relevant biopsychosocial data. This concept is rapidly growing and gaining relevance to child and adolescent psychiatry, and is connected with overarching data science themes of "big data" (extremely large data sets, including data from electronic medical records, imaging, genomics, and patients' smartphones),3,4 in addition to "machine learning" (the science of getting computers to act without being explicitly programmed)5 and "precision medicine" (the practice of custom tailoring treatments to a patient's disease processes),6 which have all received attention in this journal. We will describe principles and current applications of DP, together with its potential to facilitate improved outcomes and its limits, using depression in children and adolescents as an illustrative example.
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