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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

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.

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.

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.

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.