Heat Versus Altitude Training for Endurance Performance at Sea Level

Race course characteristics are the most important predictors in 48 h ultramarathon running

Ultra-marathon running - where races are held in distance-limited (50 km, 50 miles, 100 km, 100 miles, etc.), time-limited (6 h, 12 h, 24 h, 48 h, 72 h, etc.), and multi-stage races - is gaining in popularity. However, we have no knowledge of where the fastest 48-hour runners originate and where the fastest 48-hour races are held. This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). Model explainability tools were then used to investigate how each independent variable would influence the predicted result. A sample of 16,233 race records from 7,075 unique runners originating from 60 different countries and participating in races held in 36 different countries between 1980 and 2022 was analyzed. Participation was spread across many countries, with USA, France, Germany, and Australia at the top of the participants' rankings. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. The XG Boost model showed that elevation of the course (flat course) and the running surface (track) were the variables that had a larger influence on the running speed. The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. Men were ~ 0.5 km/h faster than women. Most finishers were 45-49 years old, and runners in this age group achieved the fastest running speeds. In summary, elevation of the course (flat course) and the running surface (track) were the most important variables for fast 48-hour races, whilst the country of origin of the athlete and the country where the event was hold would lead to the broadest difference in the predicted running speed range. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. Any athlete intending to achieve a personal best performance in this race format can benefit from these findings by selecting the most appropriate race course.

Effects of concurrent heat and hypoxic training on cycling anaerobic capacity in men

Physical training in heat or hypoxia can improve physical performance. The purpose of this parallel group study was to investigate the concurrent effect of training performed simultaneously in heat (31 °C) and hypoxia (FIO2 = 14.4%) on anaerobic capacity in young men. For the study, 80 non-trained men were recruited and divided into 5 groups (16 participants per group): control, non-training (CTRL); training in normoxia and thermoneutral conditions (NT: 21 °C, FIO2 = 20.95%); training in normoxia and heat (H: 31 °C, FIO2 = 20.95%); training in hypoxia and thermoneutral conditions (IHT: 21 °C, FIO2 = 14.4%), and training in hypoxia and heat (IHT + H: 31 °C, FIO2 = 14.4%). Before and after physical training, the participants performed the Wingate Test, in which peak power and mean power were measured. Physical training lasted 4 weeks and the participants exercised 3 times a week for 60 min, performing interval training. Only the IHT and IHT + H groups showed significant increases in absolute peak power (p < 0.001, ES = 0.36 and p = 0.02, ES = 0.26, respectively). There were no significant changes (p = 0.18) after training in mean power. Hypoxia appeared to be an environmental factor that significantly improved peak power, but not mean power. Heat, added to hypoxia, did not increase cycling anaerobic power. Also, training only in heat did not significantly affect anaerobic power. The inclusion of heat and/or hypoxia in training did not induce negative effects, i.e., a reduction in peak and mean power as measured in the Wingate Test.

Combining Heat and Altitude Training to Enhance Temperate, Sea-Level Performance

BACKGROUND

Repeated exposure to heat (ie, plasma volume expansion) or altitude (ie, increase in total hemoglobin mass), in conjunction with exercise, induces hematological adaptations that enhance endurance performance in each respective environment. Recently, combining heat and altitude training has become increasingly common for athletes preparing to compete in temperate, sea-level conditions.

PURPOSE

To review the physiological adaptations to training interventions combining thermal and hypoxic stimuli and summarize the implications for temperate, sea-level performance. Current Evidence: To date, research on combining heat and hypoxia has employed 2 main approaches: simultaneously combining the stressors during training or concurrently training in the heat and sleeping at altitude, sometimes with additional training in hypoxia. When environmental stimuli are combined in a training session, improvements in aerobic fitness and time-trial performance in temperate, sea-level conditions are generally similar in magnitude to those observed with heat, or altitude, training alone. Similarly, training in the heat and sleeping at altitude does not appear to provide any additional hematological or nonhematological benefits for temperate; sea-level performance relative to training in hot, hypoxic, or control conditions.

CONCLUSIONS

Current research regarding combined heat and altitude interventions does not seem to indicate that it enhances temperate, sea-level performance to a greater extent than "traditional" (heat or hypoxia alone) training approaches. A major challenge in implementing combined-stressor approaches lies in the uncertainty surrounding the prescription of dosing regimens (ie, exercise and environmental stress). The potential benefits of conducting heat and altitude exposure sequentially (ie, one after the other) warrants further investigation.

Prediction of plasma volume and total hemoglobin mass with machine learning

Hemoglobin concentration ([Hb]) is used for the clinical diagnosis of anemia, and in sports as a marker of blood doping. [Hb] is however subject to significant variations mainly due to shifts in plasma volume (PV). This study proposes a newly developed model able to accurately predict total hemoglobin mass (Hbmass) and PV from a single complete blood count (CBC) and anthropometric variables in healthy subject. Seven hundred and sixty-nine CBC coupled to measures of Hbmass and PV using a CO-rebreathing method were used with a machine learning tool to calculate an estimation model. The predictive model resulted in a root mean square error of 33.2 g and 35.6 g for Hbmass, and 179 mL and 244 mL for PV, in women and men, respectively. Measured and predicted data were significantly correlated (p < 0.001) with a coefficient of determination (R2 ) ranging from 0.76 to 0.90 for Hbmass and PV, in both women and men. The Bland-Altman bias was on average 0.23 for Hbmass and 4.15 for PV. We herewith present a model with a robust prediction potential for Hbmass and PV. Such model would be relevant in providing complementary data in contexts such as the epidemiology of anemia or the individual monitoring of [Hb] in anti-doping.

Hematological Adaptations Following a Training Camp in Hot and/or Hypoxic Conditions in Elite Rugby Union Players

PURPOSE

To investigate the effects of a training camp with heat and/or hypoxia sessions on hematological and thermoregulatory adaptations.

METHODS

Fifty-six elite male rugby players completed a 2-week training camp with 5 endurance and 5 repeated-sprint sessions, rugby practice, and resistance training. Players were separated into 4 groups: CAMP trained in temperate conditions at sea level, HEAT performed the endurance sessions in the heat, ALTI slept and performed the repeated sprints at altitude, and H + A was a combination of the heat and altitude groups.

RESULTS

Blood volume across all groups increased by 140 mL (95%CI, 42-237; P = .006) and plasma volume by 97 mL (95%CI 28-167; P = .007) following the training camp. Plasma volume was 6.3% (0.3% to 12.4%) higher in HEAT than ALTI (P = .034) and slightly higher in HEAT than H + A (5.6% [-0.3% to 11.7%]; P = .076). Changes in hemoglobin mass were not significant (P = .176), despite a ∼1.2% increase in ALTI and H + A and a ∼0.7% decrease in CAMP and HEAT. Peak rectal temperature was lower during a postcamp heat-response test in HEAT (0.3 °C [0.1-0.5]; P = .010) and H + A (0.3 °C [0.1-0.6]; P = .005). Oxygen saturation upon waking was lower in ALTI (3% [2% to 5%]; P < .001) and H + A (4% [3% to 6%]; P < .001) than CAMP and HEAT.

CONCLUSION

Although blood and plasma volume increased following the camp, sleeping at altitude impeded the increase when training in the heat and only marginally increased hemoglobin mass. Heat training induced adaptations commensurate with partial heat acclimation; however, combining heat training and altitude training and confinement during a training camp did not confer concomitant hematological adaptations.

Heat suit training increases hemoglobin mass in elite cross-country skiers

Purpose

The primary purpose was to test the effect of heat suit training on hemoglobin mass (Hb_mass) in elite cross‐country (XC) skiers.

Methods

Twenty‐five male XC‐skiers were divided into a group that added 5 × 50 min weekly heat suit training sessions to their regular training (HEAT; n = 13, 23 ± 5 years, 73.9 ± 5.2 kg, 180 ± 6 cm, 76.8 ± 4.6 ml·min⁻¹·kg⁻¹) or to a control group matched for training volume and intensity distribution (CON; n = 12, 23 ± 4 years, 78.4 ± 5.8 kg, 184 ± 4 cm, 75.2 ± 3.4 ml·min⁻¹·kg⁻¹) during the five‐week intervention period. Hb_mass, endurance performance and factors determining endurance performance were assessed before and after the intervention.

Results

HEAT led to 30 g greater Hb_mass (95% CI: [8.5, 51.7], p = 0.009) and 157 ml greater red blood cell volume ([29, 285], p = 0.018) post‐intervention, compared to CON when adjusted for baseline values. In contrast, no group differences were observed for changes in work economy, running velocity, and fractional utilization of maximal oxygen uptake (V̇O_2max) at 4 mmol·L⁻¹ blood lactate, V̇O_2max or 15‐min running distance performance trial during the intervention.

Conclusion

HEAT induced a larger increase in Hb_mass and red blood cell volume after five weeks with five weekly heat suit training sessions than CON, but with no detectable group differences on physiological determinants of endurance performance or actual endurance performance in elite CX skiers.

Factors Confounding the Athlete Biological Passport: A Systematic Narrative Review

BACKGROUND

Through longitudinal, individual and adaptive monitoring of blood biomarkers, the haematological module of the athlete biological passport (ABP) has become a valuable tool in anti-doping efforts. The composition of blood as a vector of oxygen in the human body varies in athletes with the influence of multiple intrinsic (genetic) or extrinsic (training or environmental conditions) factors. In this context, it is fundamental to establish a comprehensive understanding of the various causes that may affect blood variables and thereby alter a fair interpretation of ABP profiles.

METHODS

This literature review described the potential factors confounding the ABP to outline influencing factors altering haematological profiles acutely or chronically.

RESULTS

Our investigation confirmed that natural variations in ABP variables appear relatively small, likely-at least in part-because of strong human homeostasis. Furthermore, the significant effects on haematological variations of environmental conditions (e.g. exposure to heat or hypoxia) remain debatable. The current ABP paradigm seems rather robust in view of the existing literature that aims to delineate adaptive individual limits. Nevertheless, its objective sensitivity may be further improved.

CONCLUSIONS

This narrative review contributes to disentangling the numerous confounding factors of the ABP to gather the available scientific evidence and help interpret individual athlete profiles.

Effect of Exercise-Induced Reductions in Blood Volume on Cardiac Output and Oxygen Transport Capacity

We wanted to demonstrate the relationship between blood volume, cardiac size, cardiac output and maximum oxygen uptake (V.O_2max) and to quantify blood volume shifts during exercise and their impact on oxygen transport. Twenty-four healthy, non-smoking, heterogeneously trained male participants (27 ± 4.6 years) performed incremental cycle ergometer tests to determine V.O_2max and changes in blood volume and cardiac output. Cardiac output was determined by an inert gas rebreathing procedure. Heart dimensions were determined by 3D echocardiography. Blood volume and hemoglobin mass were determined by using the optimized CO-rebreathing method. The V.O_2max ranged between 47.5 and 74.1 mL⋅kg^–1⋅min^–1. Heart volume ranged between 7.7 and 17.9 mL⋅kg^–1 and maximum cardiac output ranged between 252 and 434 mL⋅kg^–1⋅min^–1. The mean blood volume decreased by 8% (567 ± 187 mL, p = 0.001) until maximum exercise, leading to an increase in [Hb] by 1.3 ± 0.4 g⋅dL^–1 while peripheral oxygen saturation decreased by 6.1 ± 2.4%. There were close correlations between resting blood volume and heart volume (r = 0.73, p = 0.002), maximum blood volume and maximum cardiac output (r = 0.68, p = 0.001), and maximum cardiac output and V.O_2max (r = 0.76, p < 0.001). An increase in maximum blood volume by 1,000 mL was associated with an increase in maximum stroke volume by 25 mL and in maximum cardiac output by 3.5 L⋅min^–1. In conclusion, blood volume markedly decreased until maximal exhaustion, potentially affecting the stroke volume response during exercise. Simultaneously, hemoconcentrations maintained the arterial oxygen content and compensated for the potential loss in maximum cardiac output. Therefore, a large blood volume at rest is an important factor for achieving a high cardiac output during exercise and blood volume shifts compensate for the decrease in peripheral oxygen saturation, thereby maintaining a high arteriovenous oxygen difference.