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Accurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors). Data from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N = 46) or a whole-room indirect calorimeter (WRIC-REE N = 44). Agreement between methods was evaluated using Bland–Altman and Lin's concordance coefficient correlation (Lin's CCC). Ninety participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼300 kcal for both methods), including the most commonly used ones, such as Harris–Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (<0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer. Most equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation. [ABSTRACT FROM AUTHOR]

Abstract Background The continuous positive airway pressure (CPAP) needed for the treatment of obstructive sleep apnea (OSA) can be determined after a manual titration study which is often expensive and time consuming. Hence, different predictive equations were suggested to simplify the treatment of OSA. The purpose of this work was to compare the CPAP identified with manual titration with that calculated using various equations in a cohort of patients with severe OSA. Methods This work was conducted on patients diagnosed with severe OSA. Data collected included full medical history, demographic and anthropometric measures, polysomnography results, and the CPAP pressure obtained after manual titration which was further compared to 15 predictive equations retrieved from the literature. Results A total of 166 patients [137 (82.5%) males and 29 (17.5%) females] with severe OSA were recruited in the study. Their mean age was 55.91 ± 12.64, and their baseline diagnostic apnea hypopnea index was 71.75 ± 23.70. The mean CPAP manual titration pressure was 11.31 ± 2.9 cmH2O. Non-significant statistical difference was found (p > 0.05) when the mean titration pressure was calculated by Eqs. 2 (11.36 ± 2), 3 (11.55 ± 1.68), 10 (11.51 ± 2.29), 11 (11.14 ± 2.04), and 14 (11.71 ± 2.06), whereas the mean titration pressure calculated by Eqs. 1, 4, 5, 6, 7, 8, 9, 12, 13, and 15 differed significantly from the manual titration pressure (p

Abstract Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Background: The continuous positive airway pressure (CPAP) needed for the treatment of obstructive sleep apnea (OSA) can be determined after a manual titration study which is often expensive and time consuming. Hence, different predictive equations were suggested to simplify the treatment of OSA. The purpose of this work was to compare the CPAP identified with manual titration with that calculated using various equations in a cohort of patients with severe OSA. Methods: This work was conducted on patients diagnosed with severe OSA. Data collected included full medical history, demographic and anthropometric measures, polysomnography results, and the CPAP pressure obtained after manual titration which was further compared to 15 predictive equations retrieved from the literature. Results: A total of 166 patients [137 (82.5%) males and 29 (17.5%) females] with severe OSA were recruited in the study. Their mean age was 55.91 ± 12.64, and their baseline diagnostic apnea hypopnea index was 71.75 ± 23.70. The mean CPAP manual titration pressure was 11.31 ± 2.9 cmH2O. Non-significant statistical difference was found (p > 0.05) when the mean titration pressure was calculated by Eqs. 2 (11.36 ± 2), 3 (11.55 ± 1.68), 10 (11.51 ± 2.29), 11 (11.14 ± 2.04), and 14 (11.71 ± 2.06), whereas the mean titration pressure calculated by Eqs. 1, 4, 5, 6, 7, 8, 9, 12, 13, and 15 differed significantly from the manual titration pressure (p < 0.05). Conclusion: Predictive equations suggested to calculate CPAP pressure, albeit simple and easy to apply, yielded variable results and should be investigated carefully before their use into clinical practice. [ABSTRACT FROM AUTHOR]

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations. [ABSTRACT FROM AUTHOR]

Summary: The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta‐analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28–55 years, BMI: 40.0–62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta‐analysis (n = 20). Six equations were meta‐analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (−12.44 kcal/d; 95%CI: −81.4; 56.5 kcal/d) and Harris & Benedict (−18.9 kcal/d; 95%CI ‐73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation. [ABSTRACT FROM AUTHOR]

Simple Summary: Simple Summary: Protein ingredients play a significant role in pet food and have been brought into focus by pet owners, which drives up the annual cost of protein sources for the pet food industry. Protein resources are a crucial part of human food. However, there is a global shortage of protein-rich food, particularly animal protein. Researching the nutritional value and metabolic properties of various protein sources is essential for the pet food industry to make environmentally sustainable pet food. Using the beagles as the model animal and the difference method, we evaluated the energy value of six protein ingredients, including fish meal, meat and bone meal, corn gluten meal, soybean meal, mealworm meal, and yeast extract, and compared the effects on biological potency. There is a correlation between the chemical composition and the effective energy of protein ingredients. This study also provided recommended predictive energy equations for protein ingredients in dog food. This study evaluated the nutrition composition, the nutrient digestibility, and the energy value of six protein ingredients used in pet food by the difference method in six beagles within a 7 × 6 incomplete Latin square design. The results showed that the apparent total tract digestibility of gross energy (GE) and organic matter (OM) in beagles fed the fish meal (FM) and corn gluten meal (CGM) diets was higher than for those fed the meat and bone meal (MBM), soybean meal (SBM), mealworm meal (MM), and yeast extract (YE) diets (p < 0.05). The digestible energy (DE), metabolizable energy (ME), and net energy (NE) of the MM diet were greater than the other diets, and MBM was the lowest (p < 0.05). The ME of protein ingredients was positively correlated with organic matter and negatively correlated with the ash content. The NE of protein ingredients was positively correlated with the crude protein content and negatively correlated with the ash content. The study resulted in predictive energy equations for protein ingredients that were more accurate than the NRC's predictive equation of ME when the ash content of the ingredient was more than 30% DM. In conclusion, the nutrient digestibility and energy value of corn gluten meal were similar to those of fish meal and those of soybean meal were similar to yeast extract. All predictive energy equations for six protein feedstuffs had slight differences with measured energy values. [ABSTRACT FROM AUTHOR]

Optimal nutritional support in childhood cancer relies on the adequate provision of energy. This study investigated the impact of chemotherapy on resting energy expenditure (REE) during the first six months of treatment and the accuracy of predictive equations in calculating said requirements of newly diagnosed children with cancer. REE was measured at diagnosis utilising a validated bioelectrical impedance analysis (BIA) mobile unit and compared with three predictive equations (Schofield 1985, World Health Organization [WHO] 1985 and the Recommended Dietary Allowance [RDA] 1989). Agreement and accuracy of these equations were tested by determining bias and agreement rates and displayed using the Bland–Altman plot. Baseline values were plotted against monthly follow-up measurements over time. Statistical significance was 5% and a priori limits of agreement set between 90% and 110% of measured REE. Forty-three newly diagnosed children with median age 4 years (IQR 2.0–7.6) were measured prior to chemotherapy initiation. Compared with measured REE (mean ± SD) 719.53 ± 206.29 kcal/day, all predictive equations significantly overestimated REE: WHO 1985 (889.75 ± 323.31 kcal/day; 23% overestimation), Schofield 1985 (899.62 ± 336.10 kcal/day; 25% overestimation) and RDA (1647.67 ± 481.06 kcal/day; 129% overestimation) (p < 0.001). Despite significant proportionate bias in all three equations (p < 0.001), the intra-class consistency coefficient showed good reliability for the Schofield 1985 (0.864) and WHO 1985 (0.849) equations. Though statistically significant (chi-square = 23.11, p < 0.003), the overall 1 kcal/kg (1.3%) increase for all cancer types at six months may not be clinically significant. Existing predictive equations are unable to calculate REE accurately at childhood cancer diagnosis, highlighting the need for future investigations into the development of cancer-specific equations. [ABSTRACT FROM AUTHOR]

Steel structures are commonly used in engineering projects and infrastructure. Steel structures should have high bearing capacity and be able to resist fire, earthquake, and corrosion. A new type of Q235 refractory steel has recently been developed in China, and the residual mechanical properties of steel structures are key indicators for estimating structural damage and reusability. Therefore, in-depth research is urgently needed. The yield stress, residual elastic modulus, ultimate tensile strength, and ultimate strain at room temperature to 900 °C and under air and water cooling conditions were detected. The residual mechanical properties of Q235FR steel were compared with other structural steels, and it was confirmed through experiments that they are closely related to temperature and cooling methods. In the case of the above cooling methods, the loss of mechanical properties can be negligible when exposed to temperatures up to 600 °C. When the temperature exceeds 600 °C, the high temperature and cooling method significantly impact the residual mechanical properties of Q235FR steel. The advantages of Q235FR steel are high strength, good ductility, strong corrosion resistance, and fire resistance. The suggested predictive equations could be used to accurately evaluate the residual mechanical properties of Q235FR steel at high temperatures. [ABSTRACT FROM AUTHOR]

This study investigates the deformation and stability of shored mechanically stabilized earth (SMSE) walls using numerical analysis. A finite element (FE) model is developed in Plaxis, and parametric sensitivity analysis is conducted to identify the most influential input parameters on the maximum lateral facing displacements of the mechanically stabilized earth (MSE) and soil nail (SN) walls and global factor of safety (FS) of the SMSE wall. Predictive equations are developed for these responses using multiple linear regression. The suitability and adequacy of the developed predictive equations are assessed by evaluating the summary of fit statistics. It is observed that the governing failure mechanism of the SMSE wall is a bilinear failure plane. Its inclination angle is 10° less than that of the Rankine failure surface. The external stability of the SMSE wall is more critical compared to the conventional MSE wall. It is concluded that special attention should be given to the interfaces and connections due to the vulnerability to reinforcement-facing connection failure. [ABSTRACT FROM AUTHOR]

Purpose: Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS. Methods: MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist. Results: Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference = − 39.71 kcal [95%CI = − 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference = − 54.60 kcal [95%CI = − 87.92; − 21.28]). Conclusion: The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS. [ABSTRACT FROM AUTHOR]

Purpose: Optimal nutritional support in childhood cancer relies on the adequate provision of energy. This study investigated the impact of chemotherapy on resting energy expenditure (REE) during the first six months of treatment and the accuracy of predictive equations in calculating said requirements of newly diagnosed children with cancer. Methods: REE was measured at diagnosis utilising a validated bioelectrical impedance analysis (BIA) mobile unit and compared with three predictive equations (Schofield 1985, World Health Organization [WHO] 1985 and the Recommended Dietary Allowance [RDA] 1989). Agreement and accuracy of these equations were tested by determining bias and agreement rates and displayed using the Bland–Altman plot. Baseline values were plotted against monthly follow-up measurements over time. Statistical significance was 5% and a priori limits of agreement set between 90% and 110% of measured REE. Results: Forty-three newly diagnosed children with median age 4 years (IQR 2.0–7.6) were measured prior to chemotherapy initiation. Compared with measured REE (mean ± SD) 719.53 ± 206.29 kcal/day, all predictive equations significantly overestimated REE: WHO 1985 (889.75 ± 323.31 kcal/day; 23% overestimation), Schofield 1985 (899.62 ± 336.10 kcal/day; 25% overestimation) and RDA (1647.67 ± 481.06 kcal/day; 129% overestimation) (p

Background: In the last decade a large number of studies proposed and/or validated equations to estimate the Resting Energy Expenditure (REE) in adults and/or older adults, however, no equation currently available showed good accuracy for older adults with severe obesity. Thus, this study aimed to develop and validate new predictive equations for REE, based on data from the indirect calorimetry, in Italian older adults with severe obesity. Methods: A retrospective study was as conducted with 764 Caucasian older adults with severe obesity (age range: 60-74 years and BMI ≥ 35 kg/m/²). Four models were used to test the accuracy of anthropometry and body composition variables in multivariable prediction of REE. All models were derived by stepwise multiple regression analysis using a calibration group of 382 subjects [295 females and 87 males] and the equations were cross-validated in the remaining 382 subjects [295 females and 87 males] as validation group. The new prediction equations and the other published equations were tested using the Bland-Altman method. Prediction accuracy was defined as the percentage of subjects whose REE was predicted within ± 10% of measured REE. Results: All the equations analyzed predicted higher energy requirements for males than females, and most of them underestimated the energy requirement values of our sample. The highest accuracy values were observed in the new equations, with 62% in the anthropometric model and 63% in the body composition model. Conclusion: Although the accuracy of our equations was slightly higher in comparison with the other taken into consideration, they cannot be considered completely satisfactory for predicting REE in Italians older adults with severe obesity. When predicting equations cannot guarantee precise or acceptable values of REE, the use of indirect calorimetry (if available) should be always recommended, especially in clinical practice. [ABSTRACT FROM AUTHOR]

This study evaluated the nutrition composition, the nutrient digestibility, and the energy value of six protein ingredients used in pet food by the difference method in six beagles within a 7 × 6 incomplete Latin square design. The results showed that the apparent total tract digestibility of gross energy (GE) and organic matter (OM) in beagles fed the fish meal (FM) and corn gluten meal (CGM) diets was higher than for those fed the meat and bone meal (MBM), soybean meal (SBM), mealworm meal (MM), and yeast extract (YE) diets (p < 0.05). The digestible energy (DE), metabolizable energy (ME), and net energy (NE) of the MM diet were greater than the other diets, and MBM was the lowest (p < 0.05). The ME of protein ingredients was positively correlated with organic matter and negatively correlated with the ash content. The NE of protein ingredients was positively correlated with the crude protein content and negatively correlated with the ash content. The study resulted in predictive energy equations for protein ingredients that were more accurate than the NRC’s predictive equation of ME when the ash content of the ingredient was more than 30% DM. In conclusion, the nutrient digestibility and energy value of corn gluten meal were similar to those of fish meal and those of soybean meal were similar to yeast extract. All predictive energy equations for six protein feedstuffs had slight differences with measured energy values.

BackgroundIn the last decade a large number of studies proposed and/or validated equations to estimate the Resting Energy Expenditure (REE) in adults and/or older adults, however, no equation currently available showed good accuracy for older adults with severe obesity. Thus, this study aimed to develop and validate new predictive equations for REE, based on data from the indirect calorimetry, in Italian older adults with severe obesity.MethodsA retrospective study was as conducted with 764 Caucasian older adults with severe obesity (age range: 60-74 years and BMI ≥ 35 kg/m/²). Four models were used to test the accuracy of anthropometry and body composition variables in multivariable prediction of REE. All models were derived by stepwise multiple regression analysis using a calibration group of 382 subjects [295 females and 87 males] and the equations were cross-validated in the remaining 382 subjects [295 females and 87 males] as validation group. The new prediction equations and the other published equations were tested using the Bland-Altman method. Prediction accuracy was defined as the percentage of subjects whose REE was predicted within ± 10% of measured REE.ResultsAll the equations analyzed predicted higher energy requirements for males than females, and most of them underestimated the energy requirement values of our sample. The highest accuracy values were observed in the new equations, with 62% in the anthropometric model and 63% in the body composition model.ConclusionAlthough the accuracy of our equations was slightly higher in comparison with the other taken into consideration, they cannot be considered completely satisfactory for predicting REE in Italians older adults with severe obesity. When predicting equations cannot guarantee precise or acceptable values of REE, the use of indirect calorimetry (if available) should be always recommended, especially in clinical practice.

This study was included 155 records of Awassi ewes and 199 records of lambs born during two seasons (2011-2012), belonging to the College of Agriculture and Forestry, Mosul University. The effect of year was highly significant on all traits except for weight gain from weaning to 180 days (GW180d). Age of dam was significantly affected daily milk yield (DMY), birth weight (BW), weaning weight (WW) and gain from birth to weaning (GBW). Type of birth had significant effect on DMY, BW,WW and weight at 180 days. The effect of lamb sex on WW, GBW was significant. Significant correlations coefficient were observed between DMY and total milk yield (TMY), months weight of lambs, GBW and GB180d being ranged from 0.46 to 0.99 and -0.44 to 0.99 respectively. The R² values of prediction equations were high and ranged between 0.81 to 0.99 to predict TMY, and 0.97 to predict lamb weight at 180 days. [ABSTRACT FROM AUTHOR]

Background: Assessment of animal growth based on live weight (LW) in traditional sheep production systems is limited by the high cost of purchase and maintenance of livestock scales. Objective: To develop and evaluate equations for LW prediction using heart girth (HG) in growing Pelibuey sheep. Methods: A dataset (n=415) of clinically healthy male Pelibuey sheep from two months to one year of age, with an average LW of 25.96 ± 10.25 kg and HG of 68.31 ± 10.53 cm, were used. Three equations were evaluated: LW (kg) = −37.70 + 0.93 × HG (Eq. 1); LW (kg) = −1.74 + 0.19 × HG + 0.008 × HG2 (Eq. 2); and LW (kg) = 0.003 × HG2.68 (Eq. 3). Results: The correlation coefficient between LW and HG was r = 0.94 (p0.05). On the other hand, for Eqs. 1 and 3 the intercept was different from zero and the slope was different from one (p

This paper contains a revision of the Harris–Benedict equations through the development and validation of new equations for the estimation of resting metabolic rate (RMR) in normal, overweight, and obese adult subjects, taking into account the same anthropometric parameters. A total of 722 adult Caucasian subjects were enrolled in this analysis. After taking a detailed medical history, the study enrolled non-hospitalized subjects with medically and nutritionally controlled diseases such as diabetes mellitus, cardiovascular disease, and thyroid disease, excluding subjects with active infections and pregnant or lactating women. Measurement of somatometric characteristics and indirect calorimetry were performed. The values obtained from RMR measurement were compared with the values of the new equations and the Harris–Benedict, Mifflin–St Jeor, FAO/WHO/UNU, and Owen equations. New predictive RMR equations were developed using age, body weight, height, and sex parameters. RMR males: (9.65 × weight in kg) + (573 × height in m) − (5.08 × age in years) + 260; RMR females: (7.38 × weight in kg) + (607 × height in m) − (2.31 × age in years) + 43; RMR males: (4.38 × weight in pounds) + (14.55 × height in inches) − (5.08 × age in years) + 260; RMR females: (3.35 × weight in pounds) + (15.42 × height in inches) − (2.31 × age in years) + 43. The accuracy of the new equations was tested in the test group in both groups, in accordance with the resting metabolic rate measurements. The new equations showed more accurate results than the other equations, with the equation for men (R-squared: 0.95) showing better prediction than the equation for women (R-squared: 0.86). The new equations showed good accuracy at both group and individual levels, and better reliability compared to other equations using the same anthropometric variables as predictors of RMR. The new equations were created under modern obesogenic conditions, and do not exclude individuals with regulated (dietary or pharmacological) Westernized diseases (e.g., cardiovascular disease, diabetes, and thyroid disease). [ABSTRACT FROM AUTHOR]

Underestimating/overestimating resting metabolic rate (RMR) affects energy prescription. The objective was to compare RMR by indirect calorimetry (RMR IC) and RMR estimated by predictive equations in women with excess body fat. This was an analytical cross-sectional study with 41 women aged 18–28 with overnutrition according to body composition. The RMR IC was measured and RMR estimated using the FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris–Benedict, and Mifflin–St Jeor equations. The percentage of adequacy (90–110%), overestimation (>110%), and underestimation (<90%) were evaluated for RMR IC. Data were described by percentiles because of non-normal distribution according to the Shapiro–Wilk test. The Kruskal–Wallis test and Bland–Altman analysis were applied at a significance level of α < 0.05. The RMR IC was 1192 and 1183 calories/day (p = 0.429) in women with obesity and overweight, respectively. The FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris–Benedict, and Mifflin–St Jeor equations overestimated the RMR IC by 283.2, 311.2, 292.7, and 203.0 calories/day and by 296.7, 413.8, 280.0, and 176.6 calories/day for women with overweight and obesity (p < 0.001), respectively. The Harris–Benedict adjusted weight (0.5) equation underestimated RMR IC by 254.7 calories/day. The predictive equations overestimated RMR IC in women with excess body fat. The Mifflin–St Jeor equation showed less overestimation and better adequacy, but was not exempt from inaccuracy. [ABSTRACT FROM AUTHOR]

Studies exploring the accuracy of equations calculating resting energy expenditure (REE) in patients with Crohn's disease (CD) are lacking. The aim of this study was to investigate the accuracy of REE predictive equations against indirect calorimetry in CD patients. REE was measured using indirect calorimetry (mREE) after an overnight fasting. Fourteen predictive equations, with and without body composition analysis parameters, were compared with mREE using different body weight approaches. Body composition analysis was performed using dual X-ray absorptiometry. One hundred and eighty-six CD outpatients (102 males) with mean age 41·3 (sd 14·1) years and 37·6 % with active disease were evaluated. Mean mREE in the total sample was 7255 (sd 1854) kJ/day. All equations underpredicted REE and showed moderate correlations with mREE (Pearson's r or Spearman's rho 0·600–0·680 for current weight, all P -values < 0·001). Accuracy was low for all equations at the individual level (28–42 and 25–40 % for current and adjusted body weight, respectively, 19–33 % for equations including body composition parameters). At the group level, accuracy showed wide limits of agreement and proportional biases. Accuracy remained low when sample was studied according to disease activity, sex, BMI and medication use. All predictive equations underestimated REE and showed low accuracy. Indirect calorimetry remains the best method for estimating REE of patients with CD. [ABSTRACT FROM AUTHOR]

Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied. [ABSTRACT FROM AUTHOR]

The Cuarto River is an important watercourse in the Córdoba Province, Argentina. Its middle-low segment crosses the eastern plains of this province, one of the most populated and (agricultural-livestock) productive regions of the country. In this segment, the channel and fluvial belt morphometric parameters exhibit notable changes downstream. Understanding the morphodynamics of the Cuarto River allows the characterization and definition of channel reach with tendencies towards migration, aggradation and overflow. In this way, it is possible to identify critical channel reaches, a fundamental aspect for effective planning. This work aims to characterize the downstream changes in the fluvial belt, meanders geometry, channel pattern and channel slope, along the middle-low segment of the Cuarto River and link these modifications with authigenic (sediment supply and transport regime) and allogenic controls. Five channel reaches (R1-R5) were selected according to geological-geomorphological criteria and different geometric parameters (channel width, channel slope, braided index, meander geometry, confinement degree) were determined for each of them. Empirical equations were used to establish relationships between these geometric parameters. Although most parameters showed a generally downward trend towards downstream reaches, marked changes were observed. Particularly, in the R1-R2 reaches, the fluvial belt widens and is unconfined, and the channel width and braided index also increases. The relationship between meander length/channel width (Lm/w) and radius of curvature/channel width (rc/w) obtained were within the ranges for free meanders (8–14) and high mobility (2–3), respectively. These parameter values are associated with alluvial channels and suggest limitations in transport and conditions for sedimentation. However, in R3, R4 and R5, the fluvial belt narrows and becomes confined, with high sinuosity, a reduction in the width of the channel and the braiding index. This pattern is expected for reaches characterized by predominance of transport and a tendency to a mixed or suspensive load. Moreover, both groups of reaches also exhibit an abrupt contrast in bedload size (from very coarse sand/gravel in R1 and R2 to fine/very fine sand in R3, R4, and R5). The similarity between the exponents of the predictive functions (Lm/w, rc/w, and Lm/rc) and those obtained for the Cuarto River, in addition to the satisfactory regression coefficients of the measured parameters suggest an acceptable balance between meander geometry and channel width regime of sediment transport. The middle-low reach of the Cuarto River shows notable changes, which can be linked to regional-scale allogenic factors such as morphostructures and lithology (R1-R2 with elevated morphostructure, R3-R4 are transitional reaches towards depressed plains, while R5 with subsidence environments). Nonetheless, the river demonstrates the capacity to regulate these factors at the reach level through authigenic processes. Thus the variability of channel width, channel slope, braiding index, meander geometry and degree of confinement are explained mainly by downstream changes in sediment supply and transport regime (authigenic controls) and secondarily by changes in regional slope and banks lithology (allogeneic controls). • Relations between morphoestructal setting and fluvial belt mophology. • Use of empirical equations to analyze behaviour of alluvial channel regime. • Significant changes downstream in meanders geometry and channel pattern. • Good correspondence between meanders geometry relations and active river reaches. • Relations between morphological changes and bedload size downstream. [ABSTRACT FROM AUTHOR]

The recovery of European wild rabbit (Oryctolagus cuniculus) populations in Spain is of great interest for wildlife conservation. Large-scale management and conservation programmes aimed to determine current population abundance.Most methods for censusing rabbits only offer estimates of relative abundance. However, we lack accurate studies about how latrine counts index is related to population density. The aim of this study is twofold: 1) to test the suitability of counting rabbit latrines as a relative abundance index and; 2) to explore if the number of latrines is a useful predictor of wild rabbit density.We compared latrine surveys conducted in 2 km-length paths with density estimates obtained from distance-based estimates. The study was conducted during two years in central Spain in mixed agriculture-woodland landscapes. Sites covered all potential range of rabbit densities in natural landscapes. Density estimates were obtained by using Distance software.We used type II major axis regression to evaluate the relationship between rabbit density and latrine counting. The predictive ability of the regression equation was performed by several predictive metrics.Results show that the number of latrines counted is a very good abundance index. We also found that latrine counts had a high predictive power to estimate rabbit density. Therefore, latrine surveys are not only a proxy of abundance, but can be used as a cost-effective tool to estimate rabbit population density at low and intermediate densities, which are the most important for the conservation practitioners. Furthermore, the method needs to be tested in other seasons and habitats to prove its generality.

Context Energy expenditure predictive equations can generate inaccurate estimates for overweight or obese individuals. Objective The objective of this review was to determine which predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) have the lowest bias and the highest precision in adults with overweight and obesity. Data Sources Searches were performed in January 2022 in MEDLINE, Web of Science, Scopus, CENTRAL, and the gray literature databases. Data Extraction Meta-analyses were performed with equations included in more than 1 study. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The Egger test was performed to assess potential publication biases, and metaregressions were conducted to explore the heterogeneity. Findings were presented separated by participants' body mass index classification (overweight and obesity). Data Analysis Sixty-one studies were included. The FAO/WHO/UNU (1985) equation, which uses only body weight in its formula, showed the lowest bias in estimating REE (mean difference [MD] = 8.97 kcal; 95% CI = –26.99; 44.94). In the subgroup analysis for individuals with obesity, the Lazzer (2007) equation showed the lowest bias (MD = 4.70 kcal; 95% CI = –95.45; 104.86). The Harris–Benedict equation (1919) showed the highest precision values for individuals with overweight (60.65%) and for individuals with obesity (62.54%). Equations with body composition data showed the highest biases. The equation proposed by the Institute of Medicine (2005) showed the lowest bias (MD = –2.52 kcal; 95% CI = –125.94; 120.90) in estimating the TEE. Most analyses showed high heterogeneity (I2 > 90%). There was no evidence of publication bias. Conclusion For individuals with overweight, the FAO/WHO/UNU (1985) and the Harris–Benedict equations (1919) showed the lowest bias and the highest precision in predicting the REE, respectively. For individuals with obesity, the Harris–Benedict equation (1919) showed the highest precision and the Lazzer equation (2007) showed the lowest bias. More studies are needed on predictive equations to estimate the TEE. Systematic Review Registration PROSPERO registration no. CRD42021262969. [ABSTRACT FROM AUTHOR]

To determine the resting energy expenditure (REE) in Chinese adults using indirect calorimetry (IC), and evaluate the validity of published predictive equations for estimating REE in overweight and obese subjects comparing to IC. 181 subjects with different body mass index (BMI) were recruited. Body composition was determined by bio-electrical impedance analysis (BIA). REE was measured by IC and estimated by 29 REE prediction equations to analyze the accuracy of REE prediction equations. For all participants combined, the Bernstein et al. and Bernestin et al. (BC) equations similarly predicted REE values between -4% and 8% (P > 0.05) of measured REE. When participant data were stratified by sex, BMI, the accuracy of each regression equation is different. In male overweight and obese group, the highest accuracy of the Bernestin et al. (BC) was 65.5% and 70.4%. In female overweight and obese group, the highest accuracy of the Bernestin et al. (BC) and Bernestin et al. was 79.6% and 69.4%. The Bernestin et al. (BC) was closest to IC in male overweight/obese and female overweight, and the Bernestin et al was closest to IC in female obese. However, because those equations have shown considerable variability, they should be used cautiously when estimating the individual REE. When the IC method cannot be used, the Bernestin et al. (BC) and Bernestin et al are more accurate. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed to compare measured and estimated resting metabolic rate (RMR) predicted by selected equations in patients with nonactive inflammatory bowel disease (IBD) on an outpatient university clinic regimen., Research Methods & Procedures: Seventy-two adult (≥20 years) IBD patients (45 with Crohn's disease-CD) had RMR measured ( m RMR) by indirect calorimetry and also estimated by predictive equations (Cunningham, Henry, Anjos et al., and Marra et al.). Body composition was assessed by DXA. Absolute Bias (estimated - m RMR) and % Bias (Bias/ m RMR) were calculated. Agreement was assessed as the limit of agreement (LoA) in the Bland & Altman approach., Results: There was no difference in age, body composition and m RMR between individuals with CD (5414.2 ± 1023.7 kJ/day) and ulcerative colitis (5443.9 ± 1008.9 kJ/day). Among the equations, only the Anjos et al.'s population-specific equation (-52.1 [642.0] kJ/day, P = 0.493; LoA: -1311; 1206 kJ/d) accurately estimated RMR. The equations of Marra et al. produced the highest % Bias (24.1 ± 14.8%). The Bland & Altman plots showed that the range of the LoA was relatively similar for all equations. In the simple regression analysis, the model with FFM resulted in a higher coefficient of determination (R 2 = 0.51 for DC 0.74 for UC) compared to the model that included BM (R 2 = 0.35 for DC and 0.65 for UC)., Conclusions: Among the equations analyzed, only Anjos et al.'s accurately estimated RMR in outpatients with nonactive IBD. However, caution is advised when applying it at the individual level, due to the wide observed LoA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes. Methods: The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, whereas the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, root mean square error (RMSE), simple linear regression, and Bland–Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ±10%, respectively) of measured RMR. Multiple linear regression models were employed to develop the new predictive equations based on traditional predictors (equation 1) and the stepwise method (equation 2). Results: The new equations 1 and 2 presented good agreement based on the mean difference (3 and −15 kcal·d−1), RMSE (200 and 192 kcal·d−1), and R 2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Jagim's equation showed the worst performance for males (mean difference = −335 kcal·d−1; RMSE = 386 kcal·d−1). Compared with measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; P ≤ 0.05), mainly in males. Conclusions: The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Jagim's equation should not be used in high-level male athletes. [ABSTRACT FROM AUTHOR]

Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris–Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro–Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women. [ABSTRACT FROM AUTHOR]

Background: The American and European guidelines recommend measuring resting energy expenditure (REE) using indirect calorimetry (IC). Predictive equations (PEs) are used to estimate REE, but there is limited evidence for their use in critically ill patients. The aim of this study is to evaluate the degree of agreement and accuracy between IC‐measured REE (REE‐IC) and 10 different PEs in mechanically ventilated critically ill patients with surgical trauma who met their estimated energy requirement. Methods: REE‐IC was retrospectively compared with REE‐PE by 10 PEs. The degree of agreement between REE‐PE and REE‐IC was analyzed by the Bland‐Altman test (BAt) and the concordance correlation coefficient (CCC). The accuracy was calculated by the percentage of patients whose REE‐PE values differ by up to ±10% in relation to REE‐IC. All analyses were stratified by gender and body mass index (BMI; <25 vs ≥25). Results: We analyzed 104 patients and the closest estimate to REE‐IC was the modified Harris‐Benedict equation (mHB) by the BAt with a mean difference of 49.2 overall (61.6 for males, 28.5 for females, 67.5 for BMI <25, and 42.5 for BMI ≥25). The overall CCC between the REE‐IC and mHB was 0.652 (0.560 for males, 0.496 for females, 0.570 for BMI <25, and 0.598 for BMI ≥25). The mHB equation was the most accurate with an overall accuracy of 44.2%. Conclusion: The effectiveness of PEs for estimating the REE of mechanically ventilated surgical‐trauma critically ill patients is limited. [Correction added on 17 February 2022, after first online publication: The word "with" was deleted before "is limited" in the preceding sentence.] Nonetheless, of the 10 equations examined, the closest to REE‐IC was the mHB equation. [ABSTRACT FROM AUTHOR]

There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASMDXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:−5.86-−5.45, Toselli's:−0.51-−0.15, Kyle's: 1.43–1.84, Rangel-Peniche's: 0.32–0.74, Sergi's: 0.83–1.23, and Yoshida's: 4.16–4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.

Summary: Background & Aims: The better equation to predict resting metabolic rate (pRMR) to use for overweight individuals mainly after weight loss is unclear. This study aimed to identify the best equation to pRMR in overweight adult women before and after a nutritional intervention aiming to lose weight. Methods: Thirty overweight adult women were enrolled in this study. Subjects underwent 6-month energy-restricted diet intervention. Anthropometrics and body composition measures were evaluated. Nine equations that are widely used to pRMR were performed based on anthropometric and body composition parameters. Measured RMR (mRMR) was obtained by indirect calorimetry. A new equation to predict resting metabolic rate (npRMR) was also developed by multiple regression analysis based on anthropometric and body composition variables. The validity of the equations was investigated through comparisons, accuracy, and agreement tests. Results: Before the nutrition intervention, only the Mifflin equation was similar to mRMR, with a mean difference of 12kcal and 83% of accuracy. The mean weight loss was 4.2% after 6 months. Following weight loss, only the Owen equation was similar to mRMR, with a mean difference of −33 kcal. However, this equation showed low accuracy (63%). All the others equations showed reduction of accuracy, increase of bias and overestimation of RMR. A npRMR was calculated, and this showed a lower mean difference to mRMR with 70% of accuracy. Conclusions: Out of the nine resting metabolic rate equations evaluated only the Mifflin equation was similar to mRMR before intervention. Interestingly, after weight loss none of the assessed equations were reliable to pRMR. Based on that we propose a new equation that showed greater accuracy and lower mean difference when compared with mRMR.

The Q1100 ultra-high strength steel (UHSS) has broad application prospects in bridges and super high-rise buildings due to its ultra-high strength properties. However, no research has been conducted on the mechanical properties of Q1100 UHSS at low and elevated temperatures. Therefore, an experiment was conducted to examine the low- and elevated-temperature mechanical properties of Q1100 UHSS at the flat and corner regions of cold-formed angle sections. The temperature considered in this study ranged from −80 ℃ to 500 ℃. The failure modes, stress-strain curves, and key mechanical parameters of the flat and corner specimens of Q1100 UHSS at different temperatures were obtained. The experimental results showed that both the flat and corner specimens underwent ductile fracture with obvious necking at different temperatures. The stress-strain curves of all specimens had no yield plateau, showing obvious nonlinear characteristics. The strength properties of Q1100 UHSS at low temperatures were increased by less than 10 %. However, the strength properties of Q1100 UHSS at elevated temperatures were significantly reduced, where the reduction exceeded 60 % at temperatures reaching 500 °C. Notably, the ultimate strength of Q1100 UHSS at 200 °C was about 5 % higher than that at ambient temperature. The cold-forming process significantly reduced the ductile properties of Q1100 UHSS, but improved its strength properties. At low and elevated temperatures, the most significant cold-formed strengthening effects of yield strength, ultimate strength, and elastic modulus occurred at −40 ℃ and 300 ℃, respectively. Moreover, the predictive equations of key mechanical parameters and a two-stage constitutive model were proposed for the Q1100 UHSS flat and corner specimens. The verification results indicated that the proposed predictive equations and constitutive model could accurately predict the mechanical parameters and stress-strain curves of the Q1100 UHSS flat and corner specimens at different temperatures. The experimental results and constitutive model provided a basis for the numerical and theoretical studies on the resistant performance of the cold-formed UHSS structures servicing in low and elevated temperatures, which could be used as the material model. • Q1100 ultra-high strength steel (UHSS) underwent ductile fracture with necking at −80 ℃ to 500 ℃. • Variations of mechanical properties of Q1100 UHSS at different temperatures were consistent with other structural steels. • Cold-forming process increased the strength properties and reduced ductile properties of Q1100 UHSS. • Predictive equations and a two-stage constitutive model of Q1100 UHSS at different temperatures were proposed. [ABSTRACT FROM AUTHOR]

Simple Summary: Intensity prescription for cardiorespiratory exercises is crucial for achieving health/fitness benefits. However, not all of the population can access a cardiopulmonary exercise test, either for economic reasons or location resources, to determine their ventilatory thresholds. Therefore, different mathematical models can predict the intensity based on the maximum or reserve heart rate. Exercise prescription guidelines indicate that people with fibromyalgia should exercise at 60% of their VO2max. However, people with fibromyalgia suffer from dysautonomia, which could lead to chronotropic incompetence, the inability to increase heart rate with increasing exercise intensities. Therefore, this study aimed to investigate the relationship and level of agreement between different mathematical models and the heart rate obtained from a cardiopulmonary exercise test at their ventilatory threshold 1. The results showed that the well-known "220 − age" at 76% and the mathematical model designed for people with fibromyalgia "209 − 0.85 × age" at 76% showed a significant level of agreement. However, Tanaka and Karvonen's formula did not show a significant level of agreement. Thus, the "220 − age" at 76% and "209 − 0.85 × age" at 76% can be used in people with FM to prescribe aerobic exercise. Objectives: This article aims to verify the agreement between the standard method to determine the heart rate achieved in the ventilatory threshold 1 in the cardiopulmonary exercise testing (VT1) and the mathematical models with exercise intensities suggested by the literature in order to check the most precise for fibromyalgia (FM) patients. Methods: Seventeen women with FM were included in this study. The VT1 was used as the standard method to compare four mathematical models applied in the literature to calculate the exercise intensity in FM patients: the well-known "220 − age" at 76%, Tanaka predictive equation "208 − 0.7 × age" at 76%, the FM model HRMax "209 – 0.85 × age" at 76%, and Karvonen Formula at 60%. Bland–Altman analysis and correlation analyses were used to explore agreement and correlation between the standard method and the mathematical models. Results: Significant correlations between the heart rate at the VT1 and the four mathematical estimation models were observed. However, the Bland-Altman analysis only showed agreement between VT1 and "220 − age" (bias = −114.83 + 0.868 × x; 95% LOA = −114.83 + 0.868 × x + 1.96 × 7.46 to −114.83 + 0.868 × x − 1.96 × 7.46, where x is the average between the heart rate obtained in the CPET at VT1 and "220 − age", in this case 129.15; p = 0.519) and "209 − 0.85 × age"(bias = −129.58 + 1.024 × x; 95% LOA = −129.58 + 1.024 × x + 1.96 × 6.619 to −129.58 + 1.024 × x − 1.96 × 6.619, where x is the average between the heart rate obtained in the CPET at VT1 and "209 − 0.85 × age", in this case 127.30; p = 0.403). Conclusions: The well-known predictive equation "220 − age" and the FM model HRMax ("209 − 0.85 × age") showed agreement with the standard method (VT1), revealing that it is a precise model to calculate the exercise intensity in sedentary FM patients. However, proportional bias has been detected in all the mathematical models, with a higher heart rate obtained in CPET than obtained in the mathematical model. The chronotropic incompetence observed in people with FM (inability to increase heart rate with increasing exercise intensities) could explain why methods that tend to underestimate the HRmax in the general population fit better in this population. [ABSTRACT FROM AUTHOR]

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Despite their body composition features, some exercisers could not have the requisites to be defined as athletes, and consequently body composition might be estimated less accurately using equations developed for the general population. Similarly, a person who carries out manual works such as farmers, builders, or a gym attender that does not compete in organized events or is not affiliated with a sports team could nevertheless have similar body composition characteristics to those of an athlete. Consequently, practitioners simply use equations for athletes when specific so-defined athletes are tested or generalized equations when exercisers or different populations are involved. Exercisers and athletes with similar body composition could show a similar phase angle, we suggest setting specific thresholds to define when generalized or formula for athletes should be used. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

This paper contains a revision of the Harris–Benedict equations through the development and validation of new equations for the estimation of resting metabolic rate (RMR) in normal, overweight, and obese adult subjects, taking into account the same anthropometric parameters. A total of 722 adult Caucasian subjects were enrolled in this analysis. After taking a detailed medical history, the study enrolled non-hospitalized subjects with medically and nutritionally controlled diseases such as diabetes mellitus, cardiovascular disease, and thyroid disease, excluding subjects with active infections and pregnant or lactating women. Measurement of somatometric characteristics and indirect calorimetry were performed. The values obtained from RMR measurement were compared with the values of the new equations and the Harris–Benedict, Mifflin–St Jeor, FAO/WHO/UNU, and Owen equations. New predictive RMR equations were developed using age, body weight, height, and sex parameters. RMR males: (9.65 × weight in kg) + (573 × height in m) − (5.08 × age in years) + 260; RMR females: (7.38 × weight in kg) + (607 × height in m) − (2.31 × age in years) + 43; RMR males: (4.38 × weight in pounds) + (14.55 × height in inches) − (5.08 × age in years) + 260; RMR females: (3.35 × weight in pounds) + (15.42 × height in inches) − (2.31 × age in years) + 43. The accuracy of the new equations was tested in the test group in both groups, in accordance with the resting metabolic rate measurements. The new equations showed more accurate results than the other equations, with the equation for men (R-squared: 0.95) showing better prediction than the equation for women (R-squared: 0.86). The new equations showed good accuracy at both group and individual levels, and better reliability compared to other equations using the same anthropometric variables as predictors of RMR. The new equations were created under modern obesogenic conditions, and do not exclude individuals with regulated (dietary or pharmacological) Westernized diseases (e.g., cardiovascular disease, diabetes, and thyroid disease).

Underestimating/overestimating resting metabolic rate (RMR) affects energy prescription. The objective was to compare RMR by indirect calorimetry (RMR IC) and RMR estimated by predictive equations in women with excess body fat. This was an analytical cross-sectional study with 41 women aged 18–28 with overnutrition according to body composition. The RMR IC was measured and RMR estimated using the FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris–Benedict, and Mifflin–St Jeor equations. The percentage of adequacy (90–110%), overestimation (>110%), and underestimation (p = 0.429) in women with obesity and overweight, respectively. The FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris–Benedict, and Mifflin–St Jeor equations overestimated the RMR IC by 283.2, 311.2, 292.7, and 203.0 calories/day and by 296.7, 413.8, 280.0, and 176.6 calories/day for women with overweight and obesity (p < 0.001), respectively. The Harris–Benedict adjusted weight (0.5) equation underestimated RMR IC by 254.7 calories/day. The predictive equations overestimated RMR IC in women with excess body fat. The Mifflin–St Jeor equation showed less overestimation and better adequacy, but was not exempt from inaccuracy.

Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.

The aim of the study is to show the differences between the measured and estimated values of resting energy expenditure and any changes occurring after the 6-month weight loss intervention program.

The current study aimed: (i) to external validate total body water (TBW) and extracellular water (ECW) derived from athlete and non-athlete predictive equations using radioisotope dilution techniques as a reference criterion in male and female athletes; (ii) in a larger sample, to determine the agreement between specific and generalized equations when estimating body fluids in male and female athletes practicing different sports. A total of 1371 athletes (men: n = 921, age 23.9 ± 1.4 y; women: n = 450, age 27.3 ± 6.8 y) participated in this study. All athletes underwent bioelectrical impedance analyses, while TBW and ECW were assessed with dilution techniques in a subgroup of 185 participants (men: n = 132, age 21.7 ± 5.1 y; women: n = 53, age 20.3 ± 4.5 y). Two specific and eight generalized predictive equations were tested. Compared to the criterion methods, no mean bias was observed using the athlete-specific equations for TBW and ECW (-0.32 to 0.05, p > 0.05) and the coefficient of determination ranged from R² = 0.83 to 0.94. The majority of the generalized predictive equations underestimated TBW and ECW (p < 0.05); R² ranged from 0.66 to 0.89. In the larger sample, all the generalized equations showed lower TBW and ECW values (ranging from -6.58 to -0.19, p < 0.05) than specific predictive equations; except for TBW in female power/velocity (one equation) athletes and team sport (two equations). The use of generalized BIA-based equations leads to an underestimation of TBW, and ECW compared to athlete-specific predictive equations. Additionally, the larger sample indicates that generalized equations overall provided lower TBW and ECW compared to the athlete-specific equations. [ABSTRACT FROM AUTHOR]

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A series of experimental tests for investigating the post-fire mechanical (PFM) and post-fire fracture (PFF) performances of high strength steel Q460 are reported in this paper. All Q460 coupon specimens are heated up to a designated temperature which is selected from 100 to 900°C and then cooled down naturally to room temperature. Tensile tests are conducted to obtain their completely full-range post-fire stress-strain curves and the corresponding mechanical properties. The obtained experimental results show that with an increase in the heating temperature, the post-fire yield strength and ultimate strength of the Q460 structural steel decrease particularly when the heating temperature is over 650°C, but the post-fire elongation enhances. Ductile fracture behaviour of the coupon specimens under axial tensile loading can also be observed through the tensile coupon tests. The obtained experimental data are compared with the other results found in the open literatures on Grade 460 high strength steel. Based on a wider range of experimental data sets, predictive equations for evaluating the PFM properties of Grade 460 high strength steel are proposed. The experimental results presented in this study will provide benchmark data for the future calibration of complex ductile fracture parameters applied in numerical simulation. [ABSTRACT FROM AUTHOR]

The S-Index assessed by means of electronic devices is a measure of Inspiratory Muscle Strength (IMS) that highly correlates with the maximal inspiratory pressure (MIP). The variables involved when using regression models for the prediction of IMS/MIP depend on both the sample characteristics and the device or protocol used. In light of the scarce information on the influence of physical activity (PA) on IMS in healthy older adults (OA), together with the incorporation of new assessment devices, the objectives of this research are: 1) to determine which factors influence the IMS in a group of trained OA, using a portable electronic device; and 2) to propose a regression model to improve its assessment and training. One hundred and fifty-nine individuals were assessed for body composition, lung capacity, IMS, and PA. A total of 92 individuals (72.73 ± 4.99 years) were considered for the final sample. Using age, sex, and weight as control variables, forced expiratory volume in the first second (FEV1) is the variable which is most likely to be included in the model (80%), without physical fitness appearing to be decisive. In the absence of spirometric variables, cardiorespiratory fitness (6MWT) takes on this role in a predictive model (16%). This is the first study proposing IMS predictive formulas considering spirometry and/or physical fitness results for a Spanish, healthy, and trained OAs population. A predictive formula including also the spirometric variables (mainly the FEV1) might better predict the inspiratory muscle strength. In addition, physical and respiratory functions confirm to be different, so it is necessary for the inspiratory muscles to be trained in a specific way. [ABSTRACT FROM AUTHOR]

Background and objective: To ensure effective carrying, scouting, patrolling, and large‐scale combat operations, the workspace design of light armored vehicles (LAVs) should be compatible with anthropometry and range of motion (ROM) measurements of the soldiers. This study examines the extent of mismatch between the anthropometric dimensions of the Ethiopian army and existing workspace dimensions of the LAV. Predictive equations have been formulated for design dimensions considering anthropometry and ROM of the target population to avoid possible incompatibility. Method: The assessment was conducted on three existing Ethiopian LAVs, and mathematical equations were framed to predict the vehicular design dimensions. Anthropometric and ROM data of Ethiopian soldiers (n = 310) from an earlier reported survey by the authors were utilized. The accommodation capacity of existing LAVs was evaluated using a one‐way or two‐way (mis)match criterion, based on individual workspace characteristics. Along with the predicted dimensions, key vehicular dimensions were compared with other globally accepted vehicular standard dimensions. Results: Twenty‐two basic design dimensions that comply with ergonomics principles were proposed. A high mismatch (in terms of the accommodating capacities of the three LAVs) between the existing and predicted design dimensions indicates the incompatibility of the existing design dimensions in their accommodation of most Ethiopian army personnel. The predicted dimensions comply with different global vehicular standards, thus validating the results. Conclusion: The research findings indicate that the incompatibilities between vehicular space dimensions and army personnel's anthropometry must be addressed to evade the adverse consequences on occupational health. The LAVs should be redesigned according to the anthropometry and ROM dimensions of Ethiopian soldiers. [ABSTRACT FROM AUTHOR]

Background: The accuracy of methods to determine resting energy expenditure (REE) contributes toward the adequate provision of nutrition support to hospitalized patients. Indirect calorimetry (IC) is considered the gold‐standard method to determine REE. The aim of this study is to evaluate the degree of agreement between the REE measured by IC (REE‐IC) and REE estimated by predictive equations in intensive care unit patients. Methods: The sample is made up of intensive care unit patients aged >18 years, both male and female, undergoing nutrition therapy. The predictive equations to estimate REE were the Harris Benedict (HB), Ireton Jones (IJ), and practical method (PM). Degree of agreement between REE‐predictive equations and REE‐IC was analyzed by the interclass correlation coefficient (ICC) and the Bland‐Altman test. Results: Average energy obtained by IC was significantly different from HB and IJ equations (P <.001). The HB equation significantly underestimated the REE‐IC for body mass index (BMI) classification. Significant concordance was observed between the REE‐IC and all estimate equations (P <.05). The IJ equation showed the greatest degree of concordance for BMI classification of underweight (ICC = 0.674; P =.011) and presented the least difference between the averages of the energy when compared with REE‐IC (107.8 kcal/d; P <.05). Conclusion: The IJ equation showed better results with IC, with the greatest degree of concordance for BMI classification of underweight. Further research should develop others equations and validate tools to measure energy expenditure for accurate dietary recommendations for hospitalized patients undergoing nutrition therapy. [ABSTRACT FROM AUTHOR]

Introduction: The reference interval for pulse oxygen saturation (SpO2) in neonates born at high altitudes has not been defined to date. The purpose of this study was to systematically review published studies and determine the reference interval of SpO2 in neonates at different altitudes.Methods: Databases of PubMed, Embase, Cochrane Library, Clinicaltrials.Gov, Chinese National Knowledge Infrastructure Database, Wanfang Database, Chinese Science Technology Journals Database, and Chinese Clinical Trial Registry were searched for studies reporting SpO2 in healthy neonates at different altitudes. Retrieval time was from inception of the database to August 16, 2021. The Agency for Healthcare Research and Quality checklist was used to evaluate the quality of studies. Python v3.8 was used to analyze the data. This systematic review was drafted in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.Results: Seven cross-sectional studies, published between 1991 and 2020, were identified. They were from US, Mexico, Israel, Ecuador, and China. Three studies were rated as high quality and four as moderate quality. The mean SpO2 (with standard deviation or standard error) of neonates born in 40 different altitudes (ranging from 25 meters to 3,100 meters) were obtained. The prediction equation for calculation of the lower limit of the reference interval was established, and the reference intervals for SpO2 at different altitudes were determined.Conclusions: In healthy neonates, the lower limit of the reference interval of SpO2 decreases with increase in altitude. High-quality prospective studies are need to confirm our findings.

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.