Body mass (BM) and body composition are the main determinants of exercise performance. Previous studies have shown that body fat (BF) and lean body mass (LBM) adversely affect aerobic performance (14,15). In sport disciplines, during which athletes perform short bouts of maximal exercise relying on anaerobic energy systems, performance is often evaluated based on power measurements. In tests measuring anaerobic power, the load is usually established in relation to BM, whereas measurement results are reported in absolute values or relative to BM. When comparing participants' absolute power values, positive correlations between absolute power and BM are expected (24). By contrast, when anaerobic power is presented relative to BM, smaller participants have an advantage, resulting in negative correlations between power output relative to BM and total BM (27). Allometric scaling is most commonly used to control the effects of BM, which sequentially allows for research results to be independent from BM: the correlation between the allometrically scaled results and BM is near zero (16,21). However, this manner of presenting peak power (PP) measurement results and the impact of BM on the final results does not take body composition into consideration.
Analysis of changes in body height (BH), BM, and body composition of athletes active between 1942 and 2011 showed that they are getting taller, with a concurrent increase in their BM and BF (1). Other similar studies (18) indicate that athlete's demonstrate body composition changes without variations in BM. Therefore, determining the effects of increases in BM and composition on anaerobic power is necessary. Increased BM may result from either an increase in BF or LBM, or a simultaneous increase of both components. Anaerobic power tests have shown that PP is higher in obese people than in those with average BM (9), but these results do not take into account the impact of body composition. In obese participants, increased BM mostly results from an increase in both BF and a concurrent increase in LBM (12). Therefore, people with similar BM may differ greatly in body fatness and LBM. Body fat is passive tissue during exercise, whereas the generation of power primarily depends on muscle mass, which is the main component of LBM (8,11). Fat mass may, however, be an additional load, particularly during locomotion (e.g., running), resulting in generation of higher power outputs necessary for rapid movement (9). Cycling is a non-weight -bearing activity in which the lower body is primarily active, but the relationship among anaerobic power, BM, and body composition may have significant influence in cross-country sports. In other words, BM and composition may significantly impact performance during uphill cycling. The study of Impellizzeri et al. (5) confirmed the importance of BM for mountain biking performance. Although the thigh muscles are mostly active during biking, Patton et al. (17) indicated that fat-free thigh mass volume did not significantly correlate with maximal power during cycling. They also suggested that maximal power could be dependent on factors related to body size rather than muscle-fiber characteristics.
The aim of this study was to determine the effect of BM and body composition on peak and mean anaerobic power in men measured through a cycling test. Our hypothesis is that absolute and body composition impact cycling anaerobic power. Body mass gains due to increased BF or increased LBM may impact cycling anaerobic power differently. Therefore, we evaluated the effect of both increased BF and LBM on cycling anaerobic power. These findings may be useful for coaches and cyclists to maximize cycling anaerobic performance: the resultant data provide information, which indicates that BM (lean mass or BF) significantly impacts performance, and due to this, should be controlled during training. Moreover, scaling the results of PP in supramaximal tests may show that not only BM but also body composition should be taken into account.
Experimental Approach to the Problem
To verify our research hypothesis, this study was designed to evaluate the effect of increased mass resulting from either from (a) increased level of BF or (b) increased LBM, on cycling anaerobic power. Thus, power measurements were conducted on 2 different groups with similar total BM (high body fat [HBF], and high lean body mass [HLBM]) and in a control group.
This study consisted of 36 men stratified into 3 groups of 12 participants (in accordance with inclusion criteria). The men's average age was 21.4 ± 1.8 years (range, 19.6–28.0 years). The details of participants' somatic body build are shown in Table 1. They were not professional athletes, although they declared recreational physical activity of various forms and intensities. Test procedures were approved by the Bioethics Commission at the Regional Medical Chamber. Participants were enrolled in testing based on preliminary anthropometric measurements. The men meeting eligibility requirements for various groups and qualifying for the test were informed about the study purpose and course and gave their written informed consent for participation.
Before the test, volunteers were examined by a sports medicine physician. The medical examination was intended to exclude any contraindications to perform the test at maximal intensity. A week before testing, participants underwent familiarization by performing a short (10 seconds) supramaximal cycle sprint. In the days preceding the test, they were to avoid physical exertion, dehydration and were recommended to rest. Anthropometric power, PP, and mean power (MP) measurements of the participants were collected. Additionally, an analysis of physical activity was evaluated. All measurements were performed in the same laboratory under similar testing conditions, with the same equipment.
Preliminary Anthropometric Measurements: Inclusion Criteria for Particular Groups of Participants
Anthropometric measurements in young, untrained men, aged 19–29 years were conducted to establish inclusion criteria for the different groups. Measurements were taken in 1,607 men. Anthropometric measurements included BH, BM, %BF, and LBM. On the basis of these data, qualifying standards were comprised for the 3 groups. The inclusion criteria for the various groups were as follows: control group (Control): men with %BF and LBM between the 40th and 60th percentile (%BF: 14.0–18.5%; LBM: 59.0–64.3 kg) of the results obtained in preliminary anthropometric measurements; high body fat group: men with LBM between the 40th and 60th percentile with %BF more than the 80th percentile (>21.5%); high lean body mass group: men with %BF between the 40th and 60th percentile with LBM above the 80th percentile (>66.3 kg). Furthermore, HBF and HLBM groups had to have similar BM and body mass index, with both being significantly higher than the control group.
Body height was measured using the anthropometer (Martin type; GPM, Pfister Inc., Carlstadt, NJ, USA) type anthropometer with 1 mm accuracy. Body mass and composition were measured using the Jawon IOI-353 analyzer (Body Composition Analyzer, Jawon Medical Co., Ltd., Kungsan-City, South Korea; 8 electrodes, 3 measurement frequencies, tetra-polar electrode method). Because of the large number of participants in the preliminary study, bioelectrical impedance (BIA) was selected to evaluate body composition, which highly correlates (r = 0.88) with dual x-ray absorptiometry (22). Moreover, the results of previous studies have shown that BIA devices are accurate in estimating body composition: tetrapolar bioelectrical impedance analysis highly correlates not only with dual-energy x-ray absorptiometry, but also with magnetic resonance imaging (26) and densitometry (13). The BIA device reasonably estimates body composition of healthy and euvolemic adults (3) under controlled conditions. For body composition, all factors that might impact BIA results were considered (3): similar ambient temperature and humidity during measurements, proper hydration (at least 1 day before estimation of body composition, participants did not perform physical activity or go into the sauna), electrodes, hands, and feet were degreased before measurement.
Maximal Cycling Sprint Test
The test was performed on the cycle ergometer (E834, Monark, Varberg, Sweden), equipped with a magnetic gauge registering complete pedal revolution duration. The cycle ergometer was also equipped with toe clips and a racing saddle. Seat height was adjusted so that the participant's leg was near complete extension at the bottom of the stroke. The cycle ergometer was connected to a computer and used software (MCE, JBA Staniak, Warsaw, Poland), which allowed calculation of the following indices: PP, MP, total work done during the test, time to attain (TA), time to maintain (TM), PP, and power decrease (PD). The test was preceded by a 4-minute warm-up performed with a 90-W load. In the second and fourth minute of the warm-up, the participant performed a 5-second acceleration until attaining their maximum pedaling rate. The test proper was performed 4 minutes after the warm-up and consisted of a 20-second supramaximal cycle ergometer sprint with a load equaling 7.5% of their BM. Every participant began the test from a stationary start with a prepared load, and their task was to achieve maximal pedaling rate in the shortest possible time and maintain it as long as possible. Participants were required to remain seated while sprinting and received energetic verbal encouragement.
Measurement results of PP and MP are presented as absolute values (in Watts) relative to BM (in Watts per kilogram) and relative to LBM (in W·kgLBM−1). To minimize the BM effect on the power results, allometric scaling was applied. Allometric scaling is based on the relationship of y = axb, where a and b are constants, y is the outcome variable (power), and x is BM (21,23). In this study, the following allometric coefficients were used; PP: 0.89, and MP: 0.86 (21). Therefore, PP and MP were calculated as follows:
Evaluation of Physical Activity
To assess participants' physical activity, the 7-day Physical Activity Recall (20) questionnaire was used. Before completing the survey, participants were instructed on how to fill in the questionnaire. This questionnaire assesses the level of recreational physical activity regarding time and intensity of exercise. Exercise intensity was evaluated according to the following categories: moderate, high, and very high. Strength exercises were classified as being of very high intensity.
The least physically active was the HBF group (9.3 ± 3.3 h·wk−1), in which moderate intensity exercise accounted for 5.6 ± 1.6 h·wk−1, high intensity for 2.9 ± 2.2 h·wk−1, and very high intensity for 0.8 ± 0.7 h·wk−1. Subjects in the HLBM and control groups devoted a similar amount of time to physical activity, respectively: 15.4 ± 5.8 and 14.9 ± 7.7 h·wk−1, although they differed in exercise intensity. For the HLBM group, the dominate exercise intensities were as follows: high (4.0 ± 2.6 h·wk−1) and very high (2.4 ± 0.9 h·wk−1) in comparison with intensities in the control group, which were 3.6 ± 2.0 h·wk−1 and 1.4 ± 1.1 h·wk−1, respectively. For moderate physical activity, both groups allocated similar amounts of time: 9.0 ± 2.5 h·wk−1 (HLBM) and 9.9 ± 5.5 h·wk−1 (control).
Data distribution was checked using the Shapiro-Wilk Test. The significance of intergroup differences was determined by 1-way analysis of variance (ANOVA), assuming that differences between groups were statistically significant at p ≤ 0.05. The significant intergroup differences shown by ANOVA were evaluated using Tukey's post hoc test. Mean and SDs were calculated for each indicator. Pearson's correlation coefficients (r) and coefficient of determinations (r2) between selected dependent variables and power (without division into groups) were calculated to determine the optimal relativization method for power values. The correlation strength was evaluated based on the following criteria (19): none 0–0.19, low 0.2–0.39, moderate 0.4–0.59, moderately high 0.6–0.79, and high >0.8 correlation. All statistical analyses were performed with STATISTICA 10 (StatSoft, Inc., Tulsa, OK, USA).
The absolute values of PP and MP were significantly higher in the HLBM group, compared with the control and HBF groups. However, PP and MP relative to BM were similar in the HLBM and control groups and significantly higher than in the HBF group. When evaluating PP and MP results using allometric scaling, there were no significant intergroup differences expressed relative to LBM. Time to attain and TM PP were comparable in all groups (Table 2).
The absolute values of MP and PP were correlated with BM as r = 0.59 and r = 0.65, respectively, and with LBM as r = 0.72 and r = 0.75, respectively. In contrast, MP and PP relative to BM displayed a significant negative correlation with %BF (r = −0.47 and r = −0.36) (Table 3). The relationship between absolute PP and BM, LBM, and BF are presented in Figures 1–3. Figure 4 presents the relationship between relative PP and %BF.
Over the past few decades, athletes' BM and BF have been significantly increasing (1), and changes in body composition have been noticed as well (18). Physical training not only improves exercise performance but may cause changes in body composition by reducing BF and increasing muscle mass. In many sports, higher BM provides an advantage over the opponent (e.g., basketball), whereas others seek to maximize sport performance at the same time maintaining minimal BM (ski jumping or sports with weight categories). The aim of our study was to determine the influence of BM and its composition on cycling anaerobic power. That is, we analyzed the effect of increased BM resulting from (a) an increase in BF or (b) an increase in LBM on PP and MP output. To the best of our knowledge, this is the first comprehensive analysis of the effect of increased BM and its composition on anaerobic power.
The test results clearly demonstrated that body composition, not total BM, has the greatest impact on anaerobic power. In our study, the absolute values of PP and MP were significantly higher in the HLBM group than in the other 2 groups. Relative to BM, PP, and MP were comparable in the control and HLBM group, and at the same time, significantly higher than in the HBF group. Although we found a significant relationship between absolute PP and BM, it should be emphasized that increased BM influences power production only when it is the result of increased LBM. Lean body mass is the indicator that most positively correlates with absolute PP. An increase in BM resulting from an increase in BF does not increase absolute PP, and it leads to a decrease in peak and MP relative to BM. Of course, another cause of BM increase exists (not evaluated in our study): a simultaneous increase in LBM and BF. This situation occurs most frequently in obese people (12). In this case, larger people (obese) have higher absolute power than persons with normal weight due to them having not only increased adiposity but also increased LBM.
Body fat is passive tissue during exercise, and it adversely affects performance, especially during weight bearing activities. Although cycling is a non-weight bearing activity, our findings suggest that BF can also negatively influence cycling anaerobic performance. The negative relationship between relative PP and %BF was significant in our study. Despite the fact that only the lower body is engaged during cycling, increased BM, due to high levels of adiposity, may be important during uphill cycling (e.g., mountain biking). Bearing in mind the huge popularity of different forms of cycling, the results of our study may be of much applied value for athletes and coaches involved in this sport. Our results also indicate the need for further research regarding the influence of BM and composition on anaerobic power, but assessed using weight bearing anaerobic tests.
The lower body and thighs are primarily active during cycling. We did not measure thigh mass and its composition, but rather total BM and composition. Patton et al. (17) indicated that there is no significant relationship between fat-free thigh volume and absolute maximal power or between muscle-fiber composition or fiber area and maximal power production. Similar to our study, they found significant correlations between body and fat-free mass and maximal power production during cycling and concluded that maximal power could be more dependent on body size. Our findings indicate that it is not absolute BM but body composition that is crucial.
The manner of data normalization is of crucial significance in data interpretation. Vanderburgh and Edmonds (24) indicated a positive correlation between absolute anaerobic power and BM. Our study only partially confirmed this finding. This type of dependency can only occur when increased BM is due to an increase in LBM. In HBF participants, for which increased BM was due to increased BF (with similar total BM to the HLBM group), the absolute PP and MP were significantly lower. In turn, Winter (27) showed a negative correlation between BM and PP relative to BM. Again, the results of our study do not confirm these data because PP and MP expressed relative to BM were similar in the HLBM and control groups, despite significant differences in BM. However, only increased BM resulting from excessive BF negatively affected PP and MP relative to BM. Thus, this data presentation manner (relative to BM) penalizes only athletes with high BF. Another way of analyzing data is allometric normalization of coefficients. The purpose of allometric scaling is to compare individuals without the confounding effect of BM. Allometric exponents have been empirically derived for a variety of populations and different exercise modalities. The allometric coefficients typically applied are 0.67 and 0.75 (16), whereas for PP and MP, coefficients of 0.89 and 0.86 have been proposed (21). Our results confirmed that these allometric coefficients make the results of PP and MP independent from BM as the correlation coefficient between BM, and the data presented in the allometric scale is near zero (r = 0.05). In light of our study, using allometric scaling may be questionable because, as in the presentation of the results relative to BM, the participant's body composition is not considered. In our opinion, the best way to present the results of anaerobic power measurements is to use power relative to LBM. This method of data normalization considers participant's body composition and simultaneously does not correlate (r = 0.12 in the case of PP, r = −0.10 in the case of MP) with BM.
The results of our study clearly indicate that the main factor (moderately high correlation) determining PP is LBM. Excessive BF, usually associated with a negative effect on aerobic endurance performance (2,15), also negatively impacts cycling anaerobic power (moderate correlation). Our results are inconsistent with the data presented by Vardar et al. (25), who demonstrated a lack of correlation between %BF and anaerobic power measured with the Wingate test in young elite wrestlers while pointing to a significant correlation between LBM and anaerobic performance. Therefore, coaches and athletes of anaerobic disciplines should not only concentrate on LBM levels (25) but also on levels of BF. This may be particularly important in sports related to an athlete's locomotion, as similar to aerobic sports, fat mass may be an additional load. The test in our study was performed on a cycle ergometer. This form of exercise does not require BM movement as in running, jumping, etc. It would therefore seem that power scaling in this type of effort is questionable. Despite this, we have noted a significant impact of body composition on PP and MP output. Scaled power output for cycle ergometers allows a more accurate comparison of individuals outside the laboratory, particularly when cycling is performed on an inclined surface. This may be particularly imperative for athletes training cross-country mountain biking (6). Gregory et al. (4) suggested that mountain bike cross-country training programs should focus on improving relative power output rather than maximizing absolute power output to improve performance. Inoue et al. (7) suggested that anaerobic power is an important determinant of performance as they found significant correlations between race time and maximal and MP. The results of our study are in opposition to the studies of Knechtle et al. (10) who showed that anthropometry is not related to race performance in a mountain bike ultramarathon.
The limitation of this study is the BH of participants from the HLBM group, which is significantly higher in comparison with the HBF and control groups. As a result of this, the increased LBM noted in this group may not only result from increased involvement of those participants in strength exercises, but also from BH. Introducing another qualifying criterion in particular groups would reduce the group size which was what we wanted to avoid. Body height does not affect anaerobic performance per se, especially since testing was performed on a cycle ergometer. For this reason, we also decided to include tall men in the study. The participants did not practice any sport professionally but engaged in various types of physical activity of different durations and intensities, which could have affected not only their BM and composition, but also their anaerobic power. Regarding the participants' results of PP (10.2–11.4 W·kg−1) to the Wingate anaerobic test classification of PP (7.5% of BM) in intercollegiate athletes, their results would be considered below average (28). This may indicate that their physical activity had little effect on their anaerobic power. Our methodology used for testing was similar to the 20-second Wingate test performed with a load equaling 7.5% of BM. The main difference was related to initiating the start. We proposed a stationary start because this allows determination of the TA PP. This indicator may be useful in estimating the starting speed in sprints. Our study showed that in trials on the ergometer, BM, and composition do not affect TA PP or MP during a maximal cycling sprint test.
Body composition is an important determinant of cycling anaerobic power. Not BM, but body composition affects cycling PP. Increased BM resulting from increased LBM does not adversely affect cycling anaerobic power. In contrast, increased BM resulting from increased BF may adversely affect cycling PP relative to BM. Coaches, instructors, and athletes of sports in which power plays an important role should pay particular attention not only to total BM but to body composition. Anaerobic, anaerobic-aerobic sport disciplines must strive to reduce BF and control LBM. For comparison of power measurement results, we also suggest presenting power relative to LBM.
No conflict of interest has been declared. This research was supported by the University of Physical Education in Krakow (Poland), grant No: 21/BS/IFC/2011. The funders had no role in the study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors would like to thank the subjects who volunteered for this study.
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Keywords:Copyright © 2015 by the National Strength & Conditioning Association.
body fat; lean body mass; Wingate anaerobic test; peak power; performance