The primary aim of this proposal is to develop and evaluate the use of state of the art machine learning approaches within a mobile intervention application for the treatment of major depressive disorder (MDD). Machine learning, a branch of artificial intelligence, focuses on the development of algorithms that automatically improve and evolve based on collected data. Machine learning models can learn to detect complex, latent patterns in data and apply such knowledge to decision making in real time. The proposed intervention, called IntelliCare, will use ongoing data collected from the patient and intervention application to continuously adapt intervention content, content form, and motivational messaging to create a highly tailored and user-responsive treatment system. Behavioral intervention technologies (BITs), including web-based and mobile interventions, have been developed and are increasingly being used to treat MDD. BITs are moderately effective in treating depression, particularly when guided by human coaching via email or telephone. However, lack of personalization and inability to adapt to patient needs or preferences, which results in a perceived lack of relevance, contributes to poorer adherence and outcomes. IntelliCare will be designed as a mobile application, but will be accessible via computer web browsers and tablets. The IntelliCare machine learning framework will use individual data obtained from use data (e.g., length of time using a treatment component), embedded sensors in the phone (e.g., GPS), and the user's self-reports (e.g., """"""""like"""""""" and usefulness ratings of treatment components) to provide a highly tailored intervention that can learn from the patient and adapt intervention and motivational materials to the patient's preferences and state. Low intensity coaching will serve as a backstop to support adherence. This project will contain three phases. Phase 1 will involve the development of IntelliCare and its optimization through usability testing. Phase 2 will be a field trial of 200 users who will receive IntelliCare for 12 weeks. The field trial has two aims: first to complete usability testing and optimization of the treatment framework, and second to develop the machine learning models and algorithms. Phase 3 will subject IntelliCare to a double blind, randomized controlled trial, comparing it to MobilCare. MobilCare will be identical to IntelliCare except that it will use standard presentation and presentation, rather than machine learning, to provide treatment and motivational materials. We will recruit half the participants from primary care settings, as this is the de facto site for treatment of depression in the United States, and half through the Internet, which is the main portal to health apps. The application of adaptive machine learning analytics to a mobile intervention has the potential to create a new generation of BITs that could revolutionize the way that such interventions are conceptualized, designed, and deployed. These innovations would have broad consequences and could be extended a broader range of BITS, including web- based interventions, and to other interventions targeting a wide range of health and mental health problems.
This project will create and evaluate IntelliCare, a mobile intervention also accessible by web browser, for depression. IntelliCare will harness modern adaptive machine learning analytics that can learn from a user's activity on the application, embedded phone sensors, and patient report to tailor intervention elements and motivational messaging to the needs and preferences of the user. This unprecedented level of tailoring could revolutionize the way such interventions are conceptualized, designed, and deployed.
|Miranda, J Jaime; BernabÃ©-Ortiz, Antonio; Diez-Canseco, Francisco et al. (2016) Towards sustainable partnerships in global health: the case of the CRONICAS Centre of Excellence in Chronic Diseases in Peru. Global Health 12:29|
|Pisani, Anthony R; Wyman, Peter A; Mohr, David C et al. (2016) Human Subjects Protection and Technology in Prevention Science: Selected Opportunities and Challenges. Prev Sci 17:765-78|
|Lattie, Emily G; Schueller, Stephen M; Sargent, Elizabeth et al. (2016) Uptake and Usage of IntelliCare: A Publicly Available Suite of Mental Health and Well-Being Apps. Internet Interv 4:152-158|
|Vittengl, Jeffrey R; Jarrett, Robin B; Weitz, Erica et al. (2016) Divergent Outcomes in Cognitive-Behavioral Therapy and Pharmacotherapy for Adult Depression. Am J Psychiatry 173:481-90|
|Mohr, David C; Schueller, Stephen M; Riley, William T et al. (2015) Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies. J Med Internet Res 17:e166|
|Saeb, Sohrab; KÃ¶rding, Konrad; Mohr, David C (2015) Making Activity Recognition Robust against Deceptive Behavior. PLoS One 10:e0144795|
|Mohr, David C; Schueller, Stephen M; Montague, Enid et al. (2014) The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res 16:e146|