Body mass and circumference of upper arm are associated with race performance in ultraendurance runners in a multistage race--the Isarrun 2006

A preliminary exploration of the regression equation for performance in amateur half-marathon runners: a perspective based on respiratory muscle function

This document presents a study on the relationship between physical characteristics, respiratory muscle capacity, and performance in amateur half-marathon runners. The aim of this study was to establish a preliminary predictive model to provide insights into training and health management for runners. Participants were recruited from the 2023 Beijing Olympic Forest Park Half-Marathon, comprising 233 individuals. Personal information including age, gender, height, weight, and other relevant factors were collected, and standardized testing methods were used to measure various parameters. Correlation analysis revealed significant associations between gender, height, weight, maximum expiratory pressure, maximal inspiratory pressure, and half-marathon performance. Several regression equations were developed to estimate the performance of amateur marathon runners, with a focus on gender, weight, maximum expiratory pressure, and height as predictive factors. The study found that respiratory muscle training can delay muscle fatigue and improve athletic performance. Evaluating the level of respiratory muscle capacity in marathon athletes is crucial for defining the potential speed limitations and achieving optimal performance. The information from this study can assist amateur runners in optimizing their training methods and maintaining their physical wellbeing.

The Relationship Between Anthropometric Variables and Race Performance

INTRODUCTION

The key elements of success in a given sports competition have become an area of interest for researchers. The reason for the success of Ethiopian runners was not proved scientifically. This study aimed at documenting the anthropometric parameters of 10,000 meter runners and to find out the association between such parameters and performances.

METHODS

A descriptive field study was conducted. 32 elite 10,000 meter runners participated. The data were collected while the athletics team was preparing for the world athletics championship. The procedure was repeated three times for each individual. Statistical analysis was performed using SPSS version 18. All the data were presented as mean ± S.D. The Pearson product-moment test was used to determine the correlation between the variables and finishing time. The level of significance for all statistical tests was set at p < 0.05.

RESULTS

The experience of male and female athletes showed a negative association with finishing time. However, there was no statistically significant correlation between the age and running time in both sexes. A significant positive association of body weight to running time was observed in both sexes. Body height correlates positively to running time in males (p<0.05), but not in females. The length of the arm, the forearm, the leg in both sexes and length of the thigh in women had no significant association with finishing time. A smaller arm and calf circumferences have a positive effect on the performance of both sexes. Smaller thigh circumference showed a positive association with the performance of men.

CONCLUSION

The age of the runners did not correlate with their performance. The anthropometric variables displayed significantly higher values in men than in women. Experienced athletes performed better in both sexes. Anthropometric parameters may be useful for selection, prediction, improving running performance besides for preventing injuries and health risk assessment.

Similarities and differences among half-marathon runners according to their performance level

This study aimed to identify the similarities and differences among half-marathon runners in relation to their performance level. Forty-eight male runners were classified into 4 groups according to their performance level in a half-marathon (min): Group 1 (n = 11, < 70 min), Group 2 (n = 13, < 80 min), Group 3 (n = 13, < 90 min), Group 4 (n = 11, < 105 min). In two separate sessions, training-related, anthropometric, physiological, foot strike pattern and spatio-temporal variables were recorded. Significant differences (p<0.05) between groups (ES = 0.55–3.16) and correlations with performance were obtained (r = 0.34–0.92) in training-related (experience and running distance per week), anthropometric (mass, body mass index and sum of 6 skinfolds), physiological (VO_2max, RCT and running economy), foot strike pattern and spatio-temporal variables (contact time, step rate and length). At standardized submaximal speeds (11, 13 and 15 km·h^-1), no significant differences between groups were observed in step rate and length, neither in contact time when foot strike pattern was taken into account. In conclusion, apart from training-related, anthropometric and physiological variables, foot strike pattern and step length were the only biomechanical variables sensitive to half-marathon performance, which are essential to achieve high running speeds. However, when foot strike pattern and running speeds were controlled (submaximal test), the spatio-temporal variables were similar. This indicates that foot strike pattern and running speed are responsible for spatio-temporal differences among runners of different performance level.

Use of Bioimpedianciometer as Predictor of Mountain Marathon Performance

This study aimed to examine the relation among body composition, training experience and race time during a mountain marathon. Body composition and training pre-race experience analyses were conducted previous to a mountain marathon in 52 male athletes. A significant correlation between race time and mountain marathon with chronological age, body fat mass, percentage of body fat (BF), level of abdominal obesity, sport experience and daily training volume was revealed. In addition, BF and athlete's chronological age were negatively associated with race performance. In contrast, the daily training volume was positively associated with mountain marathon time. A regression analysis showed that race time could be predicted (R² = .948) by the daily training load, sports experience, age, body fat mass, BF and level of abdominal obesity. The comparison between performance groups regarding to body composition and training characteristics showed that the higher performance group was lighter with lower BF, fat mass and level of abdominal obesity, and with more days of training per week compared with the lower performance group (p < .05). Therefore, coaches and fitness trainers working with mountain marathon runners should develop exercise and nutritional strategies to reduce BF and consider increasing mean daily training volume to improve performance.

Is there a correlation between body proportion and choice of profession?

OBJECTIVE

We made anthropometric measurements to compare body proportions of university students and to investigate the tendency of the students with more developed bodies in choosing profession for university education.

METHODS

A total of 204 male students [group 1: students of School of Physical Education and Sports" (SPESp) (n=88), group 2: students of Faculty of Fine Arts (FFArts) (n=59), and group 3: students of Faculty of Medicine (FMed) (n=57)] were included, and anthropometric measurements were performed.

RESULTS

"Height"; "chest circumference"; "difference of inspiration and expiration of chest circumference" values of the SPESp students were higher than the others (FFArts and FMed). In the SPESp students, chest circumference and chest expansion capacity by inspiration developed more. This shows the positive impact of sport on respiratory functions. Additionally, the legs are longer in students engaged in regular sports.

CONCLUSION

Exercise is important for all subjects in terms of body development, growth, and oxygenization and to decrease cardiovascular risk factors. For the development of the ideal body anthropometric rates, obesity prevention, and for the growth of healthy generations, governments should give importance to sports and take incentive measures to increase children and young people's interest in the sport.

What performance characteristics determine elite versus nonelite athletes in the same sport?

Context:

There are significant data comparing elite and nonelite athletes in anaerobic field and court sports as well as endurance sports. This review delineates specific performance characteristics in the elite athlete and may help guide rehabilitation.

Evidence Acquisition:

A Medline search from April 1982 to April 2012 was undertaken for articles written in English. Additional references were accrued from reference lists of research articles.

Results:

In the anaerobic athlete, maximal power production was consistently correlated to elite performance. Elite performance in the endurance athlete is more ambiguous, however, and appears to be related to the dependent variable investigated in each individual study.

Conclusion:

In anaerobic field and court sport athletes, maximal power output is most predictive of elite performance. In the endurance athlete, however, it is not as clear. Elite endurance athletes consistently test higher than nonelite athletes in running economy, anaerobic threshold, and VO_2max.

A Comparison of Anthropometric and Training Characteristics between Female and Male Half-Marathoners and the Relationship to Race Time

Purpose

Lower limb skin-fold thicknesses have been differentially associated with sex in elite runners. Front thigh and medial calf skin-fold appear to be related to 1,500m and 10,000m time in men but 400m time in women. The aim of the present study was to compare anthropometric and training characteristics in recreational female and male half-marathoners.

Methods

The association between both anthropometry and training characteristics and race time was investigated in 83 female and 147 male recreational half marathoners using bi- and multi-variate analyses.

Results

In men, body fat percentage (β=0.6), running speed during training (β=-3.7), and body mass index (β=1.9) were related to half-marathon race time after multi-variate analysis. After exclusion of body mass index, r² decreased from 0.51 to 0.49, but body fat percentage (β=0.8) and running speed during training (β=-4.1) remained predictive. In women, body fat percentage (β=0.75) and speed during training (β=-6.5) were related to race time (r²=0.73). For women, the exclusion of body mass index had no consequence on the predictive variables for half-marathon race time.

Conclusion

To summarize, in both female and male recreational half-marathoners, both body fat percentage and running speed during training sessions were related to half-marathon race times when corrected with co-variates after multi-variate regression analyses.

Master runners dominate 24-h ultramarathons worldwide-a retrospective data analysis from 1998 to 2011

BACKGROUND

The aims of the present study were to examine (a) participation and performance trends and (b) the age of peak running performance in master athletes competing in 24-h ultra-marathons held worldwide between 1998 and 2011.

METHODS

Changes in both running speed and the age of peak running speed in 24-h master ultra-marathoners (39,664 finishers, including 8,013 women and 31,651 men) were analyzed.

RESULTS

The number of 24-h ultra-marathoners increased for both women and men across years (P < 0.01). The age of the annual fastest woman decreased from 48 years in 1998 to 35 years in 2011. The age of peaking running speed remained unchanged across time at 42.5 ± 5.2 years for the annual fastest men (P > 0.05). The age of the annual top ten women decreased from 42.6 ± 5.9 years (1998) to 40.1 ± 7.0 years (2011) (P < 0.01). For the annual top ten men, the age of peak running speed remained unchanged at 42 ± 2 years (P > 0.05). Running speed remained unchanged over time at 11.4 ± 0.4 km h-1 for the annual fastest men and 10.0 ± 0.2 km/h for the annual fastest women, respectively (P > 0.05). For the annual ten fastest women, running speed increased over time by 3.2% from 9.3 ± 0.3 to 9.6 ± 0.3 km/h (P < 0.01). Running speed of the annual top ten men remained unchanged at 10.8 ± 0.3 km/h (P > 0.05). Women in age groups 25-29 (r2 = 0.61, P < 0.01), 30-34 (r2 = 0.48, P < 0.01), 35-39 (r2 = 0.42, P = 0.01), 40-44 (r2 = 0.46, P < 0.01), 55-59 (r2 = 0.41, P = 0.03), and 60-64 (r2 = 0.57, P < 0.01) improved running speed; while women in age groups 45-49 and 50-54 maintained running speed (P > 0.05). Men improved running speed in age groups 25-29 (r2 = 0.48, P = 0.02), 45-49 (r2 = 0.34, P = 0.03), 50-54 (r2 = 0.50, P < 0.01), 55-59 (r2 = 0.70, P < 0.01), and 60-64 (r2 = 0.44, P = 0.03); while runners in age groups 30-34, 35-39, and 40-44 maintained running speed (P > 0.05).

CONCLUSIONS

Female and male age group runners improved running speed. Runners aged >40 years achieved the fastest running speeds. By definition, runners aged >35 are master runners. The definition of master runners aged >35 years needs to be questioned for ultra-marathoners competing in 24-h ultra-marathons.

Increase in finishers and improvement of performance of masters runners in the Marathon des Sables

Aim

The aim of the study was to examine finisher and performance trends of ultrarunners in the Marathon des Sables, the world’s largest multistage ultramarathon.

Methods

The age and running speed was analyzed for 6945 finishes of 909 women and 6036 men between 2003 and 2012 at the Marathon des Sables covering about 240 km in the Moroccan desert.

Results

The number of finishes increased significantly for both women and men from 2003–2012. The annual number of finishes increased in age groups: 30–34 years (r² = 0.50; P = 0.021), 45–49 years (r² = 0.81; P = 0.0004), and 50–54 years (r² = 0.46; P = 0.029) for women and in all age groups older than 35 years for men (35–39 years: r² = 0.64, P = 0.0054; 40–44 years: r² = 0.67, P = 0.0036; 45–49 years: r² = 0.77, P = 0.0007; 50–54 years: r² = 0.72, P = 0.0018; 55–59 years: r² = 0.42, P = 0.041; and 60–64 years: r² = 0.67, P = 0.0038). The fastest running speed was achieved by runners in the age group of 35–39 years for both sexes. The mean age of overall finishers was 41.0 ± 9.1 years for women and 41.3 ± 9.5 years for men. For men, running speed improved for athletes in the age group of 35–39 years (r² = 0.44; P = 0.036) and of 40–44 years (r² = 0.51; P = 0.019), while it decreased for athletes in the age group of 30–34 years (r² = 0.66, P = 0.0039). For women, running speed remained stable during the study period for athletes in all age groups.

Conclusion

These data suggest that the number of finishers of masters runners older than 40 years increased for both sexes at the Marathon des Sables, as has been previously observed for single-stage ultramarathons. In contrast to women, men aged 35 to 44 years improved running speed during the study period. Future studies are needed to investigate the reasons for the growing numbers of masters athletes in endurance sports and their improvement in performance.

Estimation bias: body mass and body height in endurance athletes

Body Mass Index is associated with endurance performance in athletes. Reported and measured values of body mass and body height in 1,618 endurance athletes (1,358 men, 260 women) showed that men and women both underestimated their body mass and overestimated their body height, leading to an underestimation of Body Mass Index. There were age and sex differences in estimates of height and weight; for both women and men, underestimation of Body Mass Index amounted to 0.4 kg/m2. Master athletes tended to underestimate their body mass and overestimate their body height thus leading to significant differences between estimated and measured Body Mass Index. However, the magnitude of underestimation of BMI probably has a negligible influence on performance predictions. The differences between measured and estimated body mass, height, and BMI were within the range of normal daily variation, and for body height even within the precision of the measurement (0.5 cm).

Comparison of anthropometric and training characteristics between recreational male marathoners and 24-hour ultramarathoners

Background

Of the anthropometry and training variables used to predict race performance in a 24-hour ultrarun, the personal best marathon time is the strongest predictor in recreational male 24-hour ultramarathoners. This finding raises the question of whether similarities exist between male recreational 24-hour ultramarathoners and male recreational marathoners.

Methods

The association between age, anthropometric variables (ie, body mass, body height, body mass index, percent body fat, skeletal muscle mass, limb circumference, and skinfold thickness at the pectoral, mid axillary, triceps, subscapular, abdominal, suprailiac, front thigh, and medial calf sites), previous experience and training characteristics (ie, volume, speed, and personal best time), and race time for 79 male recreational 24-hour ultramarathoners and 126 male recreational marathoners was investigated using bivariate and multivariate analysis.

Results

The 24-hour ultramarathoners were older (P < 0.05), had a lower circumference at both the upper arm (P < 0.05) and thigh (P < 0.01), and a lower skinfold thickness at the pectoral, axillary, and suprailiac sites (P < 0.05) compared with the marathoners. During training, the 24-hour ultramarathoners were running for more hours per week (P < 0.001) and completed more kilometers (P < 0.001), but were running slower (P < 0.01) compared with the marathoners. In the 24-hour ultramarathoners, neither anthropometric nor training variables were associated with kilometers completed in the race (P > 0.05). In the marathoners, percent body fat (P < 0.001) and running speed during training (P < 0.0001) were related to marathon race times.

Conclusion

In summary, differences in anthropometric and training predictor variables do exist between male recreational 24-hour ultramarathoners and male recreational marathoners for race performance.

Is body fat a predictor of race time in female long-distance inline skaters?

PURPOSE

The aim of this study was to evaluate predictor variables of race time in female ultra-endurance inliners in the longest inline race in Europe.

METHODS

We investigated the association between anthropometric and training characteristics and race time for 16 female ultra-endurance inline skaters, at the longest inline marathon in Europe, the 'Inline One-eleven' over 111 km in Switzerland, using bi- and multivariate analysis.

RESULTS

The mean (SD) race time was 289.7 (54.6) min. The bivariate analysis showed that body height (r=0.61), length of leg (r=0.61), number of weekly inline skating training sessions (r=-0.51) and duration of each training unit (r=0.61) were significantly correlated with race time. Stepwise multiple regressions revealed that body height, duration of each training unit, and age were the best variables to predict race time.

CONCLUSION

Race time in ultra-endurance inline races such as the 'Inline One-eleven' over 111 km might be predicted by the following equation (r(2)=0.65): Race time (min)=-691.62+521.71 (body height, m)+0.58 (duration of each training unit, min)+1.78 (age, yrs) for female ultra-endurance inline skaters.

Comparison between Recreational Male Ironman Triathletes and Marathon Runners

Recent investigations described a personal best marathon time as a predictor variable for an Ironman race time in recreational male Ironman triathletes. Similarities and differences in anthropometry and training were investigated between 83 recreational male Ironman triathletes and 81 recreational male marathoners. Ironman triathletes were significantly taller and had a higher body mass and a higher skin-fold thickness of the calf compared to the marathoners. Weekly training volume in hours was higher in Ironman triathletes. In the Ironman triathletes, percent body fat was related to overall race time and both the split time in cycling and running. The weekly swim kilometres were related to the split time in swimming, and the speed in cycling was related to the bike split time. For the marathoners, the calf skin-fold thickness and running speed during training were related to marathon race time. Although personal best marathon time was a predictor of Ironman race time in male triathletes, anthropometric and training characteristics of male marathoners were different from those of male Ironman triathletes, probably due to training of different muscle groups and metabolic endurance beyond marathon running, as the triathletes are also training for high-level performance in swimming and cycling. Future studies should compare Olympic distance triathletes and road cyclists with Ironman triathletes.

The TransEurope FootRace Project: longitudinal data acquisition in a cluster randomized mobile MRI observational cohort study on 44 endurance runners at a 64-stage 4,486 km transcontinental ultramarathon

BACKGROUND

The TransEurope FootRace 2009 (TEFR09) was one of the longest transcontinental ultramarathons with an extreme endurance physical load of running nearly 4,500 km in 64 days. The aim of this study was to assess the wide spectrum of adaptive responses in humans regarding the different tissues, organs and functional systems being exposed to such chronic physical endurance load with limited time for regeneration and resulting negative energy balance. A detailed description of the TEFR project and its implemented measuring methods in relation to the hypotheses are presented.

METHODS

The most important research tool was a 1.5 Tesla magnetic resonance imaging (MRI) scanner mounted on a mobile unit following the ultra runners from stage to stage each day. Forty-four study volunteers (67% of the participants) were cluster randomized into two groups for MRI measurements (22 subjects each) according to the project protocol with its different research modules: musculoskeletal system, brain and pain perception, cardiovascular system, body composition, and oxidative stress and inflammation. Complementary to the diverse daily mobile MR-measurements on different topics (muscle and joint MRI, T2*-mapping of cartilage, MR-spectroscopy of muscles, functional MRI of the brain, cardiac and vascular cine MRI, whole body MRI) other methods were also used: ice-water pain test, psychometric questionnaires, bioelectrical impedance analysis (BIA), skinfold thickness and limb circumference measurements, daily urine samples, periodic blood samples and electrocardiograms (ECG).

RESULTS

Thirty volunteers (68%) reached the finish line at North Cape. The mean total race speed was 8.35 km/hour. Finishers invested 552 hours in total. The completion rate for planned MRI investigations was more than 95%: 741 MR-examinations with 2,637 MRI sequences (more than 200,000 picture data), 5,720 urine samples, 244 blood samples, 205 ECG, 1,018 BIA, 539 anthropological measurements and 150 psychological questionnaires.

CONCLUSIONS

This study demonstrates the feasibility of conducting a trial based centrally on mobile MR-measurements which were performed during ten weeks while crossing an entire continent. This article is the reference for contemporary result reports on the different scientific topics of the TEFR project, which may reveal additional new knowledge on the physiological and pathological processes of the functional systems on the organ, cellular and sub-cellular level at the limits of stress and strain of the human body. Please see related articles: http://www.biomedcentral.com/1741-7015/10/76 and http://www.biomedcentral.com/1741-7015/10/77.

Similarities and differences in anthropometry and training between recreational male 100-km ultra-marathoners and marathoners

Several recent investigations showed that the best marathon time of an individual athlete is also a strong predictor variable for the race time in a 100-km ultra-marathon. We investigated similarities and differences in anthropometry and training characteristics between 166 100-km ultra-marathoners and 126 marathoners in recreational male athletes. The association of anthropometric variables and training characteristics with race time was assessed by using bi- and multi-variate analysis. Regarding anthropometry, the marathoners had a significantly lower calf circumference (P < 0.05) and a significantly thicker skinfold at pectoral (P < 0.01), axilla (P < 0.05), and suprailiacal sites (P < 0.05) compared to the ultra-marathoners. Considering training characteristics, the marathoners completed significantly fewer hours (P < 0.001) and significantly fewer kilometres (P < 0.001) during the week, but they were running significantly faster during training (P < 0.001). The multi-variate analysis showed that age (P < 0.0001), body mass (P = 0.011), and percent body fat (P = 0.019) were positively and weekly running kilometres (P < 0.0001) were negatively related to 100-km race times in the ultra-marathoners. In the marathoners, percent body fat (P = 0.002) was positively and speed in running training (P < 0.0001) was negatively associated with marathon race times. In conclusion, these data suggest that performance in both marathoners and 100-km ultra-marathoners is inversely related to body fat. Moreover, marathoners rely more on speed in running during training whereas ultra-marathoners rely on volume in running training.

A comparison of ultra-endurance cyclists in a qualifying ultra-cycling race for Paris-Brest-Paris and Race Across America-Swiss cycling marathon

Ultra-endurance events test the adaptation of human physiology to extreme physical and mental demands, high levels of training, motivation, and physical conditioning among participants. To understand basic differences among participants according to the severity of the race, participants in qualifying events for two ultra-endurance cycling races, differing in length and intensity, were compared on measures of anthropometry, training, and support. One race was four times longer, required supporting teams, and racers typically had little sleep, which should lead to the qualifiers being substantially more highly trained than those from the shorter race. The qualifiers in the longer race had greater intensity in training while the qualifiers in the shorter race relied more on training volume. Different strategies and types of training reflected the different demands of the races. Future studies should evaluate personality and motivational differences in ultra-endurance events and between these athletes and athletes in other sports.

Finishers and nonfinishers in the 'Swiss Cycling Marathon ' to qualify for the 'Race Across America '

Knechtle, B, Knechtle, P, Rüst, CA, Rosemann, T, and Lepers, R. Finishers and nonfinishers in the 'Swiss Cycling Marathon' to qualify for the 'Race across America.' J Strength Cond Res 25(12): 3257-3263, 2011-We compared the characteristics of prerace anthropometry, previous experience, and training and support during the race in 39 finishers and 37 nonfinishers in the 'Swiss Cycling Marathon,' over 720 km. In this race, the cyclists intended to qualify for the 'Race across America,' the longest nonstop cycling race in the World from the West to the East of the USA. Finishers in the 'Swiss Cycling Marathon' had a lower body mass, a lower body mass index, lower circumferences of upper arm and thigh, a lower percent body fat, completed more weekly training units, covered more kilometers in the longest training ride, rode at a faster speed during training, rode more kilometers per week and for more hours, had more previous finishes in the 'Swiss Cycling Marathon' and a lighter race bike compared to the nonfinishers. In the bivariate analysis, the cycling distance per training unit (r = 0.37), the duration per training unit (r = 0.44), the speed per training unit (r = -0.59), using nutrition provided by the organizer (r = 0.50), and using own nutrition (r = 0.49) during the race were significantly and positively associated with race time. For practical applications, anthropometric characteristics such as a low body mass or low body fat were not related to race time, whereas training characteristics and nutrition during the race were associated with race time. The key to a successful finish in an ultraendurance cycling race such as the 'Swiss Cycling Marathon' seems a high speed in training and an appropriate nutrition during the race.

Age, Training, and Previous Experience Predict Race Performance in Long-Distance Inline Skaters, Not Anthropometry

The association of characteristics of anthropometry, training, and previous experience with race time in 84 recreational, long-distance, inline skaters at the longest inline marathon in Europe (111 km), the Inline One-eleven in Switzerland, was investigated to identify predictor variables for performance. Age, duration per training unit, and personal best time were the only three variables related to race time in a multiple regression, while none of the 16 anthropometric variables were related. Anthropometric characteristics seem to be of no importance for a fast race time in a long-distance inline skating race in contrast to training volume and previous experience, when controlled with covariates. Improving performance in a long-distance inline skating race might be related to a high training volume and previous race experience. Also, doing such a race requires a parallel psychological effort, mental stamina, focus, and persistence. This may be reflected in the preparation and training for the event. Future studies should investigate what motivates these athletes to train and compete.

Personal best marathon time and longest training run, not anthropometry, predict performance in recreational 24-hour ultrarunners

In recent studies, a relationship between both low body fat and low thicknesses of selected skinfolds has been demonstrated for running performance of distances from 100 m to the marathon but not in ultramarathon. We investigated the association of anthropometric and training characteristics with race performance in 63 male recreational ultrarunners in a 24-hour run using bi and multivariate analysis. The athletes achieved an average distance of 146.1 (43.1) km. In the bivariate analysis, body mass (r = -0.25), the sum of 9 skinfolds (r = -0.32), the sum of upper body skinfolds (r = -0.34), body fat percentage (r = -0.32), weekly kilometers ran (r = 0.31), longest training session before the 24-hour run (r = 0.56), and personal best marathon time (r = -0.58) were related to race performance. Stepwise multiple regression showed that both the longest training session before the 24-hour run (p = 0.0013) and the personal best marathon time (p = 0.0015) had the best correlation with race performance. Performance in these 24-hour runners may be predicted (r2 = 0.46) by the following equation: Performance in a 24-hour run, km) = 234.7 + 0.481 (longest training session before the 24-hour run, km) - 0.594 (personal best marathon time, minutes). For practical applications, training variables such as volume and intensity were associated with performance but not anthropometric variables. To achieve maximum kilometers in a 24-hour run, recreational ultrarunners should have a personal best marathon time of ∼3 hours 20 minutes and complete a long training run of ∼60 km before the race, whereas anthropometric characteristics such as low body fat or low skinfold thicknesses showed no association with performance.

Effect of heavy strength training on muscle thickness, strength, jump performance, and endurance performance in well-trained Nordic Combined athletes

The purpose of the present study was to investigate the effect of supplemental heavy strength training on muscle thickness and determinants of performance in well-trained Nordic Combined athletes. Seventeen well-trained Nordic Combined athletes were assigned to either usual training supplemented with heavy strength training (STR; n = 8) or to usual training without heavy strength training (CON; n = 9). The strength training performed by STR consisted of one lower-body exercise and two upper-body exercises [3-5 repetition maximum (RM) sets of 3-8 repetitions], which were performed twice a week for 12 weeks. Architectural changes in m. vastus lateralis, 1RM in squat and seated pull-down, squat jump (SJ) height, maximal oxygen consumption (VO(2max)), work economy during submaximal treadmill skate rollerskiing, and performance in a 7.5-km rollerski time trial were measured before and after the intervention. STR increased 1RM in squat and seated pull-down, muscle thickness, and SJ performance more than CON (p < 0.05). There was no difference between groups in change in work economy. The two groups showed no changes in total body mass, VO(2max), or time-trial performance. In conclusion, 12 weeks of supplemental strength training improved determinants of performance in Nordic Combined by improving the athletes' strength and vertical jump ability without increasing total body mass or compromising the development of VO(2max).

Personal best time and training volume, not anthropometry, is related to race performance in the 'Swiss Bike Masters' mountain bike ultramarathon

We investigated in 73 male ultraendurance mountain bikers, with (mean and SD) age 39.1 (8.6) years, weight 74.4 (8.3) kg, height 1.78 (0.07) m, and a body mass index of 23.3 (1.9) kg·m⁻², whether variables of anthropometry, training, or prerace experience were associated with race time using bi and multivariate analysis. Our investigation was conducted at the "Swiss Bike Masters," which covers a distance of 120 km and an altitude of 5,000 m. In the bivariate analysis, body mass index (r = 0.29), circumference of upper arm (r = 0.28), sum of upper body skinfolds (r = 0.38), sum of lower body skinfolds (r = 0.25), sum of 8 skinfolds (r = 0.36), percent body fat (r = 0.41), total cycling kilometers per year (r = -0.47), yearly volume in both mountain bike (r = -0.33) and road cycling (r = -0.52), number of training units per week (r = -0.48), distance per unit in road cycling (r = -0.33), average speed during training in road cycling (r = -0.33), and personal best time in the "Swiss Bike Masters"(r = 0.67) were related to race time. In the multiple linear regression analysis, personal best time in the "Swiss Bike Masters" (p = 0.000), total yearly cycling kilometers (p = 0.004), and yearly training kilometers in road cycling (p = 0.017) were related to race time. When the personal best time was the dependent variable in a separate regression model, total yearly cycling kilometers (p = 0.002) remained the single predictor variable. We concluded that finishing a particular mountain bike ultramarathon does not seem to require a special anthropometry but rather a specific skill and experience for this selective kind of race coupled with a high training volume. For practical use, we concluded that successful athletes in a mountain bike ultramarathon, in a special environment and using sophisticated equipment, need prerace experience coupled with high training volume, rather than any special anthropometry.

Predictor variables for a half marathon race time in recreational male runners

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r² = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m²) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Personal best time, not anthropometry or training volume, is associated with total race time in a triple iron triathlon

The purpose of this study was to investigate in 81 male recreational ultratriathletes (64 finishers and 17 nonfinishers) the relationship of anthropometry, prerace experience, and training with race outcome in a Triple Iron triathlon, using bi and multivariate analyses. In the bivariate analysis, the sum of 8 skinfolds (r = 0.38) and the sum of upper body skinfolds (r = 0.37) were positively related to total race time. None of the anthropometric variables was related to the swim or bike split. Circumference of upper arm (r = 0.42), percent body fat (r = 0.43), the sum of 8 skinfolds (r = 0.47), and the sum of upper body skinfolds (r = 0.45) were positively associated with the time in the run split. None of the training variables was related to total race time or split times. Personal best time in an Ironman triathlon (r = 0.59) and a Triple Iron triathlon (r = 0.82) were positively and highly significantly related to total race time. When all significant variables after bivariate analysis were included in a regression model, personal best time in a Triple Iron triathlon (p < 0.0001) remained the single predictor variable. For practical considerations, athletes with a background as an ultrarunner might have an advantage in successfully finishing a Triple Iron triathlon. However, ultrarunners should also have enough prerace experience in competing in Ironman and Triple Iron triathlons to successfully finish such a race.

What is associated with race performance in male 100-km ultra-marathoners--anthropometry, training or marathon best time?

We investigated the associations of anthropometry, training, and pre-race experience with race time in 93 recreational male ultra-marathoners (mean age 44.6 years, s = 10.0; body mass 74.0 kg, s = 9.0; height 1.77 m, s = 0.06; body mass index 23.4 kg · m(-2), s = 2.0) in a 100-km ultra-marathon using bivariate and multivariate analysis. In the bivariate analysis, body mass index (r = 0.24), the sum of eight skinfolds (r = 0.55), percent body fat (r = 0.57), weekly running hours (r = -0.29), weekly running kilometres (r = -0.49), running speed during training (r = -0.50), and personal best time in a marathon (r = 0.72) were associated with race time. Results of the multiple regression analysis revealed an independent and negative association of weekly running kilometres and average speed in training with race time, as well as a significant positive association between the sum of eight skinfold thicknesses and race time. There was a significant positive association between 100-km race time and personal best time in a marathon. We conclude that both training and anthropometry were independently associated with race performance. These characteristics remained relevant even when controlling for personal best time in a marathon.

Anthropometric and training variables related to half-marathon running performance in recreational female runners

The relationship between skin-fold thickness and running has been investigated in distances ranging from 100 m to the marathon distance (42.195 km), with the exclusion of the half-marathon distance (21.0975 km). We investigated the association between anthropometric variables, prerace experience, and training variables with race time in 42 recreational, nonprofessional, female half-marathon runners using bi- and multivariate analysis. Body weight (r, 0.60); body mass index (r, 0.48); body fat percentage (r, 0.56); pectoral (r, 0.61), mid-axilla (r, 0.69), triceps (r, 0.49), subscapular (r, 0.61), abdominal (r, 0.59), suprailiac (r, 0.55), and medial calf (r, 0.53) skin-fold thickness; mean speed of the training sessions (r, -0.68); and personal best time in a half-marathon (r, 0.69) correlated with race time after bivariate analysis. Body weight (P = 0.0054), pectoral skin-fold thickness (P = 0.0068), and mean speed of the training sessions (P = 0.0041) remained significant after multivariate analysis. Mean running speed during training was related to mid-axilla (r, -0.31), subscapular (r, -0.38), abdominal (r, -0.44), and suprailiac (r, -0.41) skin-fold thickness, the sum of 8 skin-fold thicknesses (r, -0.36); and percent body fat (r, -0.31). It was determined that variables of both anthropometry and training were related to half-marathon race time, and that skin-fold thicknesses were associated with running speed during training. For practical applications, high running speed during training (as opposed to extensive training) may both reduce upper-body skin-fold thicknesses and improve race performance in recreational female half-marathon runners.

Predictor variables for a 100-km race time in male ultra-marathoners

In 169 male 100-km ultra-marathoners, the variables of anthropometry, training, and prerace experience, in order to predict race time, were investigated. In the bivariate analysis, age (r = .24), body mass (r = .20), Body Mass Index (r = .29), circumference of upper arm (r = .26), percent body fat (r = .45), mean weekly running hours (r = -.21), mean weekly running kilometers (r = -.43), mean speed in training (r=-.56), personal best time in a marathon (r = .65), the number of finished 100-km ultra-runs (r = .24), and the personal best time in a 100-km ultra-run (r = .72) were associated with race time. Stepwise multiple regression showed that training speed (p < .0001), mean weekly running kilometers (p < .0001), and age (p < .0001) were the best correlations for a 100-km race time. Performance may be predicted (n=169, r2 = .43) by the following equation: 100-km race time (min) = 1085.60 - 36.26 x (training speed, km/hr.) - 1.43 x (training volume, km/wk.) + 2.50 x (age, yr.). Overall, intensity of training might be more important for a successful outcome in a 100-km race than anthropometric attributes. Motivation to train intensely for such an ultra-endurance run should be explored as this might be the key for a successful finish.

Similarity of anthropometric measures for male ultra-triathletes and ultra-runners

Previous research concluded that Triple Iron ultra-triathletes were close to runners in anthropometry. We assessed similarities in anthropometry between 64 Triple Iron triathletes who competed over 11.4 km swimming, 540 km cycling, and 126 km running versus 95 100-km ultra-marathoners. Variables of anthropometry such as body mass, body height, length and circumferences of limbs, skin-folds and body fat, and training such as volume and speed were compared between ultra-triathletes and ultra-runners. The Triple Iron triathletes completed their race distance within 2811 min. (SD=379) and the 100-km ultra-marathoners within 691 min. (SD=117). Triathletes were younger, had higher body mass, shorter legs, higher circumference of upper arm and thigh, lower sum of skin-folds, and lower percent body fat compared to runners. Weekly training volume was higher for triathletes, and weekly hours in running and weekly kilometres in running were higher for runners. In the Triple Iron ultra-triathletes, the sum of eight skin-folds correlated to total race time. The circumference of upper arm, the sum of eight skin-folds, and percent body fat correlated with time in the running section .42, .47, and .43, respectively. In the 100-km ultra-marathoners, the sum of eight skin-folds, the skin-fold thickness of thigh, percent body fat, weekly running hours, and weekly running kilometres correlated with race time .55, .40, .56, -.50, and -.51, respectively. However, in the triathletes, none of these training variables was significantly correlated with race time. In the ultra-marathoners, the sum of eight skin-folds, the skin-fold thickness of thigh, percent body fat, weekly running kilometres, and speed in running during training were related to race time (correlations of .55, .40, -.28, and -.51, respectively). Overall, the ultra-triathletes were not similar to ultra-runners in their anthropometric measures and training variables.

The Relationship between Anthropometry and Split Performance in Recreational Male Ironman Triathletes

PURPOSE

The aim of this study was to investigate the relation between anthropometric variables and total race time including split times in 184 recreational male Ironman triathletes.

METHODS

Body mass, body height, body mass index, lengths and circumferences of imbs, thicknesses of skin-folds, sum of skin-fold thicknesses, and percent body fat were related to total race time including split times using correlation analysis and effect size.

RESULTS

A large effect size (r>0.37) was found for the association between body mass index and time in the run split and between both the sum of skin-folds and percent body fat with total race time. A medium effect size (r=0.24-0.36) was observed in the association between body mass and both the split time in running and total race time, between body mass index and total race time, between both the circumferences of upper arm and thigh with split time in the run and between both the sum of skin-folds and percent body fat with split times in swimming, cycling and running.

CONCLUSIONS

The results of this study showed that lower body mass, lower body mass index and lower body fat were associated with both a faster Ironman race and a faster run split; lower circumferences of upper arm and thigh were also related with a faster run split.

Differential correlations between anthropometry, training volume, and performance in male and female Ironman triathletes

We investigated in 27 male Ironman triathletes aged 30.3 (9.1) years, with 77.7- (9.8) kg body mass, 1.78- (0.06) m body height, 24.3- (2.2) kg·m⁻² body mass index (BMI), and 14.4 (4.8) % body fat and in 16 female Ironman triathletes aged 36.6 (7.0) years, with 59.7- (6.1) kg body mass, 1.66- (0.06) m body height, 21.5 (1.0) kg·m⁻² BMI, and 22.8 (4.8) % body fat to ascertain whether anthropometric or training variables were related to total race time. The male athletes were training 14.8 (3.2) h·wk⁻¹ with a speed of 2.7 (0.6) km·h⁻¹ in swimming, 27.3 (3.0) in cycling, and 10.6 (1.4) in running. The female athletes trained for 13.9 (3.4) h·wk⁻¹ at 2.1 (0.8) km·h⁻¹h in swimming, 23.7 (7.6) km·h⁻¹ in cycling, and 9.0 (3.7) km·h⁻¹ in running, respectively. For male athletes, percent body fat was highly significantly (r² = 0.583; p < 0.001) associated with total race time. In female triathletes, training volume showed a relationship to total race time (r² = 0.466; p < 0.01). Percent body fat was unrelated to training volume for both men (r² = 0.001; p > 0.05) and women (r² = 0.007; p > 0.05). We conclude that percent body fat showed a relationship to total race time in male triathletes, and training volume showed an association with total race time in female triathletes. Presumably, the relationship between percent body fat, training volume, and race performance is genetically determined.

Predictor variables for half marathon race time in recreational female runners

INTRODUCTION

The relationship between skin-fold thickness and running performance has been investigated from 100 m to the marathon distance, except the half marathon distance.

OBJECTIVE

To investigate whether anthropometry characteristics or training practices were related to race time in 42 recreational female half marathoners to determine the predictor variables of half-marathon race time and to inform future novice female half marathoners.

METHODS

Observational field study at the 'Half Marathon Basel' in Switzerland.

RESULTS

In the bivariate analysis, body mass (r = 0.60), body mass index (r = 0.48), body fat (r = 0.56), skin-fold at pectoral (r = 0.61), mid-axilla (r = 0.69), triceps (r = 0.49), subscapular (r = 0.61), abdominal (r = 0.59), suprailiac (r = 0.55) medial calf (r = 0.53) site, and speed of the training sessions (r = -0.68) correlated to race time. Mid-axilla skin-fold (p = 0.04) and speed of the training sessions (p = 0.0001) remained significant after multi-variate analysis. Race time in a half marathon might be predicted by the following equation (r² = 0.71): Race time (min) = 166.7 + 1.7x (mid-axilla skin-fold, mm) - 6.4x (speed in training, km/h). Running speed during training was related to skinfold thickness at mid-axilla (r = -0.31), subscapular (r = -0.38), abdominal (r = -0.44), suprailiacal (r = -0.41), the sum of eight skin-folds (r = -0.36) and percent body fat (r = -0.31).

CONCLUSION

Anthropometric and training variables were related to half-marathon race time in recreational female runners. Skin-fold thicknesses at various upper body locations were related to training intensity. High running speed in training appears to be important for fast half-marathon race times and may reduce upper body skin-fold thicknesses in recreational female half marathoners.

Race performance in male mountain ultra-marathoners: anthropometry or training?

The association of anthropometric variables, training volume, and prerace experience with race time was investigated in 25 male mountain ultra-marathoners (M age = 44.5 yr., SD = 7.0; M body mass = 73.0 kg, SD = 7.8; M body height = 1.78 m, SD = 0.07; M Body Mass Index = 22.9 kg/m2, SD = 1.8) in a 7-day mountain ultra-marathon over 350 km with a total 11,000 m of altitude gained and lost. The relationship of anthropometry (body mass, body height, Body Mass Index, percent body fat, circumferences of limbs, and thicknesses of skin-folds), training, and prerace experience (years as active runner, average training volume in hours and kilometres per week, average running speed in training, and personal best time in marathon running) with total race time was investigated using bivariate correlation analysis. None of the variables of anthropometry were related to total race time. Average speed in running during training and personal best time in marathon running were associated with total race time. Speed in running during training was correlated with personal best time in marathon running. The finding that average speed in running during training and personal best marathon time were related to race performance suggests that training and especially intensity might be of increased importance in these ultra-runners compared to anthropometry.

Personal best time, percent body fat, and training are differently associated with race time for male and female ironman triathletes

We studied male and female nonprofessional Ironman triathletes to determine whether percent body fat, training, and/or previous race experience were associated with race performance. We used simple linear regression analysis, with total race time as the dependent variable, to investigate the relationship among athletes' percent body fat, average amount of weekly training, and best time in an Ironman triathlon. For male athletes, percent body fat (r2 = 0.57, p < .001) was related to total race time but not average weekly training. For women, percent body fat showed no association with total race time; howeven average weekly training volume was related to total race time (r = .43, p < .01). Percent body fat and average weekly training were not correlated in either gender Speed in training was not associated with race performance in either gender. For men (r2 = .56, p < .001) and women (r2 = .45, p < .05), personal best time in an Ironman triathlon was related to total race time. We concluded that percent body fat was related to race performance in male athletes and to average weekly training in female athletes. Personal best time in an Ironman triathlon was associated with total race time for both male and female athletes.

Anthropometry and Pre-Race Experience of Finishers and Nonfinishers in a Multistage Ultra-Endurance Run — Deutschlandlauf 2007

The effects of anthropometry and pre-race experience on race performance were investigated in male nonprofessional ultra-runners performing a multistage run of 1,200 km over 17 days. Of 24 athletes examined pre-race, 14 (58 %) dropped out during the race due to overuse injuries of the lower limbs. During the race, body mass, thickness of the calf skin-fold, skeletal muscle mass, and percent body fat decreased significantly, while circumference of the upper arm, thickness of triceps, and abdominal skin-fold as well as Body Mass Index decreased significantly in the finishers. Neither anthropometry nor pre-race experience and training volume nor previously finished races were associated with the race time of the finishers. In future studies of ultra-runners, the influence of psychological (cognitive and emotional) predictors on race outcome should be investigated.