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S. Khamis1,2, R. Gurel3,4, M. Arad1, B. Danino3,4
1Dana Children’s Hospital, Tel Aviv Sourasky Medical Center, Department of Pediatric Orthopedics, Gait and Motion Analysis Laboratory, Tel Aviv, Israel, 2IMACS Israeli Motion Analysis Center for Sports, Tel Aviv, Israel, 3Dana Children’s Hospital, Tel Aviv Sourasky Medical Center, Department of Pediatric Orthopedics, Tel Aviv, Israel, 4Tel Aviv University, Sackler Faculty of Medicine, Tel Aviv, Israel
Background: A biomechanical running analysis, used in sports as a diagnostic tool, assists in the understanding of the kinematics of a wide range of variables throughout the running cycle, as well as detecting abnormal biomechanics. Currently, no single score has been ascertained to identify running deviations. Such a score can be used forclassifying running deviations with a single numeric value, based on pelvis lower limb kinematics, as well as identifying movement patterns which are risk factors for running injuries.
Purpose: 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.
Methods: 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. 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.
The GPS as well as the GDI are represented by the same units (°) as the kinematic variables, but as these scores denote the distance from the normal average, the smaller values indicate less deviation from the norm or better results. In the literature, the GPS has been found to be a valid tool reflecting the quality of the gait pattern in several pathologies.The GDI, incorporates nine features, including three-dimensional rotation angles for the pelvis and the hip, the sagittal plane at the knee, and the sagittal plane at the ankle, as well as the foot progression angle. Kinematic variables include pelvic obliquity, tilt and rotation, hip ab/adduction, flexion/extension, rotations, knee flexion/extension, ankle dorsi/plantar flexion, and foot progression angle.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. p-values were corrected by the Benjamini–Hochberg procedure, thus, guaranteeing a false discovery rate control of 0.05 per measure.
The GPS as well as the GDI are represented by the same units (°) as the kinematic variables, but as these scores denote the distance from the normal average, the smaller values indicate less deviation from the norm or better results. In the literature, the GPS has been found to be a valid tool reflecting the quality of the gait pattern in several pathologies.The GDI, incorporates nine features, including three-dimensional rotation angles for the pelvis and the hip, the sagittal plane at the knee, and the sagittal plane at the ankle, as well as the foot progression angle. Kinematic variables include pelvic obliquity, tilt and rotation, hip ab/adduction, flexion/extension, rotations, knee flexion/extension, ankle dorsi/plantar flexion, and foot progression angle.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. p-values were corrected by the Benjamini–Hochberg procedure, thus, guaranteeing a false discovery rate control of 0.05 per measure.
Results: Total GPS score significantly differed between the injured and non-injured runners between groups, both on the left and right side (p= 0.000). 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.
Implications: The correlation and causative factors between running injuries and kinematic deviations is still questionable, probably because several minor kinematic deviations must occur simultaneously in order to cause injury. The GPS is capable of integrating kinematic deviations into one score. In our study, by evaluating this score, we were able to differentiate between injured and uninjured runners, thus, suggesting integrating this measurement into studies to detect and possibly prevent running injuries based on the running profile.
Funding acknowledgements: None
Keywords:
Running injuries
Running analysis
Gait Profile Score (GPS)
Running injuries
Running analysis
Gait Profile Score (GPS)
Topics:
Musculoskeletal: lower limb
Orthopaedics
Musculoskeletal: lower limb
Orthopaedics
Did this work require ethics approval? Yes
Institution: Tel Aviv Sourasky Medical Center
Committee: Tel Aviv Sourasky Medical Center, Tel Aviv, Israel’s Ethics Committee
Ethics number: (0055-20-TLV)
All authors, affiliations and abstracts have been published as submitted.
