Increase of Total Body Water With Decrease of Body Mass While Running 100 km Nonstop—Formation of Edema?

We investigated whether ultraendurance runners in a 100-km run suffer a decrease of body mass and whether this loss consists of fat mass, skeletal muscle mass, or total body water. Male ultrarunners were measured pre- and postrace to determine body mass, fat mass, and skeletal muscle mass by using the anthropometric method. In addition, bioelectrical impedance analysis was used to determine total body water, and urinary (urinary specific gravity) and hematological parameters (hematocrit and plasma sodium) were measured in order to determine hydration status. Body mass decreased by 1.6 kg (p < .01), fat mass by 0.4 kg (p < .01), and skeletal muscle mass by 0.7 kg (p < .01), whereas total body water increased by 0.8 L (p < .05). Hematocrit and plasma sodium decreased significantly (p < .01), whereas plasma urea and urinary specific gravity (USG) increased significantly (p < .01). The decrease of 2.2% body mass and a USG of 1.020 refer to a minimal dehydration. Our athletes seem to have been relatively overhydrated (increase in total body water and plasma sodium) and dehydrated (decrease in body mass and increase in USG) during the race, as evidenced by the increased total body water and the fact that plasma sodium and hematocrit were lower postrace than prerace. The change of body mass was associated with the change of total body water (p < .05), and we presume the development of     

Circles on pommel horse with a suspended aid: Spatio-temporal characteristics

Abstract

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.     

Predictor variables of performance in recreational male long-distance inline skaters

We investigated the associations between selected anthropometric and training characteristics with race time in 84 recreational male long-distance inline skaters at the longest inline marathon in Europe, the 'Inline One-eleven' over 111 km in Switzerland, using bi- and multivariate analysis. The mean (s) race time was 264 (41) min. The bivariate analysis showed that age (r = 0.30), body mass (r = 0.42), body mass index (r = 0.35), circumference of upper arm (r = 0.32), circumference of thigh (r = 0.29), circumference of calf (r = 0.38), skin-fold of thigh (r = 0.22), skin-fold of calf (r = 0.27), the sum of skin-folds (r = 0.43), percent body fat (r = 0.45), duration per training unit in inline skating (r = 0.33), and speed during training (r = -0.46) were significantly and positively correlated to race time. Stepwise multiple regression showed that duration per training unit (P = 0.003), age (P = 0.029) and percent body fat (P = 0.016) were the best correlated with race time. Race time in a long-distance inline race such as the 'Inline One-eleven' over 111 km with a mean race time of ～260 min might be predicted by the following equation (r(2) = 0.41): Race time (min) = 114.91 + 0:51* (duration per training unit, min) + 0:85* (age, years) +3:78* (body fat, %) for recreational long-distance inline skaters.     

Energetic demand and physical conditioning of table tennis players. A study review

Table tennis is a racket sport characterised by an intermittent movement profile, including short rallies interspersed with short breaks. In contrast to other racket sports, information is lacking regarding the: (i) physiological responses during table tennis matches and training; and (ii) practical recommendations for enhancing aerobic and anaerobic performance in table tennis by improving cardio-metabolic and neuro-muscular fitness, anthropometry and nutritional strategies. Therefore, this review article attempts to narratively provide an overview of the physiology of table tennis by describing the metabolic mechanisms underlying match play and outlining a framework for practical recommendations for improving cardio-metabolic and neuro-muscular fitness, anthropometry as well as nutritional strategies. A second aim was to stimulate future research on table tennis and to point out study limitations in this context. In general, the most important finding is that the rally duration is short at around 3.5s, with a longer rest time of around 8–20s, resulting in an effort-rest ratio ranging from 0.15 to 0.22 in official matches and energetic demands during match relatively low. Future studies should focus on the relationship between energetic demand and table tennis performance with a view to predicting performance in table tennis using physiological parameters.     

Influence of wheel rim width on rolling resistance and off-road speed in cross-country mountain biking

The rim width of cross-country mountain bike wheel sets has increased in recent years, but the effect of this increase on performance remains unknown. The aim of this study was to analyse the influence of rim width on rolling resistance and off-road speed. We compared 3 tubeless wheel sets: 25 mm inner width as baseline, 30 mm width with the same tyre stiffness, and 30 mm width with the same tyre pressure. Three riders conducted 75 rolling resistance tests for each wheel set on a cross-country course. We determined rolling resistance using the virtual elevation method and calculated off-road speeds for flat and uphill conditions using a mathematical model. Baseline rolling resistance (C_r) was 0.0298, 90% CI [0.0286, 0.0310], which decreased by 1.4%, [0.7, 2.2] with the wider rim and the same tyre stiffness and increased by 0.9%, [0.1, 1.6] with the wider rim and the same tyre pressure. The corresponding effects on off-road speed were most likely trivial (0.0% to 0.7% faster and 0.1% to 0.6% slower, respectively). Because the effect of rim width on off-road speed seems negligible, athletes should choose the rim width that offers the best bike handling and should experiment with low tyre pressures.     

A faster running speed is associated with a greater body weight loss in 100-km ultra-marathoners

Abstract

In 219 recreational male runners, we investigated changes in body mass, total body water, haematocrit, plasma sodium concentration ([Na⁺]), and urine specific gravity as well as fluid intake during a 100-km ultra-marathon. The athletes lost 1.9 kg (s = 1.4) of body mass, equal to 2.5% (s = 1.8) of body mass (P < 0.001), 0.7 kg (s = 1.0) of predicted skeletal muscle mass (P < 0.001), 0.2 kg (s = 1.3) of predicted fat mass (P < 0.05), and 0.9 L (s = 1.6) of predicted total body water (P < 0.001). Haematocrit decreased (P < 0.001), urine specific gravity (P < 0.001), plasma volume (P < 0.05), and plasma [Na⁺] (P < 0.05) all increased. Change in body mass was related to running speed (r = ?0.16, P < 0.05), change in plasma volume was associated with change in plasma [Na⁺] (r = ?0.28, P < 0.0001), and change in body mass was related to both change in plasma [Na⁺] (r = ?0.36) and change in plasma volume (r = 0.31) (P < 0.0001). The athletes consumed 0.65 L (s = 0.27) fluid per hour. Fluid intake was related to both running speed (r = 0.42, P < 0.0001) and change in body mass (r = 0.23, P = 0.0006), but not post-race plasma [Na⁺] or change in plasma [Na⁺] (P > 0.05). In conclusion, faster runners lost more body mass, runners lost more body mass when they drank less fluid, and faster runners drank more fluid than slower runners.     

Body Mass and Circumference of Upper Arm Are Associated With Race Performance in Ultraendurance Runners in a Multistage Race—The Isarrun 2006

In the present study, we investigated the association of anthropometric parameters with race performance in ultraendurance runners in a multistage ultraendurance run, in which athletes had to run 338 km within 5 consecutive days. In 17 male successful finishers, calculations of body mass, body height, skinfold thicknesses, extremity circumference, skeletal muscle mass (SM), and percentage body fat (%BF) were performed before the race to correlate anthropometric parameters with race performance. A positive association was shown between total running time and both body mass (r² = .29, p < .05) and upper arm circumference (r² = .23, p < .05). In contrast, body height, skinfold thicknesses, extremity circumference, SM, and %BF showed no association with race performance (p > .05). We concluded that in a multistage ultraendurance run, body mass and upper arm circumference were negatively associated with race performance in well experienced ultraendurance runners. In contrast, body height, skinfold thicknesses, circumferences of the other extremities, SM, and %BF showed no association with race performance.     

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.