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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).

Summary Objectives To measure basal metabolic rate (BMR) and to compare it with the values obtained from predictive equations in a sample of elderly (≥60 y) women. Design Cross-sectional study. Participants and setting Seventy-nine women living in Niteroi, Rio de Janeiro, Brazil enrolled in physical activity programs open to the community. Measurements Anthropometric measures were obtained using standard procedures. Percent body fat (PBF) was assessed by DXA. BMR was measured (BMRm) by indirect calorimetry under standardized conditions and compared with BMR estimated by 14 predictive equations that included elderly individuals in their development. Results Mean (±SD) age, BMI and PBF were 69.7 ± 6.5 y, 27.2 ± 4.6 kg/m2 and 42.1 ± 5.9%. BMRm (4188.3 ± 707.2 kJ/day) was significantly lower than estimated BMR by all predictive equations, including the equation developed for the Niteroian adult population (4565.6 ± 607.9 kJ/day). This population-specific equation provided the largest number of results within ±10% of BMRm and the lowest overestimation (10.6 ± 15.4%), much lower than the results from the internationally recommended Schofield equation (27.2 ± 17.6%). Regression of calf circumference (CC), age and body mass on BMRm provided similar estimates in comparison to models with fat-free mass (FFM). Conclusions All predictive equations provided biased, inaccurate estimates of BMR values in comparison to BMRm. Anthropometry and body composition explained only approximately 50% of the variability of BMRm. New equations should account for the variability of organ-metabolic rates and underlying undetected health conditions in older individuals living in tropical regions.

Aim A low-carbohydrate, healthy-fat (LCHF) dietary approach has been demonstrated as an effective strategy for improving metabolic health; however, it is often criticised for being more expensive than following a dietary approach guided by the national, Ministry of Health nutrition guidelines. This study compared the cost of these two nutritionally replete dietary approaches for one day for a family of four. Methods In this descriptive case study, one-day meal plans were designed for a hypothetical family of four representing the average New Zealand (NZ) male and female weight-stable adult and two adolescent children. National documented heights, a healthy body mass index range (18.5-25.0 kg/m2 ), and a 1.7-activity factor was used to estimate total energy requirements using the Schofield equation. Total daily costs were compared based on food prices from a popular Auckland supermarket. Meal plans were analysed for their nutritional adequacy using FoodWorks 8 dietary analysis software against national Australian and NZ nutrient reference value thresholds. Results The total daily costs were $43.42 (national guidelines) and $51.67 (LCHF) representing an $8.25 difference, or $2.06 per person, with the LCHF meal plan being the costlier option. Conclusions We consider this increased cost for an LCHF approach to be negligible. In practice, less costly food items with similar nutrition qualities can be substituted to reduce costs further should this be a goal. The LCHF approach should therefore not be disregarded as a viable dietary approach for improving health outcomes, based on its perceived expense.

ObjectiveWe aimed to evaluate the agreement between the resting energy expenditure (REE) obtained by indirect calorimetry and eight prediction equations in adult patients with renal transplantation and a newly developed REE prediction equation for use in patients with renal transplantation in the clinic.MethodsA total of 51 patients (30 males and 21 females) were involved in the study. The REE was measured by indirect calorimetry and compared with the previous prediction equations. The agreement was assessed by the interclass correlation coefficient and by Bland-Altman plot analysis.ResultsNo significant difference was found in terms of age and body mass index between the genders. Differences between the predicted and measured REEs were maximum in the Bernstein equation (−478kcal) and minimum in the Cunningham equation (−69kcal). It was found that underprediction values varied from 27.5% (chronic kidney disease equation) to 98.0% (Bernstein equation). The highest overprediction value was found in the Schofield equation (17.7%). The Cunningham equation and the new equation had the lowest root mean square error (265kcal/day). In this study, fat-free mass (FFM) was found to be the most significant variable in multiple regression analysis (r2: 0.55). The new specific equation based on FFM was generated as 424.2+24.7∗FFM (kg). Besides that, it was found that the new equation and Cunningham equation were distributed randomly according to Bland-Altman analysis. A supplementary new equation based on available anthropometric measurements was developed as −1996.8+19.1∗height (cm) + 7.2∗body weight (kg).ConclusionThis study showed that most of the predictive equations significantly underestimated REE. In patients with renal transplantation, if the REE is not measurable by indirect calorimetry, the use of the proposed equations will be more accurate.To read this ar

Summary Background & aims Basal metabolic rate (BMR) is an important physiologic measure in nutrition research. In many instances it is not measured but estimated by predictive equations. The purpose of this study was to compare measured BMR (BMRm) with estimated BMR (BMRe) obtained by different equations. Methods A convenient sample of 148 (89 women) 20–60 year-old subjects from the metropolitan area of Rio de Janeiro, Brazil participated in the study. BMRm values were measured by an indirect calorimeter and predicted by different equations (Schofield, Henry and Rees, Mifflin-St. Jeor and Anjos. All subjects had their body composition and anthropometric variables also measured. Accuracy of the estimations was established by the percentage of BMRe falling within ±10% of BMRm and bias when the 95% CI of the difference of BMRe and BMRm means did not include zero. Results Mean BMRm values were 4833.5 (SD 583.3) and 6278.8 (SD 724.0) kJ*day−1 for women and men, respectively. BMRe values were both biased and inaccurate except for values predicted by the Anjos equation. BMR overestimation was approximately 20% for the Schofield equation which was higher comparatively to the Henry and Rees (14.5% and 9.6% for women and men, respectively) and the Mifflin-St. Jeor (approximately 14.0%) equations. BMR estimated by the Anjos equation was unbiased (95% CI = −78.1; 96.3 kJ day−1 for women and −282.6; 30.7 kJ*day−1 for men). Conclusions Population-specific BMR predictive equations yield unbiased and accurate BMR values in adults from an urban tropical setting.

Optimal nutrition therapy (NT) delivery is associated with improved outcomes in critically ill children. However, avoidable barriers impede delivery of optimal energy and protein in the pediatric intensive care unit (PICU). This study aims to describe the gap between energy and protein prescription and actual intake.Single-center prospective cohort study, including consecutive children (age: 1 month to 15 years) admitted to the PICU in southern Brazil. Demographics, clinical characteristics, and NT details were recorded.We enrolled 130 patients: 37% female; median (interquartile range) age, 29.43 months (4.03, 100.63); PICU length of stay, 6 days (4, 13). Median predicted energy expenditure by Schofield equation and prescribed and actual energy intake were 47.13 kcal/kg/d (38.60, 55.38), 31.94 kcal/kg/d (13.99, 51.90), and 25.06 kcal/kg/d (10.21, 46.92), respectively. On average, actual energy intake was 47% of the predicted energy expenditure, and 68% of patients were underfed. Actual protein intake was 49% of the estimated requirement. NT was interrupted in 64% of patients.There were significant gaps among the predicted requirement, prescription, and actual delivery of energy and protein in the PICU. Suboptimal prescription and multiple feeding interruptions resulted in underfeeding.

The study aimed to determine the relationships between the basal metabolic rate (BMR) and body composition of overweight and obese Chinese adults with type 2 diabetes mellitus (DM). This cross-sectional clinical study enrolled 193 Chinese adults with type 2 DM who were overweight (24 kg/m(2)=BMI≤28 kg/m(2), n=99), or obese (BMI ≥28 kg/m(2), n=94). Ninety-seven adults with normal BMIs, including 50 DM patients and 47 healthy adults, were recruited as a control group. BMR was measured by indirect calorimetry; predicted BMR was calculated according to the Schofield equation; and the relationships between BMR, body composition, and biochemical results were determined by the Pearson correlation. The results showed that obese DM patients had significantly higher BMRs than both overweight patients (P

Accurate assessment of resting energy expenditure (REE) and metabolic state is essential to optimize nutrient intake in critically ill patients. We aimed to examine the accuracy of a simplified equation for predicting REE using carbon dioxide elimination (VCO2) values.We conducted a two-center study of metabolic data from mechanically ventilated children less than 18 years of age. Mean respiratory quotient (RQ) from the derivation set (n = 72 subjects) was used to modify the Weir equation to obtain a simplified equation based on VCO2 measurements alone. This equation was then applied to subjects at the second institution (validation dataset, n = 94) to predict resting energy expenditure. Bland-Altman analysis was used to assess the agreement between measured REE values, and REE estimated by the new equation as well as the Schofield equation. We also examined the accuracy of the new equation in classifying patients according to their metabolic state.Mean respiratory quotient (± SD) of 0.89 ± 0.09 in the derivation set was used to obtain a simplified equation, REE (kcal/day) = 5.534*VCO2 (L/min)*1440. In relation to the measured REE in the validation set, the mean bias (limits of agreement) for the REE predicted by this equation was -0.65% (-14.4-13.1%); and the overall diagnostic accuracy for classifying subjects as hypometabolic or hypermetabolic was 84%. Mean bias (limits) of agreement between measured and Schofield equation estimated REE was -0.1% (-40.5-40.7%).A simplified metabolic equation using VCO2 values was superior to the standard equation in estimating REE, and provided a reasonably accurate metabolic classification in mechanically ventilated children. In the absence of indirect calorimetry, bedside VCO2 monitoring could provide valuable continuous metabolic information to guide optimal nutrient intake.

Background Predictive equations are often used to estimate resting energy expenditure (REE). Determining the appropriate equation for different patient types, however, remains inconclusive, as in the case of Japanese children with Crohn's disease (CD). The aim of this study was to identify an appropriate predictive equation for measuring REE in Japanese children with CD. Methods Twelve Japanese children with CD managed at the National Center for Child Health and Development in Tokyo, Japan, were studied. REE (kcal/day) was measured using indirect calorimetry. The predictive equations used were the Japanese Dietary Reference Intakes (2010), the Schofield equation, the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) equation and the Cunningham equation. Difference between predicted and measured REE was analyzed on Bland–Altman plot. Results Japanese Dietary Reference Intakes (2010) had the smallest difference between predicted and measured REE. Weight was the primary predictor of REE on multiple regression analysis. As well, Japanese Dietary Reference Intakes (2010) had the highest ratio of weight to predicted REE (98.5%). Conclusions Of the four equations, Japanese Dietary Reference Intakes (2010) appeared to be the most practical and accurate predictive equation for REE in Japanese children with CD.

Objective The aim of this study was to evaluate the resting energy expenditure (REE) of postpartum women by indirect calorimetry and to provide the most appropriate predictive equations to estimate it. Methods This was a cross-sectional study with 79 women in the maternity unit of a Brazilian city hospital. Information regarding age, income, gestational age, and breastfeeding was collected. Height, weight, and body composition were measured. We measured REE by indirect calorimetry and predicted REE using eight equations. Analysis of comparison, correlation, agreement, and accuracy was performed. Results The median of measured REE was 1224 kcal (95% confidence interval [CI], 1157.4–1330), and the predicted REE ranged from 1213.8 (95% CI, 1207.3–1261.9) to 1553.1 kcal (95% CI, 1430.8–1488.5). No difference was found in REE between mothers who breastfed and those who did not ( P = 0.994); however, there was a positive correlation with lean mass ( r = 0.336; P = 0.003) and weight ( r = 0.237; P = 0.036). The best predictor of REE was the Harris Benedict equation, with lower difference ( P = 0.876), better median of adequacy (99.8%), and better interclass correlation coefficient (0.289). The Schofield equation was next, with greater percentage of accuracy (33.3%) and lower opposite agreement (7.6%). Conclusions All predictive equations showed low agreement and accuracy, and, in most cases, the results were overestimated. These findings indicate the need for continued studies to propose more suitable methods to determine the energy requirements for this population.

Summary Background & aims Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris–Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Methods Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland–Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. Results In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris–Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2 = 0.005, p = 0.54) and Harris–Benedict equations (R 2 = 0.045, p = 0.06) but was present in the Miller equation (R 2 = 0.210, p 2 = 0.213, p Conclusions Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris–Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with vascular surgery patients.

Background:The aim of this study is to establish the energy expenditure (EE) of a range of child-relevant activities and to compare different methods of estimating activity MET.Methods:27 children (17 boys) aged 9 to 11 years participated. Participants were randomly assigned to 1 of 2 routines of 6 activities ranging from sedentary to vigorous intensity. Indirect calorimetry was used to estimate resting and physical activity EE. Activity metabolic equivalent (MET) was determined using individual resting metabolic rate (RMR), the Harrell-MET and the Schofield equation.Results:Activity EE ranges from 123.7± 35.7 J/min/Kg (playing cards) to 823.1 ± 177.8 J/min/kg (basketball). Individual RMR, the Harrell-MET and the Schofield equation MET prediction were relatively similar at light and moderate but not at vigorous intensity. Schofield equation provided a better comparison with the Compendium of Energy Expenditure for Youth.Conclusion:This information might be advantageous to support the development of a new Compendium of Energy Expenditure for Youth.

Background: Paediatric onset inflammatory bowel disease (IBD) may cause alterations in energy requirements and invalidate the use of standard prediction equations. Our aim was to evaluate four commonly used prediction equations for resting energy expenditure (REE) in children with IBD. Methods: Sixty-three children had repeated measurements of REE as part of a longitudinal research study yielding a total of 243 measurements. These were compared with predicted REE from Schofield, Oxford, FAO/WHO/UNU, and Harris-Benedict equations using the Bland-Altman method. Results: Mean (±SD) age of the patients was 14.2 (2.4) years. Mean measured REE was 1566 (336) kcal per day compared with 1491 (236), 1441 (255), 1481 (232), and 1435 (212) kcal per day calculated from Schofield, Oxford, FAO/WHO/UNU, and Harris-Benedict, respectively. While the Schofield equation demonstrated the least difference between measured and predicted REE, it, along with the other equations tested, did not perform uniformly across all subjects, indicating greater errors at either end of the spectrum of energy expenditure. Smaller differences were found for all prediction equations for Crohn's disease compared with ulcerative colitis. Conclusions: Of the commonly used equations, the equation of Schofield should be used in pediatric patients with IBD when measured values are not able to be obtained. (Inflamm Bowel Dis 2010;) Copyright © 2010 Crohn's & Colitis Foundation of America, Inc.

Studies examining diet-induced thermogenesis and substrate oxidation in a whole room calorimeter (WRC) benefit from a state of energy balance in the measurement period. We found reports on four approaches to assessing energy needs in this context, and they vary considerably. This study assessed the utility of three simple alternatives for predicting energy requirements for calorimeter studies.Energy expenditure (EE) data on 37 males and 104 females was obtained from the baseline measurements of three weight-loss trials. The EE data was from 3-d RT3 accelerometer measurements in a free-living environment, and 24-h WRC measurements. A linear regression model was developed from Study 1 and Study 2 (N = 97), using mean 3-d EE as the independent variable. Twenty-four hours WRC EE (N = 29) was compared with three prediction methods: (a) Schofield equation, (b) regression model, and (c) free-living EE × 85%.Energy expenditure measured by accelerometers was 2454.1 ± 491.9 kcal/d in the free-living condition and 2022.7 ± 295.8 kcal/d during the 24-h calorimeter stay (83.8% ± 10.6% of free-living). Energy requirements in the whole room calorimeter was estimated as 0.405 × mean [3-d RT3 energy expenditure] + 1009.6 kcal/d (N = 97, R(2) = 0.479, P0.001). Predicted energy requirements were not significantly different from the WRC EE but the Schofield method produced the lowest mean bias and standard deviation.Schofield equations are an inexpensive and convenient alternative for predicting energy requirements in WRC studies.

Background & aims: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women.Methods: Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE.Results: Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18–71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris–Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias −1.7, −0.5, +1.1, +2.2, and −1.8%, RMSE 168, 170, 163, 167, and 173 kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45 kg/m2, the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group.Conclusions: In Belgian women, the original Harris–Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5–50). Estimations for the BMI range between 30 and 40 kg/m2, however, should be improved.

Estimation of resting energy expenditure (REE) involves predicting basal metabolic rate (BMR) plus adjustment for metabolic stress. The aim of this study was to investigate the methods used to estimate REE and to identify the impact of the patient's clinical condition and the dietitians' work profile on the stress factor assigned. A random sample of 115 dietitians from the United Kingdom with an interest in nutritional support completed a postal questionnaire regarding the estimation of REE for 37 clinical conditions. The Schofield equation was used by the majority (99%) of dietitians to calculate BMR; however, the stress factors assigned varied considerably with coefficients of variation ranging from 18.5 (cancer with cachexia) to 133.9 (HIV). Dietitians specializing in gastroenterology assigned a higher stress factor to decompensated liver disease than those not specializing in gastroenterology (19.3 vs 10.7, P=0.004). The results of this investigation strongly suggest that there is wide inconsistency in the assignment of stress factors within specific conditions and gives rise to concern over the potential consequences in terms of under- or overfeeding that may ensue.

The most commonly used predictive equation for basal metabolic rate (BMR) is the Schofield equation, which only uses information on body weight, age and sex to derive the prediction. However, because body composition is a key influencing factor, there will be error in calculating an individual's basal requirements based on this prediction. To investigate whether adding additional anthropometric measures to the standard measures can enhance the predictability of BMR and to cross-validate this within a separate subgroup. Cross-sectional study of 150 Caucasian adults from Scotland, with a body mass index range of 16.7–49.3 kg/m2. All subjects underwent measurement of BMR, body composition, and 148 also had basic skinfold and circumference measures taken. The resultant equation was tested in a subgroup of 39 obese males. The average difference between the predicted (Schofield equation) and measured BMR was 502 kJ/day. There was a slight systematic bias in this error, with the Schofield equation underestimating the lowest values. The average discrepancy between predicted and actual BMR was reduced to 452 kJ/day, with the addition of fat mass, fat-free mass, an overall 10% improvement on the Schofield equation (P=0.054). Using an equation derived from principal components analysis of anthropometry measurements similarly decreased the difference to 458 kJ/day (P=0.039). Testing the equation in a separate group indicated a 33% improvement in predictability of BMR, compared to the Schofield equation. In the absence of detailed information on body composition, utilizing anthropometric data provides a useful alternative methodology to improve the predictability of BMR beyond that achieved from the standard Schofield prediction equation. This should be confirmed in more individuals, both within the obese and normal weight category.

Summary Introduction The aim of the study was to assess if the estimated average requirements for energy for normal children (EAR) and the Schofield equation could reliably predict energy requirements in children with inactive Crohn's disease (CD). Methods Twenty-three children with inactive CD were studied, median age 14.3 years (range 7.8–16.9). Resting energy expenditure (REE) was measured by indirect calorimetry and compared with that predicted using the Schofield equation (BMR). Total energy expenditure (TEE) was measured using REE and a 3-day activity diary and compared with EAR. Results REE ranged from 79% to 136% of BMR. Mean REE was not significantly greater than mean BMR ( P = 0.25 2-tailed t-test). TEE ranged from 72% to 163% of estimated average requirements for energy for children of that weight (EARw). EARw tended to underestimate TEE in large children and overestimate TEE in small children (Bland–Altman plot R = 0.5 , P = 0.002 ). EARw was a poor predictor of TEE ( R = 0.35 , P = 0.1 ). EAR underestimated energy requirements by >500 kcal/day in 40% of the children. Conclusions The Schofield equation and EAR are unreliable methods of predicting total energy requirements in children with inactive CD with a significant potential to underestimate energy needs. When energy requirements were greater than EAR it was due to physical activity and body habitus rather than raised REE.

Objective To examine differences in low energy intake reporting between intervention and control groups during a dietary intervention trial. Design Retrospective data analysis from a subcohort of participants in the Polyp Prevention Trial (PPT), a 4-year, multisite, randomized, controlled dietary intervention trial. Intervention consisted of educational material and counseling sessions supporting a low-fat, high-fiber diet. Baseline and annual demographics, behavioral characteristics, energy intake (EI) based on self-reported 4-day food records, and height and weight of participants were collected at baseline and annually. Basal metabolic rate (BMR) was estimated (using the Schofield equation) to calculate EI/BMR. Subjects Of the 443 participants (302 male, 141 female) at baseline, 195 (43.3%) were younger than 60 years, and 394 (91%) were white. At Year 4, 383 participants remained: 186 (122 men, 64 women) in the intervention group, and 197 (133 men, 64 women) in the control group. Statistical analyses Using either paired t tests or analysis of variance, the differences between the means for EI, weight, and EI/BMR were compared at baseline, Year 1, and Year 4 for the participants who remained at Year 4. The Goldberg EI/BMR cutoff value of 1.06 (for plausible EI) identified participants who reported low EI. Linear regression was used to quantify the association of various risk factors to EI/BMR and for multivariate analyses within groups. χ 2 contingency table analysis quantified differences of low energy reporting within groups. Results At baseline, 46.8% of women and 11.6% of men reported lower than plausible EI. Only men had a significant increase in low energy reporting after randomization. At Year 1, 18.9% of intervention group men reported low EI compared with 9.8% of control group men ( P P Conclusions/applications Difference in low EI reporting between intervention and control groups could distort results from dietary intervention trials; interpretation of findings from dietary trials must include this potential bias. Intervention study design should include dietary intake data collection methods that are not subject to such bias (ie, biomarkers and performance criteria) to measure intervention compliance.

During intercurrent illness children with methylmalonic acidemia were found to have increased resting energy expenditure (REE). We measured REE in children with disorders of propionate metabolism (methylmalonic and propionic acidemia) when they were well and compared the values with those predicted by the Schofield equation.Prospective study in tertiary care facility. REE was measured with open-circuit indirect calorimetry under standardized conditions. Predicted REE values were calculated with the Schofield equation. Fourteen subjects with propionic acidemia (n = 3) and methylmalonic acidemia (n = 11) were studied.The median REE was 690 kcal/d (range 186 to 1687 kcal/d), which is significantly reduced, representing 80% +/- 18% of that predicted by the Schofield height and weight equation (P.01). REE was significantly lower in female compared with male patients for unknown reasons. There were no differences with age or neurologic state. REE was not further reduced in those with chronic renal failure.REE in patients with disorders of propionate metabolism is reduced when they are well.

The aim of this study was to check the validity of predictive equations for the calculation of resting metabolic rate (RMR) in moderately active adolescents. The RMR was measured in a sample of 25 healthy 15.5-18.2-year-old boys practicing soccer. The RMR was assessed by indirect calorimetry for 30 min following an overnight fast. Body composition was estimated from skinfold thickness measurements. Among the available equations to predict RMR, we decided to use those a of Molnar et al., Harris-Benedict, Schofield, and Cunningham. Measured and predicted values were compared by means of a one-way ANOVA. Also the Bland-Altman test was performed in order to evaluate the accuracy of the prediction equations compared to the measured value. The measured RMR was found to be 1834 +/- 160 kcal/day (mean +/- SD), while the Molnar et al., Schofield, Harris-Benedict, and Cunningham predicted values were 1707 +/- 78, 1866 +/- 89, 1779 +/- 84 and 1830 +/- 87 kcal/day, respectively. On average, compared to the measured values only the Molnar et al. equation differed significantly. On an individual basis, all the equations demonstrated considerable variability between measured and predicted RMRs. The predicted values also differed significantly. As regards the moderately active subjects (16-18 years old), we recommend the use of the Schofield equation, based on simple anthropometric parameters and also that of Cunningham, even if the estimation or measurement of fat-free mass may be cumbersome for everyday pediatric use.

Background Accurate assessment of energy expenditure and anthropometry in older people is important for targeted nutritional support. The present study aimed to compare measured and calculated resting metabolic rate (m-RMR and c-RMR) and measured, calculated and estimated weight and height in older people aged ≥70 years. Methods Participants were healthy older people aged ≥70 years. Indirect calorimetry using a ventilated hood calorimeter was performed for 30 min on fasted participants, and was compared with c-RMR, as calculated using six commonly used equations. Measured, calculated and estimated height and weight were compared. Results Subjects comprised 14 males and 20 females and mean (SD) m-RMR was 5243 (845) kJ day(-1) [1253 (202) kcal day(-1) ]. The Mifflin St-Jeor equation was the most consistently accurate, with the smallest mean difference between m-RMR and c-RMR of 58 (553) kJ day(-1) [14 (132) kcal day(-1) ] and c-RMR was within 10% of m-RMR in the greatest number of participants (n = 24; 70%). The Schofield equation was among the least accurate in this age group. In older males, self-reported height and weight were accurate, whereas, in females or those unable to self-report height, ulna length was the most accurate alternative to measured height. Conclusions Current equations used to calculate RMR in older people have inaccuracies, although the Mifflin St-Jeor equation was most accurate. Future studies should investigate the validity, reliability, cost and practicality of using fat free mass as an item in novel equations to calculate RMR in this age group. Self-reported height and weight in males, and height calculated from ulna length in females, were the most accurate alternatives to measured values in the present study.

Summary Background Critically ill children are at high risk of underfeeding and AKI, which may lead to further nutritional deficiencies. This study aimed to determine the adequacy of nutrition support during the first 5 days of intensive care unit (ICU) stay. Design, setting, participants, & measurements A chart review of pediatric patients admitted to the pediatric ICU for .72 hours between August 2007 and March 2008 was conducted. Patients were classified as having no AKI versus AKI by modified pediatric RIFLE criteria. All nutrition was analyzed. Basal metabolic rate (BMR) was estimated by the Schofield equation and protein needs by American Society for Parenteral and Enteral Nutrition guidelines. Results Of the 167 patients, 102 were male and 65 were female (median age 1.4 years). Using the RIFLE criteria, 102 (61%) patients had no AKI, whereas 44 (26%) were classified as category R (risk), 12 (7%) as category I (injury), and 9 (5%) as category F (failure). The median 5-day energy intake was lower relative to estimated BMR. Overall protein provision (19%) was lower than energy provision (55%) compared with estimated needs (P,0.001). I/F patients were more likely to be fasted versus receiving enteral/parenteral nutrition (n=813 patient days) and to receive ,90% of BMR (n=832 patient days) than No AKI/R patients. Conclusions Underfeeding, common in critically ill children, was accentuated in AKI. Protein underfeeding was greater than energy underfeeding in the first 5 days of PICU stay. Efforts should be made to provide adequate nutrition in ICU patients with AKI.

The cross sectional design was used in this study to elaborate physical activity, energy consumption, and work productivity of men workers. The total number of sample is 72 men. The inclusion criteria of the sample is the head of household’s women tea pickers at Tea Plantation PTPN VIII Bandung, West Java who have children 0-72 month age and willing to be interviewed. The place of the research was chose purposively. Malabar Plantation was chosen from the other five plantations (clusters) cause of its easier access and the homogenous characteristics inter cluster. Data collected was analyzed statistically. In general, more than a half of samples had active or moderately physical activity level (PAL=1.89). The physical activity level of samples during work day (average PAL=1.93) was higher than holiday (average PAL=1.77) (p87.2 - 121.2%. Majority of samples (76.4%) had wages per month below the regional minimum wages. The working hours of more than a half of samples were below seven hours per day and had wages per hour below standard regional minimum wage. Based on correlation analysis, there are significant correlation between 1) education level with physical activity level; 2) age, income per capita, and family size with wage per month; 3) age and physical activity level with working hours; and 4) age and income per capita with wages per hour. Keywords: physical activity, energy expenditure, energy adequacy level, work productivity, tea picker’s household head.

The aim of this study was to assess the validity of the commonly used equations (Harris-Benedict (HB), Schofield (S) and equations based on midarm circumference (MAC) and midarm muscle circumference (MAMC) in predicting resting energy expenditure (REE) in a population of patients with musculoskeletal deformities. 20 kyphoscoliotic patients (15 female (F); 5 male (M); mean age 59.6 years) and 10 controls (7 F; 3M; 59.8 years) were studied. REE measured by indirect calorimetry (IC) with a ventilated canopy system (Deltatrac metabolic monitor) was not significantly different between patients and controls (Mean (SD) REE (MJ/24 h): Patients: 5.48 (1.1); controls: 5.28(0.8)). In patients with deformities the Schofield equation gave values which were closest to measured REE (mean difference and limits of agreement IC vs S: 0.098 MJ/24 h; −0.822 and 1.018). The Harris-Benedict equation using height (Ht) and armspan (AS) in lieu of height also gave acceptable results (IC vs HB (Ht): 0.34; −0.638 and 1.318; IC vs HB (AS): 0.255; −0.683 and 1.253). Equations based on MAC and MAMC compared poorly (IC vs MAC equation: 0.398; −1.530 and 2.326; IC vs MAMC equation 0.687; −0.911 and 2.285). On regression analysis the equation REE = 0.295 (MAMC) + 0.0483 (AS) −0.0324 (age) −6.25 predicted REE best in the patient population (r2 = 0.861).

Summary Objective To examine the accuracy of present resting energy expenditure (REE) predictive equations among obese adult Taiwanese, and obtain new predictive equations for this population. Method A total of 80 participants (men=42, women=38) aged 18–64 years (mean 41.5±13.1 years) with body mass index (BMI) ≥30kg/m 2 were recruited from obesity clinic of a tertiary hospital. Anthropometric data including weight, height, waist circumference (WC), hip circumference (HipC) were collected. Body fat and fat free mass (FFM) were measured using bioelectric impedance analysis. Indirect calorimeter (Vmax 29n) was used for measurement of REE. The commonly used predictive equations (Harris–Benedict equation, Bernstein equation, Mifflin equation, Owen equation, Schofield equation, Cunningham equation, Wang equation, and Liu equation) were tested for group means and individual prediction accuracy for our target population. New predictive equations were formulated using multiple linear regression analyses. Result The mean BMI was 34.3±4.6kg/m 2 . All of the commonly used predictive equations showed statistically significant differences with the measured REE values. As for individual prediction accuracy, these predictive equations showed poor performance and only Mifflin equation provided an individual accuracy over 40%. The new predictive equation recommended for obese adult Taiwanese is below: REE (kcal/d)=11.6×weight (kg)−204.84×gender−4.8×height (cm)+1594.6 (gender: men=1; women=2). Conclusion The previous commonly used predictive equations were less accurate in obese Taiwanese. For this specific population, the newly developed equation should be applied for better prediction of REE.

Resting energy expenditure (REE) is often used to estimate total energy needs. The Schofield equation based on weight and height has been reported to underestimate REE in female children with phenylketonuria (PKU). The objective of this observational, cross-sectional study was to evaluate the agreement of measured REE with predicted REE for female adolescents with PKU. A total of 36 females (aged 11.5 to 18.7 years) with PKU attending Emory University's Metabolic Camp (June 2002 to June 2008) underwent indirect calorimetry. Measured REE was compared to six predictive equations using paired Student's t tests, regression-based analysis, and assessment of clinical accuracy. The differences between measured and predicted REE were modeled against clinical parameters to determine whether a relationship existed. All six selected equations significantly under predicted measured REE (P0.005). The Schofield equation based on weight had the greatest level of agreement, with the lowest mean prediction bias (144 kcal) and highest concordance correlation coefficient (0.626). However, the Schofield equation based on weight lacked clinical accuracy, predicting measured REE within +/-10% in only 14 of 36 participants. Clinical parameters were not associated with bias for any of the equations. Predictive equations underestimated measured REE in this group of female adolescents with PKU. Currently, there is no accurate and precise alternative for indirect calorimetry in this population.

The present study measuring resting energy expenditure (REE; kJ/d) longitudinally using indirect calorimetry in six elderly women aged ≥70 years following surgery for hip fracture, describes changes over time (days 10, 42 and 84 post-injury) and compares measured values to those calculated from routinely applied predictive equations. REE was compared to REE predicted using the Harris Benedict and Schofield equations, with and without accounting for the theoretical increase in energy expenditure of 35 % secondary to physiological stress of injury and surgery. Mean (95 % CI) measured REE (kJ/d) was 4704 (4354, 5054), 4090 (3719, 4461) and 4145 (3908, 4382) for days 10, 42 and 84, respectively. A time effect was observed for measured REE,P=0·003. Without adjusting for stress the mean difference and 95 % limits of agreement for measured and predicted REE (kJ/kg per d) for the Harris Benedict equation were 1 (−9, 12), 10 (2, 18) and 9 (1, 17) for days 10, 42 and 84, respectively. The mean difference and 95 % limits of agreement for measured and predicted REE (kJ/kg per d) for the Schofield equation without adjusting for stress were 8 (−3, 19), 16 (6, 26) and 16 (10, 22) for days 10, 42 and 84, respectively. After adjusting for stress, REE predicted from the Harris Benedict or Schofield equations overestimated measured REE by between 38 and 69 %. Energy expenditure following fracture is poorly understood. Our data suggest REE was relatively elevated early in recovery but declined during the first 6 weeks. Using the Harris Benedict or Schofield equations adjusted for stress may lead to overestimation of REE in the clinical setting. Further work is required to evaluate total energy expenditure before recommendations can be made to alter current practice for calculating theoretical total energy requirements of hip fracture patients.

In the Redfern area in metropolitan Sydney, there are many people on low incomes living in public housing. To investigate their nutritional status, a sample of 60 parents in this area was interviewed; 51 participants completed a food frequency questionnaire. Participants also completed weighed food records. The weighed food records showed energy intakes lower than expenditure calculated as necessary to maintain body weight (as estimated by the Schofield equation and activity factor). Therefore, the data are unlikely to be accurate and are not reported here. The food-frequency questionnaire indicated energy intakes close to those required to maintain body weight. Recorded nutrient intakes were similar to those recorded in other Australian studies. Fat supplied 35.6 per cent of the energy in the diets of the sample, compared to 37.3 per cent in the National Dietary Survey. Although the results were obtained by convenience sampling and may not be representative, the results do suggest that some parents on low incomes are able to maintain nutrient intake similar to that of the wider community.

Quality of human resources is very important to improve productivity. The worker productivity is correlated with nutritional status and health status. The objective of the research is to analyze physical activity, energy intake, nutritional status, of women workers at Tea Plantation PTPN VIII Bandung, West Java. The cross sectional design was used in this study to elaborate physical activity, nutritional status, and productivity of tea picker’s women. The criteria of study sample were tea picker’s women at cluster area of Malabar tea plantation of PTPN VIII Bandung, having infant and they were willing to be interviewed. The total number of 92 women sample was chosen randomly. Primary data consisted of physical activity recall (2x24 hours), food consumption recall (2x24 hours), anthropometry data (weight and height), and productivity (passage of tea sprout). Secondary data were included data of PT Perkebunan Nusantara VIII Bandung, West Java. The result showed that more than a half of samples having active or moderate physical activity level (PAL). The physical activity level of samples during work day (average PAL=1.87) was higher than holiday (average PAL=1.69) (p2362 kcal and 2134 kcal. The average energy adequacy level during workday and day off according to Schofield and Oxford equation were not significantly different, respectively 97,2% and 103,3%. The majority of samples had normal nutritional status, and 30.4% of the sample was overweight. The energy adequacy level according to Schofield and Oxford equation are related to nutritional status of women workers. Keywords: physical activity, energy expenditure, nutritional status, plantation women

OBJECTIVE: In view of the practical need to use equations for the evaluation of energy expenditure in HIV-infected patients, the objective of the present study was to determine the concordance between the energy expenditure values obtained by indirect calorimetry as the gold standard and those obtained by predictive equations elaborated from data for the healthy population: Harris-Benedict, Schofield and Cunningham, and by equations elaborated from data for HIV-infected patients: Melchior (1991-1993). METHODS: The study was conducted at the Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto on 32 HIV-infected men under treatment with highly active antiretroviral therapy. Resting energy expenditure was measured by indirect calorimetry and estimated on the basis of measurement of O2 consumption and CO2 production. RESULTS: Statistical analysis revealed weak concordance for the Harris-Benedict (0.38) and Cunningham (0.34) equations and satisfactory concordance for the Schofield equation (0.47). Only the two Melchior equations (1991 and 1993) showed strong concordance with the values obtained by indirect calorimetry (0.63 and 0.66, respectively) and could be used in practice. CONCLUSION: The best equations seem to be population-specific, such as the Melchior equations elaborated for HIV-infected patients. OBJETIVO: Frente à necessidade prática da utilização de equações para avaliar gasto energético em portadores do HIV, o presente estudo avaliou a concordância dos valores de gasto energético obtidos pelo padrão ouro de calorimetria indireta com equações preditivas desenvolvidas a partir de população saudável: Harris-Benedict, Schofield e Cunningham e equações desenvolvidas a partir de portadores do HIV: Melchior (1991 a 1993). MÉTODOS: O estudo foi realizado no Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto, com 32 homens portadores do HIV em terapia antirretroviral de alta potência. O gasto energético de repouso foi medido por calometria indireta e estimado a partir de medida de consumo de O2 e produção de CO2. RESULTADOS: De acordo com análise estatística, a concordância foi fraca para equações de Harris-Benedict (0,38) e Cunningham (0,34) e satisfatória para Schofield (0,47). Apenas as duas equações de Melchior apresentaram forte concordância com valores obtidos por calometria indireta (respectivamente 0,63 e 0,66) e poderiam ser utilizadas na prática. CONCLUSÃO:: As melhores equações parecem ser população específica, como as de Melchior, desenvolvidas para portadores do HIV

In chronic kidney disease (CKD) there are many metabolic changes that can influence resting energy expenditure (REE). Protein energy wasting is highly prevalent in hemodialysis patients. Estimating REE is the first step in the process to advise an adequate nutritional therapy. There are many prediction equations for REE. The purpose of this study is to determine which equation predicts REE in a reliable way. In the VU University Medical Centre 56 hemodialysis patients (33 men), mean age 56±14.8 year, BMI (kg/m2) 27.0±6 indirect calorimetry was performed. Measured REE was compared with sixteen different prediction equations based on weight, height, sex, and/or age. The Root Mean Squared prediction Error (RMSE) are presented as well as the average difference between estimation and measurement (bias). 90–110% adequacy of measured REE was accepted as accurate prediction. The measured REE was 1526±299 kcal/d (mean±SD). The percentage accurate predictions, percentage bias and RMSE were: for original Harris Benedict equation (1919), Schofield equation based on weight and height and Cole equation respectively 48%, 50%, 57%; bias: +2.9%, +4.4%, -0.5%; RMSE: 288, 286,271 kcal/d. Conclusion In this study Coles prediction equations provide an acceptable estimate of resting energy expenditure of HD patients. Further insight into explaining factors is warranted.