Determination of energy expenditure in professional cyclists using power data: Validation against doubly labeled water

Isotope Dilution for Measuring Total Energy Expenditure, Water Turnover, and Total Body Water in Athletes: A Systematic Review

Isotope-based tracer methods allow the determination of total energy expenditure (TEE), water turnover (rH2O), and total body water (TBW) in free-living conditions. These methods have exciting applications in athletes. However, the limited number of available measurements constrains their applicability. The aim was to describe the application of isotope dilution techniques for measuring TEE, rH2O, and TBW in athletic populations. A comprehensive search (https://doi.org/10.17605/OSF.IO/7932T) was performed in three databases: PubMed, EBSCO (CINAHL, MEDLINE, and SPORTDiscus), and Cochrane Library. A total of 1,540 records were identified (564 excluded) and 174 through other sources. After excluding 53 duplicates, 1,097 articles were screened. A total of 121 studies were included, totaling 3,244 measurements from different types of sports, age range, and tier level, with 1,020 from female athletes and 139 measurements where sex was not reported. For TEE, 75 studies were included with values ranging from 1,939 to 10,070 kcal/day. For rH2O, 15 studies were included with values ranging from 2.7 to 13.4 L/day. For TBW, 77 studies were included with values ranging from 29.8 to 76.8 kg. Variability was observed across the studies among the variables of interest. Overall, males showed higher TEE, rH2O, and TBW values than females, with endurance sports showing the greatest variability in energy and water flux, and TBW values varying most in team and mixed sports. Future research should increase representation of females, athletes with disabilities, and Tier 5 "world-class" athletes to establish normative values across sports, age groups, and sex while applying standardized isotope dilution methodologies.

Minimum Overnight Interstitial Glucose Concentration in Professional Cyclists During Two Consecutive Annual Training Camps: The Limited Impact of Daily Exercise Energy Expenditure

This observational study investigated the use of continuous glucose monitoring (CGM) in a team of professional cyclists without diabetes during two consecutive annual training camps. The goal of the study was twofold: to present the aggregated CGM metrics such as day/overnight CGM average (DAYAVG/OVNAVG) for this group of professional cyclists and to study the association between exercise energy expenditure (megajoules per day), carbohydrate intake (grams), and minimum overnight CGM values (millimoles per liter). Linear mixed models were employed in the analysis. Data were available for 26 cyclists (22 participated in both training camps). CGM levels reported (DAYAVG = 6.37 ± 0.54 mmol/L and OVNAVG = 5.30 ± 0.52 mmol/L), are not typically seen in healthy individuals not engaged in intensive exercise routines. Results showed that minimum overnight CGM values significantly fluctuated throughout the training camp, but a statistically significant association between exercise energy expenditure (p = .0839) or carbohydrate intake (p = .059) and minimum overnight CGM values could not be detected. This research contributes to the literature on CGM use in professional athletes and underscores the need for further studies to fully understand the benefits and limitations of CGM to guide sports performance.

Current Resting Metabolic Rate Prediction Equations Lack Sensitivity and Specificity to Indicate Relative Energy Deficiency in Sport: A Large Cohort Study in Elite Athletes

OBJECTIVES

Measured resting metabolic rate (RMR) was compared to predicted RMR equations (RMRratio) to see whether a low RMRratio relates to the Relative Energy Deficiency in Sport (REDs) Clinical Assessment Tool 2 (CAT2) severity/risk score.

METHODS

Female (n = 127) and male (n = 53) athletes (performance Tiers 3-5) were assigned green/yellow/orange/red light according to CAT2. RMR and submaximal exercise energy expenditure (via cycle ergometer) were assessed fasted on the same morning via indirect calorimetry. Low RMR was defined as RMRratio < 0.90, with 11 RMR prediction equations tested for sensitivity, specificity, and predictive validity against the CAT2.

RESULTS

RMRratio (Cunningham) was only lower in red versus green light (0.90 ± 0.07 vs. 0.99 ± 0.10; p = .023; but RMRratio was only low in 44% of red light athletes). The prevalence of low RMRratio ranged from 1% (Owen equation) to 68% (van Hooren equation) despite the overall prevalence of REDs being 46%. As a diagnostic (no REDs [green] vs. REDs [yellow + orange + red]), Cunningham equation reported sensitivity (true positives) of 0.77 at RMRratio of 1.00 and specificity (true negatives) of 1.00 at RMRratio of 0.70. Exercise energy expenditure was significantly lower in green versus orange (0.131 ± 0.013 vs. 0.142 ± 0.008 kcal·kg fat-free mass-1·min-1; p < .001) but was greater in red (0.127 ± 0.011) versus orange.

CONCLUSION

Interpretation of RMRratio is radically impacted by choice of prediction equation. Although there may be some utility in cross-sectionally detecting extreme REDs cases (red light) via RMRratio, more research with a focus on sport/phenotype-specific prediction equations and varying risk thresholds is required to strengthen the validity and reliability of RMR as a part of REDs diagnostics.

Training volume and total energy expenditure of an Olympic and Ironman world champion: approaching the upper limits of human capabilities

Research on world-class athletes in endurance events, such as cycling Grand Tours, has reported extreme levels of total energy expenditure. However, it has been argued that over extended periods, such as months, sustained energy expenditure is capped at approximately 2.5 times the basal metabolic rate. Triathlon is particularly notable for its high energetic demands due to its multimodal nature, requiring athletes to maintain high training volumes. In this case study, we analyzed the total energy expenditure of world-class triathlete Kristian Blummenfelt using doubly labeled water over two specific periods, along with 3 yr of training data. Total energy expenditure ranged from 7,019 to 8,506 kcal/day. Reported energy intake ranged from 4,899 to 6,360 kcal/day. The annual training volumes for the years 2020-2022 were 1,480, 1,350, and 1,308 h, respectively, following a pyramidal intensity distribution. Approximately 53% of the entire three-year period matched with the doubly labeled water measurement periods in terms of training volume, indicating that the recorded total energy expenditure is representative of the majority of the observed data. Hence, the greater part of the 3-yr period likely exceeds the proposed metabolic ceiling for sustained total energy expenditure. This not only questions the validity of the current metabolic limits but also suggests a new perspective on what is physiologically achievable in world-class athletes.NEW & NOTEWORTHY The current paper presents unprecedented data on the training volume and intensity distribution of a world-class triathlete. Furthermore, using doubly labeled water measurements and training data, we argue that our findings challenge the proposed alimentary limit for sustained energy expenditure, thereby raising the upper boundary of what is physiologically possible in humans.

Energetics of a World-Tour Female Road Cyclist During a Multistage Race (Tour de France Femmes)

Despite the increased popularity of female elite road cycling, research to inform the fueling requirements of these endurance athletes is lacking. In this case study, we report for the first time the energetics of a female world-tour cyclist competing in the 2023 Tour de France Femmes, an 8-day race of the Union Cycliste Internationale. The 29-year-old athlete presented with oligomenorrhea and low T3 before the race. Total daily energy expenditure assessed with the doubly labeled water technique was 7,572 kcal/day (∼4.3 physical activity levels), among the highest reported in the literature to date for a female. Crank-based mean maximal power was consistent with female world-tour cyclists (5 min, mean 342 W, 4.8 W/kg; 20 min 289 W, 4.1 W/kg). The average daily energy intake measured with the remote food photography method (Stage Days 1-7) was 5,246 kcal and carbohydrate intake was 13.7 g/kg (range 9.7-15.9 g/kg), and 84 g/hr during stages, and an average fat intake of 15% of daily energy intake. An estimated 2,326 kcal/day energy deficit was evidenced in a 2.2 kg decrease in body mass. Notwithstanding the high carbohydrate intake, the athlete was unable to match the energy requirements of the competition. Despite signs of energy deficiency preexisting (oligomenorrhea and low T3), and other further developing during the race (weight loss), performance was in line with that of other world-tour cyclists and a best personal performance was recorded for the last stage. This case study emphasizes the need for further research to inform energy requirements for female athletes' optimal performance and health.

The Tour de France, also possible for mortals? A comparison of a recreational and a World Tour cyclist

Cycling Grand Tours are arguably the epitome of strenuous endurance exercise, and they have been reported to represent the ceiling of sustained energy expenditure for humans. It remains unknown, however, if an average recreational athlete could endure such an event. Through the analysis of power output (PO), we compared data from the 2023 Tour de France (21 stages, total distance = 3,405 km, elevation gain = 51,815 m) in a recreational (male, age = 58 yr; height = 191 cm; body mass = 96.1 kg; estimated maximum oxygen uptake = 45.4 mL·kg-1·min-1) and a sex-matched professional (World-Tour) cyclist (28 yr; 180 cm; 67.0 kg; 80.5 mL·kg-1·min-1). The recreational and professional cyclist completed the event in 191 and 87 h, respectively (average PO of 1.50 and 3.45 W·kg-1), with the latter spending a greater proportion of time in high-intensity zones. The recreational cyclist showed an estimated total daily energy expenditure (TDEE) of 35.9 MJ [or 8,580 kcal, or ∼4.3× his daily basal metabolic rate (BMR)], whereas lower absolute values were estimated for the professional cyclist (29.7 MJ, 7,098 kcal, ∼3.8× his BMR). Despite such high TDEE values, both individuals lost minimal body mass during the event (0-2 kg). The present report therefore suggests that, partly due to differences in exercise intensity and duration, not only professional cyclists but also recreational athletes can reach currently known ceilings of TDEE for humans.NEW & NOTEWORTHY This case report indicates that a recreationally trained 58-year-old man can reach similar or even higher values of energy expenditure (∼4 times their basal metabolic rate) than professional cyclists, who are likely near the ceiling of sustained energy expenditure for humans. This was possible owing to a total longer exercise time coupled with a lower absolute and relative intensity in the recreational athlete.

Accuracy of respiratory gas variables, substrate, and energy use from 15 CPET systems during simulated and human exercise

PURPOSE

Various systems are available for cardiopulmonary exercise testing (CPET), but their accuracy remains largely unexplored. We evaluate the accuracy of 15 popular CPET systems to assess respiratory variables, substrate use, and energy expenditure during simulated exercise. Cross-comparisons were also performed during human cycling experiments (i.e., verification of simulation findings), and between-session reliability was assessed for a subset of systems.

METHODS

A metabolic simulator was used to simulate breath-by-breath gas exchange, and the values measured by each system (minute ventilation [V̇E], breathing frequency [BF], oxygen uptake [V̇O2 ], carbon dioxide production [V̇CO2 ], respiratory exchange ratio [RER], energy from carbs and fats, and total energy expenditure) were compared to the simulated values to assess the accuracy. The following manufacturers (system) were assessed: COSMED (Quark CPET, K5), Cortex (MetaLyzer 3B, MetaMax 3B), Vyaire (Vyntus CPX, Oxycon Pro), Maastricht Instruments (Omnical), MGC Diagnostics (Ergocard Clinical, Ergocard Pro, Ultima), Ganshorn/Schiller (PowerCube Ergo), Geratherm (Ergostik), VO2master (VO2masterPro), PNOĒ (PNOĒ), and Calibre Biometrics (Calibre).

RESULTS

Absolute percentage errors during the simulations ranged from 1.15%-44.3% for V̇E, 1.05-3.79% for BF, 1.10%-13.3% for V̇O2 , 1.07%-18.3% for V̇CO2 , 0.62%-14.8% for RER, 5.52%-99.0% for Kcal from carbs, 5.13%-133% for Kcal from fats, and 0.59%-12.1% for total energy expenditure. Between-session variation ranged from 0.86%-21.0% for V̇O2 and 1.14%-20.2% for V̇CO2 , respectively.

CONCLUSION

The error of respiratory gas variables, substrate, and energy use differed substantially between systems, with only a few systems demonstrating a consistent acceptable error. We extensively discuss the implications of our findings for clinicians, researchers and other CPET users.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

Energy constraint and compensation: Insights from endurance athletes

The Constrained Model of Total Energy Expenditure predicts that increased physical activity may not influence total energy expenditure, but instead, induces compensatory energetic savings in other processes. Much remains unknown, however, about concepts of energy expenditure, constraint and compensation in different populations, and it is unclear whether this model applies to endurance athletes, who expend very large amounts of energy during training and competition. Furthermore, it is well-established that some endurance athletes consciously or unconsciously fail to meet their energy requirements via adequate food intake, thus exacerbating the extent of energetic stress that they experience. Within this review we A) Describe unique characteristics of endurance athletes that render them a useful model to investigate energy constraints and compensations, B) Consider the factors that may combine to constrain activity and total energy expenditure, and C) Describe compensations that occur when activity energy expenditure is high and unmet by adequate energy intake. Our main conclusions are as follows: A) Higher activity levels, as observed in endurance athletes, may indeed increase total energy expenditure, albeit to a lesser degree than may be predicted by an additive model, given that some compensation is likely to occur; B) That while a range of factors may combine to constrain sustained high activity levels, the ability to ingest, digest, absorb and deliver sufficient calories from food to the working muscle is likely the primary determinant in most situations and C) That energetic compensation that occurs in the face of high activity expenditure may be primarily driven by low energy availability i.e., the amount of energy available for all biological processes after the demands of exercise have been met, and not by activity expenditure per se.

Quantifying physical activity energy expenditure based on doubly labelled water and basal metabolism calorimetry: what are we actually measuring?

PURPOSE OF REVIEW

Physical activity impacts energy balance because of its contribution to total energy expenditure. Measuring physical activity energy expenditure (PAEE) is often performed by subtracting the estimated 24 h expenditure on basal metabolism (called basal energy expenditure or BEE) from the total energy expenditure (TEE) measured by doubly labelled water minus an estimate of the thermic effect of food (TEF). Alternatively it can be measured as the ratio of TEE/BEE, which is commonly called the physical activity level (PAL).

RECENT FINDINGS

PAEE and PAL are widely used in the literature but their shortcomings are seldom addressed. In this review, we outline some of the issues with their use.

SUMMARY

TEE and BEE are both measured with error. The estimate of PAEE by difference magnifies these errors and consequently the precision of estimated PAEE is about 3× worse than TEE and 25-35× worse than BEE. A second problem is that the component called PAEE is actually any component of TEE that is not BEE. We highlight how the diurnal variation of BEE, thermoregulatory expenditure and elevations of RMR because of stress will all be part of what is called PAEE and will contribute to a disconnect between what is measured and what energy expenditure is a consequence of physical activity. We emphasize caution should be exerted when interpreting these measurements of PAEE and PAL.