Health-Related Behavioral Sciences
Health-Related Behavioral Sciences, AD&T's newest research theme, is built on growing collaborations with psychologists, sociologists, and other social scientists. It strives to address problems related to the effective delivery of healthcare, such as patient adherence to prescribed medical regimens and human interaction with medical devices and technology.
Some current Health-Related Behavioral Sciences projects include:
Dynamic Analysis of Self-Management of Patients with Type 2 Diabetes
This study will collect pilot data on adherence to recommendations from physicians--regarding medication, diet, physical activities, sleep, and self-administered blood tests--in forty Type 2 diabetes patients and develop statistical methods to analyze the intensive longitudinal data that are produced. The study will involve both patients and spouses who are recruited through the project’s clinical partner, the Tianjin Metabolic Diseases Treatment and Prevention Center in Tianjin, China. In addition to the standard diabetes treatment plan, participants will complete questionnaires measuring such factors as demographic and personality characteristics, attachment styles, and stress. A smartphone app will be installed on patients’ and spouses’ phones that will record physiological measures of fasting blood glucose, physical activity, weight, and sleep. (PIs: Zhang, Wang and Luo)
Comparing the Efficacy of a Single-Sessions Virtual Reality Treatment for Acrophobia to a Gold Standard Treatment and No Treatment
Acrophobia (an excessive or unrealistic fear of situations that involve heights) is a common condition that causes significant impairment. A current dilemma in acrophobia treatment is the fact that while in vivo exposure is an effective treatment, rates of treatment utilization are low, likely due to the anticipated discomfort of directly confronting a feared stimulus. This collaboration between Notre Dame and Florida State University aims to address this problem by testing the efficacy of virtual reality exposure treatment with the potential to increase accessibility to treatment and change the way specific phobias are treated. (PIs: Hames, Rose, Villano and Cougle)
Using Integrative Data Mining to Improve the Prediction of Suicide: An Initial Application
Suicide is significant public health concern, and given the inherent difficulty in predicting this complex behavior, the current proposal aims to identify important correlates and improve prediction of distinct suicide outcomes. This project will utilize a combination of machine learning and data integration to elucidate the relationship between risk factors at multiple levels of analysis as well as lifetime and past 12-month suicidal ideation, plans, and attempts. The results of this project will provide insights into important variables to target in suicide prevention and intervention strategies. (PIs: Ammerman and Jacobucci)
For more information on the program, please contact Arnie Phifer, Associate Director.