Can Your Innovation Help Improve Player Health?

The Football Players Health Study at Harvard University (FPHS) is a unique multidisciplinary, strategic research and translation initiative addressing the challenge of player health in American style football (ASF). The FPHS is a portfolio of studies designed to deepen the understanding of the benefits and risks of participation in ASF, identify risks that are potentially reversible or preventable, and develop interventions or approaches to improve the health and wellbeing of players.

The FPHS is currently soliciting proposals for innovative “ultra near term” solutions (e.g. the application of technologies such as imaging, sensing, diagnostic, preventative or therapeutic technologies) to improve the health of former and/or current professional players. Successfully funded awards will have an emphasis on improving health by applying a technology to either prevent or treat a medical condition with high prevalence among American style football players (such as sleep apnea, cardiometabolic and cardiovascular disease, neurocognitive impairment, musculoskeletal injury, chronic pain).

The focus of this current RFA is to accelerate innovative solutions towards commercialization that are directly applicable to this population. In addition, eligible solutions will have matured past the Proof-of-Concept Stage of the CIMIT Healthcare Innovation Cycle and be on a path for commercial investment within 12 months. Technologies with current or near FDA-approval that have been developed for similar or related use cases in different populations are welcome. For example, adaptation of innovative solutions/ technologies that have been applied to treat musculoskeletal conditions (such as tendon, bone, muscle or joint disease), detect inflammation and metabolic dysfunction, or reduce risk for long-term sequelae of concussion. Specifically, research should focus on next step of translation to commercialization. Note funding from this RFA is not meant for definitive phase 3 clinical trials.

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