We anticipate the next call for proposals will be announced in November/December 2017. See below for information related to the first round of ESSI grant funding.
ESSI projects aim to optimize physical performance and health
Four research teams will share $800,000 in awards from the University of Michigan’s Exercise & Sport Science Initiative.
The projects measure the effects of physical activity on brain aging, examine the influence of sleep on athletic performance, devise new techniques for integrating and analyzing exercise and physical data, and develop a model for predicting injury in runners.
Launched in 2016, ESSI draws on the expertise of faculty from a wide range of disciplines across campus, Michigan Athletics and industry partners to optimize performance and health for people of all ages and abilities.
“Science and technology are creating a host of new opportunities that have potential to transform the world of exercise and sport,” said Ellen Arruda, professor of mechanical engineering, biomedical engineering, and macromolecular science and engineering, who co-directs ESSI with Ron Zernicke, professor of kinesiology, and of orthopaedic surgery and biomedical engineering.
“The projects awarded as part of our first round of pilot grants highlight the multidisciplinary nature of this field, which will ultimately lead to improvements in health, well-being and performance. By working together, we are exploring the science underlying new advances, with the eventual goal of translating these new ideas into practice.”
Proposals for this initial round of awards were submitted by researchers at all three U-M campuses, and included 13 schools, colleges and units on the Ann Arbor campus, ranging from engineering and education to social research and kinesiology.
Funding will be awarded in four, equal, six-month increments of up to $50,000. Each installment will be dispersed after successful biannual project reviews.
The four research projects selected are:
Determining the effects of objectively measured physical activity on circuit-specific brain aging in a massive multi-modal neuroimaging community sample
Team: Mark D. Peterson (Medical School), Chandra Sripada (Medical School and LSA), Jenna Wiens (College of Engineering) and Stephen Smith (Oxford University)
Goal: Researchers aim to better understand the role of exercise on brain structural and functional health, as well as to inform early, targeted interventions to preserve cognitive and physical health through the lifespan.
Mobile sleep and circadian rhythm assessment for enhanced athletic performance
Team: Olivia Walch and Cathy Goldstein (Medical School), and Danny Forger (LSA and Medical School)
Goal: Researchers aim to identify a product that will use well-established sleep and circadian science, as well as validated mathematical algorithms, to develop a software suite that allows coaches and athletes, or other exercising individuals, to estimate both circadian state and sleep non-invasively to optimize performance.
End-user techniques for aggregating and analyzing exercise and physical data
Team: Steve Oney and Michael Nebeling (School of Information and CoE) and Sun Young Park (Penny W. Stamps School of Art and Design, and School of Information)
Goal: Researchers aim to develop techniques to allow non-programmers to collect and integrate “big data” from multiple sensors; visualize, analyze and share these data; and act on the collected data to improve athletic performance and training.
Development of a multi-level, systems-based model for injury resiliency at the individual and team level in collegiate running sports
Team: Richard Gonzalez (LSA, Stephen M. Ross School of Business, Institute for Social Research and CoE), Cristine Agresta and Jessica Deneweth Zendler (School of Kinesiology), Vineet Raichur (ISR), Jeffrey Housner (Medical School), and Bo Sandoval and Christina Fanning (Athletics)
Goal: Researchers aim to develop a multi-level, systems-based model for studying running-related injury predictions in collegiate runners. The proposed framework will facilitate the development of analytical models that can predict individual risk, as well as identify unique determinants that can and should be corrected to promote resilience.
These projects reflect the five grand research challenges that ESSI has identified: improved physical activity across the lifespan; wise wearable sensor technology; injury prevention, diagnosis and management; individualized augmented reality and virtual reality; and sports and learning analytics.