New Considerations for Wearable Technology Data: Changes in Running Biomechanics During a Marathon

in Journal of Applied Biomechanics
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The purpose of this study was to use wearable technology data to quantify alterations in subject-specific running patterns throughout a marathon race and to determine if runners could be clustered into subgroups based on similar trends in running gait alterations throughout the marathon. Using a wearable sensor, data were collected for cadence, braking, bounce, pelvic rotation, pelvic drop, and ground contact time for 27 runners. A composite index was calculated based on the “typical” data (4–14 km) for each runner and evaluated for 14 individual 2-km sections thereafter to detect “atypical” data (ie, higher indices). A cluster analysis assigned all runners to a subgroup based on similar trends in running alterations. Results indicated that the indices became significantly higher starting at 20 to 22 km. Cluster 1 exhibited lower indices than cluster 2 throughout the marathon, and the only significant difference in characteristics between clusters was that cluster 1 had a lower age–grade performance score than cluster 2. In summary, this study presented a novel method to investigate the effects of fatigue on running biomechanics using wearable technology in a real-world setting. Recreational runners with higher age–grade performance scores had less atypical running patterns throughout the marathon compared with runners with lower age–grade performance scores.

Clermont, Benson, Edwards, and Ferber are with the Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada. Hettinga is with adidas, Portland, OR. Ferber is also with the Faculty of Nursing, University of Calgary, Calgary, AB, Canada; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; and Running Injury Clinic, Calgary, AB, Canada.

Clermont (Christian.clermont@ucalgary.ca) is corresponding author.
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