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

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.

Development and validation of new predictive equations for resting energy expenditure in physically active boys

Measurement or estimation of resting energy expenditure (REE) should be the first step in determining energy demand in physically active boys. The purpose of this study was to develop and validate new equations for resting energy expenditure in male children and adolescents practicing soccer. The cross-sectional studywas carried out among 184 boys in the derivation group and 148 boys in the validation group (mean age 13.20 ± 2.16 years and 13.24 ± 1.75 years, respectively). The calorimeter and device for assessing body composition by bioelectrical impedance analysis (BIA) were used. Model of multiple regression showed that REE can be predicted in this population with Eq. (1) (with height and weight data) or Eq. (2) (with age, height, and fat free mass data). Predictive Eq. (1) had an average error of 51 ± 199 kcal and predictive Eq. (2) - 39 ± 193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was 4.7% and 3.9%, respectively. The accuracy was 61.2% in the population for predictive Eq. (1) and 66.2% for predictive Eq. (2). Therefore, the new equations developed and validated in this study are recommended for the estimation of REE in physically active boys, when the use of IC is not feasible or available.

The LEAF questionnaire is a good screening tool for the identification of the Female Athlete Triad/Relative Energy Deficiency in Sport among young football players

Background

It has been noticed that Female Athlete Triad (Fat) and Relative Energy Deficiency (Red-S) in Sport are characterized by the symptoms of impaired endocrine-metabolic function and bone health in female athletes. In addition, it may be evaluated with a qualitative tool, such as Low Energy Availability in Females questionnaire (LEAF-Q) and quantitative measurements: bone mineral density (BMD), resting energy expenditure (REE), body composition, 24-hour dietary recall.

Methods

The aim of this study was to assess the prevalence of Triad and Red-S using the LEAF-Q in youth female football players. Additionally, the difference in the BMD, body composition, REE and energy intake (EI) were assessed between the Triad/Red-S risk and not at-risk groups.

Results

Almost two thirds (64.7%) of participants are classified as being at-risk for the triad according to their LEAF-Q scores. There were no statistically significant differences (p > 0.05) between most of the values among children from the analyzed groups. There was a statistically significant difference (p < 0.001) between the EI values among girls from the two analyzed groups: at-risk (1,773.18 kcal ±  232.57) and not at-risk (2,054.00 kcal ±  191.39). Girls who did not meet the energy intake recommendations were 10.00 as likely to be in the Triad/Red-S risk group.

Conclusion

Early identification of Fat/Red-S symptoms by screening tools such as the LEAF questionnaire is important in protecting young athletes from long-term damage due to the progression of the risk factors associated with the Fat/Red-S.