Technical Report
The Influence of SKINS A400 Lower Body Compression Garments on Running and Neuromuscular Performance
Human Performance Unit The University of Essex, UK, CO4 3SQ
by
Chris McManus, Human Performance Unit Manager, The University of Essex Kelly Murray, Human Performance Unit Sport Scientist, The University of Essex Nicholas Morgan, Sports Integrated Ltd
EXECUTIVE SUMMARY In the present study we assessed the effects of (1) correctly fitted and (2) oversized, fulllength compression tights on parameters of running and vertical jump performance, metabolic response and ratings of perceived exertion. Study outcome During steady state running at a fixed intensity of 60% vVO2max (12.1 Âą 1.3 km/h), running economy was significantly lower (p < 0.05) in correctly fitted compression tights when compared with running shorts. We observed no significant changes in any other performance, mechanistic or subjective measure. What is running economy and why is it important? Running economy is defined as the energy required to run at a set speed. It has been demonstrated to be a strong predictor of endurance performance, and a better predictor than VO2max. Put simply the lower the running economy, the better the endurance athlete. What does the finding of this study mean? When wearing correctly fitted compression compared to running shorts, the runners demonstrated that they used less energy when running at a sub maximal speed. They were more economical and efficient. What does this mean in practice? It is widely accepted that runners who are more economical during sub maximal speeds have the ability to push harder or run longer during their training and/or events. How does compression improve running economy? It is suggested to occur as a result of one or a combination of: > Enhanced proprioception > Reduced muscle oscillation and vibration, therefore optimising neurotransmission > Enhanced running technique / posture > Improved circulation coupled with decreased muscle oscillations reduce energy cost
INTRODUCTION Compression claims are leveraged generically across brands to help provide evidence of their performance benefit. However, it is important to recognise that this assumes an appropriate size, fit and compression profile. Furthermore, the type of garment appears to play a pivotal role in the efficacy of whether particular performance, mechanistic or subjective variables are influenced, further complicating the issue of extrapolating specific observations into generic ‘compression-wide’ claims. Consequently, no garment is “proven” unless they have been shown to provide a performance benefit under research conditions. SKINS have led the way in proving that their garments are efficacious under various research conditions. With the launch of the new A400 range it is important to provide evidence that they maintain a proven benefit.
METHODS Participants Eleven healthy, recreationally active (>3 sport specific training sessions per week) males (mean ± SD; age 28.7 ± 6.6 years, weight 68.2 ± 5.3 kg, VO2max 54.15 ± 4.9 ml/kg/min, vVO2max: 19.2 ± 1.4 km h-1 (corrected for 1% gradient)) participated in the study, which was approved by the university’s ethics review board. Subjects were instructed to continue with normal dietary practices whilst participating in the study, and to keep a 3-day food and activity diary prior to testing. They were requested to follow the same dietary intake the day prior and day of testing for all subsequent testing dates. Participants were asked to refrain from exercise, caffeine and alcohol intake 24 hours prior to testing and refrain from strenuous or competitive exercise 48 hours prior. Furthermore, subjects did not consume any food or fluid (other than water) in the 2 hours prior to testing. The Garment The compression garments used in the current study were Skins
TM
Men’s Compression
A400 Long Tights (Zug, Switzerland), and the correctly fitted garment for each subject was in accordance with the manufacturer’s instructions (correctly-sized garments; CSG). For the oversized garment condition (over-sized garments; OSG), subjects wore 2 sizes above the manufacturer’s instructions (M = XL; L = XXL). Subjects were blinded to the garment condition by removing the size label. The pressure exerted by the compression garments on the lower limbs were evaluated by the Picopress® pressure monitor (CV = 2.79%, Partsch and Mosti., 2010). Pressure measures were recorded a 6 anatomical locations (5cm above superior sphyrion, medical calf, posterior calf, anterior thigh, posterior thigh and gluteus maximus). The pressure sensor was inserted from the bottom for the sphyrion and calf measures and inserted from the top for the thigh and gluteus maximus. The average of 3 measures was recorded for each anatomical location for both compression garment conditions.
The control garment consisted of loose fitting running shorts, thereby providing a comparison between compression garments and garments typically worn by recreational runners. The same short sleeve top was worn on every testing occasion, as were the same running shoes always worn.
Experimental Approach The experimental protocol consisted of 4 sessions, held between 2-4 days apart for all subjects. Each subject attended their sessions at the same time of day, with similar environmental conditions (temperature: 18 ¹ 1.0° C) to minimise circadian rhythm. A randomised, crossover design was incorporated into the study.
The initial session was used to determine individual maximal aerobic capacity (VO2max), maximal aerobic velocity (vVO2max) and maximal heart rate (HRmax). After a 5 min warm-up at 7 km h-1 (0% gradient) subjects undertook an incremental exercise test on a treadmill (Saturn, HP-Cosmos, Nussdorf, German) wearing the control garments previously described. The progressive exercise test was used from a previously published method (Goh et al., 2011), to determine maximal performance parameters. Following the 5 min warm-up, treadmill speed was increased by 1 km h-1 every minute until 16 km h-1 was achieved. At this point, gradient was increased by 2% each minute thereafter until volitional exhaustion. VO2max was determined as the point where (a) a plateau was observed in VO2 consumption over a 30 s period and decreased thereafter with increasing workload, (b) HR was within 10 beat min-1 of age predicted maximum HR, (c) a respiratory exchange ratio (RER) of >1.1 was observed and (d) volitional fatigue was achieved (Dupont et al., 2003). To determine vVO2max, gradient increases were converted to a running velocity whereby a gradient rise of 1.5% equated to an increase in speed of 1 km h -1 (Margaria et al., 1963).
Following the incremental test, a familiarisation process was undertaken which included subjects running for 5 min at a pre-determined sub-maximal intensity (60% vVO2max) and completing two sets of five counter-movement jumps (CMJ) using a force platform (0.36 m x 0.36 m, PASCO Scientific PS-2142, Roseville, CA) collecting at 1,000 Hz. Subjects were provided with verbal instructions and a physical demonstration of the correct CMJ technique. Subjects were required to achieve a jump height coefficient of variance value of <5% for the second set of CMJ, otherwise additional attempts were requested to ensure consistency in technique.
During sessions 2-4, subjects provided a urine sample upon arrival to assess specific gravity (Atago Co., Ltd., Tokyo, Japan) and provided a 24 hour dietary intake record to
ensure subjects had adhered to the food and fluid recommendations. Subjects were randomly assigned to each testing condition in an attempt to limit any learning effects. For those sessions when a compression garment was worn, pressure measures were recorded prior to undertaking a cycle warm-up (Monark 818 E, Sweden) of 5 min at 100W. Exactly 3 minutes following the warm-up, 5 x counter-movement jumps were performed. Subjects were required to jump as high as possible for 5 consecutive efforts with a 3 s pause between jumps. Countermovement depth was self-selected by the subject. A self-selected countermovement depth was chosen to assess reliability of variables using a technique requiring minimal intervention thereby maximizing the potential application to practical settings where time limitations may exist. Each trial was then analyzed using customdesigned software (Forcedecks, UK) capable of automatically detecting values for the variables of interest. Jump height, flight time, mean and peak concentric force were variables of interest, whereby the mean of 5 jumps was used for data analysis. Five minutes after the completion of 5 x CMJ, subjects underwent a 15 min steady state (SS) running task at 60% vVO2max, at a gradient of 1%. During the SS run the VO2, VCO2, minute ventilation and RER were measured constantly with a breath-by-breath gas analyser (Jaeger Oxycon Pro, Erich Jaeger GmbH, Hoechberg, Germany). Values for VO2 were smoothed over 5 s to de-emphasis breath-to-breath variation. Running economy (ml/kg/km -1) was calculated using the VO2 data from the final 3 min of the SS run task. Ratings of perceived exertion (RPE) where recorded at minute 3, 6, 9, 12 and 15 using the Borg 6-20 scale (Borg, 1970) and a SS session mean was established from the 5 RPE values provided. After 15 min of SS running, subjects straddled the treadmill belt and a capillary blood sample was obtained to determine lactate concentration. All blood samples for lactate concentration measurement were collected in a capillary tube (Eppendorf AG, Hamburg, Germany) from the right ear lobe and analysed using a Biosen lacate analyser (EKF Industrie, Elektronik GmbH, Barleben, Germany). A second set of CMJ performances were then assessed, exactly 5 min following the completion of the SS run task, followed by the final exercise task, requiring subjects to run to exhaustion. The time to exhaustion (TTE) test would begin 10 min after the completion of the SS run task, whereby subjects would run at 100% vVO2max (1% gradient) for as long as possible. Timing would begin when subjects released the handrail and stopped when subjects made contact with the handrail at the point of volitional fatigue.
Statistical Analysis To investigate the effects of wearing a compression garment on running and neuromuscular performance, all data were calculated with conventional procedures and are presented as mean values and standard deviations. Subsequently, all data were initially compared using a paired t-test, after which, a statistical analysis was performed using a specifically designed spreadsheet available for crossover studies. We used a contemporary statistical approach because small performance changes can be beneficial for high performing athletes, whereas conventional statistics can be less sensitive to such small but worthwhile changes. From the spreadsheet, we used magnitude-based inferences about effect sizes (Ρ2), and then to make inferences about true (population) values of the effect, the uncertainty in the effect was expressed as 90% confidence limits. Changes and errors were expressed as percents via analysis of log-transformed values, to reduce bias arising from non-uniformity of error and back transformed to obtain changes in means in raw values. The probability that the true value of the effect was practically negative, trivial, or positive accounted for the observed difference, and typical error of measurement. The effect size, Cohenâ&#x20AC;&#x2122;s d (defined as (difference in means)/standard deviation (Cohen, 1988)), was calculated for all variables between each clothing condition. Thresholds for small, moderate, and large effects were 0.20, 0.50, and 0.80, respectively (Cohen, 1988). All statistical tests were processed using the statistical package SPSS (Version 18) and Microsoft Excel (Microsoft CorporationTM, Redmond, WA, USA).
RESULTS Sub-garment pressures Pressure differences were significantly lower in the OSG condition when compared to CSG for 5 of the 6 anatomical locations (see Table 1).
Table 1. Compression profiles (mmHg) of correctly fitted and oversized garments. Oversized Correct size p value Ankle 1.3 ± 0.9 3.1 ± 1.3 <0.001* M. Calf 9.4 ± 3.1 11.5 ± 3.0 <0.05* P. Calf 9.2 ± 2.7 10.6 ± 3.1 >0.05 A. Thigh 3.7 ± 1.0 6.7 ± 0.6 <0.001* P. Thigh 4.6 ± 1.7 7.1 ± 2.0 <0.001* P. Gluteal 3.1 ± 0.7 5.3 ± 0.6 <0.001* Physiological and perceptual values All physiological and perceptual data is presented in Table 2. Most variables identified from the CMJ performance did not differ between trials, and this is evident for both before and after the 15 min SS run task (P>0.05). However, when wearing the CSG, peak (53:44:3%; η2 = 0.2) and mean (39:58:3%; η2 = 0.2) concentric force following the SS run demonstrated a small effect size when compared with the control condition. RPE during and blood lactate following the 15 min SS run were unaffected by the garment condition, as was TTE when running at 100% vVO2max (P>0.05). Wearing the CSG resulted in an improved running economy at 60% vVO2max (Fig. 1) when compared with control and OSG (P=0.02; 96:4:0%; η2 = 0.6).
Fig 1. Running economy during steady state at 60% vVO2max 230
Economy (ml/kg/km)
225
*
220 215
210 205 200 195 190 185 180 Control
Oversize
Correct
Table 2. Physiological and perceptual values when wearing CSG, OSG and control garments.
Variable
None
Compression Oversized
Correct Size
Best effect size
Best P
Correct Size-None Effect size (%) ± 90% confidence limit
Chances (% and qualitative) of a substantial improvement or impairment Improvement Impairment
Counter-movement Jump^ Pre steady state: Mean concentric force (N) Peak concentric force (N) Jump height (cm) Flight time (s)
1344.6 1707 31.0 0.499
± ± ± ±
119.9 199.6 7.9 0.064
1353.1 1691.1 30.8 0.497
± ± ± ±
118.8 156.9 7.8 0.065
1353.2 1716.9 30.4 0.495
± ± ± ±
104.1 216.7 6.6 0.053
0.1 0.1 0.1 0.1
0.6 0.5 0.7 0.7
0.7 0.5 -1.3 -0.7
± ± ± ±
3.0 2.8 6.2 3.0
25; Possibly 15; Unlikely 3; Very unlikely 4; Very unlikely
9; Unlikely 5; Unlikely 19; Unlikely 19; Unlikely
1359.5 1706.4 31.8 0.507
± ± ± ±
106.3 155.7 6.8 0.054
1361.3 1716.3 31.7 0.505
± ± ± ±
121.0 171.7 8.0 0.064
1375.8 1739.7 31.5 0.505
± ± ± ±
105.4 198.6 6.2 0.050
0.2 0.2 0 0
0.2 0.3 0.7 0.7
1.2 1.7 -0.8 -0.4
± ± ± ±
2.4 3.0 3.7 1.8
39; Possibly 53; Possibly 1; Very unlikely 2; Very unlikely
3; Very unlikely 3; Very unlikely 8; Unlikely 8; Unlikely
Post steady state: Mean concentric force (N) Peak concentric force (N) Jump height (cm) Flight time (s) Steady state running^^ Running economy (ml/kg/km) RPE Blood lactate (mmol/L)
214.19 ± 11.58 11.44 ± 2.28 3.0 ± 1.1
211.21 ± 10.35 11.58 ± 1.77 2.9 ± 1.0
207.38 ± 10.79 11.25 ± 2.02 3.0 ± 1.0
0.6 0.1 0.1
0.02* 0.6 0.5
3.2 ± 2.1 1.2 ± 4.8 4.1 ± 13.6
96; Likely 18; Unlikely 30; Possibly
0; Almost certainly not 3; Very unlikely 6; Unlikely
134.6 ± 37.3
132.4 ± 40.2
138.3 ± 36.1
0.1
0.3
3.3 ± 5.8
23; Unlikely
2; Very unlikely
Maximal Testing^^ Time to exhaustion (s) *
denotes statistical significance p <0.05 ^ 10 subjects ^^ 11 subjects
DISCUSSION Running Economy Running economy (RE) can be defined as the energy required for a sub-maximal running speed and is determined by measuring oxygen uptake (VO2) in steady-state conditions. RE has been demonstrated to be a better predictor of performance than maximal oxygen uptake (VO2max) in athletes who have a similar VO2max (Hausswirth et al, 2001; Saunders et al 2004). RE is closely associated with performance since a good RE would reduce the % of VO2max required to maintain a given mechanical load (Lucia et al., 2002). The results of this study demonstrated that during steady state running at a fixed intensity of 60% vVO2max (12.1 ± 1.3 km/h), RE was significantly lower (p < 0.05) in correctly fitted compression tights when compared with running shorts. The results of this study are similar to Bringard et al., (2006), who reported an improvement in RE when compression tights were worn and running velocity was ~12 km·h-1. Furthermore, a non-statistically significant, but large effect size (ES = 0.9) was reported in RE when compression stockings were worn while running at ~15-17 km·h-1 (Varela-Sanz et al., 2011). It is important to recognise that not all study’s report positive findings with regards to RE (Sperlich et al., 2010; Lovell et al., 2011). However, comparisons between studies are difficult due to variations in study design, whilst the impact of a varied individual response requires further investigation. When trying to understand the mechanism behind improved RE, alterations in running technique offer a plausible explanation. In 2014, Born et al. investigated the influence of a novel, long compression tight with adhesive strips, on repeated sprint performance. In the final 10 sprints (~20km/h; 30 x 30 m sprint, one sprint per minute), hip flexion angle reduced, while step length and activation of the m. rectus femoris significantly increased. Whilst, the garment used is uniquely different to SKINS and the pressure compression profile exerted higher (~20 mmHg), this is the first to report a measured change in running mechanics when wearing compression tights, therefore suggesting that altered sprint mechanics may explain improved performance, unlike previously proposed physiological mechanisms; such as changes in hemodynamics and oxygen uptake. Currently, this area of research remains immature (Valera-Sanz et al., 2011; Stickford et al., 2015; Born et al., 2014) and consequently clear conclusions have yet to be reached. In
investigating further, consistency in garment use and the variables measured is required. Further, it has been hypothesised that athletes may need an accommodation period for systematically experiencing the benefits of a compression garment (Valera-Sanz et al., 2011) and as such the measurement of an acute response may not provide the true extent of the benefits of compression. This provides an interesting angle for future investigation. Increased proprioception and muscle coordination have also been suggested as possible mechanisms to explain a reduced metabolic cost of running when wearing compression tights. In 1995, Perlau et al reported improved technique in a stationary knee extension task when elastic bandages are applied to the leg. In addition, Kuster et al, (1999) reported that a sleeve worn on the knee improved the integration of the balance control system and muscle coordination in subject’s recovery ACL surgery. Currently, the evidence to support the claims remains largely unproven in a dynamic, athletic population. During low-moderate exercise intensity, reduced oscillation / vibration of the musculature has been reported as a result of wearing compression clothing (Bakken, 2011; Doan et al., 2003). Cardinale et al., (2003) demonstrated that vibration of the muscle results in increased activity (as measured by EMG), and consequently, cardiorespiratory and metabolic demands are increased (Rittweger et al. 2000; Rittweger et al. 2001). Therefore, it was a justifiable proposition put forward by Bringard et al., (2006) to suggest that a reduction in muscle vibration by wearing compressive tights will cause a reduction in oxygen uptake during steady state, sub maximal exercise. Compression Profile As to be expected, the oversized compression tights applied a significantly lower level of pressure at most anatomical landmarks when compared with the correctly fitted garment. However, the lack of distinct differences between these values (mean compression values <12 mmHg for both garments) supports the proposal from Brophy-Williams et al., (2014) that future research studies should aim to standardise garments based upon compression profiles rather than manufacturers recommendations, which has previously been the norm (Driller & Halson, 2013; Ménétrier et al., 2011; Rugg & Sternlicht, 2013). Whilst no predetermined ‘ideal’ compression profile at specific anatomical landmarks, or gradient has been defined in the literature, it has been reported that a pressure of 18 mmHg at the ankle, dissipating to 8 mmHg at the calf (mean pressure = 12 mmHg) to be most
effective in increasing venous flow velocity when compared to higher and lower pressures (Lawrence and Kakkar, 1980). Conversely, Watanuki and Murata (1994) suggest that 17.3 mmHg is the minimum physiologically effective pressure at the calf, decreasing to 15.1 mmHg at the thigh. Although sports compression tights and leggings exert different pressures, the optimal pressure to induce the greatest increase in venous blood flow, muscle oxygenation, EMG and many other parameters is yet to be determined. Although compression values reported in the literature vary greatly, the weight of evidence is currently suggestive that values between 15-25 mmHg are optimal for physiological change. In light of the lower values observed in this current study, this may have had an impact on the results observed. Blood Lactate Despite some authors reporting a reduction in lactate concentration following exercise with compression garments (Berry & McMurray, 1987; Chatard et al., 2004), the current study found no difference in blood lactate between any clothing conditions. RPE Similar to that of Bringard et al., (2006), RPE was not different between the garment conditions when running at ~12 km/hr in the current study. As with all compression exercise studies, it is not possible to truly blind the subject to the garment condition, therefore prior knowledge of the presumed benefits of compression garments may predispose subjects to believing that their performance would benefit from using the garment (Goh et al., 2011; Desharnais et al. 1993). Interestingly, it has been reported that SKINS long tights reduce RPE at both 10 and 20 min during a sub-maximal run at 32째C when compared to normal running shorts. Furthermore, a reduced RPE following fifteen minutes of continual running (5 min at 50, 70 and 85% heart rate reserve) was reported when compression tights were worn (Rugg and Sternlicht, 2013). The compression profiles of the tights were 18, 12.6 and 7.2 mmHg at the ankle, calf and thigh respectively, therefore similar profiles at the calf and thigh to that of the current study.
Vertical Jump Performance No significant differences were observed in vertical jump performance either prior too or post completing a 15 minute steady state run between garment conditions. The variables analysed include the mean and peak concentric force, jump height and flight time. A small effect size (ES = 0.2) was observed in mean and peak concentric force following the steady state run when wearing correctly fitted compression tights. These findings are similar to that of Ali et al., (2010), who reported no difference in jump height or peak power between garment conditions following a 40 minute sub maximal run (~80% VO2max). Rugg and Sternlicht, (2013) report a significantly greater mean counter-movement jump height in graduated compression tights following 15 minutes of steady state running. The difference in finding between this and the present study may be related to the method of jump height assessment (Vertec vs force platform). Furthermore, countermovement jump has been shown to improve (or attenuated a decline) following various exercise modalities when wearing compression clothing (Jakeman et al., 2010; Kraemer et al., 1996; Kraemer et al., 1998). Future research should identify if vertical jump height is improved during athletic performance. Data in this area is lacking and would ultimately identify if any identifiable benefit is gained, does this translate into ‘on field’ performance i.e. peak and mean jump height of ‘blockers’ during volleyball game. Time to Exhaustion The time to exhaustion data presented in this study is generally aligned to previous findings, in that compression garments have no statistical or practical significance on running time to exhaustion at vVO2max (Goh et al., 2011; Sperlich et al., 2010). Goh et al., (2011) investigated running performance undertaken at the same exercise intensity, with SKINS long tights, reporting a similar compression profile (13.6 ± 3.4 and 8.6 ± 1.9 mmHg at the calf and thigh respectively) supports the current finding that time to exhaustion is not improved. A worthwhile point is that whilst at 10°C, TTE was not improved, a small effect size (ES = 0.48) was reported at 32°C when wearing full length SKINS compression tights.
Future Research In light of the improved RE, it would seem prudent to further explore this finding in relation to exercise performance. Firstly, future research should look to explore if a change in RE at ~12 km/h translates into a measureable performance enhancement. This investigation should require subjects to exercise for a prolonged period of time as most literature is based upon 3-15 min of exercise. Secondly, a deeper understanding into the mechanistic explanation behind why these findings are observed may shed light on what physiological/biomechanical variables(s) contributes towards this ergogenic benefit. Furthermore, whilst speculative at present, it could be hypothesised that potential benefits gained from wearing compression clothing could be strongly associated with the desired exercise intensity and compression profile (mmHg). It might be that when running at a lower intensity (jogging), a lower compression profile (5-15 mmHg) would suffice and bring about physiological benefits such as a reduced oxygen cost. Whereas when considering sprinting performance, applying appropriate (i.e. higher) levels of compression, altered running gait/posture may explain the possible ergogenic benefit. However this hypothesis remains to be explored and future research should look to address this. Finally, it should be mentioned that there is an increasing prevalence of authors speculating about a possible individual response when wearing compression. Possible inter-individual differences in sub-maximal running economy due to experience has been reported (Stickford et al., 2015), while the question regarding whether an optimal timecourse of wearing compression (i.e. number of wears/required duration of experience) to elicit the greatest physiological benefit has also been mentioned (Valera-Sanz et al., 2011). However, differentiating between ‘sub-conscious’ measures that cannot be altered by an athlete’s perception of compression clothing (i.e. energy cost) and ‘conscious’ variables than can (Time to exhaustion; TTE, and rating of perceived exertion; RPE), should be vital when attempting to understand any effects of ‘compression experience’. It has previously been reported in a study using compression shorts, that 93% of participants using compression garments believed that the garment was beneficial. This, in conjunction with mass-participation compression clothing-usage data, suggests that there may be a significant perceptual component to the ergogenic effects associated with compression apparel (Bernhardt and Anderson, 2005).
CONCLUSION In conclusion, the results of this study support that of Bringard et al (2006) that in the same environmental conditions, a lower running economy is produced, despite no observed change in subjective rating of perceived exertion. When wearing correctly fitted SKINS A400 long tights compared to running shorts, the runners demonstrated that they used less energy when running at a sub maximal speed. Wearing compression clothing during running may decrease muscle oscillations and alter running gait/posture, thereby promoting lower energy expenditure at a given intensity. Future studies are required to elucidate on the mechanisms integrating with the observed changes in running economy, plus identify if these physiological changes translate into a measurable performance enhancement.
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