What predicts performance in ultra-triathlon races? - a comparison between Ironman distance triathlon and ultra-triathlon

Ultra-Cycling- Past, Present, Future: A Narrative Review

BACKGROUND

Ultra-endurance events are gaining popularity in multiple exercise disciplines, including cycling. With increasing numbers of ultra-cycling events, aspects influencing participation and performance are of interest to the cycling community.

MAIN BODY

The aim of this narrative review was, therefore, to assess the types of races offered, the characteristics of the cyclists, the fluid and energy balance during the race, the body mass changes after the race, and the parameters that may enhance performance based on existing literature. A literature search was conducted in PubMed, Scopus, and Google Scholar using the search terms 'ultracycling', 'ultra cycling', 'ultra-cycling', 'ultra-endurance biking', 'ultra-bikers' and 'prolonged cycling'. The search yielded 948 results, of which 111 were relevant for this review. The studies were classified according to their research focus and the results were summarized. The results demonstrated changes in physiological parameters, immunological and oxidative processes, as well as in fluid and energy balance. While the individual race with the most published studies was the Race Across America, most races were conducted in Europe, and a trend for an increase in European participants in international races was observed. Performance seems to be affected by characteristics such as age and sex but not by anthropometric parameters such as skin fold thickness. The optimum age for the top performance was around 40 years. Most participants in ultra-cycling events were male, but the number of female athletes has been increasing over the past years. Female athletes are understudied due to their later entry and less prominent participation in ultra-cycling races. A post-race energy deficit after ultra-cycling events was observed.

CONCLUSION

Future studies need to investigate the causes for the observed optimum race age around 40 years of age as well as the optimum nutritional supply to close the observed energy gap under consideration of the individual race lengths and conditions. Another research gap to be filled by future studies is the development of strategies to tackle inflammatory processes during the race that may persist in the post-race period.

Using Physiological Laboratory Tests and Neuromuscular Functions to Predict Extreme Ultratriathlon Performance

Purpose: This study aims to investigate the relationship between split disciplines and overall extreme ultra-triathlon (EUT) performance and verify the relationship among physiological and neuromuscular measurements with both fractional and total EUT performance while checking which variables could predict partial and overall EUT race time. Methods: Eleven volunteers (37 ± 6 years; 176.9 ± 6.1 cm; 77.9 ± 10.9 kg) performed two maximal graded tests (cycling and running) for physiological measurements and muscle strength/power tests to assess neuromuscular functions. Results: The correlation of swimming split times to predict overall EUT race times was lower than for cycling and running split times (r2 = 0.005; p > .05; r2 = 0.949; p < .001 and r2 = 0.925; p < .001, respectively). VO2peak obtained during running test (VO2peakrun) and VT power output assessed during cycling test (VTPO) were the highest predictors of cycling performance (r2 = 0.92; p = .017), whereas VO2peakrun and peakpower output in the cycling test (PPO) were the highest predictors of running performance (r2 = 0.94; p = .008). Conclusion: VO2peakrun and VTPO, associated to jump height assessed during countermovement jump (CMJ) test were the highest correlated variables to predict total EUT performance (r2 = 0.99; p = .007). In practical terms, coaches should include the assessment of VO2peakrun, VTPO, and CMJ to evaluate the athletes' status and monitor their performance throughout the season. Future studies should test how the improvement of these variables would affect EUT performance during official races.

Impacts of different triathlon races on systemic cytokine profile and metabolic parameters in healthy individuals: a systematic review

The present systematic review aimed to discuss the impacts of different triathlon protocols on the level of pro and anti-inflammatory cytokines, as well as biomarkers related to the performance of healthy individuals. Four databases [PubMed (28 articles), Scopus (24 articles), Science Direct (200 articles), and SPORT Discus (1101 articles) were assessed. The eligibility criteria were applied, and the selected articles were used in the peer review, independently, as they were identified by March 2022. Of the 1359 articles found, 10 were included in this systematic review. Despite the difference in triathlon protocols, it was observed an increase in pro and anti-inflammatory cytokines including IL-4 and IL-10, and chemokines, such as IL-8 and MCP-1. Moreover, the anti-inflammatory serum levels increase after triathlon. Overall, the studies also reported enhancement in the serum levels of cortisol, creatine kinase, C reactive protein, Endothelial Growth Factor, Vascular Endothelial Growth Factor, Myostatin, Lactate dehydrogenase, free fatty acids, and lactate dehydrogenase in triathlon athletes. This systematic review indicates that different triathlon race promotes an acute elevation of circulating cytokines and chemokines levels which return to standard levels after triathlon races. The findings of this systematic review demonstrate that the modulation of inflammatory parameters may be associated with an increase in metabolic indicators (CK, Cortisol, and LDH) after the end of different types of triathlon races.

Changes in pacing variation with increasing race duration in ultra-triathlon races

Despite the increasing scientific interest in the relationship between pacing and performance in endurance sports, little information is available about pacing and pacing variation in ultra-endurance events such as ultra-triathlons. Therefore, we aimed to investigate the trends of pacing, pacing variation, the influence of age, sex, and performance level in ultra-triathlons of different distances. We analysed 969 finishers (849 men, 120 women) in 46 ultra-triathlons longer than the original Ironman® distance (e.g., Double-, Triple-, Quintuple- and Deca Iron ultra-triathlons) held from 2004 to 2015. Pacing speed was calculated for every cycling and running lap. Pacing variation was calculated as the coefficient of variation (%) between the average speed of each lap. Performance level (i.e., fast, moderate, slow) was defined according to the 33.3 and 66.6 percentile of the overall race time. A multivariate analysis (two-way ANOVA) was applied for the overall race time as the dependent variable with 'sex' and 'age group' as independent factors. Another multivariate model with 'age' and 'sex' as covariates (two-way ANCOVA) was applied with pacing variation (cycling and running) as the dependent variable with 'race' and 'performance level' as independent factors. Different pacing patterns were observed by event and performance level. The general pacing strategy applied was a positive pacing. In Double and Triple Iron ultra-triathlon, faster athletes paced more evenly with less variation than moderate or slower athletes. The variation in pacing speed increased with the length of the race. There was no significant difference in pacing variation between faster, moderate, and slower athletes in Quintuple and Deca Iron ultra-triathlon. Women had a slower overall performance than men. The best overall times were achieved at the age of 30-39 years. Successful ultra-triathlon athletes adapted a positive pacing strategy in all race distances. The variation in pacing speed increased with the length of the race. In shorter ultra-triathlon distances (i.e., Double and Triple Iron ultra-triathlon), faster athletes paced more evenly with less variation than moderate or slower athletes. In longer ultra-triathlon distances (i.e., Quintuple and Deca Iron ultra-triathlon), there was no significant difference in pacing variation between faster, moderate, and slower athletes.

Physiological Features of Olympic-Distance Amateur Triathletes, as Well as Their Associations with Performance in Women and Men: A Cross-Sectional Study

The purpose of this study was to verify the physiological and anthropometric determinants of triathlon performance in female and male athletes. This study included 40 triathletes (20 male and 20 female). Dual-energy X-ray absorptiometry (DEXA) was used to assess body composition, and an incremental cardiopulmonary test was used to assess physiological variables. A questionnaire about physical training habits was also completed by the athletes. Athletes competed in the Olympic-distance triathlon race. For the female group, the total race time can be predicted by V̇O₂max (β = -131, t = -6.61, p < 0.001), lean mass (β = -61.4, t = -2.66, p = 0.018), and triathlon experience (β = -886.1, t = -3.01, p = 0.009) (r² = 0.825, p < 0.05). For the male group, the total race time can be predicted by maximal aerobic speed (β = -294.1, t = -2.89, p = 0.010) and percentage of body fat (β = 53.6, t = 2.20, p = 0.042) (r² = 0.578, p < 0.05). The variables that can predict the performance of men are not the same as those that can predict the triathlon performance of women. These data can help athletes and coaches develop performance-enhancing strategies.

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.

The beginning of success: Performance trends and cut-off values for junior and the U23 triathlon categories

Background

This study sought to determine cut-off values for each triathlon discipline to achieve podium in Junior (short distance; 750 m swim, 20 km cycle and 5 km run) and U23 (standard/Olympic distance; 1.5 km swim, 40 km cycle and 10 km run) triathlon events. Additionally, we aimed to investigate which discipline has the largest relationship with overall Junior and U23 triathlon performance, and the effect of sex and time in performance trends.

Methods

We included all data from Junior and U23 official races (International Triathlon Federation; ITU) of Junior (n = 3,314 finishes) and U23 (n = 5,092 finishes) categories held from 1999 to 2018.

Results

Men were significantly faster than women in both Junior (11.13%) and U23 (12.28%) categories. Swimming and cycling times were faster in 2009-2018 than in the 1999-2008 decade for men (3.36%; 6.49%), women junior (6.50%; 7.09%), men (0.15%; 3.46%) and women U23 (1.61%; 3.31%) respectively. Cycling was the discipline with the greatest influence on overall triathlon performance in Junior and U23 categories, regardless of sex or rank position. The cut-off values for the Junior category were (men/women): swimming, 9.2/9.4 min; cycling, 31.9/38.2 min; running, 16.8/18.9 min. U23's cut-off values were (men/women): swim, 18.0/19.4 min, cycling: 63.4/70.1 min; run, 33.9/38.7 min.

Conclusion

Cycling was the discipline with the greatest influence on overall performance for both men and women in Junior and U23 categories. Moreover, swimming and cycling performances increased over the years for both sexes.

Vegan vs. omnivore diets paradox: A whole-metagenomic approach for defining metabolic networks during the race in ultra-marathoners- a before and after study design

BACKGROUND

The effect of vegan diets on metabolic processes in the body is still controversial in ultra endurance athletes. The study aims to determine gut microbiome adaptation to extreme exercise according to vegan or omnivore diet consumed in ultra-marathoners. We also seek to evaluate long-term vegan diets' effects on redox homeostasis, and muscle fatigue, and assess energy availability.

METHODS

Seventy participants will be assigned to the study, including 35 vegan ultra-marathoners and 35 omnivores competing in the Sri-Chinmoy ultra marathon race. Research data will be collected from the participants at four steps (three visits to the research laboratory and the race day) throughout the study. At the first visit (seven days before the race), fecal samples, and anthropometric measurements will be collected. Body composition will be measured using DXA. Participants will be informed about keeping detailed food records and will be asked to record their diet data and activity logs during the entire study period. At second visit, maximum oxygen consumption will be measured on treadmill. On race day, blood samples will be collected immediately before, and 0. min, 2 hours, and 24 hours after the race. Body weight will be measured before and after the race. The blood and fecal samples will be stored at -80 C until analysis. Plasma malondialdehyde, reactive oxygen metabolites, total antioxidant capacity, Heatshockprotein-70, and serum Orosomucoid-1 will be analyzed in blood samples. Fecal samples will be analyzed with shotgun metagenomic analysis and interpreted using bioinformatics pipeline (HumanN2). Statistical tests will be analyzed using SPSS version 23.0 and R Software.

DISCUSSION

Study findings will determine the effects of the vegan diet on sports performance, revealing the multiple interactions between host and gut microbiome at the whole metagenomic level. Additionally, results will show the possible adaptation throughout the race by analyzing blood and fecal samples. Furthermore, by assessing energy availability and determining host-metabolite crosstalk for ultra-endurance athletes, possible nutritional deficiencies can be identified. Thus, advanced nutritional strategies can be developed based on metabolic needs.

TRIAL REGISTRATION

Current controlled trials, ISRCTN registry 69541705. Registered on 8 December 2019.

Influence of a 2-km Swim on the Cycling Power-Duration Relationship in Triathletes

Rothschild, J, Sheard, AC, and Crocker, GH. Influence of a 2-km swim on the cycling power-duration relationship in triathletes. J Strength Cond Res XX(X): 000-000, 2020-Triathletes must cycle after swimming, and so, it is important to understand how cycling performance may be affected by prior swimming. Therefore, the purpose of this study was to determine the effects of a 2-km swim at a self-selected race-pace intensity on the cycling power-duration relationship. Eighteen trained triathletes (12 M, 6 F; 37.1 ± 10.6 years, V[Combining Dot Above]O2max 54.8 ± 10.1 ml·kg·min) performed two 3-minute all-out cycling tests (3MTs) on separate days with one 3 MT immediately after a 2-km swim (swim-bike [SB]) and one without prior swimming (bike-only [BO]). The power-duration relationship was expressed as the total work done (TWD) and subdivided into end-test power (EP) and work done above EP. To assess swimming intensity, heart rate (HR) was continuously monitored during the 2-km swim and blood lactate was assessed on completion of the swim. End-swim lactate was 4.2 ± 1.8 mM, and mean swimming HR was 147 ± 18 b·min. The 2-km swim decreased TWD during the 3MT by 6% (BO: 62.8 ± 12.7 kJ; SB: 58.9 ± 13.4 kJ; p = 0.001) though neither EP (BO: 281 ± 65 W; SB: 269 ± 68 W; p = 0.102) nor work done above EP (BO: 12.1 ± 3.8 kJ; SB: 10.5 ± 4.2 kJ; p = 0.096) differed between trials. In conclusion, TWD while cycling decreases after a 2-km race-pace swim. Results from this study suggest that triathletes should determine racing cycling power following a simulated race-pace swim.

Self-Selected Pacing During a World Record Attempt in 40 Ironman-Distance Triathlons in 40 Days

The present case study analyzed performance, pacing, and potential predictors in a self-paced world record attempt of a professional triathlete to finish 40 Ironman-distance triathlons within 40 days. Split times (i.e., swimming, cycling, running) and overall times, body weight, daily highest temperature, wind speed, energy expenditure, mean heart rate, and sleeping time were recorded. Non-linear regressions were applied to investigate changes in split and overall times across days. Multivariate regression analyses were performed to test which variables showed the greatest influence on the dependent variables cycling, running and overall time. The athlete completed the 40×Ironman distances in a total time of 444:22 h:min. He spent 50:26 h:min in swimming, 245:37 h:min in cycling, 137:17 h:min in running and 11:02 h:min in transition times. Swimming and cycling times became slower across days, whereas running times got faster until the 20th day and, thereafter, became slower until the 40th day. Overall times got slower until the 15th day, became faster to 31st, and started then to get slower until the end. Wind speed, previous day’s race time and average heart race during cycling were significant independent variables influencing cycling time. Body weight and average heart rate during running were significant independent variables influencing running performance. Cycling performance, running performance, and body weight were significant independent variables influencing overall time. In summary, running time was influenced by body weight, cycling by wind speed, and overall time by both running and cycling performances.

Core Temperature Response During the Marathon Portion of the Ironman World Championship (Kona-Hawaii)

The Ironman triathlon consists of a 3.8 km swim, 180 km bike, and 42.195 km run. Thermoregulation responses play an important role in performance optimization and injury prevention. Factors such as environmental conditions including heat and humidity, athlete training level, and race duration can affect thermoregulation. Hyperthermia occurs when the core temperature rises above 38.5°C. The present study aims to describe core temperature (Tcore) in top-level and well-trained age group triathletes during the marathon of Ironman World Championship 2014 in Kona-Hawaii under thermal stress conditions. Tcore of 15 triathletes (age: 36.11 ± 7.36 years, body mass: 71.14 ± 7.12 kg, height: 179 ± 0.04 cm, and fat %: 8.48 ± 0.85) who classified for the Ironman World Championship was measured by an ingestible pill telemetry system prior to competition, during the marathon and 60 min after finishing the race. Mean wet bulb globe temperature (WBGT) during the marathon was 24.66°C (range 22.44–28.50°C). Body mass index (BMI) and perceived exertion (Borg Scale and Visual Analog Scale-Pain) were collected before the race and 60 min after the event. Time variables were extracted from their official race time and split times. Finish time was 10: 06:56 ± 0:48:30. Tcore was initially 36.62 ± 0.17°C, increased at the end of the event (38.55 ± 0.64; p < 0.01) and remained elevated 60 min after the event (38.65 ± 0.41°C; p < 0.002). BMI significantly decreased after the event (22.85 ± 1.11 vs. 21.73 ± 1.36; p < 0.05), whereas both exercise perceived exertion [Borg Scale (10.2 ± 1.64 vs. 18.60 ± 1.67; p < 0.003)] and perceived muscle pain [VAS Pain (2.75 ± 1.59 vs. 9.08 ± 1.13; p < 0.001)] increased significantly after the event. Tcore during competition correlated negatively with position in age group (r − 0.949, p = 0.051), but not with race time (r = −0.817; p = 0.183). High-level age group triathletes competing under thermal stress conditions in the Kona Ironman reached a state of hyperthermia during the marathon. After 60 min of recovery the hyperthermia persisted. Strategies to aid post-event cooling and recovery should be considered to avoid the potentially dangerous adverse health effects of hyperthermia.

Bioelectrical Impedance Vector Analysis (BIVA) and Body Mass Changes in an Ultra-Endurance Triathlon Event

This study aimed to provide the first description of the whole-body bioimpedance vector of nine non-professional triathletes, and to assess body mass (BM) and vector variations evoked by an ultra-endurance triathlon event. Anthropometric and bioelectrical assessments were performed before (PRE), after (POST), and 48 hours following the race (POST48h). Bioimpedance vector analysis (BIVA) showed triathletes' vectors placed to the left of the major axis and mostly outside the 50% tolerance ellipse of the reference population. Vector migration in POST indicated dehydration, paralleled by a decrease in BM (p = 0.0001). Increased hydration status from POST to POST48h was suggested by a reversed vector migration and increased BM (p = 0.0001). Compared to PRE, POST48h values reflected fluid retention by changes in BIVA, while BM was still lower (p = 0.0001). Racing time was positively related to basal resistance -R/h- (r = 0.68; p = 0.04) and bioimpedance -Z/h- (r = 0.68; p = 0.045). Besides, basal R/h and Z/h were positively related to PRE-to-POST changes of R/h and Z/h (r = 0.80; p = 0.009). PRE-to-POST changes of R/h and Z/h were positively related to racing time (r = 0.80, p = 0.01) and internal workload (r = 0.80, p ≤ 0.02). Notwithstanding the lack of significant correlation between BM and bioelectrical parameters, the vector's behavior was explained from a multifactorial perspective (including BM variations) by using multiple regression analysis. On the other hand, BM changes were not related to racing time, internal workload or energy deficit (ranges: r = - 0.46 to 0.65; p = 0.06 to 0.98). In conclusion, these triathletes exhibit a specific bioelectrical distribution. Furthermore, vector migration was consistent with fluid loss induced by the event. Finally, vector analysis seems to provide additional information about hydration changes 48h after the event in comparison with BM alone.

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.

Substance use and misuse in a mountain ultramarathon: new insight into ultrarunners population?

Endurance and ultra-endurance events have become increasingly popular. The aim of our study was to explore the use of medication among endurance runners participating in the 2014 Infernal Trail des Vosges. Among the 389 runners engaged, 297 (76.3%) completed a specific questionnaire dealing with substance use/misuse. Our results show a 27% (before the race) and 18% (during the race) prevalence of substance use. The two major classes of substances used were non-steroidal anti-inflammatory drugs (NSAIDs; 9.8%) and painkillers (6.7%), principally because of osteo-articular pain (29.6%) or to prevent pain (28.2%). A positive correlation was found between substance consumption before (past month) and during the race (overall medication: p < 0.0001; NSAIDs: p = 0.008). Our results could be explained by the specific characteristics of ultrarunners predominantly motivated by personal achievement and general health (recreational approach). However, education interventions should further be delivered regarding the risks of substance use in ultra-endurance events.