Bipolar (BP) disorder is a chronic illness of profound shifts in mood ranging from mania to depression. BP is successfully treated by combining medication with psychosocial therapy, but care can prove inadequate in practice. With gaps in coverage and medication, along with imprecise guidelines on when, where, and how to intervene, promising psychosocial therapies require adaptive strategies to better address the specific needs of individuals in a timely manner (NIMH Strategy 2). To accomplish this, however, requires evidence-based practices for adapting a psychosocial therapy. This Mentored Research Scientist Development Award aims to address this knowledge gap, by (1) establishing a mobile health platform for translating a psychosocial therapy in BP into an effective adaptive intervention and (2) facilitating the transition of a junior researcher, at the interface of mathematics and psychiatry, into an independent researcher of effective adaptive interventions. The research effort is founded on a mobile health platform that combines evidence-based markers of mood for long-term monitoring with a micro-randomized trial, designed for optimizing mobile health adaptive interventions.
In Aim 1, we use modeling to characterize and test new markers of mood course that account for volatility, a feature that masks effects of a therapy on mood.
In Aim 2, we explore the potential for long-term monitoring of BP with interpretable markers from actigraphy.
In Aim 3, best practices from Aim 1 and 2 are integrated with a micro-randomized trial into a mobile health platform. We then test the feasibility of using the platform to translate a psychosocial therapy, clinical phone call, into an adaptive intervention. If successful, this work will advance the Candidate's independent goal of adaptive scheduling of phone-calls with BP individuals. To complement the research agenda, the award will expand the Candidate's background in Computational Psychiatry into the area of Translational Psychiatry by providing training in five strategic areas: (1) clinical assessments, (2) psychosocial therapy, (3) mobile health interventions, (4) adaptive clinical trials, and (5) open-access scheduling. Dr. Melvin McInnis, Thomas B and Nancy Upjohn Woodworth Professor of Bipolar Disorder and Depression and Professor of Psychiatry at the University of Michigan, will be the primary mentor and will guide clinical aspects of the training (Training Objectives 1?3); Dr. Amy Kilbourne, Professor of Psychiatry and Acting Director of VA/HSR&D's Quality Enhancement Research Initiative, will guide training in the application of adaptive trial designs that involve psychosocial therapy (Training Objectives 2,4?5); and Dr. Susan Murphy, Herbert E. Robbins Distinguished Professor of Statistics, will guide training into methodology for adaptive trial design and mobile health interventions (Training Objectives 3?4). The proposed K01 award promises to train a junior scholar to address technically-challenging problems in mental health. This work is aligned with the NIH and NIMH missions of providing precise clinical care.
Bipolar disorder, a chronic disease of profound shifts in mood, is ranked as the seventh and eighth highest cause of disability among male and female adults by the World Health Organization. The aim of this project is to measure mood course in the moment, over time, in individuals, thus directing therapy to individuals when it is most needed. If successful, this project will (1) establish a mobile health platform for general use in developing effective adaptive interventions in bipolar disorder and (2) facilitate the transition of a junior researcher, at the interface of mathematics and psychiatry, into an independent researcher of effective adaptive interventions.
|Cochran, Amy; Belman-Wells, Livia; McInnis, Melvin (2018) Engagement Strategies for Self-Monitoring Symptoms of Bipolar Disorder With Mobile and Wearable Technology: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 7:e130|
|Cochran, Amy L; Schultz, André; McInnis, Melvin G et al. (2018) Testing frameworks for personalizing bipolar disorder. Transl Psychiatry 8:36|