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Applied Sciences
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Sports Injuries: Prevention and Rehabilitation
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Sports Injuries: Prevention and Rehabilitation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1003

Special Issue Editors

Dr. Iwona Sulowska-Daszyk

E-Mail Website
Guest Editor
Institute of Clinical Rehabilitation, University of Physical Education in Krakow, Poland Al. Jana Pawla II 78, 31-571 Krakow, Poland
Interests: foot; short foot; injuries; injury prevention; movement; biomechanics; athletes; gait; motion analysis; FMS; running; runners
Prof. Dr. Anna Mika

E-Mail Website
Guest Editor
Institute of Clinical Rehabilitation, University of Physical Education in Kraków, 31-571 Krakow, Poland
Interests: rehabilitation medicine; sports medicine; exercise science; biomechanics; bioengineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Participating in sports is inherently associated with the risk of injury and overuse. The most common issues involve dysfunctions of the musculoskeletal system.

Currently, this is not an aspect that concerns only competitive sports. Amateur sports have also achieved incredible development, which is why they are often practiced at a very high level, consequently leading to a risk of injuries comparable to that of professional athletes.

Competitive athletes typically exhibit a very high level of overall physical preparedness, a factor that is often lacking among amateur athletes. This preparedness significantly influences the frequency of sports-related injuries.

The goal of this Special Issue is to examine methods for preventing and treating sports injuries across all levels of athletic performance, from professional to amateur athletes. Original research, narratives, and systematic reviews are welcome, as these will provide valuable perspectives and enhance strategies for injury prevention and rehabilitation across various sports disciplines. Such contributions will enrich existing knowledge and support its practical application by athletes, coaches, and physiotherapists.

Dr. Iwona Sulowska-Daszyk
Prof. Dr. Anna Mika
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sports-related injuries
  • injury prevention
  • rehabilitation
  • treatment
  • athletes

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Published Papers (2 papers)

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Research

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13 pages, 1049 KiB  
Article
Injury Prediction in Korean Adult Field Hockey Players Using Machine Learning and SHAP-Based Feature Importance Analysis
by Minkyung Choi, Kumju Lee and Kihyuk Lee
Appl. Sci. 2025, 15(16), 8946; https://doi.org/10.3390/app15168946 - 13 Aug 2025
Viewed by 234
Abstract
Field hockey involves repetitive high-intensity movements and physical contact, posing a high risk of injury. However, studies developing injury prediction models without relying on expensive tools such as GPS remain limited. This study aimed to develop an explainable AI model that predicts injury [...] Read more.
Field hockey involves repetitive high-intensity movements and physical contact, posing a high risk of injury. However, studies developing injury prediction models without relying on expensive tools such as GPS remain limited. This study aimed to develop an explainable AI model that predicts injury occurrence using only simple questionnaire-based data and visually identifies key predictors. Survey data were collected from 239 adult players registered with the Korea Field Hockey Association in 2024, including university and professional team athletes. Ten variables were used: sex, team affiliation, playing experience, player level, warm-up duration, weekly training hours and days, and physical indicators (age, height, weight). Injury was defined as an event within the past year that resulted in being unable to train for more than 24 h. Logistic Regression, Random Forest, and XGBoost models were compared. The final model—Logistic Regression—underwent SHAP-based visualization for interpretability. The Logistic Regression model showed the best performance in recall (0.6810 ± 0.0983), F1-score (0.6260 ± 0.0499), and AUC (0.6515 ± 0.0393). SHAP analysis identified Group, Training Time, Weight, and Player Level as key predictors, and visualized their contributions to individual predictions. This study demonstrates that a lightweight, interpretable injury prediction model using only simple survey data can achieve practical performance. This approach offers valuable insights for real-world applications and the development of injury prevention strategies. Full article
(This article belongs to the Special Issue Sports Injuries: Prevention and Rehabilitation)
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Review

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13 pages, 665 KiB  
Review
Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
by Luke Canavan Dignam, Lisa Ryan, Michael McCann and Ed Daly
Appl. Sci. 2025, 15(14), 7874; https://doi.org/10.3390/app15147874 - 14 Jul 2025
Viewed by 508
Abstract
Sport injury recognition is rapidly evolving with the integration of new emerging technologies. This systematic review aims to identify and evaluate technologies capable of detecting injuries during sports participation. A comprehensive search of PUBMED, Sport Discus, Web of Science, and ScienceDirect was conducted [...] Read more.
Sport injury recognition is rapidly evolving with the integration of new emerging technologies. This systematic review aims to identify and evaluate technologies capable of detecting injuries during sports participation. A comprehensive search of PUBMED, Sport Discus, Web of Science, and ScienceDirect was conducted following the PRISMA 2020 guidelines. The review was registered on PROSPERO (CRD42024608964). Inclusion criteria focused on prospective studies involving athletes of all ages, evaluating tools which are utilised to identify injuries in sports settings. The review included research between 2014 and 2024; retrospective, conceptual, and fatigue-focused studies were excluded. Risk of bias was assessed using the Critical Appraisal Skills Program (CASP) tool. Of 4283 records screened, 70 full-text articles were assessed, with 21 studies meeting the final inclusion criteria. The technologies were grouped into advanced imaging (Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DFI), and Quantitative Susceptibility Mapping (QSM), with biomarkers (i.e., Neurofilament Light (NfL), Tau protein, Glial Fibrillary Acidic Protein (GFAP), Salivary MicroRNAs, and Immunoglobulin A (IgA), and sideline assessments (i.e., the King–Devick test, KD-Eye Tracking, modified Balance Error Scoring System (mBESS), DETECT, ImPACT structured video analysis, and Instrumented Mouth Guards (iMGs)), which demonstrated feasibility for immediate sideline identification of injury. Future research should improve methodological rigour through larger, diverse samples and controlled designs, with real-world testing environments. Following this guidance, the application of emerging technologies may assist medical staff, coaches, and national governing bodies in identifying injuries in a sports setting, providing real-time assessment. Full article
(This article belongs to the Special Issue Sports Injuries: Prevention and Rehabilitation)
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