Age of peak performance in 50-km ultramarathoners - is it older than in marathoners?

Purpose: Despite the increasing popularity of 50-km ultramarathons during the last few years, only limited information is available regarding the trends in its performance and participation. The aim of the present study was to examine the age of peak running performance in female and male 50-km ultramarathoners using second-order nonlinear regression analyses. Methods: Data from 494,414 runners (124,045 women and 370,369 men) who finished a 50-km ultramarathon between 1975 to 2016 were analyzed. Results: When the top ten finishers in 1-year age-groups were analyzed, the age of peak running speed was 41 years in both women and men. When the fastest finishers in 1-year age-group intervals were analyzed, the age of peak running speed was 40 years in women and 39 years in men. Conclusion: In summary, the age of peak running speed in 50-km ultramarathoners is older than what has been reported by previous studies for marathons. Women seem to achieve the best race time in a 50-km ultramarathon later in life compared with men. These findings are of great practical value for coaches and fitness trainers when setting performance goals for 50-km ultramarathon runners

Sedentarism in Recreational Marathon Runners

AIM Although it has been previously observed that sedentary behavior (SB) was not related to training duration in marathon runners, little information existed about the relationship of SB with training, anthropometric and physiological characteristics in this population. This study aimed to investigate the prevalence of SB and its correlation with performance parameters (such as body fat percentage, maximal oxygen uptake and weekly training volume) as well as its variation by sex and day (ie, weekdays versus weekend) in recreational marathon runners. METHODS A total of 151 finishers (women, n = 29; men, n = 122; age 43.1 (8.7) years, mean (standard deviation)) in the Athens marathon 2017 performed a series of anthropometric and physiological tests, and completed the Multi-context sitting time questionnaire. RESULTS SB did not correlate with anthropometric and physiological characteristics and no difference in these characteristics was shown between low and high sedentary participants (p > 0.05). SB did not differ between women and men (p > 0.05), but differed between working and non-working days (p < 0.05). CONCLUSION In contrast to previous findings on the general population indicating an association of a high SB with a low cardiorespiratory and muscular fitness, our finding of no correlation between SB and physical fitness in marathon runners suggested that endurance exercise might offset the negative effects of SB

Differences in Force-velocity Characteristics of Upper and Lower Limbs of Non-competitive Male Boxers

Int J Exerc Sci 5(2) : 106-113, 2012. Despite the increasing popularity of boxing, only a few studies have been conducted on the physiology or the biomechanics of this sport. The aim of the present study is to examine the ratios of mechanical characteristics (maximal anaerobic power, Pmax, theoretical maximal force, F0, and velocity, v0) between upper and lower limbs of male boxers. Twelve male caucasians, all members of a local fitness club, aged 29.5 (3.2) yr [mean (standard deviation)], stature 1.74 (.05) m, body mass 77.9 (8.1) kg, body fat 22.4 (3.9) % and somatotype 5.5-5.5-1.1, performed a force-velocity (F-v) test for both legs and arms. The F-v test included five supramaximal pedal sprints, each lasting 7 sec, against incremental braking force of 20-60 N for arms and 30-70 N for legs, on modified arm-cranking and on cycle ergometer (Ergomedics 874, Monark, Sweden). The legs had higher Pmax (910 W vs. 445 W, t11=22.9, p\u3c.001), Pmax expressed in relative to body mass values (rPmax, 11.8 W.kg-1 vs. 5.8 W.kg-1, t11=20.6,p\u3c.001), F0 (168 N vs. 102 N, t11=21.7, p\u3c.001), v0 (217 rpm vs. 177 rpm, t11=46.6, p\u3c.001) and lower v0/F0 (1.33 rpm.N-1 vs. 1.82 rpm.N-1, t11=15.3, p\u3c.001) than the arms. Pmax of upper limbs was associated with Pmax of lower limbs (r=.70, p\u3c.05) and their ratio was .49 (.06). The respective values of rPmax was r=.76 (p\u3c.01), F0, r=.35 (p=.26) and .61 (.13), and of velocity, v0,r=.17 (p=.59) and .812 (.10). In spite of moderate associations between upper and lower limbs’ F0 and v0, a stronger relationship was found with regard to Pmax. These findings emphasize the need for separate evaluation of arms’ and legs’ F-v characteristics on a regular basis and the consideration of these measures in training design

Predictors of half-marathon performance in male recreational athletes

Few research has been conducted on predictors of recreational runners' performance, especially in half-marathon running. The purpose of our study was (a) to investigate the relationship of half-marathon race time with training, anthropometry and physiological characteristics, and (b) to develop a formula to predict half-marathon race time in male recreational runners. Recreational runners (n=134, age 44.2±8.7 years; half-marathon race time 104.6±16.2 min) underwent a physical fitness battery consisting of anthropometric and physiological tests. The participants were classified into five performance groups (fast, 73-92 min; above average, 93-99 min; average 100-107 min; below average, 108-117 min; slow group, 118-160 min). A prediction equation was developed in an experimental group (EXP, n=67), validated in a control group (CON, n=67) and prediction bias was estimated with 95 % confidence intervals (CI). Performance groups differed in half-marathon race time, training days, training distance, age, weight, (body mass index) BMI, body fat (BF) and maximum oxygen uptake (VO$_{2}$max) (p≤0.001, η$^{2}$≥0.132), where faster groups had better scores than the slower groups. Half-marathon race time correlated with physiological, anthropometric and training characteristics, with the faster the runner, the better the score in these characteristics (e.g., VO$_{2}$max, r=0.59; BMI, r=-0.55; weekly running distance, r=-0.53, p<0.001). Race time in EXP might be calculated (R$^{2}$=0.63, standard error of the estimate=9.9) using the equation 'Race time (min)=80.056+2.498×BMI-0.594×VO$_{2}$max-0.191×weekly training distance in km'. Validating this formula in CON, no bias was shown (difference between observed and predicted value 2.3±12.8 min, 95 % CI -0.9, 5.4, p=0.153). Half-marathon race time was related to and could be predicted by BMI, VO$_{2}$max and weekly running distance. Based on these relationships, a prediction formula for race time was developed providing a practical tool for recreational runners and professionals working with them

Sports and Health, Second Edition

The International Journal of Environmental Research and Public Health (IJERPH) has increased its publications of scientific papers related to exercise; a search of Pubmed (on 22 June 2022) using IJERPH and exercise as keywords showed 1788 entries for 2021 compared to 80 entries in 2016 [...

Swimming during COVID-19: Operational recommendations and considerations for South African swimming venues

Swimming is one of the most popular recreational activities in South Africa. Since the emergence of the Coronavirus disease 2019 (COVID-19), South Africa imposed one of the strictest lockdown measures to contain and control the spread of the virus. These measures included the closure of gyms, fitness centres and swimming pools across the country. However, as the restrictions begin to ease, it is important to consider how swimming facilities can reopen whilst simultaneously ensuring appropriate measures are in place to reduce COVID-19 infections. Outlined are recommendations and considerations for swimming facilities in South Africa. Currently there is no evidence to suggest that COVID-19 transmission to humans is possible through water, making swimming one of the safer options for physical activity indoors. However, participation is still not without risk and compliance with government mandates and public health officials take precedent over the recommendations outlined in this article

Pacing Strategies in the ‘Athens Classic Marathon’: Physiological and Psychological Aspects

Despite the increased scientific interest in the relationship between pacing and performance in marathon running, little information is available about the association of pacing with physiological and psychological parameters. Therefore, the aim of the present study was to examine the role physical fitness and training characteristics on pacing in the ‘Athens Classic Marathon.’ Finishers in this race in 2017 (women, n = 26, age 40.8 ± 9.4 years; men, n = 130, age 44.1 ± 8.6 years) were analyzed for their pacing during the race, completed the Motivation of Marathon Scale (MOMS) and performed a series of physiological tests. Women and faster recreational runners adopted a more even pacing. A more even pacing was related with a higher aerobic capacity and lower muscle strength in men, but not in women. Men with more even pacing scored higher in psychological coping, self-esteem, life meaning, recognition and competition than their counterparts with less even pacing. Considering the increasing number of participants in marathon races, these findings might help a wide range of professionals (fitness trainers, physiologists, and psychologists) working with runners to optimize the pacing of their athletes

Swimming three ice miles within fifteen hours

Ice Mile swimming (1608 m in water of below 5 °Celsius) is becoming increasingly popular. This case study aimed to identify body core temperature and selected haematological and biochemical parameters before and after repeated Ice Miles. An experienced ice swimmer completed three consecutive Ice Miles within 15 h. Swim times, body core temperatures, and selected urinary and haematological parameters were recorded. Body core temperature reached its maximum between 5, 8 and 15 min after immersion (37.7°C, 38.1°C, and 38.0°C, respectively). The swimmer suffered hypothermia during the first Ice Mile (35.4°C) and body core temperature dropped furthermore to 34.5°C during recovery after the first Ice Mile. He developed a metabolic acidosis in both the first and the last Ice Mile (pH 7.31 and pH 7.34, respectively). We observed hyperkalaemia ([K⁺] > 5.5 mM) after the second Ice Mile (6.9 mM). This was followed by a drop in [K⁺] to3.7 mM after the third Ice Mile. Anticipatory thermogenesis (i.e. an initial increase of body core temperature after immersion in ice cold water) seems to be a physiological response in a trained athlete. The results suggest that swimming in ice-cold water leads to a metabolic acidosis, which the swimmer compensates with hyperventilation (i.e. leading to respiratory alkalosis). The shift of serum [K⁺] could increase the risk of a cardiac arrhythmia. Further studies addressing the physiology and potential risks of Ice Mile swimming are required to substantiate this finding

Skinfold thickness variation and associations with cardiorespiratory fitness in male soccer players of different ages

OBJECTIVE: The aim of the present study was to examine skinfold thickness (SKF) distribution in youth and adult male soccer players regarding cardiorespiratory fitness (CRF) and the role of age. PATIENTS AND METHODS: Participants were youth [n=83, age 16.2 (1.0) years, mean (standard deviation)] and adult male soccer players [n=121, 23.2 (4.3) years], who were tested for SKF on 10 anatomical sites and Conconi test was used to assess velocity at maximal oxygen uptake (vVO2max). RESULTS: A between-within-subjects analysis of variance revealed a small interaction between the anatomical site and age group on SKF (p=0.006, η2=0.022), where adolescents had larger cheek (+0.7 mm; p=0.022; 95% confidence intervals - CI  - 0.1, 1.3), triceps (+0.9 mm; p=0.017; 95% CI 0.2, 1.6) and calf (+0.9 mm; p=0.014; 95% CI 0.2, 1.5) SKF, while adults had larger chin (+0.5 mm; p=0.007; 95% CI 0.1, 0.8) SKF, and no difference was observed for the rest of the anatomical sites. No difference between adolescent and adult age groups was observed in average SKF (SKFavg) [9.0 (2.7) vs. 9.1 (2.5) mm; difference -0.1 mm; 95% CI, -0.8, 0.6; p=0.738]. Compared to adults, adolescents had a lower SKF coefficient of variation (SKFcv) [0.34 (0.10) vs. 0.37 (0.09); difference-0.03; 95% CI, -0.06, -0.01; p=0.020] and subscapular-to-triceps ration (STR) [1.08 (0.28) vs. 1.29 (0.37); difference-0.21; 95% CI, -0.31, -0.12; p<0.001]. The largest Pearson moment correlation coefficient between vVO2max and SKF was shown in the subscapular (r=-0.411; 95% CI, -0.537, -0.284; p<0.001) and the smallest in the patellar anatomical site (r=-0.221; 95% CI, -0.356, -0.085; p=0.002). In addition, vVO2max correlated moderately with SKFavg (r=-0.390; 95% CI, -0.517, -0.262; p<0.001) and SKFcv (r=-0.334; 95% CI, -0.464, -0.203; p<0.001). CONCLUSIONS: In summary, CRF was related to the thickness of specific SKF and the magnitude of thickness variation by the anatomical site (i.e., the smaller the variation, the better the CRF). Considering the relevance of specific SKF for CRF, their further use would be recommended for monitoring physical fitness in soccer players