Personal Best Time and Training Volume, Not Anthropometry, is Related to Race Performance in the ‘Swiss Bike Masters’ Mountain Bike Ultramarathon

2011 ◽  
Vol 25 (5) ◽  
pp. 1312-1317 ◽  
Author(s):  
Beat Knechtle ◽  
Patrizia Knechtle ◽  
Thomas Rosemann ◽  
Oliver Senn
Author(s):  
Jeremy McAdam ◽  
Kaitlin McGinnis ◽  
Rian Ory ◽  
Kaelin Young ◽  
Andrew D. Frugé ◽  
...  

2010 ◽  
Vol 20 (5) ◽  
pp. 418-426 ◽  
Author(s):  
Noel Pollock ◽  
Claire Grogan ◽  
Mark Perry ◽  
Charles Pedlar ◽  
Karl Cooke ◽  
...  

Low bone-mineral density (BMD) is associated with menstrual dysfunction and negative energy balance in the female athlete triad. This study determines BMD in elite female endurance runners and the associations between BMD, menstrual status, disordered eating, and training volume. Forty-four elite endurance runners participated in the cross-sectional study, and 7 provided longitudinal data. Low BMD was noted in 34.2% of the athletes at the lumbar spine, and osteoporosis in 33% at the radius. In cross-sectional analysis, there were no significant relationships between BMD and the possible associations. Menstrual dysfunction, disordered eating, and low BMD were coexistent in 15.9% of athletes. Longitudinal analysis identified a positive association between the BMD reduction at the lumbar spine and training volume (p = .026). This study confirms the presence of aspects of the female athlete triad in elite female endurance athletes and notes a substantial prevalence of low BMD and osteoporosis. Normal menstrual status was not significantly associated with normal BMD, and it is the authors’ practice that all elite female endurance athletes undergo dual-X-ray absorptiometry screening. The association between increased training volume, trend for menstrual dysfunction, and increased loss of lumbar BMD may support the concept that negative energy balance contributes to bone loss in athletes.


2016 ◽  
Vol 28 (4) ◽  
pp. 565-571 ◽  
Author(s):  
Kristine E. Lynch ◽  
Alun Thomas ◽  
Bryan Gibson

Purpose:There has long been a debate regarding the importance of talent versus training in athletic performance. In this study we sought to quantify their relative contributions to the race performance of high-school sprinters.Methods:Using race results from the athletic.net website, we identified high-school athletes who participated in at least one race in both 9th and 12th grade in the 100 m, 200 m or 400 m. Athletes with a record of racing before high school were excluded from the analyses. Using separate linear regression models for each event and gender, we analyzed the effect of baseline ability, race experience and training exposure on race time in the 12th grade.Results:35,909 athletes, running a total of 1,627,652 races, contributed to the final analyses. The proportion of variance (R2) in 12th grade race times accounted for by baseline ability ranged from 40% to 51% depending on the event, and was consistently higher for females than males. Race experience explained 3.6–4.4% of the variance and training exposure explained 0.8–1.7%.Conclusion:Although race experience and training exposure impact high-school sprinters’ performance, baseline ability is the dominant influence.


2019 ◽  
Vol 7 (3_suppl) ◽  
pp. 2325967119S0012 ◽  
Author(s):  
Matthew D. Milewski ◽  
Caitlin M. McCracken ◽  
Bill Meehan ◽  
Andrea Stracciolini

BACKGROUND Sleep duration and sport specialization have been shown to affect injury profile in young athletes. The interplay between training hours per week, and, multiple versus single sports participation on sleep hours in young athletes is unknown. Purpose/Objective To investigate associations between single sport participation and training volume, with sleep hours, in pediatric and adolescent athletes. METHODS Study design: Cross-sectional epidemiological study was conducted using electronic questionnaire data from an injury prevention evaluation (IPE) at a sports injury prevention center affiliated with a tertiary level pediatric medical center between April 2013 and February 2018. Data analysis included sports participation, previous injury history, training regimen, and sleeping habits. For each sport selected, athletes were asked about average number of practice hours for each sport and number of seasons training for the sport during the year. All athletes aged 11-18 years were included in the study. Main outcome measures include sleep duration, single sport, and training hours/seasons. Single sport athletes were defined as those athletes who listed participation in only one sport year-round. Binary measures were created to indicate 1)any participant that listed practicing > 10 hour/week for any sport during a season and 2) any participant that trains three or more seasons for any sport in which they participate. Multivariate regression models (M1, M2, M3) were created for soccer athletes to control for sport training differences while testing the independent effect of gender, age and sport training. Based on the results univariate linear regression of hours of sleep was stratified by age and gender and regressed by self-reported hours of practice per week, identification as single sport athlete, training three or more seasons for soccer. RESULTS There were 756 athletes, 11-18 years old, included (mean age 13.5±2.5 years; 56% female (N=426)). For female athletes, figure skating (46%, 11/24), dance (42%, 28/67), and gymnastics (25%, 12/47) lead the list for single sport athletes. In comparison, for male athletes, swimming (26%, 5/19), tennis (19%, 5/26) and soccer (13%, 16/120) lead the list. The overwhelming majority of gymnasts, dancers, and figure skaters (88% (38/43), 83% (54/66) and 83%, (20/24)) train = 3 seasons of the year. In comparison, for male athletes, tennis athletes (62%, 16/25) seem to train = 3 seasons of the year followed by soccer (41%, 49/119) and swimming (39%, 7/18). (Tables 1 and 2) Table 3 presents multivariate linear regression coefficient of weeknight hours slept by practice hours, gender, age and sport characteristics for soccer participants using three different models (M1, M2, M3). Younger athletes, ages 11-14 years, slept nearly an hour more than participants aged 15-18 years across all training types. Only female soccer athletes training = 3 seasons slept significantly less (ß -0.24, SE 0.12, 95%) than their male counterparts. Participants that practiced soccer > 10 hours/week slept significantly, and substantively, less than their peers practicing =3 or less hours/week (ß -0.61, SE 0.17, 95%). Table 4 presents all participants and sport type stratified by age and sex. Practicing more than 10 hours/week was significant in males ages 11-14 years. Middle school aged males, practicing > 10 hours/week for any sport in which they participate over the course of the year, slept over half an hour less than their peers that practiced fewer hours (ß -0.65, SE 0.2, 95%). CONCLUSIONS/SIGNIFICANCE Training volume appears to affect sleep in young athletes. Middle school male athletes practicing greater than 10 hours/week appear to sleep less than their peers. Anticipatory guidance surrounding training may help to improve sleep hygiene in pediatric and adolescent athletes. [Table: see text][Table: see text][Table: see text][Table: see text]


2011 ◽  
Vol 6 (1) ◽  
pp. 25-37 ◽  
Author(s):  
Martin D. Hoffman ◽  
Kevin Fogard

Purpose:Despite increased 161-km ultramarathon participation in recent years, little is known about those who pursue such an activity. This study surveyed entrants in two of the largest 161-km trail ultramarathon runs in North America to explore demographic characteristics and issues that affected race performance.Methods:All entries of the 2009 Western States Endurance Run and the Vermont 100 Endurance Race were invited to complete a postrace questionnaire.Results:There were 500 respondents among the 701 race entries (71.3% response). Finish time was found to have a significant (P <.01) negative association with training volume and was generally directly associated with body mass index. Among nonfinishers, the primary reason for dropping out was nausea and/or vomiting (23.0%). Finishers compared with nonfinishers were more likely (P <.02) to report blisters (40.1% vs 17.3%), muscle pain (36.5% vs 20.1%), and exhaustion (23.1% vs 13.7%) as adversely affecting race performance, but nausea and/or vomiting was similar between groups (36.8% vs 39.6%). Nausea and/or vomiting was no more common among those using nonsteroidal anti-infammatory drugs (NS AIDs), those participating in the event with higher ambient temperatures, those with a lower training volume, or those with less experience at finishing 161-km races. Overall use of NSAIDs was high, and greater (P = .006) among finishers (60.5%) than nonfinishers (46.4%).Conclusions:From this study, we conclude that primary performance-limiting issues in 161 -km ultramarathons include nausea and/or vomiting, blisters, and muscle pain, and there is a disturbingly high use of NSAIDs in these events.


2008 ◽  
Vol 26 (8) ◽  
pp. 863-873 ◽  
Author(s):  
John H. M. Brooks ◽  
Colin W. Fuller ◽  
Simon P. T. Kemp ◽  
Dave B. Reddin

2021 ◽  
Author(s):  
Rhaí André Arriel ◽  
Moacir Marocolo

Abstract Although in recent years, cross-country short track (XCC) mountain biking became more popular among athletes and coaches, no study has analyzed the main determinants of performance in this modality. Thus, this study investigates performance and pacing profile of professional cross-country cyclists on different technical sections during a XCC competition. Twenty male professional cross-country cyclists (25.9 ± 5.4 years; eight under 23 and twelve elite), performed 6 laps of a XCC 2020 UCI International Mountain Bike Cup. Average speed (lap by lap and in five different technical sections of the track) were analyzed according to athletes, categories and race performance group. For race performance analyses, cyclists were divided into 4 groups (1-4; n=5 each), according to total race time, presenting group 1 the better performance. In general, XCC athletes adopted a positive pacing profile during competition but no differences in speed over the race or in each circuit section were found between categories (p > 0.05). Race performance groups adopted different pacing profiles: group 1 maintained a more even pacing profile, groups 2 and 3 adopted a positive pacing profile and group 4 adopted a reverse J-shaped pacing profile. No difference in speed was found between categories across track sections. Group 1 was 17.9% and 8.3% faster than the group 4 (p < 0.05) on the non-technical uphill section and more technical uphill/downhill section, respectively. A general positive pacing profile during XCC is adopted by the mainly of athletes and this choice of pacing profile is influenced by race performance, regardless of cyclist category. Furthermore, physical fitness is more relevant than technical ability in this competition.


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