Personal best marathon time and longest training run, not anthropometry, predict performance in recreational 24-hour ultrarunners

The whole-body and skeletal muscle metabolic response to 14 days of highly controlled low energy availability in endurance-trained females

This study investigated the effects of 14 days low energy availability (LEA) versus optimal energy availability (OEA) in endurance-trained females on substrate utilization, insulin sensitivity, and skeletal muscle mitochondrial oxidative capacity; and the impact of metabolic changes on exercise performance. Twelve endurance-trained females (V̇O2max 55.2 ± 5.1 mL × min-1 × kg-1) completed two 14-day randomized, blinded, cross-over, controlled dietary interventions: (1) OEA (51.9 ± 2.0 kcal × kg fat-free mass (FFM)-1 × day-1) and (2) LEA (22.3 ± 1.5 kcal × kg FFM-1 × day-1), followed by 3 days OEA. Participants maintained their exercise training volume during both interventions (approx. 8 h × week-1 at 79% heart rate max). Skeletal muscle mitochondrial respiratory capacity, glycogen, and maximal activity of CS, HAD, and PFK were unaltered with LEA. 20-min time trial endurance performance was impaired by 7.8% (Δ -16.8 W, 95% CI: -23.3 to -10.4, p < .001) which persisted following 3 days refueling post-LEA (p < .001). Fat utilization was increased post-LEA as evidenced by: (1) 99.4% (p < .001) increase in resting plasma free fatty acids (FFA); (2) 270% (p = .007) larger reduction in FFA in response to acute exercise; and (3) 28.2% (p = .015) increase in resting fat oxidation which persisted during submaximal exercise (p < .001). These responses were reversed with 3 days refueling. Daily glucose control (via CGM), HOMA-IR, HOMA-β, were unaffected by LEA. Skeletal muscle O2 utilization and carbohydrate availability were not limiting factors for aerobic exercise capacity and performance; therefore, whether LEA per se affects aspects of training quality/recovery requires investigation.

Best practice recommendations for body composition considerations in sport to reduce health and performance risks: a critical review, original survey and expert opinion by a subgroup of the IOC consensus on Relative Energy Deficiency in Sport (REDs)

BACKGROUND

The assessment of body composition (BC) in sport raises concern for athlete health, especially where an overfocus on being lighter or leaner increases the risk of Relative Energy Deficiency in Sport (REDs) and disordered eating.

METHODS

We undertook a critical review of the effect of BC on performance (29 longitudinal, prospective or intervention studies) and explored current practice related to BC considerations via a follow-up to a 2013 internationally distributed survey.

RESULTS

The review found that a higher level of body fat was negatively associated with endurance performance, while a gain in muscle mass resulted in performance benefits across sports. BC did not contribute to early talent identification, and no unique cut-off to signify a performance advantage for BC was identified. BC appears to be one of an array of variables impacting performance, and its influence should not be overstated. The survey (125 practitioners, 61 sports and 26 countries) showed subtle changes in BC considerations over time, such as an increased role for sport dietitian/nutrition practitioners as BC measurers (2013: 54%, 2022: 78%); less emphasis on reporting of body fat percentage (2013: 68%, 2022: 46%) and reduced frequency of BC assessment if ≥every fourth week (2013: 18%, 2022: 5%). Respondents remained concerned about a problematic focus on BC (2013: 69%, 2022: 78%). To address these findings, we provide detailed recommendations for BC considerations, including an overview of preferable BC methodology.

CONCLUSIONS

The 'best practice' guidelines stress the importance of a multidisciplinary athlete health and performance team, and the treatment of BC data as confidential medical information. The guidelines provide a health focus around BC, aiming to reduce the associated burden of disordered eating, problematic low energy availability and REDs.

Prediction of performance in a 100-km run from a simple equation

This study aimed to identify predictive variables of performance for a 100-km race (Perf100-km) and develop an equation for predicting this performance using individual data, recent marathon performance (Perfmarathon), and environmental conditions at the start of the 100-km race. All runners who had performed official Perfmarathon and Perf100-km in France, both in 2019, were recruited. For each runner, gender, weight, height, body mass index (BMI), age, the personal marathon record (PRmarathon), date of the Perfmarathon and Perf100-km, and environmental conditions during the 100-km race (i.e., minimal and maximal air temperatures, wind speed, total amount of precipitation, relative humidity and barometric pressure) were collected. Correlations between the data were examined, and prediction equations were then developed using stepwise multiple linear regression analyses. Significant bivariate correlations were found between Perfmarathon (p<0.001, r = 0.838), wind speed (p<0.001, r = -0.545), barometric pressure (p<0.001, r = 0.535), age (p = 0.034, r = 0.246), BMI (p = 0.034, r = 0.245), PRmarathon (p = 0.065, r = 0.204) and Perf100-km in 56 athletes The, 2 prediction equations with larger sample (n = 591) were developed to predict Perf100-km, one including Perfmarathon, wind speed and PRmarathon (model 1, r² = 0.549; standard errors of the estimate, SEE = 13.2%), and the other including only Perfmarathon and PRmarathon (model 2, r² = 0.494; SEE = 14.0%). Perf100-km can be predicted with an acceptable level of accuracy from only recent Perfmarathon and PRmarathon, in amateur athletes who want to perform a 100 km for the first time.

The Relationship between 24 h Ultramarathon Performance and the "Big Three" Strategies of Training, Nutrition, and Pacing

BACKGROUND

The present case study examined the relationship between 24 h ultramarathon performance and the "big three" strategies of training, nutrition, and pacing.

METHODS

A 32-year-old male ultramarathon runner (body mass: 68.5 kg, height: 179 cm) participated in a 24 h ultramarathon race. Training status was quantified based on from a GPS sports watch. The nutritional status was evaluated during the week leading up to the race, and blood glucose level and heart rate were measured during the race.

RESULTS

His aim of the distance was 200 km, but the actual performance was 171.760 km. The blood glucose level was stable because of adequate CHO intake before (7.2 ± 0.8 g/kg/day) and during the race (48 g/h). The running speed decreased in the middle and later stages of the race despite adequate CHO intake and a lack of high intensity running in the early stage of the race. The longest training session before the race (80 km) had to be significantly shorter compared to the aim.

CONCLUSIONS

For optimal 24 h ultramarathon performance, the "big three" strategies of training, nutrition, and pacing are all important. However, the performance level estimated based on previous studies may be achievable even with insufficient training, as long as the nutritional and pacing strategies are appropriate.

Potential Long-Term Health Problems Associated with Ultra-Endurance Running: A Narrative Review

It is well established that physical activity reduces all-cause mortality and can prolong life. Ultra-endurance running (UER) is an extreme sport that is becoming increasingly popular, and comprises running races above marathon distance, exceeding 6 h, and/or running fixed distances on multiple days. Serious acute adverse events are rare, but there is mounting evidence that UER may lead to long-term health problems. The purpose of this review is to present the current state of knowledge regarding the potential long-term health problems derived from UER, specifically potential maladaptation in key organ systems, including cardiovascular, respiratory, musculoskeletal, renal, immunological, gastrointestinal, neurological, and integumentary systems. Special consideration is given to youth, masters, and female athletes, all of whom may be more susceptible to certain long-term health issues. We present directions for future research into the pathophysiological mechanisms that underpin athlete susceptibility to long-term issues. Although all body systems can be affected by UER, one of the clearest effects of endurance exercise is on the cardiovascular system, including right ventricular dysfunction and potential increased risk of arrhythmias and hypertension. There is also evidence that rare cases of acute renal injury in UER could lead to progressive renal scarring and chronic kidney disease. There are limited data specific to female athletes, who may be at greater risk of certain UER-related health issues due to interactions between energy availability and sex-hormone concentrations. Indeed, failure to consider sex differences in the design of female-specific UER training programs may have a negative impact on athlete longevity. It is hoped that this review will inform risk stratification and stimulate further research about UER and the implications for long-term health.

Association between dietary practice, body composition, training volume and sport performance in 100-Km elite ultramarathon runners

BACKGROUND & AIM

Sport performance during competitions is a central goal for athletes, and several factors have been identified that appear to have an association with better performance in different sport disciplines. However, the data are still not conclusive in ultramarathon runners. Accordingly, this study aimed to assess the potential associations between anthropometric, body composition, dietary and training factors and athletic performance in 100-Km elite ultramarathon runners.

METHODS

Body mass index (BMI), body composition, training volume, Mediterranean dietary adequacy score (MDAS) and "100-Km race competition record" were assessed in 10 elite ultramarathon runners from the Italian Ultramarathon and Trail Association (IUTA) of the Italian national team.

RESULTS

The study sample had a mean age of 41.1 ± 7.59 years and BMI of 21.66 ± 1.11 kg/m². Female athletes had a lower appendicular skeletal muscle index (ASMI) and 100-Km race competition record, and a higher trunk fat percentage and MDAS compared to males. Correlation analysis revealed a significant association between the 100-Km race competition record and age, gender, ASMI, training volume, total body and trunk fat percentages. However, after correcting for confounders, partial correlation analysis confirmed only the association between training volume and 100-Km race competition record (ρ = -0.891, p = 0.009).

CONCLUSION

Our findings provide evidence that a higher training volume expressed as Kilometers per week is an independent variable associated with better performance in 100-Km race competitions in elite ultramarathon runners. Future studies are needed to assess the usefulness of programs based on the increase of training volume as a strategy to improve athletic performance in 100-Km races in this specific population.

To Be a Champion of the 24-h Ultramarathon Race. If Not the Heart ... Mosaic Theory?

This comprehensive case analysis aimed to identify the features enabling a runner to achieve championship in 24-h ultramarathon (UM) races. A 36-year-old, multiple medalist of the World Championships in 24-h running, was assessed before, one and 10 days after a 24-h run. Results of his extensive laboratory and cardiological diagnostics with transthoracic echocardiography (TTE) and a one-time cardiopulmonary exercise test (CPET) were analyzed. After 12 h of running (approximately 130 km), the athlete experienced an increasing pain in the right knee. His baseline clinical data were within the normal range. High physical efficiency in CPET (VO₂max 63 mL/kg/min) was similar to the average achieved by other ultramarathoners who had significantly worse results. Thus, we also performed genetic tests and assessed his psychological profile, body composition, and markers of physical and mental stress (serotonin, cortisol, epinephrine, prolactin, testosterone, and luteinizing hormone). The athlete had a mtDNA haplogroup H (HV0a1 subgroup, belonging to the HV cluster), characteristic of athletes with the highest endurance. Psychological studies have shown high and very high intensity of the properties of individual scales of the tools used mental resilience (62–100% depending on the scale), openness to experience (10th sten), coherence (10th sten), positive perfectionism (100%) and overall hope for success score (10th sten). The athlete himself considers the commitment and mental support of his team to be a significant factor of his success. Body composition assessment (%fat 13.9) and the level of stress markers were unremarkable. The tested athlete showed a number of features of the champions of ultramarathon runs, such as: inborn predispositions, mental traits, level of training, and resistance to pain. However, none of these features are reserved exclusively for “champions”. Team support’s participation cannot be underestimated. The factors that guarantee the success of this elite 24-h UM runner go far beyond physiological and psychological explanations. Further studies are needed to identify individual elements of the putative “mosaic theory of being a champion”.

Relationship between Biological Maturation, Physical Fitness, and Kinanthropometric Variables of Young Athletes: A Systematic Review and Meta-Analysis

There is a growing interest in knowing the relationship between biological maturation and sport performance-related variables of young athletes. The objective of this study is to analyze the relationship between biological maturation, physical fitness, and kinanthropometric variables of athletes during their growing period, according to their sex. The systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and the search protocol was registered in PROSPERO, code: CRD42020208397. A search through the PubMed, Web of Sciences, and EBSCO databases was performed. A total of 423 studies were screened and 13 were included in the meta-analysis. The meta-analysis was completed by using the mean and standard deviation of each variable according to each maturation status (early, on time, or late). Differences depending on maturation were found on physical fitness, with better results in the advanced maturational groups in the male population (standard mean difference (SMD) = 0.17-2.31; p < 0.001-0.05). Differences depending on maturation were found for kinanthropometric variables in males (SMD = 0.37-2.31; p < 0.001-0.002) and height and body mass in females (SMD = 0.96-1.19; p < 0.001). In conclusion, the early maturation group showed higher values in kinanthropometric variables and better results in physical fitness, highlighting the importance of the maturational process in the talent selection programs. Despite that, more research is needed to clarify the relationship of maturation with the other variables on female populations and the changes in the muscle and bone variables during the maturation processes of both sexes.

Long distance running - Can bioprofiling predict success in endurance athletes?

The TransEuropeFootRace (TEFR) was one of the most extreme multistage competitions worldwide. The ultramarathon took the runners over a distance of 4487 km, from Bari, Italy, to the North Cape, Norway, in 64 days. The participating ultra-long-distance runners had to complete almost two marathons per day (~70 km). The race was accompanied by a research team analysing adaptations of different organ systems of the human body that were exposed to a chronic lack of regeneration time. Here, we analyzed runner's urine using mass spectrometric profiling of thousands of low-molecular weight compounds. The results indicated that pre-race molecular factors can predict finishers and separate them from nonfinishers already before the race. These observations were related to the training volume as finishers ran about twice as many kilometers per week before TEFR than nonfinishers, thus apparently achieving a higher performance level and resistance against overuse. While this hypothesis needs to be validated in future long-distance races, the bioprofiling experiments suggest that the competition readiness of the runners is measurable and might be adjustable.

The Impact of Sex and Performance Level on Pacing Behavior in a 24-h Ultramarathon

Purpose: We analyzed the impact of sex, performance level and substantial speed reductions (SSR) on pacing in the VI Rio 24-h Marines Ultramarathon. This will provide insights into the importance of minimizing speed variations in relation to optimal pacing in endurance events.

Methods: Runners (30 males and 21 females), classified as high- (HP) and low-performance (LP) ran the race while having their time recorded every 400 m. The pacing was analyzed as the first 10% (initial epoch), the following 80% (intermediate epoch) and the last 10% of the race (final epoch). The time percentage spent at speeds <3.5 km·h⁻¹ (SSR), 3.5 to 5.9 km·h⁻¹ (walking speed), 6.0 to 8.0 km·h⁻¹ (walk-to-running transition speed) and > 8.0 km·h⁻¹ (running speed) was calculated.

Results: Runners showed a reverse J-shaped pacing (P < 0.001) regardless of sex and performance level, although male (P < 0.004) and HP runners (P < 0.001) have preserved a higher mean speed throughout the race. Male and HP runners spent more time at running speed (P < 0.001) and less time at SSR (P < 0.001) than female and LP runners. Total distance was inversely correlated with the number of SSR and speed CV in male (r = −0.47 and r = −0.64, respectively) and female (r = −0.61 and r = −0.47, respectively).

Conclusion: Male, HP runners showed less SSR, conserving a higher mean speed with less variation throughout the race. Results suggest that conservative pacing strategies, with lower speeds in the beginning and higher speeds toward the end, may be the most adequate for different endurance running disciplines. Results also show different competition dynamics between men and women, which warrants further exploration in ultramarathons as well as other IAAF events.

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.

Training and Body Composition during Preparation for a 48-Hour Ultra-Marathon Race: A Case Study of a Master Athlete

Although the acute effects of ultra-endurance exercise on body composition have been well studied, limited information exists about the chronic adaptations of body composition to ultra-endurance training. The aim of the present study was to examine the day-by-day variation of training and body composition of a master athlete during the preparation for a 48-hour ultra-marathon race. For all training sessions (n = 73) before the race, the running distance, duration, and pace were recorded, and body mass, body fat (BF), body water (%), visceral fat, fat-free mass (FFM), four circumferences (i.e., waist, upper arm, thigh and calf), and eight skinfolds (i.e., chest, mid-axilla, triceps, subscapular, abdomen, iliac crest, thigh and calf) were measured accordingly in a 53-year-old experienced ultra-endurance athlete (body mass 80.1 kg, body height 177 cm, body mass index 25.6 kg·m⁻²). The main findings of the present study were that (a) the training plan of the ultra-endurance master athlete followed a periodization pattern with regard to exercise intensity and training volume, which increased over time, (b) the body mass, BF, and FFM decreased largely during the first 30 training sessions, and (c) the circumferences and skinfolds reflected the respective decrease in BF. The findings of this case study provided useful information about the variation of training and body composition during the preparation for an ultra-marathon race in a male master ultra-marathoner. The preparation for an ultra-endurance race seems to induce pronounced changes in body mass and body composition.

Physiology and Pathophysiology in Ultra-Marathon Running

In this overview, we summarize the findings of the literature with regards to physiology and pathophysiology of ultra-marathon running. The number of ultra-marathon races and the number of official finishers considerably increased in the last decades especially due to the increased number of female and age-group runners. A typical ultra-marathoner is male, married, well-educated, and ~45 years old. Female ultra-marathoners account for ~20% of the total number of finishers. Ultra-marathoners are older and have a larger weekly training volume, but run more slowly during training compared to marathoners. Previous experience (e.g., number of finishes in ultra-marathon races and personal best marathon time) is the most important predictor variable for a successful ultra-marathon performance followed by specific anthropometric (e.g., low body mass index, BMI, and low body fat) and training (e.g., high volume and running speed during training) characteristics. Women are slower than men, but the sex difference in performance decreased in recent years to ~10–20% depending upon the length of the ultra-marathon. The fastest ultra-marathon race times are generally achieved at the age of 35–45 years or older for both women and men, and the age of peak performance increases with increasing race distance or duration. An ultra-marathon leads to an energy deficit resulting in a reduction of both body fat and skeletal muscle mass. An ultra-marathon in combination with other risk factors, such as extreme weather conditions (either heat or cold) or the country where the race is held, can lead to exercise-associated hyponatremia. An ultra-marathon can also lead to changes in biomarkers indicating a pathological process in specific organs or organ systems such as skeletal muscles, heart, liver, kidney, immune and endocrine system. These changes are usually temporary, depending on intensity and duration of the performance, and usually normalize after the race. In longer ultra-marathons, ~50–60% of the participants experience musculoskeletal problems. The most common injuries in ultra-marathoners involve the lower limb, such as the ankle and the knee. An ultra-marathon can lead to an increase in creatine-kinase to values of 100,000–200,000 U/l depending upon the fitness level of the athlete and the length of the race. Furthermore, an ultra-marathon can lead to changes in the heart as shown by changes in cardiac biomarkers, electro- and echocardiography. Ultra-marathoners often suffer from digestive problems and gastrointestinal bleeding after an ultra-marathon is not uncommon. Liver enzymes can also considerably increase during an ultra-marathon. An ultra-marathon often leads to a temporary reduction in renal function. Ultra-marathoners often suffer from upper respiratory infections after an ultra-marathon. Considering the increased number of participants in ultra-marathons, the findings of the present review would have practical applications for a large number of sports scientists and sports medicine practitioners working in this field.

Ultra-obligatory running among ultramarathon runners

Participants in the Ultrarunners Longitudinal TRAcking (ULTRA) Study were asked to answer "yes" or "no" to the question "If you were to learn, with absolute certainty, that ultramarathon running is bad for your health, would you stop your ultramarathon training and participation?" Among the 1349 runners, 74.1% answered "no". Compared with those answering "yes", they were younger (p < 0.0001), less likely to be married (p = 0.019), had less children (p = 0.0095), had a lower health orientation (p < 0.0001) though still high, and higher personal goal achievement (p = 0.0066), psychological coping (p < 0.0001) and life meaning (p = 0.0002) scores on the Motivations of Marathoners Scales. Despite a high health orientation, most ultramarathon runners would not stop running if they learned it was bad for their health as it appears to serve their psychological and personal achievement motivations and their task orientation such that they must perceive enhanced benefits that are worth retaining at the risk of their health.

Race predictors and hemodynamic alteration after an ultra-trail marathon race

OBJECTIVE

Unique rough-terrain ultra-trail running races have increased in popularity. Concerns regarding the suitability of the candidates make it difficult for organizers to manage safety regulations. The purpose of this study was to identify possible race predictors and assess hemodynamic change after long endurance races.

METHODS

We studied 228 runners who competed in a 66 km-trail running race. A questionnaire and noninvasive hemodynamic flow assessment including blood pressure, heart rate, stroke volume, stroke volume variation, systemic vascular resistance, cardiac index, and oxygen saturation were used to determine physiologic alterations and to identify finish predictors. One hundred and thirty volunteers completed the questionnaire, 126 participants had a prerace hemodynamic assessment, and 33 of these participants completed a postrace assessment after crossing the finish line. The participants were divided into a finisher group and a nonfinisher group.

RESULTS

The average age of all runners was 37 years (range of 24-56 years). Of the 228 runners, 163 (71.5%) were male. There were 189 (82.9%) finishers. Univariable analysis indicated that the finish predictors included male gender, longest distance ever run, faster running records, and lower diastolic pressure. Only a lower diastolic pressure was a significant predictor of race finishing (diastolic blood pressure 74-84 mmHg: adjusted odd ratio 3.81; 95% confidence interval [CI] =1.09-13.27 and diastolic blood pressure <74 mmHg: adjusted odd ratio 7.74; 95% CI =1.57-38.21) using the figure from the multivariable analysis. Among the finisher group, hemodynamic parameters showed statistically significant differences with lower systolic blood pressure (135.9±14.8 mmHg vs 119.7±11.3 mmHg; p<0.001), faster heart rate (72.6±10.7 bpm vs 96.4±10.4 bpm; p<0.001), lower stroke volume (43.2±13.6 mL vs 29.3±10.1 mL; p<0.001), higher stroke volume variation; median (interquartile range) (36% [25%-58%] vs 53% [33%-78%]; p<0.001), and lower oxygen saturation (97.4%±1.0% vs 96.4%±1.0%; p<0.001). Systemic vascular resistance and cardian index did not change significantly.

CONCLUSION

The only race finishing predictor from the multivariable analysis was lower diastolic pressure. Finishers seem to have a hypovolemic physiologic response and a lower level of oxygen saturation.

The age of the best ultramarathon performance - the case of the "Comrades Marathon"

The aim of the present study was to determine the age of the fastest running speed in 202,370 runners (34,090 women and 168,280 men) competing in the "Comrades Marathon" between 1994 and 2015 using non-linear regression analysis (second order polynomial function). When all runners were considered in 1-year age intervals, the fastest running speed (9.61 ± 1.65 km/h) was achieved at the age of 29.89 years in men, whereas women achieved it at the age of 35.96 years 8.60 ± 1.10 km/h. When the fastest runners were considered in 1-year intervals, the fastest running speed (16.65 km/h) was achieved in men at the age of 36.38 years. For the fastest women, the age of the fastest running speed (13.89 km/h) was 32.75 years. To summarize, for all runners, men achieved the best ultramarathon performance ~6 years earlier than women. When the fastest runners were considered, however, men achieved the best performance ~4 years later than women.

Do women reduce the gap to men in ultra-marathon running?

The aim of the present study was to examine sex differences across years in performance of runners in ultra-marathons lasting from 6 h to 10 days (i.e. 6, 12, 24, 48, 72, 144, and 240 h). Data of 32,187 finishers competing between 1975 and 2013 with 93,109 finishes were analysed using multiple linear regression analyses. With increasing age, the sex gap for all race durations increased. Across calendar years, the gap between women and men decreased in 6, 72, 144 and 240 h, but increased in 24 and 48 h. The men-to-women ratio differed among age groups, where a higher ratio was observed in the older age groups, and this relationship varied by distance. In all durations of ultra-marathon, the participation of women and men varied by age (p < 0.001), indicating a relatively low participation of women in the older age groups. In summary, between 1975 and 2013, women were able to reduce the gap to men for most of timed ultra-marathons and for those age groups where they had relatively high participation.

The aspect of experience in ultra-triathlon races

Previous experience seems to be an important predictor for endurance and ultra-endurance performance. The present study investigated whether the number of previously completed races and/or the personal best times in shorter races is more predictive for performance in longer non-stop ultra-triathlons such as a Deca Iron ultra-triathlon. All female and male ultra-triathletes who had finished between 1985 and 2014 at least one Double Iron ultra-triathlon (i.e. 7.6 km swimming, 360 km cycling and 84.4 km running), one Triple Iron ultra-triathlon (i.e. 11.4 km swimming, 540 km cycling and 126.6 km running), one Quintuple Iron ultra-triathlon (i.e. 19 km swimming, 900 km cycling and 221 km running) and one Deca Iron ultra-triathlon (i.e. 38 km swimming, 1,800 km cycling and 422 km running) were identified and their best race times for each distance were recorded. Multiple regression analysis (stepwise, forward selection, p of F for inclusion <0.05, p of F for exclusion >0.1, listwise deletion) was used to determine all variables correlating to overall race time and performance in split disciplines for both Quintuple and Deca Iron ultra-triathlon. The number of finished shorter races (i.e. Double and Triple Iron ultra-triathlon) was not associated with the number of finished longer races (i.e. Quintuple and Deca Iron ultra-triathlon) whereas both split and overall race times correlated to split and overall race times of the longer races with the exception of the swimming split times in Double Iron ultra-triathlon showing no correlation with swimming split times in both Quintuple and Deca Iron ultra-triathlon. In summary, previous experience seemed of importance in performance for longer ultra-triathlon races (i.e. Quintuple and Deca Iron ultra-triathlon) where the personal best times of shorter races (i.e. Double and Triple Iron ultra-triathlon) were important, but not the number of previously finished races. For athletes and coaches, fast race times in shorter ultra-triathlon races (i.e. Double and Triple Iron ultra-triathlon) are more important than a large of number finished races in order to achieve a fast race time in a longer ultra-triathlon (i.e. Quintuple and Deca Iron ultra-triathlon).

Exercise behavior of ultramarathon runners: baseline findings from the ULTRA study

Little is known about exercise habits of those who compete in foot races longer than the standard 42-km marathon distance. The purpose of this work was to describe the past-year and lifetime exercise patterns of a large cohort of ultramarathon runners. Information on exercise history was collected on 1,345 current and former ultramarathon runners as baseline data for participation in a longitudinal observational study. Median age at the first ultramarathon was 36 years, and the median number of years of regular running before the first ultramarathon was 7 (interquartile range, 3-15). Age at first ultramarathon did not change across the past several decades, but there was evidence of an inverse relationship (r = -0.13, p < 0.0001) between number of years of regular running before the first ultramarathon and calendar year. The active ultramarathon runners (n = 1,212) had a previous year median running distance of 3,347 km, which was minimally related to age (r = -0.068, p = 0.018), but mostly related to their longest ultramarathon competition of the year (p < 0.0001). Running injuries represented the most common reason for discontinuation of regular running, whereas work and family commitments were reported as the main reasons for not running an ultramarathon in the previous year among those who were regularly running and intending to run ultramarathons again. We conclude that runners tend to be well into adulthood and with several years of running experience before running their first ultramarathon, but 25% have only been regularly running for 3 years or less at the time of their first ultramarathon.

What is the age for the fastest ultra-marathon performance in time-limited races from 6 h to 10 days?

Recent findings suggested that the age of peak ultra-marathon performance seemed to increase with increasing race distance. The present study investigated the age of peak ultra-marathon performance for runners competing in time-limited ultra-marathons held from 6 to 240 h (i.e. 10 days) during 1975-2013. Age and running performance in 20,238 (21%) female and 76,888 (79%) male finishes (6,863 women and 24,725 men, 22 and 78%, respectively) were analysed using mixed-effects regression analyses. The annual number of finishes increased for both women and men in all races. About one half of the finishers completed at least one race and the other half completed more than one race. Most of the finishes were achieved in the fourth decade of life. The age of the best ultra-marathon performance increased with increasing race duration, also when only one or at least five successful finishes were considered. The lowest age of peak ultra-marathon performance was in 6 h (33.7 years, 95% CI 32.5-34.9 years) and the highest in 48 h (46.8 years, 95% CI 46.1-47.5). With increasing number of finishes, the athletes improved performance. Across years, performance decreased, the age of peak performance increased, and the age of peak ultra-marathon performance increased with increasing number of finishes. In summary, the age of peak ultra-marathon performance increased and performance decreased in time-limited ultra-marathons. The age of peak ultra-marathon performance increased with increasing race duration and with increasing number of finishes. These athletes improved race performance with increasing number of finishes.

The best triathletes are older in longer race distances - a comparison between Olympic, Half-Ironman and Ironman distance triathlon

The purpose of this study was (i) to determine the age of peak triathlon performance for world class athletes competing in Olympic, Half-Ironman and Ironman distance races and (ii) to investigate a potential change in the age of the annual fastest athletes across years. Data of ages and race times of all finishers in the international top races over the three distances between 2003 and 2013 were collected and the annual top ten women and men were analysed using linear, non-linear and hierarchical multivariate regression analyses. The age of peak male performance was 27.1 ± 4.9 years in the Olympic, 28.0 ± 3.8 years in the Half-Ironman and 35.1 ± 3.6 years in the Ironman distance and the age of peak male performance was higher in the Ironman compared to the Olympic (p < 0.05) and the Half-Ironman distance (p < 0.05) triathlon. The age of peak female performance was 26.6 ± 4.4 years in the Olympic, 31.6 ± 3.4 years in the Half-Ironman and 34.4 ± 4.4 years in the Ironman distance and the age of peak female performance was lower in the Olympic compared to the Half-Ironman (p < 0.05) and Ironman distance (p < 0.05) triathlon. The age of the annual top ten women and men remained unchanged over the last decade in the Half-Ironman and the Ironman distance. In the Olympic distance, however, the age of the annual top ten men decreased slightly. To summarize, the age of peak triathlon performance was higher in the longer triathlon race distances (i.e. Ironman) and the age of the annual top triathletes remained mainly stable over the last decade. With these findings top athletes competing at world class level can plan their career more precisely as they are able to determine the right time in life to switch from the shorter (i.e. Olympic distance) to the longer triathlon race distances (i.e. Half-Ironman and Ironman) in order to continuously compete in triathlon races at world class level.

Age group performances in 100 km and 100 miles ultra-marathons

Improved performance has been reported for master runners (i.e. athletes older than 40 years) in both single marathons and single ultra-marathons. This study investigated performance trends of age group ultra-marathoners competing in all 100 km and 100 miles races held worldwide between 1971 and 2013. Changes in running speeds across years were investigated for the annual ten fastest 5-year age group finishers using linear, non-linear and multi-level regression analyses. In 100 km, running speed remained unchanged in women in 25–29 years, increased non-linearly in 30–34 to 55–59 years, and linearly in 60–64 years. In men, running speed increased non-linearly in 18–24 to 60–64 years and linearly in 65–69 to 75–79 years. In 100 miles, running speed increased in women linearly in 25–29 and 30–34 years, non-linearly in 35–39 to 45–49 years, and linearly in 50–54 and 55–59 years. For men, running speed increased linearly in 18–24 years, non-linearly in 25–29 to 45–49 years, and linearly in 50–54 to 65–69 years. Overall, the faster race times over the last 30 years are a result of all top ten finishers getting faster. These findings suggest that athletes in younger to middle age groups (i.e. 25–35 to 50–65 years depending upon sex and distance) have reached their limits due to a non-linear increase in running speed whereas runners in very young (i.e. younger than 25–35 years) and older age groups (i.e. older than 50–65 years) depending upon sex and distance might still improve their performance due to a linear increase in running speed.

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.

Runners in their forties dominate ultra-marathons from 50 to 3,100 miles

OBJECTIVES

This study investigated performance trends and the age of peak running speed in ultra-marathons from 50 to 3,100 miles.

METHODS

The running speed and age of the fastest competitors in 50-, 100-, 200-, 1,000- and 3,100-mile events held worldwide from 1971 to 2012 were analyzed using single- and multi-level regression analyses.

RESULTS

The number of events and competitors increased exponentially in 50- and 100-mile events. For the annual fastest runners, women improved in 50-mile events, but not men. In 100-mile events, both women and men improved their performance. In 1,000-mile events, men became slower. For the annual top ten runners, women improved in 50- and 100-mile events, whereas the performance of men remained unchanged in 50- and 3,100-mile events but improved in 100-mile events. The age of the annual fastest runners was approximately 35 years for both women and men in 50-mile events and approximately 35 years for women in 100-mile events. For men, the age of the annual fastest runners in 100-mile events was higher at 38 years. For the annual fastest runners of 1,000-mile events, the women were approximately 43 years of age, whereas for men, the age increased to 48 years of age. For the annual fastest runners of 3,100-mile events, the age in women decreased to 35 years and was approximately 39 years in men.

CONCLUSION

The running speed of the fastest competitors increased for both women and men in 100-mile events but only for women in 50-mile events. The age of peak running speed increased in men with increasing race distance to approximately 45 years in 1,000-mile events, whereas it decreased to approximately 39 years in 3,100-mile events. In women, the upper age of peak running speed increased to approximately 51 years in 3,100-mile events.

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.

Sex differences in 24-hour ultra-marathon performance--a retrospective data analysis from 1977 to 2012

OBJECTIVES

This study examined the changes in running performance and the sex differences between women and men in 24-hour ultra-marathons held worldwide from 1977 to 2012.

METHOD

Changes in running speed and ages of the fastest 24-hour ultra-marathoners were determined using single- and multi-level regression analyses.

RESULTS

From 1977 to 2012, the sex differences in 24-hour ultra-marathon performance were 4.6±0.5% for all women and men, 13.3% for the annual fastest finishers, 12.9±0.8% for the top 10 and 12.2±0.4% for the top 100 finishers. Over time, the sex differences decreased for the annual fastest finishers to 17%, for the annual 10 fastest finishers to 11.3±2.2% and for the annual 100 fastest finishers to 14.2±1.8%. For the annual fastest men, the age of peak running speed increased from 23 years (1977) to 53 years (2012). For the annual 10 and 100 fastest men, the ages of peak running speed were unchanged at 40.9±2.5 and 44.4±1.1 years, respectively. For women, the ages of the annual fastest, the annual 10 fastest and the annual 100 fastest remained unchanged at 43.0±6.1, 43.2±2.6 and 43.8±0.8 years, respectively.

CONCLUSION

The gap between the annual top, annual top 10 and annual top 100 female and male 24-hour ultra-marathoners decreased over the last 35 years; however, it seems unlikely that women will outrun men in 24-hour ultra-marathons in the near future. The fastest 24-hour ultra-marathoners worldwide achieved their peak performance at the age of master athletes (>35 years).

Finisher and performance trends in female and male mountain ultramarathoners by age group

Background

This study examined changes according to age group in the number of finishers and running times for athletes in female and male mountain ultramarathoners competing in the 78 km Swiss Alpine Marathon, the largest mountain ultramarathon in Europe and held in high alpine terrain.

Methods

The association between age and performance was investigated using analysis of variance and both single and multilevel regression analyses.

Results

Between 1998 and 2011, a total of 1,781 women and 12,198 men finished the Swiss Alpine Marathon. The number of female finishers increased (r² = 0.64, P = 0.001), whereas the number of male finishers (r² = 0.18, P = 0.15) showed no change. The annual top ten men became older and slower, whereas the annual top ten women became older but not slower. Regarding the number of finishers in the age groups, the number of female finishers decreased in the age group 18–24 years, whereas the number of finishers increased in the age groups 30–34, 40–44, 45–49, 50–54, 55–59, 60–64, and 70–74 years. In the age groups 25–29 and 35–39 years, the number of finishers showed no changes across the years. In the age group 70–74 years, the increase in number of finishers was linear. For all other age groups, the increase was exponential. For men, the number of finishers decreased in the age groups 18–24, 25–29, 30–34, and 35–39 years. In the age groups 40–44, 45–49, 50–54, 55–59, 60–64, 70–74, and 75–79 years, the number of finishers increased. In the age group 40–44 years, the increase was linear. For all other age groups, the increase was exponential. Female finishers in the age group 40–44 years became faster over time. For men, finishers in the age groups 18–24, 25–29, 30–34, 40–44, and 45–49 years became slower.

Conclusion

The number of women older than 30 years and men older than 40 years increased in the Swiss Alpine Marathon. Performance improved in women aged 40–44 years but decreased in male runners aged 18–49 years.

Increase in finishers and improvement of performance of masters runners in the Marathon des Sables

Aim

The aim of the study was to examine finisher and performance trends of ultrarunners in the Marathon des Sables, the world’s largest multistage ultramarathon.

Methods

The age and running speed was analyzed for 6945 finishes of 909 women and 6036 men between 2003 and 2012 at the Marathon des Sables covering about 240 km in the Moroccan desert.

Results

The number of finishes increased significantly for both women and men from 2003–2012. The annual number of finishes increased in age groups: 30–34 years (r² = 0.50; P = 0.021), 45–49 years (r² = 0.81; P = 0.0004), and 50–54 years (r² = 0.46; P = 0.029) for women and in all age groups older than 35 years for men (35–39 years: r² = 0.64, P = 0.0054; 40–44 years: r² = 0.67, P = 0.0036; 45–49 years: r² = 0.77, P = 0.0007; 50–54 years: r² = 0.72, P = 0.0018; 55–59 years: r² = 0.42, P = 0.041; and 60–64 years: r² = 0.67, P = 0.0038). The fastest running speed was achieved by runners in the age group of 35–39 years for both sexes. The mean age of overall finishers was 41.0 ± 9.1 years for women and 41.3 ± 9.5 years for men. For men, running speed improved for athletes in the age group of 35–39 years (r² = 0.44; P = 0.036) and of 40–44 years (r² = 0.51; P = 0.019), while it decreased for athletes in the age group of 30–34 years (r² = 0.66, P = 0.0039). For women, running speed remained stable during the study period for athletes in all age groups.

Conclusion

These data suggest that the number of finishers of masters runners older than 40 years increased for both sexes at the Marathon des Sables, as has been previously observed for single-stage ultramarathons. In contrast to women, men aged 35 to 44 years improved running speed during the study period. Future studies are needed to investigate the reasons for the growing numbers of masters athletes in endurance sports and their improvement in performance.

Analysis of performance and age of the fastest 100-mile ultra-marathoners worldwide

OBJECTIVES

The performance and age of peak ultra-endurance performance have been investigated in single races and single race series but not using worldwide participation data. The purpose of this study was to examine the changes in running performance and the age of peak running performance of the best 100-mile ultra-marathoners worldwide.

METHOD

The race times and ages of the annual ten fastest women and men were analyzed among a total of 35,956 finishes (6,862 for women and 29,094 for men) competing between 1998 and 2011 in 100-mile ultra-marathons.

RESULTS

The annual top ten performances improved by 13.7% from 1,132±61.8 min in 1998 to 977.6±77.1 min in 2011 for women and by 14.5% from 959.2±36.4 min in 1998 to 820.6±25.7 min in 2011 for men. The mean ages of the annual top ten fastest runners were 39.2±6.2 years for women and 37.2±6.1 years for men. The age of peak running performance was not different between women and men (p>0.05) and showed no changes across the years.

CONCLUSION

These findings indicated that the fastest female and male 100-mile ultra-marathoners improved their race time by ∼14% across the 1998-2011 period at an age when they had to be classified as master athletes. Future studies should analyze longer running distances (>200 km) to investigate whether the age of peak performance increases with increased distance in ultra-marathon running.

Participation and performance trends in ultra-endurance running races under extreme conditions - 'Spartathlon' versus 'Badwater'

BACKGROUND

The aim of the present study was to compare the trends in participation, performance and age of finishers in 'Badwater' and 'Spartathlon' as two of the toughest ultramarathons in the world of more than 200 km of distance.

METHODS

Running speed and age of male and female finishers in Badwater and Spartathlon were analyzed from 2000 to 2012. Age of peak performance and sex difference in running speed were investigated during the studied period.

RESULTS

The number of female and male finishes increased in Badwater and Spartathlon. Women accounted on average for 21.5% ± 6.9% in Badwater and 10.8% ± 2.3% in Spartathlon. There was a significant increase in female participation in Badwater from 18.4% to 19.1% (p < 0.01) and in Spartathlon from 11.9% to 12.5% (p = 0.02). In men, the age of finishers was higher in Badwater (46.5 ± 9.3 years) compared to Spartathlon (44.8 ± 8.2 years) (p < 0.01). The age of female finishers of both races was similar with 43.0 ± 7.5 years in Badwater and 44.5 ± 7.8 years in Spartathlon (p > 0.05). Over the years, the age of the annual five fastest men decreased in Badwater from 42.4 ± 4.2 to 39.8 ± 5.7 years (p < 0.05). For women, the age remained unchanged at 42.3 ± 3.8 years in Badwater (p > 0.05). In Spartathlon, the age was unchanged at 39.7 ± 2.4 years for men and 44.6 ± 3.2 years for women (p > 0.05). In Badwater, women and men became faster over the years. The running speed increased from 7.9 ± 0.7 to 8.7 ± 0.6 km/h (p < 0.01) in men and from 5.4 ± 1.1 to 6.6 ± 0.5 km/h (p < 0.01) in women. The sex difference in running speed remained unchanged at 19.8% ± 4.8% (p > 0.05). In Spartathlon, the running speed was stable over time at 10.8 ± 0.7 km/h for men and 8.7 ± 0.5 km/h for women (p > 0.05). The sex difference remained unchanged at 19.6% ± 2.5% (p > 0.05).

CONCLUSIONS

These results suggest that for both Badwater and Spartathlon, (a) female participation increased, (b) the fastest finishers were approximately 40 to 45 years, and (c) the sex difference was at approximately 20%. Women will not outrun men in both Badwater and Spartathlon races. Master ultramarathoners can achieve a high level of performance in ultramarathons greater than 200 km under extreme conditions.

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).

Ultramarathon runners: nature or nurture?

Ultramarathon running is increasingly popular. An ultramarathon is defined as a running event involving distances longer than the length of a traditional marathon of 42.195 km. In ultramarathon races, ~80% of the finishers are men. Ultramarathoners are typically ~45 y old and achieve their fastest running times between 30 and 49 y for men, and between 30 and 54 y for women. Most probably, ultrarunners start with a marathon before competing in an ultramarathon. In ultramarathoners, the number of previously completed marathons is significantly higher than the number of completed marathons in marathoners. However, recreational marathoners have a faster personal-best marathon time than ultramarathoners. Successful ultramarathoners have 7.6 ± 6.3 y of experience in ultrarunning. Ultramarathoners complete more running kilometers in training than marathoners do, but they run more slowly during training than marathoners. To summarize, ultramarathoners are master runners, have a broad experience in running, and prepare differently for an ultramarathon than marathoners do. However, it is not known what motivates male ultramarathoners and where ultramarathoners mainly originate. Future studies need to investigate the motivation of male ultramarathoners, where the best ultramarathoners originate, and whether they prepare by competing in marathons before entering ultramarathons.

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.

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.

Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes

Background

The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes.

Methods

Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis.

Results

A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001) and in an Olympic distance triathlon (P < 0.0001) were the best variables related to Ironman race time. Ironman race time (minutes) might be partially predicted by the following equation: (r² = 0.65, standard error of estimate = 56.8) = 152.1 + 1.332 × (personal best time in a marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes).

Conclusion

These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes.

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

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r² = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m²) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Leg skinfold thicknesses and race performance in male 24-hour ultra-marathoners

The association of skinfold thicknesses with race performance has been investigated in runners competing over distances of ≤50 km. This study investigated a potential relation between skinfold thicknesses and race performance in male ultra-marathoners completing >50 km in 24 hours. Variables of anthropometry, training, and previous performance were related to race performance in 63 male ultra-marathoners aged 46.9 (standard deviation [SD] 10.3) years, standing 1.78 (SD 0.07) m in height, and weighing 73.3 (SD 7.6) kg. The runners clocked 146.1 (SD 43.1) km during the 24 hours. In the bivariate analysis, several variables were associated with race performance: body mass (r = -0.25); skinfold thickness at axilla (r = -0.37), subscapula (r = -0.28), abdomen (r = -0.31), and suprailiaca (r = -0.30); the sum of skinfold thicknesses (r = -0.32); percentage body fat (r = -0.32); weekly kilometers run (r = 0.31); personal best time in a marathon (r = -0.58); personal best time in a 100-km ultra-run (r = -0.31); and personal best performance in a 24-hour run (r = 0.46). In the multivariate analysis, no anthropometric or training variable was related to race performance. In conclusion, in contrast to runners up to distances of 50 km, skinfold thicknesses of the lower limbs were not related to race performance in 24-hour ultra-marathoners.