Acute Effect of Blood Flow Restricted High-Intensity Resistive Training

Recommended by many professional organizations such as the American College of Sports Medicine (ACSM) and the National Strength and Conditioning Association (NSCA), resistance exercise training improves muscle strength, body composition, performance, functionality and quality of life in healthy young adults. Blood flow restriction (BFR), also known as Kaatsu training, is proposed as a new method to improve muscle strength and muscle hypertrophy in a short time. Although there are studies in the literature in which BFR is applied with low-intensity resistance training, no studies have been found examining the effects of BFR applied with high-intensity resistance training on the neuromuscular adaptations of the quadriceps femoris muscle. Determining whether high-intensity resistance training with BFR is more effective on the quadriceps femoris muscle compared to high-intensity resistance training applied without BFR may provide faster and more effective responses by arranging resistance training protocols in terms of neuromuscular gains. Between 07.03.2022 and 07.06.2022, in the Biruni University Physiotherapy and Rehabilitation Department Application Laboratory, in which 24 healthy young adults between the ages of 18-25 (from 12 BFR, 12 BFR) will be included, exercise training to be applied with and without BFR was determined. Its effects will be compared with electromyographic measurement, single leg squat test, and measurement of late-onset muscle pain (Visual Analog Scale). IBM SPSS 21.0 statistical program will be used for statistical analysis. Whether the variables are suitable for normal distribution will be analyzed with the Shapiro-Wilk test. If the variables show normal distribution, the variation within the group will be analyzed with the paired samples t test, and if it does not show normal distribution, the Wilcoxon Signed rank test will be analyzed. Comparison of groups will be made with independent samples t-test in independent groups if the variables show normal distribution, and with Mann Whitney-U test if they do not show normal distribution. Categorical data distributions will be evaluated with the Chi-square test.