Variables that influence Ironman triathlon performance - what changed in the last 35 years?

Origin and age group of the fastest amateur triathletes competing in 'Ironman Hawaii' between 2003 and 2019

Little is known about the prevalent nationalities among the best-placed athletes participating in "Ironman Hawaii." Moreover, the age at which athletes achieve their best performances remains unclear. The present study aimed to compare the prevalent nationalities among the athletes, their respective placement among the top five, and the performance difference between the different age groups in 'Ironman Hawaii' from 2003 to 2019. A total of 30 354 amateur triathletes were selected from the Obsessed Triathlete (OBSTRI) website. A "TOP 5" division filter was applied for further analysis, resulting in 1 851 athletes being included in this study. Among the male runners, Americans participated the most in Ironman events (39%), followed by Germans (10%). Among female runners, Americans participated the most (54%), followed by Australian runners (8%). Male Americans also featured most among the top five (30%), followed by Germans (16%). Female Americans were the most prevalent among the top five (47%), followed by Australian Americans (10%). There were no significant performance differences (p ​> ​0.05) between the 25-29 and 40-44 age groups for either sex. The 45-49 age group presented significantly worse performance than the 35-39 age group for both sexes (p ​< ​0.001). North Americans were the most performant and frequent participants in "Ironman Hawaii." The expected performance decline due to aging was observed after 45 years in both sexes.

Cycling is the most important predictive split discipline in professional Ironman® 70.3 triathletes

Introduction

Our study examined 16,611 records of professional triathletes from 163 Ironman® 70.3 races across 97 countries (2004-2020). The aim was to identify the most predictive discipline-swim, bike, or run-for overall race time.

Methods

We used correlation matrices to compare the dependent variable "finish time" with independent variables "swim time," "bike time," and "run time." This analysis was conducted separately for male and female athletes. Additionally, univariate and multiple linear regression models assessed the strength of these associations.

Results

The results indicated that "bike time" had the strongest correlation with finish time (0.85), followed by "run time" (0.75 for females, 0.82 for males) and "swim time" (0.46 for females, 0.63 for males). Regression models confirmed "bike time" as the strongest predictor of overall race time (R² = 0.8), with "run time" and "swim time" being less predictive.

Discussion

The study concludes that in Ironman 70.3 races, "bike time" is the most significant predictor of overall race performance for both sexes, suggesting a focus on cycling in training and competition strategies. It also highlights a smaller performance gap between genders in swimming than in cycling or running.

Performance and pacing of professional IRONMAN triathletes: the fastest IRONMAN World Championship ever-IRONMAN Hawaii 2022

Pacing during cycling and running in an IRONMAN triathlon has been investigated in only one study with elite IRONMAN triathletes. We have, however, no knowledge of how professional triathletes pace during an IRONMAN World Championship. To investigate the split-by-split speed, pacing strategies and pacing variability in professional female and male IRONMAN World Championship participants in the fastest IRONMAN World Championship ever in IRONMAN Hawaii 2022. For both cycling and running, 25 specific split times were recorded in each discipline. The best 30 men and 30 women overall were chosen from the official IRONMAN website database for further analysis. They were divided into three performance groups: Top 10, 11-20th place, and 21st-30th place. Mean speed, individual linear regressions with the corresponding correlation coefficients, and coefficient of variation were calculated to assess split-by-split speed, pacing strategies, and pacing variability, respectively. In both men's and women's cycling and running segments, the top ten participants exhibited faster split times compared to the slower performance groups. Notably, no discernible differences existed between the 11-20th and 21st-30th place in men's cycling and women's running times. Conversely, in men's running and women's cycling segments, those in the 11-20th place displayed quicker times than those in the 21st-30th place. In the cycling segment across all groups, men demonstrated a more negative pacing pattern (indicating an increase in speed), whereas women exhibited more consistent pacing. In the running segment, the top 10 men and all women's groups showcased relatively similar slightly positive pacing profiles. However, men ranking 11-20th and 21st-30th displayed more pronounced positive pacing strategies, implying a more significant decline in speed over time. In terms of cycling, the variability in pacing remained relatively consistent across the three performance groups. Conversely, during the running segment, the top ten male triathletes and those in the 11-20th place displayed lower pacing variability than their counterparts in the 21st-30th position place and all women's groups. In summary, performance and pacing were examined in professional male and female IRONMAN World Championship participants during IRONMAN Hawaii 2022. Top performers showed faster cycling and running split times, with differences in pacing strategies between sexes. The pacing was more consistent in cycling, while running pacing varied more, particularly among male triathletes in different performance groups.

A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in Ironman® 70.3 Races between 2004 and 2020

Our purpose was to find the fastest race courses for elite Ironman® 70.3 athletes, using machine learning (ML) algorithms. We collected the data of all professional triathletes competing between 2004 and 2020 in Ironman 70.3 races held worldwide. A sample of 16,611 professional athletes originating from 97 different countries and competing in 163 different races was thus obtained. Four different ML regression models were built, with gender, country of origin, and event location considered as independent variables to predict the final race time. For all the models, gender was the most important variable in predicting finish times. Attending to the single decision tree model, the fastest race times in the Ironman® 70.3 World Championship of around ~4 h 03 min would be achieved by men from Austria, Australia, Belgium, Brazil, Switzerland, Germany, France, the United Kingdom, South Africa, Canada, and New Zealand. Considering the World Championship is the target event for most professional athletes, it is expected that training is planned so that they attain their best performance in this event.

I. Shalfawi S. Progression in training volume and perceived psychological and physiological training distress in Norwegian student athletes: A cross-sectional study

This cross-sectional study examined self-reported weekly training volume and perceived training distress in Norwegian student athletes according to gender, type of sport, school program, and school year. The Norwegian version of the Multicomponent Training Distress Scale (MTDS-N) was completed by 608 student athletes (M age = 17.29 ± .94). Univariate and multivariate techniques were used in data analyses. Results revealed significant differences in weekly training volume between sport types. No significant differences in weekly training volume were found for gender, school year, or school program. However, a multivariate effect was found for gender, with females perceiving higher levels of training distress than males. A multivariate interaction effect between school year and training volume was also observed. We recommend that practitioners use a conceptual framework to periodize training and monitor training distress in student athletes, particularly in females, to preserve physiological and psychological well-being and ensure a progressive training overload leading to positive performance development.

Effects of Training on Cardiorespiratory Fitness in Triathletes: A Systematic Review and Meta-Analysis

Triathlon is an aerobic sport, which is commonly measured by maximal aerobic consumption (VO₂max). Objective: to analyze the changes produced in cardiorespiratory and physiological measurements during practice, which determine triathletes’ performance level. A systematic review and a meta-analysis based on PRISMA protocol and registered in PROSPERO (CRD42020189076) was conducted. The research was performed using PubMed, SPORTDiscus, Embase, Dialnet, Web of Science (WOS) and MEDLINE databases during February and March 2020. Studies that measured cardiorespiratory variables in triathletes published in the last 10 years were included. Results: 713 articles were identified, with 25 studies selected for the systematic review and five articles for the meta-analysis. These articles concluded that the main cardiorespiratory variables that determine triathletes’ performance were modified depending on the triathlon segment performed and the athletes’ sex and age. The meta-analysis showed no conclusive results related to the effects of changes in VO₂max in triathletes’ performance [SMD = −0.21; 95%CI: (−0.84 to 0.43)]. Conclusions: cardiorespiratory fitness, in terms of VO₂max and ventilatory thresholds, is the strongest predictor of performance in triathlon. This response may be affected depending on the triathlon segment performed and the athlete’s age or sex, leading to both physiological and biomechanical alterations that affect competition performance.

What Is the Best Discipline to Predict Overall Triathlon Performance? An Analysis of Sprint, Olympic, Ironman® 70.3, and Ironman® 140.6

Objective: To analyze the proportion of dedication in each triathlon discipline (swimming, cycling, and running) and the importance of each separate discipline to predict overall performance of elite triathletes across different triathlon distances.

Methods: Data from 2015 to 2020 (n = 16,667) from official races and athletes in Sprint, Olympic distance, IM 70.3 (Half-Ironman distance), and IM 140.6 (Full-Ironman distance) competitions were included. The proportion of each discipline was calculated individually and compared using general linear models by event distance, sex, and performance level. Automatic linear regression models were applied for each distance considering overall performance as the dependent variable.

Results: A within-distance analysis showed that the best predictor for Sprint is cycling, for Olympic is swimming, for IM 70.3 is cycling, and for IM 140.6 is running. A between-distance analysis revealed that swimming is a better predictor in Olympic distance than in other triathlon distances. Cycling is a poor predictor for overall performance in IM 140.6, and the importance of running to predict overall performance is the highest in IM 140.6 and diminishes with decreasing race distance.

Conclusion: Each discipline represents a different relative portion and importance to predict overall performance depending on the triathlon distance. Swimming is the most important predictor discipline in Sprint- and Olympic-distance triathlon, cycling in IM 70.3, and running in IM 140.6.

Impact of training volume and experience on amateur Ironman triathlon performance

PURPOSE

To investigate the association between training volume, sleep time, signs and symptoms of excessive training (overtraining), and  previous triathlon experience with overall and split race times in the Ironman distance triathlon.

METHODS

Ninety-nine triathletes (19 women and 80 men) answered an online survey containing questions about anthropometric characteristics (body mass and height), weekly training volume (hours per day and days per week), previous experience in Ironman distance triathlon race, and signs and symptoms of excessive training. Data of race times of all participants were collected by a single race (the Ironman Brazil 2019 - Florianópolis). All surveys were collected between 28 and 30 days before the race. The athlete was instructed to answer the questions according to what was happening in the week before completing the survey.

RESULTS

Total race time did not differ among those who trained up to 14 h per week (11:28:46±01:54:30 h:min:sec), between 15 and 20 h per week (11:37:31±01:20:26 h:min:sec) or more than 20 h per week (11:30:18±01:31:28 h:min:sec) (p = 0.922). Total race time of the triathletes who presented (12:42:22±01:49:36 h:min:sec) or no (11:23:06±01:29:02 h:min:sec) unintentional body mass loss (p = 0.006), feeling (12:46:17±02:03:13 h:min:sec) or no (11:24:09±01:28:07 h:min:sec) of decreased performance (p = 0.009) or feeling (12:08:58±01:47:12 h:min:sec) or no (11:16:34±01:24:53 h:min:sec) loss of energy (p = 0.011) in the week prior to the race were significantly different. Triathletes who had a previous experience in Ironman races achieved a better performance (11:15:21±01:32:04 h:min:sec) than those without previous experience (12:06:38±01:32:10 h:min:sec) (p = 0.010).

CONCLUSION

In summary, high volumes of training (more than 20 h per week), when performed forty days before a race, may not have a positive impact on performance compared to lower volumes of training (up to 14 h per week). However, athletes who had a previous experience in Ironman race presented better results in swimming splits and overall race time. Moreover, the presence of overtraining symptoms, such as unintentional loss of weight, sensation of fatigue and/or performance decrease impact negatively triathlon performance.

Cut-Off Values in the Prediction of Success in Olympic Distance Triathlon

Cut-off points and performance-related tools are needed for the development of the Olympic distance triathlon. The purposes of the present study were (i) to determine cut-off values to reach the top three positions in an Olympic distance triathlon; (ii) to identify which discipline present the highest influence on overall race performance and if it has changed over the decades. Data from 1989 to 2019 (n = 52,027) from all who have competed in an official Olympic distance triathlon events (World Triathlon Series and Olympics) were included. The cut-off value to achieve a top three position was calculated. Linear regressions were applied for performance trends overall and for the top three positions of each race. Men had cut-off values of: swimming = 19.5 min; cycling = 60.7 min; running = 34.1 min. Women's cut-off values were: swimming = 20.7 min; cycling = 71.6 min; running = 38.1 min. The running split seemed to be the most influential in overall race time regardless of rank position or sex. In conclusion, cut-offs were established, which can increase the chances of achieving a successful rank position in an Olympic triathlon. Cycling is the discipline with the least influence on overall performance for both men and women in the Olympic distance triathlon. This influence pattern has not changed in the last three decades.

HRV-Based Training for Improving VO2max in Endurance Athletes. A Systematic Review with Meta-Analysis

This review aimed to synthesize evidence regarding interventions based on heart rate variability (HRV)-guided training for VO_2max improvements in endurance athletes and address the issues that impact this performance enhancement. The Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CINAHL Complete, the Web of Science Core Collection, Global Health, Current Contents Connect, and the SciELO citation index were searched. Inclusion criteria were: randomized controlled trials; studies with trained athletes enrolled in any regular endurance training; studies that recruited men, women, and both sexes combined; studies on endurance training controlled by HRV; studies that measured performance with VO_2max. A random-effects meta-analysis calculating the effect size (ES) was used. Moderator analyses (according to the athlete's level and gender) and metaregression (according to the number of participants in each group) were undertaken to examine differences in ES. HRV-guided training and control training enhanced the athletes' VO_2max (p < 0.0001), but the ES for the HRV-guided training group was significantly higher (p < 0.0001; ES_HRVG-CG = 0.187). The amateur level and female subgroup reported better and significant results (p < 0.0001) for VO_2max. HRV-guided training had a small (ES = 0.402) but positive effect on endurance athlete performance (VO_2max), conditioned by the athlete's level and sex.

The Age-Related Performance Decline in Ironman 70.3

Although the age-related decline in sport events has been well studied, little is known on such a decline in recreational triathletes for the Half Ironman distance. Indeed, the few existing studies concentrated on specific aspects such as top events, elite groups, some consecutive years, single locations, or age categories instead of analyzing all the data available. Therefore, the aim of the present study was to examine recreational triathletes’ performance in three split disciplines (swimming, cycling, and running) as well as in overall race time by analyzing all data of Half Ironman finishers found on ironman.com (i.e., 690 races; years 2004 through 2018; 206,524 women (24.6%) and 633,576 men (75.4%), in total 840,100 athletes). The age-dependent decline in Half Ironman started earliest in swimming (from the very first age group on) with a smallest age group delta between 35–49 years in men and 40–54 years in women. The performance decline started at 26 and 28 years in men and women for running; at 34 years for men and 35 years for women in cycling; and at 32 years for men and 31 years for women with regard to overall race time. The results may be used by coaches and recreational athletes alike to plan a triathlon career.

Ultra-endurance triathlon performance and markers of whole-body and gut-specific inflammation

PURPOSE

To examine the influence of the Ultraman Florida triathlon (3 days of non-continuous racing; stage 1: 10 km swim and 144.8 km cycle; stage 2: 275.4 km cycle; stage 3: 84.4 km run) on circulating plasma concentrations of whole-body (C-reactive protein (CRP), interleukin (IL)-6 (IL-6), and IL-10 and surrogate gut-specific inflammatory markers (IL-17 and IL-23), and determine whether these variables are associated with performance.

METHODS

Eighteen triathletes (N = 18; 15 men, 3 women; age: 37 ± 8 yrs) were evaluated at baseline and post-race for circulating concentrations of CRP, IL-6, IL-10, IL-17, and IL-23. Blood samples were drawn two days prior to stage 1 (1600 h) and one day after stage 3 (1200 h).

RESULTS

Plasma CRP significantly increased from baseline (1985.8 ± 5962.3 ng/mL) to post-race (27,013.9 ± 12,888.8 ng/mL, p < 0.001, 13-fold increase). Both plasma IL-6 and IL-10 did not significantly change from baseline to post-race. Baseline and post-race concentrations of IL-17 and IL-23 were below detectable limits. Pearson's correlation between mean finish time and post-race IL-10 revealed a significant positive correlation (r = 0.54, p < 0.05).

CONCLUSIONS

Our results suggest that cytokines such as IL-6 and IL-10 involved in the inflammatory response return to near-baseline concentrations rapidly even after ultra-endurance events of extreme duration. The absence of IL-17 and IL-23 may suggest positive gut adaptations from ultra-endurance training. A significant positive correlation between post-race IL-10 concentrations and mean finish time may indicate that a relationship between anti-inflammatory responses and performance exists.

Contribution of select physiological variables to sprint triathlon performance

BACKGROUND

The purpose of this study was to examine the relationship between select physiological variables and sprint triathlon performance.

METHODS

Moderately trained male triathletes (N.=15) performed two graded maximal exercise tests, on a cycle ergometer and a treadmill. Anaerobic capacity was evaluated, on a cycle ergometer and a treadmill. Within two weeks before or after the testing, all triathletes participated in a sprint triathlon race (750 m sea swim, 20 km cycle, 5 km run).

RESULTS

The results showed that significant correlations exist between VO2max during cycling (r=-0.811, P<0.05) or running (r=-0.757, P<0.05) and overall triathlon performance. Stepwise multiple linear regression analysis, with triathlon performance as the dependent variable and the physiological measures during running as the independent variables, showed that VO2max (mL.min-1.kg-1), Time_CF, CHO_rate @ VT and % body fat yielded the best prediction of performance (R2=0,912). When only the physiological variables from cycling were included into the model, the results showed that VO2max (mL.min-1), VO2max (mL.min-1.kg-1) and PFmax (rpm) explained 88% of the variation in sprint triathlon performance.

CONCLUSIONS

Our data indicate that overall race time for moderately trained triathletes, competing over the sprint distance can be accurately predicted from maximal laboratory tests.

Cycling as the Best Sub-8-Hour Performance Predictor in Full Distance Triathlon

For any triathlon distance (short, Olympic, half-distance and full-distance), competitors spend more time cycling than swimming or running, but running has emerged as the discipline with the greatest influence on overall performance at the Olympic distance. However, there is a lack of evidence on which discipline has the greatest influence on performance in the overall full-distance triathlon (3.8 km swimming/180 km cycling/42.195 km running), especially for the fastest performing athletes of all time. The total race times of 51 fastest triathletes (sub-8-hour) were studied, while for the split times, a sample of 44 participants was considered. The discipline that seemed to better predict total race time was cycling (coefficient = 0.828; p < 0.001), followed by running (coefficient = 0.726; p < 0.001) and swimming (coefficient = 0.476; p < 0.001). Furthermore, cycling was the discipline with the highest performance improvement over the years, whereas running had a slightly decrease. In conclusion, cycling seems to be the discipline with greater influence in final result for the full-distance triathlon.

Pacing Strategy of a Full Ironman Overall Female Winner on a Course With Major Elevation Changes

Pryor, JL, Adams, WM, Huggins, RA, Belval, LN, Pryor, RR, and Casa, DJ. Pacing strategy of a full Ironman overall female winner on a course with major elevation changes. J Strength Cond Res 32(11): 3080-3087, 2018-The purpose of this study was to use a mixed-methods design to describe the pacing strategy of the overall female winner of a 226.3-km Ironman triathlon. During the race, the triathlete wore a global positioning system and heart rate (HR)-enabled watch and rode a bike outfitted with a power and cadence meter. High-frequency (every km) analyses of mean values, mean absolute percent error (MAPE), and normalized graded running pace and power (accounting for changes in elevation) were calculated. During the bike, velocity, power, cadence, and HR averaged 35.6 km·h, 199 W, 84 rpm, and 155 b·min, respectively, with minimal variation except for velocity (measurement unit variation [MAPE]: 7.4 km·h [20.3%], 11.8 W [7.0%], 3.6 rpm [4.6%], 3 b·min [2.3%], respectively). During the run, velocity and HR averaged 13.8 km·h and 154 b·min, respectively, with velocity varying four-fold more than HR (MAPE: 4.8% vs. 1.2%). Accounting for elevation changes, power and running pace were less variable (raw [MAPE] vs. normalized [MAPE]: 199 [7.0%] vs. 204 W [2.7%]; 4:29 [4.8%] vs. 4:24 min·km [3.6%], respectively). Consistent with her planned pre-race pacing strategy, the triathlete minimized fluctuations in HR and watts during the bike and run, whereas velocity varied with changes in elevation. This case report provides observational evidence supporting the utility of a pacing strategy that allows for an oscillating velocity that sustains a consistent physiological effort in full Ironman races.

Changes in physiological and performance variables in non-professional triathletes after taking part in an Olympic distance triathlon

Given increasing popularity of triathlon, the objective of this study was to evaluate the acute effects upon the health of triathletes. To do so, with a sample of 23 male athletes (34.4 ± 7.9 years old), an assessment was carried out both before and after an Olympic distance triathlon, of the bodily composition, the jumping ability and the BORG and VAS scales, as well as a blood analysis of the following: Lactate (mmol/L), Hematrocrit (%), Glucose (mg/dL), Total proteins (mg/dL), Triglycerides (mg/dL), Bilirubin (mg/dL), GOT (IU/L), GPT (IU/L), LDH (IU/L), CPK (IU/L). The results showed an increase (p < 0.001) in the different markers of metabolic stress and muscular damage following the triathlon, but always within a normal range considered to be healthy, with the exception of CPK (IU/L) (PRE 149.33 ± 108.16 vs POST 290.10 ± 102.48). Therefore, it would seem that competing in an Olympic-distance triathlon does not pose health risks for trained subjects.

Sex differences in pacing during 'Ultraman Hawaii'

Background

To date, little is known for pacing in ultra-endurance athletes competing in a non-stop event and in a multi-stage event, and especially, about pacing in a multi-stage event with different disciplines during the stages. Therefore, the aim of the present study was to examine the effect of age, sex and calendar year on triathlon performance and variation of performance by events (i.e., swimming, cycling 1, cycling 2 and running) in ‘Ultraman Hawaii’ held between 1983 and 2015.

Methods

Within each sex, participants were grouped in quartiles (i.e., Q1, Q2, Q3 and Q4) with Q1 being the fastest (i.e., lowest overall time) and Q4 the slowest (i.e., highest overall time). To compare performance among events (i.e., swimming, cycling 1, cycling 2 and running), race time in each event was converted in z score and this value was used for further analysis.

Results

A between-within subjects ANOVA showed a large sex × event (p = 0.015, η² = 0.014) and a medium performance group × event interaction (p = 0.001, η² = 0.012). No main effect of event on performance was observed (p = 0.174, η² = 0.007). With regard to the sex × event interaction, three female performance groups (i.e., Q2, Q3 and Q4) increased race time from swimming to cycling 1, whereas only one male performance group (Q4) revealed a similar trend. From cycling 1 to cycling 2, the two slower female groups (Q3 and Q4) and the slowest male group (Q4) increased raced time. In women, the fastest group decreased (i.e., improved) race time from swimming to cycling 1 and thereafter, maintained performance, whereas in men, the fastest group decreased race time till cycling 2 and increased it in the running.

Conclusion

In summary, women pace differently than men during ‘Ultraman Hawaii’ where the fastest women decreased performance on day 1 and could then maintain on day 2 and 3, whereas the fastest men worsened performance on day 1 and 2 but improved on day 3.