Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle

Abstract

Background An accurate estimation of athletes’ energy needs is crucial in diet planning to improve sport performance and to maintain an appropriate body composition. This study aimed to develop and validate in elite athletes new equations for estimating resting energy expenditure (REE) based on anthropometric parameters as well as bioimpedance analysis (BIA)-derived raw variables and to validate the accuracy of selected predictive equations. Methods Adult elite athletes aged 18–40 yrs were studied. Anthropometry, indirect calorimetry and BIA were performed in all subjects. The new predictive equations were generated using different regression models. The accuracy of the new equations was assessed at the group level (bias) and at the individual level (precision accuracy), and then compared with the one of five equations used in the general population or three athletes-specific formulas. Results One-hundred and twenty-six male athletes (age 26.9 ± 9.1 yrs; weight 71.3 ± 10.9 kg; BMI 22.8 ± 2.7 kg/m2) from different sport specialties were randomly assigned to the calibration (n = 75) or validation group (n = 51). REE was directly correlated with individual characteristics, except for age, and raw BIA variables. Most of the equations from the literature were reasonably accurate at the population level (bias within ±5%). The new equations showed a mean bias −0.3% (Eq. A based on anthropometric parameters) and −0.6% (Eq. B based on BIA-derived raw variables). Precision accuracy (individual predicted-measured differences within ±5%) was ~75% in six out of eight of the selected equations and even higher for Eq. A (82.4%) and Eq. B (92.2%). Conclusion In elite athletes, BIA-derived phase angle is a significant predictor of REE. The new equations have a very good prediction accuracy at both group and individual levels. The use of phase angle as predictor of REE requires further research with respect to different sport specialties, training programs and training level.

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

“…Wt, 78.0 ± 11.5 kg; ht, 178.4 ± 7.1 cm; body fat 12.4 ± 4.1% Overestimation Balci (2021)—Female, members of the Turkish National Olympic Team, 17%, (P 42%) [ 32 ] Underestimation Jagim (2019)—Males, NCAA Div III mixed sports, 12%, (P NR%) [ 12 ] Tinsley (2019) – Male, bodybuilders, 13%, (P NR%) [ 23 ] Overestimation Jagim (2019)—Females, NCAA Div III mixed sports, 7%, (P NR%) [ 12 ] Tinsley (2019)—Female, bodybuilders, 7%, (P NR%) [ 23 ] Ten-Haaf (2014)—Females, mix of athletics, endurance, and team sports, 7%, (P 59%) [ 21 ] Nichols (2020)—< 25 BMI Females, recreational exercisers, 7%, (P 52%) [ 51 ] Nichols (2020)—> 25 BMI Females, recreational exercisers, 6%, (P 57%) [ 51 ] Nichols (2020)—< 25 BMI Males, recreational exercisers, 8%, (P 46%) [ 51 ] Balci (2021)—Males, members of the Turkish National Olympic Team (P 40%) [ 32 ] Marra (2021)—Males, mix of endurance, combat, and water polo (P 75%) [ 33 ] Ten-Haaf (2014)—Males, mix of athletics, endurance, and team sports, (P 77%) [ 21 ] Wong (2012)—Males, mix of combat, team, and skilled sports, (P NR%) [ 61 ] Nichols (2020)—> 25 BMI Males, recreational exercisers, (P 57%) [ 51 ] Taguchi (2011) (weight), Japan [ 76 ] Females ( kcal/24 h ): 26.9 × FFM (kg) + 36 n = 93 F; 20.3 ± 1.2 y n = 93 Collegiate female athletes competing in Japan in inter-collegiate games from various sports including track and field, swimming, lacrosse, basketball, judo, rhythmic gymnastics, rowing, cheerleading, badminton, weightlifting. Wt, 57.0 ± 9.2 kg, ht, 162.8 ± 6.4 cm, body fat 20.0 ± 3.9% Underestimation Watson (2019)—Females, NCAA Div II mixed sports, 7%, (P NR%) [ 25 ] Wong (2012) (weight), Malaysia [ 61 ] Unisex ( kcal/24 h ): 669 + (13 × wt [kg]) + (192 × sex [M = 1, F = 0]) n = 92 M, n = 33 F; 18–31 y ‘Elite’ National Malaysian athletes from 15 sports (combat, racquet, team, s...…”

“…Studies had to report the following outcomes (or have them extrapolatable): (i) accuracy of a prediction equation (i.e., predicted energy expenditure expressed as a percentage of the measured energy expenditure) and/or (ii) precision of a prediction equation (i.e., the percentage/number of participants predicted correctly within 10% of their measured values). An accurate RMR prediction equation has previously been defined as predicting within 10% of measured values in studies and reviews in athletes [ 9 , 27 , 28 , 32 , 33 ], and in the general population [ 34 – 36 ]. This has been justified as consistent with IC measurement errors of ≤ 5% [ 36 , 37 ].…”

Dietary Habits Are Related to Phase Angle in Male Patients with Non-Small-Cell Lung Cancer

“…There are no data on the relationship between PhA and RMR in lung cancer patients. Interestingly, PhA has been found to be a significant predictor of resting energy expenditure in athletes [ 36 ], healthy subjects [ 37 , 38 ], and patients with Crohn’s disease [ 39 ], possibly reflecting the association of PhA with lean mass and muscle quality [ 40 ]. This discrepancy may be due to the inherent differences in the studied populations since we included only male subjects with advanced-stage NSCLC.…”

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

“…Ten Haaf et al [19] 11.936 × Wt (kg) + 587.728 × Ht (m)-8.129 × Age (yrs.) + 191.027 × G + 29.279 Athletes in the Netherlands 90 2014 7 Marra et al [20] (17.2 × Wt)-( 5.95 × Age) + 748 Athletes in Italy 126 2021 8 Freire et al [21] 729. Kim et al [23] 730.4 + 15 × FFM Athletes in Korea 50 2015 FFM = fat-free mass, G = gender (1 for men and 0 for women), Ht = height, RMR = resting metabolic rate, Wt = body weight.…”