International Journal of Human Movement and Sports Sciences Vol. 9(6), pp. 1219 - 1229
DOI: 10.13189/saj.2021.090616
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A Statistical Model for Prediction of Lower Limb Injury of Active Sportsperson

Hemantajit Gogoi ^1,*, Poli Borah ², Lakshyajit Gogoi ³, Yajuvendra Singh Rajpoot ⁴, Tadang Minu ⁵, Joseph Singh ², Mantu Baro ^6
1 Department of Sports Coaching, Sri Sri Aniruddhadeva Sports University, India
² Department of Sports Biomechanics, Lakshmibai National Institute of Physical Education, India
³ North East Regional Centre, Lakshmibai National Institute of Physical Education, India
⁴ Department of Physical Education Pedagogy, Lakshmibai National Institute of Physical Education, India
⁵ Department of Physical Education, Rajiv Gandhi University, India
⁶ Department of Physical Education, Dibrugarh University, India

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.

KEYWORDS
Gait Kinematics, Lower Limb Injury, Injury Prediction, Injury Prediction Model and Logistic Regression

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Hemantajit Gogoi , Poli Borah , Lakshyajit Gogoi , Yajuvendra Singh Rajpoot , Tadang Minu , Joseph Singh , Mantu Baro , "A Statistical Model for Prediction of Lower Limb Injury of Active Sportsperson," International Journal of Human Movement and Sports Sciences, Vol. 9, No. 6, pp. 1219 - 1229, 2021. DOI: 10.13189/saj.2021.090616.

(b). APA Format:
Hemantajit Gogoi , Poli Borah , Lakshyajit Gogoi , Yajuvendra Singh Rajpoot , Tadang Minu , Joseph Singh , Mantu Baro (2021). A Statistical Model for Prediction of Lower Limb Injury of Active Sportsperson. International Journal of Human Movement and Sports Sciences, 9(6), 1219 - 1229. DOI: 10.13189/saj.2021.090616.