129683 20241125101202.0 doi 10.3390/s23239336 sideral 135857 ART-2023-135857 eng Rodríguez, Javier Universidad de Zaragoza KeepRunning: A MoCap-Based Rapid Test to Prevent Musculoskeletal Running Injuries 2023 Access copy available to the general public Unrestricted The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner’s anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test–retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test–retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners’ body segments using elastic bands and the volunteers’ movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test–retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.4 2023 CHEMISTRY, ANALYTICAL 34 / 106 = 0.321 2023 Q2 T1 INSTRUMENTS & INSTRUMENTATION 24 / 76 = 0.316 2023 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 122 / 353 = 0.346 2023 Q2 T2 0.786 2023 Instrumentation 2023 Q1 Analytical Chemistry 2023 Q1 Atomic and Molecular Physics, and Optics 2023 Q1 Information Systems 2023 Q2 Medicine (miscellaneous) 2023 Q2 Biochemistry 2023 Q2 Electrical and Electronic Engineering 2023 Q2 7.3 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Marín, Javier Universidad de Zaragoza (orcid)0000-0003-4527-3267 Royo, Ana C. Universidad de Zaragoza (orcid)0000-0002-1625-1475 Padrón, Luis Universidad de Zaragoza Pérez-Soto, Manuel Universidad de Zaragoza Marín, José J. Universidad de Zaragoza (orcid)0000-0002-2463-7271 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5002 720 Universidad de Zaragoza Dpto. Ingeniería Diseño Fabri. Área Proyectos de Ingeniería 5002 305 Universidad de Zaragoza Dpto. Ingeniería Diseño Fabri. Área Expresión Gráfica en Ing. 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 5004 590 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Máquinas y Motores Térmi. 23, 23 (2023), 9336 [25 pp.] Sensors Sensors 1424-8220 4255706 http://zaguan.unizar.es/record/129683/files/texto_completo.pdf Versión publicada 2627971 http://zaguan.unizar.es/record/129683/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:129683 articulos driver 2024-11-22-12:12:02 ARTICLE