Subscribe to the UTP Journals Collection Today! Extend your current subscriptions to provide library users with the interdisciplinary content they need to complete their research and ensure the high quality of their scholarship. Dive deeper with expanded access to 38 highly respected journals with interdisciplinary content from numerous journals within similar fields of study. Keep users connected on or off campus with UTPJ Collection Campus Activated Subscriber Access (CASA). Gain full access to 38 journals, including 22 complete online journal archives encompassing 3,750+ issues, totaling 504,000+ pages, and 45,000+ articles and reviews from 1920 to 2020. Save 25% in subscription costs by subscribing to the UTPJ Collection. UTP Journals Collection INCREASE ACCESS, IMPROVE RESEARCH UTP JOURNALS COLLECTION utpjournals.press/the-collection The UTP Journals Collection gathers the most sought-after scholarship and packages it into a cost-effective solution. Ensure your library users have seamless access to interdisciplinary research by diverse and internationally renowned authors and editors in an abundance of subject areas in the arts, humanities, and sciences: history, Canadian and cultural studies, literature and languages, theatre and modern drama, religion, health and information sciences, and law and criminology.
Objective: Running is an easy way of meeting physical activity recommendations for individuals with knee osteoarthritis (KOA); however, it remains unknown how their cartilage reacts to running. The objective of this pilot study was to compare the effects of 30 min of running on T2 and T1ρ relaxation times of tibiofemoral cartilage in female runners with and without KOA. Methods: Ten female runners with symptomatic KOA (mean age 52.6 ± 7.6 years) and 10 without KOA (mean age 52.5 ± 7.8 years) ran for 30 min on a treadmill. Tibiofemoral cartilage T2 and T1ρ relaxation times were measured using magnetic resonance imaging prior to and immediately after the bout of running. Repeated-measures analyses of covariance (ANCOVA) were conducted to examine between-group differences across scanning times. Results: No Group × Time interactions were found for T2 (P ≥ 0.076) or T1ρ (P ≥ 0.288) relaxation times. However, runners with KOA showed increased T2 values compared with prerunning in the medial and lateral femur 55 min post-running (5.4 to 5.5%, P b 0.022) and in all four tibiofemoral compartments 90 min post-running (6.9 to 11.1%, P b 0.01). A significant group effect was found for T1ρ in the medial femur, with greater values in those with KOA compared with controls. Conclusion: While Group × Time interactions in T2 and T1ρ relaxation times remained statistically insignificant, the observed significant increases in T2 in runners with tibiofemoral osteoarthritis TFOA may suggest slower and continuing changes in the cartilage and thus a need for longer recovery after running. Future research should investigate the effects of repeated exposure to running.
Objective
The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA).
Methods
In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm3/year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force‐measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined.
Results
The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R2 = 0.35, β = −0.119, P < 0.001).
Conclusion
Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.