ABSTRACT THE INFLUENCE OF BODY COMPOSITION ON CADENCE EFFICIENCY IN COMPETITIVE CYCLISTS by Tate Bross Devlin The primary aim of this investigation was to determine the relationship between body composition and gross efficiency at various cadences. In 23 subjects (M/F=15/8, age=32.1 (11.1)yrs) body fat: 18.1(7.6)%; body mass: 72.9(7.6) kg; fat free mass: 59.7(8.3) kg; thigh circumference: 50.0(3.1) cm; VO2max: 57.1(9.0) ml/kg/min; and preferred cadence: 89(4.1) rpm) were measured. Efficiency was assessed during two cadence sessions where 3, 5-minute intervals, followed by 5 minutes of rest at a freely chosen cadence. The intervals were set at an intensity of 70% of the power output reached at VO2max. During the interval sessions the participants were instructed to maintain cadences of 80-85-90rpm and 95-100-105 rpm for the first and second sessions, respectively. Fat free mass, body fat %, thigh circumference, and body mass were not significantly associated with cadence efficiency. Preferred cadence was correlated with fat free mass, thigh circumference, and body mass. In agreement with previous studies, cyclists were most efficient at 80 rpm (23.7(1.8)%), and least efficient at 105 rpm (22.2(1.9)%) (p=0.000). It appears that body composition measurements do not influence the cadence at which a cyclist is the most efficient. However, body composition does appear to influence cadence selection and racing strength in competitive cyclists.
THE INFLUENCE OF BODY COMPOSITION ON CADENCE EFFICIENCY IN COMPETITIVE CYCLISTS Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Science by Tate Bross Devlin Miami University Oxford, Ohio 2016 Advisor: Dr. Julie Cousins Reader: Dr. Ronald Cox Reader: Dr. Randal Claytor 2016 Tate Bross Devlin
This Thesis titled THE INFLUENCE OF BODY COMPOSITION ON CADENCE EFFICIENCY IN COMPETITIVE CYCLISTS by Tate Bross Devlin has been approved for publication by College of Education, Health & Society and Department of Kinesiology and Health Dr. Ronald Cox Dr. Julie Cousins Dr. Randal Claytor
Table of Contents Introduction 1 Methods 2 Results 3 Discussion 6 Conclusion 8 References 9 iii
List of Tables Table 1. Subject s descriptive statistics 3 Table 2. Subject s body composition measurements 4 Table 3. Correlation matrix of racing strength 3 Table 4. Gross efficiency at each of the cadences 6 iv
List of Figures Figure 1. Scatterplot of self-reported preferred cadence vs. body weight 5 Figure 2. Scatterplot of self-reported preferred cadence vs. thigh circumference at one-half the length of the thigh 5 Figure 3. Scatterplot of self-reported preferred cadence vs. fat-free mass 5 Figure 4. Scatterplot of gross efficiency vs. cadence 6 v
Acknowledgements The authors would like to thank the subjects for their participation and dedication to this study. Additionally, all undergraduate assistants that helped with running the data collection sessions. vi
INTRODUCTION In the sport of cycling, a large volume of training is required in order to get to the elite level. Races can be won and lost by hundredths of a second, in an effort to stay on top of the competition; cyclists are continuously in search of ways to maximize their performance. Gross efficiency in cycling is defined as the ratio of work done to the total energy expended, and is commonly expressed as a percentage (Sidossis, Horowitz, Coyle, 1992). Several research studies (Horowitz, Sidossis, Coyle, 1994) have suggested that efficiency plays a significant role in the determination of cycling performance. One of the easiest variables to manipulate in cycling is cadence. Previous research has reported that trained cyclists are the most mechanically efficient at a cadence of 55-65 RPM (Vercruyssen, Brisswalter, 2010; Biuzen, Vercruyssen, Hausswirth, 2007; Hansen, Andersen, Nielsen, 2012; Marsha, Martin, 1993; Marsh, Martin, 1997). Despite this finding, competitive cyclists choose to ride at cadences of 80-95 RPM (Vercruyssen, Brisswalter, 2010). Additionally, the 1-hour records have been set at cadences of 100.8, 102, and 104.2 RPM, with cadence increasing throughout successful attempts. (Vercruyssen, Brisswalter, 2010). Upon examining the literature and the history of the sport, it is clear that optimal performance requires the adoption of a higher, less efficient cadence. Emanuele and colleagues (2012) referred to this phenomenon as the cadence paradox. Several studies have been conducted with the hope of finding what factors determine freely chosen cadence in competitive cyclists. Some of the variables that have been tested include oxygen consumption (Vercruyssen, Brisswalter, 2010; Biuzen, Vercruyssen, Hausswirth, 2007; Hansen, Andersen, Nielsen, 2012; Marsha, Martin, 1993; Marsh, Martin, 1997), muscular parameters (Lucia, Hoyos, Chicharro, 2000), pedaling technique (Rossato, Bini, 2010), and performance (Lucia, Hoyos, Chicharro, 2000). Yet, the reason cyclists choose higher cadences than what is the most energetically optimal for them remains unclear. In a study conducted by Lucia, Hoyos, Chicharro (2000), the preferred cadence of professional cyclists was examined during stages of the Giro d Italia, Tour de France, and Vuelta a Espana. The researchers observed that the larger, more powerful cyclists adopted lower cadences of 80-90 RPM, while the lighter riders adopted higher cadences of 90-100 RPM. To the best of our knowledge, no additional research has been conducted investigating the relationship between body composition and cadence. The purpose of the current investigation was to determine the relationship between body composition and gross efficiency at a range of cadences adopted by cyclists in racing situations. We hypothesized that there will be a significant association between cadence efficiency and body composition. More specifically, individuals with a greater amount of fat free mass will be more efficient at a lower cadence when compared to individuals with less fat free mass. 1
METHODS SUBJECTS Twenty-three healthy male (15) and female (8) cyclists with a valid USA Cycling license participated in this study. All of the subjects had been competing in USAC sanctioned cycling races with in the previous calendar year. Prior to participation in the study, the subjects provided written informed consent. The Institutional Review Board at Miami University approved this study. PROCEDURES Subjects completed three laboratory sessions. During the first session, height, weight, cycling history questionnaire, body composition, and VO 2 max tests were completed. The cycling history questionnaire addressed the participants preferred cadence, racing strengths, weekly training time, and years of competition. Six types of racing (criterium, road, time-trial, climbing, sprinting, and break-away) were ranked categorically on a scale of 1-6, with 1 being the most preferred and 6 the least. Height was measured to the nearest 0.1 cm using a SECA stadiometer. Body mass was measured in minimal clothing to the nearest 0.1 kg using a digital scale (Tanita Corporation, Japan). Body composition was analyzed by air displacement plethysmography (BOD POD, COSMED, Concord, CA). The BOD POD was calibrated according to manufacturer s specifications. All subjects wore a swim cap and spandex shorts for the test. Females also wore a sports bra. The BOD POD software predicted thoracic gas volume. From the data collected for body mass, body volume and thoracic lung volume, computer software determined body density and body fat percentage using the Siri (1961) equation. Thigh length was measured from the proximal end of the greater trochanter to the distal lateral femoral condyle. Thigh circumference measurements were taken at one-quarter, one-half, and three-quarters of the length of the thigh. The maximal graded exercise test was performed on a calibrated Wahoo Kickr (Atlanta, GA) on their personal race bike. A TrueOne Gas Analyzer (Parvomedics, Sandy, UT) was used to determine maximal oxygen uptake. The test began at 100 W for females and 150 W for males and increased by 25 W every minute. The test concluded when the participant reached volitional fatigue. The ventilation and gas exchange variables were monitored throughout the test and averaged over a 30 second period. The metabolic cart was calibrated following manufacturer s instruction prior to conducting any of the tests. Following the initial data collection, gross efficiency was assessed during sessions two and three. VO2 and CO2 were measured by a TrueOne Gas Analyzer and were used to determine the respiratory exchange ratio (RER). During session two and three, the subjects were connected to the metabolic cart and performed 30 minutes of exercise. They completed a five-minute warm-up at 100 W following the warm-up the subjects began the cadence testing session. Each subject performed three, 5-minute intervals (intensity was set to 70% of the power output reached at VO 2 max), followed by 5 minutes of recovery at 100 W. During session two, the subjects were asked to maintain cadences of 80, 85 and 90 rpm; while during session three, they maintained 2
cadences of 95, 100, and 105 rpm. Cadence was monitored by attaching a Garmin Bike Speed and Cadence Sensor (Olathe, KS) to their bicycle. The subjects completed sessions two and three on their personal race bike which was connected to the Wahoo Kickr. Gross efficiency was calculated for each of the 5-minute cadence intervals using the following equation: Gross Efficiency= Work Performed/ Energy Cost x 100, where, Work Performed= Power Output(kg*m/min)/ 426.8 kcal/kg*m and Energy Cost= VO2 (L/min) x kcals/l (using VO2 and energy equivalent for oxygen corresponding to each RER value from the tables of Lusk & Bois (1924). Prior to each of the sessions, subjects were asked to record their dietary intake and daily activity. Subjects were asked to consume a similar diet and partake in the same level of activity before each session. Sessions 2 and 3 were scheduled during the same time of day and approximately 1 week apart. Statistical Analysis Descriptive statistics were completed on the 23 participants and data was checked for normality. Repeated measures ANOVA and Pearson Product Moment correlations were used to determine overall differences between the efficiencies at each cadence and measures of body composition. The Mauchly s test for sphericity was significant, which indicates that the scores from the repeated measures ANOVA vary significantly at the p<.05 level. Therefore the Greenhouse- Geisser test was used. Statistical significance was set to p<.05. Results are presented as means (standard deviations) unless otherwise noted. All statistical analyses were conducted using SPSS (Version 23). RESULTS Descriptive Statistics A total of 23 USA Cycling licensed, category 1-5 racers, underwent cadence efficiency testing. The mean age of participants was 32.1 (2.3) years. The participants had a mean VO2max of 57.1 (1.9) ml/kg/min and a maximum power output of 387.8 (16.5) watts during the VO2max test. The subject s descriptive characteristics are presented in table 1. Body composition measurements, including body fat percentage, fat free mass, and thigh circumference, are presented in table 2. Table 1. Subject s descriptive characteristics. Characteristics (N=23) Mean±SD Age (Years) 32.1±2.3 Height (cm) 175.8±1.9 Weight (kg) 72.9±1.6 VO2max (ml/kg/min) 57.1±1.9 Max HR (BPM) 182.7±2.0 Max Power Output (Watts) 387.8±16.5 Competition (Years) 4.9±1.0 Weekly Training Time (Hours) 10.7±.9 Preferred Cadence (RPM) 89±4.1 3
Table 2. Subject s body composition measurements. Characteristics (N=23) Mean±SD Body Fat (%) 18.1±1.6 Fat Free Mass (kg) Thigh Circumference (cm) 59.7±8.3 50.0±0.6 Racing Strength and Body Composition A preference for criterium racing was negatively correlated with body fat percentage, while a preference for climbing was positively correlated with body fat percentage. Additionally, a preference for climbing was negatively correlated with a preference for sprinting. A preference for criterium racing was negatively correlated with a preference for climbing and road racing. Correlations between racing strength and body composition measurements are presented in table 3. Table 3. Correlation matrix of ranked strength (1 being best, 6 being worst) of sprinting, climbing, break-away, road race, and time trial, fat free mass, body fat percentage, and thigh circumference at one-half the length of the thigh. Measure Sprint Climb Break-Away Road Race Crit Time Trial Body Fat % Fat Free Mass Thigh Circ. Sprint Rank --.738** -.283 -.375.367 -.101 -.190 -.121 -.322 Climb Rank --.066.332 -.544** -.077.548** -.087 -.413 Breakaway Rank -- -.192.014 -.268.098 -.087.116 Road race Rank. -- -.477* -.210.302 -.148.022 Crit Rank -- -.405 -.468*.342 -.023 TT Rank -- -.136 -.072 -.300 Body Fat % --.697* *.326 Fat Free Mass --.251 -- Thigh Circumference Note: *Correlation is significant (p<0.05) between variables. ** Correlation is significant (p<0.01) between variables. Preferred Cadence and Body Composition A significant and negative correlation exists between preferred cadence and total body weight (r= -0.604, p= 0.006); this relationship is displayed in figure 1. A significant negative correlation exists between preferred cadence and thigh circumference (r= -0.630, p= 0.004); this relationship is displayed in figure 2. A significant negative correlation exists between preferred cadence and fat free mass (r= -0.465, p= 0.045); this relationship is displayed in figure 3. Preferred cadence was not significantly correlated to fat mass (r=-0.131, p=0.592). 4
Preferred Cadence (RPM) Preferred Cadence (RPM) Preferred Cadence (RPM) Figure 1. Scatterplot of self-reported preferred cadence vs. body weight. 95 r= -.604 p=.006 90 85 80 55 60 65 70 75 80 85 90 95 Body Weight (kg) Figure 2. Scatterplot of self-reported preferred cadence vs. thigh circumference at one-half the length of the thigh. 95 r= -.630 90 85 80 75 40 45 50 55 60 Thigh Circumference (cm) Figure 3. Scatterplot of self-reported preferred cadence vs. fat free mass. 95 90 r=-.465 p=.045 85 80 75 45 50 55 60 65 70 75 80 Fat Free Mass (kg) 5
Gross Efficiency(%) Gross Efficiency and Cadence At an intensity of 70% of the maximal power output reached at VO2max, gross efficiency was greatest at 80 RPM (23.7 (1.8) %), and lowest at 105 RPM (22.2 (1.9) %) (Table 4). Figure 4 displays the gross efficiency of all participants at each cadence tested. On average, the cyclists were 6.33% more efficient at 80 RPM when compared to 105 RPM. An inverse relationship was observed between cadence and gross efficiency from 80 to 105 RPM. A repeated measures ANOVA revealed that gross efficiency at each of the cadences were statistically significant, F(1.876) = 10.377, p <.001. Gross efficiency at 105 RPM was significantly less than 80, 85, 90, 95, and 100 RPM. Table 4. Gross efficiency at each of the cadences. Cadence (RPM) Gross Efficiency (%) Mean±SD 80 85 90 95 100 105 23.7±1.8 23.4±1.5 23.3±1.7 22.8±1.4 22.6±1.9 22.2±1.9 Figure 4. Scatterplot of gross efficiency vs. cadence. 30 r= -0.290 p=0.0 25 20 15 10 80 85 90 95 100 105 Cadence (RPM) Cadence, Gross Efficiency and Body Composition Fat free mass, body fat percentage, thigh circumference, and body mass were not significantly associated with cadence efficiency. More specifically, the participant s efficiency scores across the cadences do not differ as a function of fat free mass (p=0.135). Additionally, the efficiency scores across the cadences do not differ as a function of total body mass, body fat percentage, or thigh circumference. DISCUSSION Gross efficiency is an important component to consider for training and racing in competitive cycling. In the current study the relationship between gross efficiency and body composition in competitive cyclists was examined. The initial hypothesis was not supported by the data 6
collected. Gross efficiency at 80, 85, 90, 95, 100, and 105 RPM was not found to be significantly correlated with the total body mass, fat free mass, body fat percentage, and thigh circumference. Body Composition and Racing Strength A significant correlation between racing strength and body fat percentage was found. More specifically, individuals with a lower body fat percentage are more likely to prefer climbing, while individuals with a higher body fat percentage are more likely to prefer criterium racing. This was inline with a study conducted by Padilla, Mujika, and Cuesta, (1999), which found that uphill cyclists are significantly lighter when compared to time trial and flat terrain cyclists. Professional cyclists are required to perform in a wide variety of situations, including short, technical and intense criterium races, long endurance road races, individual time trials, and on flat and mountainous terrains. Body mass has been found to have a major influence during uphill cycling performance, as it determines gravity-dependent resistance (Mujika, Padilla, 2001). Previous research has suggested that variability in body composition measurements can be highly influential on a cyclist s performance level during the diverse racing situations (Padilla, Mujika, Cuesta, 1999; Mujika, Padilla, 2001; Swain, Coast, Clifford, 1987). Body Composition and Preferred Cadence Few studies have examined the association of preferred cadence and measures of body composition. A study by Lucia, Hoyos, Chicharro, (2000) found that the larger, more powerful cyclists tend to adopt cadences of 80-90 RPM while smaller, lighter cyclist pedaled at slightly faster cadences of 90-100 RPM. They used visual inspection of the cyclist to determine cyclist size. The current study used laboratory tests to measure body composition and self-reported preferred cadence. The cyclist in this study preferred to ride at cadences between 80 and 95 RPM. An inverse relationship was found between preferred cadence and body mass, fat free mass, and thigh circumference. Thus, individuals with a greater total body mass (kg), fat free mass (kg), and thigh circumference (cm) were found to prefer lower cadences compared to individuals on the lower end of the body composition measures. Additional research studies need to be conducted to further explore the relationship of body composition and cadence selection. Cadence and Gross Efficiency In agreement with previous research a statistically significant and inverse relationship between percent efficiency and cadence was found. The lowest cadence tested was 80 RPM, with an average efficiency of 23.7%, where the highest cadence tested, 105 RPM, had an average efficiency of 22.2%. A decrease in efficiency as cadence is increased has been reported by various investigators (Vercruyssen, Brisswalter, 2010). Researchers Gotshall, Bauer, and Fahmer (1996) explained the decline in efficiency with an increase in cadence through hemodynamic principles. As pedal rate is increased, blood flow to the muscles will also be increased, increasing oxygen consumption, and decreasing efficiency. In line with previous research, we found that preferred cadence was unrelated to the most efficient cadence. The average preferred cadence for our cyclists tested was 89 (4.1) RPM, while they were found to be most efficient at 80 RPM. While we did not test cadences as low as 55 and 65 RPM, our lowest cadence tested (80 RPM) 7
was found to be the most efficient. Our range of cadences (80-105 RPM) was chosen because it reflects typical cadences adopted for the duration of a race. Cadence, Gross Efficiency and Body Composition The reason why body composition measurements, including fat free mass, thigh circumference, and body fat percentage, were not found to influence the cadence at which a cyclist is the most efficient remains unclear. It is possible that cadence is chosen regardless of efficiency. Emanuele, Horn, and Denoth (2012) found that cyclist tend to ride at a cadence that produces the lowest neuromuscular fatigue, rather than the cadence that is the most energetically efficient. Future research examining the relationship between body composition and the cadence at which neuromuscular fatigue is minimized would be beneficial to further understanding this relationship. Limitations There are several limitations to this study that are worth noting. First, correlations do not imply causation. Another limitation is that preferred cadence was measured by self-report. A further limitation is cadence intervals were set to 70% of maximal power at VO2max. Additionally, the participants were instructed to maintain a similar food, hydration and training status prior to each session, however, this was not controlled. Finally, the cadence intervals were completed on two separate days approximately one week apart. CONCLUSION In conclusion, it appears that body composition measurements, including fat free mass, thigh circumference, and body fat percentage, do not influence the cadence at which a cyclist is the most efficient. However, body fat percentage does appear to be correlated with cadence selection and racing strength in competitive cyclists. While cyclists were found to be most efficient at 80 rpm, they train and race at faster cadences. Therefore, additional research is needed to determine what factors contribute to a competitive cyclist s cadence selection and how body composition plays a role. 8
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