ArticleJournal of sport and health science2026
Data driven shoe design improves running economy beyond state-of-the-art Advanced Footwear Technology running shoes.
Article in Journal of sport and health science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdvanced 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 its traditionally developed predecessor model and 2 other state-of-the-art AFT models.
methodsWe quantified running economy (i.e., the rate of metabolic energy consumption while running at a specified speed) for 15 trained runners (11 males and 4 females) in this prototype AFT shoe and 3 commercially available AFT models: the PUMA Fast-R 2 (FR2), the Nike Alphafly 3 (NIKE), and the Adidas Adios Pro Evo 1 (ADI).
resultsRunning economy in the FR3 was 3.15% ± 1.24%, 3.62% ± 1.25%, and 3.54% ± 1.16% (mean ± SD) 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).
conclusionOur results suggest that shoes designed using computational design analysis from biomechanical data can improve running economy. This approach appears to be a promising frontier in performance running shoe design, offering potential for further improvements and personalized AFT models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.