Predictor variables for half marathon race time in recreational female runners

Participation and performance characteristics in half-marathon run: a brief narrative review

Half-marathon (HM) is a running sport of increasing popularity in both sexes and in all age groups worldwide during the last years. Many studies have examined several aspects of HM, such as performance and participation trends, sex and age differences, physiological correlates, and training; however, no comprehensive review has ever been contacted to summarize the recently accumulated knowledge. Therefore, the aim of the present study was to review all previous research in this sport, focusing on participation and performance aspects. It was shown that HM runners had similar anthropometric and physiological characteristics as full-marathon runners which should be attributed to the affinity of these two races in terms of metabolic demands. Performance in HM was related with superior scores in aerobic capacity (maximal oxygen uptake, anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, body mass index, body fat percentage and skinfold thickness). Considering the popularity of HM race and the lack of many original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical applications, since the majority of HM runners are amateur runners in need of sex-, age- and performance-tailored exercise prescription.

Are Individual and Environmental Characteristics Associated With Running Performance in Female Runners of Different Age Categories?

The purpose of this study was to identify the individual and environmental predictors associated with performance in female runners of different ages. The sample comprised 440 female Brazilian runners, who answered an online questionnaire, that provided information regarding height, weight, age (categories: “young adult”, “adult”, “early middle-age”, and “older adults”), socioeconomic status, and training characteristics (frequency and volume per week, running pace, race event, and running club participation). Information about environmental variables was obtained from the official institutes and comprised the human development index (HDI), athletics events, athletic tracks, and female homicide. A linear regression model, clustered by state and performed by age groups, was computed. The sample presented a mean running pace of 5:57min/km, and a mean BMI of 23.51kg/m⁻². An increase in running pace and volume/week was observed with increasing age. In “young adults”, any of the variables were significantly associated with the performance. In “adult” group, only individual characteristics were statistically significantly related with the performance. In “early middle-age”, besides BMI (β=5.72; 95%CI=3.65–7.79) and training volume (β=−0.67; 95%CI=−1.07 − −0.27), the HDI was associated with the performance (β=−23.30; 95%CI=−44.11 − −2.49). In older adults, it was found an association between socioeconomic status (β=−19.47; 95%CI=−32.29 − −6.65), practice time (β=142.92; 95%CI=89.34–196.50), running event participation (β=−80.12; 95%CI=−114.35− −45.88), athletic events (β=33.44; 95%CI=15.16–51.72), and female homicide (β=−0.11; 95%CI=−0.17 − −0.05) with the performance, highlighting the influence of both individual and environmental characteristics. Information about the role of these constraints, and their relationships, in female runners’ performance, can be used to guide the development of projects/strategies aiming to increase their involvement in physical activities and sports practice, through the promotion of a more “friendly environment” to women, and providing support for decision-makers when suggesting/implementing public policies.

Training and Racing Behavior of Recreational Runners by Race Distance-Results From the NURMI Study (Step 1)

The present study investigated pre-race preparation of a large sample of recreational runners competing in different race distances (e.g., shorter than half-marathon, half-marathon, marathon and ultra-marathon). An online questionnaire was used and a total of 3,835 participants completed the survey. Of those participants, 2,864 (75%) met the inclusion criteria and 1,628 (57%) women and 1,236 (43%) men remained after data clearance. Participants were categorized according to race distance in half-marathon (HM), and marathon/ultra-marathon (M/UM). Marathon and ultra-marathon data were pooled since the marathon distance is included in an ultra-marathon. The most important findings were (i) marathon and ultra-marathon runners were more likely to seek advice from a professional trainer, and (ii) spring was most commonly reported across all subgroups as the planned season for racing, (iii) training volume increased with increasing race distance, and (iv) male runners invested more time in training compared to female runners. In summary, runners competing in different race distances prepare differently for their planned race.

Clinical Trial Registration:www.ClinicalTrials.gov, identifier ISRCTN73074080. Retrospectively registered 12th June 2015.

Predictive Performance Models in Long-Distance Runners: A Narrative Review

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.

Subcutaneous Adipose Tissue in Female Volleyball Players: Is It Related with Performance Indices?

Background and objectives: The aim of the present study was to examine subcutaneous adipose tissue distribution in female volleyball players with regards to (a) variation by anatomical site, (b) differences among age groups and playing positions, and (c) physiological characteristics associated with performance. Materials and Methods: Participants were adolescent (n = 89, age 15.6 ± 0.9 years, mean ± standard deviation) and adult female volleyball players (n = 78, 24.8 ± 5.3 years), who performed a series of anthropometric and performance tests including skinfold thickness in 10 sites, Abalakov jump (AJ) and handgrip test (HG). Results: Chin had the smallest thickness, and iliac crest and abdomen the largest. The largest correlations of skinfold thickness were shown with regards to AJ ad HG. Coefficient of variations in skinfold thickness correlated with performance indices with small magnitude. Triceps and chin were the most frequent predictors of performance indices. The anatomical site of skinfold was near the active muscle groups related to performance in HG. Conclusions: In conclusion, performance indices such AJ and HG were related with thickness of specific skinfolds and with the variation of thickness by anatomical site (i.e., the less the variation, the better the performance). Considering the relevance of specific skinfolds (e.g., triceps and chin) for performance, their further use would be recommended for purposes of training monitoring, volleyball players’ selection and talent identification.

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

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) (R ² = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R ² = 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.

Predicting Competition Performance in Short Trail Running Races with Lactate Thresholds

Trail running is a popular sport, yet factors related to performance are still not fully understood. Lactate thresholds have been thoroughly investigated in road running and correlate strongly with race performance, but to date few data are available about the value in trail running performance prediction. We examined 25 trail runners (age 31.2 ± 5.1 years, BMI 22.2 ± 1.82 kg/m²) with an initial graded exercise test for measurement of VO_2max (59.5 ± 5.2 ml.kg^‐1.min^{‐ 1}) and lactate thresholds (LT): LTAET (LT aerobic) 1.03 ± 0.59 mmol/l; 11.2 ± 1.1 km/h), IAT (individual lactate threshold) (2.53 ± 0.59 mmol/l; 15.4 ± 1.6 km/h) and LT4 (lactate threshold at 4 mmol/l) (16.2 ± 1.9 km/h). All runners subsequently participated in a 31.1 km XS trail race and 9 runners in a 21 km XXS trail race. Race performance times correlated negatively with the XS trail run (LTAET: r = ‐0.65, p < 0.01; LT4: r = ‐0.87, p < 0.01; IAT: r = ‐0.84, p < 0.01) and regression analysis showed that race performance could be predicted by: LT4: ‐324.15×LT4+13195.23 (R² = .753, F_1,23 = 70.02, p < 0.01). A subgroup analysis showed higher correlations with race performance for slower than faster runners. No correlations were found with the XXS race. Lactate thresholds can be of value in predicting trail race performance and help in designing training plans.

Different Predictor Variables for Women and Men in Ultra-Marathon Running-The Wellington Urban Ultramarathon 2018

Ultra-marathon races are increasing in popularity. Women are now 20% of all finishers, and this number is growing. Predictors of performance have been examined rarely for women in ultra-marathon running. This study aimed to examine the predictors of performance for women and men in the 62 km Wellington Urban Ultramarathon 2018 (WUU2K) and create an equation to predict ultra-marathon race time. For women, volume of running during training per week (km) and personal best time (PBT) in 5 km, 10 km, and half-marathon (min) were all associated with race time. For men, age, body mass index (BMI), years running, running speed during training (min/km), marathon PBT, and 5 km PBT (min) were all associated with race time. For men, ultra-marathon race time might be predicted by the following equation: (r² = 0.44, adjusted r² = 0.35, SE = 78.15, degrees of freedom (df) = 18) ultra-marathon race time (min) = −30.85 ± 0.2352 × marathon PBT + 25.37 × 5 km PBT + 17.20 × running speed of training (min/km). For women, ultra-marathon race time might be predicted by the following equation: (r² = 0.83, adjusted r² = 0.75, SE = 42.53, df = 6) ultra-marathon race time (min) = −148.83 + 3.824 × (half-marathon PBT) + 9.76 × (10 km PBT) − 6.899 × (5 km PBT). This study should help women in their preparation for performance in ultra-marathon and adds to the bulk of knowledge for ultra-marathon preparation available to men.

Running performance in a timed city run and body composition: A cross-sectional study in more than 3000 runners

OBJECTIVE

The importance of body composition for running performance is unclear in the general population. The aim of this study was to evaluate whether body composition influences running speed and whether it is a better predictor of running speed than body mass index (BMI).

METHODS

The study included 1353 women (38.2 ± 12.1 y of age) and 1771 men (39.6 ± 12.1 y of age) who underwent, for the first time, a measurement of body composition by bioelectrical impedance analysis between 1999 and 2016, before a timed run occurring annually in Geneva. The running distances and times were converted to average speed (km/h). Body composition was expressed as sex-specific quartiles, where quartile 1 (lowest values) was the reference quartile. The relationships between speed and BMI or body composition were analyzed by multivariate linear regressions.

RESULTS

Multivariate regressions showed that the higher the fat mass index (FMI) quartile, the lower the running speed in women and men (all P < 0.001). In men, a fat-free mass index (FFMI) in quartile 4 (>20 kg/m²) was associated with a poor running performance (r = -0.50, P < 0.001), whereas in women, an FFMI in quartile 2 or 3 (15-16.4 kg/m²) was associated with a higher running speed (r = 0.23, P = 0.04; r = 0.28, P = 0.01, respectively). Body composition predicted speed better than BMI in women (R² = 26.8% versus 14.4%) and men (R² = 29.8% versus 25.4%).

CONCLUSIONS

Running speed is negatively associated with BMI and FMI in both sexes. Body composition is a better predictor of running performance than BMI.

How accurate are runners' prospective predictions of their race times?

Metacognition is a domain which has illuminated our understanding of the regulation of cognition, but has yet to be applied in detail to more physical activities. We used half marathon finish time predictions from 7211 runners to investigate the factors that influence running performance metacognitive accuracy. In particular, we were concerned with the effects of experience, gender, and age on calibration. We expected more experienced runners to be better calibrated than less experienced ones. Given analogous findings in the domain of metacognition, we expected women to be less overconfident in their predictions, and better calibrated than male runners. Based on the metacognition literature, we expected that if older runners have effectively learned from previous experience, they would be as well-calibrated as younger runners. In contrast, uninformed inferences not based on performance feedback would lead to overestimating performance for older compared to younger runners. As expected, experience in terms of both club membership and previous race completion improved calibration. Unexpectedly though, females were more overconfident than males, overestimating their performance and demonstrating poorer calibration. A positive relationship was observed between age and prediction accuracy, with older runners showing better calibration. The present study demonstrates that data, collected before a test of physical activity, can inform our understanding of how participants anticipate their performance, and how this ability is affected by a number of demographic and situational variables. Athletes and coaches alike should be aware of these variables to better understand, organise, plan, and predict running performance, potentially leading to more appropriate training sessions and faster race finish times.

Should Body Size Categories Be More Common in Endurance Running Events?

Thousands of endurance running events are held each year in the United States, and most of them use age and sex categories to account for documented effects of those factors on running performance. However, most running events do not provide categories of body mass, despite abundant evidence that it, too, dramatically influences endurance running performance. The purposes of this article are to (1) discuss how body mass affects endurance running performance, (2) explain several mechanisms through which body mass influences endurance running performance, and (3) suggest possible ways in which body mass might be categorized in endurance running events.

Anthropometric characteristics of top-class Olympic race walkers

BACKGROUND

Typical training programmes in elite race walkers involve high training volumes at low and moderate intensities, which have been reported to induce functional and structural adaptations at an anthropometric level. Since anthropometrical variables are closely related to movement efficiency and performance in endurance events, the aim of this study was to describe the anthropometric profile of world-class race walkers.

METHODS

Twenty-nine world-class race walkers (21 men and 8 women) participated in this study. Anthropometric characteristics, including height, body mass, eight skinfolds, five girths and four bone breadths were measured. Body composition, somatotype, somatotype dispersion mean, somatotype attitudinal mean and height to weight ratio, as well as skinfolds extremity to trunk ratio were also calculated.

RESULTS

Mean height, body mass and body mass index were 177.1±7.1 cm, 66.4±5.8 kg, and 21.2±1.3 kg·m2 for men and 165.6±4.5 cm, 53.6±3.7 kg, and 19.6±1.6 kg· m2 for women, respectively. Women presented greater body fat content (6.7±0.6 vs. 12.2±0.8%; very large effect), less muscle mass (65.6±4.6 vs. 61.6±2.6 kg; large effect), and were more endomorphic (large effect) than men. Men specialists in 20-km showed greater muscle mass (66.7±4.9 vs. 64.4±4.3 kg; moderate effect), and slightly higher skinfolds, girths, body fat content and were more mesomorphic than 50-km specialists (moderate effect).

CONCLUSIONS

The present study expands the limited knowledge on the anthropometric characteristics and somatotype elements of elite top-class race walkers. The characterisation of the morphology of elite race walkers provides coaches a reference values to control the training development of the race walker, as well as providing reference values to improve talent identification.

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

Relationship of anthropometric and training characteristics with race performance in endurance and ultra-endurance athletes

A variety of anthropometric and training characteristics have been identified as predictor variables for race performance in endurance and ultra-endurance athletes. Anthropometric characteristics such as skin-fold thicknesses, body fat, circumferences and length of limbs, body mass, body height, and body mass index were bi-variately related to race performance in endurance athletes such as swimmers in pools and in open water, in road and mountain bike cyclists, and in runners and triathletes over different distances. Additionally, training variables such as volume and speed were also bi-variately associated with race performance. Multi-variate regression analyses including anthropometric and training characteristics reduced the predictor variables mainly to body fat and speed during training units. Further multi-variate regression analyses including additionally the aspects of previous experience such as personal best times showed that mainly previous best time in shorter races were the most important predictors for ultra-endurance race times. Ultra-endurance athletes seemed to prepare differently for their races compared to endurance athletes where ultra-endurance athletes invested more time in training and completed more training kilometers at lower speed compared to endurance athletes. In conclusion, the most important predictor variables for ultra-endurance athletes were a fast personal best time in shorter races, a low body fat and a high speed during training units.

Prediction of half-marathon race time in recreational female and male runners

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r² = 0.42, adjusted r² = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) – 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r² = 0.68, adjusted r² = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) – 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the models.

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.

Reduced performance difference between sexes in master mountain and city marathon running

Background

The performance in master marathoners has been investigated in flat city marathons but not in mountain marathons. This study examined changes in the sex differences in performance across time in female and male master runners competing in a mountain marathon compared to a flat city marathon.

Methods

The association between age and performance of finishers in the Jungfrau Marathon, Switzerland, with 1830 meter changes in altitude and a flat city marathon (Lausanne Marathon), Switzerland, were analyzed from 2000 to 2011.

Results

In both events, athletes in the 35–44 years age group showed the highest number of finishers. In the mountain marathon, the number of female master runners aged > 35 years increased in contrast to female finishers aged < 35 years, while the number of male finishers was unchanged in all age groups. In the city marathon, the number of female finishers was unchanged while the number of male finishers in the age groups for 25–34-year-olds and 35–44-year-olds decreased. In female marathoners, performance improved in athletes aged 35–44 and 55–64 years in the city marathon. Male marathoners improved race time in age group 45–54 years in both the city marathon and the mountain marathon. Female master runners reduced the sex difference in performance in the 45–54-year age group in both competitions and in the 35–44-year age group in the mountain marathon. The sex difference in performance decreased in the 35–44-year age group from 19.1% ± 4.7% to 16.6% ± 1.9% in the mountain marathon (r² = 0.39, P = 0.03). In age groups 45–54 years, the sex difference decreased from 23.4% ± 1.9% to 15.9% ± 6.1% in the mountain marathon (r² = 0.39, P < 0.01) and from 34.7% ± 4.6% to 11.8% ± 6.2% in the city marathon (r² = 0.39, P < 0.01).

Conclusion

These findings suggest that female master runners aged 35–54 years reduced sex differences in their performance in both mountain and city marathon running.

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).

Exercise and its interactions with various aspects of man and animal lives

To review recently published papers in the Brazilian Scientific press on the general subject of physical exercise. All articles published in 2010/2011 found through the keyword exercise were collected from 11 Brazilian Journals. They were hand filtered to exclude all but original research papers. They were grouped according to subject categories and subcategories. A brief summary of all included articles was produced, comparing similar articles between them. The most commonly found interactions refer to exercise vs. the cardiovascular system, metabolism and the locomotor system, in this order. The volume of scientific research in the field is high and of sufficient quality to justify highlighting.

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.

Predictor variables for marathon race time in recreational female runners

PURPOSE

We intended to determine predictor variables of anthropometry and training for marathon race time in recreational female runners in order to predict marathon race time for future novice female runners.

METHODS

Anthropometric characteristics such as body mass, body height, body mass index, circumferences of limbs, thicknesses of skin-folds and body fat as well as training variables such as volume and speed in running training were related to marathon race time using bi- and multi-variate analysis in 29 female runners.

RESULTS

The marathoners completed the marathon distance within 251 (26) min, running at a speed of 10.2 (1.1) km/h. Body mass (r=0.37), body mass index (r=0.46), the circumferences of thigh (r=0.51) and calf (r=0.41), the skin-fold thicknesses of front thigh (r=0.38) and of medial calf (r=0.40), the sum of eight skin-folds (r=0.44) and body fat percentage (r=0.41) were related to marathon race time. For the variables of training, maximal distance ran per week (r=- 0.38), number of running training sessions per week (r=- 0.46) and the speed of the training sessions (r= - 0.60) were related to marathon race time. In the multi-variate analysis, the circumference of calf (P=0.02) and the speed of the training sessions (P=0.0014) were related to marathon race time. Marathon race time might be partially (r(2)=0.50) predicted by the following equation: Race time (min)=184.4 + 5.0 x (circumference calf, cm) -11.9 x (speed in running during training, km/h) for recreational female marathoners.

CONCLUSIONS

Variables of both anthropometry and training were related to marathon race time in recreational female marathoners and cannot be reduced to one single predictor variable. For practical applications, a low circumference of calf and a high running speed in training are associated with a fast marathon race time in recreational female runners.

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.

Running speed during training and percent body fat predict race time in recreational male marathoners

BACKGROUND

Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners.

METHODS

Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times.

RESULTS

After multivariate regression, running speed of the training units (β = -0.52, P < 0.0001) and percent body fat (β = 0.27, P < 0.0001) were the two variables most strongly correlated with marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r (2) = 0.44): race time ( minutes) = 326.3 + 2.394 × (percent body fat, %) - 12.06 × (speed in training, km/hours). Running speed during training sessions correlated with prerace percent body fat (r = 0.33, P = 0.0002). The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics.

CONCLUSION

The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour) are two key factors for a fast marathon race time in recreational male marathoner runners.

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.

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.