Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study

Objective: The goal of this study was to utilize Gait Profile Score (GPS) and Gait Deviation Index (GDI), to assess its capability of differentiating between injured and non-injured runners. Design: In total, 45 long-distance runners (15 non-injured, 30 injured), diagnosed with one of the following...

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Autores principales:	Sam Khamis, Ron Gurel, Moran Arad, Barry Danino
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	running gait analysis injury kinematics Gait Profile Score (GPS) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/bbbd7543962a4cfbaf2182ef1b2c6c4e
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id	oai:doaj.org-article:bbbd7543962a4cfbaf2182ef1b2c6c4e
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spelling	oai:doaj.org-article:bbbd7543962a4cfbaf2182ef1b2c6c4e2021-11-25T16:40:08ZIdentifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study10.3390/app1122108982076-3417https://doaj.org/article/bbbd7543962a4cfbaf2182ef1b2c6c4e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10898https://doaj.org/toc/2076-3417Objective: The goal of this study was to utilize Gait Profile Score (GPS) and Gait Deviation Index (GDI), to assess its capability of differentiating between injured and non-injured runners. Design: In total, 45 long-distance runners (15 non-injured, 30 injured), diagnosed with one of the following running related injuries, patella femoral pain syndrome, iliotibial pain syndrome, and medial tibial stress syndrome, were recruited. Methods: Data were obtained from a running analysis gait laboratory equipped with eight infrared motion-capturing cameras and a conventional treadmill. Running kinematics were recorded according to the Plug-In Gait model, measuring running deviations of the pelvis and lower extremities at a sampling rate of 200 Hz. GPS and GDI were calculated integrating pelvis and lower limb kinematics. Movement Analysis Profile results were compared between injured and non-injured runners. The non-parametric two-sample Wilcoxson test determined whether significant kinematic differences were observed. Results: Total GPS score significantly differed between the injured and non-injured runners. Not all running kinematics expressed by GDI differed between groups. Conclusions: GPS score was capable of discriminating between the injured and non-injured runners’ groups. This new running assessment method makes it possible to identify running injuries using a single numerical value and evaluate movements in individual joints.Sam KhamisRon GurelMoran AradBarry DaninoMDPI AGarticlerunninggait analysisinjurykinematicsGait Profile Score (GPS)TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10898, p 10898 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	running gait analysis injury kinematics Gait Profile Score (GPS) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	running gait analysis injury kinematics Gait Profile Score (GPS) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Sam Khamis Ron Gurel Moran Arad Barry Danino Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study
description	Objective: The goal of this study was to utilize Gait Profile Score (GPS) and Gait Deviation Index (GDI), to assess its capability of differentiating between injured and non-injured runners. Design: In total, 45 long-distance runners (15 non-injured, 30 injured), diagnosed with one of the following running related injuries, patella femoral pain syndrome, iliotibial pain syndrome, and medial tibial stress syndrome, were recruited. Methods: Data were obtained from a running analysis gait laboratory equipped with eight infrared motion-capturing cameras and a conventional treadmill. Running kinematics were recorded according to the Plug-In Gait model, measuring running deviations of the pelvis and lower extremities at a sampling rate of 200 Hz. GPS and GDI were calculated integrating pelvis and lower limb kinematics. Movement Analysis Profile results were compared between injured and non-injured runners. The non-parametric two-sample Wilcoxson test determined whether significant kinematic differences were observed. Results: Total GPS score significantly differed between the injured and non-injured runners. Not all running kinematics expressed by GDI differed between groups. Conclusions: GPS score was capable of discriminating between the injured and non-injured runners’ groups. This new running assessment method makes it possible to identify running injuries using a single numerical value and evaluate movements in individual joints.
format	article
author	Sam Khamis Ron Gurel Moran Arad Barry Danino
author_facet	Sam Khamis Ron Gurel Moran Arad Barry Danino
author_sort	Sam Khamis
title	Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study
title_short	Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study
title_full	Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study
title_fullStr	Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study
title_full_unstemmed	Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study
title_sort	identifying running deviations in long distance runners utilizing gait profile analysis: a case control study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bbbd7543962a4cfbaf2182ef1b2c6c4e
work_keys_str_mv	AT samkhamis identifyingrunningdeviationsinlongdistancerunnersutilizinggaitprofileanalysisacasecontrolstudy AT rongurel identifyingrunningdeviationsinlongdistancerunnersutilizinggaitprofileanalysisacasecontrolstudy AT moranarad identifyingrunningdeviationsinlongdistancerunnersutilizinggaitprofileanalysisacasecontrolstudy AT barrydanino identifyingrunningdeviationsinlongdistancerunnersutilizinggaitprofileanalysisacasecontrolstudy
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Identifying Running Deviations in Long Distance Runners Utilizing Gait Profile Analysis: A Case Control Study

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