Big Data Meets Mind-body Complementary Therapy: Data-driven Musculoskeletal Models that Guide the Use of Yoga for Treatment of Chronic Disease

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. Our project will develop advanced computational models to provide a scientific understanding of muscle contributions to yoga and to develop a novel framework for revealing the most effective yoga therapy poses and sequences for those with chronic disease.  Specifically, we will develop musculoskeletal simulations of yoga poses, use these simulations to test current intuition regarding muscle targets, determine how muscle weakness influences the ability to achieve poses, and ultimately prescribe modifications that will 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, we will layer big data analytic methods, including clustering and principal component analyses, onto the analysis approach.

Kelley Mitchell Virgilio Researcher Bio

Kelley Mitchell Virgilio’s 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. As a member of an interdisciplinary team exploring the efficacy of yoga therapy, she is utilizing full-body musculoskeletal models to simulate yoga poses and optimize modifications to tailor yoga therapy for treatments of chronic disease. This project forms a vital connection between the insights gained from computational models and the application of these in designing rehabilitative practices.

Advisor: Silvia Salinas Blemker, PhD, Associate Professor, Biomedical Engineering, Mechanical & Aerospace Engineering, and Orthopaedic Surgery

 

Tamara Fischer-White, MSN, RN, RYT Researcher Bio

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. In collaboration with the Department of Biomedical Engineering, the use of computational modeling in a big data environment provides a novel means of combing existing therapeutic yoga practice and theory with computerized modeling of the musculoskeletal effects of yoga poses on the entire body. The data obtained provides the rigor necessary to validate therapeutic yoga application in rehabilitation and chronic disease symptom management by identifying the most appropriate, effective yoga style for a particular chronic disease state.

Advisors:  Ann Gill Taylor, EdD, RN, FAAN

Betty Norman Norris Professor of Nursing; Director, Center for the Study of Complementary and Alternative Therapies; Adjunct Professor, Department of Physical Medicine and Rehabilitation