Compression Sportswear Improves Speed, Endurance, and Functional Motor Performances: A Meta-Analysis

ression sportswear is widely used for enhancing exercise performances, facilitating recovery, and preventing injuries. Despite prior ndings that con rmed positive effects on physical recovery after exercises, whether compression sportswear can enhance exercise performances has not been determined. Thus, this systematic meta-analysis examined the effects of compression sportswear on exercise performances including speed, endurance, strength and power, functional motor performance, and sport-related performance. We calculated effect sizes by comparing changes in exercise performances between the compression garment and the control group. Two additional moderator variable analyses determined whether altered exercise performances were different based on the types of participants and compression sportswear. For the total 769 participants from 42 in- cluded studies, the random-effect model found that compression sportswear signi cantly improved speed, endurance, and functional motor performances. Additional moderator variable analyses identi ed signi cant positive effects on speed for athletes, and endurance and functional motor performance for moderately trained adults. Further, whole-body compression garments were bene - cial for improving speed, and lower-body compression garments effectively advanced endurance performances. For functional motor performances, both upper- and lower-body suits were effective. These ndings suggest that wearing compression sportswear may be a viable strategy

signi cant positive effects on speed for athletes, and endurance and functional motor performance for moderately trained adults. Further, whole-body compression garments were bene - cial for improving speed, and lower-body compression garments effectively advanced endurance performances. For functional motor performances, both upper- and lower-body suits were effective. These ndings suggest that wearing compression sportswear may be a viable strategy to enhance overall exercise performances. Keywords:compression sportswear; garment; exercise performance; meta-analysis 1. Introduction Compression sportswear is one of the sports technologies, broadly used for improving performances, facilitating recovery, and preventing injuries for athletes from recreational to elite [1,2]. The initial use of compression garments was in the medical eld in the mid-20th century for surgeons; compression stockings were used for preventing blood clots after surgery. In recent years, further studies additionally reported the potential bene ts of compression garments in improving blood circulation, muscle fatigue, and recovery in post-exercise [3]. Common types of compression sportswear include shirts, shorts, sleeves, socks, and underwear, typically made of an elastic material so that the compression sportswear may be bene cial for improving physiological, biomechanical, and subjective components during and after exercises [4,5]. For example, the use of lower- compression sportswear reduced blood lactate, blood ow, and heart rate during endurance exercises[6–8]. Further, participants wearing waist-to-ankle tights showed more ef cient movement executions (i.e., greater muscle activations in the agonists and less hip exion angle during sprint performances [9] and wearing compression sportswear might modulate soft tissue movements (i.e., reduced muscle oscillations) attenuating excessive impact forces [10,11]. Presumably, these physiological and biomechanical effects of compression sportswear positively in uenced an individual's perceptual pain and fatigue [12]. Appl. Sci.2023,13, 13198.

Appl. Sci.2023,13, 13198 2 of 19 Despite the positive effects of compression sportswear inconsistently reported in individual studies, several meta-analytic ndings evidenced better improvements in re- covery post exercises [13–16]. Speci cally, the use of compression sportswear facilitated restoring potential muscle damage after exercise, as indicated by overall positive effects on the severity of delayed onset of muscle soreness as well as blood lactate levels [13,14]. Brown and colleagues additionally provided speci c information that signi cant recovery of muscle strength was observed at more than 24 h after performing resistance training [15]. Importantly, most people wear compression garments while exercising because they expect potential effects on improving physical performance as well [17–19]. However, beyond facilitated recovery post-exercise, the overall effect of compression sportswear on exercise performances has not been quantitatively investigated in multiple studies. Recently, two review studies tried to systematically summarize potential effects on exercise performances [4,20]. Despite inconsistent results among the included studies, the authors suggested a possibility that compression sportswear improved cycling, counter- movement jump, and baseball performances [21–23]. Considering the individual ndings that physiological, biomechanical, and perceptual bene ts of compression sportswear were observed during movement execution [24,25], new meta-analytic approaches are necessary to determine whether the compression sportswear effectively improves athletic performances based on potential moderator variables such as types of motor performances and garments. Thus, this meta-analysis examined the effect of compression sportswear on exercise performances including speed, power, endurance, strength, functional motor performance, and sport-related performance. In addition, we determined whether different types of participants and compression garments modulated positive effects on speci c ex- ercise performances. We would expect that the current meta-analytic ndings may provide information on whether compression garments positively in uence ongoing movement ex- ecutions and whether these effects are differentiated by certain exercise functions, physical levels, and types of garments. 2. Materials and Methods 2.1. Study Identi cation The current systematic review and meta-analysis were performed consistent with sug- gestions by the Preferred Reporting Items for the Systematic Reviews and Meta-Analysis (PRISMA) statement. We reported all checklist items (Supplementary Table S1) and the PRISMA ow diagram [26].

effects are differentiated by certain exercise functions, physical levels, and types of garments. 2. Materials and Methods 2.1. Study Identi cation The current systematic review and meta-analysis were performed consistent with sug- gestions by the Preferred Reporting Items for the Systematic Reviews and Meta-Analysis (PRISMA) statement. We reported all checklist items (Supplementary Table S1) and the PRISMA ow diagram [26]. Given that we already completed data extraction and analyses, the protocol registration for this study failed. According to the PRISMA statement [27], we established the inclusion criteria following the PICOS format [28]: (1) Population: healthy adults including athletes, high, moderately, or lightly trained adults, and no trained adults: (2) Intervention: compression garments; (3) Comparison: control group without wearing compression garments; (4) Outcome: exercise performances (i.e., endurance, speed, power and strength, functional motor performances, and sport-related performances); (5) Study design: randomized control trials (RCT) including either crossover or parallel design. Fur- ther, literature review studies, case studies, and studies without suf cient information for effect size calculation were removed. Two authors (HL and RK) completed an independent literature identi cation process using two search engines (i.e., PubMed and Web of Science) from 12 September to 30 October 2023. We used the following keywords: (compression OR compressive OR elastic OR spandex OR nylon OR neoprene OR latex) AND (garment OR sportswear OR stocking OR sleeves OR underwear OR braces OR swimwear) AND (physical function OR physical performance OR tness OR exercise performance). Initially, we found 1692 potential studies (i.e., 1073 studies from PubMed and 619 stud- ies from the Web of Science), and a further 279 duplicated studies were excluded. Then, we additionally eliminated 1371 studies (i.e., 119 review studies, 20 case studies, 13 animal studies, and 1219 studies that did not meet our inclusion criteria). Accordingly, we included 42 studies in data synthesis for the meta-analysis [6,7,9,11,17–19,21,29–62]. Figure

Appl. Sci.2023,13, 13198 3 of 19 the overall procedures of literature search and identi cation. Additionally, we added bibliometric data for the included studies in Supplementary Table S2.Appl. Sci. 2023, 13, 13198 3 of 18 Then, we additionally eliminated 1371 studies (i.e., 119 review studies, 20 case studies, 13 animal studies, and 1219 studies that did not meet our inclusion criteria). Accordingly, we included 42 studies in data synthesis for the meta-analysis [6,7,9,11,17–19,21,29–62]. Fig- ure 1 shows the overall procedures of literature search and identification. Additionally, we added bibliometric data for the included studies in Supplementary Table S2. Figure 1. Flow chart for study identification procedure. 2.2. Data Synthesis Procedures for Meta-Analysis Using version 4.0 of Comprehensive Meta-Analysis software (Englewood, NJ, USA), we conducted a meta-analysis. For effect size calculations, we used the standardized mean difference (SMD) by quantifying differences in exercise performances between compres- sion garments and control groups (e.g., sample size, mean, and standard deviation values) for the parallel design [63]. For the crossover design, a paired analysis was applied using sample size and mean difference with standard error based on prior suggestions [63–66]. We defined that greater values of SMD indicate more increases in following exercise per- formances with wearing compression garments as compared with those for the control group: (1) endurance, (2) speed, (3) power, and strength, (4) functional motor perfor- mances, and (5) sport-related performances. Using the random-effect model, we statisti- cally synthesized individual effect sizes into overall effect sizes because of the assumption that common effect size does not exist across the included studies [63,67]. Finally, we ex- cluded potential outliers that exceeded upper and lower limits (i.e., ±2 × SD of individual effect sizes). Specifically, potential outliers could distort the overall effect size, increase the distribution of data, decrease statistical power, and elevate the inaccuracy of the model. Thus, removing outliers allows data to be normally distributed and minimizes biased re- sults [68]. The level of heterogeneity among individual effect sizes was estimated by quantify- ing I 2 [63,69]. For calculating I 2 , we quantified Q statistic (i.e., summing the squared dif-

increase the distribution of data, decrease statistical power, and elevate the inaccuracy of the model. Thus, removing outliers allows data to be normally distributed and minimizes biased re- sults [68]. The level of heterogeneity among individual effect sizes was estimated by quantify- ing I 2 [63,69]. For calculating I 2 , we quantified Q statistic (i.e., summing the squared dif- ferences between the effect size of each study and the overall effect size that is weighted Figure 1.Flow chart for study identi cation procedure. 2.2. Data Synthesis Procedures for Meta-Analysis Using version 4.0 of Comprehensive Meta-Analysis software (Englewood, NJ, USA), we conducted a meta-analysis. For effect size calculations, we used the standardized mean difference (SMD) by quantifying differences in exercise performances between compression garments and control groups (e.g., sample size, mean, and standard deviation values) for the parallel design [63]. For the crossover design, a paired analysis was applied using sample size and mean difference with standard error based on prior suggestions [63–66]. We de ned that greater values ofSMDindicate more increases in following exercise perfor- mances with wearing compression garments as compared with those for the control group: (1) endurance, (2) speed, (3) power, and strength, (4) functional motor performances, and (5) sport-related performances. Using the random-effect model, we statistically synthesized individual effect sizes into overall effect sizes because of the assumption that common effect size does not exist across the included studies [63,67]. Finally, we excluded potential outliers that exceeded upper and lower limits (i.e., 2 SD of individual effect sizes). Speci cally, potential outliers could distort the overall effect size, increase the distribution of data, decrease statistical power, and elevate the inaccuracy of the model. Thus, removing outliers allows data to be normally distributed and minimizes biased results [68]. The level of heterogeneity among individual effect sizes was estimated by quantifying I 2 [63,69]. For calculatingI 2 , we quantified Q statistic (i.e., summing the squared differences between the effect size of each study and the overall effect size that is weighted by the inverse variance of each study). Then, the percent ofI 2 equals (Q-degrees of

results [68]. The level of heterogeneity among individual effect sizes was estimated by quantifying I 2 [63,69]. For calculatingI 2 , we quantified Q statistic (i.e., summing the squared differences between the effect size of each study and the overall effect size that is weighted by the inverse variance of each study). Then, the percent ofI 2 equals (Q-degrees of freedom)/Q 100. Normally, greater values ofI 2 than 75% denote substantial variability among the included studies [70]. To assess potential publication bias, we provided revised funnel plots after

Appl. Sci.2023,13, 13198 4 of 19 trim and fill methods [63], and further performed the Egger regression test. Ap-value for intercept (b0) less than 0.05 represents a significant level of publication bias among the included studies [71]. In addition, we conducted additional moderator variable analyses for each component of exercise performance. Two analyses determined whether different physical activity levels of participants (i.e., athlete, highly trained adult, moderately trained adult, and healthy adult) [72,73] and garment types (i.e., upper-body, lower-body, and whole-body suits) influence changes in exercise performances. 2.3. Methodological Quality Estimation To assess potential methodological quality issues, we measured the Physiotherapy Evidence Database (PEDro) scores by completing yes-or-no responses on 11 item checklists (i.e., group allocation, blinding, attrition, statistical analyses, and data variability) [74]. Consistent with previous studies, the studies that were included could be categorized into four levels: (a) high quality (scoring 9–10 points), (b) moderate quality (scoring 6–8points), (c) satisfactory quality (scoring 4–5 points), and (d) low quality (scoring below 4 points) [75,76]. 3. Results 3.1. Participant Characteristic The 42 studies focused on the effects of wearing compression garments during performance in 769 with 366 athletes and 403 healthy participants (range of mean age: 18.3–46.3years). Twenty-three studies included athletes of various sports (i.e., cyclist, track, soccer, football, golf, netball, marathon, half-marathon, cross-country, running, triathletes, track and eld, cricket, and tennis) and nineteen studies investigated recreationally active participants (i.e., highly trained adults, moderately trained adults, and healthy adults). Details of the demographic information are summarized in Table. Table 1.Participant characteristics. Study Study Design Total (N) Age (yrs) Gender (F/M) Stature (m) Body Mass (kg) Population Ali 2007 [32] Crossover Exp. 1 14 22.0 0.4 14 M 1.74 0.01 72.9 2.0 Moderately trained adultExp. 2 14 23.0 0.5 14 M 1.76 0.01 74.2 2.1 Ali 2011 [45] Crossover 12 33.0 10.0 3 F, 9 M 1.74 0.06 68.5 6.2 Athlete (runner) Barwood 2014 [40] Crossover 8 21.0 2.0 NA 1.77 0.06 72.8 7.1 Healthy adult Birmingham 1998 [18] Crossover 36 24.0 2.1 18 F, 18 M 1.70 0.10 66.8 9.6 Healthy adult Bodendorfer 2019 [29] Crossover 10 23.6 1.4 5

1.76 0.01 74.2 2.1 Ali 2011 [45] Crossover 12 33.0 10.0 3 F, 9 M 1.74 0.06 68.5 6.2 Athlete (runner) Barwood 2014 [40] Crossover 8 21.0 2.0 NA 1.77 0.06 72.8 7.1 Healthy adult Birmingham 1998 [18] Crossover 36 24.0 2.1 18 F, 18 M 1.70 0.10 66.8 9.6 Healthy adult Bodendorfer 2019 [29] Crossover 10 23.6 1.4 5 F, 5 M 1.76 0.01 72.9 16.7 Healthy adult Born 2014 [52] Crossover Exp. 1 12 25.0 3.0 NA 1.67 0.03 61.0 5.0 Athlete Exp. 2 12 23.0 2.0 1.69 0.03 61.0 6.0 Broatch 2017 [34] Crossover Male 9 28.0 6.0 9 M 1.81 0.07 83.8 9.3 Moderately trained adultFemale 11 25.0 2.0 11 F 1.69 0.06 62.6 9.5 Broatch 2019 [11] Crossover Exp. 1 13 22.0 3.0 13 M 1.85 0.06 84.1 9.4 Healthy adult Exp. 2 14 27.0 5.0 14 M 1.81 0.07 77.8 8.4 Bruden 2012 [31] Crossover 10 34.6 6.8 10 M 1.80 0.05 82.2 10.4 Athlete (triathletes and cyclist) Chang 2022 [62] Crossover 18 35.3 8.5 9 F, 9 M 1.70 0.10 60.5 9.8 Athlete (half-marathon runner) Cheng 2019 [51] Crossover 16 22.5 0.9 16 M 1.71 0.05 63.5 6.9 Healthy adult Dascombe 2011 [7] Crossover 11 28.4 10.0 11 M 1.77 0.05 72.6 8.0 Athlete (MD-runner and triathletes) de Glanville 2012 [48] Crossover 14 33.8 6.8 14 M 1.80 0.10 74.6 4.4 Athlete (multisport)

Appl. Sci.2023,13, 13198 5 of 19 Table 1.Cont. Study Study Design Total (N) Age (yrs) Gender (F/M) Stature (m) Body Mass (kg) Population Del Coso 2013 [42] Parallel CG 19 35.0 5.3 NA 1.76 0.08 73.2 5.2 Athletes (triathletes) Control 17 35.8 6.3 1.76 0.05 73.2 6.0 Doan 2003 [9] Crossover Male 10 20.0 0.9 10 M 1.79 0.07 74.1 8.3 Athlete (track) Female 10 19.2 1.3 10 F 1.69 0.03 60.2 5.2 Driller 2013 [49] Crossover 12 30.0 6.0 12 M 1.80 0.05 75.6 5.8 Athlete (cyclist) Duf eld 2007 [44] Crossover 10 22.1 1.1 10 M 1.85 0.07 84.7 5.9 Athlete (cricket) Faulkner 2013 [43] Crossover 11 23.7 5.7 11 M 1.78 0.08 75.3 10.0 Athlete (runner) Ganzit 2007 [54] Crossover 20 29.9 4.3 NA 1.78 0.04 71.9 4.5 Athlete (cyclist) Geldenhuys 2019 [39] Parallel CG 20 34.0 4.8 6 F, 14 M 1.70 0.90 72.1 10.5 Highly trained adult Control 21 34.0 6.4 6 F, 15 M 1.80 0.09 76.6 9.9 Gimenes 2019 [35] Parallel CG 10 18.4 0.5 10 M 1.79 0.05 67.8 7.2 Athlete (soccer) Control 10 18.3 0.5 10 M 1.78 0.05 73.7 7.1 Higgins 2009 [17] Crossover 9 22.6 4.6 9 F 1.76 0.04 67.8 6.6 Athlete (netball) Hong 2022 [21] Crossover 20 22.8 2.2 6 F, 6 M 1.70 0.07 67.2 12.7 Healthy adult Kemmler 2009 [50] Crossover 21 39.3 10.9 21 M 1.79 0.05 75.4 7.4 Moderately trained adult Lee 2021 [30] Crossover 13 20.9 1.4 13 M 1.73 0.05 65.9 7.8 Healthy adult Limmer 2022 [57] Crossover Male 12 30.0 7.2 12 M 1.81 0.06 74.8 10.2Moderately trained adultFemale 12 28.2 6.2 12 F 1.67 0.06 59.4 5.6 Machek 2020 [33] Crossover 15 22.1 4.1 15 M 1.78 0.06 87.8 7.8 Moderately trained adult Macrae 2012 [37] Crossover 12 26.0 7.0 12 M 1.80 0.07 79.0 9.0 Moderately trained adult McManus 2022 [61]Crossover 26 27.9 7.0 26 M 1.79 0.06 76.1 8.4 Moderately trained adult Michael 2014 [56] Crossover 12 24.0 7.2 12 F 1.68 0.06 57.8 6.1 Athlete Mortaza 2012 [36] Crossover 31 21.2 1.5 31 M NA NA

7.8 Moderately trained adult Macrae 2012 [37] Crossover 12 26.0 7.0 12 M 1.80 0.07 79.0 9.0 Moderately trained adult McManus 2022 [61]Crossover 26 27.9 7.0 26 M 1.79 0.06 76.1 8.4 Moderately trained adult Michael 2014 [56] Crossover 12 24.0 7.2 12 F 1.68 0.06 57.8 6.1 Athlete Mortaza 2012 [36] Crossover 31 21.2 1.5 31 M NA NA Athlete (football) Pereira 2014 [55] Parallel 24 24.1 5.2 24 M 1.76 0.06 78.6 9.7 Moderately trained adult Rhee 2011 [59] Crossover 32 45.0 1.1 32 F NA NA Healthy adult Rider 2014 [6] Crossover Male 7 21.0 1.3 7 M 1.73 0.04 68.7 9.7 Athlete (cross-country) Female 3 18.7 0.6 3 F 1.63 0.05 56.7 3.3 Sear 2010 [47] Crossover 8 20.6 1.2 8 M NA 72.9 5.9 Athlete (A-team sports) Song 2015 [58] Crossover 11 46.3 16.0 11 M 1.72 0.01 86.4 11.8 Athlete (golf) Sperlich 2010 [41] Crossover 15 27.1 4.8 15 M 1.83 0.08 76.3 7.6 Athlete (runner and triathletes) Stickford 2015 [38] Crossover 16 22.4 3.0 16 M 1.81 0.05 66.4 5.2 Athlete (PD-runner) Tsuruike 2013 [46] Crossover Tennis 12 19.8 0.9 12 M 1.73 0.06 64.4 5.0 Athlete (tennis and soccer)Soccer 12 19.9 0.3 12 M 1.75 0.06 66.5 7.0 Yang 2021 [19] Crossover 14 24.6 3.6 8 F, 6 M 1.67 0.06 58.9 5.5 Healthy adult Zhang 2016 [60] Crossover 12 21.2 1.4 12 M 1.78 0.04 67.1 6.4 Athlete (track and eld) Zhang 2019 [53] Crossover 16 25.5 2.6 8 F, 8 M 1.67 0.10 61.1 6.3 Healthy adult Abbreviations. CG: compression garment group; Exp: experiment; F: female; M: male; MD: middle distance; NA: not available; A: amateur; CG: compression garment group; PD: professional distance. 3.2. Exercise Performance and Compression Sportswear The 42 studies distribute compression garments on motor functions into ve different domains speed, endurance, power and strength, functional motor performance, and sport- related performance (Table). Speed was measured as time to complete the full cutting

42 studies distribute compression garments on motor functions into ve different domains speed, endurance, power and strength, functional motor performance, and sport- related performance (Table). Speed was measured as time to complete the full cutting

The study analyzes the impact of compression sportswear on various exercise performances.