Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents

Dietary intake and adherence to the Mediterranean diet in semi-professional female soccer players: a cross-sectional study

Background

Adequate energy and nutrient intakes in athletes contribute to optimal performance and recovery, decrease the risk of injury, and help preserve athletes' health. The Mediterranean diet (MD) is considered suitable for covering the nutritional needs of athletes, while contributing to improve eating habits. The aim of the present study was to investigate the energy and nutrient intakes of semi-professional female soccer players and their adherence to the MD, during the competitive season.

Methods

A cross-sectional observational study was conducted on twenty-three female soccer players, who were invited to fill in a 3-day food diary twice, one month apart, to assess their energy and nutrient intakes and a validated questionnaire (MEDI-LITE) to evaluate their adherence to MD. Exercise energy expenditure during three training and match-play days was monitored by GPS.

Results

On average, the participants consumed 1,981 kcal/day, with 44% of their energy deriving from carbohydrates, 21% from protein, and 34% from fat; the mean MD adherence score was 10.1 ± 1.8, corresponding to a good MD adherence. A substantial percentage of athletes were at risk of insufficient nutrient intakes for vitamin D (100%), iodine (87%), potassium (87%), vitamin E (39%), iron and zinc (17 and 30%, respectively).

Conclusion

The evaluation of the dietary intake in female soccer players showed energy deficiency in relation to training level, mainly due to the insufficient intake of carbohydrates, and micronutrient deficiencies. The technical staff should promote adequate consumption of starchy foods in female athletes and emphasize the importance of a proper distribution of energy intake among different eating occasions, including snacks. Periodic monitoring of the nutritional status of micronutrients (vitamin D and some minerals) could help reduce deficiency risk. Over all, nutrition education to improve eating habits of athletes seems worthy of attention, in order to preserve athletes' health and performance.

Development and validation of novel equation for prediction of resting energy expenditure in active Saudi athletes

Being the most stable component of energy expenditure, resting metabolic rate (RMR) is usually used in the calculation of energy requirements for athletes. An adequate energy prescription is essential in supporting athlete development. This work aims to develop and validate an equation for calculating energy requirements for Arabic Saudi athletes. This cross-sectional study included 171 active athletes aged 18 to 45 years. The sample was divided into a development group (n = 127) and a validation group (n = 44). Anthropometry, indirect calorimetry, and body composition analysis via bioelectric impedance analysis were performed on all participants. The novel predictive equations were created by using stepwise linear regression analyses. The accuracy of the novel equations was compared with 10 equations, and Bland and Altman plots were used to estimate the limits of agreement between measured RMR and novel equations. The first novel equation used a set of basic measures, including weight, gender, and age, was [RMR = 1137.094 + (Wt × 14.560)-(Age × 18.162) + (G × 174.917)] (R = 0.753, and R2 = 0.567, wt = weight, G = gender; for male use 1 and female 0). The second equation used fat-free mass, age, and weight [RMR = 952.828 + (fat-free mass × 10.970)-(Age × 18.648) + (Wt × 10.297)] (R = 0.760 and R2 = 0.577). Validation of the second novel equation increased the prediction of measured RMR to 72.7% and reduced the amount of bias to 138.82 ± 133.18 Kcal. Finally, the new set of equations was designed to fit available resources in clubs and showed up to 72.73% accurate prediction and good agreement with measured RMR by Bland and Altman plots.

Relative Energy Deficiency in Sport (REDs) Indicators in Male Adolescent Endurance Athletes: A 3-Year Longitudinal Study

Longitudinal measurements of Relative Energy Deficiency in Sport (REDs) among adolescent male elite athletes are lacking. We aimed to monitor REDs indicators and their possible impact on performance in elite high-school cross-country skiing and biathlon athletes (n = 13) (16.3 ± 0.4 years, 179.4 ± 7.6 cm, 63.6 ± 8.2 kg body mass (BM), and peak oxygen uptake (VO2peak): 61.5 ± 5.3 mL/kg BM/min) every 6 months for 3 years. Protocols included assessments of energy availability (EA), body composition and bone mineral density (BMD), resting metabolic rate (RMR), disordered eating behavior, exercise addiction, VO2peak, and muscle strength. Data were analyzed using a linear mixed model. At baseline, 38% had low lumbar BMD (Z-score ≤ -1), and overall, bone health increased only slightly. VO2peak and muscle strength improved (p < 0.001), RMR decreased (p = 0.016), and no change was observed in EA or physiological or psychological REDs indicators. Conclusively, many of these young male athletes had poor bone health at baseline, and most either lost or did not achieve the expected pubertal bone mineral accrual, although no other indication of REDs was observed, while performance improved during the study period. Our findings highlight the importance of elite sports high schools focusing on screening for early detection of impaired bone health in male athletes.

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.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Accuracy of equations for estimating resting energy expenditure in children and adolescents living with phenylketonuria

BACKGROUND

Measuring resting energy expenditure (REE) in individuals living with phenylketonuria (PKU) using indirect calorimetry (IC) is unusual in healthcare facilities because it requires specific protocols and expensive equipment. Considering that determining REE is crucial for devising nutritional strategies for the management of PKU, the aim of this study was to identify the predictive equations that provide the best estimates of REE in children and adolescents living with PKU and to propose a predictive equation for determining REE in this population.

METHODS

An REE concordance study was conducted with children and adolescents living with PKU. Anthropometric and body composition assessments using bioimpedance and REE assessment using IC were performed. The results were compared to 29 predictive equations.

RESULTS

Fifty-four children and adolescents were evaluated. The REE obtained using IC differed from all estimated REE, except Henry's equation for male children (p = 0.058). Only this equation showed good agreement (0.900) with IC. Eight variables were associated with the REE obtained using IC with emphasis on fat-free mass (kg) (r = 0.786), weight (r = 0.775), height (r = 0.759) and blood phenylalanine (r = 0.503). With these variables, three REE equations were suggested, with R2  = 0.660, 0.635 and 0.618, respectively, and the third equation, which involves weight and height, showed adequate sample size for a statistical power of 0.942.

CONCLUSION

Most equations, not specific for individuals living with PKU, overestimate the REE of this population. We propose a predictive equation for assessing REE for children and adolescents living with PKU to be used in settings where IC is not available.

Effect of gas exchange data selection methods on resting metabolic rate estimation in young athletes

This cross-sectional study analysed the effect of the gas exchange data selection methods on the resting metabolic rate (RMR) estimation and proposed a protocol shortening providing a suitable RMR estimation for young athletes. Sixty-six healthy young Brazilian athletes performed a 30-minute RMR assessment. Different methods of gas exchange data selection were applied: short and long-time intervals, steady-state (SSt), and filtering. A mixed one-way ANOVA was used to analyse the mean differences in gas exchange, RMR, respiratory exchange ratio (RER), and coefficients of variation across all methods. Additionally, paired Student's t-test were used to compare the first and best SSt RMR values for each SSt method (3, 4, and 5-min). The 5-min SSt method provided the lowest RMR estimate (1454 kcal.day-1). There was a statistical difference between methods (F = 2.607, p = 0.04), but they presented a clinically irrelevant absolute difference (~36 kcal.day-1). There were no differences in RER among methods. In addition, using the SSt method, 12 minutes of assessment were enough to obtain a valid estimation of RMR. The 5-min SSt method should be employed for assessing the RMR among young athletes, considering the possibility of obtaining a shortened assessment (~12 min) with an acceptable and low coefficient of variation.

Predicting resting energy expenditure among athletes: a systematic review

Resting energy expenditure (REE) is often estimated in athletes using equations developed from the general population however, the application in athletic-specific populations is questionable. The aim of this systematic review was to compare measured REE and estimations of REE obtained from non-sport participants and athletes. Inclusion criteria met PICO criteria: population - participants involved in organized sport; intervention - resting energy expenditure was obtained by calorimetry; comparator - equations to estimate REE; outcomes - comparisons between measured REE and predicted REE. The search was conducted in Web of Science all databases, PubMed and Scopus. Comparisons between measured REE and predicted REE as well the potential models to estimate REE developed among athletes were summarized. Allowing for variation among studies, equations developed within general populations were not comparable to REE measured by calorimetry in athletes. Equations across athletic samples were obtained but, few studies tested their validity across independent samples of sport participants. Nevertheless, equations developed within athlete populations seem to be widely unused in sports nutrition literature and practice. De Lorenzo and ten Haaf equations appear to present an acceptable agreement with measured REE. Finally, equations used among adults should not be generalised for youth sport participants.

Comparison between predicted and measured resting energy expenditures in Korean male collegiate soccer players

[Purpose]

This study aimed to evaluate the differences between predicted resting energy expenditure (REE), using fat-free mass (FFM)-based prediction equations, and measured REE in Korean male collegiate soccer players.

[Methods]

Fifteen male collegiate soccer players (18-21 years) participated in this study. The REE measurements were conducted using the Douglas bag method. Body composition was measured by dual-energy X-ray absorptiometry (DXA). The differences between the measured REE and predicted REE, using the five FFM-based REE equations, were analyzed using the t-test, calculation of errors, regression analysis, and the Bland-Altman method.

[Results]

The Cunningham (1980) and ten Haaf and Weijs (2014) equations showed significantly overestimated REE (1,808 ± 99 kcal/d, p <0.01; 1,838 ± 103 kcal/d, p <0.01; respectively), but the Owen (1988), Taguchi (2011), and Kim (2015) equations’ estimations were not significantly different from the measured REE (1,589 ± 106 kcal/d, 1,640 ± 124 kcal/d, and 1,622 ± 68 kcal/d, respectively). The Taguchi equation gave the best prediction of REE with the lowest constant error (-6 ± 125) and effect size (-0.05), and a non-significant proportional bias (p = 0.95).

[Conclusion]

The Taguchi equation is recommended for predicting REE in Korean collegiate soccer players. The selection process of a REE-prediction equation must take into consideration the target population’s characteristics. Future studies are recommended to evaluate the validity of the different FFM-based REE-prediction equations in various Korean athletes.

Predictive equations for resting metabolic rate are not appropriate to use in Brazilian male adolescent football athletes

High accuracy in estimating energy expenditure is essential for enhancing sports performance. The resting metabolic rate (RMR), as a primary component of total energy expenditure (TEE), is commonly estimated using predictive equations. However, these references may not be applicable to adolescent athletes. The purpose of this cross-sectional study was to analyse the differences between predicted RMR in relation to energy expenditure measured by indirect calorimetry (IC) among 45 Brazilian male adolescent football athletes. Indirect calorimetry (IC) and anthropometric (bioimpedance) measurements were recorded at a single visit to the laboratory after fasting overnight. The mean age was 15.6 ± 1.14 years, body mass was 63.05 ± 7.8 kg, and height was 172 ± 7.5 cm. The RMR values predicted by equations proposed by the Food and Agriculture Organization (FAO) (United Nations), Henry and Rees (HR), Harris Benedict (HB), and Cunningham (CUN) were compared with IC RMR values, by correlation analysis. The FAO and HR predictive equations yielded different values from IC (IC: 1716.26 ± 202.58, HR: 1864.87 ± 147.78, FAO: 1854.28 ± 130.19, p = 0.001). A moderate correlation of 0.504 was found between the results of HB and IC. In the survival-agreement model, the CUN equation showed low disagreement with the IC RMR, with error values between 200 and 300 kcal/day. The results showed that HB and CUN yielded similar values as IC, with the CUN equation showing low disagreement with IC; hence, adolescent athletes should undergo evaluation with precise laboratory methods to ensure that accurate information about RMR is recorded.

Association of Low Energy Availability and Suppressed Metabolic Status in Korean Male Collegiate Soccer Players: A Pilot Study

Low energy availability (EA) can impair physiological function in athletes. The purpose of this study was to investigate EA status, metabolic status, and bone metabolism with biochemical analysis in Korean male soccer players. Twelve male athletes (18–20 years) completed the study. Body composition and bone mineral density were measured using dual energy X-ray absorptiometry (DXA), while VO₂ max was determined by an incremental exercise test. Blood samples were taken for bone marker and hormone analyses. Resting energy expenditure (REE) was measured using the Douglas bag method and predicted using the DXA method. Food diaries and heart rates (HR) during training were recorded, and the Profile of Mood States 2 and Eating Attitude Test 26 were completed. Group differences between low EA (LEA <30 kcal/kg FFM/d, n = 5) and high EA (HEA ≥30 kcal/kg FFM/d, n = 7) were evaluated. The mean EA of the all participants was 31.9 ± 9.8 kcal/kg FFM/d with only two participants having an EA above 45 kcal/kg FFM/d. LEA showed suppressed REE (LEA: 26.0 ± 1.7 kcal/kg/d, HEA: 28.8 ± 1.4 kcal/kg/d, p = .011) with a lower REE_ratio (LEA: 0.91 ± 0.06, HEA: 1.01 ± 0.05, p = .008) as well as a lower insulin-like growth factor 1 (IGF-1) level (LEA: 248.6 ± 51.2 ng/mL, HEA: 318.9 ± 43.4 ng/mL, p = .028) compared to HEA. There were no group differences in bone markers or other hormone levels. Korean male athletes exhibited low EA status with suppressed metabolism, but there was limited evidence on the effect of EA on bone metabolism, endocrine system, and psychological parameters.

Resting Energy Expenditure of Physically Active Boys in Southeastern Poland-The Accuracy and Validity of Predictive Equations

Optimization of energy intake in the diet of young athletes is of primary importance. In addition to the energy expenditure associated with their body development, the demand resulting from intensive physical activity also increases. The aim of this study was to compare the accuracy of formulas commonly used for resting energy expenditure (REE) calculations with values obtained from measurements using indirect calorimetry among male children and adolescents practicing football. The study was conducted among 184 boys aged 9 to 17 using a calorimeter and a device for assessing body composition by means of electrical bioimpedance using a segment analyzer. The mean error ranged from −477 kcal/d by the Maffeis formula to −182 kcal/d for the Institute of Medicine of the National Academies (IMNA) formula. A statistically significant difference was found for all formulas in the calculated value in relation to the measured REE value (p < 0.0001). Most “ready-to-use” formulas underestimate REE, which can be a risk in determining the total energy demand in a group that requires more calories, especially when due to intensive growth and development and the expenditure associated with regular training and increased physical activity.

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Background

Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults.

Methods

With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group.

Results

EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930).

Conclusion

Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.

Feasibility of the Energy Expenditure Prediction for Athletes and Non-Athletes from Ankle-Mounted Accelerometer and Heart Rate Monitor

Due to the nature of micro-electromechanical systems, the vector magnitude (VM) activity of accelerometers varies depending on the wearing position and does not identify different levels of physical fitness. Without an appropriate energy expenditure (EE) estimation equation, bias can occur in the estimated values. We aimed to amend the EE estimation equation using heart rate reserve (HRR) parameters as the correction factor, which could be applied to athletes and non-athletes who primarily use ankle-mounted devices. Indirect calorimetry was used as the criterion measure with an accelerometer (ankle-mounted) equipped with a heart rate monitor to synchronously measure the EE of 120 healthy adults on a treadmill in four groups. Compared with ankle-mounted accelerometer outputs, when the traditional equation was modified using linear regression by combining VM with body weight and/or HRR parameters (modified models: Model A, without HRR; Model B, with HRR), both Model A (r: 0.931 to 0.972; ICC: 0.913 to 0.954) and Model B (r: 0.933 to 0.975; ICC: 0.930 to 0.959) showed the valid and reliable predictive ability for the four groups. With respect to the simplest and most reasonable mode, Model A seems to be a good choice for predicting EE when using an ankle-mounted device.

Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models

We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models' performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) - (340 male or 537 female).

Cross-sectional comparison of body composition and resting metabolic rate in Premier League academy soccer players: Implications for growth and maturation

For the first time we aimed to: (1) assess fat-free mass (FFM) and RMR in youth soccer players, (2) compare measured RMR to estimated RMR using previously published prediction equations, and (3) develop a novel population-specific prediction equation. In a cross-sectional design, 99 males from a Premier League academy underwent assessments of body composition (DXA) and RMR (indirect-calorimetry). Measured RMR was compared to estimated values from five prediction equations. A novel RMR prediction equation was developed using stepwise multiple regression. FFM increased (P<0.05) between U12 (31.6±4.2 kg) and U16 (56.3±5.3 kg) after which no further increases occurred (P>0.05). RMR in the U12s (1655±195 kcal.day^-1), U13s (1720±205 kcal.day^-1) and U14s (1846±218kcal.day^-1) was significantly lower than the U15s (1957±128 kcal.day^-1), U16s (2042±155 kcal.day^-1), U18s (1875±180 kcal.day^-1) and U23s (1941±197 kcal.day^-1) squads (P>0.05). FFM was the single best predictor of RMR (r²=0.43; P<0.01) and was subsequently included in the novel prediction equation: RMR (kcal.day^-1) = 1315 + (11.1 x FFM in kg). Both FFM and RMR increase from 12-16 years old, thus highlighting the requirement to adjust daily energy intake to support growth and maturation. The novel prediction RMR equation developed may help to inform daily energy requirements.

Nutrition for Female Soccer Players-Recommendations

Soccer is one of the most popular sports in the world. As its number of players is increasing, the number of female players is also on the rise. However, there are limited data about how the diets of female soccer players should be designed. Thus, the aim of our work is to deliver concise nutritional recommendations for women practicing this sport. Based on a literature review, we emphasize that individual adjustment of the energy value of the diet is the key factor for the physical performance of female soccer players. Appropriate macronutrient intake makes it possible to achieve the proper energy value of the diet (5-10 g/kg body mass/day carbohydrates; 1.2-1.7 g/kg body mass/day proteins; <30% fats from energy). The micronutrients should be consumed in amounts corresponding to individual values recommended in national standards. Soccer players should pay special attention to the proper consumption of such micronutrients, as well as vitamins such as iron, calcium, and vitamin D. The right amount of fluid intake, consistent with the player's needs, is crucial in maximizing exercise performance. The diet of a female practicing soccer is usually characterized with low energy values, which increases the risk of various health consequences related to low energy availability. Monitoring the diets of female soccer players is, therefore, necessary.

Accuracy of Predictive Resting-Metabolic-Rate Equations in Chinese Mainland Adults

Accurate measurement of the resting metabolic rate (RMR) is necessary when we make energy requirements and nutrition suggestions in clinical. However, indirect calorimetry is not always available. The objectives of this study were to make a comparison between RMR measured by indirect calorimetry and RMR predicted by different kinds of equations, and to develop new predictive equations for Chinese mainland adults. In this study, 315 Chinese mainland adults from different provinces all over China were recruited. Subjects underwent half a day of testing, which consisted of anthropometric assessment and RMR measurement. Measured and predicted RMR were compared; new optimal equations for Chinese mainland adults were developed and tested by splitting the subjects into a development and validation group. The measured RMR was in the range of 831–2776 kcal/day (mean 1651 ± 339 kcal/day). Our findings indicated that, except for the Harris–Benedict and Schofield equations, three Chinese equations and two fat-free mass (FFM) modeling equations all significantly underestimated RMR compared to the measured value (all p < 0.01). There were no significant differences between predicted and measured RMR using the new equations for females and males. Of the pre-existing equations, Schofield’s is the most suitable for Chinese mainland adults. However, the two new equations developed in this study seem to be more effective for predicting the RMR of Chinese mainland adults, and need to be validated by a larger independent sample with different physiological and anthropometric characteristics.

Low RMRratio as a Surrogate Marker for Energy Deficiency, the Choice of Predictive Equation Vital for Correctly Identifying Male and Female Ballet Dancers at Risk

Ballet dancers are reported to have an increased risk for energy deficiency with or without disordered eating behavior. A low ratio between measured (m) and predicted (p) resting metabolic rate (RMRratio < 0.90) is a recognized surrogate marker for energy deficiency. We aimed to evaluate the prevalence of suppressed RMR using different methods to calculate pRMR and to explore associations with additional markers of energy deficiency. Female (n = 20) and male (n = 20) professional ballet dancers, 19–35 years of age, were enrolled. mRMR was assessed by respiratory calorimetry (ventilated open hood). pRMR was determined using the Cunningham and Harris–Benedict equations, and different tissue compartments derived from whole-body dual-energy X-ray absorptiometry assessment. The protocol further included assessment of body composition and bone mineral density, blood pressure, disordered eating (Eating Disorder Inventory-3), and for females, the Low Energy Availability in Females Questionnaire. The prevalence of suppressed RMR was generally high but also clearly dependent on the method used to calculate pRMR, ranging from 25% to 80% in males and 35% to 100% in females. Five percent had low bone mineral density, whereas 10% had disordered eating and 25% had hypotension. Forty percent of females had elevated Low Energy Availability in Females Questionnaire score and 50% were underweight. Suppressed RMR was associated with elevated Low Energy Availability in Females Questionnaire score in females and with higher training volume in males. In conclusion, professional ballet dancers are at risk for energy deficiency. The number of identified dancers at risk varies greatly depending on the method used to predict RMR when using RMRratio as a marker for energy deficiency.