Grantee: Rajani S. Sadasivam, PhD
Rajani S. Sadasivam, PhD
- University of Massachusetts Medical School
Dr. Rajani Sadasivam's research aims to increase the evidence for and uptake of digital health, with the ultimate goal of increasing the health and well-being of individuals. Dr. Sadasivam is particularly passionate about applying digital health to address health disparities.
Smoking-related disparities are widespread and growing. Socio-economically disadvantaged individuals smoke at higher rates and suffer more from smoking-related diseases. It is therefore critically important to identify individualized approaches to cessation support that are both scalable and effective.
This project's primary aim is to test whether text messages selected for an individual smoker using a new computer-tailored health communication (CTHC) method (the Adapt2Quit collective intelligence system) will improve smoking cessation effectiveness when compared to standard messaging. The system will be tested with socio-economically disadvantaged individuals who smoke. CTHC is focused on selecting the best message for an individual. Adapt2Quit uses machine-learning algorithms to select messages based upon multiple attributes, including 1) the smoker's personal characteristics; 2) the smoker's continuous feedback to the system; and 3) data from thousands of prior smokers' profiles and their feedback patterns.
Because of the potential for broad reach and effectiveness, health messaging programs (especially texting) are increasingly being adopted in real-world settings (public health and within healthcare systems). The current study focuses on innovative methods for increasing the effectiveness of health messaging; these findings are intended to increase the impact of these real-world systems.
Dr. Sadasivam's PhD training was in computer engineering. During his training, he worked with health care researchers to develop digital health for behavior change interventions. Through this work, Dr. Sadasivam came to recognize and enjoy the challenges inherent in engaging patients for meaningful, sustained behavior change. Dr. Sadasivam's training in computer engineering, combined with his many years of work in health care research settings, has positioned him to make unique contributions to the design of digital health.
I tested computer-tailored health communication (CTHC) techniques I learned in a 2007 workshop in a nationwide randomized control trial with 900 smokers. The system worked, and I realized the power of CTHC to help patients. During this time, companies like Amazon were using big data methods to adapt to users' continuous feedback (their collective intelligence) to tailor content. I realized that these methods could make CTHC even more powerful, and I have pursued this line of research since then.”
Selected Grants
| Project Title | Grant Number | Program Director |
|---|---|---|
| Adapt2Quit - A Machine-Learning, Adaptive Motivational System: RCT for Socio-Economically Disadvantaged smokers" | 1R01CA240551-01A1 | Yvonne Prutzman |
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