A Statistical Model for Prediction of Lower Limb Injury of Active Sportsperson

Hemantajit Gogoi,Tadang Minu,Poli Borah,Yajuvendra Singh Rajpoot,Lakshyajit Gogoi,Joseph Singh,Mantu Baro

doi:10.13189/saj.2021.090616

Abstract

For an active sportsperson, running is the most common physical activity, but it carries a high risk of musculoskeletal injuries. Half of the running injuries are identified as overuse injuries, with the most affected areas being the lower limbs. Previous studies had revealed several factors responsible for the development of running-related lower-limb injuries of sportspersons. However, there have been few studies aiming at predicting them. Therefore, the present study aimed to develop a predictive model to predict lower limb injury of active sportsperson. The BTS G-WALK system synchronised with two GoPro Hero 6 cameras were used to conduct the study on seventy-five (N=75) healthy male subjects without any lower limb injury history. The BTS G-WALK system provided spatio-temporal parameters while Kinovea software was used to extract kinematic data from raw videos of treadmill running movement of the subjects. A prospective cohort study design was used to investigate how the difference in running gait kinematic affects the outcome of lower limb injury occurrences of active sportspersons. Further, a prediction model was developed using binary logistic regression, for which IBM® SPSS® version 25 was used. All statistical analyses were tested at 0.05 (p = 0.05) level of significance. The model indicated that Range of Pelvic Obliquity (RPO) and Maximum Toe Out (MTO) were positively and Symmetry Index (SI) was negatively associated with an increased likelihood of exhibiting lower limb injury. The model explained 85.7% variance and correctly classified 93.3% cases of lower limb injury of an active sportsperson. The risk factors for lower limb injuries of a sportsperson can be identified and prediction of lower limb injury of a sportsperson is theoretically possible. To generalize the model for practical implications, the researcher suggested further research with larger sample size.

Highlights

Prediction is a feature of statistical interpretation with several approaches
The injury assessment questionnaire reported that 24% (n=18) of total subjects suffered from any lower limb injury during one-year follow-up period
The present study reported that the range of pelvic tilt was 8.30±2.55 degrees, the range of pelvic obliquity was 8.13±1.69 degrees and the range of pelvic rotation was 9.49±2.47 degrees

Summary

Introduction

Prediction is a feature of statistical interpretation with several approaches. While statistical analysis generally provides knowledge about a population based on a sample population, this is not always the case with predictive statistical analysis. To address the prevalence of running injuries including medial tibial stress syndrome (MTSS), patellofemoral pain (PFP), iliotibial band syndrome (ITBS), and achilles tendinopathy (AT); a great amount of study has been conducted already. It was reported that greater hip adduction or hip internal rotation may produce increased rearfoot eversion [2] The majority of such researches have been emphasising and analysing the pattern of injuries to develop training protocols aiming at extenuating them, rather than examining the factors that contribute to the risk of injury, while others have been conducted at a more sophisticated level to use the information to prevent future injuries [3]. In the present study, the researchers intend to develop a statistical predictive model to predict lower limb injury of an active sportsperson

Results

Discussion

Conclusion