Research Overview

One robust finding in depression research is that life stress is the single most important trigger of depressive episodes. Understanding how life stress leads to depression has the potential to transform our ability to prevent and treat depression. Unfortunately, the capacity to capture the effects of stress accurately and in real-time has been limited because assessments of psychiatric phenotypes has traditionally relied on patients’ self-report of symptoms. Mobile electronic technology holds great promise in overcoming these limitations by capturing continuous, real-time, passive measures likely to be related to the progression from stress to depression. Dr. Sen’s research has demonstrated that the onset of stress and depression can be prospectively predicted in a large group of individuals: medical interns. Medical internship, the first year of professional medical training, is characterized by long work hours, emotionally difficult situations and inconsistent and insufficient sleep. Internship is a rare situation whereby the onset of a major, uniform stressor and a dramatic increase in depressive symptoms can be accurately predicted.

In this project, the research team will determine the dynamic relationships between mood, sleep, and circadian rhythms, and develop a micro-randomized intervention trial for depression under stress using an app platform that integrates mobile phones signals and wearable data. The team will build personalized models of sleep, circadian rhythms, physical activity, and mood, and assess and optimize the efficacy of objective mobile sensor to detect depression onset.  The researchers believe that when real-time data on an individual is fit to the right mathematical model, simple and insightful information can be easily communicated back to the end user as well as their caregiver and/or domain expert.  The clinical aim, therefore, is to allow individuals at risk to be identified early and provided the right treatment at the right time, and empower the individuals to manage health and remain engaged with the mobile health technology.

The impact of this project goes beyond depression.  Advanced tools and models with large-scale computing power and data integration capacity are critical in studying physiology, psychosocial behavior and environment in real-time, and their role in defining risk for mental illness.  The research team’s multi-disciplinary approach in analyzing real-time, multi-modal mobile data can be generalized to the prediction of onset and preventive treatment of many other diseases.

Updates

  • The team recruited 2,051 subjects in the mobile aim of the study from the cohort starting internship in July 2018.
  • With agreements with Fitbit and Mindstrong, these subjects will be passively and continually assessed ​for sleep time, sleep stages, heart rate variability, physical activity and cognitive function.
  • All subjects are also enrolled in microrandomized feedback to assess the efficacy of personalized timing of mobile data feedback.
  • The team has received the following external grants that are directly related to their MIDAS project:
    • Identifying Digital Cognitive Predictors of Depression under Stress (MindStrong Health)
    • Broad Scale Genomic Analysis to Find Genes Associated with Depression Under Stress (NIH)

(July 2018)

Previous updates

The team is focusing on recruiting the first cohort of participants; we have recruited 404 participants into the mobile aim of the study with a goal of 500-550.  The team has also added a passive cognitive assessment component to the study through a collaboration with Mindstrong.

(July 2017)

Research Team

  • Srijan Sen, Associate Professor, Department of Psychiatry and Molecular and Behavioral Neuroscience Institute
  • Margit Burmeister, Professor, Departments of Computational Medicine and Bioinformatics, Human Genetics, Psychiatry and Molecular and Behavioral Neuroscience Institute
  • Olivia Walch, postdoctoral fellow, Department of Neurology
  • Daniel Forger, Professor, Departments of Mathematics and Computational Medicine and Bioinformatics
  • Ambuj Tewari, Associate Professor, Department of Statistics, and Department of Electrical Engineering and Computer Science
  • Zhenke Wu, Assistant Professor, Department of Biostatistics and Michigan Institute for Data Science