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

2019 ◽  
Vol 16 (10) ◽  
pp. 1844 ◽  
Author(s):  
Emma O’Loughlin ◽  
Pantelis T. Nikolaidis ◽  
Thomas Rosemann ◽  
Beat Knechtle
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Degrees Of Freedom ◽  
Predictor Variables ◽  
Marathon Running ◽  
Running Speed ◽  
Race Time ◽  
Marathon Race

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.

scholarly journals Development and Validation of Prediction Equation of “Athens Authentic Marathon” Men’s Race Speed

2021 ◽  
Vol 12 ◽  
Author(s):  
Pantelis T. Nikolaidis ◽  
Thomas Rosemann ◽  
Beat Knechtle
Keyword(s):  
Body Mass ◽  
Prediction Equation ◽  
Physiological Characteristics ◽  
Control Group ◽  
Running Speed ◽  
Marathon Runners ◽  
Race Time ◽  
Marathon Race ◽  
Weekly Training

AimDespite the increasing popularity of outdoor endurance running races of different distances, little information exists about the role of training and physiological characteristics of recreational runners. The aim of the present study was (a) to examine the role of training and physiological characteristics on the performance of recreational marathon runners and (b) to develop a prediction equation of men’s race time in the “Athens Authentic Marathon.”MethodsRecreational male marathon runners (n = 130, age 44.1 ± 8.6 years)—who finished the “Athens Authentic Marathon” 2017—performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO2max), anaerobic power, squat, and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, n = 65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, n = 65).ResultsIn the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF, and VO2max (p ≤ 0.003, η2 ≥ 0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (r = −0.36, p < 0.001) and largely with the number of weekly training days (r = 0.52, p < 0.001) and weekly running distance (r = 0.58, p < 0.001), but not with the number of previously finished marathons (r = 0.08, p = 0.369). With regard to physiological characteristics, running speed correlated largely with body mass (r = −0.52, p < 0.001), BMI (r = −0.60, p < 0.001), BF (r = −0.65, p < 0.001), VO2max (r = 0.67, p < 0.001), moderately with isometric muscle strength (r = 0.42, p < 0.001), and small with anaerobic muscle power (r = 0.20, p = 0.021). In EXP, race speed could be predicted (R2 = 0.61, standard error of the estimate = 1.19) using the formula “8.804 + 0.111 × VO2max + 0.029 × weekly training distance in km −0.218 × BMI.” Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12 ± 1.09 km/h, 95% confidence intervals −0.15, 0.40, p = 0.122).ConclusionThese findings highlighted the role of aerobic capacity, training, and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.

scholarly journals Predictor variables for half marathon race time in recreational female runners

Clinics ◽  
2011 ◽  
Vol 66 (2) ◽  
pp. 287-291 ◽  
Author(s):  
Beat Knechtle ◽  
Patrizia Knechtle ◽  
Ursula Barandun ◽  
Thomas Rosemann ◽  
Romuald Lepers
Keyword(s):  
Predictor Variables ◽  
Race Time ◽  
Marathon Race

scholarly journals World Records in Half-Marathon Running by Sex and Age

2018 ◽  
Vol 26 (4) ◽  
pp. 629-636 ◽  
Author(s):  
Pantelis T. Nikolaidis ◽  
Stefania Di Gangi ◽  
Beat Knechtle
Keyword(s):  
Sex Difference ◽  
Marathon Running ◽  
Nonlinear Regression Analysis ◽  
Race Time ◽  
Marathon Race ◽  
The World ◽  
Order Polynomial ◽  
The Relationship ◽  
World Records ◽  
Fourth Order Polynomial

The relationship between age and elite marathon race times is well investigated, but little is known for half-marathon running. This study investigated the relationship between half-marathon race times and age in 1-year intervals by using the world single age records in half-marathon running and the sex difference in performance from 5 to 91 years in men and 5 to 93 years in women. We found a fourth-order polynomial relationship between age and race time for both women and men. Women achieve their best half-marathon race time earlier in life than men, 23.89 years compared with 28.13 years, but when using a nonlinear regression analysis, the age of the fastest race time does not differ between men and women, with 26.62 years in women and 26.80 years in men. Moreover, the sex difference in half-marathon running performance increased with advancing age.

scholarly journals Predictor Variables for Marathon Race Time in Recreational Female Runners

2012 ◽  
Vol 3 (2) ◽  
Author(s):  
Wiebke Schmid ◽  
Beat Knechtle ◽  
Patrizia Knechtle ◽  
Ursula Barandun ◽  
Christoph Alexander Rüst ◽  
...  
Keyword(s):  
Predictor Variables ◽  
Race Time ◽  
Marathon Race

scholarly journals Relationship Between Physical Fitness, BMI, WHR and Hypertension in Elderly Men and Women

2013 ◽  
Vol 52 (4) ◽  
pp. 275-284
Author(s):  
Saša Pantelić ◽  
Radmila Kostić ◽  
Ratomir Djurašković ◽  
Slavoljub Uzunović ◽  
Zoran Milanović ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Physical Fitness ◽  
Body Mass ◽  
Elderly Women ◽  
Upper Body ◽  
Predictor Variables ◽  
Elderly Men ◽  
Men And Women ◽  
Arterial Blood

Abstract Aim: The aim of this study was to determine the structure, characteristics and significance of the relationship between physical fitness, BMI and WHR on one hand and hypertension of elderly men and women on the other. Methods: The sample consisted of 1288 participants (594 men and 694 women) who live in their own households in the cities and villages of Central, Eastern and South Serbia. After the obtained classification of participants based on arterial blood pressure, 231 patients with hypertension aged 60-80 years were selected. The subsample consisted of 138 male participants, while the subsample of women was 93 participants. Predictor variables consisted of 6 variables for the evaluation of physical fitness, Body mass index (BMI) and Waist-to Hip Ratio index (WHR). Criterion variables consisted of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Results: The results showed that there is a statistically significant correlation (p <0.05) between predictor variables and hypertension. Higher values of higher SBP in elderly men causes an increase in body weight due to increased body fat (BMI, WHR). In elderly women, these changes occur under the influence of increased body mass index and reduced CRF. Higher values of high DBP in elderly men cause more power and flexibility of the upper body and in elderly women greater strength in the arms and less strength in legs and CRF. Conclusions: Being overweight in both subsamples could be considered as a factor that contributes to high blood pressure.

scholarly journals An Increase in Fat Mass Index Predicts a Deterioration of Running Speed

Nutrients ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 701 ◽  
Author(s):  
Laurence Genton ◽  
Julie Mareschal ◽  
Véronique L. Karsegard ◽  
Najate Achamrah ◽  
Marta Delsoglio ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Composition ◽  
Body Mass ◽  
Fat Mass ◽  
High Body Mass Index ◽  
Bioelectrical Impedance ◽  
Fat Free Mass ◽  
Running Speed ◽  
Fat Mass Index ◽  
Running Performance

A low fat mass is associated with a good running performance. This study explores whether modifications in body composition predicted changes in running speed. We included people who underwent several measurements of body composition by bioelectrical impedance analysis between 1999 and 2016, at the “Course de l’Escalade”, taking place yearly in Geneva. Body composition was reported as a fat-free mass index (FFMI) and fat mass index (FMI). Running distances (men: 7.2 km; women: 4.8 km) and running times were used to calculate speed in km/h. We performed multivariate linear mixed regression models to determine whether modifications of body mass index, FFMI, FMI or the combination of FFMI and FMI predicted changes in running speed. The study population included 377 women (1419 observations) and 509 men (2161 observations). Changes in running speed were best predicted by the combination of FFMI and FMI. Running speed improved with a reduction of FMI in both sexes (women: ß −0.31; 95% CI −0.35 to −0.27, p < 0.001. men: ß −0.43; 95% CI −0.48 to −0.39, p < 0.001) and a reduction of FFMI in men (ß −0.20; 95% CI −0.26 to −0.15, p < 0.001). Adjusted for body composition, the decline in running performance occurred from 50 years onward, but appeared earlier with a body mass, FFMI or FMI above the median value at baseline. Changes of running speed are determined mostly by changes in FMI. The decline in running performance occurs from 50 years onward but appears earlier in people with a high body mass index, FFMI or FMI at baseline.

scholarly journals A Comparison of Anthropometry between Ironman Triathletes and Ultra-swimmers

2010 ◽  
Vol 24 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Beat Knechtle ◽  
Barbara Baumann ◽  
Patrizia Knechtle ◽  
Andrea Wirth ◽  
Thomas Rosemann
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Body Fat ◽  
Body Height ◽  
Percent Body Fat ◽  
Male And Female ◽  
Race Time ◽  
Variate Analysis

A Comparison of Anthropometry between Ironman Triathletes and Ultra-swimmersWe intended to compare the anthropometry of male and female Ironman triathletes with the anthropometry of male and female ultra-swimmers. Body mass, body mass index and body fat were lower in both male and female triathletes compared to swimmers. Body height and length of limbs were no different between the two groups. In the multi-variate analysis, in male triathletes, body mass (p=0.015) and percent body fat (p=0.0003) were related to race time; percent body fat was also related to the swim split (p=0.0036). In male swimmers, length of the arm was related to race time (p=0.0089). In female triathletes and swimmers, none of the investigated anthropometric variables showed an association with race time. We concluded that Ironman triathletes and ultra-swimmers were different regarding anthropometry and that different anthropometric variables were related to race time. We assume that other factors, such as training and equipment, as opposed to anthropometry, may better predict race time in male and female Ironman triathletes.

scholarly journals PREDICTION OF PHYSICAL VARIABLE TO SQUAT LONG JUMP ACHIEVEMENT (A MULTIVARIATE CORRELATIONAL STUDY ON TRAINED STUDENTS OF A STATE SENIOR HIGH SCHOOL IN PEKALONGAN REGENCY)

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Abdurachman Abdurrachman ◽  
Sugiyanto Sugiyanto ◽  
Muchsin Doewes
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Negative Relationship ◽  
Total Sample ◽  
Limb Length ◽  
Running Speed ◽  
Long Jump ◽  
Physical Abilities ◽  
Correlational Method ◽  
Linearity Test

Achievement of squat long jump needs to be supported with several components that are divided into anthropometric elements and physical abilities. This study aims to determine the relationship and the extent to which squat long jump achievement can predict limb length, body mass index, body flexibility, and running speed. The subjects of this research were trained students of state senior high schools in Pekalongan Regency, with a total sample of 60 students. The independent variables in this research included limb length, body mass index, body flexibility, and running speed, whereas the dependent variables consisted of long jump and squat style achievement. The data were obtained through the test and measurement of each variable. This study used multivariate correlational method. Data were analyzed with normality test, linearity test, simple and multiple regression analysis, and hypotheses were tested with t-test and F-test. The results showed that limb length, body mass index, and flexibility had a positive relationship, whereas body mass index and running speed had a negative relationship with the squat long jump achievement. The prediction value of limb length was 0.027, body mass index -0.049, body flexibility 0.026, and running speed -0.234. The conclusion of this study is that there was a significant relationship among the variables, and limb length, body mass index, body flexibility and running speed can be predicted based on squat long jump achievement.

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

2011 ◽  
pp. 113 ◽  
Author(s):  
Beat Knechtle ◽  
Rüst ◽  
Knechtle ◽  
Barandun ◽  
Romuald Lepers ◽  
...  
Keyword(s):  
Predictor Variables ◽  
Race Time ◽  
Marathon Race

scholarly journals Relation between Dental Caries and Body Mass Index-for-age among Schoolchildren of Jazan City, Kingdom of Saudi Arabia

2017 ◽  
Vol 18 (4) ◽  
pp. 277-282 ◽  
Author(s):  
Mir FA Quadri ◽  
Bassam M Hakami ◽  
Asma AA Hezam ◽  
Raed Y Hakami ◽  
Fadwa A Saadi ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Saudi Arabia ◽  
Oral Health ◽  
Dental Caries ◽  
Body Mass ◽  
World Health ◽  
Predictor Variables ◽  
P Value ◽  
Kingdom Of Saudi Arabia ◽  
Low Bmi

ABSTRACT Objective To analyze and report the type of relation present between dental caries and body mass index (BMI)-for-age among schoolchildren in Jazan region of Kingdom of Saudi Arabia. Materials and methods A cross-sectional study with multistaged random sampling technique was designed to recruit the sample of schoolchildren. Caries was examined using the World Health Organization recommended “decayed and filled teeth”/“decayed missing and filled teeth (dft/DMFT)” method. The BMI-for-age was calculated using the value obtained from body weight and height (kg/m2) of each child. The obtained results were plotted on age- and gender-specific percentile curves by the Centers for Disease Control and Prevention and categorized accordingly. Chi-squared test was conducted to analyze the relation between BMI-for-age and dental caries. Logistic regression was performed to judge the predictor variables. The p-value < 0.05 was considered as significant. Results A total of 360 children were part of this study with equal recruitment from both genders. The mean dft/DMFT value for girls (2.52) was more than that for boys (1.88); and the (p = 0.00) calculated value was statistically significant. Most of the children had normal BMI-for-age (60.6%) and very few were obese (4.7%). Dental caries, fast food, and snacks between meals were significant independent predictor variables for BMI (p < 0.05). Dental caries was a strong predictor, and the analysis showed that children with untreated caries had 81% (odds ratio = 0.19; confidence interval = 0.65, 0.58) higher chance of suffering from low BMI. Conclusion To conclude, this is the first study attempted to see the relationship between BMI-for-age and dental caries among schoolchildren in Jazan city of Kingdom of Saudi Arabia. Negative relation between dental caries and BMI should warrant health promoters about dental caries as a reason for low BMI in a subset of children. Clinical significance High and alarming percentage of untreated dental caries demonstrates the oral health needs among the schoolgoing children in Jazan region. Public health dentists should develop and implement prevention programs so that the oral health issues among schoolchildren are addressed. How to cite this article Quadri MFA, Hakami BM, Hezam AAA, Hakami RY, Saadi FA, Ageeli LM, Alsagoor WH, Faqeeh MA, Dhae MA. Relation between Dental Caries and Body Mass Index-for-age among Schoolchildren of Jazan City, Kingdom of Saudi Arabia. J Contemp Dent Pract 2017;18(4):277-282.

Sign in / Sign up

Export Citation Format

Share Document