Based on power data, basal metabolic rate (BMR), and a non-exercise physical activity level (PAL) value, Bas Van Hooren et al. (2022) devised a method to calculate TDEE in professional cyclists and compared energy expenditure (EE) across multi-day and single-day races.
Introduction
Performance in (extreme) endurance sports, such as multi-day cycling contests, is heavily influenced by the athlete’s ability to balance their energy levels and the availability of energy. A negative energy balance, which occurs when there is an imbalance between the amount of energy consumed and the amount of energy expended during exercise, can lead to fatigue, impaired endurance performance, symptoms of “overreaching,” and problems with immune, hormonal, and metabolic processes as well as reproductive and bone health. As body mass is a key factor in movement economy, a high positive energy balance can, on the other hand, have a negative impact on performance due to increases in body/fat mass and, consequently, a decreased movement economy. Additionally, a heavier body mass will hinder performance during mountainous segments. It is crucial to quantify EE and energy intake to assure peak performance and lower the risk of illness and damage.
The most accurate way to assess EE in free-living situations for extended periods of time (like several days) is with doubly labeled water. Two 3-week cycling competitions, the Tour de France and the Giro d’Italia, yielded average total daily energy expenditure (TDEE) values of 33.7 and 32.3 MJ/day, respectively, in prior studies that employed DLW to measure EE. The variability in EE across athletes and between contests limits the use of these general estimations for dietary prescription in practice, despite the need of understanding the normal TDEE. A negative energy balance in one person, but a positive balance in another, could result from utilizing an average estimate of 32 MJ per day for matching energy intake, which could result in subpar performance. Alternative techniques are needed to reliably estimate the TDEE because it is neither practicable nor inexpensive to measure TDEE using DLW during every race or training session.
Predictive equations for the basal metabolic rate (BMR) and a component for the level of physical activity can also be used to estimate energy expenditure (PAL). Accurate measurement of the BMR and the PAL-value are crucial because in our suggested method, the BMR will be multiplied by a PAL-value to calculate TDEE. However, neither professional cyclists nor any other category of extreme endurance athletes currently have a BMR predictive equation created expressly for them. For instance, even though ten Haaf and colleagues created two BMR equations (one based on body mass and one based on fat-free mass), both equations were based on a group of physically active individuals rather than professional athletes.
The proposed method for determining EE is represented schematically. In order to create predictive equations for professional cyclists, the BMR must first be assessed. To determine the relationship between power output and EE, an incremental exercise test is carried out in the second step. The exercise efficiency (EE) is then calculated from power measurements during competition and training using this relation. Third, DLW is used to calculate total EE. Fourth, to calculate non-exercise EE, the BMR and exercise EE are removed from the total EE. To calculate a non-exercise PAL, the BMR and non-exercise EE are added together and divided by the BMR. The ratios for each component are calculated using the average values recorded during the study’s multi-day cycling event.
Goal of the study
This study’s main goal was to suggest a step-by-step methodology that would enable a more precise prediction of TDEE during both training and competition in professional cyclists. To achieve this, the researchers (1) assessed the efficacy of current BMR predictive equations in professional cyclists and created a new predictive equation, and (2) examined the non-exercise PAL-value during competition and training in professionals. The researchers also compared the EE results (TDEE, EEE, and EEN) between a multi-day cycling competition (Vuelta a Espaa) and a single day cycling competition (Ardennes Classics) as a secondary goal.
Methods
The BMR and TDEE of elite cyclists were examined in this cross-sectional observational study with the goal of enhancing tailored nutrition plans. To develop a model for the sequential determination of TDEE, the following variables were measured: (1) power output and respiratory gas exchange during an incremental cycling test, (2) BMR using indirect calorimetry, and (3) TDEE using DLW. Prior to the measurements, each participant completed an informed permission form after receiving written and spoken information about the project. This study was carried out as part of the cyclist’s regular training and racing program; thus ethics approval was not necessary.
The participants, 21 male and 4 female professional cyclists from a UCI World Tour Team, had mean ages of 27 ± 4 years, heights of 180.6 ± 6.8 cm, and body weights of 66.8 ± 7.5 kg, respectively. Because not all athletes competed in every event or because they were unavailable for the BMR measurements, the number of athletes who completed each measurement varied. Notably, only female athletes took part in the BMR assessments. In contrast to all other given data, which only includes males, BMR data are presented for both sexes together as well as separately.
Basal metabolic rate
Between 8:30 and 9:30 a.m., 17 men and 4 women showed up at the Metabolic Research Unit Maastricht at Maastricht University to measure O2 intake and CO2 production using an indirect calorimeter and a vented hood system (Omnical, Maastricht Instruments, Maastricht University). During simulated BMR measurements, this technology has demonstrated to be extremely accurate. The measurements were done either once on a day without a race preceding it or twice the day before and after a one-day race in the Ardennes. The participant’s upper torso was covered with plastic sheeting affixed to the hood to create a seal between the air within and outside the hood. Participants were told to input the measurements after an overnight fast and refrain from exercise for at least 8 hours prior to the measurements as a means of standardization. All subjects engaged in their usual exercise regimen the day before the measurements. Participants were told to lie down in a supine position and remain still for 30 to 35 minutes for the measurements. Measurements were carried out in a thermoneutral setting when the subjects were asleep but awake. For the purpose of removing the impacts of becoming used to the testing technique, the first 10-15 minutes of data were removed. Using the Weir formula, BMR was calculated from O2 consumption and CO2 generation.
Test of the power-energy expenditure relationship with incremental exercise
A VO2-max test was completed by 13 male cyclists who competed in the Vuelta, the Ardennes Classics, or both 4.9 +/- 0.2 months before the events. Female athletes were not included in these metrics because no information about their training or race was collected from them. The test was conducted according to the following protocol: 2 W/kg of beginning power was increased by 0.5 W/kg every three minutes until volitional exhaustion. Continuous measurements were made throughout the test of power output (Pioneer SGX-CA500; Kawasaki, 1 sample/s), oxygen consumption, and CO2 production (Omnical; Maastricht Instruments, 0.2 samples/s). Weir’s non-protein equation was used to compute EE using O2 consumption and CO2 generation. For comparison, EE was also determined using Jeukendrup’s equation for moderate-to-vigorous exercise. To ascertain the association between power output and EE for everyone, two regression formulas were performed using EE (based on the Weir or Jeukendrup equation) as the dependent variable and power output as the independent variable. Power output throughout each training session and race was recorded (Pioneercyclo, Pioneer Benelux). Prior to conducting further analysis, a 30-s rolling average was obtained to decrease noise. Then, for each training session and race event, EEE was determined by replacing the rolling-average power of that session into the power-EE regression equation.
Doubly labeled water for total EE
For the purpose of calculating TDEE, DLW measurements were taken twice in 2019: once during the three-week Vuelta a Espana and once during an eight-day span that included three 1-day events in the Ardennes. The 2019 Vuelta’s DLW measures involved seven individuals, and the 2019 Ardennes classics’ DLW measurements involved another seven males. Both measurement times were attended by a single male participant.
Results
The mean TDEE for the Vuelta a Espana and Ardennes classics, respectively, was 31.7 2.8 and 27.3 2.8 MJ/day. Average daily energy expenditure for these activity activities was 10.1 1.4 MJ/day and 17.4 1.8 MJ. Professional cyclists have been reported to have non-exercise PAL values of 1.8 and 2.0, respectively, which is higher than the currently used general figure. Male riders’ measured BMR was much higher than what the Oxford models anticipated. This led to the creation of a BMR prediction equation (MJ/day) for male endurance athletes.
The newly proposed step-by-step method for determining TDEE includes calculating EE during exercise using the measured power and determining EE during non-exercise periods using a measured or estimated BMR along with a non-exercise PAL value. The researchers demonstrate that this method, which is also rather simple to apply in practice, can result in a more accurate calculation of EE than the use of a general mean PAL value in conjunction with BMR predictive equations created for non-elite athletes. This in turn can help professional cyclists make better nutritional decisions.
Power output and EE during the VO2max test are correlated. Inter-individual variances in energy cost at a given power output are highlighted by differences in intercepts. However, as seen by the identical slopes, the increase in EE with increases in power output is basically comparable. The other lines show the regression lines for each individual, whereas the bold thick line shows the mean regression line. A symbol is used to represent a single data point.
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Reference
Van Hooren, B, Cox, M, Rietjens, G, Plasqui, G. Determination of energy expenditure in professional cyclists using power data: Validation against doubly labeled water. Scand J Med Sci Sports. 2022; 00: 1- 13. doi:10.1111/sms.14271