Background: Nowadays, the development of training programs for speed, agility and reaction time responses in football players is increasing widely. Motor imagery is a new method that uses collateral with physical training. However, there is still a scarcity of evidence concerning the addition of motor imagery protocol to routine training programs.
Objective: The main objective was to compare speed, agility and reaction time after motor imagery training in university athletes and amateur athletes who received and did not receive motor imagery training for 2 weeks.
Methods: Participants were divided into 4 subgroups as follows: university athlete group with motor imagery training and control group, amateur athlete group with motor imagery training and control group. This study collected the training effects of speed, agility and reaction time. The Wilcoxon signed-rank test and the Mann-Whitney U test were selected to analyse the differences within and between groups, respectively.
Results: The result presented positive changes in all variables after training sessions for 2 weeks in all groups. Speed at 20 m, agility, and reaction time were found to be significantly different after motor imagery training in both university athletes and amateur athletes.
Conclusion: This finding demonstrated that the addition of the motor imagery training along with routine physical training promotes physical performance in athletes at all experience levels. In further studies, the retention effect after practice should be considered.
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http://dx.doi.org/10.1142/S1013702524500021 | DOI Listing |
J Burn Care Res
January 2025
School of Physical and Occupational Therapy, McGill University.
Fear avoidance (FA) describes beliefs and behaviors related to avoiding movements or activities after a painful event. FA is a prevalent issue that limits the recovery outcomes and social reintegration of burn survivors. However, as current literature focuses on chronic conditions, understanding the impact and treatment of FA within sudden onset musculoskeletal (MSK) conditions, specifically in the burn survivor population, is lacking.
View Article and Find Full Text PDFAnn N Y Acad Sci
January 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences.
View Article and Find Full Text PDFRev Sci Instrum
January 2025
Shenyang Bluewisdom Technology Co., Ltd., Shenyang, Liaoning Province 110623, China.
Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms.
View Article and Find Full Text PDFHRB Open Res
September 2024
UCD School of Public Health, Physiotherapy and Sports Science, Health Sciences Centre, University College Dublin, Dublin, Leinster, Ireland.
Background: Following Spinal Cord Injury (SCI), 53% of people develop neuropathic pain (NP). NP can be more debilitating than other consequences of SCI, and a persistent health issue. Pharmacotherapies are commonly recommended for NP management in SCI, although severe pain often remains refractory to these treatments in many sufferers.
View Article and Find Full Text PDFFront Neural Circuits
January 2025
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan.
Introduction: Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.
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