Consumer Grade Wearable Sensor Metrics Are Associated with Internal Forces at Common Running Injury Locations.

Authors

John J Davis Iv, Stacey A Meardon, John S Raglin, Andrew W Brown, Jaroslaw Harezlak, Allison H Gruber

Published in

Medicine and science in sports and exercise. Oct 16, 2026. Epub Oct 16, 2026.

Abstract

Inexpensive consumer-grade wearable sensors have expanded possibilities to assess, monitor, or modify a runner's gait. However, it is unclear which sensor-measured gait metrics are strongly associated with internal loading at common injury locations.
To measure the association between gait metrics measured by consumer-grade wearable sensors and internal forces at the Achilles tendon and the patellofemoral joint.
55 healthy runners wearing consumer-grade chest straps foot-pods completed a 38-minute run on an instrumented treadmill at 12 speeds from 70%-125% of self-reported typical running pace. A scaled musculoskeletal model derived from motion capture data and driven by static optimization was used to calculate Achilles tendon and patellofemoral joint forces during stance for each speed. Functional regression models were used to estimate the associations of each of nine sensor-measured gait metrics with internal forces across stance, after adjusting for body mass and running speed.
At both the Achilles tendon and the patellofemoral joint, cadence, vertical oscillation, and vertical ratio (vertical oscillation divided by step length) showed the strongest associations with internal forces (maximum of 0.4-0.6 BW change in force per one standard deviation change in the gait metric). Other wearable sensor-measured gait metrics, including stance time, leg stiffness, and stance time percent showed weaker associations (<0.4 BW change in force per one standard deviation change in the gait metric).
Runners with higher-than-average cadence, vertical oscillation, or vertical ratio values experience marked differences in internal forces at the Achilles tendon and patellofemoral joint. Of these gait metrics, cadence is the most practical for out-of-lab use because of its ease of accurate measurement with consumer-grade devices.

PMID:
42885854
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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