AUTHOR=Benjamin Courteney Leigh , Sekiguchi Yasuki , Fry Lauren Amanda , Casa Douglas James 

TITLE=Performance Changes Following Heat Acclimation and the Factors That Influence These Changes: Meta-Analysis and Meta-Regression

JOURNAL=Frontiers in Physiology

VOLUME=10

YEAR=2019

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2019.01448

DOI=10.3389/fphys.2019.01448

ISSN=1664-042X

ABSTRACT=<p>Heat acclimation (HA) is the process of intentional and consistent exercise in the heat that results in positive physiological adaptations, which can improve exercise performance both in the heat and thermoneutral conditions. Previous research has indicated the many performance benefits of HA, however, a meta-analysis examining the magnitude of different types of performance improvement is absent. Additionally, there are several methodological discrepancies in the literature that could lead to increased variability in performance improvement following HA and no previous study has examined the impact of moderators on performance improvement following HA. Therefore, the aim of this study was two-fold; (1) to perform a meta-analysis to examine the magnitude of changes in performance following HA in maximal oxygen consumption (VO<sub>2max</sub>), time to exhaustion, time trial, mean power, and peak power tests; (2) to determine the impact of moderators on results of these performance tests. Thirty-five studies met the inclusion/exclusion criteria with 23 studies that assessed VO<sub>2max</sub> (<italic>n</italic> = 204), 24 studies that assessed time to exhaustion (<italic>n</italic> = 232), 10 studies that performed time trials (<italic>n</italic> = 101), 7 studies that assessed mean power (<italic>n</italic> = 67), and 10 papers that assessed peak power (<italic>n</italic> = 88). Data are reported as Hedge's g effect size (ES), and 95% confidence intervals (95% CI). Statistical significance was set to <italic>p</italic> &lt; 0.05, a priori. The magnitude of change following HA was analyzed, with time to exhaustion demonstrating the largest performance enhancement (ES [95% CI], 0.86 [0.71, 1.01]), followed by time trial (0.49 [0.26, 0.71]), mean power (0.37 [0.05, 0.68]), VO<sub>2max</sub> (0.30 [0.07, 0.53]), and peak power (0.29 [0.09, 0.48]) (<italic>p</italic> &lt; 0.05). When all of the covariates were analyzed as individual models, induction method, fitness level, heat index in time to exhaustion (coefficient [95% CI]; induction method, −0.69 [−1.01, −0.37], <italic>p</italic> &lt; 0.001; fitness level, 0.04 [0.02, 0.06], <italic>p</italic> &lt; 0.001; heat index, 0.04 [0.02, 0.07], <italic>p</italic> &lt; 0.0001) and induction length in mean power (coefficient [95% CI]; induction length 0.15 [0.05, 0.25], <italic>p</italic> = 0.002) significantly impacted the magnitude of change. Sport scientists and researchers can use the findings from this meta-analysis to customize HA induction. For time to exhaustion improvements, HA implementation should focus on induction method and baseline fitness, while the training and recovery balance could lead to optimal time trial performance.</p>