International Journal of Sports Physiology and Performance, 2015, 10, 76-83 http://dx.doi.org/10.1123/ijspp.2014-0003 © 2015 Human Kinetics, Inc.

www.IJSPP-Journal.com ORIGINAL INVESTIGATION

Lower-Leg Compression, Running Mechanics, and Economy in Trained Distance Runners Abigail S.L. Stickford, Robert F. Chapman, Jeanne D. Johnston, and Joel M. Stager The efficacy of and mechanisms behind the widespread use of lower-leg compression as an ergogenic aid to improve running performance are unknown. The purpose of this study was to examine whether wearing graduated lower-leg compression sleeves during exercise evokes changes in running economy (RE), perhaps due to altered gait mechanics. Sixteen highly trained male distance runners completed 2 separate RE tests during a single laboratory session, including a randomized-treatment trial of graduated calf-compression sleeves (CS; 15–20 mm Hg) and a control trial (CON) without compression sleeves. RE was determined by measuring oxygen consumption at 3 constant submaximal speeds of 233, 268, and 300 m/min on a treadmill. Running mechanics were measured during the last 30 s of each 4-min stage of the RE test via wireless triaxial 10-g accelerometer devices attached to the top of each shoe. Ground-contact time, swing time, step frequency, and step length were determined from accelerometric output corresponding to foot-strike and toe-off events. Gait variability was calculated as the standard deviation of a given gait variable for an individual during the last 30 s of each stage. There were no differences in VO2 or kinematic variables between CON and CS trials at any of the speeds. Wearing lower-leg compression does not alter the energetics of running at submaximal speeds through changes in running mechanics or other means. However, it appears that the individual response to wearing lower-leg compression varies greatly and warrants further examination. Keywords: endurance athletes, energy cost, gait, ground-contact time, performance clothing Endurance runners are using compression garments presumably as a means to improve training, performance, and recovery, yet there is little consistent, conclusive research exploring the efficacy of and/ or mechanisms underlying the use of lower-leg compression as an ergogenic aid specifically during running. Improved venous return and clearance of metabolites,1 proprioception,2,3 force production,2–4 thermoregulation,3 and subjective measures2,5 have been evaluated as potential mechanisms to improve athletic performance using compression, with varying results. With regard to distance running in particular, studies evaluating the use of compression stockings to improve performance have been equivocal.5–8 A relatively constant finding among studies evaluating compression garments is a decrease in active hip-joint range of motion, suggesting the likelihood of an “ergogenic interplay” between compression and biomechanical factors.2,3,7 In addition, there is evidence to suggest that compression garments reduce longitudinal and anterior–posterior muscle vibrations on landing during repetitive jumping.3,4 This finding has been attributed to enhanced muscle activation, as soft-tissue vibrations with ground impact can be attenuated by increased muscle activity.9 Similarly, compression garments appear to allow greater maintenance and consistency of repeated-jump performances, although whether this would translate to any kinematic measure during other modes of exercise is unknown. A potentially comparable measure during running is stride-to-stride variability, which has been shown to relate to the energy cost of locomotion and increase with fatigue.10,11 Only recently have kinematics been examined with subjects wearing The authors are with the Dept of Kinesiology, Indiana University, Bloomington, IN. Address author correspondence to Abigail Stickford at [email protected].

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lower-leg compression garments during endurance running, and no differences were observed when the results were compared with a noncompressive control visit.8 However, the runners used in the study can be considered moderately trained (mean 10-km best: 38 min), and no indication of gait stability was measured. It is well known that individual differences in running mechanics influence the oxygen cost of running at submaximal workloads (ie, running economy [RE])10–14 and that a strong relationship exists between RE and distance-running performance.15–17 Small differences in RE often influence performance outcomes, particularly at the elite level, where all athletes possess a high maximal oxygen uptake (VO2max) and can sustain efforts at a high percentage of their VO2max for prolonged periods of time.17 Thus, any change in running mechanics that influences RE could ultimately affect an athlete’s performance. It is currently unknown whether lower-leg compression sleeves alter gait mechanics and/or stride-to-stride variability in young, highly trained runners and, if so, what impact this alteration will have on RE. We hypothesized that wearing compression sleeves would reduce stride-to-stride variability and improve economy during treadmill running.

Methods Subjects Subjects (N = 16) were highly trained men, all either current college or professional distance runners. Subject anthropometrics and personal-best race performances are displayed in Table 1. Subjects reported running 6.5 ± 0.9 d/wk and 100 ± 32 km/wk during the 6 months before the study. All runners were determined to be fit to participate in the study, as assessed by physical activity and training

Effect of Compression on Gait and Economy   77

Table 1  Subject Characteristics and Reported Personal-Best Race Performances During the 1 Year Before the Study, Mean ± SD Characteristic

Value

Age (y)

22.4 ± 3.0

Height (cm)

180.6 ± 4.6

Mass (kg)

66.4 ± 5.2

Event average time (min:s)   1500 m (n = 8)

3:56.2 ± 0:12.6

  5000 m (n = 12)

14:47 ± 1:02.2

  10,000 m (n = 4)

29:22 ± 0:35.7

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Note: Some subjects (n = 9) reported personal bests in more than 1 event.

questionnaires. Their specialty racing events included the 1500-m, 3000-m steeplechase, 5000-m, and 10,000 m. Primary inclusion criteria were active training, age 18 to 30 years, and a 5000-m time of ≤16:30 within the past year. Runners who had not raced a 5000-m in the past year were deemed highly trained by VO2max measures (>65 mL · kg–1 · min–1) obtained within the past year. The study was carried out from late July through early September. Subjects were informed of the risks and benefits of the study and gave written informed consent before testing. All protocols and procedures were approved by the institutional review board of Indiana University.

Design Subjects completed a single experimental session. Since subjects could not be blinded to wearing compression garments, they also completed a questionnaire defining their a priori experiences with and beliefs regarding compression garments. The 7 dichotomous questions were summed to give an overall belief score, with high positive values indicating substantial experience with and positive opinions on the effectiveness of compression garments. Two separate RE tests took place during the session. The sequence of tests, a trial with calf-compression sleeves (CS) and a control trial without compression sleeves (CON), was randomized and counterbalanced, with approximately 10 to 15 minutes rest between. Graduated calf-compression sleeves (Zensah, Miami, FL) generating 15 to 20 mm Hg of compression (per manufacturer’s statement) were worn by the subjects during the CS trial; manufacturer recommendations for sizing based on height and maximal calf circumference were used. The elastic of the sleeve reached from ~2 cm above the ankle to ~4 cm below the knee. For both trials runners wore the same pair of their own lightweight shoes and low-cut socks.

Methodology RE was assessed by measuring VO2 during 4-minute stages at each of 3 constant submaximal speeds of 233, 268, and 300 m/min on a motorized treadmill (Quinton, model 18-72, Bothell, WA). Treadmill speed was verified through the use of a laser tachometer (Mastech, model DT-2234C, San Jose, CA). RE was calculated from the VO2 measured over the final 60 seconds of each 4-minute stage at each speed and the slope of line relating VO2 to speed. Ventilatory and metabolic variables were continuously measured during exercise using a computer-interfaced, open-circuit, indirect calorimetry system. Minute ventilation (VE) was deter-

mined using a pneumotach (Hans Rudolph #3813, Kansas City, MO) and amplifier (Hans Rudolph #1110) on the inspired side. Subjects breathed through a low-resistance 2-way valve (#2700, Hans Rudolph), and a 5-L mixing chamber was used for collection of expired gases. Fractional concentrations of O2 (FEO2) and CO2 (FEO2) were determined from dried expired gas sampled at a rate of 300 mL/min, using separate O2 and CO2 gas analyzers (SA-3 and CD-3A, respectively, AEI Technologies, Pittsburg, PA). Analyzers were calibrated before each test using commercially available gas mixtures within the physiological range. VE, VO2, and VCO2 were averaged over each minute of exercise, with VE corrected to BTPS (body temperature and pressure, saturated) and VO2 and VCO2 corrected to STPD (standard temperature and pressure, dry). These variables, as well as FEO2 and FECO2, were continuously measured and monitored with a data-acquisition control system (DASYLab 10.0, National Instruments, Norton, MA) sampling at 50 Hz. To measure select kinematic variables related to running gait, accelerometric data were gathered during the last 30 seconds of each 4-minute stage of the RE test. Separate wireless triaxial 10-g accelerometers (G-link, Microstrain, Williston, VT) were attached to the shoelaces on each foot using plastic ties. The accelerometers sampled each axis at 1024 Hz, with data from each 30-second stage being stored in separate files. Accelerometer data were analyzed using a custom in-house program, following a technique described previously.18 Briefly, the unfiltered output of the accelerometer in the vertical and horizontal planes was used to identify contact and toe-off time points for each step, allowing quantification of (1) foot ground-contact time (tc), defined as the time (s) from when the foot contacts the ground to when the foot toes off (ie, breaks contact with the ground); (2) swing time (tsw), defined as the time (s) from toe-off to ground contact of consecutive footfalls of the same foot; (3) step frequency (SF), defined as the number of ground-contact events (ie, steps taken) per second; and (4) step length (SL), defined as the length (m) that the treadmill belt moved from toe-off to ground contact in successive steps (opposite feet), calculated from SF (steps/min) and treadmill speed (m/min). Values of tc, tsw, SL, and SF were determined from the average of accelerometric values obtained from a minimum 20 consecutive steps. Gait variability was calculated as the standard deviation of a given gait variable for an individual during the last 30 seconds of each stage. Mechanical parameters of the spring-mass model during running, including vertical displacement of the center of mass at lowest point, peak displacement of the leg spring, maximal ground-reaction force, vertical stiffness, and leg spring stiffness, were calculated as described by Morin et al19 using measures of body mass, height, flight time, and ground-contact time.

Statistical Analysis Descriptive statistics were used to describe the characteristics of the group, and Pearson correlations were used to quantify relationships between mechanical and metabolic variables (SPSS 18, Chicago, IL). To assess differences in the outcome measures of tc, tsw, SL, SF, and submaximal VO2 at the different running speeds during CS and CON testing sessions, a 2 × 3 repeated-measures ANOVA was conducted. When the F value was considered statistically significant, differences between CS and CON at each speed were determined using simple main effects. A modified Bonferroni adjustment was done to account for the multiple planned comparisons. Data are reported as mean ± standard error of the mean (SE) unless otherwise stated. Significance was set at P < .05.

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Results Running Economy

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There were no differences in VO2 between CON and CS trials at any of the speeds (P = .70–1.00) (Figure 1). At 233 m/min, VO2 was 46.7 ± 1.6 mL · kg–1 · min–1 (CON) and 46.5 ± 1.5 mL · kg–1 · min–1 (CS). At 268 and 300 m/min, submaximal VO2 was 54.0 ± 1.6 mL · kg–1 · min–1 for CON and 54.0 ± 1.7 mL · kg–1 · min–1 for CS

and 62.1 ± 1.7 mL · kg–1 · min–1 for CON and 62.2 ± 1.8 mL · kg–1 · min–1 for CS, respectively. In addition, there was no difference in the slope of the lines relating submaximal VO2 and running speed between the 2 experimental conditions (slope = 0.230 and 0.233 for CON and CS, respectively; P = .54) (Figure 1).

Running Mechanics There were no differences in tc, tsw, SF, and SL between CON and CS trials at any of the running speeds (P = .28–.94) (Table 2A). Gait variability was also not different from CON to CS conditions at any of the speeds (P = .42–.73) (Table 2B). Spring-mass model parameters were comparable to those previously reported19 and did not differ between CON and CS. As seen previously, tc was inversely correlated with submaximal VO2. Figure 2 shows the relationship between the inverse of tc (tc–1) and VO2 for each individual during the 3 speeds of the control condition. The mean coefficient of determination (R2) was .96 ± .02. A similar relationship was seen between these variables during the treatment condition.

Individual Response to Compression

Figure 1 — Submaximal oxygen consumption (VO2) during control (black circles) and compression (white circles) conditions. Slopes of the running economy lines are 0.230 during control and 0.233 during compression trials. Values are mean ± SE.

The individual metabolic response to compression was quite variable (ΔVO2 range –4.8% to 5.1%), with some runners consistently showing a higher VO2 during the CON condition and others displaying higher VO2 while wearing compression (Figure 3). We completed a post hoc analysis using subjects with the greatest improvements (n = 4) and largest decrements (n = 4) in RE (from CON to CS) to determine if there were any distinguishing characteristics that explained these individual responses to compression. There were no differences between groups in spring-mass model parameters (Table

Table 2A  Gait Variables, Mean ± SE Gait Variable Speed (m/min)

Condition

233

Control

Ground-contact time (s)

Swing time (s)

Step frequency (Hz)

Step length (m)

0.204 ± 0.003

0.507 ± 0.010

2.831 ± 0.038

1.38 ± 0.02

Compression sleeve

0.205 ± 0.003

0.506 ± 0.009

2.827 ± 0.035

1.38 ± 0.02

268

Control

0.188 ± 0.003

0.503 ± 0.010

2.907 ± 0.037

1.53 ± 0.02

Compression sleeve

0.189 ± 0.003

0.500 ± 0.009

2.910 ± 0.035

1.53 ± 0.02

300

Control

0.175 ± 0.002

0.495 ± 0.008

2.990 ± 0.037

1.68 ± 0.02

Compression sleeve

0.175 ± 0.002

0.495 ± 0.009

2.994 ± 0.036

1.67 ± 0.02

Table 2B  Calculated Variability (SD) in Measured Gait Variables, Mean ± SE Gait Variability (SD) Speed (m/min)

Condition

Ground-contact time (s)

Swing time (s)

Step frequency (Hz)

Step length (m)

233

Control

0.0059 ± 0.0012

0.0096 ± 0.0013

1.62 ± 0.12

0.013 ± 0.001

Compression sleeve

0.0066 ± 0.0014

0.0088 ± 0.0012

1.57 ± 0.17

0.013 ± 0.001

Control

0.0055 ± 0.0010

0.0086 ± 0.0012

1.71 ± 0.22

0.015 ± 0.002

Compression sleeve

0.0052 ± 0.0011

0.0080 ± 0.0009

1.72 ± 0.21

0.015 ± 0.002

Control

0.0054 ± 0.0008

0.0086 ± 0.0010

1.93 ± 0.21

0.018 ± 0.002

Compression sleeve

0.0051 ± 0.0014

0.0082 ± 0.0015

1.81 ± 0.19

0.017 ± 0.002

268 300

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Figure 2 — Mass-specific rates of oxygen consumption (VO2) increase linearly with inverse ground-contact time (1/tc) for all subjects. Average coefficient of determination (R2) is .96.

Figure 3 — Individual percentage changes in oxygen consumption (VO2) from the control (CON) to compression (CS) condition across 3 submaximal speeds.

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80  Stickford et al

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3). However, there was a clear trend for runners who improved RE with compression to have lower SF- and SL-variability measures (Cohen d = 0.77–2.24; Table 4). At the slowest running speed, subjects with improved RE tended to further reduce gait-variability measures when wearing compression, while subjects with worsened RE increased gait variability (d = 1.22, 1.17, P = .09, .10 for SF and SL variability, respectively; Figure 4). Runners with improved RE also showed greater consistency in absolute SF and SL measures from CON to CS than subjects whose RE worsened (d = 1.02, 1.08, and 0.67, P = .14, .13, .21 for SF across speeds; d = 0.66, 1.08, and 0.67, P = .20, .13, .21 for SL across speeds; Figure 5 shows averages). In addition, there was a significant inverse correlation between belief scores and changes in VO2 with compression (r = –.52, P = .04), as runners who exhibited more positive feelings about compression garments displayed larger decrements in submaximal VO2 during CS (Figure 6).

Discussion This study investigated the impact of wearing lower-leg compression sleeves on the RE and mechanics of highly trained distance runners. Our conclusions can be divided into 3 major categories: RE, running mechanics, and the individual variability in response to compression. While the use of lower-leg compression sleeves did not affect group measures of RE or mechanics, subjects who had a positive or negative change in RE with compression treatment displayed different biomechanical responses to, and a priori beliefs regarding, compression.

Running Economy In the current study we found that lower-leg compression sleeves do not alter whole-body VO2 during submaximal running. The

Table 3  Main Mechanical Parameters During Treadmill Running (Mean ± SD) Speed

Parameter

Group CON

Group CS

Positive CON

Positive CS

Negative CON

Negative CS

233 m/min

Δyc (m)

0.05 ± 0.01

0.05 ± 0.01

0.05 ± 0.01

0.05 ± 0.01

0.05 ± 0.01

0.06 ± 0.01

ΔL (m)

0.14 ± 0.02

0.14 ± 0.02

0.14 ± 0.01

0.14 ± 0.01

0.15 ± 0.03

0.15 ± 0.03

268 m/min

300 m/min

Fmax (kN)

1.80 ± 0.27

1.79 ± 0.24

1.82 ± 0.09

1.84 ± 0.05

1.73 ± 0.44

1.69 ± 0.38

kvert (kN/m)

36.53 ± 8.47

35.84 ± 7.44

35.26 ± 4.98

36.00 ± 3.49

33.60 ± 13.66

32.38 ± 11.59

kleg (kN/m)

13.67 ± 3.28

13.41 ± 2.88

13.33 ± 1.71

13.62 ± 1.11

12.63 ± 5.37

12.15 ± 4.90

Δyc (m)

0.04 ± 0.01

0.04 ± 0.01

0.05 ± 0.00

0.05 ± 0.00

0.05 ± 0.01

0.05 ± 0.01

ΔL (m)

0.14 ± 0.02

0.14 ± 0.02

0.14 ± 0.01

0.15 ± 0.01

0.15 ± 0.02

0.15 ± 0.03

Fmax (kN)

1.88 ± 0.26

1.86 ± 0.25

1.91 ± 0.05

1.89 ± 0.05

1.84 ± 0.46

1.84 ± 0.44

kvert (kN/m)

43.42 ± 9.38

42.97 ± 9.20

43.12 ± 4.73

42.04 ± 4.91

41.95 ± 14.94

42.70 ± 15.89

kleg (kN/m)

13.41 ± 3.01

13.27 ± 2.97

13.49 ± 1.30

13.14 ± 1.35

13.03 ± 4.90

13.27 ± 5.22

Δyc (m)

0.04 ± 0.00

0.04 ± 0.00

0.04 ± 0.00

0.04 ± 0.00

0.04 ± 0.00

0.04 ± 0.00

ΔL (m)

0.15 ± 0.02

0.14 ± 0.02

0.15 ± 0.01

0.15 ± 0.01

0.15 ± 0.02

0.14 ± 0.02

Fmax (kN)

1.96 ± 0.25

1.97 ± 0.27

1.97 ± 0.05

1.96 ± 0.08

1.95 ± 0.43

1.96 ± 0.43

kvert (kN/m)

52.14 ± 10.49

53.30 ± 12.77

49.45 ± 1.44

48.79 ± 3.97

52.44 ± 16.50

53.20 ± 16.38

kleg (kN/m)

13.68 ± 2.92

14.00 ± 3.54

13.14 ± 1.35

12.97 ± 1.15

13.84 ± 4.66

14.05 ± 4.62 al19).

Note: Spring-mass model parameters based on a sine-wave modeling of the force–time curve during contact (calculations based on Morin et Positive refers to improved running economy with compression (n = 4); negative refers to worsened running economy with compression (n = 4). No significant differences in any measures between conditions or groups. Abbreviations: CON, control; CS, compression trial. Δyc, vertical displacement of the center of mass when it reaches its lowest point; ΔL, peak displacement of the leg spring; Fmax, maximal ground-reaction force during contact; kvert, vertical stiffness; kleg, stiffness of the leg spring.

Table 4  Comparisons in Stride-Frequency (SF) and Stride-Length (SL) Variability (SD) During the Compression Trial Between Subjects Who Showed Improvements (n = 4) and Decrements (n = 4) in Running Economy SF Variability

SL Variability

Speed (m/min)

Improved running economy

Worsened running economy

P

Effect size (Cohen d)

233

1.11 ± 0.18

1.77 ± 0.38

.02*

2.22

268

1.37 ± 0.20

2.24 ± 1.23

.14

1.00

300

1.56 ± 0.29

2.20 ± 1.02

.18

0.85

233

0.009 ± 0.001

0.014 ± 0.003

.04*

2.24

268

0.012 ± 0.002

0.020 ± 0.012

.17

0.93

300

0.015 ± 0.001

0.021 ± 0.011

.20

0.77

Note: P value reported from comparison between groups (1-tailed independent-groups t test). Values are mean ± SD. *Significantly different at P < .05.

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Effect of Compression on Gait and Economy   81

Figure 4 — Percentage changes in average stride-frequency (SF) and stride-length (SL) variability from control to compression in individuals who showed an improvement in running economy (black bars) and those whose running economy worsened (gray bars) at 233 m/min (d = 1.22, 1.17; P = .09, .10, for SF and SL variability, respectively). No differences between groups at other speeds (d = 0.05–0.23, P = .38–.49). Values are mean ± SE.

Figure 6 — Correlation between change in oxygen consumption (ΔVO2) from control (CON) to compression (CS) and belief score (r = –.517, P = .041). Score was determined from answers to pretesting questionnaire about experience with and opinions on compression garments—positive values assigned to experience/positive responses; negative values assigned to no experience/negative responses.

with regular elastic tights in trained runners (speeds 167–268 m/ min). However, they did observe a significant reduction in the slow component of VO2 while compression tights were worn, presumably indicating improved energetics. Similarly, in subjects with fitness levels more comparable to that of our subjects (VO2max = 70 mL · kg–1 · min–1; 10-km best ≈ 38:30), there was no effect of compression on VO2 during a 40-minute run (at a speed corresponding to the slowest speed, 233 m/min, of our study).6 Our findings add to and support the literature regarding compression garments and RE in healthy individuals, providing additional evidence for no gross effect of compression on submaximal VO2, specifically in a group of elite runners.

Running Mechanics

Figure 5 — Percent changes in stride frequency and stride length from control to compression at a speed of 233 m/min in individuals who showed an improvement in running economy (black bars) and those whose running economy worsened (gray bars). Values are mean ± SE.

absence of a group effect of compression on submaximal VO2 is consistent with previous findings. Kemmler et al7 saw no change in the VO2 of moderately trained runners (VO2max = 52 mL · kg–1 · min–1; 10-km best = 40:36) at various workloads (established based on blood lactate values; submaximal speeds lower than those in the current study) when wearing compressive stockings as compared with during control conditions. Bringard et al20 also showed no effect of compression on submaximal running gross VO2 compared

Few studies have investigated the effect of compression on the selected mechanical variables measured in our study.8 However, previous findings indicate that lower-body compression garments may influence lower-body mechanics by limiting range of motion (ROM).2,3 Decreased ROM is associated with increased stiffness,17 which, in turn, can alter the selected gait variables measured in the current study. Furthermore, compression garments appear to allow greater consistency in mechanics/performance during repetitive power movements.4 Thus, previous findings would suggest that lower-body compression may affect the mechanics of movement. Bringard et al7 and Kemmler et al20 seem to support this hypothesis by proposing that compression treatment may act through increased biomechanical support of the muscle–tendon unit, ultimately resulting in greater mechanical efficiency. We were unable to confirm this, however, as we found no overall effect of wearing lower-limb compression on leg stiffness or running mechanics. The inability of compression to exert an effect on running mechanics in the current study could be due to a number of factors including the targeted gait variables, the level and/or location of compression exerted by the calf sleeve used, and, perhaps more likely, the subject population.21 Nonetheless, as will be discussed further, our findings suggest that there may be distinct individual metabolic and mechanical responses

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to compression, perhaps partially explaining the lack of a group response in our, and previous, studies. During submaximal running, the majority of stored energy comes from muscles and tendons supporting the ankle and knee; for the lower leg, this refers specifically to the triceps surae and the Achilles tendon.22 The lower band of the compression sleeve used in our study sat immediately superior to the lateral malleolus. Thus, although it covered most of the triceps surae and a portion of the tendon, the compression did not cover the entire Achilles tendon, nor did it surround the ankle joint, itself. In previous investigations finding decreased ROM, the garment completely covered the joint about which ROM was reduced.2,3 On the other hand, jumpperformance consistency improves with a garment covering just the hip joint, despite multiple muscles and joints being involved in the movement,4 indicating that complete compression about all involved muscles and joints is not necessary to see changes in global mechanical variables. As running, too, involves the coordination of various muscles and joints, it is unlikely that the lack of gross mechanical effect in the current study was due to the garment not covering a joint. Also of note, as is the case in the majority of studies on compression garments,21 the precise level of pressure exerted by the sleeves on the calf in our study is unknown; we relied fully on the manufacturer’s statement. As such, the magnitude of compression used in our study may be different from that of garments used in previous studies. Direct measurements of compression-garment pressures have been shown to be comparable to manufacturer ratings,6 so based on available reports, the compression appears to be similar between our study (15–20 mm Hg) and others (range = 8–26 mm Hg).21 We chose this particular compression-sleeve garment as it is relatively inexpensive, widely available, and commonly used; in other words, findings regarding the garment are quite applicable to the target athlete population. The subjects in our study were highly trained distance runners, whereas many previous studies used less-fit subjects and athletes specializing in other events (eg, sprinters, skiers).2–7,20 Attempts to alter running mechanics have been successful in clinical populations and in novice or moderately trained runners,23,24 but it appears that altering habitual gait patterns in highly trained athletes is more difficult. It has been demonstrated that highly trained runners consistently select the most economically optimal locomotion style; deviating from the preferred gait mechanics results in significant decrements in economy of locomotion.11,13 Furthermore, trained runners are able to maintain similar gait patterns by adjusting, for example, leg stiffness or muscle activity despite external interferences (eg, changes in surface properties).25 Therefore, our subject cohort, being highly trained, may have been able to continue to select the most economical running pattern despite the “interference” of lower-leg compression.

Individual Response to Compression Although the group mean metabolic response to compression was not different from CON, there was substantial interindividual variability. A change of 1% in submaximal VO2 is reported to have significant performance implications, demonstrating a tight link between RE and running performance.26,27 Therefore, we completed a post hoc analysis to determine if there were any distinguishing characteristics that explained these individual responses to compression. When subjects were grouped according to changes in RE, it became evident that those who improved RE while wearing

compression had lower measures of gait variability, particularly at the slowest speed during CS (P = .04 [SL], 0.02 [SF]; Table 4). Variability in intraindividual stride length, already lower in highly trained runners than in nonrunners,28 decreases with faster running speeds,29 so it is reasonable that we did not see significant differences in gait variability between groups at the faster speeds. In trained runners, a large proportion of the increased energy cost of running with fatigue is due to increased step variability,10 so decreased gait variability may contribute to the decrease in submaximal VO2 with compression seen in some subjects in our investigation. Differences between groups in the degree/direction of change in SL and SF may also have affected RE responses to compression. Subjects whose economy worsened while wearing compression tended to decrease SL and increase SF, whereas subjects with improvements in RE had little to no change in SL and SF across conditions. As mentioned previously, highly trained athletes typically select gait patterns that minimize metabolic cost, and deviations from preferred kinematics result in increases in oxygen cost. If the control condition represents a preferred movement pattern, it appears that the compression caused some subjects to deviate from their most economical running mechanics for a given speed. The underlying cause as to why compression would affect the stride mechanics and gait variability of some subjects and not others is unknown. One possible explanation may be subjective perceptions of the garment, as runners with personal experience and/or positive opinions of the garment may have felt more comfortable (psychologically and/or physically) wearing compression. However, as there were 4 individuals who exhibited positive responses, 8 showing neutral responses, and 4 exhibiting negative responses to compression, there is certainly a possibility that the variation in response occurred by chance, and the sleeves truly have no effect on metabolic or mechanical variables in highly trained runners.

Practical Applications In general, previous research and the current investigation indicate that wearing lower-leg compression sleeves has no substantial effect on distance-running performance.5–8,21 However, if, in fact, differences do exist in metabolic response to lower-leg compression among individuals, the performance implications could be particularly important for highly trained runners. In the current study, the average percentage change in submaximal VO2 with compression treatment ranged from –4.8% to +5.1%. A 5% improvement in RE has been shown to induce ~3.8% improvement in distancerunning performance, and a 10% improvement in RE results in ~7% improvement in 3000-m performance,27,28 indicating that there could certainly be a range of performance implications for the subjects in our study when choosing to wear compression garments. Finally, it is not unreasonable to suspect that the different responses to compression are due to psychological effect. A significant correlation was found between overall viewpoints on the efficacy of compression and metabolic changes while wearing compression, and the 2 subjects in our study with the greatest improvements in RE with compression treatment were the only subjects who had worn compression sleeves themselves and believed that compression aided in training, racing, and recovery. Thus, in determining whether an athlete should use compression garments for training, competitive, and/or recovery purposes, coaches would be well-advised to consider the athlete’s expectations, whether positive or negative, of the garment.

Effect of Compression on Gait and Economy   83

Conclusions Wearing lower-leg compression sleeves does not alter running mechanics or economy in highly trained distance runners during submaximal running. However, while there is no group difference with the sleeves, it appears that the individual response to wearing lower-leg compression varies greatly; as such, future research should examine underlying physiological, anatomical, and psychological characteristics in relation to the metabolic and/or kinematic outcomes of wearing leg-compression garments.

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Acknowledgments Supported by a grant from USA Track and Field. We would like to acknowledge Dr Dave Tanner for his work on the accelerometric gaitanalysis program and Dr S. Lee Hong for input regarding study design and theoretical implications. None of the authors of this article has any conflicts of interest or financial conflicts to report. The results of the current study do not constitute endorsement of the product by the authors or the journal.

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Lower-leg compression, running mechanics, and economy in trained distance runners.

The efficacy of and mechanisms behind the widespread use of lower-leg compression as an ergogenic aid to improve running performance are unknown. The ...
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