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This paper reviews the most relevant aspects of measuring basal metabolic rate (BMR). The authors discuss individual and lifestyle factors (age, anthropometry, smoking, physical activity, diet, menses, and ethnicity) as well as possible environmental factors at time of measurement which may alter BMR values. Various available predictive equations, especially those recommended by the World Health Organization in 1985, and subsequent validation studies, particularly in populations living in the tropics, are presented. The review indicates the need for more information on BMR from populations living in different parts of the world so that better estimates can be provided for clinicians and epidemiologists.

O estudo avaliou a reprodutibilidade e a validade relativa de um questionário de freqüência alimentar (QFA) em 83 indivíduos adultos, do Sul do Brasil. Utilizou-se o coeficiente de correlação Intraclasse (ICC), classificação em quartos de ingestão pelo QFA e IR24h e índice Kappa ponderado. Os nutrientes foram log transformados e corrigidos para calorias. A atenuação nas correlações foi corrigida pela variabilidade intrapessoal. Para a reprodutibilidade, os valores das correlações variaram de 0,44 (IC95%: -0,19-0,76) a 0,83 (IC95%: 0,72-0,90) para carboidrato e vitamina C, respectivamente. No estudo de validade, os coeficientes deatenuados variaram de 0,23 (IC95%: -0,22-0,61) a 0,86 (IC95%: 0,21-1,16) para a vitamina A e lipídeos, no QFA1, e de 0,50 (IC95%: -016-0,91) a 0,95 (IC95%: 0,79-1,05) para vitamina A e energia, no QFA2. O índice Kappa ponderado apresentou valor mais baixo para o cálcio (0,10; IC95%: 0,00-0,26) e valores mais altos para vitamina C no QFA1 (0,38; IC95%: 0,23-0,53) e energia no QFA2 (0,38; IC95%: 0,24-0,52). Os resultados indicam reprodutibilidade satisfatória para todos os nutrientes, com exceção do carboidrato, e validade relativa razoável, especialmente para os macronutrientes e vitamina C.

The objective of this study was to investigate the effect of seasonality on nutrient intake in healthy adults from the southernmost metropolitan area of Brazil. The dietary intake (24-hour dietary recall on six different days) in a sample of 162 adults (114 women), aged 20 to 69, was obtained during the year 2007. The nutrient intake was averaged for each season and adjusted for energy intake using the residual method. The effect of season on energy, macro and micro-nutrient intake was investigated based on the Generalized Estimate Equations (GEE) model. There were statistically significant differences between seasons for carbohydrate and total fat intake after controlling for gender, age, education, and interactions. In summer carbohydrate intake was higher than other seasons while the total fat intake was lower. These findings highlight the importance of considering seasonal variations not only for evaluating dietary intake but also nutrition and public health policy recommendations, particularly in adult populations living in temperate regions.

Background/Objective: Because studies have evidenced variations in nutrient intake, 1

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Objective: To measure BMR in a probability sample of adults from an urban city of Brazil and to compare indirectly measured BMR (BMR i ) with BMR predicted from different equations. Design: BMR data were obtained by indirect calorimetry and estimated by different predictive equations (Schofield; Harris and Benedict; Henry and Rees). Anthropometric and body composition measures were also obtained. Setting: The Nutrition, Physical Activity and Health Survey (PNAFS), a household survey conducted in Niterói, Rio de Janeiro state, Brazil. Subjects: Representative sample of 529 adults (aged $20 years; 339 females) living in Niterói, Rio de Janeiro state, Brazil. Results: Mean BMR i values were 5839?7 (SE 73?9) kJ/d and 4758?1 (SE 39?5) kJ/d for men and women, respectively. Predicted BMR by all equations was significantly higher (difference between means and 95 % CI did not include zero) than BMR i in both men and women of all ages. Overall bias in BMR (predicted BMR minus BMR i ) using the Schofield equations (overestimation of about 20 %) was higher than when using the Henry and Rees equations (13 % and 16 % overestimation for males and females, respectively). The percentage of individuals whose BMR predicted by the Schofield equations fell within 10 % of BMR i was very low (7?8 % and 14?1 % of males and females, respectively). Conclusions: Current available predictive equations of BMR are not adequate to estimate BMR in Brazilians living in Niterói, Rio de Janeiro, Brazil.