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# VO2 max and training indices as determinants of competitive running performance

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## Abstract

The importance of the maximal oxygen uptake (VO2 max) for competitive running performance is established. Although of clear importance, the quantitative association between the volume and intensity of training, and running performance has not been established. The purpose of this investigation was to quantify the relative importance of VO2 max, training volume (miles/week) and intensity for running performance at distances ranging from 1.0 to 26.2 miles. Seventy‐eight well‐trained runners of widely varying ability were studied during uphill treadmill running to determine VO2 max. They provided training records to determine training volume and intensity, and participated in races of 1.0 (n = 31), 2.0 (n = 55), 3.0 (n = 28), 6.0 (n= 17), 10.0 (n = 20) and 26.2 (n = 25) miles. The relationship of VO2 max and training volume and intensity to performance was determined using multiple regression. Performance (running time) was highly correlated with VO2 max (r= ‐0.91, ‐0.92, ‐0.94, ‐0.96, ‐0.95 and ‐0.96 for 1.0, 2.0, 3.0, 6.0, 10.0 and 26.2 miles, respectively). The addition of training measures improved the multiple correlations in some (1.0, 2.0, 3.0 and 6.0 miles) but not all (10.0 and 26.2 miles) events. However, even when addition of one or both training indices improved the multiple correlation, the net reduction in the standard error of estimate was small. The results imply that the volume and intensity of training, per se, are relatively minor determinants of cross‐sectional differences in competitive running performance.
... The variables included in such prediction equations might be roughly classified into three distinct groups: anthropometry, physiology and training (Foster, 1983;Tanda, 2011). Body mass index (BMI; Doherty et al., 2020), body fat percentage (BF; Salinero et al., 2017), skinfold thickness (Arrese and Ostáriz, 2006), and somatotype (Bale et al., 1985) have been used among anthropometric-related variables, where high scores in BMI or fat indices were associated with a slower race speed. ...
... Therefore, the aim of the present study was to (a) profile anthropometric, physiological, and training characteristics of men recreational marathon runners by performance level and (b) develop and validate a prediction equation of race speed in the "Athens Authentic Marathon." It was hypothesized that runners with a faster running speed would present better scores in the characteristics associated with performance (e.g., VO 2 max, body composition, and training) than their slower counterparts (Foster, 1983;Tanda, 2011). It was also assumed that these characteristics could be used to predict race speed in the particular marathon race. ...
... The best predictor of race speed was VO 2 max, explaining a large portion of variance (∼40%). This finding was in agreement with previous research including VO 2 max in prediction equations of race speed or time (Foster, 1983). Moreover, it was in line with a comparative study of recreational marathon runners of different performance levels showing that the faster runners had higher VO 2 max than the slower ones (Gordon et al., 2017). ...
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Aim: The aim of the present study was to examine (a) the role of training and physiological characteristics on the performance of recreational marathon runners, and (b) to develop a prediction equation of men’s race time in the 'Athens Authentic Marathon'. Methods: Recreational male marathon runners (n=130, age 44.1±8.6 years) - who finished the 'Athens Authentic Marathon' 2017 - performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO2max), anaerobic power, squat and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, n=65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, n=65). Results: In the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF and VO2max (p≤0.003, η2≥0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (r=-0.36, p<0.001), and largely with the number of weekly training days (r=0.52, p<0.001) and weekly running distance (r=0.58, p<0.001), but not with the number of previously finished marathons (r=0.08, p=0.369). With regards to physiological characteristics, running speed correlated largely with body mass (r=-0.52, p<0.001), BMI (r=-0.60, p<0.001), BF (r=-0.65, p<0.001), VO2max (r=0.67, p<0.001), moderately with isometric muscle strength (r=0.42, p<0.001), and small with anaerobic muscle power (r=0.20, p=0.021). In EXP, race speed could be predicted (R2=0.61, standard error of the estimate=1.19) using the formula ‘8.804+0.111×VO2max +0.029×weekly training distance in km-0.218×BMI’. Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12±1.09 km/h, 95% confidence intervals -0.15, 0.40, p=0.122). Conclusion: These findings highlighted the role of aerobic capacity, training and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.
... Analysis: Table 2 presents 12 studies from 1983 to 2015 [12,13,[22][23][24][25][26][28][29][30][31][32]. The most notable are the physiological variables such as VO2max [12,23,25,32] and vVO2max, [13,22,28,31] and RE measurements [12,29,30,33]. ...
... Analysis: Table 2 presents 12 studies from 1983 to 2015 [12,13,[22][23][24][25][26][28][29][30][31][32]. The most notable are the physiological variables such as VO2max [12,23,25,32] and vVO2max, [13,22,28,31] and RE measurements [12,29,30,33]. Table 2. Multiple regression models associated with performance in 5000 m races. ...
... Analysis: Table 3 presents 13 studies from 1983 to 2014 [13,23,[26][27][28][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. Physiological variables such as VO2max [23,[32][33][34]38] and vVO2max continue to be prominent [27,28,33]. Of the 13 studies, seven have a prediction equation [7,23,26,28,34,37,44]. ...
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Physiological variables such as maximal oxygen uptake (VO2max), velocity at maximal oxygen uptake (vVO2max), running economy (RE) and changes in lactate levels are considered the main factors determining performance in long-distance races. The aim of this review was to present the mathematical models available in the literature to estimate performance in the 5000 m, 10,000 m, half-marathon and marathon events. Eighty-eight articles were identified, selections were made based on the inclusion criteria and the full text of the articles were obtained. The articles were reviewed and categorized according to demographic, anthropometric, exercise physiology and field test variables were also included by athletic specialty. A total of 58 studies were included, from 1983 to the present, distributed in the following categories: 12 in the 5000 m, 13 in the 10,000 m, 12 in the half-marathon and 21 in the marathon. A total of 136 independent variables associated with performance in long-distance races were considered, 43.4% of which pertained to variables derived from the evaluation of aerobic metabolism, 26.5% to variables associated with training load and 20.6% to anthropometric variables, body composition and somatotype components. The most closely associated variables in the prediction models for the half and full marathon specialties were the variables obtained from the laboratory tests (VO2max, vVO2max), training variables (training pace, training load) and anthropometric variables (fat mass, skinfolds). A large gap exists in predicting time in long-distance races, based on field tests. Physiological effort assessments are almost exclusive to shorter specialties (5000 m and 10,000 m). The predictor variables of the half-marathon are mainly anthropometric, but with moderate coefficients of determination. The variables of note in the marathon category are fundamentally those associated with training and those derived from physiological evaluation and anthropometric parameters.
... Some of the more notable measures that have been examined are VO 2max , time-toexhaustion (TTE) tests, and time-trial (TT) tests. VO 2max is highly correlated with race performance [17] but has limitations because it does not account for additional physiological differences [18,19]. The reliability of TTE tests has been shown to be lower than TT tests [20]. ...
... In addition to TT performance, the studies all measured change in VO 2max and once again there was no response difference between men and women. The similarity in responses between changes in TT performance and VO 2max is an important finding because it demonstrates consistency between two outcome measures that have been shown to be highly correlated [17]. ...
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Background Interval training has become an essential component of endurance training programs because it can facilitate a substantial improvement in endurance sport performance. Two forms of interval training that are commonly used to improve endurance sport performance are high-intensity interval training (HIIT) and sprint interval training (SIT). Despite extensive research, there is no consensus concerning the optimal method to manipulate the interval training programming variables to maximize endurance performance for differing individuals.Objective The objective of this manuscript was to perform a systematic review and meta-analysis of interval training studies to determine the influence that individual characteristics and training variables have on time-trial (TT) performance.Data SourcesSPORTDiscus and Medline with Full Text were explored to conduct a systematic literature search.Study SelectionThe following criteria were used to select studies appropriate for the review: 1. the studies were prospective in nature; 2. included individuals between the ages of 18 and 65 years; 3. included an interval training (HIIT or SIT) program at least 2 weeks in duration; 4. included a TT test that required participants to complete a set distance; 5. and programmed HIIT by power or velocity.ResultsTwenty-nine studies met the inclusion criteria for the quantitative analysis with a total of 67 separate groups. The participants included males (n = 400) and females (n = 91) with a mean group age of 25 (range 19–45) years and mean $$V{\text{O}}_{{2{\text{max}}}}$$ of 52 (range 32–70) mL·kg−1·min−1. The training status of the participants comprised of inactive (n = 75), active (n = 146) and trained (n = 258) individuals. Training status played a significant role in improvements in TT performance with trained individuals only seeing improvements of approximately 2% whereas individuals of lower training status demonstrated improvements as high as 6%. The change in TT performance with HIIT depended on the duration but not the intensity of the interval work-bout. There was a dose–response relationship with the number of HIIT sessions, training weeks and total work with changes in TT performance. However, the dose–response was not present with SIT.Conclusion Optimization of interval training programs to produce TT performance improvements should be done according to training status. Our analysis suggests that increasing interval training dose beyond minimal requirements may not augment the training response. In addition, optimal dosing differs between high intensity and sprint interval programs.
... Maximal aerobic power (VO 2max ) is an established determinant of endurance performance (Blagrove et al., 2018;Foster, 1983;JDR Bassett & Howley, 1997;Joyner, 1991;Pollock et al., 1980). Training methods to improve VO 2max are characterized in two modes: continuous training (CT) and interval training (IT) methods (Laursen & Jenkins, 2002; KS Seiler & Kjerland, 2006). ...
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Success in endurance running is primarily determined by maximal aerobic power (VO2max), fractional utilization, and running economy (RE). Within the literature, two training modalities have been identified to improve VO2max; continuous training (CT) and interval-training (IT). The efficacy of IT to improve VO2max in well-trained runners remains equivocal, as does whether a dose-response relationship exists between the IT training load performed and changes in VO2max. A keyword search was performed in five electronic databases. Seven studies met the inclusion criteria for this systematic review. The training impulse (TRIMP) was calculated to analyse relationships between training load and changes in VO2max, by calculating the time accumulated in certain intensity domains throughout a training intervention. Non-significant (P>0.05) improvements in VO2max were reported in six studies, with only one study reporting a significant (P<0.05) improvement following the IT interventions. A relationship between the training session impulse of the interval-training performed (IT STRIMP) and VO2max improvements were observed. The efficacy of IT to improve VO2max in well-trained runners remains equivocal, nevertheless, the novel method of training-load analysis demonstrates a relationship between the IT STRIMP and VO2max improvements. This provides practical application for the periodization of IT within the training regime of well-trained distance runners.
... The highest rate at which a person can take up and utilize oxygen, VO 2Max , is limited by cardiovascular characteristics and sets the upper limit of her or his performance in endurance sports (Costill, 1967;Foster, 1983;Bassett & Howley, 2000). After increasing during the first 2 months of training, VO 2Max reaches a plateau but the percent of VO 2Max an athlete can maintain during a run increases and is positively correlated with performance (Bassett & Howley, 2000). ...
Article
Objective Lower digit ratios between the lengths of fingers 2 (2D) and 4 (4D) (2D:4D) are associated with superior distance running and athletic performance. We examined relationships between 2D:4D, aerobic fitness, physical skills, and overall physical fitness of elite adolescent boy and girl distance runners. Methods Subjects were top five finishers for their sex and age in 10 or more races of 10 km or longer in Michigan in 1981. We calculated 2D:4D of 15 girls and 11 boys from radiographs. Subject peak O2 consumption (VO2Peak), ventilatory threshold (VT), and point of equivalent change (PEC) were collected during intermittent treadmill protocol tests. Performances on physical skills tests (flex‐arm hang, broad jump, vertical jump, figure‐8‐run, sit ups, and sit‐and‐reach test) were collected in the laboratory. We examined the interrelationships between 2D:4D, subject sex, aerobic fitness, physical skills test performance, and overall physical fitness, a composite of aerobic and physical skills performance with correlation, linear regression, t tests, and principle component analyses. Results Girls had significantly larger right hand (R) 2D:4D than boys. Boys had greater VO2Peak by mass than girls. Boys with lower R2D:4D had significantly greater VO2Peak and PEC. Girls with lower R2D:4D had significantly greater VT. Factors associated with aerobic fitness explained most of the variation in composite physical fitness scores. Composite aerobic fitness, physical skills, and overall physical fitness scores of boys were negatively correlated with R2D:4D. Conclusions These data suggest that R2D:4D may help predict distance running performance in girls and boys and overall physical fitness in boys and provide additional insights into the innate factors influencing youth physical fitness.
... Τις τελευταίες δεκαετίες παρατηρείται έντονο ερευνητικό ενδιαφέρον από τη διεθνή επιστημονική κοινότητα για την επίδραση που έχουν οι διαφορετικές μορφές προπόνησης δύναμης στη βελτίωση της δρομικής οικονομίας σε αθλητές αντοχής (8,38). Η απόδοση των αθλητών αντοχής καθορίζεται από πολλούς παράγοντες, οι σημαντικότεροι εκ των οποίων είναι η μέγιστη πρόσληψη οξυγόνου (VO2max) (16,12,29), το αναερόβιο κατώφλι (24,35,69) και η δρομική οικονομία (17,20,30). Ένας αθλητής που έχει υψηλή VO2max και υψηλό αναερόβιο κατώφλι έχει όλα τα στοιχεία για να επιτύχει υψηλού επιπέδου επιδόσεις, ωστόσο, σε πολύ καλά προπονημένους αθλητές, οι οποίοι εμφανίζουν παρόμοιες τιμές στη VO2max, ο καλύτερος παράγοντας πρόβλεψης της επίδοσης τους θεωρείται η δρομική οικονομία (17,21,34). ...
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Research has shown that, concurrent strength and endurance training has been considered an effective method to improve running economy (RE) and performance in endurance running athletes. Strength training improves the RE 2% -8%, making the runner consume less O2 for the same submaximal running velocity. This improvement was due to neural adaptations without observable muscle hypertrophy. However, no improvements were found in relative VO2max, when strength training was performed in conjunction with aerobic training. The purpose of this narrative review is to examine various strength training programs that have attempted to improve RE. More specifically, the effect of a) resistance training programs, aiming to improve Maximal Force, such as heavy weight training, isometric training and vibration training with heavy weight, and b) explosive training, aiming to improve Maximal Power, such as low-middle intensity resistors with explosive repetitions, plyometric training or a combination of the two above, was investigated.Complex training was also investigated. The results showed that heavy weight training and explosive training are effective concurrent training methods aiming to improve RE. In particular, improved lower-limb coordination, muscle coactivation and increased muscle stiffness, which enhances the ability of the muscles to store and utilize elastic energy more efficiently, result in reduced energy expenditure. Similarly, other neuromuscular adaptations such as vertical jump (5J) and contact time correlated with the speed in the anaerobic threshold which was associated with improved RE and running performance. The different magnitude of improvement of the RE for each specific type of strength training, probably, due to the different characteristics of the exercise protocols and trainees. Therefore, more research is needed to determine which style of strength training is more beneficial than any other. Furthermore, future studies should examine movement-specific forms of resistance training.
... In addition, most reviews that address aerobic performance use VO 2max as the primary outcome measure. Although VO 2max has been correlated with race performance [22], strong evidence suggest other variables may positively influence performance outcomes [23,24]. An alternative measure, timetrial (TT) performance, has demonstrated a high correlation with endurance performance, and may directly simulate the physiological responses required during competition [25,26]. ...
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Background: Two forms of interval training commonly discussed in the literature are high-intensity interval training (HIIT) and sprint interval training (SIT). HIIT consists of repeated bouts of exercise that occur at a power output or velocity between the second ventilatory threshold and maximal oxygen consumption (VO2max). SIT is performed at a power output or velocity above those associated with VO2max Objective: The primary objective of this study is to systematically review published randomized and pair- matched trials to determine which mode of interval training, HIIT versus SIT, leads to a greater improvement in TT performance in active and trained individuals. The second objective of this review is to perform a subgroup analysis to determine if there is a distinction between HIIT programs that differ in work-bout duration. Data Sources: SPORTDiscus (1800 - present) and Medline with Full Text (1946 - present) were used to conduct a systematic literature search Study Selection: Studies were selected for the review if they met the following criteria: 1. individuals (males and females) who were considered at least moderately-trained (~3-hours per week of activity) as specified by the authors of the included studies; 2. between the ages of 18 and 45 years; 3. randomized or pair-matched trials that included a HIIT and a SIT group; 4. provided detailed information about the interval training program; 5. were at least 2-weeks in duration; 6. included a TT test that required participants to complete a set distance. Results: A total of 6 articles met the inclusion criteria for the subjective and objective analysis. The pooled analysis was based on a random-effects model. There was no difference in the change in TT performance when comparing all HIIT versus SIT (0.9%; 90% CI: -0.1% to 1.9%, p = 0.18). However, subgroup analysis based on duration of work interval indicated a 2% greater improvement in TT performance following long-HIIT ( 4 min) when compared to SIT. There was no difference in change in VO2max/peak oxygen consumption (VO2peak) between groups. There was a moderate effect (ES = 0.70) in favour of HIIT over SIT in maximal aerobic power (MAP) or maximal aerobic velocity (MAV). Conclusion: The results of the meta-analysis indicate that long-HIIT may be the optimal form of interval training to augment TT performance. Additional research that directly compares HIIT exercise differing in work-bout duration would strengthen these results and provide further insight into the mechanisms behind the observed benefits of Long- HIIT.
... The physiological variables related to performance have been previously described (Ramsbottom et al., 1987). In the case of long-distance runners, those variables obtained in the incremental laboratory tests, essentially maximal oxygen consumption (VO 2 max) and its related variables, have been very useful for observing the adaptations produced by training (Legaz Arrese et al., 2006), and to predict performance in competition (Foster, 1983). Additional laboratory tests and associated variables have been proposed that may be determinants of long-duration aerobic performance. ...
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This study compared the ability to predict performance in half-marathon races through physiological variables obtained in a laboratory test and performance variables obtained in the Cooper field test. Twenty-three participants (age: 41.6 ± 7.6 years, weight: 70.4 ± 8.1 kg, and height: 172.5 ± 6.3 cm) underwent body composition assessment and performed a maximum incremental graded exercise laboratory test to evaluate maximum aerobic power and associated cardiorespiratory and metabolic variables. Cooper’s original protocol was performed on an athletic track and the variables recorded were covered distance, rating of perceived exertion, and maximum heart rate. The week following the Cooper test, all participants completed a half-marathon race at the maximum possible speed. The associations between the laboratory and field tests and the final time of the test were used to select the predictive variables included in a stepwise multiple regression analysis, which used the race time in the half marathon as the dependent variable and the laboratory variables or field tests as independent variables. Subsequently, a concordance analysis was carried out between the estimated and actual times through the Bland-Altman procedure. Significant correlations were found between the time in the half marathon and the distance in the Cooper test (r = −0.93; p < 0.001), body weight (r = 0.40; p < 0.04), velocity at ventilatory threshold 1, (r = −0.72; p < 0.0001), speed reached at maximum oxygen consumption (vVO2max), (r = −0.84; p < 0.0001), oxygen consumption at ventilatory threshold 2 (VO2VT2) (r = −0.79; p < 0.0001), and VO2max (r = −0.64; p < 0.05). The distance covered in the Cooper test was the best predictor of time in the half-marathon, and might predicted by the equation: Race time (min) = 201.26 – 0.03433 (Cooper test in m) (R2 = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R2 = 0.77, SEE 5.28 min. predicted by equation: Race time (min) = 156.7177 – 4.7194 (vVO2max) – 0.3435 (Weight). Concordance analysis showed no differences between the times predicted in the models the and actual times. The data indicated a high predictive power of half marathon race time both from the distance in the Cooper test and vVO2max in the laboratory. However, the variable associated with the Cooper test had better predictive ability than the treadmill test variables. Finally, it is important to note that these data may only be extrapolated to recreational male runners.
... Peak aerobic power (V Ȯ2peak) is a notable predictor of performance in long-distance running events (Basset & Howley, 2000). Differences in V Ȯ2peak can account for interindividual differences in race times from one mile to the marathon (Foster, 1983). Among master runners, defined as runners aged 40 years and older, V Ȯ2peak is an important predictor of distance running performance (Wiswell et al., 2000). ...
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The aim of this study was to analyse the performance development and training structure of three Norwegian brothers, HI, FI and JI who are all European 1500 m champions, and to examine to what extent training, environment and family support has been decisive in their development. Their performance development and training from the age of 13 was examined through analysis of the Norwegian Athletic Federation (Norges Friidrettsforbund) all-time best results for boys in 800 m and 1500 m; analysis of training diaries; observation of training sessions; and dialogue with the three runners, their father and coach and their mother. All three were very physically active from a young age, and they have taken part in different sports. In the preparation period leading up to the 2018 and 2019 seasons, these three athletes ran an average of 140–160 km·week –1 , with 23–25% at and above anaerobic threshold pace. Training intensity was monitored and controlled via blood lactate measurements taken during all interval sessions. Throughout childhood and adolescence, the boys were highly motivated and strongly encouraged to take part in sport and training by their close family. All the three were coached by their father. An active childhood, a gradual progression in training volume, strong family support, mental toughness, a high volume of training at and above anaerobic threshold, and mindful monitoring and regulation of training intensity have brought these brothers to a top international level in distance running.
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Muscle biopsy samples were obtained from the gastrocnemius of 26 well-trained runners of widely varying ability. Portions of the sample were analyzed for succinate dehydrogenase (SDH) activity and for muscle fiber composition. $\dot V$ O2 max was determined during uphill treadmill running. Mean values for muscle SDH activity (14.6 U/g), fiber composition (55% slow twitch) and $\dot V$ O2 max (60.9 ml/kg×min−1) were lower than reported previously for groups of elite and sub-elite runners. The physiological data were consistent with the performance ability of the sample [5∶12, 11∶20 and 36∶40 (min∶s) for 1, 2 and 6 miles, respectively]. Within the sample, performance was most strongly related to $\dot V$ O2 max (r=−0.84, −0.87 and −0.88 for 1, 2, and 6 miles). There was little relationship between muscle SDH activity and either performance (r=−0.11, −0.14, −0.20 for 1, 2, and 6 miles) or $\dot V$ O2 max (r=0.23). The relationship between muscle fiber composition and performance was only modestly strong (r=−0.52, −0.54, −0.55 for 1, 2, and 6 miles). The results indicate that the primary determinant of cross-sectional differences in running performance is $\dot V$ O2 max. Skeletal muscle metabolism apparently contributes little to these cross-sectional differences and may be of much greater importance to variations in performance within an individual.
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This study was undertaken to determine the response of $\dot V$ O2 max and of running performance (805 and 3218 m) to the onset of training in untrained individuals and to an increase in the volume and intensity of training in well trained individuals. In series A, $\dot V$ O2 max and performances of 12 previously untrained individuals were determined before and after 4 and 8 weeks of training. In series B, performances, $\dot V$ O2 max and $\dot V$ O2 submax of 15 previously well trained runners were determined before and after 4 and 8 weeks of controlled training. In series A, $\dot V$ O2 max increased during the first 4 weeks of training but failed to increase further even in the presence of an increased training load (80 total km for the first 4 weeks, 130 total km for the second 4 weeks). Running performances improved throughout the training period. In series B, neither $\dot V$ O2 max nor $\dot V$ O2 submax changed but running performance improved throughout the experimental period. The results indicated that not all of the improvement in running performance subsequent to training is attributable to changes in $\dot V$ O2 max. Further the results indicate that changes in running economy are not a likely explanation for performance improvement among previously well trained runners. It is suggested that physiological adaptations not integrated in the test of $\dot V$ O2 max, or improvement in pacing contribute to training induced improvements in running performance.
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Muscle biopsies were obtained from the gastrocnemius of 14 elite distance runners, 18 middle distance runners, and 19 untrained men. The middle distance runners were all highly trained, but had significantly slower performance times than the elite runners at distances greater than 3 miles. Fiber composition and mean cross sectional areas were determined from muscle sections incubated for histochemical activity. A portion of the specimen was used to determine succinate dehydrogenase (SDH), lactate dehydrogenase (LDH) and phosphorylase activities. All subjects were tested for maximal oxygen uptake on a treadmill. As previously demonstrated by others, the elite runners' muscles were characterized by a high percentage (79%) of slow twitch (ST) fibers. On the average, the cross sectional area of their ST fibers was found to be 22% larger than the FT fibers (P<0.05). SDH activity of whole muscle homogenates from elite and middle distance runners was 3.4 and 2.8 fold greater, respectively, than that measured in the untrained men. Since the LDH and phosphorylase activities were similar for the runners and untrained men, it appears that training for distance running has little influence on the enzymes of glycogenolysis.
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The author presents this book as a supplement to his Psychometric methods (see 10: 6010). This new text presupposes no previous knowledge of statistics and does not include the emphasis on measurement nor psychophysics which was contained in the previous one. There is also no description of factor analysis, for which the reader is referred to the earlier volume. Within the 14 chapters the author presents the newer work of Fisher and others, such as methods of statistical inference and analysis of variance. Two new chapters, on testing hypotheses, and on predictions and errors of prediction, have been added and include such concepts as the null hypothesis, chisquare distribution, and the use of attributes in statistical prediction. The statistical illustrations are taken from modern psychological research. Exercises are presented after each chapter. Six tables in an appendix and an author and subject index are given. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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The present study confirms earlier observations that the musculature of elite distance runners is characterized by a high predominance of ST fibers. Although the percent ST fibers effectively discriminates between good and elite distance runners, fiber composition alone is a poor predictor of distance running success within the group of elite runners. Muscle enzyme measurements suggest that the 11 to 20 miles (17.7 to 32.2 km) of daily training performed by the elite runners produced a significantly greater increase in muscle SDH activity than was observed in the good distance runners, who were running 7 to 11 miles (11.3 to 17.7 km) per day, Although such endurance training enhances the oxidative capacity of the muscle, it apparently has little influence on the enzymes of glycogenolysis.
The aerobic performance of thirteen male ultramarathon and nine female marathon runners were studied in the laboratory and their results were related to their times in events ranging in distance from 5 km to 84.64 km. The mean maximal aerobic power output (VO2 max) of the men was 72.5 ml/kg·min compared with 58.2 ml/kg·min (p<0.001) in the women but the O2 cost (VO2) for a given speed or distance of running was the same in both sexes. The 5 km time of the male athletes was closely related to their VO2 max (r=−0.85) during uphill running but was independent of relative power output (%VO2 max). However, with increasing distance the association of VO2 max with male athletic performance diminished (but nevertheless remained significant even at 84.64 km), and the relationship between VO2 max and time increased. Thus, using multiple regression analysis of the form: $$\begin{gathered} 42.2 km (marathon) time (h) = 7.445 - 0.0338 \dot V{\text{O}}_{{\text{2 max}}} ({\text{ml/kg }} \cdot {\text{ min}}) \hfill \\ - 0.0303\% \dot V{\text{O}}_{{\text{2 max}}} (r = 0.993) \hfill \\ \end{gathered}$$ and $$\begin{gathered} 84.64 {\text{km (London}} - {\text{Brighton) time (h) = 16}}{\text{.998 }} - {\text{ 0}}{\text{.0735 }}\dot V{\text{O}}_{{\text{2 max}}} \hfill \\ ({\text{ml/kg }} \cdot \min ) - 0.0844\% \dot V{\text{O}}_{{\text{2 max}}} (r = 0.996) \hfill \\ \end{gathered}$$ approximately 98% of the total variance of performance times could be accounted for in the marathon and ultramarathon events. This suggests that other factors such as footwear, clothing, and running technique (Costill, 1972) play a relatively minor role in this group of male distance runners. In the female athletes the intermediate times were not available and they did not compete beyond 42.2 km (marathon) distance but for this event a similar association though less in magnitude was found with VO2 max (r=−0.43) and %VO2 max (= −0.49). The male athletes were able to sustain 82% VO2 max (range 80–87%) in 42.2 km and 67% VO2 max (range 53–76%) in 84.64 km event. The comparable figure for the girls in the marathon was 79% VO2 max (ranges 68–86%). Our data suggests that success at the marathon and ultramarathon distances is crucially and (possibly) solely dependent on the development and utilisation of a large VO2 max.
Maximal oxygen uptake (max VO2) in leg and arm work, succinate dehydrogenase activity (SDH) and percentage of slow twitch fibers (%ST fibers) in M. vastus lateralis (VL), M. gastrocnemius c.l. (GL) and M. deltoideus (D) were studied in 89 athletes practising 11 different sport events. It was found that maximal oxygen uptake correlated positively with %ST fibers and SDH activity in M. VL. The SDH activity and %ST fibers in M. VL correlated also with one another. The results suggest that oxidative capacity of the muscles is not the limiting factor for maximal oxygen uptake. The role of the oxidative capacity of the muscles might be important during submaximal work of long duration and when a relatively small muscle mass is activated (long-distance running). MaxVO2 might be the most important determinant of performance when large muscle mass is activated during maximal work of a duration from several minutes up to 1 h (cross-country skiing).