The measurement of so-called “anaerobic threshold” is widely used in the prediction of a subject’s ability to maintain submaximal work rate as well as for measuring oxygen uptake above which the cardiovascular system insufficiently meets tissue oxygen requirements. Such data can provide information about submaximal work performance that may be independent of maximal oxygen uptake (V˙O2max) (11).
Studies on age-related decline in aerobic capacity have used various methods to obtain threshold parameters. In research with sedentary and untrained middle and older participants, the ventilatory threshold (VT) is usually determined, based mainly on a systematic increase in ventilatory equivalent for oxygen (V˙E/V˙O2) or end-tidal oxygen partial pressure with no concomitant rise in ventilatory equivalent for carbon dioxide (V˙E/V˙CO2) or end-tidal carbon dioxide partial pressure during an incremental exercise test (4,10,11,28,30,31,33,35). Untrained individuals (UT) very seldom undergo invasive tests to identify the LT, i.e., the exercise V˙O2 above which a net increase in lactate production is observed, resulting in a sustained increase in blood lactate concentration (16). In aging competitive athletes and physically active elderly people, VT is also determined (6,21,34,39), but the majority of examinations rely on LT identification (1,22,23,32,38) and on the determination of maximal lactate steady state, i.e., the highest concentration of blood lactate that can be maintained during the last 20 min of a 30-min constant-workload test, representing an equilibrium between lactate production and clearance (24). According to all given results, regardless of the variety of methods and controversies about relation between VT and LT and their metabolic basis (2,15,27), a significant decline in absolute threshold parameters with age is observed in any case. So far, gas exchange threshold (GET) was not used for evaluation of age-related changes in cardiorespiratory threshold parameters in adult athletes. GET identifies the point during an incremental exercise test where V˙CO2 begins to rise disproportionately faster than V˙O2 and the slope of the V˙CO2 and V˙O2 curve becomes >1.0. It directly addresses the onset of excess CO2 production in response to lactate accumulation (5). GET is independent of respiratory chemoreceptor sensitivity and thus the ventilatory response to exercise; the measurement correlates with LT and is reproducible (5,27). Besides, the measurement is noninvasive and usually enables to encourage and test a larger number of athletes (12) in a relatively shorter period than for LT determination (1,16,23,32).
The available body of research with participation of trained populations relates only to endurance-trained individuals (runners, cyclists, and triathletes). Surprisingly, the effect of chronic speed–power training on LT, VT, or GET has not been explored yet, although a numerous group of young and master athletes, especially track and field competitors, practices speed–power events (sprint running, jumping, throwing, and combined events). To our knowledge, speed–power athletes (SP) have never been the subject of any study in the context of age-related changes in “anaerobic threshold,” probably because their training modality was not of interest in the context of aerobic capacity and threshold parameters that are not used as predictors of strength or speed performance. However, it should be noted that SP use aerobic exercise in combination with submaximal and maximal-intensity exercise (9,20). It is known that higher intensities of exercise, especially high-intensity interval training, may be as or more effective in elevating threshold and change in pulmonary O2 uptake kinetics parameters compared high-volume endurance training (25). Thus, one may expect that SP would tend to maintain threshold cardiorespiratory parameters on a higher level than observed in UT and relatively close to that of endurance athletes.
The aim of this study was to evaluate oxygen uptake at the GET (V˙O2GET) in highly trained speed–power and endurance young and master athletes in a wide age range from 20 to 90 yr compared with untrained subjects. Our hypothesis was that the V˙O2GET in SP would be lower than that in endurance runners (ER), but it would exceed the V˙O2GET of untrained healthy participants over the whole age range. We also hypothesized that the three groups, representing dissimilar training modalities, would differ significantly in the rates of age-related cross-sectional decline in V˙O2GET.
One hundred and ninety-nine healthy men ages 20–90 yr were studied. They were assigned to three groups: i) speed–power trained track and field athletes (SP; 51 athletes specialized in sprint, jumps, throws, and combined events; age range, 20–90 yr), ii) endurance athletes (ER; 87 long-distance and middle-distance runners; 20–80 yr), and iii) UT (61 participants; 20–70 yr). Athletes ages 20–35 yr were included into this study within a routine periodical physical evaluation, as a result of cooperation between our university and the coaches of the Polish national team. Athletes older than 35 yr were regular participants in masters European and world championships, ranked 1–10 in their age categories, and were recruited on a voluntary basis during international events by the agency of national teams’ leaders, via leaflets, and personal communication. The UT subjects were encouraged to participate in the study and invited to undergo preventive physical examinations through announcements via local mass media. The UT group had no previous or current competitive sport experience. These participants occasionally practiced recreational forms of exercise (e.g., jogging, swimming, and team games) in their leisure time, but they did not exceed 150 min of moderate to vigorous physical activity per week, including nonsport activities such as heavy housework, gardening, etc. The aims of the research and testing methodology were explained to both athletes and untrained subjects who gave their informed consent before their inclusion in the study. The project has been approved by the Ethics Committee at the Karol Marcinkowski Medical University in Poznań.
Both athletes and UT underwent preliminary interview and clinical examination to evaluate their health status, especially cardiovascular, pulmonary, and musculoskeletal systems. Body mass and height were measured using certified digital medical scale series WPT 60/150.O (Radwag, Radom, Poland), accuracy 0.01 kg, with mechanical measuring rod for height, accuracy 0.5 cm. Body mass index (BMI) was calculated as body weight (kg) divided by height squared (m2). Blood pressure and 12-lead ECG measurements were performed after 5–10 min of seated rest. Subjects included into the study were those who i) did not report history of a cardiovascular/cardiopulmonary disease (myocardial infarction, stroke, angina pectoris, or hypertension) or other severe or chronic diseases such as diabetes; ii) had no major orthopedic injury or illness resulting in inability to run; iii) did not take medications that could affect circulatory function (e.g., β-blockers); iv) had a normal resting ECG; v) had BMI below 30.0 kg·m−2; and vi) were nonsmokers. Resting hemoglobin (Hb) concentration and hematocrit (HCT) were measured using Cobas b121 apparatus (Roche, Mannheim, Germany). Because of voluntary character of the participation and mentioned criteria, the UT group was probably healthier than if it was randomly selected. On the other hand, UT subjects older than 70 yr, who would be able to perform an exercise test to exhaustion without health risk, did not volunteer.
Subjects were instructed to avoid strenuous (high-volume or high-intensity) physical activity for at least 24 h before testing session. In both athletes and untrained participants, all examinations were performed in the period between May and July, i.e., in the period of specific preparation or competition in track and field athletics. This ensured the level of aerobic capacity as high as possible in athletes and reduced seasonal variation in fitness in all groups. Information was obtained from each athlete regarding training history (years of competitive sport participation) and current training volume (hours per week over the past year). The exercise tests were conducted between 8:00 a.m. and noon, 2 h after consuming a light breakfast (bread and butter, water, without coffee or tea). An incremental running treadmill test (Woodway ES1, Waukesha, WI) was performed.
After 10 min of warm-up, subjects started running at a speed of 6 km·h−1. Subsequently, the speed was progressively increased by 2 km·h−1 every 3 min until volitional exhaustion. Each treadmill test lasted 12–21 min depending on age and training status. Subjects were verbally encouraged to give a maximal effort throughout the test. Respiratory parameters (V˙E, V˙O2, and V˙CO2) were measured continuously (breath-by-breath) using CPX-D computer system (Medical Graphics Corporation, St. Louis, MO). Before each trial, the system was calibrated according to the manufacturer’s instructions. Temperature, humidity, and barometric pressure were recorded by the sensors. In a two-point volume calibration (0.2 and 2 L·s−1), the flow values were measured automatically at the set measuring points. Gas analyzer calibration was done with standard gas mixture 5% CO2 and 16% O2. HR was recorded every 5 s with a Polar Accurex Plus device (Polar Elektro, Kempele, Finland). To detect the point of the GET, the V-slope method was administered using computerized regression analysis of the slopes of the CO2 output versus O2 uptake plot, which detects the beginning of the excess CO2 output generated from the buffering of [H+] (5). The method involves the analysis of the behavior of V˙CO2 as a function of V˙O2 during progressive exercise tests with a consequent increase in V˙CO2. This results in a transition in the relation between the V˙CO2 and V˙O2. The software supplied by Medical Graphics was used supported with visual inspection by two experienced researchers. As a secondary criterion, they additionally used the point when V˙E/V˙O2 as well as end-tidal oxygen partial pressure raised without concomitant raise in V˙E/V˙CO2 and end-tidal carbon dioxide partial pressure. V˙O2max (mL·kg−1·min−1) was recorded and considered to be achieved if the test met at least three of the following criteria: i) a plateau in V˙O2 with increasing workload, ii) RER >1.15, iii) HR within 5 beats·min−1 of the age-predicted maximal HR (36), and iv) blood lactate concentration after exercise greater than 7 mmol·L−1. The following threshold parameters were measured or calculated for the purposes of this study: V˙O2GET expressed in mL·min−1 and mL·kg−1·min−1, percentage V˙O2GET in relation to V˙O2max, and HR corresponding to the V˙O2GET (HRGET, beat·min−1).
Descriptive data were expressed as mean values and SD. Comparisons between the three groups of subjects were made by one-way ANOVA and post hoc Scheffe tests when indicated by a significant F value, with Shapiro–Wilk and Levene tests to assess the normal distribution of a sample and equality of variances, respectively. Using ANCOVA, we obtained age- and weight-adjusted means as well as differences in exercise variables. The relation between age and the parameters of maximal aerobic function were obtained by linear regression analysis. Multiple regression was used to identify significant contributors to the variance in V˙O2GET. A test for parallelism of regression lines was used to determine differences between slopes. Although second-order polynomial functions yielded a somewhat better fit in regression equations for some cases (r2 increase by 1%–6%), the linear model was used because of its simplicity. Pearson correlation coefficients were used to describe the relations between the variables analyzed. Statistical significance was set at P < 0.05. The statistical power of ANOVA and regression analyses ranged between 0.88 and 1.00 for exercise variables and between 0.66 and 0.96 for other variables. All statistics were performed by using Statistica 9.1 software package (StatSoft, Inc., Tulsa, OK).
The characteristics of the subjects are presented in Table 1. Body mass of the ER group was lower than that of the SP and UT groups. The ER group had a higher Hb level than the UT group and a higher HCT than the SP group. The ER group had higher V˙O2max and O2 Pulsemax than the SP and UT groups. The SP group showed a significantly higher V˙O2max than the UT group. HRmax was higher in the UT group than that in the SP and ER groups. V˙O2GET and O2 PulseGET were higher in the ER group compared with the SP and UT groups and higher in the SP compared with UT group. A greater HRGET was found in the ER than SP and UT groups.
Main threshold characteristics in relation to age are expressed graphically in Figure 1A–C. V˙O2GET was inversely related to age in the SP, ER, and UT groups. A greater absolute reduction in V˙O2GET was found in the ER than that in the SP and UT groups and greater in the SP than UT group (Table 2). The percentage decline was similar in all three groups. A similar pattern of between-group differences was found for age ranges before and after 50 yr. However, the rate of absolute decline in V˙O2GET did not differ between younger and older SP athletes, whereas it was accelerated in older compared with younger groups of the ER and UT subjects. About 85 yr old, the predicted level of V˙O2GET reached virtually the same value in the SP and ER groups (Fig. 2). The percentage decline in V˙O2GET was greater in older than that in younger subjects in the ER, SP, and UT groups, but the acceleration of decline after 50 yr old was less in the SP than in the ER and UT groups.
HRGET and O2 PulseGET were strongly inversely related to age in the SP, ER, and UT groups (Fig. 3A, B). The rate of absolute decline in HRGET was greater in the ER group compared with the SP and UT groups, whereas it did not differ between the SP and UT groups (Table 2). The absolute decline in O2 PulseGET was higher in the ER than SP and UT groups and in the SP compared with the UT group. The percentage decline in HRGET and O2 PulseGET was similar in all three groups.
A stepwise regression analysis was used to identify variables that best predicted V˙O2GET for both groups of athletes. Cardiorespiratory factors but not age were predominant predictors of V˙O2GET. The pair O2 PulseGET and HRGET alone explained 99.8% of variance in V˙O2GET, 89.9%–95.6% and 4.1%–9.8%, respectively. In this model, the third significant variable was training volume that explained, however, only 0.8%–0.9% of variance in both groups of athletes.
The absolute decline in V˙O2max was greater in the ER group compared with the SP and UT groups and in the SP compared with UT group (Table 2). The percentage declines were similar in all three groups, however, with the tendency to the lowest rates in the SP compared with the ER and UT groups. V˙O2max correlated negatively with age in the ER (r = −0.86), SP (r = −0.76), and UT (r = −0.88) groups (P = 0.000 for each case). Total Hb levels showed moderate inverse correlations with age for the SP (r = −0.34, P = 0.16), ER (r = −0.36, P = 0.001), and UT (r = −0.42, P = 0.001) groups. There were no significant differences in the rate of absolute decline in Hb between groups investigated. Similarly, the percentage changes ranged narrowly between 0.9% and 1.4% (Table 2).
Training history and volume
The SP and ER groups did not differ significantly regarding years of competitive sport experience (Table 1). The relation between training history and age was very strong (r = 0.93 and 0.92, respectively, P = 0.000). Figure 4 shows the relation between weekly training volume and V˙O2GET in the combined group of speed–power and endurance athletes. The increase in weekly training volume by 1 h was related to a predicted increase in V˙O2GET by 2.0 mL·kg−1·min−1.
We found that ER had, as expected, higher levels of V˙O2GET than SP, expressed in absolute, body mass–adjusted, and relative values. Nevertheless, the V˙O2GET of the SP group remarkably exceeded that of the UT. The cross-sectional rate of absolute decline in V˙O2GET was significantly smaller in the SP than ER group and least pronounced in the UT group. The percentage decline was comparable in all groups investigated. However, when considering rates of decline separately in groups before and after 50 yr old, the absolute rates of decline were virtually the same in younger and older SP athletes, whereas in ER and UT, a considerably greater loss was observed in the older groups. Consequently, the regression lines for V˙O2GET converged to a similar value in the SP and ER groups by 85 yr old. These results correspond to the findings from studies focusing on age-related decline in V˙O2max in athletes and untrained healthy individuals (e.g., 17,29,37) and add further information regarding the changes in functional aerobic capacity.
Age-related change in GET
To our best knowledge, studies analyzing age-related changes in GET in speed–power trained athletes are not available. The rate of decline in V˙O2GET in our untrained participants (0.22 mL·kg−1·min−1·yr−1) was greater than obtained by other researchers for VT measured using ventilatory equivalents (from nonsignificant to 0.16 mL·kg−1·min−1·yr−1) (4,11,28,35). The difference may be connected with different age ranges and activity status as well as specific methods of threshold identification. Comparable studies on athletes used only LT as the threshold criterion. The rate of decline in V˙O2GET in our ER group (0.56 mL·kg−1·min−1·yr−1) was greater than obtained by other researchers for LT, both in untrained participants (0.11 mL·kg−1·min−1·yr−1) (16) and former athletes (0.39 mL·kg−1·min−1·yr−1) (22); however, any comparisons of these two methods may be deceptive. Although it seems that the rate of age-related decline in threshold parameters is smaller in untrained than trained individuals, further research is needed to support this finding.
Age-related change in GET versus V˙O2max
In UT, it has been observed that absolute V˙O2 at both VT (4,10,28,30,31) and LT (22) declines at a slower rate than V˙O2max in wide age ranges from 15 to 85 yr. A longitudinal study by Stathokostas et al. (35) has confirmed these observations for VT. Only in one study using VT, opposite results were obtained (11). In general, the rates of decline ranged from 0.13 to 0.15 mL·kg−1·min−1 for V˙O2 at VT and from 0.31 to 0.43 mL·kg−1·min−1 for V˙O2max. Our findings for untrained subjects are in line with the mentioned data. In SP and ER, however, the values had a reverse tendency to greater cross-sectional decline in absolute V˙O2GET than V˙O2max, suggesting the effect of training status and modality on this relation.
Our results suggest a significant age-related decrease in GET as a percentage of V˙O2max in athletes and insignificant change in UT. Available reports on age-related change in threshold V˙O2 as a percentage of V˙O2max are inconsistent. The VT-based cross-sectional data suggest an increase in percentage threshold in UT (4) and lack of significant changes in competitive athletes (39). The longitudinal study by Stathokostas et al. (35) did not reveal age-related percentage VT alterations in healthy older adults (73.5 ± 6.4 yr) living independently in the community. Contrary to VT, LT as a percentage of V˙O2max was found to increase in master athletes both in cross-sectional (1,23,38) and longitudinal studies (23) whereas it remained constant (22) across age in untrained participants (16). Quite distinct results were obtained for maximal lactate steady state that decreased with age (r = −0.68) in highly trained cyclists (24). The discrepancies between studies may be connected with different methods for threshold determination as well as age ranges, training status, and study design. It was also found that that absolute but not relative LT was a significant predictor of performance in master athletes (32). Therefore, the diagnostic and clinical value of percentage threshold values is questionable.
Contributors to the age-related decline
V˙O2GET was greater in the SP than UT group despite nonsignificant differences in HRGET, Hb, and HCT. At the same time, O2 PulseGET was greater in the SP than UT group and was the strongest predictor of V˙O2GET in both groups. This suggests either a greater stroke volume or O2 extraction in the SP group. The rates of absolute decline in O2 PulseGET as well as HRGET were less in the SP than ER group, which was associated with a smaller decline in V˙O2GET. A very small age-related decline in O2 PulseGET in the UT group was related to the smallest absolute decline in V˙O2GET compared with the SP and ER groups. The absolute level of Hb declined almost insensibly in all three groups and could not affect the between-group differences. The percentage decline rates in HRGET, O2 PulseGET, and Hb were very similar regardless of training type and activity status. It should be noticed that O2 PulseGET made a much greater contribution to the percentage decline in V˙O2GET than HRGET and Hb did. Age did not contribute significantly to the explained variance of V˙O2GET. This suggests that it is only a mediated variable. Muscle properties and body composition were not considered in this study; however, they can affect threshold parameters (12,26,30,33) because muscle structure and function differ between sprint-trained athletes, ER, and UT across the lifespan (3,7,8,14,18,19).
Physical activity, exercise training, and GET across the lifespan
The maintenance of regular physical activity in the elderly provides high submaximal aerobic fitness, as was shown by higher measured than predicted levels of VT in octogenarians (34). Even simple exercise habits in adulthood, e.g., 30 min per session twice a week for a duration of three months, may bring about an increased level of GET compared with nonactive lifestyle (26). A greatly elevated and stable level of VT may be maintained up to 50 yr old in highly trained subjects (6). Also in our study, we revealed a significant positive relation between weekly training volume and V˙O2GET in the combined group of speed–power and endurance athletes. In this light, it is surprising that training volume did contribute to the explained variance of V˙O2GET to a very small extent (<1%) in our athletes, with cardiorespiratory factors foreground. Nevertheless, the age-related decline in threshold parameters may be delayed or even prevented through physical activity during longer periods of life, despite of a low contribution to regression models (35). Moreover, training modality or type of physical activity are surely of great importance. Although training data were not collected here in detail, training profiles of young and older speed–power and endurance athletes are clearly distinct (9,20,40). It seems that the combination of regular aerobic and high-intensity anaerobic exercise brings about an increased level of threshold parameters in SP, far above the capabilities of untrained subjects, across the whole age range analyzed.
Another interesting question is the adherence to different types of exercise training. Contrary to the common view, it appears that the continuation of endurance training may be more burdensome than physical activity on the basis of intensive short-term or mixed exercise. It was demonstrated that endurance exertion can be more time consuming and perceived as being more fatiguing than sprint exercise (13). That might conceivably be the reason for which speed–power master athletes usually participate longer (25–34 yr) in sport activity and competition than long-distance runners (16–18 yr) (9).
The cross-sectional design of this study is the major limitation that makes it difficult to assess real rates of decline in GET with age. Consequently, expressions we used such as “rate of decline” or “change” are simplifications. In UT, longitudinal data showed a faster loss in VT than cross-sectional design as reported by Stathokostas et al. (35); however, only subjects between the ages of 55 and 85 yr were examined. Also, careful attention is required when analyzing our cross-sectional results, because of data scattering. The trends depicted by regression lines are generalizations, and the between-subject variability in V˙O2GET is considerable. Consequently, the V˙O2GET bandwidths of the ER, SP, and UT groups partly overlap along the age axis. Thus, conclusions about the age-related decline apply mainly to general trends. A possible sample bias due to voluntary participation must be also taken into consideration because the health status of our participants was better than average in population. However, such a recruitment was justified in the case of competitive athletes who were intended to serve as an idealized model of successful aging from the physiological point of view.
Improvement in or maintenance of “threshold” aerobic capacity is advantageous, on the one hand, to sport performance and, on the other hand, to functioning in everyday life, where various activities can be carried out with less fatigue, without restraint and limitations, which is particularly important for older people. Our findings show that the “speed–power model” of lifelong physical activity is associated with a considerably increased level of V˙O2GET and its relatively slow age-related decline.
This work was funded by the Polish Ministry of Science and Higher Education (application and grant number: N N404 191536).
Authors do not have professional relations with any companies or manufacturers who will benefit from the results of the present study.
The authors thank the coaches, athletes, and volunteers for their full participation in the study.
The results of the present study do not constitute endorsement of the product by the authors or by the American College of Sports Medicine.
1. Allen WK, Seals DR, Hurley BF, Ehsani AA, Hagberg JM. Lactate threshold and distance-running performance in young and older endurance athletes. J Appl Physiol
. 1985; 58 (4): 1281–4.
2. Amann M, Subudhi AW, Foster C. Predictive validity of ventilatory and lactate thresholds for cycling time trial performance. Scand J Med Sci Sports
. 2006; 16 (1): 27–34.
3. Andersen JL. Muscle fiber type adaptation in the elderly human muscle. Scand J Med Sci Sports
. 2003; 13 (1): 40–7.
4. Babcock MA, Paterson DH, Cunningham DA. Influence of ageing on aerobic parameters determined from a ramp test. Eur J Appl Physiol Occup Physiol
. 1992; 65 (2): 138–43.
5. Beaver WL, Wasserman K, Whipp BJ. A new method for detecting anaerobic threshold by gas exchange. J Appl Physiol
. 1986; 60 (6): 2020–7.
6. Burtscher M, Förster H, Burtscher J. Superior endurance performance in aging runners. Gerontology
. 2008; 54 (5): 268–71.
7. Coggan R, Abduljalil AM, Swanson SC, et al.. Muscle metabolism during exercise in young and older untrained and endurance-trained men. J Appl Physiol
. 1993; 75 (5): 2125–33.
8. Coggan AR, Spina RJ, Rogers MA, et al.. Histochemical and enzymatic characteristics of skeletal muscle in master athletes. J Appl Physiol
. 1990; 68 (5): 1896–901.
9. Conzelmann A. Competitive Sport in the Second Part of Life: An Example of Masters Athletics
. Cologne (Germany): Sport und Buch Strauß; 1993. p. 128 German.
10. Cunningham DA, Nancekievill EA, Paterson DH, Donner AP, Rechnitzer PA. Ventilation threshold and aging. J Gerontol
. 1985; 40 (6): 703–7.
11. Cunningham DA, Paterson DH, Koval JJ, St. Croix CM. A model of oxygen transport capacity changes for independently living older men and women. Can J Appl Physiol
. 1997; 22 (5): 439–53.
12. Davis JA, Storer TW, Caiozzo VJ. Prediction of normal values for lactate threshold estimated by gas exchange in men and women. Eur J Appl Physiol
. 1997; 76 (2): 157–64.
13. Desgorces FD, Sénégas X, Garcia J, Decker L, Noirez P. Methods to quantify intermittent exercises. Appl Physiol Nutr Metab
. 2007; 32 (4): 762–9.
14. Faulkner JA, Davis CS, Mendias CL, Brooks SV. The aging of elite male athletes: age-related changes in performance and skeletal muscle structure and function. Clin J Sport Med
. 2008; 18 (6): 501–7.
15. Gladden LB, Yates JW, Stremel W, Stamford BA. Gas exchange and lactate anaerobic thresholds: inter and intra evaluator agreement. J Appl Physiol
. 1985; 58 (6): 2082–9.
16. Iredale KF, Nimmo MA. The effect of aging on the lactate threshold in untrained men. J Aging Phys Act
. 1997; 5: 39–49.
17. Jackson AS, Sui X, Herbert JR, Church TS, Blair SN. Role of life style and aging on the longitudinal change in cardiorespiratory fitness. Arch Intern Med
. 2009; 169 (19): 1781–7.
18. Kaczor JJ, Ziółkowski W, Antosiewicz J, Hac S, Tarnopolsky MA, Popinigis J. The effect of aging on anaerobic and aerobic enzyme activities in human skeletal muscle. J Gerontol A Biol Sci Med Sci
. 2006; 61 (4): 339–44.
19. Korhonen MT, Cristea A, Alén M, et al.. Aging, muscle fiber type, and contractile function in sprint-trained athletes. J Appl Physiol
. 2006; 101 (3): 906–17.
20. Kusy K, Zieliński J, Osik T. The structure of exercise loads for sprint training: a profile of the best Polish 200m sprinter. In: Lühnenschloß D, Wastl P, editors. Quo Vadis Olympic Track and Field? Problems, Balances, Prospects
. Hamburg: Czwalina; 2008. pp. 113–24. German.
21. Lenti M, De Vito G, di Palumbo AS, Sbriccoli P, Quattrini F, Sacchetti M. Effects of aging and training status on ventilatory response during incremental cycling exercise. J Strength Cond Res
. 2011; 25 (5): 1326–32.
22. Ładyga M, Faff J, Burkhard-Jagodzińska K. Age-related decrease of the indices of aerobic capacity in the former elite rowers and kayakers. Biol Sport
. 2008; 25 (3): 245–61.
23. Marcell TJ, Hawkins SA, Tarpenning KM, Hyslop DM, Wiswell RA. Longitudinal analysis of lactate threshold in male and female master athletes. Med Sci Sports Exerc
. 2003; 35 (5): 810–7.
24. Mattern CO, Gutilla MJ, Bright DL, Kirby TE, Hinchcliff KW, Devor ST. Maximal lactate steady state declines during the aging process. J Appl Physiol
. 2003; 95 (6): 2576–82.
25. McKay BR, Paterson DH, Kowalchuk JM. Effect of short-term high-intensity interval training vs. continuous training on O2 uptake kinetics, muscle deoxygenation, and exercise performance. J Appl Physiol
. 2009; 107 (1): 128–38.
26. Miyatake N, Miyachi M, Tabata I, et al.. Evaluation of ventilatory threshold and its relation to exercise habits among Japanese. Environ Health Prev Med
. 2010; 15 (6): 374–80.
27. Myers J, Ashley E. Dangerous curves. A perspective on exercise, lactate, and the anaerobic threshold. Chest
. 1997; 111 (3): 787–95.
28. Paterson DH, Cunningham DA, Koval JJ, St Croix CM. Aerobic fitness in a population of independently living men and women aged 55–86. Med Sci Sports Exerc
. 1999; 31 (12): 1813–20.
29. Pimentel AE, Gentile CL, Tanaka H, Seals DR, Gates PE. Greater rate of decline in maximal aerobic capacity with age in endurance-trained than in sedentary men. J Appl Physiol
. 2003; 94 (6): 2406–13.
30. Posner JD, Gorman KM, Klien HS, Cline CJ. Ventilatory threshold: measurement and variation with age. J Appl Physiol
. 1987; 63 (4): 1519–25.
31. Reinhard U, Müller PH, Schmülling RM. Determination of anaerobic threshold by the ventilation equivalent in normal individuals. Respiration
. 1979; 38 (1): 36–42.
32. Rossuello AE, Hawkins SA, Wiswell RA. Absolute lactate threshold predicts endurance performance in master athletes. Biol Sport
. 2009; 26 (2): 105–12.
33. Sanada K, Kuchiki T, Miyachi M, McGrath K, Higuchi M, Ebashi H. Effects of age on ventilatory threshold and peak oxygen uptake normalized for regional skeletal muscle mass in Japanese men and women aged 20–80 years. Eur J Appl Physiol
. 2007; 99 (5): 475–83.
34. Simar D, Malatesta D, Dauvilliers Y, Préfaut C, Varray A, Caillaud C. Aerobic and functional capacities in a selected active population of European octogenarians. Int J Sports Med
. 2005; 26 (2): 128–33.
35. Stathokostas L, Jacob-Johnson S, Petrella RJ, Paterson DH. Longitudinal changes in aerobic power in older men and women. J Appl Physiol
. 2004; 97 (2): 781–9.
36. Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol
. 2001; 37 (1): 153–6.
37. Wilson TM, Tanaka H. Meta-analysis of the age-associated decline in maximal aerobic capacity in men: relation to training status. Am J Physiol Heart Circ Physiol
. 2000; 278 (3): H829–34.
38. Wiswell RA, Jaque SV, Mercell TJ, et al.. Maximal aerobic power, lactate threshold, and running performance in master athletes. Med Sci Sports Exerc
. 2000; 32 (6): 1165–70.
39. Wyatt FB, McCarthy JP. Age associated declines in exercise time to exhaustion and ventilatory parameters in trained cyclists. J Exerc Physiol Online
. 2003; 6 (1): 12–7.
40. Zieliński J, Rychlewski T, Kusy K, Domaszewska K, Laurentowska M. The effect of endurance training on changes in purine metabolism: a longitudinal study of competitive long-distance runners. Eur J Appl Physiol
. 2009; 106 (6): 867–76.