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Review

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

by
Hanall Lee
1,†,
Rye-Kyeong Kim
1,†,
Woen-Sik Chae
2,* and
Nyeonju Kang
1,*
1
Division of Sport Science, Health Promotion Center, Sport Science Institute, Incheon National University, Incheon 22012, Republic of Korea
2
Department of Physical Education, Kyungpook National University, Daegu 41566, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2023, 13(24), 13198; https://doi.org/10.3390/app132413198
Submission received: 17 November 2023 / Revised: 6 December 2023 / Accepted: 11 December 2023 / Published: 12 December 2023
(This article belongs to the Section Applied Biosciences and Bioengineering)
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Versions Notes

Abstract

:
Compression sportswear is widely used for enhancing exercise performances, facilitating recovery, and preventing injuries. Despite prior findings that confirmed 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 included studies, the random-effect model found that compression sportswear significantly improved speed, endurance, and functional motor performances. Additional moderator variable analyses identified significant positive effects on speed for athletes, and endurance and functional motor performance for moderately trained adults. Further, whole-body compression garments were beneficial 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 findings suggest that wearing compression sportswear may be a viable strategy to enhance overall exercise performances.

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 field 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 benefits 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 beneficial 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 flow, and heart rate during endurance exercises [6,7,8]. Further, participants wearing waist-to-ankle tights showed more efficient movement executions (i.e., greater muscle activations in the agonists and less hip flexion 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 influenced an individual’s perceptual pain and fatigue [12].
Despite the positive effects of compression sportswear inconsistently reported in individual studies, several meta-analytic findings evidenced better improvements in recovery post exercises [13,14,15,16]. Specifically, 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 specific information that significant 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 [17,18,19]. 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, countermovement jump, and baseball performances [21,22,23]. Considering the individual findings that physiological, biomechanical, and perceptual benefits 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 specific exercise performances. We would expect that the current meta-analytic findings may provide information on whether compression garments positively influence ongoing movement executions and whether these effects are differentiated by certain exercise functions, physical levels, and types of garments.

2. Materials and Methods

2.1. Study Identification

The current systematic review and meta-analysis were performed consistent with suggestions 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 flow 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. Further, literature review studies, case studies, and studies without sufficient information for effect size calculation were removed. Two authors (HL and RK) completed an independent literature identification 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 fitness OR exercise performance).
Initially, we found 1692 potential studies (i.e., 1073 studies from PubMed and 619 studies 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 [6,7,9,11,17,18,19,21,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62]. Figure 1 shows the overall procedures of literature search and identification. Additionally, we added bibliometric data for the included studies in Supplementary Table S2.

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 [63,64,65,66]. We defined that greater values of SMD indicate more increases in following exercise performances 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). 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 results [68].
The level of heterogeneity among individual effect sizes was estimated by quantifying I2 [63,69]. For calculating I2, 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 of I2 equals (Q-degrees of freedom)/Q × 100. Normally, greater values of I2 than 75% denote substantial variability among the included studies [70]. To assess potential publication bias, we provided revised funnel plots after trim and fill methods [63], and further performed the Egger regression test. A p-value for intercept (β0) 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 6–8 points), (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: 18.3–46.3 years). 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 field, 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 1.

3.2. Exercise Performance and Compression Sportswear

The 42 studies distribute compression garments on motor functions into five different domains speed, endurance, power and strength, functional motor performance, and sport-related performance (Table 2). Speed was measured as time to complete the full cutting maneuver and Y-excursion, sprints time, walking and running speed, and throw speed from ten studies. Endurance was evaluated for various types of long-distance running, continuous cycling, half marathon, ultramarathon, ball throw, and endurance-related treadmill tests from 17 studies. Power and strength were evaluated by cycling using a cycle ergometer, golf swing, climbing, short distance sprint test, hand grip strength, isokinetic flexion and extension, joint power output, peak torque to body weight ratio, and average power at angular velocity from 18 studies. Functional motor performance included degree of motion, joint angle measurement, step frequency, step length, swing time, ground contact time, jump height, squat depth, balance task, hand function test, and cadence from 16 studies. Sport-related performance (i.e., cricket, netball, and 400 m sprint) from three studies.
We categorized compression garment type into upper-body, lower-body, and whole-body suits: (1) five sleeve compression garment on upper-body (i.e., arm, elbow, forearm, wrist, and palm), (2) one glove compression garment on upper-body, (3) four studies with graduated compression stockings on lower-body (i.e., ankle length, knee-high, calf and thigh, and ankle to knee), (4) three studies with neoprene sleeves on lower-body (i.e., knee joint and below the knee), (5) one study with prophylactic knee braces on lower-body (i.e., knee joint), (6) twelve studies with compression garment on lower-body (i.e., ankle, calf, knee, hip, and thigh and shank), (7) seven studies with compression stockings on lower-body (i.e., ankle to tibia, ankle, calf, below knee, and knee joint), (8) three compression tights on lower-body (i.e., ankle, thigh and calf), (9) one compression running pants on lower-body (i.e., thigh and calf), and (10) five studies with compression garment on whole-body.

3.3. Methodological Quality Estimation

We found that the range of PEDro scores for the included studies was 7 to 10 (mean ± SD of PEDro score = 7.5 ± 0.92). These findings indicated that a relatively high methodological quality appeared in the included studies for meta-analysis [75,76]. Additionally, among 42 included studies, four confirmed that the therapists were blinded for the compression garment condition, and all the included studies explicitly mentioned obtaining data over 85% from the initially assigned participants. The specific details on all PEDro scores are shown in Supplementary Table S3.

3.4. Meta-Analytic Findings

3.4.1. Compression Garment on Exercise Performances

The random-effect meta-analysis model found that compression garments revealed a significant effect on enhancing speed (25 comparisons from 10 studies; SMD = 0.195; 95% CI = 0.065 to 0.326; p = 0.003; I2 = 0.0%). A funnel plot indicated potential asymmetry with two imputed values, and this bias was additionally observed in the Egger’s regression test (β0 = 4.737 with p = 0.003; Supplementary Figure S1). Additionally, wearing compression garments significantly improved endurance (34 comparisons from 17 studies; SMD = 0.112; 95% CI = 0.019 to 0.205; p = 0.018; I2 = 0.0%). A funnel plot showed minimal publication bias, and this level was found in Egger’s regression test (β0 = 0.348 with p = 0.677; Supplementary Figure S2). For the functional motor performance, the compression garment showed positive effectiveness (63 comparisons from 16 studies; SMD = 0.162; 95% CI = 0.093 to 0.232; p < 0.001; I2 = 0.0%). A funnel plot showed minimal publication bias, and this level was found in Egger’s regression test (Egger’s β0 = −0.009 with p = 0.989; Supplementary Figure S3). However, the meta-analysis failed to show significant effects of compression garments on power and strength (45 comparisons from 18 studies; SMD = 0.005; 95% CI = −0.071 to 0.082; p = 0.889; I2 = 0.0%; Egger’s β0 = −0.128 with p = 0.780) and sport performance (eight comparisons from three studies; SMD = −0.122; 95% CI = −0.407 to 0.163; p = 0.401; I2 = 30.56%; Egger’s β0 = −6.147 with p = 0.003).

3.4.2. Moderator Variable Analysis on Participant Type

For speed, moderator variable analysis identified significant effects for athletes (18 comparisons from six studies; SMD = 0.265; 95% CI = 0.110 to 0.420; p = 0.001; I2 = 00.0%; Figure 2), and a funnel plot indicated potential asymmetry with two imputed values, and this bias was additionally observed in the Egger’s regression test (Egger’s β0 = 4.518 with p = 0.008; Supplementary Figure S4). However, no significant effects appear in moderately trained adults (two comparisons from two studies) and healthy adults (five comparisons from three studies) (Supplementary Table S4). For endurance, positive effects of compression garments were observed for moderately trained adults (11 comparisons from four studies; SMD = 0.219; 95% CI = 0.079 to 0.358; p = 0.002; I2 = 0.0%; Figure 3). A funnel plot indicated potential asymmetry with four imputed values, and this bias was additionally observed in the Egger’s regression test (Egger’s β0 = 5.064 with p = 0.021; Supplementary Figure S5). However, compression garments failed to improve endurance in athletes (20 comparisons from 10 studies), highly trained adults (two comparisons from two studies), and healthy adults (one comparison from one study) (Supplementary Table S4). For functional motor performance, the analysis revealed significant positive effects for moderately trained adults (25 comparisons from four studies; SMD = 0.171; 95% CI = 0.063 to 0.279; p = 0.002; I2 = 0.0%; Figure 4), and a funnel plot showed minimal publication bias, and this bias was additionally observed in the Egger’s regression test (Egger’s β0 = 16.019 with p = 0.0004; Supplementary Figure S6). However, no significant effects appeared in athletes (20 comparisons from six studies), highly trained adults (one comparison from one study), and healthy adults (12 comparisons from four studies) (Supplementary Table S4). For remaining power and strength, and sport-related performances, moderator variable analyses failed to report any significant effects depending on different participant types (Supplementary Table S4).

3.4.3. Moderator Variable Analysis on Garment Type

For speed, the moderator variable analysis revealed significant effects on whole-body suit (14 comparisons from four studies; SMD = 0.277; 95% CI = 0.100 to 0.454; p = 0.002; I2 = 0.0%; Figure 5), and a funnel plot indicated potential asymmetry with three imputed values, and this bias was additionally observed in the Egger’s regression test (Egger’s β0 = 6.424 with p = 0.0004; Supplementary Figure S7), but lower-body suit failed to show positive effectiveness (10 comparisons from five studies) (Supplementary Table S5). The analysis on endurance revealed positive effects on a lower-body suit (27 comparisons from 14 studies; SMD = 0.136; 95% CI = 0.030 to 0.241; p = 0.012; I2 = 0.0%; Figure 6), and a funnel plot showed minimal publication bias, and this level was found in the Egger’s regression test (Egger’s β0 = −0.841 with p = 0.333; Supplementary Figure S8). However, no significant effects were observed for the upper-body suit (two comparisons from one study) and whole-body suit (four comparisons from two studies) (Supplementary Table S5). For functional motor performance, the moderator variable analysis found positive effects on upper-body suit (four comparisons from two studies; SMD = 0.460; 95% CI = 0.153 to 0.766; p = 0.003; I2 = 32.33%; Figure 7) and lower-body suit (55 comparisons from 14 studies; SMD = 0.126; 95% CI = 0.051 to 0.200; p = 0.001; I2 = 0.0%; Figure 8), and each funnel plots showed minimal publication bias, and this level was found in the Egger’s regression test on both upper- (Egger’s β0 = 3.861 with p = 0.136; Supplementary Figure S9) and lower-body suit (%; Egger’s β0 = −0.405 with p = 0.524; Supplementary Figure S10). For remaining power and strength, and sport-related performances, moderator variable analyses failed to report any significant effects depending on different compression garment types (Supplementary Table S5).

4. Discussion

This meta-analysis examined the effects of compression sportswear on various exercise performances. The meta-analytic findings revealed that wearing compression sportswear significantly improved speed, endurance, and functional motor performance. Additional moderator variable analyses identified significant positive effects on speed for athletes and endurance and functional motor performance for moderately trained adults. Further, whole-body compression garments were beneficial for improving speed, and lower-body compression garments effectively advanced endurance performances. For functional motor performances, both upper- and lower-body suits were effective.
Previous studies reported that wearing compression garments can facilitate recovery after the execution of various exercises [13,14,15,16]. Beyond beneficial effects on recovery, two meta-analysis studies investigated whether lower-body compression garments improved running performances and found no significant positive effects [12,77]. To the best of our knowledge, the current meta-analytic findings are the first to report specific effects of compression sportswear on speed, endurance, and functional motor performances. Despite relatively small positive effects, the findings support a proposition that compression sportswear may improve exercise performances because of potential benefits on physiological, biomechanical, neuromuscular, and perceptual components [20].
The moderator variable analysis found that wearing compression garments advanced speed performances for athletes. However, these effects were not confirmed for other populations including highly and moderately trained adults and healthy adults due to insufficient sample sizes (e.g., 0–2 comparisons used for data synthesis). Improved speed functions were consistent with prior findings that the use of compression garments improved performances during a short burst of high-intensity effort [78] because of maintaining high energy phosphates for subsequent short and anaerobic bursts of energy [17] and increasing biomechanical efficiency (e.g., greater step length improving running performances in elite athletes) [52]. Moreover, positive effects of compression sportswear on endurance and functional motor performances (e.g., countermovement jump and visuomotor task) were observed for moderately trained adults, and no significant effects appeared in athletes. Several studies suggested that wearing compression garments during endurance exercise protocols was effective for decreasing blood lactate concentration [6,31,79] and limiting excessive soft tissue vibrations of muscles [9,11]. Further, Yang and colleagues suggested that compression garments may increase sensory inputs contributing to the consolidation of sensorimotor integrations, as indicated by greater corticomuscular connectivity [19]. Perhaps, facilitated sensorimotor processing might influence motor control strategies resulting in advanced movement control and execution during various functional motor tasks. In addition, endurance and functional motor performance improvements increased for moderately trained adults because they had a greater possibility of physiological and neuromuscular benefits from compression garments in comparison to highly trained athletes who might have already competitive levels of neurophysiological functions.
In addition, we found that whole-body suits were effective for improving speed performance, and lower-body suits enhanced endurance performances. Both upper and lower-body suits were effective for functional motor performances. These findings tentatively suggest that supporting upper and lower extremities with whole-body suits may be suitable for some exercise protocols required for speed-related motor functions because of the potential contributions of properties of upper and lower limbs to increasing speed performances such as sprint velocity [80,81]. Similarly, given that functional motor performance tasks in this meta-analysis consisted of either upper or lower limb movements, both upper- and lower-body suits might be effective. Not surprisingly, the findings on endurance performances indicated that lower-body suits may be an option whereas the effects of whole-body suits from a small sample size (i.e., four comparisons) were insignificant. Presumably, the included studies estimated endurance performances using running and cycling tasks requiring repetitive and continuous lower limb movements, so that wearing lower-body suits including (e.g., hip-thigh, calf-ankle, and knee joint sleeves) may provide physiological and biomechanical benefits for improving endurance capabilities.
Although our comprehensive meta-analysis identified potential effects of compression sportswear on exercise performances, these findings are necessary to be cautiously interpreted. Importantly, significant effects of compression garments on exercise performances were relatively small (e.g., most values of effect size less than 0.3) so wearing compression garments seems to be insufficient to expect greater improvements in exercise performances based on the current meta-analytic findings. Second, small changes in exercise performances in this meta-analysis were the immediate effects of compression garments. For power and strength and athletic performances, we failed to identify significant effects. Presumably, wearing compression garments without repetitive practices may be insufficient to immediately increase muscle strength as well as highly skilled movements. Thus, future studies can investigate the long-term effects of repetitive training protocols with compression garments on exercise performances. Perhaps, long-term training with compression garments may prevent the overuse of muscles and joints and athletic injuries presumably facilitating the efficacy of training programs [82]. Further, despite the potential advantages of compression garments with respect to neurophysiological, biomechanical, or perceptual components, how these improvements in the motor system support specific exercise performances is still inconclusive. Thus, more studies will be necessary to identify potential mechanisms underlying the treatment effects of compression garments on the execution of various motor actions.
In conclusion, the current meta-analytic approaches found that wearing compression garments revealed small positive effects on exercise performances including speed, endurance, and functional motor performances. Moreover, these positive effects were observed for speed performances of athletes and endurance and functional motor performances for moderately trained adults. Wearing whole-body suits was effective for improving speed and lower-body suits improved endurance capabilities. For functional motor performances, both upper- and lower-body suits were effective. These findings provided some practical implications for improving exercise performance. First, wearing compression garments can reveal acute effects depending on the characteristics of performers (e.g., physical capabilities) and types of exercise performances. Further, these positive effects may be additionally affected by certain types of garments. For example, for athletes engaging in explosive movements such as sprints, whole-body suits may be suitable, and moderately trained adults may select lower-body suits for improving long-distance running. In future studies, investigating the effects of compression garments on various populations such as adolescents, older adults, or neurological patients may be necessary by focusing on identifying optimal types of compression garments (e.g., pressure levels and materials) for increasing their exercise performances.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app132413198/s1, Supplementary Table S1: PRISMA checklist items, Supplementary Table S2: Bibliometric basics, Supplementary Table S3: Methodological quality assessment using PEDro Score, Supplementary Table S4: Results of participant type effects for exercise performance, Supplementary Table S5: Results of garment type effects for exercise performance, Supplementary Figure S1: Funnel plot from the 25 comparisons on speed, Supplementary Figure S2: Funnel plot from the 34 comparisons on endurance, Supplementary Figure S3: Funnel plot from the 63 comparisons on functional motor performance, Supplementary Figure S4: Funnel plot from the 18 comparisons for athletes on speed, Supplementary Figure S5: Funnel plot from the 11 comparisons for moderately trained adult on endurance, Supplementary Figure S6: Funnel plot from the 25 comparisons for moderately trained adult on functional motor performance, Supplementary Figure S7: Funnel plot from the 14 comparisons for whole-body suit on speed, Supplementary Figure S8: Funnel plot from the 27 comparisons for lower-body suit on endurance, Supplementary Figure S9: Funnel plot from the four comparisons for upper-body suit on functional motor performance, Supplementary Figure S10: Funnel plot from the 55 comparisons for lower-body suit on functional motor performance. References mentioned in Supplementary Materials file are [6,7,9,11,17,18,19,21,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,83].

Author Contributions

Conceptualization, N.K. and W.-S.C.; methodology, H.L., R.-K.K., W.-S.C. and N.K.; software, H.L., R.-K.K., W.-S.C. and N.K.; validation, H.L., R.-K.K., W.-S.C. and N.K.; writing—original draft preparation, H.L., R.-K.K., W.-S.C. and N.K.; supervision, N.K. and W.-S.C.; project administration, N.K. and W.-S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flow chart for study identification procedure.
Figure 1. Flow chart for study identification procedure.
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Figure 2. The effect of the compression garment on the speed of athletes. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals. More positive size indicates increased speed performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [9,17,42,44,47,52].
Figure 2. The effect of the compression garment on the speed of athletes. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals. More positive size indicates increased speed performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [9,17,42,44,47,52].
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Figure 3. The effect of the compression garment on the endurance for moderately trained adults. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals. A more positive size indicates enhanced endurance performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [32,33,50,57].
Figure 3. The effect of the compression garment on the endurance for moderately trained adults. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals. A more positive size indicates enhanced endurance performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [32,33,50,57].
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Figure 4. The effect of the compression garment on the functional motor performance for moderately trained adults. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals. A more positive size indicates improved functional performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [11,19,21,33].
Figure 4. The effect of the compression garment on the functional motor performance for moderately trained adults. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals. A more positive size indicates improved functional performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [11,19,21,33].
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Figure 5. The effect of the compression garment on the speed for the whole-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, h: hour. A more positive size indicates increased speed performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [17,37,44,47].
Figure 5. The effect of the compression garment on the speed for the whole-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, h: hour. A more positive size indicates increased speed performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [17,37,44,47].
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Figure 6. The effect of the compression garment on the endurance for the lower-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, AnT: anaerobic threshold, AT: aerobic threshold, min: minute, w: watt. A more positive size indicates impr0oved endurance performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [6,7,32,33,35,39,40,41,42,45,50,54,60,62].
Figure 6. The effect of the compression garment on the endurance for the lower-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, AnT: anaerobic threshold, AT: aerobic threshold, min: minute, w: watt. A more positive size indicates impr0oved endurance performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [6,7,32,33,35,39,40,41,42,45,50,54,60,62].
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Figure 7. The effect of the compression garment on the functional motor performance for the upper-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, JTHFT: Jabsen Taylor hand function test, CV: coefficient of variance. A more positive size indicates improved functional motor performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [19,59].
Figure 7. The effect of the compression garment on the functional motor performance for the upper-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, JTHFT: Jabsen Taylor hand function test, CV: coefficient of variance. A more positive size indicates improved functional motor performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [19,59].
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Figure 8. The effect of the compression garment on the functional motor performance for the lower-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, GAS: gastrocnemii muscles, VAS: vasti muscles, RMS: root mean square, ROM: Range of motion, rpm: revolution per minute, Hz: hertz. A more positive size indicates improved functional motor performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [9,11,18,21,30,33,36,38,45,51,52,53,56,62].
Figure 8. The effect of the compression garment on the functional motor performance for the lower-body suit. Abbreviations. SMD: standardized mean difference, LL: lower limit, UL: upper limit, CIs: confidence intervals, GAS: gastrocnemii muscles, VAS: vasti muscles, RMS: root mean square, ROM: Range of motion, rpm: revolution per minute, Hz: hertz. A more positive size indicates improved functional motor performance. Note that gray square indicates individual effect size with 95% CI. Red diamond demonstrates overall effect size [9,11,18,21,30,33,36,38,45,51,52,53,56,62].
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Table 1. Participant characteristics.
Table 1. Participant characteristics.
StudyStudy
Design		 Total (N)Age
(yrs)		Gender
(F/M)		Stature (m)Body Mass (kg)Population
Ali 2007 [32]CrossoverExp. 11422.0 ± 0.414 M1.74 ± 0.0172.9 ± 2.0Moderately trained adult
Exp. 21423.0 ± 0.514 M1.76 ± 0.0174.2 ± 2.1
Ali 2011 [45]Crossover 1233.0 ± 10.03 F, 9 M1.74 ± 0.0668.5 ± 6.2Athlete (runner)
Barwood 2014 [40]Crossover 821.0 ± 2.0NA1.77 ± 0.0672.8 ± 7.1Healthy adult
Birmingham 1998 [18]Crossover 3624.0 ± 2.118 F, 18 M1.70 ± 0.1066.8 ± 9.6Healthy adult
Bodendorfer 2019 [29]Crossover 1023.6 ± 1.45 F, 5 M1.76 ± 0.0172.9 ± 16.7Healthy adult
Born 2014 [52]CrossoverExp. 11225.0 ± 3.0NA1.67 ± 0.0361.0 ± 5.0Athlete
Exp. 21223.0 ± 2.01.69 ± 0.0361.0 ± 6.0
Broatch 2017 [34]CrossoverMale928.0 ± 6.09 M1.81 ± 0.0783.8 ± 9.3Moderately trained adult
Female1125.0 ± 2.011 F1.69 ± 0.0662.6 ± 9.5
Broatch 2019 [11]CrossoverExp. 11322.0 ± 3.013 M1.85 ± 0.0684.1 ± 9.4Healthy adult
Exp. 21427.0 ± 5.014 M1.81 ± 0.0777.8 ± 8.4
Bruden 2012 [31]Crossover 1034.6 ± 6.810 M1.80 ± 0.0582.2 ± 10.4Athlete (triathletes and cyclist)
Chang 2022 [62]Crossover 1835.3 ± 8.59 F, 9 M1.70 ± 0.1060.5 ± 9.8Athlete (half-marathon runner)
Cheng 2019 [51]Crossover 1622.5 ± 0.916 M1.71 ± 0.0563.5 ± 6.9Healthy adult
Dascombe 2011 [7]Crossover 1128.4 ± 10.011 M1.77 ± 0.0572.6 ± 8.0Athlete (MD-runner and triathletes)
de Glanville 2012 [48]Crossover 1433.8 ± 6.814 M1.80 ± 0.1074.6 ± 4.4Athlete (multisport)
Del Coso 2013 [42]ParallelCG1935.0 ± 5.3NA1.76 ± 0.0873.2 ± 5.2Athletes (triathletes)
Control1735.8 ± 6.31.76 ± 0.0573.2 ± 6.0
Doan 2003 [9]CrossoverMale1020.0 ± 0.910 M1.79 ± 0.0774.1 ± 8.3Athlete (track)
Female1019.2 ± 1.310 F1.69 ± 0.0360.2 ± 5.2
Driller 2013 [49]Crossover 1230.0 ± 6.012 M1.80 ± 0.0575.6 ± 5.8Athlete (cyclist)
Duffield 2007 [44]Crossover 1022.1 ± 1.110 M1.85 ± 0.0784.7 ± 5.9Athlete (cricket)
Faulkner 2013 [43]Crossover 1123.7 ± 5.711 M1.78 ± 0.0875.3 ± 10.0Athlete (runner)
Ganzit 2007 [54]Crossover 2029.9 ± 4.3NA1.78 ± 0.0471.9 ± 4.5Athlete (cyclist)
Geldenhuys 2019 [39]ParallelCG2034.0 ± 4.86 F, 14 M1.70 ± 0.9072.1 ± 10.5Highly trained adult
Control2134.0 ± 6.46 F, 15 M1.80 ± 0.0976.6 ± 9.9
Gimenes 2019 [35]ParallelCG1018.4 ± 0.510 M1.79 ± 0.0567.8 ± 7.2Athlete (soccer)
Control1018.3 ± 0.510 M1.78 ± 0.0573.7 ± 7.1
Higgins 2009 [17]Crossover 922.6 ± 4.69 F1.76 ± 0.0467.8 ± 6.6Athlete (netball)
Hong 2022 [21]Crossover 2022.8 ± 2.26 F, 6 M1.70 ± 0.0767.2 ± 12.7Healthy adult
Kemmler 2009 [50]Crossover 2139.3 ± 10.921 M1.79 ± 0.0575.4 ± 7.4Moderately trained adult
Lee 2021 [30]Crossover 1320.9 ± 1.413 M1.73 ± 0.0565.9 ± 7.8Healthy adult
Limmer 2022 [57]CrossoverMale1230.0 ± 7.212 M1.81 ± 0.0674.8 ± 10.2Moderately trained adult
Female1228.2 ± 6.212 F1.67 ± 0.0659.4 ± 5.6
Machek 2020 [33]Crossover 1522.1 ± 4.115 M1.78 ± 0.0687.8 ± 7.8Moderately trained adult
Macrae 2012 [37]Crossover 1226.0 ± 7.012 M1.80 ± 0.0779.0 ± 9.0Moderately trained adult
McManus 2022 [61]Crossover 2627.9 ± 7.026 M1.79 ± 0.0676.1 ± 8.4Moderately trained adult
Michael 2014 [56]Crossover 1224.0 ± 7.212 F1.68 ± 0.0657.8 ± 6.1Athlete
Mortaza 2012 [36]Crossover 3121.2 ± 1.531 MNANAAthlete (football)
Pereira 2014 [55]Parallel 2424.1 ± 5.224 M1.76 ± 0.0678.6 ± 9.7Moderately trained adult
Rhee 2011 [59]Crossover 3245.0 ± 1.132 FNANAHealthy adult
Rider 2014 [6]CrossoverMale721.0 ± 1.37 M1.73 ± 0.0468.7 ± 9.7Athlete (cross-country)
Female318.7 ± 0.63 F1.63 ± 0.0556.7 ± 3.3
Sear 2010 [47]Crossover 820.6 ± 1.28 MNA72.9 ± 5.9Athlete (A-team sports)
Song 2015 [58]Crossover 1146.3 ± 16.011 M1.72 ± 0.0186.4 ± 11.8Athlete (golf)
Sperlich 2010 [41]Crossover 1527.1 ± 4.815 M1.83 ± 0.0876.3 ± 7.6Athlete (runner and triathletes)
Stickford 2015 [38]Crossover 1622.4 ± 3.016 M1.81 ± 0.0566.4 ± 5.2Athlete (PD-runner)
Tsuruike 2013 [46]CrossoverTennis1219.8 ± 0.912 M1.73 ± 0.0664.4 ± 5.0Athlete (tennis and soccer)
Soccer1219.9 ± 0.312 M1.75 ± 0.0666.5 ± 7.0
Yang 2021 [19]Crossover 1424.6 ± 3.68 F, 6 M1.67 ± 0.0658.9 ± 5.5Healthy adult
Zhang 2016 [60]Crossover 1221.2 ± 1.412 M1.78 ± 0.0467.1 ± 6.4Athlete (track and field)
Zhang 2019 [53]Crossover 1625.5 ± 2.68 F, 8 M1.67 ± 0.1061.1 ± 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.
Table 2. Compression sportswear and exercise performance.
Table 2. Compression sportswear and exercise performance.
StudyCompression Sportswear CharacteristicsExercise Performance
ExtremitiesTypePressure (mmHg)TypeTask
Ali 2007 [32]Lower
(ankle length)		GCS18–22Endurance
Endurance		1. Intermittent running
(shuttle running)
2. Continuous running
(10 km running)
Ali 2011 [45]Lower
(knee-high)		GCS12–32Endurance
Functional motor performance		1. 10 km running
2. Countermovement jump
Barwood 2014 [40]Lower
(calf and thigh)		GCSCalf: 17–20
Thigh: 10–11		Endurance5 km treadmill running
Birmingham 1998 [18]Lower
(knee joint)		NSNAFunctional motor performanceOpen and close kinetic chain test
Bodendorfer 2019 [29]Lower
(knee joint)		NS
PKB		NASpeedCutting movements
Born 2014 [52]Lower
(lower body)		CGExp. 1: 17.5–21.7
Exp. 2: 18.2–20.2		Functional motor performance
Speed		1. Hip flexion angle, step length
and frequency
2. 30 m repeated sprints
Broatch 2017 [34]Lower
(thigh, calf, and ankle)		CGThigh: 11.7
Calf: 26.4
Ankle: 21.5		Power and StrengthSprint cycling-peak power
and work
Broatch 2019 [11]Lower
(thigh and calf)		CT2XU: 10.7–21.8
Nike: 9.1–21.5
Under Armor: 7.7–18.9		Functional motor performance9 min treadmill running
(lower limb displacements)
Bruden 2012 [31]Lower
(thigh and shank)		CG
(Ionized and nonionized)		Midthigh: 11–15
Midshank: 16–21		Endurance
Power and Strength		1. Fatigue rate
2. Sprint and cycling
(mean and peak power)
Chang 2022 [62]Lower
(thigh and calf)		CRPThigh: 12–20
Calf: 15–22		Endurance
Functional motor performance		1. 21 km treadmill running trials
2. Knee flexion proprioception
(lunge squat)
Cheng 2019 [51]Lower
(above the ankle to below the tibia)		CS30–40Power and Strength
Functional motor performance		1. Single joint movement
(peak and power output)
2. Walking on the treadmill
(step length and frequency)
Dascombe 2011 [7]Lower (lower body)CGrLBCG: 13.7–19.2
uLBCG: 15.9–32.7		Endurance
Endurance		1. Progressive maximal tests
2. Time-to-exhaustion tests
de Glanville 2012 [48]Lower (lower body)CG6.0–11.8Power and StrengthSubsequent 40 km cycling time
(power output)
Del Coso 2013 [42]Lower (ankle to knee)GCSNAEndurance
Speed
Speed
Speed		1. Half iron marathon
2. Swimming (m/s)
3. Cycling (m/s)
4. Running (m/s)
Doan 2003 [9]Lower
(hip, thigh, and knee)		CGNAFunctional motor performance
Functional motor performance
Functional motor performance
Functional motor performance		1. 60 m sprint range of motion test
2. Jump height
3. Squat depth
4. Range of motion (knee)
Driller 2013 [49]Lower
(full-length lower body)		CGNAPower and StrengthIncremental cycling test
(power output)
Duffield 2007 [44]Whole-bodyCGNAEndurance
Sport performance
Speed		1. Total distance
2. Cricket Ball throws
3. 10 and 20 m sprint
Faulkner 2013 [43]Lower
(hip to ankle, hip to knee, and ankle to knee)		CGNASport performance40 m sprint
Ganzit 2007 [54]Lower (ankle)CS20Endurance
Power and strength		1. Incremental cycling test
2. Maximal workload
Geldenhuys 2019 [39]Lower (below knee)CGNAEnduranceUltramarathon-race pace
(0–42, 42–56 km)
Gimenes 2019 [35]Lower
(calf and ankle)		CS20–30Endurance1. Sprints (rep)-match 1 and 2
2. Distance covered (m)-match 1 and 2
Higgins 2009 [17]Whole-bodyNetball CGNASpeed
Sport performance
Sport performance		1. Netball 20 m sprint time (s)
2. Netball countermovement jumps-flight time (s)
3. Netball Distance traveled
Hong 2022 [21]Lower
(lower body)		CG16.8–27.3Functional motor performance
Power and strength		1. Proprioception (displacements of COP and AE)
2. Countermovement jump
Kemmler 2009 [50]Lower
(below knee)		CS18–24Endurance
Power and Strength		1. Stepwise incremented treadmill test
2. Incremented treadmill (work output)
Lee 2021 [30]Lower
(ankle)		CT28.6 ± 9.4Functional motor performance
Power and Strength		1. Cadence
2. 20 min fatiguing preload cycling power output
Limmer 2022 [57]Upper
(forearm)		Sleeve CG12.4–22.4Endurance
Power and Strength		1. Finger hang and lap climbing
2. Hand grip strength
Machek 2020 [33]Lower
(below knee)		NSNAEndurance
Functional motor performance
Power and Strength		1. One leg extension (total volume)
2. Countermovement jump, barbell, and squat jump
3. One leg extension (1RM-kg)
Macrae 2012 [37]Whole-bodyCGCSG: 11–15
OSG: 8–13		Power and Strength
Speed		1. 6 km cycling power output
2. 6 km cycling time
McManus 2022 [61]Lower
(calf and thigh)		CT6.8–8.9SpeedRunning speed at 60, 62.5, and 65% VO2 max for 15 min
Michael 2014 [56]Lower
(lower body)		CGNAFunctional motor performanceSingle-leg balance task
Mortaza 2012 [36]Lower
(knee joint)		1. NS
2. PKB		NAFunctional motor performance
Power and Strength		1. Single-leg vertical jump and Cross-over hop
2. Isokinetic knee flexion and extension
(at 60, 180, 300 u/sec)
Pereira 2014 [55]Upper
(elbow)		Sleeve CGNAPower and StrengthUnilateral maximal isokinetic eccentric/concentric elbow flexion
Rhee 2011 [59]Upper
(hand)		Glove CG25–32Functional motor performance
Power and Strength		1. Hand function test using the JTHFT
2. Hand grip strength
Rider 2014 [6]Lower
(below knee)		CS15–20EnduranceMaximal workload treadmill test
Sear 2010 [47]Whole-bodyCGUpper: 5.3–7.3
Lower: 9.2–17.8		SpeedProlonged high-intensity intermittent treadmill exercise
Song 2015 [58]UpperSleeve CGNAPower and StrengthModern golf swing
Sperlich 2010 [41]Whole-body1. CG (socks)
2. CG (tight)
3. Whole-body CG		20EnduranceTime to exhaustion (on the treadmill)
Stickford 2015 [38]Lower
(calf)		CS15–20Functional motor performanceWalking tests (step length, frequency, swing time, and ground contact time)
Tsuruike 2013 [46]UpperSleeve CGNAPower and Strength External rotation of glenohumeral joint with isotonic contraction force output
Yang 2021 [19]Upper
(arm, elbow, forearm, wrist, and palm)		Sleeve CG10–20Functional motor performance
Functional motor performance
Functional motor performance		1. Visual tracking motor tasks
2. Reaction time
3. Joint position sense
Zhang 2016 [60]Lower
(above the knee)		CGNAEndurance
Power and Strength		1. Isokinetic knee extensions at 60 and 300°/s assessing work fatigue
2. Isokinetic knee extensions at 60 and 300°/s on a dynamometer
Zhang 2019 [53]Lower
(knee joint)		CSNAFunctional motor performanceKnee joint position-active target-matching task
Abbreviations. C: compression; CG: compression garment; CRP: compression running pants; CS: compression stockings; CT: compression tights; GCS: graduated compression stockings; NA: not available; rLBCG: manufacturer-recommended lower-body compression garments; uLBCG: undersized lower-body compression garments; NS: neoprene sleeves; PKB: prophylactic knee braces; AE: absolute error; COP: center of pressure; CSG: correctly-sized garments; JTHFT: Jebsen-Taylor hand function test; NS: neoprene sleeves; OSG: over-sized garments; S: socks; T: tights.
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Lee, H.; Kim, R.-K.; Chae, W.-S.; Kang, N. Compression Sportswear Improves Speed, Endurance, and Functional Motor Performances: A Meta-Analysis. Appl. Sci. 2023, 13, 13198. https://doi.org/10.3390/app132413198

AMA Style

Lee H, Kim R-K, Chae W-S, Kang N. Compression Sportswear Improves Speed, Endurance, and Functional Motor Performances: A Meta-Analysis. Applied Sciences. 2023; 13(24):13198. https://doi.org/10.3390/app132413198

Chicago/Turabian Style

Lee, Hanall, Rye-Kyeong Kim, Woen-Sik Chae, and Nyeonju Kang. 2023. "Compression Sportswear Improves Speed, Endurance, and Functional Motor Performances: A Meta-Analysis" Applied Sciences 13, no. 24: 13198. https://doi.org/10.3390/app132413198

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