Exercising in high environmental temperatures may cause precocious hyperthermia induced fatigue resulting in a decreased athletes’ performance output. This systematic review with meta-analysis investigated the possible effects of pre-exercise cooling on performance output. This study was performed according to the PRISMA guidelines and the PICO-model was used to establish the research question. The Cochrane Risk of Bias Tool was applied to assess the validity of the included studies. Study eligibility was given when the studies compared the effects between any kind of pre-cooling and non-cooling strategies prior to exercise on performance output. Twenty-nine studies met the inclusion criteria for quantitative analysis. Risk of bias was high or unclear but the performance bias was low. The estimated standardized mean difference revealed that external pre-cooling (21 studies) enhanced performance (Hedges’ g = 0.49 [95% CI: 0.33 to 0.64]), with the main effect observed in endurance cycling or running. Internal (7 studies) and mixed-method (5 studies) pre-cooling failed to significantly affect performance parameters. However, the main output parameter, evaluated in these studies, was peak power output. Subgroup analysis for different outcome measures was not possible because meaningful grouping was not plausible. Limitations of this meta-analysis were the high or unclear risk of bias and the comparability of the included studies. Future studies should also determine the effects of different pre-cooling applications on female and untrained participants. Based on the results of this meta-analysis, it can be concluded that there is some evidence in favour of external pre-cooling to avoid precocious hyperthermia induced fatigue in endurance athletes exercising in hot environments.
High environmental temperatures may negatively influence the athletes’ performance output due to precocious hyperthermia induced fatigue [
The research question was defined by the PICO-model in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [
An electronic systematic search, according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, was conducted between October 2016 and January 2017 on the MEDLINE (PubMed) database [
shows the flow-chart of the selection process.
Data extraction from the studies was performed by two researchers (EH, RC). These authors extracted independently from each other the data into spreadsheets. This method was used to extract relevant data on study eligibility and content of the study (including the cooling procedure, environmental conditions, exercise protocols and outcome measures). In case of disagreement, a third researcher (RS) checked the variable in the original study and agreement was sought by consensus. The primary outcomes considered in this meta-analysis were time to finish or exhaustion, maximum speed, covered distance or PPO of a specified exercise task. Data were extracted at the end of the performance of a specified exercise. The methodological quality of the studies was assessed with the Cochrane Risk of Bias Tool [
Several meta-analyses and forest plot drawings were conducted using the Comprehensive Meta-Analysis software (CMA II, Biostat Inc., Englewood, NJ 07631, US). A priori it was decided to use a random-effects model because the studies under investigation were not exact replicates of each other. The DerSimonian and Laird inversed-variance method was used to calculate the weighting factors. Because individual studies reported study results in different metrics, the calculated individual study effect sizes were standardized and expressed as Hedges’ g to correct for overestimation of the true effect size in small study samples. The corresponding 95% confidence intervals (95% CI) around the individual studies effect sizes as well as around the overall weighted effect were calculated. The latter indicates the range in which the mean effect size may fall, based on the universe from which this set of studies was sampled. Cohen’s benchmarking for the interpretation of the effect size was followed: g lower than 0.2 (neglible effect size), g between 0.21 and 0.49 (small effect size), g between 0.50 and 0.79 (moderate effect size), and g equal to or higher than 0.8 (very high effect size) [
To test the Null hypothesis of heterogeneity (i.e. that all studies have a common effect size), a Cochran’s Q-test was conducted and the Q-value was reported together with its corresponding degrees of freedom (df(Q)) and exact p-value. The significance level of this Q-test was set at 0.1 because this test is known to lack statistical power. Higgins’ I2 was calculated to express the amount of the total observed variance that can be explained by the true between studies variance rather than random sampling error and was reported as a relative number (i.e. in %). Higgins’ suggested benchmarking values for the interpretation of I2 was followed: I2 around 25% (low heterogeneity), I2 around 50% (moderate heterogeneity) and I2 around 75% or more (high heterogeneity) [
Subgroup meta-analysis was conducted to explain a part of the observed heterogeneity only if this was plausible (i.e. in case of a sufficient number of studies). Groups were established based on the type of exercise protocols (cycling, running and functional strength) and outcomes (cycle time to exhaustion, cycle time to finish, power output, running distance, running time, running time to exhaustion, sprint time and lifted weight) used in the individual studies. Because of the low numbers of studies within the subgroups it was decided to assume a common variance. Thus, to obtain a more accurate value of T2 in the subgroup analysis, T2 was pooled and used as the common between studies variance across all the subgroups.
The likelihood for publication bias was assessed only in the overall meta-analysis using the classic fail-safe N and the Duval and Tweedie’s trim and fill methods [
In the present work, the results of eight studies with healthy and active volunteers (k = 91), four studies with team-sport players (k = 49), eight studies with moderately trained (k = 57) and nine studies with well trained volunteers (k = 81) were included in different meta-analyses. Hence, a total of 278 participants (k = 272 males and k = 6 females [
endurance cycling (k = 11) or running (k = 5), repeated cycling (k = 3) or running (k = 6) sprints and sport specific tasks (k = 4).
External cooling techniques comprised of cooling vests (k = 4), local ice applications (k = 4), cold-water immersion (CWI; k = 3), combinations of external cooling (k = 11) and whole-body cryotherapy (WBC; k = 1). In total, 23 external cooling data sets were extracted from 21 studies.
Internal cooling techniques comprised of ingesting cold water (k = 3) and crushed ice/ice slurry/slushies (k = 4). In total, seven internal cooling data sets were extracted from seven studies. Mixed-method cooling comprised of the combination of ingesting ice slushies and waring cooling vests (k = 3) or ice towel applications (k = 2). In total, five mixed-method cooling data sets were extracted from five studies comprising of ingesting ice/drinking slushies and wearing cooling vests [
In these analysis, the effects of external cooling was studied on following outcome variables: PPO (k = 5) [
Authors | Sample size | Cooling method | Cooling duration | Environmental conditions | Exercise protocol | Outcome | p-value for cooling vs. control | |
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Temp (˚C) | RH (%) | |||||||
Bogerd et al. (2010) | k = 8 males | Ice vest | 45 min | 29 | 80 | Endurance cycling at 65% VO2max | Cycle time to exhaustion | <0.001 |
Brade et al. (2013)a | k = 10 males | Slushy & cooling vest | Ingesting 7 g/kg ice every 10 min up to 30 min & 8 min (vest) | 27 | N/a | Repeated sprint running for 80 min | Sprint time | 0.64 |
Brade et al. (2013)b | k = 10 males | Slushy & cooling vest | Ingesting 7g/kg ice every 10 min up to 30 min & 8 min (vest) | 35 | 58 | Repeated cycling sprint for 70 min | PPO | 0.60 |
Brade et al. (2014) | k = 12 males | Ice Vest & Slushy. + Ice Vest. + Slushy | Vest worn and ingesting 7 g/kg ice for max. 30 min. + vest worn for max. 30 min. + ingesting 7 g/kg ice up to 30 min. | 35 | 60 | Repeated cycling sprint for 70 min | PPO | 0.22+ 0.28+ 0.28 |
Byrne et al. (2011) | k = 7 males | Ingesting cold water | Ingesting 900 ml of cold water (2˚C) during 35 min | 32 | 60 | 30 min self-paced cycling trial | PPO | 0.04 |
Castle et al. (2006) | k = 12 males | Ice packs on upper legs | 20 min | 34 | 52 | Intermittent cycling sprint for 20 × 5 sec | PPO | 0.01 |
Duffield et al. (2007) | k = 9 males | CWI | 15 & 10 min | 32 | 30 | Intermittent sprint protocol for 2 × 30 min | Running distance | 0.33 |
Duffield et al. (2009) | k = 7 males | Cooling vest & cold towels to the neck & ice packs on the upper legs | 20 min | 32 | 44 | Intermittent sprint exercise for 30 min | Running distance | 0.001 |
Duffield et al. (2010) | k = 8 males | CWI | 20 min | 33 | 50 | Cycle time trial for 40 min | PPO | 0.01 |
Duffield et al. (2011) | k = 6 males and k = 2 females | Ice vest & cold towels to the neck & head | 20 min | 35 | 55 | On court tennis drills for 5 × 5 min | Running distance | 0.13 |
Faulkner et al. (2015) | k = 10 males | Cooling vest & cooling sleeves | 9 min | 35 | 50 | Cycling 60 min at 75% Wmax as fast as possible | PPO | 0.03 |
Galoza et al. (2011) | k = 16 males | Ice bags on the upper arm | 3 × 1 min | n/a | n/a | 4 sets of biceps curls at 70% of 1 RM | Weight lifted | 0.05 |
Gonzales et al. (2014) | k = 10 males | Cooling vest & headband | 5 & 15 min | 30 | 79 | Cycle time trial for 20 min | PPO | 0.01 |
Hue et al. (2013) | k = 5 males and k = 4 females | Ingesting ice water | Ingesting 190 ml water before exercise | 28 | 73 | 10 × 100 m swimming | Swimming time | 0.66 |
Ihsan et al. (2010) | k = 7 males | Ingesting crushed ice | Ingesting 6.8 g/kg ice within 30 min | 30 | 75 | Cycle time trial for 40 min | PPO | 0.12 |
James et al. (2015) | k = 12 males | Ice slurry + Cold towels to the neck and head & water immersion of hands & cooling vest & ice packs on upper legs | Ingesting 7.5 g/kg of ice within 20 min + 20 min | 32 | 62 | Incremental treadmill test | Running speed | 0.73 (HI), 0.63 (LI)+ 0.27 (HI), 0.14 (LI) |
Lee et al. (2008) | k = 8 males | Ingesting cold drink | 3 bottles of 300 ml of old water within 20 min each bottle in 2 min. | 35 | 69 | Cycling at 65% VO2max until exhaustion | Cycle time to exhaustion | <0.001 |
Minett et al. (2011) | k = 10 males | Cold towels to the neck and head & water immersion of hands & cooling vest & ice packs on upper legs | 20 min | 33 | 33 | Intermittent sprint protocol for 2 × 35 min | Running distance | 0.003 |
Minett et al. (2012)a | k = 8 males | Cold towels to the neck and head & water immersion of hands & cooling vest & ice packs on upper legs | 20 min | 33 | 34 | Intermittent sprint protocol for 2 × 35 min | Running distance | 0.001 |
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Minett et al. (2012)b | k = 10 males | Cold towels to the neck and head & water immersion of hands & cooling vest & ice packs on upper legs | 20 min | 32 | 64 | 6-over spell bowling performance | Running speed | 0.83 |
Morrison et al. (2014) | k = 10 males | CWI | 60 min | 30 | 50 | Cycling at 95% of ventilatory threshold until exhaustion | Cycle time to exhaustion | 0.001 |
Quod et al. (2008) | k = 6 males | CWI & cooling vest | 30 min (CWI) & 40 min (cooling vest) | 34 | 41 | Cycle time trial for 40 min | Cycle time to finish | 0.008 |
Randall et al. (2015) | k = 8 males | Cooling vest + ice packs on upper legs | 30 min + 30 min | 32 | 48 | Treadmill running at 70% VO2max until exhaustion | Running time | 0.22 + 0.002 |
Ross et al. (2011) | k = 11 males | Cold towels & ingesting ice | Ingesting 14 g/kg of ice while wearing cold towels 30 min | 34 | 55 | Cycle time trial of 46.4 km | PPO | 0.04 |
Ross et al. (2012) | k = 12 males | Cold towels & ingesting ice | Ingesting 14 g/kg of ice while wearing cold towels 30 min | 33 | 50 | Cycle time trial of 46.4 km | PPO | 0.70 |
Siegel et al. (2012) | k = 8 males | Ice slurry + CWI | Ingesting 7.5 g/kg of ice within 30 min + CWI for 30 min | 34 | 52 | Running till exhaustion | Running time to exhaustion | 0.005+ <0.001 |
Skein et al. (2012) | k = 10 males | CWI & ice towels over shoulder | 15 & 5 min | 31 | 33 | Intermittent sprint protocol for 50 min | Sprint time | 0.08 |
Tyler et al. (2011) | k = 8 males | Cooling collar | N/a | 32 | 53 | Treadmill running at 70% of VO2max until exhaustion | Running time to exhaustion | 0.01 |
Uckert et al. (2007) | k = 20 males | Cooling vest | 20 min | 31 | 50 | Incremental treadmill test | Running time to exhaustion | <0.001 |
& = indicates combinations within a cooling intervention; + = indicates additional cooling intervention within one study; PPO = peak power output; CWI = cold water immersion; N/a = not available, RM = repetition maximum; VO2max = maximal oxygen uptake; Wmax = maximal power output; HI = high intensity, LI = low intensity.
To test the hypothesis that external pre-cooling had an enhancing effect on performance parameters, an overall meta-analysis was conducted. This calculation showed that external pre-cooling techniques had a moderate but statistical significant effect on performance output compared to non-cooling strategies (Hedges’ g = 0.49 [95% CI: 0.33 to 0.64]), which can be observed in
overall weighted effect-size would yield an adjusted value of Hedges’ g = 0.43 [95% CI: 0.28 to 0.58]).
In an effort to explain a part of the observed high heterogeneity in this overall meta-analysis, different subgroup meta-analyses were conducted by stratifying studies based on their type of exercise protocols (cycling, running and functional strength) and outcomes (cycle time to exhaustion, cycle time to finish, power output, running distance, running time, running time to exhaustion, sprint time, running speed and lifted weight). Pre-cooling was effective for enhancing performance characteristics in humans for both cycling (Hedges’ g = 0.53, [95% CI: 0.26 to 0.80]; Q = 14.7, df(Q) = 7, p = 0.037; I2 = 52.5%) and running (Hedges’ g = 0.45, [95% CI: 0.25 to 0.65]; Q = 59.9, df(Q) = 13; p < 0.001; I2 = 78.3%) exercises.
Pre-cooling had very strong, positive and statistical significant effects on cycle time to exhaustion (Hedges’ g = 1.02; [95% CI = 0.53 to 1.51]), cycling time to finish (Hedges’ g = 0.88; [95% CI: 0.23 to 1.52]) and running time to exhaustion (Hedges’ g = 0.82; [95% CI: 0.53 to 1.11]), while low respectively moderate, positive and statistical significant effects of pre-cooling were observed on PPO (Hedges’ g = 0.33; [95% CI: 0.11 to 0.55]), running distance (Hedges’ g = 0.54; [95% CI: 0.28 to 0.80]) and running time (Hedges’ g = 0.48; [95% CI: 0.08 to 0.88]). In these subgroups heterogeneity disappeared except for the “running time to exhaustion” subgroup where heterogeneity remained high (Q = 17.0, df(Q) = 2, p < 0.001; I2 = 88.2). External pre-cooling had no main effect on sprint time (Hedges’ g = −0.55; [95% CI: −1.16 to 0.07]), running speed (Hedges’ g = 0.15; [95% CI: −0.04 to 0.35]) and lifted weight (Hedges’ g = 0.98; [95% CI: −0.003 to 1.97]).
Seven studies revealed the effect of internal pre-cooling on performance parameters [
Five studies examined the effects of mixed-method internal and external cooling versus non-cooling strategies on PPO and performance times [
The aim of this systematic review and meta-analysis was to evaluate the effects of
pre-cooling (external cooling, internal cooling and mixed-method cooling) vs. non-cooling strategies on different performance parameters in hot and humid environmental conditions. The analysis demonstrated that, prior to exercise tasks, external cooling methods had a significant better effect on performance parameters compared to non-cooling strategies. However, internal cooling and mixed method cooling strategies were not superior compared to non-cooling strategies to enhance performance.
The overall weighted mean difference revealed a significant (p < 0.001) effect of pre-exercise external cooling applications on the performance output compared to non-cooling strategies. The observed effect size for this analysis was small (Hedges’ g: 0.49). The strongest effect of pre-cooling was observed for cycling performance (Hedges’ g: 0.53; p < 0.001), primarily cycle time to exhaustion (p < 0.001) and cycling time to finish (p = 0.008) under hot (ranging from 29 to 34˚C) and humid (ranging from 41 to 80%) conditions. Only one study in the subgroup analysis showed a non-significant result for external pre-cooling on cycle performance [
Pre-cooling demonstrated to be overall significant effective for running tasks (p < 0.001). Open-end exercise tests, like the determination of total running distance and running time to exhaustion seem to be primarily positive affected from pre-cooling (p < 0.001 and p = 0.003), especially under hot (ranging from 31˚C to 35˚C) and humid (ranging from 30% to 53%) conditions. However, the subgroup analysis demonstrated that it was ineffective to enhance running speed and sprint times and weight lifting. It has already been reported that decreased muscle temperature from excessive cooling can negatively influence voluntary force production and sprint performance [
The results of this meta-analysis show some evidence that external pre-cooling methods may be effective for enhancing performance parameters with combinations of external cooling methods (k = 5; Hedges’ g range: 0.32 to 1.12), CWI (k = 3; Hedges’ g range: 0.69 to 1.34), cooling vests (k = 2; Hedges’ g range: 0.27 to 1.20) and local cooling applications (k = 3; Hedges’ g range: 0.29 to 0.56) demonstrating to have the largest effect.
The overall weighted effect indicated that internal pre-cooling is not effective vs. non-cooling strategies (p = 0.05) to enhance performance under hot and humid environmental conditions. As it can be observed in
This meta-analysis could not demonstrate a statistically significant (p = 0.28) overall weighted effect of mixed-method pre-cooling on performance output. However, it has to be mentioned, that the included studies used acclimatization periods prior to the pre-cooling method (for the experimental and control group). Acclimatization to hot environmental conditions has been demonstrated to be a key component for reducing the negative effects of heat strain [
Based on the results of this meta-analysis, external pre-exercise cooling appears to be an effective intervention to enhance performance in hot (between 29˚C and 34˚C) and humid (41% to 80%) conditions. Endurance running and cycling tasks were primarily affected from external pre-cooling. Combinations of external cooling applications, CWI and wearing cooling vests demonstrated to have the largest effects. Internal and mixed-method cooling demonstrated to have no main effect on performance enhancement, although internal cooling might have a significant positive effect on both cycling and running endurance tasks. However, it has to be considered that the main outcome parameter for internal and mixed-method pre-cooling was PPO. The low number of studies made an evaluation of the possible effects of internal and mixed-method pre-cooling on endurance performance impossible. The high and unclear risk of bias of the included studies has to be taken into account when using the current results, although the risk for publication bias was very low.
The authors thank the “Thim van der Laan foundation” for the financial support.
The authors declare no conflict of interest.
Conceived and designed the experiments: EH PC RC JT. Data extraction and quality assessment: EH RS TD JT. Risk of bias assessment: EH RS RC. Analysis of the data: EH JT. Wrote the paper: EH PC JT RC. Read and approved final version of manuscript: EH RS PC RC TD JT.
Hohenauer, E., Stoop, R., Clarys, P., Clijsen, R., Deliens, T. and Taeymans, J. (2018) The Effect of Pre-Exercise Cooling on Performance Characteristics: A Systematic Review and Meta-Analysis. International Journal of Clinical Medicine, 9, 117-141. https://doi.org/10.4236/ijcm.2018.93012