Assessing the utility of digital health technology to improve our capacity to assess and intervene in depression
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When it comes to mental health, no country is considered developed. In the last decade, the burden of mental health disorders (MHD) has risen in all countries due to disparities in timely diagnosis and access to evidence-based treatments. Additionally, scientists, are still conducting research to understand the underlying mechanisms behind MHD. Part of the problem is that measures of symptom severity are all based on self-reports by patients and clinician observation often resulting in an imprecise measurement of MHD. Those that are more objective (e.g. MRI) are costly and not widely available, nor are they ecologically valid measures of behavior. Additionally, in-clinic assessments tend to be episodic and often miss capturing the lived experience of disease over time including the potential impact of social and environmental factors that are suspected to be linked to neurodevelopmental and psychological processes. To improve long term outcomes in MHD, there is a critical need to develop new ways to objectively assess specific underlying constructs of behavior patterns linked with neuropsychiatric conditions. The pervasive network of smartphones offers researchers a unique opportunity to study MH at a population scale and at a fraction of the cost of traditional clinical research. The high-frequency daily usage of smartphones also provides new ways to capture the individualized momentary experience of living with mental health issues based on “real-world data” (RWD) in an objective, momentary and nonreactive way. The principal findings of this dissertation research show the feasibility of utilizing smartphones to reach, enroll and engage a diverse and nationally representative population as well as the potential of using RWD in predicting mental health outcomes. The RWD collected from more than 2000 participants showed notable inter-/intra-person heterogeneity highlighting the challenges of developing a robust cohort level machine learning model to predict depression. However, personalized N-of-1 models show the promise of “precision digital psychiatry” by assessing an individual’s drifts from their own average “digital behavior” as a more reliable predictor of a person’s daily mood. Of note, participant enrollment and retention in large-scale digital health research studies remains a significant challenge. Cross study analysis using data from >100,000 participants showed significant underlying biases in technology access and utilization based on participants’ demographics that could impact the generalizability of the statistical inference drawn. In addition, the results from a survey-based study on a large and diverse sample show growing concerns among the general public about the security and privacy of their digital data which if left unaddressed can negatively influence people’s decision to participate and share data in digital health research. These findings are contemporary and extend the on-going efforts to objectively evaluate the potential fit of technology in psychiatry in engaging the general population to monitor their mental health in the real world outside the clinic. However, while the technology shows the promise to move the psychiatric research from subjective to objective measures, episodic to continuous monitoring, provider-based to ubiquitous and reactive to proactive care; accomplishing these goals does come with measurable challenges. Further research is needed to develop robust and validated digital biomarkers of behavioral health. This includes large scale behavioral phenotyping studies (N > 100,000) that are powered to detect the association between RWD and behavioral anomalies, the ability to integrate RWD across similar studies, improve equitable utilization of technology across a diverse and representative population and address people’s concerns about data security and privacy.
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