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Applied Sciences: Physical Fitness and Performance

Differences in ultra-endurance exercise in performance-matched male and female runners


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Medicine & Science in Sports & Exercise: March 1996 - Volume 28 - Issue 3 - p 359-365
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When the progression of male and female athletic world records(100-1500 m) was compared by Whipp and Ward in 1992(29), it was found that records were being improved at a greater rate by the female athletes. They predicted that by the year 2025 female athletes would perform as well as their male counterparts (over these shorter distances) if this trend continued.

A review of the literature shows that when comparisons on performances are made between the genders, top female runners are consistently 9-11% slower than their male counterparts over all distances raced(18). Studies have shown that the champion male athlete owes this greater performance level to a greater physiological capacity(5,6,8,13,28).

Furthermore, it is well documented that a greater physiological capacity also exists in males in average population groups. The average male has less body fat (6,8,10,19,28), a greater aerobic capacity (˙VO2max) and more favorable cardiovascular ratios (heart size, left ventricular mass, cardiac output) thereby allowing for a greater work capacity in the average male athlete in comparison to the average female athlete(6,13,18,19,28).

Much of the data that exist on gender-related differences in the physiology of endurance athletes have focused mainly on aerobic, cardiovascular, metabolic, and muscular differences(5,6,12,24,27,28), in events less than 20 km (7,28). However, one study(19) has made a physiological comparison after equating performance (time to run 24.1 km/15 miles) between a group of average male and female athletes. The major finding of that study was that over 24.1 km, performances were similar because of similar physiologies. A preliminary study conducted in this laboratory however indicated that when aerobic capacity(˙VO2max, running economy, and training level) was matched, female athletes tended to outperform their male counterparts in events longer than 24.1 km (unpublished data). It was noted that some female endurance-athletes outperformed male endurance-athletes with similar aerobic capacities by more than 30 min over 90 km.

As a result of the paucity of studies that have investigated performance differences in performance-matched male and female athletes particularly at distances greater than 24.1 km, and since in a country where the premiere road race is 90 km long and attracts a field of some 12 000 runners, an opportunity existed to test the hypothesis that females would outperform those males who were able to perform as well as the females at half the distance (42.2 km).


Twenty runners, 10 males and 10 females, volunteered to participate in this study after providing informed consent. Each male runner was matched with a female runner according to the time taken to complete a given standard marathon (42.2 km). The female group ran an average of 3:36 ± 0:42 h:min, and the male group an average of 3:39 ± 0:47 h:min for a standard marathon. A prerequisite for subject participation was that each subject intended competing in a selected ultra-endurance event (the 90-km Comrades Marathon), which is the major event on the South African running calendar. In addition, each subject pair was required to participate in the same 10-km and 21.1-km race prior to the ultra-endurance event.

The subjects reported to a laboratory situated at an altitude of 1800 m(625 Torr) and regulated at a room temperature of 20-22°C. All subjects were familiarized with the testing equipment and procedures prior to the commencement of testing.

Body fat composition was estimated using the method of Durnin and Womersley(10), by measuring skinfold thickness at the biceps, triceps, suprailiac, and subscapular sites (Harpenden calipers, British Indicators Ltd., England). Body mass (accurate to 0.05 kg, Mettler TE/J, Zurich, Switzerland) and height (accurate to 0.1 cm, SECA, Germany) were also measured.

Each subject was then asked to run on a motorized treadmill (Powerjog, E10, Sport Engineering Limited, Birmingham, England). Oxygen consumption(˙VO2) was measured during steady-state treadmill running using a precalibrated on-line system (Oxycon-4, Mijnhardt, Bunnik, Holland), and heart rate was monitored by means of subcostal ECG leads (Hewlett Packard 78351, Andover, MA). Measurements to calculate running economy (RE) were made during steady state (2-5 min) submaximal exercise (12 km·h-1 and 0.0% gradient) (4). Running economy was calculated from:Equation

Maximal aerobic capacity (˙VO2max) was measured using a discontinuous incremental technique (30). The speed of the treadmill remained constant (13.0 km·h-1), and load was increased by increasing the gradient (1.0% increments). Each workload was of 3-min duration. The ˙VO2max was considered to have been reached when the change in the ˙VO2 was not more than 1.0 ml O2·min-1·kg-1 with an increment in the gradient of 1%.

The blood lactate curve for each subject was determined as a measure of training level (14). Blood was collected from an in-dwelling winged infusion set inserted into a forearm vein. The blood samples were collected into tubes containing an anticoagulant (potassium oxalate) and a metabolic inhibitor (sodium fluoride). The subjects performed a graded exercise protocol on a treadmill in which the gradient was progressively increased every 3.5 min until a ˙VO2 of at least 90%˙VO2max had been reached. Blood was drawn and steady state˙VO2 was measured at each level. The plasma was frozen at -20°C until analysis using an enzymatic-spectrophotometric method (Boehringer Mannheim, Mannheim, Germany). The lactate curve for the female group and for the male group, respectively, was constructed using least squares-regression on an exponential model (Fig. 1). In addition to this, a training history was obtained from each subject (Table 2).

The subjects were also asked to complete the Profile of Mood States (POMS) and Personal Motivation (PM) questionnaires under normal resting conditions. The PM test is divided into three and two subfactors, respectively: goal directedness (AA) and personal excellence (BB) comprise the overall PM. Individuals who score highly in the AA factor are intent on achieving personal goals and will persevere despite adversity. The AA is comprised of (A) persistence, (B) awareness of time, and (C) action orientation. The second subfactor (BB), is challenge-oriented. Individuals who score highly on the BB factor believe that their fate is entirely in their hands, and goals will be achieved by taking the initiative rather than leaving them to luck. The BB is comprised of (D) aspirational level and (E) personal causation(21).

The average fraction of the ˙VO2max (F) of each athlete sustained at each of the four distances examined was estimated from: F =(Vs·RE/˙VO2max) (9) where vs is the average running speed (in km·min-1) at a given distance(s), RE is the running economy (in ml O2·kg-1·km-1), and ˙VO2max is the maximal aerobic capacity of each athlete (in ml O2·min-1·kg-1).

Data from the 10-km, 21.1-km, and 42.2-km distances were obtained from races run at an altitude of 1800 m (625 Torr). The 90-km race, however, was run at sea level (760 Torr) and thus a correction for the ˙VO2max measured at 625 Torr had to be made for sea-level conditions, which allowed a correction to the calculation for the average F at the 90-km distance. This correction was achieved by using the equation of Peronnet et al.(20), and the corrected ˙VO2max (at sea level) values are given in Table 1. The running economy was assumed to remain unchanged between altitude and sea level. The average fractions of the ˙VO2max (F) and relative energy expenditures are depicted in Figure 2.

Venous blood samples were obtained ±20-30 min before the 90-km race(Comrades Marathon) and ±5-10 min upon finishing the race. The blood was used to measure plasma concentrations of glucose (Glucose GOD/PAP; colorimetric method with deproteinisation, Randox Laboratories, Antrim, N. Ireland), free fatty acids (gas chromatography with 50% v/v/ ortho-phosphoric acid preparation), and plasma osmolalities (5500 vapor pressure osmometer, Wescor Inc., Logan, UT). The subjects were also asked to complete the POMS questionnaire under normal resting conditions, and then again immediately after the ultra-endurance event.

Student's t-statistic for independent data(12) was used to analyze differences between groups. The null hypothesis was rejected if P < 0.05. The data are presented as means ± SD.


The female group weighed less (P < 0.01) than the male group(57.3 ± 6.4 kg vs 72.1 ± 11.4 kg, respectively). The percentage body fat values of the female group were significantly higher (P< 0.01) than that of the male group (22.0 ± 3.2% vs 16.1 ± 3.0%) (Table 1).

Table 1 shows that the female ˙VO2max values were 5.7% lower (P < 0.05) than their male counterparts when expressed relative to body mass (48.3 ± 2.8 ml O2·min-1·kg-1 vs 51.3 ± 3.3 ml O2·min-1·kg-1), but were not significantly different when they were expressed relative to the fat-free mass (61.6± 2.7 ml O2·min-1·kg-1 vs 61.1± 3.4 ml O2·min-1·kg-1). After correcting for the change in altitude from 625 Torr to 760 Torr, the female group still had a significantly lower (P < 0.05)˙VO2max value (51.5 ± 3.0 ml O2·min-1·kg-1) compared with their male counterparts (54.6 ± 3.5 ml O2·min-1·kg-1). There were no significant differences in the running economies of the two groups (180.0 ± 14.7 ml O2·kg-1·km-1 for the females vs 177.0± 11.3 ml O2·kg-1·km-1 for the males) (Table 1) or between the lactate curves for the two groups (Fig. 1).

Table 2 shows the training histories of the two gender groups (obtained from training log-books). The average running time per session did not differ significantly (63.0 ± 8.4 min·session-1 for the females vs 63.3 ± 11.7 min·session-1 for the males. Training frequency averaged 5.6± 1.1 sessions·wk-1 in the female group compared to 4.9± 1.1 sessions·wk-1 in the male group (P > 0.05). There were no differences (P > 0.05) in the average training distances that the two groups ran each training session, (12.5± 1.4 km·session-1 in the female group vs 12.6 ± 1.8 km·session-1 in the male group). Training intensity averaged 5.1 ± 0.6 min·km-1 for the females compared with 4.9± 0.6 min·km-1 for the males (P > 0.05). In addition, Table 3 shows the times of the year that performances were posted, the races from which data were collected, and the number of performance-matched male-female pairs participating in each race.

Figure 2 shows the average running speeds over the four distances (performances were matched at 42.2 km). The speeds at 10 km were 227.4 ± 16.4 m·min-1 in the female group vs 233.2± 35.8 m·min-1 in the male group (P > 0.05). At the 21.1-km distance, the female group ran an average speed of 211.8± 13.7 m·min-1 compared with the 213.8 ± 21.9 m·min-1 of the males (P > 0.05). At 42.2 km, the average speeds were similar (194.8 ± 12.9 m·min-1 for the female group vs 192.6 ± 16.3 m·min-1 for the male group). The female group ran significantly faster (171.0 ± 11.7 m·min-1) than the male group (155.2 ± 14.7 m·min-1) over the 90-km distance (P < 0.05).

The average fractions of ˙VO2max (F) that could be sustained at the respective distances (s) are depicted in Figure 3. At 10 km, F10 in the females was 84.4 ± 7.5% vs 79.9 ± 7.1% in the males. F21.1 in the females was larger (P < 0.05)(78.7 ± 6.1%) than that observed in the males (73.5 ± 3.8%). The females exercised at a higher (P < 0.01) F42.2 value (73.4± 5.5) compared with the males (66.3 ± 3.7%). At 90 km, the females also exercised at a higher (P < 0.01) F90 value(59.8 ± 6.2%) compared with their male counterparts (50.2 ± 3.1%). The slope of the regression curve representing the average fraction of the ˙VO2max used for the females was significantly less(P < 0.05) than the slope of the corresponding curve for the males (Fig. 3). Figure 3 also depicts the average relative energy expenditure (in watts·kg-1) at each distance studied (11). At 10 km, the female group performed at 13.8 ± 1.7 W·kg-1 vs 13.9 ± 1.9 W·kg-1 in the males. The power output observed in the female group at 21.1 km was 12.8 ± 1.5 W·kg-1 compared with 12.7± 1.4 W·kg-1 in the male group. At 42.2 km, the females' power output was 11.8 ± 1.4 W·kg-1 vs 11.5 ± 1.0 W·kg-1 of the males (P > 0.05). At 90 km, the average energy expenditure of the females was greater (P < 0.05)(10.4 ± 1.3 W·kg-1) than their male counterparts (9.2± 0.9 W·kg-1). Over the 90-km distance the females used on average 30.8 ± 4.0 ml O2·min-1·kg-1 compared with 27.4 ± 2.8 ml O2·min-1·kg-1 consumed by the males(P < 0.05).

There was no significant gender-related difference in the concentration of glucose that occurred in the blood plasma as a result of running the Comrades ultra-marathon (Fig. 4). The initial resting values of glucose for both groups (5.3 ± 0.97 mM in the females vs 5.71 ± 1.28 mM in the males) were within the normal resting range (3.0-6.0 mM). Resting FFA levels were 0.23 ± 0.31 mM for the females and 0.24± 0.26 mM for the males. Figure 4 shows a significantly lower (P < 0.01) plasma FFA concentration in the females upon completion of the 90-km Comrades ultra-marathon. The female [FFA] was 0.370 ± 0.28 mM compared with 0.880 ± 0.47 mM noted in the male group (Fig. 4). The pre-90-km osmolality samples were 299.0 ± 23.2 mOsmol·kg-1 vs 304.7 ± 34.8 mOsmol·kg-1, females versus males, respectively. The post-90-km osmolalities of 314.1 ± 16.9 mOsmol·kg-1 in the females versus 315.2 ± 7.1 mOsmol·kg-1 in the males were not significantly different (Fig. 4).

There were no significant differences between the genders in any of the personality traits after running the 90-km distance (Fig. 5). There were also no gender-related differences in the PM test, with the female group scoring 64.4 ± 8.0 versus the 57.5 ± 17.3 personal motivation units noted in the male group (Fig. 6).


In spite of matching performances at 42.2 km, our female subjects outran their male counterparts over a distance of 90 km. The greater performance by the females could not be ascribed to a higher maximal aerobic capacity nor to a better running economy, nor to greater endurance-training (the lactate curves shown in Fig. 1 and the training histories shown in Table 2 were similar when comparing the two groups).

However, the greater performance at 90 km and an equal performance at 42.2 km in spite of a lesser aerobic capacity in the females was related to a greater fraction of ˙VO2max that could be sustained during each of these races (Fig. 3). Over 42.2 km a greater F compensated for a lower ˙VO2max in the female group so that the energy expended, and hence performances, were the same between the two groups. Over 90 km, the difference in F between the two groups was sufficiently large to result in a significantly greater energy expenditure and hence better performance by the female group. When we compared F's at each of the running distances studied, the degree of decline of F as the distance increased was significantly less in the female group compared with that in the male group(Fig. 3). While differences in performance were not detected at the shorter distances studied, the differences tended to become increasingly larger as running distance increased.

In an attempt to identify how the female group was able to sustain exercise at a significantly greater fraction of ˙VO2max than the males, we measured plasma FFA. Costill et al. (6) found that when athletes ran at a pace of ≅60% ˙VO2max, there were no gender-related differences in plasma FFA levels during at least the first 60 min of exercise. The latter study did, however, find that male athletes exhibited a greater muscle carnitine palmityl transferase (CPT) activity compared with female athletes, and it is muscle CPT, which may in part control the rate of FFA oxidation. Furthermore, they postulated that males should be capable of an enhanced level of β-oxidation merely as a result of their larger mitochondrial network existing in larger muscles, thereby being able to oxidize lipids at a greater rate (7).

These conclusions, however contradict the findings of Tarnopolsky et al.(26), who presented evidence that females were able to utilize fat to a greater extent (and thus utilize less carbohydrate) than equally trained and nourished males during moderate-intensity, long-duration exercise. They speculated that as a result of a slower rate of muscle glycogen depletion, females should be expected to perform better (than males) in long distance exercise.

However, since it has been postulated that lipid oxidation during muscular activity is positively related to the concentration of circulating FFA(1,22) and because the overall contribution of lipid oxidation to the energy demands of the exercise is inversely related to the exercise intensity (3), it is possible that the process of β-oxidation was not employed to the same degree as an energy supply in the females as in the males in our study. Thus, at 90 km, plasma FFA levels were approximately half as high in the female group compared with the male group upon completion of the ultra-endurance event, and during the race the females were working at ≅60% of ˙VO2max compared with≅50% of ˙VO2max in the males.

On the other hand, since the male group had higher FFA levels but did not perform as well as the females, one could postulate that FFA plasma concentration is a balance between the rate of fat mobilization and the rate of fat utilization. The higher FFA levels in the males therefore could reflect that in our study fat mobilization outstripped fat utilization, and the opposite could explain the lower FFA levels observed in the females. If this were a possibility, it would not be clear whether fat utilization was greater in the male group or in the female group. Though prevailing evidence(1,5,6) might favor the conclusion that in our study fat utilization was greater in the male group, and that theβ-oxidation of fat may not explain why the females outperformed their male counterparts, it is not clear what happens to the fat mobilization: fat utilization ratio during ultra-endurance exercise over a 90-km distance. In addition, factors such as variability in dietary regimen and menstrual cycle variation in the female subjects may have an effect on FFA utilization(26). Under the conditions of our study, we were not able to control for dietary regimen and phases of the menstrual cycle, and we are unable to infer what effects these factors might have had on our results.

Both plasma osmolality and blood glucose levels did not differ significantly between the two groups before and after the 90-km race(Fig. 4) and therefore were not influencing factors in the respective performances.

It has also been postulated that female athletes have a psychologically stronger disposition in endurance exercise(2,15-18,23), which could explain the better female ultra-endurance performance observed in the present study. The results of the psychological assessments used in this study, however, failed to supply the reason why the females outperformed the males during the ultra-endurance exercise. Difference in psychological fortitude was apparently not a factor in this study.

In conclusion, when male and female athletes were matched on their performance at a standard marathon (42.2-km) level, males showed a tendency for larger performance decrements with increasing distance, and although the groups had been matched at 42.2 km, the females outperformed the males at the 90-km distance. The females exercised at higher levels of their˙VO2max, and these data suggest that females are able to maintain a higher work rate for longer during ultra-endurance exercise.

In the study by Pate et al. (19), who found that similar performances over a distance of 15 miles resulted from similar physiological function, it was not clear whether the male and female groups were at a similar training level. In our study design we were particularly careful to ensure that differences in training level did not influence our results.

We wish to emphasize, however, that there is no evidence to suggest that the elite female athlete can match or outperform the elite male athlete in the foreseeable future. It has been postulated that the rate of improvement in a world record is dependent on factors such as newness of the event, the number of participants, and the amount of training (25). Therefore, the results shown by Whipp and Ward (29) probably reflect, at least partly, the advantage the female athlete has enjoyed of using latter day advances in sports science. Full female participation in athletics only began around the 1960s because of antiquated social mores, compared with the turn of the 20th century for males(25). Therefore, only within the last two to three decades have girls and women had similar opportunities to train and compete as boys and men have had over the previous nine to ten decades.

It appears that female ultra-endurance athletes may well have an edge over matched male counterparts (14). A close investigation should be made of variables such as hormonal and dietary variation, metabolic and muscle function, substrate utilization, and psychological profiles to unlock the key to the mechanism(s) involved and minimize the degree of speculation required to attempt to explain these results.

Figure 1-The curves comparing lactate accumulation as work intensity increases. The individual data points are plotted (○ female, • male). Both curves were fitted by least squares regression using an exponential model(
Figure 1-The curves comparing lactate accumulation as work intensity increases. The individual data points are plotted (○ female, • male). Both curves were fitted by least squares regression using an exponential model(:
dotted line , female; solid line , male).
Figure 2- Average running speeds as racing distance increases in the female group (○) and in the male group (•). The speed of running 90 km was performed at sea level (760 Torr) compared with altitude (625 Torr) for the other distances.
Figure 2- Average running speeds as racing distance increases in the female group (○) and in the male group (•). The speed of running 90 km was performed at sea level (760 Torr) compared with altitude (625 Torr) for the other distances.
Figure 3-The differences in O2-consumption with increasing distance between the female (○) and male (•) groups.
Figure 3-The differences in O2-consumption with increasing distance between the female (○) and male (•) groups.:
Top: the fraction of Vo2max (F) at which each group works; bottom: the approximate relative energy expenditure (in W·kg-1) between the two groups over the four different racing distances studied. F is obtained from the equation: F = Vs·RE/VO2max (Di Prampero, P. E., G. Atchou, J.-C. Bruckner, and C. Moia. The energetics of endurance running. Eur. J. Appl. Physiol . 55:259-266, 1986). Note that at the distance of equal performance (i.e., 42.2 km), the females were working at a higher fraction of their VO2max than their male counterparts, and that at 90 km, the females are able to utilize more O2, and thereby expend more energy, compared with the males. The F values and energy expenditures during the 90-km distance have been corrected for performance at sea level (760 Torr) for comparison with the altitude (625 Torr) performances for the other distances; * indicates a significant difference( P < 0.05) and ** ( P < 0.01).
Figure 4-The effects of running 90 km on glucose(
Figure 4-The effects of running 90 km on glucose(:
top ) and FFA ( middle ) concentrations, and plasma osmolality ( bottom ). Blood samples were obtained before and after running the 90-km distance in females(□) and in males (▪); ** indicates a significant difference( P < 0.01) between males and females.
Figure 5-The psychological effects of running 90 km (Comrades Marathon) in females (□) and in males (▪). The POMS test was conducted under normal resting conditions, and then completed again immediately on completion of the 90-km race. The figure depicts the change in each of the six personality traits with running 90 km compared with normal resting values.
Figure 5-The psychological effects of running 90 km (Comrades Marathon) in females (□) and in males (▪). The POMS test was conducted under normal resting conditions, and then completed again immediately on completion of the 90-km race. The figure depicts the change in each of the six personality traits with running 90 km compared with normal resting values.
Figure 6-Absolute scores from the PM test performed under resting conditions in females (□) and in males (▪).
Figure 6-Absolute scores from the PM test performed under resting conditions in females (□) and in males (▪).


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©1996The American College of Sports Medicine