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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, ${\eta }_p^2=.189$ ). 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.

Many endurance athletes perform specific blocks of training in hot environments in “heat stress training camps.” It is not known if physiological threshold heart rates measured in temperate conditions are reflective of those under moderate environmental heat stress. A total of 16 endurance-trained cyclists and triathletes performed incremental exercise assessments in 18°C and 35°C (both 60% relative humidity) to determine heart rates at absolute blood lactate and ventilatory thresholds. Heart rate at fixed blood lactate concentrations of 2, 3, and 4 mmol·L⁻¹ and ventilatory thresholds were not significantly different between environments (P > .05), despite significant heat stress-induced reductions in power output of approximately 10% to 17% (P < .05, effect size = 0.65–1.15). The coefficient of variation for heart rate at these blood lactate concentrations (1.4%−2.9%) and ventilatory thresholds (2.3%−2.7%) between conditions was low, with significant strong positive correlations between measurements in the 2 environments (r = .92–.95, P < .05). These data indicate heart rates measured at physiological thresholds in temperate environments are reflective of measurements taken under moderate environmental heat stress. Therefore, endurance athletes embarking on heat stress training camps can use heart rate–based thresholds ascertained in temperate environments to prescribe training under moderate environmental heat stress.

Athletes may choose to perform exercise in the overnight-fasted state for a variety of reasons related to convenience, gut comfort, or augmenting the training response, but it is unclear how many endurance athletes use this strategy. We investigated the prevalence and determinants of exercise performed in the overnight-fasted state among endurance athletes using an online survey and examined differences based on sex, competitive level, and habitual dietary pattern. The survey was completed by 1,950 endurance athletes (51.0% female, mean age 40.9 ± 11.1 years). The use of fasted training was reported by 62.9% of athletes, with significant effects of sex (p < .001, Cramer’s V [φ_c] = 0.18, 90% CI [0.14, 0.22]), competitive level (p < .001, φ_c = 0.09, 90% CI [0.5, 0.13]), and habitual dietary pattern noted (p < .001, φ_c = 0.26, 90% CI [0.22, 0.29]). Males, nonprofessional athletes, and athletes following a low-carbohydrate, high-fat diet were most likely to perform fasted training. The most common reasons for doing so were related to utilizing fat as a fuel source (42.9%), gut comfort (35.5%), and time constraints/convenience (31.4%), whereas the most common reasons athletes avoided fasted training were that it does not help their training (47.0%), performance was worse during fasted training (34.7%), or greater hunger (34.6%). Overall, some athletes perform fasted training because they think it helps their training, whereas others avoid it because they think it is detrimental to their training goals, highlighting a need for future research. These findings offer insights into the beliefs and practices related to fasted-state endurance training.

A common practice among endurance athletes is to purposefully train in hot environments during a “heat stress camp.” However, combined exercise-heat stress poses threats to athlete well-being, and therefore, heat stress training has the potential to induce maladaptation. This case study describes the monitoring strategies used in a successful 3-week heat stress camp undertaken by 2 elite Ironman triathletes, namely resting heart rate variability, self-report well-being, and careful prescription of training based on previously collected physiological data. Despite the added heat stress, training volume very likely increased in both athletes, and training load very likely increased in one of the athletes, while resting heart rate variability and self-report well-being were maintained. There was also some evidence of favorable metabolic changes during routine laboratory testing following the camp. The authors therefore recommend that practitioners working with endurance athletes embarking on a heat stress training camp consider using the simple strategies employed in the present case study to reduce the risk of maladaptation and nonfunctional overreaching.

Purpose: To determine the effect of different high-intensity interval-training (IT) sessions on the postexercise recovery response and time course across varying recovery measures. Methods: A total of 13 highly trained rowers (10 male and 3 female, peak oxygen uptake during a 6-min maximal test 4.9 [0.7] L·min⁻¹) completed 3 IT sessions on a rowing ergometer separated by 7 d. Sessions consisted of 5 × 3.5 min, 4-min rest periods (maximal oxygen uptake [VO₂max]); 10 × 30 s, 5-min rest periods (glycolytic); and 5 × 10 min, 4-min rest periods (threshold). Participants were instructed to perform intervals at the highest maintainable pace. Blood lactate and salivary cortisol were measured preexercise and postexercise. Resting heart-rate (HR) variability, post-submaximal-exercise HR variability, submaximal-exercise HR, HR recovery, and modified Wingate peak and mean power were measured preexercise and 1, 10, 24, 34, 48, 58, and 72 h postexercise. Participants resumed training throughout the measurement period. Results: Between-groups short-term response differences (1 h post-IT) across IT sessions were trivial or unclear for all recovery variables. However, post-submaximal-exercise HR variability demonstrated the longest recovery time course (threshold = 37.8 [14.2], glycolytic = 20.2 [11.0], and VO₂max = 20.6 [15.2]; mean [h] ± confidence limits). Conclusion: Short-term responses to threshold, glycolytic, and VO₂max IT in highly trained male and female rowers were similar. Recovery time course was greatest following threshold compared with glycolytic and VO₂max-focused training, suggesting a durational influence on recovery time course at HR intensities ≥80% HR_max. As such, this provides valuable information around the programming and sequencing of high-intensity IT for endurance athletes.

Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart-rate sensor in the measurement of heart-rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing and normal breathing using smartphone PPG, a heart-rate chest strap, and electrocardiography (ECG). The root mean sum of the squared differences between R–R intervals (rMSSD) was determined from each device. Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% [90% CI] = 6.35 [5.13; 8.5]). When assessed as a standardized difference, all differences were deemed “trivial” (average standard difference [90% CI] = 0.10 [0.08; 0.13]). Both PPG- and heart-rate-sensor-derived measures had almost perfect correlations with ECG (R = 1.00 [0.99; 1.00]). Conclusion: Both PPG and heart-rate sensors provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.

The aim of this study was to compare 2 different methodological assessments when analyzing the relationship between performance and heart-rate (HR) -derived indices (resting HR [RHR] and HR variability [HRV]) to evaluate positive adaptation to training. The relative change in estimated maximum aerobic speed (MAS) and 10-km-running performance was correlated to the relative change in RHR and the natural logarithm of the square root of the mean sum of the squared differences between R-R intervals on an isolated day (RHR_day; Ln rMSSD_day) or when averaged over 1 wk (RHR_week; Ln rMSSD_week) in 10 runners who responded to a 9-wk training intervention. Moderate and small correlations existed between changes in MAS and 10-km-running performance and RHR_day (r = .35, 90%CI [–.35, .76] and r = –.21 [–.68, .39]), compared with large and very large correlations for RHR_week (r = –.62 [–.87, –.11] and r = .73 [.30, .91]). While a trivial correlation was observed for MAS vs Ln rMSSD_day (r = –.06 [–.59, .51]), a very large correlation existed with Ln rMSSD_week (r = .72 [.28, .91]). Similarly, changes in 10-km-running performance revealed a small correlation with Ln rMSSD_day (r = –.17 [–.66, .42]), vs a very large correlation for Ln rMSSD_week (r = –.76 [–.92, –.36]). In conclusion, the averaging of RHR and HRV values over a 1-wk period appears to be a superior method for evaluating positive adaption to training compared with assessing its value on a single isolated day.