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      Effects of post-exercise sodium bicarbonate ingestion on acid-base balance recovery and time-to-exhaustion running performance: a randomised crossover trial in recreational athletes

      1 , 1 , 2 , 1
      Applied Physiology, Nutrition, and Metabolism
      Canadian Science Publishing

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          Abstract

          This study investigated the effect of post-exercise sodium bicarbonate (NaHCO 3) ingestion on acid-base balance recovery and time-to-exhaustion (TTE) running performance. Eleven male runners (stature, 1.80 ± 0.05 m; body mass, 74.4 ± 6.5 kg; maximal oxygen consumption, 51.7 ± 5.4 mL·kg −1·min −1) participated in this randomised, single-blind, counterbalanced and crossover design study. Maximal running velocity (v-V̇O 2max) was identified from a graded exercise test. During experimental trials, participants repeated 100% v-V̇O 2max TTE protocols (TTE1, TTE2) separated by 40 min following the ingestion of either 0.3 g·kg −1 body mass NaHCO 3 (SB) or 0.03 g·kg −1 body mass sodium chloride (PLA) at the start of TTE1 recovery. Acid-base balance (blood pH and bicarbonate, HCO 3 ) data were studied at baseline, post-TTE1, after 35 min recovery and post-TTE2. Blood pH and HCO 3 concentration were unchanged at 35 min recovery (p > 0.05), but HCO 3 concentration was elevated post-TTE2 for SB vs. PLA (+2.6 mmol·L −1; p = 0.005; g = 0.99). No significant differences were observed for TTE2 performance (p > 0.05), although a moderate effect size was present for SB vs. PLA (+14.3 s; g = 0.56). Post-exercise NaHCO 3 ingestion is not an effective strategy for accelerating the restoration of acid-base balance or improving subsequent TTE performance when limited recovery is available.

          Novelty: Post-exercise sodium bicarbonate ingestion did not accelerate the restoration of blood pH or bicarbonate after 35 min. Performance enhancing effects of sodium bicarbonate ingestion may display a high degree of inter-individual variation. Small-to-moderate changes in performance were likely due to greater up-regulation of glycolytic activation during exercise.

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          Most cited references34

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          Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

          Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
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            Generalized eta and omega squared statistics: measures of effect size for some common research designs.

            The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variety of research designs.
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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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                Author and article information

                Journal
                Applied Physiology, Nutrition, and Metabolism
                Appl. Physiol. Nutr. Metab.
                Canadian Science Publishing
                1715-5312
                1715-5320
                September 2021
                September 2021
                : 46
                : 9
                : 1111-1118
                Affiliations
                [1 ]Surrey Human Performance Institute, University of Surrey, Guildford GU2 7XH, United Kingdom.
                [2 ]Research Centre for Life and Sport Sciences (CLaSS), School of Health Sciences, Birmingham City University, Birmingham B15 3TN, United Kingdom.
                Article
                10.1139/apnm-2020-1120
                33730517
                e26e84c7-9264-4eda-9852-daf153fca54f
                © 2021

                http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining

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