Yoga therapy is used as a key element of multimodal treatment strategies for symptom management of chronic disease. However, it is currently prescribed based on intuition of the relationships between body poses and muscle exertion and is yet to be challenged by rigorous quantitative analyses.
Tamara Fischer-White and Kelley Virgilio developed advanced computational models to provide a scientific understanding of muscle contributions to yoga and to form a novel framework for revealing the most effective yoga therapy poses and sequences for those with chronic disease.
Specifically, they developed musculoskeletal simulations of yoga poses, used these simulations to test current intuition regarding muscle targets, determined how muscle weakness influences the ability to achieve poses, and ultimately prescribed modifications to optimize yoga poses for individuals with chronic disease.
Because of the vast number of joints and muscles involved in yoga poses, the parameters and results of the simulations involve large data sets, particularly as numerical approaches are used to optimize the pose modifications for targeted treatment of chronic disease. Therefore, they layered big data analytic methods, including clustering and principal component analyses, onto the analysis approach.
Tamara Fischer-White is a doctoral student in the School of Nursing, Center for the Study of Complementary and Alternative Therapies. Her research goal is to provide individuals and health care providers the scientific evidence upon which to base their application of complementary health-enhancing therapies.
Kelley Virgilio is a PhD candidate in the Department of Biomedical Engineering. Her current biomechanical research utilizes a system of multiscale computational models to elucidate relationships between musculoskeletal structure and function and to predict the effect of rehabilitative exercises on the growth and regeneration of muscle and tendon.
