The Association Between Knee Biomechanics During Running And Resting Trochlea Cartilage Thickness

There is ambiguity in the role of loading on knee cartilage development and degeneration in distance runners. PURPOSE: To examine the association between knee joint kinetics during running and resting cartilage thickness. It was hypothesized that greater joint kinetics would be associated with thicker resting cartilage. METHODS: Sixteen runners participated (7 females and 9 males; Height = 1.73 ± 0.07 m, Mass = 66.7 ± 8.3 kg, Age = 21.5 ± 3.7 years, self-selected running Speed = 3.34 ± 0.4 m/s, Weekly Running Amount = 51.9 ± 25.7 km, Running Experience = 6.53 ± 3.5 years). Runners were defined as anyone who runs at least 3 times per week, and 16 kilometers in that week for the past 6 months. Participants rested supine for 30 minutes before ultrasound imaging of trochlear cartilage. After imaging, participants ran at a self-selected speed for 35 minutes on a force instrumented treadmill while kinetics and kinematics were recorded at 2400 Hz and 240 Hz, respectively. Variables of interest included peak patellofemoral joint stress (PFJS), peak patellofemoral joint force (PFJF), peak knee flexion angle (KFA), peak knee extensor moment (KEM), medial, central, and lateral cartilage region thickness and total thickness of femoral trochlea cartilage. Spearman’s rho assessed the association between biomechanical and cartilage outcomes (α = 0.05). RESULTS: Peak PFJS was associated with medial (rho = 0.653, p = 0.006), central (rho = 0.503, p = 0.047), lateral (rho = 0.529, p = 0.035) and total (rho = 0.618, p = 0.011) cartilage thickness. Peak PFJF was associated with medial cartilage thickness (rho = 0.524, p = 0.037) but not central, lateral, or total cartilage thickness (all rho<0.32, all p > 0.05). Peak KEM and peak KFA were not associated with medial, central and lateral cartilage thickness and total cartilage thickness (all rho<0.48, all p > 0.06). CONCLUSIONS: Peak PFJS was consistently associated with all cartilage outcomes, but PFJF, KEM and KFA were not. Therefore, robust biomechanical modeling is useful when quantifying knee loading characteristics. Greater PFJS during running may provide a beneficial mechanical stimulus for cartilage maintenance and development. Supported by: This project received funding from the Natural Science and Engineering Research Council of Canada (RGPIN-2022-04804, PI: DN Pamukoff), the Canadian Foundation for Innovation John R. Evans Leader Fund (Project ID: 42110, PI: DN Pamukoff) and the Ontario Graduate Scholarship 2024-2025 (RJ Evans).

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2025-09-16
DOI
https://doi.org/10.1249/01.mss.0001154556.99736.51
Primary Topic
Osteoarthritis Treatment and Mechanisms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Association Between Knee Biomechanics During Running And Resting Trochlea Cartilage Thickness

Derek N. Pamukoff, Richard W. Willy, Harry S. Battersby, Ryan J. Evans et al.
Medicine & Science in Sports & Exercise
Osteoarthritis Treatment and Mechanisms
article

The Association Between Knee Biomechanics During Running And Resting Trochlea Cartilage Thickness

Derek N. Pamukoff, Richard W. Willy, Harry S. Battersby, Ryan J. Evans, Tom Boers
article en

Abstract

There is ambiguity in the role of loading on knee cartilage development and degeneration in distance runners. PURPOSE: To examine the association between knee joint kinetics during running and resting cartilage thickness. It was hypothesized that greater joint kinetics would be associated with thicker resting cartilage. METHODS: Sixteen runners participated (7 females and 9 males; Height = 1.73 ± 0.07 m, Mass = 66.7 ± 8.3 kg, Age = 21.5 ± 3.7 years, self-selected running Speed = 3.34 ± 0.4 m/s, Weekly Running Amount = 51.9 ± 25.7 km, Running Experience = 6.53 ± 3.5 years). Runners were defined as anyone who runs at least 3 times per week, and 16 kilometers in that week for the past 6 months. Participants rested supine for 30 minutes before ultrasound imaging of trochlear cartilage. After imaging, participants ran at a self-selected speed for 35 minutes on a force instrumented treadmill while kinetics and kinematics were recorded at 2400 Hz and 240 Hz, respectively. Variables of interest included peak patellofemoral joint stress (PFJS), peak patellofemoral joint force (PFJF), peak knee flexion angle (KFA), peak knee extensor moment (KEM), medial, central, and lateral cartilage region thickness and total thickness of femoral trochlea cartilage. Spearman’s rho assessed the association between biomechanical and cartilage outcomes (α = 0.05). RESULTS: Peak PFJS was associated with medial (rho = 0.653, p = 0.006), central (rho = 0.503, p = 0.047), lateral (rho = 0.529, p = 0.035) and total (rho = 0.618, p = 0.011) cartilage thickness. Peak PFJF was associated with medial cartilage thickness (rho = 0.524, p = 0.037) but not central, lateral, or total cartilage thickness (all rho<0.32, all p > 0.05). Peak KEM and peak KFA were not associated with medial, central and lateral cartilage thickness and total cartilage thickness (all rho<0.48, all p > 0.06). CONCLUSIONS: Peak PFJS was consistently associated with all cartilage outcomes, but PFJF, KEM and KFA were not. Therefore, robust biomechanical modeling is useful when quantifying knee loading characteristics. Greater PFJS during running may provide a beneficial mechanical stimulus for cartilage maintenance and development. Supported by: This project received funding from the Natural Science and Engineering Research Council of Canada (RGPIN-2022-04804, PI: DN Pamukoff), the Canadian Foundation for Innovation John R. Evans Leader Fund (Project ID: 42110, PI: DN Pamukoff) and the Ontario Graduate Scholarship 2024-2025 (RJ Evans).

Medicine & Science in Sports & ExerciseVol. 57(10S)
Western University (CA), University of Montana (US)
Openalex Percentile: Top 63%
Osteoarthritis Treatment and Mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.