Data driven shoe design improves running economy beyond state-of-the-art Advanced Footwear Technology running shoes

Abstract

Advanced Footwear Technology (AFT) has enabled remarkable improvements in running performance over traditional marathon racing shoes. However, reported differences between state-of-the-art AFT models are small, and vary across individuals. To assess if the benefits of AFT have been fully realized or if further running economy improvements can be unlocked using modern computational design and optimization techniques, we compared a prototype AFT shoe developed using a data-driven computational design process (PUMA Fast-R 3; FR3) against state-of-the-art AFT models. We quantified running economy for 15 trained runners (11M, 4F) in this prototype AFT shoe and three commercially available AFT models: the PUMA Fast-R 2 (FR2), the Nike Alphafly 3 (NIKE), and the Adidas Adios Pro Evo (ADI). Running economy in the FR3 was 3.15 {+/-} 1.24%, 3.62 {+/-} 1.25%, and 3.54 {+/-} 1.16% better than in the FR2, NIKE and ADI (all p < 0.001), respectively, and every individual performed best in the FR3 shoes. While step parameters were similar between FR3 and FR2, the FR3 had a lower step frequency than the ADI (p = 0.013) and longer contact time than the NIKE and ADI (both p<0.001). Our results suggest that computational design analysis is a promising frontier in performance running shoe design, offering potential for further improvements and personalized AFT models.