Preventive Health Care
My interest in pursuing research in early intervention, preventive care, and health informatics has grown in recent times, especially with the rapid adoption of digital health tools, which could either improve a system depending on its implementation. A significant portion of my research efforts has been focused on analyzing longitudinal data for identifying the impact of risky adolescent behaviors and characteristics on adulthood health outcomes. By collaborating with a MU cardiologist, our research examined the long-term effects of adolescent depression, negative emotions, and positive well-being on adulthood cardiovascular risk. Using longitudinal datasets that were systematically collected by federally-sponsored research studies for over 20 years, we identified behavior and biomarkers of a representative sample of US individuals beginning in adolescence through adulthood. Subsequently, statistical models were developed to ascertain its impact on cardiovascular health later in life. Our research was among the first to associate the adverse implications of adolescent self-reported depressive symptoms and negative emotions on adulthood cardiovascular risk factors such as hypertension and obesity. This research has also been recognized by Science Trends (scientific blog), and other researchers on Twitter. Some of the publications from this research as as follows:
- Srinivas, S., Anand, K., & Chockalingam, A. (2020). Longitudinal association between adolescent negative emotions and adulthood cardiovascular disease risk: an opportunity for healthcare quality improvement. Benchmarking: An International Journal.
- Srinivas, S., Rajendran, S., Anand, K., & Chockalingam, A. (2018). Self-reported depressive symptoms in adolescence increase the risk for obesity and high BP in adulthood. International journal of cardiology, 269, 339-342.
Currently, I am collaborating with healthcare professionals to develop a screening tool to estimate the long-term risk of chronic diseases (such as cardiovascular disease and type 2 diabetes) among adolescents using machine learning models.