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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH100482-02
Application #
8705032
Study Section
Special Emphasis Panel (ZMH1)
Program Officer
Sherrill, Joel
Project Start
2013-08-01
Project End
2018-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Northwestern University at Chicago
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
City
Chicago
State
IL
Country
United States
Zip Code
60611
Stiles-Shields, Colleen; Montague, Enid; Kwasny, Mary J et al. (2018) Behavioral and cognitive intervention strategies delivered via coached apps for depression: Pilot trial. Psychol Serv :
Saeb, Sohrab; Lattie, Emily G; Kording, Konrad P et al. (2017) Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR Mhealth Uhealth 5:e112
Stiles-Shields, Colleen; Montague, Enid; Lattie, Emily G et al. (2017) Exploring User Learnability and Learning Performance in an App for Depression: Usability Study. JMIR Hum Factors 4:e18
Mohr, David C; Zhang, Mi; Schueller, Stephen M (2017) Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annu Rev Clin Psychol 13:23-47
Mohr, David C; Lyon, Aaron R; Lattie, Emily G et al. (2017) Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment. J Med Internet Res 19:e153
Saeb, Sohrab; Cybulski, Thaddeus R; Schueller, Stephen M et al. (2017) Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles. J Med Internet Res 19:e118
Saeb, Sohrab; Lonini, Luca; Jayaraman, Arun et al. (2017) The need to approximate the use-case in clinical machine learning. Gigascience 6:1-9
Rubanovich, Caryn Kseniya; Mohr, David C; Schueller, Stephen M (2017) Health App Use Among Individuals With Symptoms of Depression and Anxiety: A Survey Study With Thematic Coding. JMIR Ment Health 4:e22
Mohr, David C; Tomasino, Kathryn Noth; Lattie, Emily G et al. (2017) IntelliCare: An Eclectic, Skills-Based App Suite for the Treatment of Depression and Anxiety. J Med Internet Res 19:e10
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

Showing the most recent 10 out of 16 publications