2018
DOI: 10.1016/j.humov.2018.01.005
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Relationship between jumping abilities and skeletal muscle architecture of lower limbs in humans: Systematic review and meta-analysis

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Cited by 13 publications
(9 citation statements)
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“…The architectural characteristics of the muscles, namely muscle thickness, fascicle angle and fascicle length, have been associated with muscle strength, muscle power and RFD [21,[24][25][26]. Traditional resistance training induces significant changes in muscle architecture by increasing mainly the muscle thickness and fascicle angle [27,28].…”
Section: Introductionmentioning
confidence: 99%
“…The architectural characteristics of the muscles, namely muscle thickness, fascicle angle and fascicle length, have been associated with muscle strength, muscle power and RFD [21,[24][25][26]. Traditional resistance training induces significant changes in muscle architecture by increasing mainly the muscle thickness and fascicle angle [27,28].…”
Section: Introductionmentioning
confidence: 99%
“…In addition, unilateral sports with a high volume of strong eccentric movements of the dominant limb would appear to cause an increase in the length of the muscle fascicle and a reduction of pennation angles [49] that have been described as asymmetric changes in modalities such as snowboarding and soccer [25, 50]. Contrary to these findings, in the work of Aeles et al [51] involving well-trained jumping athletes and untrained subjects, no significant differences were found in muscle architecture between the dominant and non-dominant lower limbs, and additionally some of the most recent reviews indicate that muscle architecture could be unrelated to achieving an enhanced sport performance in jumping [51, 52]. Therefore, changes in muscle architecture in trained athletes would not show so many chronic adaptations to exercise but would represent acute changes resulting from muscle fatigue caused by the activity and would disappear after 15 minutes of recovery [53, 54].…”
Section: Discussionmentioning
confidence: 99%
“…The Pearson product-moment correlation coefficient (r) is used as the effect size of this study. Since the variance is strongly dependent on the correlation, the r coefficient is transformed by the formula into Fisher 'z ( 25 ). The sample correlation r is converted into Fisher's Z by formula (1), and the standard error is calculated by formula (2), where n is the sample size.…”
Section: Methodsmentioning
confidence: 99%