Physiology, Power Output, and Racing Strategy of a Race Across America Finisher

A century of exercise physiology: lung fluid balance during and following exercise

PURPOSE

This review recalls the principles developed over a century to describe trans-capillary fluid exchanges concerning in particular the lung during exercise, a specific condition where dyspnea is a leading symptom, the question being whether this symptom simply relates to fatigue or also implies some degree of lung edema.

METHOD

Data from experimental models of lung edema are recalled aiming to: (1) describe how extravascular lung water is strictly controlled by "safety factors" in physiological conditions, (2) consider how waning of "safety factors" inevitably leads to development of lung edema, (3) correlate data from experimental models with data from exercising humans.

RESULTS

Exercise is a strong edemagenic condition as the increase in cardiac output leads to lung capillary recruitment, increase in capillary surface for fluid exchange and potential increase in capillary pressure. The physiological low microvascular permeability may be impaired by conditions causing damage to the interstitial matrix macromolecular assembly leading to alveolar edema and haemorrhage. These conditions include hypoxia, cyclic alveolar unfolding/folding during hyperventilation putting a tensile stress on septa, intensity and duration of exercise as well as inter-individual proneness to develop lung edema.

CONCLUSION

Data from exercising humans showed inter-individual differences in the dispersion of the lung ventilation/perfusion ratio and increase in oxygen alveolar-capillary gradient. More recent data in humans support the hypothesis that greater vasoconstriction, pulmonary hypertension and slower kinetics of alveolar-capillary O2 equilibration relate with greater proneness to develop lung edema due higher inborn microvascular permeability possibly reflecting the morpho-functional features of the air-blood barrier.

Racing and Training Physiology of an Elite Ultra-Endurance Cyclist: Case Study of 2 Record-Setting Performances

PURPOSE

To present a case report of an elite ultra-endurance cyclist, who was the winner and course record holder of 2 distinct races within a 4-month span: a 24-hour solo cycling race and a 2-man team multiday race (Race Across America).

METHODS

The athlete's raw data (cycling power, heart rate [HR], speed, and distance) were obtained and analyzed for 2 ultra-endurance races and 11 weeks of training in between.

RESULTS

For the 24-hour race, the athlete completed 861.6 km (average speed 35.9 km·h-1, average power 210 W [2.8 W·kg-1], average HR 121 beats per minute) with a 37% decrease in power and a 22% decrease in HR throughout the race. During the 11 weeks between the 24-hour race and Race Across America, training intensity distribution (Zone 1/2/3) based on HR was 51%/39%/10%. For the Race Across America, total team time to complete the 4939-km race was 6 days, 10 hours, 39 minutes, at an average speed of 31.9 km·h-1. Of this, the athlete featured in this case study rode 75.2 hours, completing 2532 km (average speed 33.7 km·h-1, average power 203 W [2.7 W·kg-1]), with a 12% decrease in power throughout the race. Power during daytime segments was greater than nighttime (212 [25] vs 189 [18] W, P < .001, ηp2=.189).

CONCLUSIONS

This case report highlights the performance requirements of elite ultra-endurance cycling. Although average power was similar when riding for 24 hours continuously and 75 hours intermittently over 6.5 days, there were large differences in pacing strategies and within-day power-output changes.

Participating In The Race Across AMerica In A Team Of Eight Cyclists: Do Not Neglect Crew Preparation

Background

The Race Across AMerica (RAAM) is considered as one of the longest, and most difficult cycling races in the world. It can be performed in solo or in relay of two, four or eight persons.

Purpose/method

The aim of the present study was to investigate several physiological, perceptual and psychological responses before, during and after RAAM in a team of eight amateur cyclists. Because logistics of all team is demanding and complex, crew members have followed the same testing procedure.

Results

The main result is that parameters were globally not altered to a greater extent in cyclists than in crew members over the course of the RAAM, and that all variables returned to pre-race levels 1 week after the end of the race in both groups. In crew, body fat was decreased (p < 0.05) at mid-race (Mid) vs 1 week before the RAAM (Pre) (−1.5%) and total body water was increased (p < 0.05) at Mid vs Pre (+2.5%). In pre-relay quadriceps strength in cyclists was significantly lower (p < 0.05) at Mid vs Pre (41.6 ± 9.1 vs 45.0 ± 11.2 N, d = 0.36). Therefore, performing the race with eight well-prepared amateur cyclists seems to decrease potential risks on health. In crew, quadriceps strength remained stable at each assessment time but general fatigue increased all along the race. Anger was increased (p < 0.05) at Mid vs Pre in crew.

Conclusion

The most important message of this study is that crew members for a team of eight require adequate and sufficient preparation and training. Roles and responsibilities need to be thoroughly defined; individuals need to know each other beforehand and they must be prepared for sleep deprivation. The results of this study show the importance of the preparation of two teams: crew members, as well as cyclists.

Psychosocial factors as predictors of dropout in ultra-trailers

OBJECTIVE

Although research on the physiological and psychological factors of endurance performance has been extensive, the factors related to dropping out of ultra-trail races have not been well documented. The aim of this study was to examine psychosocial factors as predictors of dropout in ultra-trail runners.

METHODS

Two hundred and twenty-one volunteer athletes completed a survey that included measures of: (a) motivational variables (self-determined motivation, basic needs satisfaction, achievement goals), (b) theory of planned behavior constructs (attitudes, subjective norms, self-efficacy and intention to finish the race), and (c) coping strategies in sport.

RESULTS

The results showed that finishers had higher scores for self-efficacy and intention to finish the race than withdrawers, whereas withdrawers had higher scores for avoidance coping. Multiple logistic regression showed that the number of started and finished ultra-trail races (OR, 0.44; 95% CI 0.22-0.88; p<0.02), self-efficacy (OR, 2.03; 95% CI 1.06-3.89; p<0.04), intention to finish the race (OR, 0.34; 95% CI 0.16-0.71; p<0.004), mastery-approach goals (OR, 0.56; 95% CI 0.31-1.00; p<0.05), and coping strategies of seeking social support (OR, 0.43; 95% CI 0.26-0.71; p<0.001) were associated with a lower risk of race dropout, whereas avoidance coping strategies (OR, 2.26; 95% CI 1.46-3.52; p<0.001) were associated with an increased dropout rate.

CONCLUSION

Interventions promoting self-efficacy constructs and specific coping strategies might contribute to preventing dropout in ultra-trailers.

Training Intensity Distribution and Changes in Performance and Physiology of a 2nd Place Finisher Team of the Race across America Over a 6 Month Preparation Period

Aim: To monitor the training intensity distribution (TID) and the development of physiological and performance parameters.

Methods: During their preparation period for the RAAM, 4 athletes (plus 1 additional backup racer) performed 3 testing sessions; one before, one after 3, and one after 6 months of training. VO_2max, maximal rate of lactate accumulation (dLa/dt_max), critical power, power output at lactate minimum (MLSS_P), peak and mean power output during a sprint test, heart rate recovery, isometric strength, jumping height, and body composition were determined. All training sessions were recorded with a power meter. The endurance TID was analyzed based on the time in zone approach, according to a classical 3-zone model, including all power data of training sessions, and a power specific 3-zone model, where time with power output below 50% of MLSS_P was not considered.

Results: The TID using the classical 3-zone model reflected a pyramidal TID (zone 1: 63 ± 16, zone 2: 28 ± 13 and zone 3: 9 ± 4%). The power specific 3-zone model resulted in a threshold-based TID (zone 1: 48 ± 13, zone 2: 39 ± 10, zone 3: 13 ± 4%). VO_2max increased by 7.1 ± 5.3% (P = 0.06). dLa/dt_max decreased by 16.3 ± 8.1% (P = 0.03). Power output at lactate minimum and critical power increased by 10.3 ± 4.1 and 16.8 ± 6.2% (P = 0.01), respectively. No changes were found for strength parameters and jumps.

Conclusion: The present study underlines that a threshold oriented TID results in only moderate increases in physiological parameters. The amount of training below 50% of MLSSp (~28% of total training time) is remarkably high. Researchers, trainers, and athletes should pay attention to the different ways of interpreting training power data, to gain realistic insights into the TID and the corresponding improvements in performance and physiological parameters.

Sleep Management Strategy and Performance in an Extreme Mountain Ultra-marathon

We intended to assess the relationship between sleep strategies and performance during the North-Face Ultra-Trail du Mont-Blanc 2013, to test the hypothesis that sleep management can influence athletic performance. Almost all runners specifically adopted sleep management strategies before the race. Among the finishers 72% didn't sleep at all during the race and 28% took a least one break for sleep. Non-sleepers completed the race faster than the sleepers (P = 0.0008). Race time was positively correlated with drowsiness (P < 0.0001) and negatively correlated with the number participations in this race (P = 0.0039). Runners who adopted a sleep management strategy based on increased sleep time before the race completed the race faster (P = 0.0258). Most finishers seemed to be aware of the importance of developing sleep management strategies and increasing sleep time some nights before the race appeared to be the most relevant strategy to improve performance.

Factors influencing pacing in triathlon

Triathlon is a multisport event consisting of sequential swim, cycle, and run disciplines performed over a variety of distances. This complex and unique sport requires athletes to appropriately distribute their speed or energy expenditure (ie, pacing) within each discipline as well as over the entire event. As with most physical activity, the regulation of pacing in triathlon may be influenced by a multitude of intrinsic and extrinsic factors. The majority of current research focuses mainly on the Olympic distance, whilst much less literature is available on other triathlon distances such as the sprint, half-Ironman, and Ironman distances. Furthermore, little is understood regarding the specific physiological, environmental, and interdisciplinary effects on pacing. Therefore, this article discusses the pacing strategies observed in triathlon across different distances, and elucidates the possible factors influencing pacing within the three specific disciplines of a triathlon.

A comparison of ultra-endurance cyclists in a qualifying ultra-cycling race for Paris-Brest-Paris and Race Across America-Swiss cycling marathon

Ultra-endurance events test the adaptation of human physiology to extreme physical and mental demands, high levels of training, motivation, and physical conditioning among participants. To understand basic differences among participants according to the severity of the race, participants in qualifying events for two ultra-endurance cycling races, differing in length and intensity, were compared on measures of anthropometry, training, and support. One race was four times longer, required supporting teams, and racers typically had little sleep, which should lead to the qualifiers being substantially more highly trained than those from the shorter race. The qualifiers in the longer race had greater intensity in training while the qualifiers in the shorter race relied more on training volume. Different strategies and types of training reflected the different demands of the races. Future studies should evaluate personality and motivational differences in ultra-endurance events and between these athletes and athletes in other sports.