Volume 12, Issue 1 (6-2026)
J Sport Biomech 2026, 12(1): 70-88 |
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Ahanjan S, Dashti Rostami K, Jalalvand A. Investigation of Chronic Ankle Instability in Athletes Using Artificial Neural Networks. J Sport Biomech 2026; 12 (1) :70-88
URL: http://biomechanics.iauh.ac.ir/article-1-444-en.html
URL: http://biomechanics.iauh.ac.ir/article-1-444-en.html
1- Department of Sport Sciences and Health, Amirkabir University of Technology, Tehran, Iran.
2- Department of Motor Behavior and Biomechanics, Faculty of Sport Sciences, University of Mazandaran, Babolsar, Iran.
3- Department of Sport Biomechanics, Ha.C., Islamic Azad University, Hamedan, Iran.
2- Department of Motor Behavior and Biomechanics, Faculty of Sport Sciences, University of Mazandaran, Babolsar, Iran.
3- Department of Sport Biomechanics, Ha.C., Islamic Azad University, Hamedan, Iran.
Abstract: (14 Views)
Objective An artificial neural network (ANN) is an information-processing concept inspired by the biological nervous system, capable of handling data in a manner similar to the human brain. The key feature of this approach is its architecture, which consists of numerous interconnected processing elements that operate collectively to solve complex problems. Chronic ankle instability (CAI) is one of the most common and troublesome sequelae of acute ankle sprains. It is typically attributed to mechanical and functional instability, yet these categories do not fully capture the range of pathological conditions contributing to CAI. Therefore, the present study aimed to estimate and classify chronic ankle instability in athletes using artificial neural networks.
Methods Forty athletes participated in this study and were divided into two groups: 20 athletes with chronic ankle instability and 20 without a history of ankle injury. The analyzed variables included height, weight, forefoot width, foot pronation, active and passive dorsiflexion, plantarflexion, inversion, and eversion ranges of motion (ROM), static postural control, and performance in the Illinois agility test. Independent t-tests and discriminant analyses were used for statistical comparisons. Subsequently, ANN modeling was applied to identify the most influential predictors of CAI.
Results Data modeling with the optimal neural network achieved an R value of 0.9997, with a minimal error between the experimental and predicted data, indicating very high predictive accuracy. The ANN results identified forefoot width and passive inversion ROM as the most critical factors influencing ankle instability.
Conclusion The artificial neural network demonstrated exceptional accuracy in predicting ankle instability. The findings suggest that increased forefoot width and excessive inversion range of motion are major contributors to chronic ankle instability. Preventive strategies such as the use of ankle braces or taping are recommended for athletes exhibiting these characteristics.
Methods Forty athletes participated in this study and were divided into two groups: 20 athletes with chronic ankle instability and 20 without a history of ankle injury. The analyzed variables included height, weight, forefoot width, foot pronation, active and passive dorsiflexion, plantarflexion, inversion, and eversion ranges of motion (ROM), static postural control, and performance in the Illinois agility test. Independent t-tests and discriminant analyses were used for statistical comparisons. Subsequently, ANN modeling was applied to identify the most influential predictors of CAI.
Results Data modeling with the optimal neural network achieved an R value of 0.9997, with a minimal error between the experimental and predicted data, indicating very high predictive accuracy. The ANN results identified forefoot width and passive inversion ROM as the most critical factors influencing ankle instability.
Conclusion The artificial neural network demonstrated exceptional accuracy in predicting ankle instability. The findings suggest that increased forefoot width and excessive inversion range of motion are major contributors to chronic ankle instability. Preventive strategies such as the use of ankle braces or taping are recommended for athletes exhibiting these characteristics.
Type of Study: Research |
Subject:
Special
Received: 2025/09/27 | Accepted: 2025/10/30 | Published: 2025/11/1
Received: 2025/09/27 | Accepted: 2025/10/30 | Published: 2025/11/1
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