Injury rates among runners are high, with the knee injured most frequently. The interaction of running experience and running mechanics is not well understood but may be important for understanding relative injury risk in low vs higher mileage runners. The study aim was to apply a principal component analysis (PCA) to test the hypothesis that differences exist in kinematic waveforms and coordination between higher and low mileage groups. Gait data were collected for 50 subjects running at 3.5 m/s assigned to either a low (< 15 miles/wk) or higher (> 20 miles/wk, 1 year experience) mileage group. A PCA was performed on a matrix of trial vectors of all force, joint kinematic, and center of pressure data. The projection of the subjects' trial vectors onto the linear combination of PC7, PC10, PC13, and PC19 was significantly different between the higher and lower mileage groups (d = 0.63, P = .012). This resultant PC represented variation in transverse plane pelvic rotation, hip internal rotation, and hip and knee abduction and adduction angles. These results suggest the coordination of lower extremity segment kinematics is different for lower and higher mileage runners. The adopted patterns of coordinated motion may explain the lower incidence of overuse knee injuries for higher mileage runners.
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http://dx.doi.org/10.1123/jab.2013-0261 | DOI Listing |
Sensors (Basel)
December 2024
School of Urban Construction and Transportation, Hefei University, Hefei 230601, China.
Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis.
View Article and Find Full Text PDFSci Adv
December 2024
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to = 225,212 middle-aged and older adults (mean age, 56.
View Article and Find Full Text PDFTraffic Inj Prev
November 2024
University of Michigan Transportation Research Institute, University of Michigan, Ann Arbor, Michigan.
Objectives: Motor vehicle travel shifts during the COVID-19 pandemic resulted in fewer vehicle miles traveled yet paradoxically higher fatality rates. Anecdotally, the paradox was blamed on increases in risky behavior in the absence of regular traffic and enforcement. We examined three hypotheses to explain the fatality paradox using Michigan crash data: (1) lack of congestion led to higher-speed impacts; (2) increased risky driver/driving; and (3) low-risk driving miles decreased.
View Article and Find Full Text PDFPhys Ther Sport
January 2025
Faculty of Sport, Technology and Health Sciences, St Mary's University, London, UK. Electronic address:
Objectives: To determine the incidence, severity, and nature of injuries sustained by female trail runners and investigate selected training variables as risk factors for injuries.
Design: Cross-sectional, retrospective cohort study.
Setting: Online questionnaire (Jisc Online Surveys).
Environ Int
November 2024
Chengdu Technical Center of Vehicle Exhaust Pollution, No. 69, Haitong Street, Chengdu, China. Electronic address:
Accurately estimating vehicle emissions is crucial for effective air quality management. As key data for emission inventory construction, emission factors (EFs) are influenced by vehicle usage characteristics and experience deterioration. Current deterioration models often employ single-factor approaches based on vehicle age or accumulated mileage, which fail to capture the effects of varying usage intensities within the same mileage or age intervals.
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