HCII Thesis Proposal: Julian Andres Ramos Rojas, "The Personalization of Mobile Health Interventions"

Monday, December 2, 2019 - 1:30pm to 3:00pm
Newell-Simon Hall 3305
Thesis Committee:
Anind K. Dey (Co-Chair), Information School, University of Washington
Mayank Goel (Co-chair), Human-Computer Interaction Institute, Carnegie Mellon University
Carissa Low, Department of Medicine, University of Pittsburgh
Tanzeem Choudhury, Department of Information Science, Cornell University
Robert Kraut, Human-Computer Interaction Institute, Carnegie Mellon University
Personalized medicine is the adjustment of medical treatment by taking into account people's unique demographics, genetic makeup, and lifestyle. This approach, however, relies on domain knowledge that is often limited and forces medical practitioners to explore multiple treatments with a patient until finding an appropriate one. During this process, patients are on their own: They have to remember the specifics of the treatment, and they need to identify when and what treatment to put into practice. To overcome these challenges, I envision equipping with the means to personalize and provide health interventions the most popular computing device available today: The mobile phone. This personalized mobile health approach would give access to health interventions to anyone with a phone, and it would be especially impactful for populations that lack access to basic health services.

At the core of this proposal, I investigate methods for the personalization of mobile health interventions using artificial intelligence (AI), smartphones and wearables, and the patient’s feedback. In my work so far, I have explored two fundamental challenges: when to intervene (identifying intervention points) and what treatment to use (treatment selection). I approached these challenges by integrating human-computer interaction work in interruptibility (i.e., receptivity) and contextual bandits; an AI method for solving sequential decision-making problems. This work was applied to a sleep intervention and compared to standard clinical treatment. The results show that my integrated approach is as good or better than clinical treatment, and for a stratum of the study’s sample, the results are clinically meaningful.

For my remaining thesis work, I propose to investigate methods for how to predict the short-term effect of a treatment (models of effects), and how to predict patient adherence to treatment (models of behavior). Mobile health researchers have identified the proposed work as crucial for the advancement of the field. Behavior models are necessary for reducing intervention burden and increasing engagement. Models of effects can inform the direction and strength treatments. My hypothesis is that both models could be used to compute the expected value of treatment effect. This expected value could be used to select the best treatment: one that takes into account the effect and adherence to treatment. I plan to use these models to inform the treatment selection of my SleepU system which will then be deployed to college students in a sleep intervention. This model-based approach for a mobile health intervention will be compared against my completed work that does not use an explicit model, and a survey-based approach; where treatment is selected from the patient's own preferences and forecast of effects of treatment. Additionally, I will measure the patient’s engagement gains from using this model-based approach. The overall results from this work will inform the development and deployment of effective and efficient personalized mobile health interventions in the real world.

Document: Download here
Queenie Kravitz

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