PHYSIOLOGICAL CHARACTERISTICS MASTERS-LEVEL CYCLISTS OF JEREMIAH J. PEIFFER,1 CHRISTOPHER R. ABBISS,1 DALE CHAPMAN,1,2 PAUL B. LAURSEN,1 3 AND DARYL L. PARKER 1 School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, Australia; 2Department of Physiology, Australian Institute of Sport, Canberra, Australia; and 3College of Health and Human Services, California State University, Sacramento, Sacramento, California ABSTRACT Peiffer, JJ, Abbiss, CR, Chapman, D, Laursen, PB, and Parker, DL. Physiological characteristics of masters-level cyclists. J Strength Cond Res 22(5): 1434–1440, 2008—Although a considerable amount of research is available describing the physiological characteristics of competitive young-adult cyclists, research describing these same characteristics in Masters-level cyclists is rare. Therefore, the purpose of this study was to describe and compare the effect of aging on physiological fitness parameters of Masters-level cyclists in an attempt to provide normative fitness data. Thirty-two male cyclists (35–73 years) completed one 15-minute economy test and one graded exercise test (GXT) on a cycle ergometer. During the GXT, maximal oxygen uptake (V_ O2max), maximal heart rate (HRmax), the first (VT1) and second (VT2) ventilatory thresholds, and peak power output (PPO) were recorded. For the purpose of analysis, subjects were allocated into three age groups (35–45 years, 45–54 years, $55 years). Maximal oxygen uptake and absolute PPO were significantly lower among subjects 55 years and older (45.9 6 4.6 mL
kg21
min21 and 324 6 51 W, respectively) compared with the 45- to 54-year group (54.2 6 6.6 mL
kg21
min21 and 392 6 36 W, respectively), and both were significantly less compared with the 35- to 44-year group (60.7 6 5.1 mL
kg21
min21 and 434 6 32 W, respectively). Maximal heart rate was significantly greater in both the 35- to 44-year and 45- to 54-year age groups compared with the $55-year group. The first ventilatory threshold was significantly greater in the subjects who were 55 years and older group compared with the 35- to 44-year and 45- to 54-year age groups, and VT2 was significantly greater in subjects 55 years and older compared with the 35- to 44-year group. Economy was not different amongst groups. In conclusion, increases in age resulted in a significant reduction in fitness parameters across age groups. Address correspondence to Jeremiah Peiffer, j.peiffer@ecu.edu.au. 22(5)/1434–1440 Journal of Strength and Conditioning Research Ó 2008 National Strength and Conditioning Association 1434 the TM Journal of Strength and Conditioning Research The comparison of the fitness characteristics of Masters-level cyclists with established young-adult cyclist data should be avoided, because this may lead to inaccurate assessments of fitness. KEY WORDS training, performance, age, aging, cycling INTRODUCTION he Baby Boom years (1946–1964) resulted in an exponential increase in the world population, with approximately 76 million births in the United States alone. Today, the age of the Baby Boom generation is 40–60 years old and represents one quarter of the population in the United States. Therefore, ‘‘Masterslevel’’ athletes (.35 years) represent a large fraction of today’s recreational sporting population. Currently, the majority of research on Masters-aged individuals examines disease control (i.e., diabetes, heart diseases) aimed at improving the functional quality of life (21,22). Indeed, this research provides interesting insight into physiological changes that occur with aging. For example, research has shown that beyond 40 years of age, maximal oxygen uptake (V_ O2max) (19), heart rate maximum (16), and neuromuscular function (7) significantly decline in a progressive manner. It has also been suggested that an increase in the number of competitive years of sport participation can attenuate the decline in aerobic fitness normally associated with aging (20). As such, studies on Masters athletes present interesting insight into the fitness levels that can be achieved through continued participation in competitive sport (20,23,24). However, most Masters-level research completed to date has focused on competitive runners, leaving a void in the research of Masters-level cyclists. Physiological indices such as V_ O2max (4), peak power output (PPO) (8), metabolic thresholds (1), and economy of motion (14) can be used to identify the training status of an athlete and predict cycling performance. Routinely, cyclists are classified by exercise scientists as being either untrained, trained, or elite on the basis of established maximal exercise test normative data (11). Unfortunately, physiological changes associated with aging may complicate the T the TM Journal of Strength and Conditioning Research comparison of Masters cyclists’ data with already-established normative data. It is necessary to acknowledge the influence of age on fitness, and although it is possible that some variables (i.e., PPO, economy, ventilatory thresholds) may be similar between younger and older competitive cyclists (20), it should not be assumed that all variables will be similar. Indeed, doing so could result in the inappropriate categorization of these athletes. A comprehensive physiological profile of Masters cyclists would aid exercise scientists in their ability to properly categorize these athletes. Furthermore, such data would provide interesting insight into which physiological indices among Masters-level cyclists are most influenced by age. Therefore, the purpose of this study was twofold. We first sought to establish a physiological profile for Masters-level cyclists between the ages of 35 and 75 years. Second, we aimed to provide individual physiological profiles throughout different decades of the Masters-level cyclist (35–44 years, 45– 54 years, and $55 years). We hypothesized that the physiological profile of Masters-level cyclists would not be similar to that of the young-adult population norms. Additionally, we hypothesized that an increase in competitive years of racing would attenuate the decline in aerobic capacity that has been previously highlighted in noncompetitive aging adults. | www.nsca-jscr.org regularly participating in cycle training (Table 2), with no other training (i.e., weight lifting, running) reported during this time. Subjects were given written and verbal instructions regarding the study procedures and the possible risks and benefits resulting from their participation in the study. Written informed consent was obtained before the study, which was approved by the Edith Cowan University human research ethics committee. Subjects were required to refrain from intense physical activity the day before and the day of all physical testing. Procedures Subjects were required to complete a 15-minute submaximal economy test (15EC) and a maximal GXT on a Veltron cycle ergometer (RacerMate, Seattle, Wash). Before the 15EC and GXT, the Velotron cycle ergometer was adjusted (i.e., seat height, handlebar height, top tube length) to mimic each subject’s own personal bicycle, and subjects were fitted with a telemetry heart-rate–monitoring system (Polar 810i, Kempele, Finland). During both tests, respiratory data were collected as 15-second averages via a Parvo Medics metabolic cart (Parvo Medics, Sandy, Utah). The gas analyzers were calibrated before each subject’s trial using gases of known concentration (16% O2, 4% CO2), and the pneumotach was calibrated using a 3-L syringe (Hans Rudolph, series 5530; Kansas City, Mo) throughout a range of flow rates. During the 15EC, subjects were required to ride at three separate intensities (100, 150, 200 W) in sequential order at 5-minute intervals. Data from the 200-W intensity were used for analysis, because this intensity has previously been used to assess economy in trained cyclists (17). Subjects were instructed to maintain their preferred cycling cadence throughout the duration of the test. The average volume of oxygen consumption (V_ O2) recorded during the final 2 minutes of the 200-W stage was determined, and economy was then calculated as follows: METHODS Experimental Approach to the Problem This study examined physiological data recorded during a maximal graded exercise test (GXT) on a cycle ergometer for 32 Masters-level cyclists. Specifically, V_ O2max, PPO, maximal heart rate (HRmax), and the first and second ventilatory thresholds (VT1 and VT2) were analyzed because these variable have previously been shown, in a normal population, to be influenced by aging. Comparisons were made between decades (35–44 years, 45–54 years, and $55 years) to evaluate age-related changes within the 32 Masterslevel cyclists, and additional comparisons were made to preexisting normative data established from a younger cohort of cyclists. Economy ¼ W =V_ o2 Subjects Thirty-two Masters-level male cyclists (Table 1) volunteered to participate in this study. All subjects were recruited from Western Australia cycling clubs (Cat. A–C; U.S. equivalent, Cat. 1–3) and were actively participating in cycle racing at the time of testing. All testing was conducted during the roadrace season (February to April), with a majority of races conducted for an average distance of 70–120 km. At the time of testing, subjects reported to be TABLE 1. Descriptive characteristics (mean 6 SD) for 32 Masters-level cyclists as a single cohort (ALL) and separated by decade. Age N Age (y) Height (cm) Weight (kg) Body fat (%) ALL 35–44 y 45–54 y $ 55 y 32 49 6 11 176.7 6 5.8 78.6 6 7.4 14.4 6 3.5 14 39 6 3 178.2 6 4.8 80.3 6 6.6 12.7 6 3.0 10 49 6 3 176.9 6 5.9 78.5 6 8.5 14.9 6 4.0 8 65 6 4 173.7 6 6.7 75.9 6 7.3 16.4 6 2.8* *Significantly greater than 35- to 44-year age group; p , 0.05. VOLUME 22 | NUMBER 5 | SEPTEMBER 2008 | 1435 Physiology of Masters Cyclists where Pcs is the power output corresponding to the last comTABLE 2. Weekly training data (mean 6 SD) for 32 Masters-level cyclists as a single pleted stage, and Tc is the time cohort (ALL) and separated by decade. in seconds of the uncompleted stage. Age To determine VT1 and VT2, ALL 35–44 y 45–54 y $ 55 y the ventilatory equivalents _ V_ O2 and VE/ _ V_ CO2) for (VE/ Days 5.2 6 1.3 5.1 6 1.4 5.0 6 1.3 5.5 6 1.4 each subject were plotted over Hours 10.9 6 5.1 11.4 6 9.3 9.5 6 2.7 15.4 6 7.5 the corresponding V_ O2 values. Kilometers 313 6 94 301 6 99 283 6 61 359 6 120 VT1 was defined as a sudden rise _ V_ CO2 _ V_ O2 while VE/ in VE/ remained at a plateau, otherwise known as the isocapnic buffering _ V_ O2 point. VT2 was determined as the point where both VE/ where W is the workload for each stage (W) and V_ O2 is the _ _ and VE/VCO2 displayed an exponential increase (15). Ventilaaverage volume of oxygen consumed in liters per minute. tory threshold graphs (Figure 1) were assessed for After the 15EC, subjects were given 15 minutes of passive VT1 and VT2 by two independent reviewers; a third recovery before beginning the GXT. The GXTcommenced at reviewer was used in cases where discrepancies in threshold an intensity of 70 W and increased by 35 W
min21, during placement occurred. which the subjects were again instructed to pedal at a cadence reflective of their normal road riding. The test was terminated Statistical Analyses when subjects were no longer able to maintain a cadence of Maximal oxygen uptake, PPO, HRmax, VT1, VT2, and _ at least 60 rpm. During the GXT, VO2max, PPO, HRmax, economy were compared between age groups (35–44 years, VT1, and VT2 were recorded. V_ O2max was defined as 45–54 years, $55 years), using a one-way analysis of variance a change in V_ O2 of no greater than 2 mL
kg21
min21 with (ANOVA). If the ANOVA indicated a difference between three successive stages, RER greater than 1.1, and heart rate conditions, a Tukey honestly significant differences post hoc greater than 85% of age-predicted max. PPO was calculated test was used to determine the point of interaction. Data were as the power output corresponding to the highest stage analyzed using Statistica for Windows, with the level of completed, including the fractional representation of any significance set at 0.05. Normative percentiles were deuncompleted stage as followed: termined using the percentile function within Microsoft PPO ¼ P cs þ ððT c =60Þ 3 35 W Þ Excel. Data are presented as means 6 standard deviations. RESULTS _ V_ O2; n = VE/ _ V_ CO2; VT1 = first ventilatory threshold; VT2 = second Figure 1. Ventilatory equivalents vs. V_ O2. ¤ = VE/ ventilatory threshold. 1436 the TM Journal of Strength and Conditioning Research Values for all variables of interest measured during the 15EC and GXT are presented in Table 3. Absolute V_ O2max (L
min21) and PPO (W) were shown to significantly decline with increases in age group. Moreover, a significantly lower relative V_ O2max (ml
kg21
min21) and PPO (W
kg21) were observed in the subjects who were 55 years and older compared with the 35- to 44-year age group and the 45- to 54-year age group. Compared with the $55-year age group, HRmax was significantly greater in both the 35- to 44-year and 45- to 54-year age groups; however, no differences were observed between the 35- to 44-year and the TM | www.nsca-jscr.org 45- to 54-year age groups. Compared with the $ 55-year age group, VT1 expressed as a percentage of V_ O2max was significantly lower in the 35- to 44-year and 45- to 54-year age groups, and VT2 was significantly greater in the $55-year age group compared with the 35- to 44-year age group. No difference in economy was observed between any age group. Table 2 shows a listing of percentiles (10–90%) for the GXT variables in each age group. 8 45.89 6 4.61*† (38.25–51.66) 3.49 6 0.58*† (2.66–4.17) 324 6 51*† (265–407) 4.25 6 0.41*† (3.80–4.79) 160 6 14*† (153–175) 75.6 6 6.3 (67–85) 82 6 7*† (67–87) 93 6 5* (86–98) DISCUSSION Values in brackets represent ranges. V_ O2max = maximal aerobic capacity; PPO = peak power; VT1= first ventilatory threshold; VT2= second ventilatory threshold. *Significant difference from 35- to 45-year age group (p , 0.05). †Significant difference from 45- to 55-year age group (p , 0.05). 10 55.19 6 6.59 (43.70–64.56) 4.26 6 0.25 (3.76–4.65) 392 6 36* (341–435) 4.80 6 0.72 (3.80–6.00) 177 6 6 (169–185) 71.9 6 7.0 (61–83) 73 6 6 (66–82) 89 6 6 (82–97) 14 60.73 6 5.08 (52.27–69.78) 4.87 6 0.48 (3.91–5.49) 434 6 32 (372–475) 5.42 6 0.47 (3.80–6.33) 185 6 8 (170–202) 76.3 6 6.5 (68–90) 66 6 6 (56–78) 86 6 6 (72–95) 32 55.29 6 8.02 4.33 6 0.71 393 6 58 5.01 6 0.72 176 6 14 74.5 6 6.6 72 6 9 89 6 6 N V_ O2max (mL
kg21
min21) V_ O2max (L
min21) PPO (W) PPO (W
kg21) Heart rate (bpm) Economy (W
L21
min21) VT1(%V_ O2max) VT2(%V_ O2max) $55 y 45–54 y All 35–44 y Age TABLE 3. Maximal oxygen uptake (V_ O2max), peak power output (PPO), maximal heart rate, economy, and first and second ventilatory thresholds (VT1, VT2) determined during a graded exercise test performed by Masters-level cyclists (mean 6 SD, range). Journal of Strength and Conditioning Research The aims of this study were to determine the effects of aging on V_ O2max, PPO, HRmax, VT1, VT2, and economy in a group of Masters (.35 years)-level cyclists and to determine whether Masters-level cycling data can be accurately assessed using established young-adult normative cycling data. The interesting findings from this study are that 1) V_ O2max, PPO, and HRmax significantly declined with increases in age, 2) an increase in VT1 was observed with increases in age, and 3) economy was found to be similar between the various age groups tested in this study. Unlike other studies that have used a case study approach (5), to the authors’ knowledge this study is the first to provide a detailed assessment of the physiological characteristics of Masters-level cyclists. Decreases in aerobic capacity of approximately 10% per decade have been reported for trained as well as untrained individuals (19,24). However, Pollock et al. (20) have shown that Masters runners (50–82 years) who continued to compete for a 10-year time period did not experience declines in V_ O2max to levels observed in their noncompetitive counterparts. In the present study, absolute and relative V_ O2max declined by approximately 10% between the 35- to 44-year age group compared with the 45- to 54year age group. Additionally, this decline continued a further 16% between the 45- to 54-year and $ 55-year age groups (Table 3). Interestingly, our subjects were all engaged in a high level of training (Table 2), and personal communication with the subjects indicated that most had been cycling for more than 10 years. This observation suggests that continued participation in competitive cycling does not necessarily attenuate the age-related decreases in aerobic fitness that have been described previously. One possible explanation for the decline in V_ O2max is the lower HRmax observed in our older subjects (Table 3). Heart rate has been shown to be a limiting factor to V_ O2max (25), and an agerelated decrease in heart rate has been shown to occur in trained and untrained individuals (23). Interestingly, declining V_ O2max values were in contrast to an observed increase in VT1. VT1 is noninvasive indicator of lactate threshold. In comparison, Coggan et al. (3) observed an age-related increase in lactate threshold in older runners, possibly attributable to increases in oxidative enzymes (succinate dehydrogenase and b-hydroxyacyl-CoA) and a decrease in lactate dehydrogenase found to exist in the older runners. This finding may help explain why MastersVOLUME 22 | NUMBER 5 | SEPTEMBER 2008 | 1437 the Journal of Strength and Conditioning Research Age V_ O2max (L
min21) V_ O2max (mL
kg21
min21) Percentile TM 90 80 70 60 50 40 30 20 10 All 63.34 62.78 60.05 58.38 56.18 54.42 50.58 47.99 44.21 35–44 y 45–54 y $55 y 67.21 63.28 62.85 62.49 60.94 59.90 59.11 56.25 54.36 63.01 59.66 58.01 56.78 56.18 55.69 53.20 48.75 47.51 50.58 49.37 48.18 47.78 47.08 55.69 44.21 42.04 40.01 All 5.25 5.04 4.64 4.48 4.37 4.19 4.05 3.93 3.45 35–44 y 45–54 y $55 y 5.935 5.27 5.23 5.08 5.00 4.79 4.60 4.55 4.20 4.44 4.39 4.38 4.36 4.33 4.27 4.19 4.08 3.99 PPO (W
kg21) PPO (W) 4.09 5.05 4.00 3.71 3.53 3.37 3.15 2.96 2.79 All 35–44 y 45–54 y $55 y 459 439 426 424 418 381 366 342 317 461 459 456 445 439 429 425 425 388 425 422 421 417 399 380 371 351 345 388 364 338 329 322 311 294 276 265 Age VT1(%V_ O2max) Percentile 90 80 70 60 50 40 30 20 10 All 83 82 79 74 71 69 67 64 63 VT2(%V_ O2max) 35–44 y 45–54 y $55 y 74 72 69 69 66 63 63 63 58 82 80 77 74 71 69 68 67 67 87 87 87 84 83 82 80 80 76 All 97 95 92 91 89 88 87 84 78 Economy (W
L21
min21) 35–44 y 45–54 y $55 y All 92 90 90 88 87 87 85 80 78 82.9 78.0 76.0 76.0 75.5 73.4 70.0 69.0 68.0 95 94 93 92 91 89 88 84 82 97 97 97 97 97 94 89 88 87 35–44 y 45–54 y $55 y 87.2 80.0 78.0 76.0 76.0 75.2 73.9 71.8 68.6 82.1 78.8 73.8 70.8 69.5 69.0 69.0 68.4 65.5 83.6 80.2 76.0 76.0 76.0 75.6 74.2 70.4 67.7 All 5.94 5.78 5.41 5.23 4.93 4.70 4.60 4.47 4.02 35–44 y 45–54 y $55 y 5.93 5.85 5.62 5.50 5.34 5.16 5.09 4.94 4.67 5.87 5.42 4.88 4.69 4.67 4.62 4.53 4.37 3.98 4.73 4.63 4.50 4.45 4.32 4.12 3.86 3.81 3.80 Physiology of Masters Cyclists 1438 TABLE 4. Percentile values for maximal oxygen uptake (V_ O2max), peak power output (PPO), first (VT1) and second (VT2) ventilatory thresholds, and economy determined during a graded exercise test performed by Masters-level cyclists. the TM Journal of Strength and Conditioning Research level cyclists are still competitive with younger cyclists, even in the face of decreasing aerobic capacity. Similar to the decline in V_ O2max, PPO was also found to be significantly different between the various age groups tested in this study. In the present study, it was found that absolute and relative PPO was 9% lower in the 45- to 54-year age group, compared with the 35- to 44-year age group, with a further 14% average decrease in the $55-year age group compared with cyclists ages 45–54 years (Table 1). Such a decrease in PPO is an interesting finding because PPO has previously been found to be highly correlated with overall cycling performance (2,8). Therefore, because Masters-level cyclists often compete as a single age group (35 years), such findings may indicate possible disadvantages for older riders. The ability to generate power during cycling is highly dependent on the muscular strength of the cyclist. Consequently, it is possible that the dramatic differences in maximal power output observed may be related to reductions in the ability of older athletes to produce force. An increase in age from 45 to 80 years has been shown to result in a 15–29% decline in absolute lower-body muscular strength (6), possibly because of reductions in muscle mass. Indeed, Lexell et al. (13) have shown a 40% reduction in muscle cross-sectional area between the ages of 20 and 80 years, with 30% of that reduction occurring after the age of 50 years (23). Although research has shown resistance training to reverse the loss in muscle mass (23), few cyclists engage in such practices, because many cyclists believe that additional bodyweight gains from strength training will decrease cycling performance. Despite this, there is a paucity of research examining the influence of resistance training on cycling performance in Masters-level cyclists (10). It is possible that by incorporating resistance training, Masters-level cyclists may be able to attenuate the loss of muscle mass and strength, consequently maintaining performance with aging (12). | www.nsca-jscr.org Models of cycling performance indicate that the economy of motion makes an important contribution to overall cycling performance (18). It has been shown that an inverse relationship exists between aerobic fitness and economy (9,14), where those with lower levels of aerobic fitness have a compensatory increase in economy of motion. Further, it has been suggested that the inverse relationship may be attributable to an increase in the percentage of more efficient Type I fibers in exercised muscle (9). The loss of muscle mass associated with aging is primarily attributable to a reduction in Type II fibers (16) resulting in a greater percentage of Type I fibers. This latter finding might be thought to contribute to a higher economy of motion in older compared with younger cyclists. However, results of this study do not support this theory, because similar levels of economy were observed between all age groups (Table 3). Unfortunately, in the present study, muscle fiber type was not determined, and, therefore, it is not known whether similar fiber types existed between age groups. The physiological categorization of cyclists provides a tool to predict exercise performance, create appropriate training protocols, aid in the identification of talent, and distinguish groups of cyclists within a given population. On the basis of previous guidelines (Table 5), the physiological characteristics of all subjects in the present study would suggest that this sample pool generally consists of ‘‘trained cyclists’’ (11). However, when individual age groups are examined, only the 35- to 44-year age group could be classified as ‘‘trained,’’ whereas the lower aerobic capacity and PPO that were observed in our older cyclists ($ 55 years) would have resulted in these cyclists being classified as ‘‘untrained.’’ This categorization would have occurred despite that fact that the training load of this group of cyclists would be considered ‘‘elite.’’ Additionally, three of the subjects who were 55 years and older had qualified and were scheduled to compete in the World Masters Road Cycling Championships later that year. TABLE 5. Criteria for the classification of trained, well-trained, elite, and world-class road cyclists. Training and race status Training frequency Training duration Training background Race days per year International Cycling Union (UCI) ranking Physiological variables Wmax (W) Wmax (W
kg21) V_ O2max (L
min21) V_ O2max (mL
kg21
min21) Economy (W
L21
min21) Trained Well trained Elite World class 2–3 times a week 30–60 min 1y 0–10 3–7 times a week 60–240 min 3–5 y 0–20 5–8 times a week 60–360 min 5–15 y 50–100 5–8 times a week 60–360 min 5–30 y 90–110 — — First 2000 First 200 250–400 4.0–5.0 4.5–5.0 64–70 72–74 300–450 5.0–6.0 5.0–5.3 70–75 74–75 400–600 6.0–7.0 5.2–6.0 72–80 76–77 400–600 6.5–8.0 5.4–7.0 75–90 . 78 Values represent ranges. Adapted from Jeukendrup, Craig, and Hawley (11). VOLUME 22 | NUMBER 5 | SEPTEMBER 2008 | 1439 Physiology of Masters Cyclists This discrepancy highlights the inaccuracy of comparing data from competitive Masters-level cyclists with data derived from younger cyclists. The authors recognize that the small sample sizes (n = 14, n = 11, n = 8) do not provide the statistical power to label our data ‘‘normative.’’ However, it should also be noted that the normative data that are commonly used to describe cyclists are based on a small sample size (11). Therefore, it is our intention to provide the information in Tables 3 and 4 as a current guide to assess the physical fitness level of Masters-level cyclists. Further research using a larger sample size is needed to provide an overall representation and true normative data for the Masters-level cyclist. In summary, whereas Masters-level cyclists were shown to possess relatively high levels of aerobic capacity, a decrease in physical fitness parameters was shown with increases in age. The decrease in aerobic capacity (;10% per decade) was consistent with previous literature and may be related to the decrease in HRmax. Further, economy of motion was not found to be significantly different between the age groups tested in this study, despite a progressive reduction in PPO. Although Masters-level cyclists may present with a high degree of physical fitness compared with a normal population, the present study has shown that normative data established from younger cyclists should not be used to categorize the fitness levels of Masters-level cyclists. PRACTICAL APPLICATIONS Maximal GXT data provide an effective way to predict performance, determine training intensities, and categorize cyclists. However, continued competitive cycling does not attenuate the declines in aerobic fitness and muscular power associated with aging. Masters-level cyclists can be highly competitive, but the comparison of physiological indices from Masters-level cyclists with normative data established from younger cyclists can lead to a misinterpretation of the data and subsequent inaccuracies in predicting performance, determining training intensities, and categorization of Masters-level cyclists. Therefore, it is important for coaches, athletes, and exercise scientists to use age-appropriate data when comparing cyclists between age groups. REFERENCES 1. Amann, M, Subudhi, AW, and Foster, C. Predictive validity of ventilatory and lactate thresholds for cycling time trial performance. Scand J Med Sci Sports 16: 27–34, 2006. 2. Balmer, J, Davison, RC, and Bird, SR. Peak power predicts performance power during an outdoor 16.1-km cycling time trial. Med Sci Sports Exerc 32: 1485–1490, 2000. 3. Coggan, AR, Spina, RJ, Rogers, MA, King, DS, Brown, M, Nemeth, PM, and Holloszy, JO. Histochemical and enzymatic characteristics of skeletal muscle in master athletes. J Appl Physiol 68: 1896–1901, 1990. 4. Faria, EW, Parker, DL, and Faria, IE. The science of cycling: physiology and training—part 1. Sports Med 35: 285–312, 2005. 5. Faria, I and Frankel, M. Anthropometric and physiologic profile of a cyclist—age 70. Med Sci Sports 9: 118–121, 1977. 1440 the TM Journal of Strength and Conditioning Research 6. Frontera, WR, Hughes, VA, Lutz, KJ, and Evans, WJ. A cross-sectional study of muscle strength and mass in 45to 78-yr-old men and women. J Appl Physiol 71: 644–650, 1991. 7. Goodpaster, BH, Park, SW, Harris, TB, Kritchevsky, SB, Nevitt, M, Schwartz, AV, Simonsick, EM, Tylavsky, FA, Visser, M, and Newman, AB. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci 61: 1059–1064, 2006. 8. Hawley, JA and Noakes, TD. Peak power output predicts maximal oxygen uptake and performance time in trained cyclists. Eur J Appl Physiol Occup Physiol 65: 79–83, 1992. 9. Hunter, GR, Bamman, MM, Larson-Meyer, DE, Joanisse, DR, McCarthy, JP, Blaudeau, TE, and Newcomer, BR. Inverse relationship between exercise economy and oxidative capacity in muscle. Eur J Appl Physiol 94: 558–568, 2005. 10. Izquierdo, M, Hakkinen, K, Ibanez, J, Anton, A, Garrues, M, Ruesta, M, and Gorostiaga, EM. Effects of strength training on submaximal and maximal endurance performance capacity in middle-aged and older men. J Strength Cond Res 17: 129–139, 2003. 11. Jeukendrup, AE, Craig, NP, and Hawley, JA. The bioenergetics of World Class Cycling. J Sci Med Sport 3: 414 –433, 2000. 12. Laursen, PB, Chiswell, SE, and Callaghan, JA. Should endurance athletes supplement resistance training into their training program to improve performance? Strength Cond J 27: 50–55, 2005. 13. Lexell, J, Taylor, CC, and Sjostrom, M. What is the cause of the ageing atrophy? Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15- to 83-year-old men. J Neurol Sci 84: 275–294, 1988. 14. Lucia, A, Hoyos, J, Perez, M, Santalla, A, and Chicharro, JL. Inverse relationship between V_ O2max and economy/efficiency in worldclass cyclists. Med Sci Sports Exerc 34: 2079–2084, 2002. 15. Lucia, A, Joyos, H, and Chicharro, JL. Physiological responses to professional road cycling: climbers vs. time trialists. Int J Sports Med 21: 505–512, 2000. 16. Maharam, LG, Bauman, PA, Kalman, D, Skolnik, H, and Perle, SM. Masters athletes: factors affecting performance. Sports Med 28: 273–285, 1999. 17. Moseley, L and Jeukendrup, AE. The reliability of cycling efficiency. Med Sci Sports Exerc 33: 621–627, 2001. 18. Olds, TS, Norton, KI, Lowe, EL, Olive, S, Reay, F, and Ly, S. Modeling road-cycling performance. J Appl Physiol 78: 1596–1611, 1995. 19. Pimentel, AE, Gentile, CL, Tanaka, H, Seals, DR, and Gates, PE. Greater rate of decline in maximal aerobic capacity with age in endurance-trained than in sedentary men. J Appl Physiol 94: 2406–2413, 2003. 20. Pollock, ML, Foster, C, Knapp, D, Rod, JL, and Schmidt, DH. Effect of age and training on aerobic capacity and body composition of master athletes. J Appl Physiol 62: 725–731, 1987. 21. Schroeder, TE, Hawkins, SA, Hyslop, D, Vallejo, AF, Jensky, NE, and Wiswell, RA. Longitudinal change in coronary heart disease risk factors in older runners. Age Ageing 36: 57–62, 2007. 22. Stewart, KJ. Physical activity and aging. Ann N Y Acad Sci 1055: 193–206, 2005. 23. Trappe, S. Master athletes. Int J Sport Nutr Exerc Metab 11(Suppl.): S196–S207, 2001. 24. Trappe, SW, Costill, DL, Vukovich, MD, Jones, J, and Melham, T. Aging among elite distance runners: a 22-yr longitudinal study. J Appl Physiol 80: 285–290, 1996. 25. Wagner, PD. New ideas on limitations to V_ O2max. Exerc Sport Sci Rev 28: 10–14, 2000.