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      Six Weeks of Unilateral Flywheel Hip-Extension and Leg-Curl Training Improves Flywheel Eccentric Peak Power but Does Not Enhance Hamstring Isokinetic or Isometric Strength

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          Abstract

          Purpose: This preregistered trial investigated how 6 weeks of unilateral flywheel leg-curl and hip-extension training impact isokinetic, isometric, and flywheel strength and power outcomes. Methods: The study involved 11 male university athletes (age 22 [2] y; body mass 77.2 [11.3] kg; height 1.74 [0.09] m) with one leg randomly allocated to flywheel training and one leg to control. Unilateral eccentric and isometric knee-flexion torque and flywheel unilateral leg-curl and hip-extension peak power were tested. Training intensity and volume (3–4 sets of 6 + 2 repetitions) were progressively increased. Results: The intervention enhanced hip-extension concentric ( P < .01, d = 1.76, large) and eccentric ( P < .01, d = 1.33, large) peak power more than the control (significant interaction effect). Similarly, eccentric ( P = .023, d = 1.05, moderate) peak power was enhanced for the leg curl. No statistically significant differences between conditions were found for isokinetic eccentric ( P = .086, d = 0.77, moderate) and isometric ( P = .431, d = 0.36, small) knee-flexor strength or leg-curl concentric peak power ( P = .339, d = 0.52, small). Statistical parametric mapping analysis of torque–angle curves also revealed no significant ( P > .05) time–limb interaction effect at any joint angle. Conclusion: Unilateral flywheel hamstring training improved knee-flexor eccentric peak power during unilateral flywheel exercise but not flywheel concentric, isokinetic eccentric, or isometric (long-lever) knee-flexor strength.

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          Progressive statistics for studies in sports medicine and exercise science.

          Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
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            Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine.

            Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
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              Hamstring injuries have increased by 4% annually in men's professional football, since 2001: a 13-year longitudinal analysis of the UEFA Elite Club injury study.

              There are limited data on hamstring injury rates over time in football.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
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                Journal
                International Journal of Sports Physiology and Performance
                Human Kinetics
                1555-0265
                1555-0273
                January 1 2024
                January 1 2024
                : 19
                : 1
                : 34-43
                Affiliations
                [1 ]School of Health and Sports Sciences, University of Suffolk, Ipswich, United Kingdom
                [2 ]Institute of Health and Wellbeing, University of Suffolk, Ipswich, United Kingdom
                [3 ]School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom
                Article
                10.1123/ijspp.2023-0035
                fcae0dfb-5735-4e3a-b3ea-b19d20c9dc8b
                © 2024
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