Identifying Biomechanical Risk Factors for Lower Limb Injuries in High Jump Athletes Using Penalised Logistic Regression

Background. High jump athletes are exposed to considerably lower limb injury risk due to repetitive high-impact loading and asymmetrical force application during the approach and take-off phases. Despite the biomechanical demands of the event, limited research has examined the predictive value of combined kinematic and neuromuscular factors in identifying athletes at elevated risk of musculoskeletal injury. Objectives. This study aimed to identify biomechanical predictors of lower limb injury risk in competitive male high jump athletes using penalised logistic regression. Materials and Methods. Twenty-one male national-level high jump athletes (age 21.14 ± 2.22 years; height 187.04 ± 5.36 cm; body mass 74.09 ± 5.04 kg) underwent 3D motion capture, ground reaction force analysis, and surface electromyography. Key predictors included cadence (steps/min), pelvic obliquity (°), pelvic rotation (°), and muscle activation asymmetry (% difference in EMG amplitude between limbs). Injury classification followed the International Olympic Committee’s consensus criteria, with injury history verified by medical records. Correlation analyses were followed by LASSO logistic regression with leave-one-out cross-validation. Model performance was assessed using AUC, sensitivity, specificity, predictive values, F1 score, calibration slope, intercept, and Brier score. Results. Four variables were retained in the final model: cadence (OR = 1.60, p = 0.021), pelvic obliquity (OR = 1.48, p = 0.033), pelvic rotation (OR = 1.36, p = 0.072), and muscle activation asymmetry (OR = 1.66, p = 0.018). The model demonstrated moderate discriminative ability (AUC = 0.78, 95% CI: 0.64–0.92), sensitivity of 0.75, and specificity of 0.71. However, calibration was suboptimal (slope = 0.24, intercept = 0.47, Brier score = 0.21), suggesting risk underestimation and potential overfitting. Conclusions. Muscle activation asymmetry, cadence, and pelvic kinematic deviations were associated with an increased risk of lower limb injury in high jump athletes. These findings highlight the importance of neuromuscular balance and lumbopelvic stability in injury screening. While the results demonstrate preliminary utility, small sample size and calibration limitations necessitate validation in larger, prospective cohorts before clinical application.

Authors

Institutions

Publication Details

Journal
Physical Education Theory and Methodology
Published
2025-11-26
DOI
https://doi.org/10.17309/tmfv.2025.6.12
Primary Topic
Sports injuries and prevention
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identifying Biomechanical Risk Factors for Lower Limb Injuries in High Jump Athletes Using Penalised Logistic Regression

Suchishrava Dubey, Hilmainur Syampurma, Yajuvendra Singh Rajpoot, Prashant Kumar Choudhary et al.
Physical Education Theory and Methodology
Sports injuries and prevention
article

Identifying Biomechanical Risk Factors for Lower Limb Injuries in High Jump Athletes Using Penalised Logistic Regression

Suchishrava Dubey, Hilmainur Syampurma, Yajuvendra Singh Rajpoot, Prashant Kumar Choudhary, Sohom Saha, Ritesh Bhardwaj
article en

Abstract

Background. High jump athletes are exposed to considerably lower limb injury risk due to repetitive high-impact loading and asymmetrical force application during the approach and take-off phases. Despite the biomechanical demands of the event, limited research has examined the predictive value of combined kinematic and neuromuscular factors in identifying athletes at elevated risk of musculoskeletal injury. Objectives. This study aimed to identify biomechanical predictors of lower limb injury risk in competitive male high jump athletes using penalised logistic regression. Materials and Methods. Twenty-one male national-level high jump athletes (age 21.14 ± 2.22 years; height 187.04 ± 5.36 cm; body mass 74.09 ± 5.04 kg) underwent 3D motion capture, ground reaction force analysis, and surface electromyography. Key predictors included cadence (steps/min), pelvic obliquity (°), pelvic rotation (°), and muscle activation asymmetry (% difference in EMG amplitude between limbs). Injury classification followed the International Olympic Committee’s consensus criteria, with injury history verified by medical records. Correlation analyses were followed by LASSO logistic regression with leave-one-out cross-validation. Model performance was assessed using AUC, sensitivity, specificity, predictive values, F1 score, calibration slope, intercept, and Brier score. Results. Four variables were retained in the final model: cadence (OR = 1.60, p = 0.021), pelvic obliquity (OR = 1.48, p = 0.033), pelvic rotation (OR = 1.36, p = 0.072), and muscle activation asymmetry (OR = 1.66, p = 0.018). The model demonstrated moderate discriminative ability (AUC = 0.78, 95% CI: 0.64–0.92), sensitivity of 0.75, and specificity of 0.71. However, calibration was suboptimal (slope = 0.24, intercept = 0.47, Brier score = 0.21), suggesting risk underestimation and potential overfitting. Conclusions. Muscle activation asymmetry, cadence, and pelvic kinematic deviations were associated with an increased risk of lower limb injury in high jump athletes. These findings highlight the importance of neuromuscular balance and lumbopelvic stability in injury screening. While the results demonstrate preliminary utility, small sample size and calibration limitations necessitate validation in larger, prospective cohorts before clinical application.

Physical Education Theory and MethodologyVol. 25(6)
Lakshmibai National Institute of Physical Education (IN), State University of Padang (ID)
Openalex Percentile: Top 54%
Sports injuries and prevention
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.