Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology Abstract. Developing a better understanding of how life stress leads to the onset of psychiatric disorders, such as major depression has the potential to transform our ability to prevent and treat these disorders. Unfortunately, the capacity to capture the effects of stress accurately and in real-time has been limited because the assessment of psychiatric phenotypes has traditionally relied on the long-term recall and self-report of symptoms by affected individuals. 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 disease. Unfortunately, the results of mobile electronic technology studies conducted, to date, have been limited, in large part because it is difficult to anticipate the onset of stress and depression. To advance, there is a critical need to understand the temporal relationship between stress and depression with real-time, objective measures. Our lab has demonstrated that we can prospectively predict the onset of stress and depression in a large group of individuals utilizing the medical internship paradigm. 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; we can accurately predict that a cohort will experience the onset of a major, uniform stressor and a dramatic increase in depressive symptoms1, 2. This model allows for the same individuals to be followed, first under normal conditions and then under the conditions of high stress. Over the past 10 years, we have enrolled over 13,000 interns from 55 U.S. institutions and are enrolling 3,000-3,500 new interns each year. We have found that rates of depression increase dramatically, from 4% prior to internship to 26% during internship year. Here, we propose to utilize existing and developing technology (see support letter from Dr. Insel, Google/Verily) to examine physiology, behavior, environment, spatial, and temporal dimensions in real-time, and establish how, together, they contribute to the relationship between stress and onset of depression. Specifically we aim to: 1) identify mobile electronic technology factors that change with stress, and 2) develop a weighted mobile electronic technology factor that predicts depressive mood. We believe that completion of these aims will have profound impact in multiple domains: 1) most directly, detecting signature predictors of depression risk will inform micro-randomized intervention trials so that individuals at risk are identified early and provided the right treatment at the right time 2) better temporal and dynamic understanding of how risk factors (such as sleep perturbations) lead to depression, guiding development of new and more effective interventions 3) finally, identifying mobile signatures of depression risk will provide richer and more effective phenotypes that will facilitate genetic and biomarker studies of depression and stress. Principal Investigator: Co-Principal Investigator: Srijan Sen, M.D., Ph.D. Margit Burmeister, Ph.D. Associate Professor of Psychiatry, Medical School Research Professor, MBNI Research Associate Professor, Molecular and Behavioral Associate Chair and Professor, Department of Neuroscience Institute (MBNI) Computational Medicine & Bioinformatics Associate Chair for Research/Research Faculty Devel Professor, Department of Psychiatry Email: [email protected]; Tel.: (734) 615-8666 Professor, Department of Human Genetics Website: http://www.srijan-sen-lab.com/ University of Michigan (U-M) Team Members: Susan Murphy, Ph.D. ISR & Medical School, Department of Psychiatry Lawrence C. An, M.D CHCR Rex Timbs, B.S.C.S CHCR Marcio Mourao, Ph.D. CSCAR John Brussolo, Ph.D MSIS Amy Cochran, Ph.D. LSA, Department of Mathematics Daniel Forger, Ph.D. LSA, Department of Mathematics Elena Frank, M.A., Ph.D Medical School, MBNI Zhuo (Joan) Zhao, M.S. Medical School, MBNI Industry Partner(s): Thomas Insel, M.D Verily Life Sciences a division of Google Technical Description Problem. According to the World Health Organization, major depression is the second leading cause of disability, affecting more than 350 million people worldwide3. Unfortunately, the burden of depression has been growing over the past decades, with a concomitant increase of death due to suicide while death for other health factors decreased4. The field has struggled to develop new, effective treatments and prevention strategies for the disorder. One important and robust finding in depression research has been that life stress is the single most important trigger for the development of depressive episodes5,6. Approximately 80% of depressive episodes are preceded by a major stressor7, 8, 9. Understanding the mechanisms through which life stress leads to depression has the potential to predict when an episode is imminent, informing preventative and treatment strategies. Unfortunately, the capacity to capture the effects of stress accurately and in real-time has been limited because the assessment of psychiatric phenotypes has traditionally relied on long-term recall and self-report of symptoms by affected individuals. Once depressed, individuals develop negative cognitive biases. In fact, individuals often recall fixed periods of time (like childhood) as more stressful when they are in a depressive episode compared to when they are in a healthy period10. There is a critical need to understand the temporal relationship between stress and depression with real-time, objective measures to intervene effectively in real- time. Mobile electronic technology holds great promise in overcoming these limitations by capturing continuous, real-time, passive measures likely to be related to depression11. Previous studies suggest that insufficient and poor quality sleep, a sedentary lifestyle, social isolation, and decreased cognitive function are associated with mood changes12. Because it is almost impossible to prospectively predict the onset of stress and depression in a large group of subjects, mobile electronic technology studies conducted, to date, have been small and largely focused on patients already experiencing psychiatric symptoms13,14,15. Solution. Medical internship has long been considered the most stressful year during the career of a physician, with long work hours, little sleep and life and death decisions to make. Unlike most stressors, internship is a rare situation where we can accurately predict that a cohort will experience the onset of a major, uniform stressor and a dramatic increase in depressive symptoms16, 17. This model allows for the same individuals to be followed, first under normal conditions and then under the conditions of high stress. Our laboratory has also shown feasibility that mobile monitoring allows prospective, real-time monitoring of continuous, real-time, passive measures from this large National group of medical interns to effectively predict short-term risk for depressive episodes (R01 MH101459, K23 MH095109). Here, we propose to examine physiology, psychosocial behavior, environment, spatial and temporal dimensions in real-time, and establish how, together, they contribute to the relationship between stress and onset of depression. Specifically we aim to: 1) identify mobile electronic technology factors that change with stress, and 2) develop a weighted mobile electronic technology factor that predicts depressive mood. Approach. Summary statement of work, justification of team, and expected impact is outlined below. I. Objectives and Expected Outcomes Our primary objective in this application is to identify weighted sensor stream signatures that predict near-term risk of a major depressive episode. Specifically, we will collect daily mood ratings along with a rich set of passive and active data streams including sleep duration and quality, physical activity, geolocation, social interactions and cognitive function. We will then employ state-of-the-art analytical methodologies to develop prediction models that will be tested in subsequent cohorts of training physicians and general population samples. There are also a series of important secondary objectives for this study. First, beyond identifying unbiased integrated predictors of near-term depressive episodes, we will also better understand the temporal relationship between specific factors and depression. For instance, the study should better elucidate the timing and nature of sleep changes that precede depression and may suggest potential interventions (see preliminary data below). Finally, mobile interventions hold promise in improving access to effective mental health care but we know little about when these tools will be most effective. Our team proposes to successfully accomplish: Aim #1: Identify mobile electronic technology factors that that change with stress and Aim #2: Identify a weighted mobile electronic technology factor signature that predict depressive mood. At the completion of this project, we expect to have identified mobile signatures that change with stress and prospectively predict changing levels of depressive symptoms. Further, we will establish a nimble platform well positioned to rapidly assess new mobile electronic intervention technologies as they emerge. II. Nature of the Data – The Internship Model To date, we have enrolled over 13,000