Objetivos: Quantificar o gasto energético de crianças e adolescentes com bronquiolite obliterante pós-infecciosa acompanhados em ambulatório de pneumologia pediátrica e comparar com crianças e adolescentes hígidos. Metodologia: Estudo transversal com grupo controle incluindo 72 crianças e adolescentes de 8 a 18 anos. Comparou-se dois grupos de 36 indivíduos, um com diagnóstico de BO e outro hígido, os quais foram pareados pelo gênero, idade e classificação do IMC. Para avaliação nutricional utilizou-se a antropometria e a composição corporal. O gasto energético foi medido pela calorimetria indireta; o fator atividade pelo recordatório 24h de atividades físicas e a ingestão energética pelos inquéritos alimentares. Resultados: Os resultados a seguir são apresentados, respectivamente, para o grupo BO e para o grupo controle: idade (11,8 ± 2,7) e (12,3 ± 2,8); índice de massa corporal (18,9 ± 4,0 Kg/m2) e (18,8 ± 3,4 Kg/m2); gasto energético de repouso (GER) (1717,6 ± 781,5) e (2019,9 ± 819); gasto energético total (GET) (2677,5 ± 1514,0 Kcal/dia) e (3396,1 ± 1557,9Kcal/dia); estimativa da ingestão energética (2294,1 ± 746,7Kcal/dia) e (2116,5 ± 612,1Kcal/dia). O GER e GET não foram estatisticamente diferentes entre os grupos (p= 0,102; p=0,051). O GER não foi estatisticamente diferente nem quando ajustado para massa magra (p=0,116). O GER está associado a MM (r=0,490; p0,001), sendo que quanto maior a MM, maior o gasto energético. Não houve diferenças estatisticamente significativas entre o GET e o consumo energético no grupo BO (p=0,202). O grupo controle consumiu, em média, 1.279,6Kcal a menos do que o previsto pelo GET (p<0,001). Houve diferença estatisticamente significante entre o gasto energético medido pela calorimetria indireta e o estimado pelas fórmulas de predição. Todas as fórmulas de predição subestimaram a necessidade energética. Conclusão: O GER e o GET foram semelhantes entre os grupos. A estimativa da ingestão energética das crianças e adolescentes hígidos foi menor que o GET. Os resultados sugerem que todas as fórmulas de predição utilizadas subestimaram as necessidades energéticas quando comparadas com a calorimetria indireta. / Objectives: To quantify energy expenditure in children and adolescents with post-infectious obliterans bronchiolitis (OB) receiving outpatient pediatric pulmonology care and compare them with healthy children and adolescents. Methods: Cross-sectional study with control group including 72 children and adolescents aged 8-18 years. Two groups of 36 individuals were compared – one diagnosed with OB and another healthy – which were paired as to gender, age, and body mass index classification. Anthropometry and body composition were used for nutritional assessment. Energy expenditure was measured by indirect calorimetry, activity factor was assessed through 24-hour recall, and energy intake was measured by nutrition surveys. Results: The following results are respectively presented to the OB group and to the control group: age (11.8 ± 2.7) and (12.3 ± 2.8); body mass index (18.9 ± 4.0 Kg/m2) and (18.8 ± 3.4 Kg/m2); resting energy expenditure (REE) (1717.6 ± 781.5) and (2019.9 ± 819); total energy expenditure (TEE) (2677.5 ± 1514.0 Kcal/day) and (3396.1 ± 1557.9 Kcal/day); energy intake estimate (2294.1 ± 746.7 Kcal/day) and (2116.5 ± 612.1 Kcal/day). REE and TEE were not statistically different between groups (p=0.102; p=0.051). REE was not statistically different even when adjusted for lean mass (p=0.116). REE is associated with lean mass (r=0.490; p<0.001) – the higher the lean mass, the higher the energy expenditure. There were no statistically significant differences between TEE and energy expenditure in the OB group (p=0.202). The control group had an average intake 1279.6 Kcal lower than that predicted by TEE (p<0.001). There was a statistically significant difference between energy expenditure measured by indirect calorimetry and that estimated by prediction equations. All prediction equations underestimated energy needs. Conclusion: REE and TEE weresimilar between groups. Energy intake estimate in the healthy children and adolescents was lower than TEE. Results suggest that all prediction equations used in this study underestimated the energy needs when compared with indirect calorimetry.

The two most common methods to measure resting metabolic rate using indirect calorimetry are steady state or time interval. Steady state is commonly defined as the first five minutes in which oxygen consumption and carbon dioxide production vary by <10%. A time interval measurement generally lasts 20-60 minutes. Using steady state criteria is often harder to achieve, but many suggest it more accurately measures resting metabolic rate. Our objective was to determine if there were differences between steady state and time interval measurements in a healthy adult population. Seventy seven subjects were measured for 45 minutes. Inclusion criteria included healthy subjects ages 18-65, excluding pregnant and lactating women. Paired t-tests analyzed differences between measures, and Bland-Altman plots evaluated bias, precision, and accuracy. Of 77 subjects, 84% achieved steady state, and 95% achieved SS by minute 30. Most differences between steady state and time intervals were statistically but not practically significant. Bland-Altman plots showed steady state measurements were generally lower indicating that steady state is more indicative of resting metabolic rate. Minutes 6-25 were most precise, accurate and fairly unbiased compared to steady state. We recommend measuring a subject for 30 minutes and using steady state criteria of <10% variation of oxygen consumption and carbon dioxide production for five minutes if a subject is able to achieve it. However, if a subject cannot achieve steady state, we recommend averaging minutes 6-25.

Purpose: Accelerometry is commonly used to objectively measure physical activity (PA), however, differential data collection methods and analysis techniques yield dissimilar outcomes. The aims of this research were to (1) understand how accelerometer output varies among accelerometers worn on the non-dominant wrist (NDW), dominant wrist (DW), and hip; (2) develop site-specific algorithms to predict activity type classification, activity intensity classification, and estimates of metabolic intensity; and (3) compare the algorithms in a free-living setting. Methods: Forty participants (16.8 – 64.2 yr) completed a sequence of sedentary and physical activities in a laboratory while wearing accelerometers on the NDW, DW, and hip. Participants also wore a portable metabolic analyzer to objectively measure oxygen consumption (VO2). One-second accelerometer output was compared across wear locations by activity type and intensity classifications (Aim 1). Accelerometer output data were transformed into variables related to the magnitude (ϒ), horizontal angle (φ), and inclination (θ) of acceleration, and used to develop algorithms for the NDW, DW, and hip. Random forest algorithms were developed to predict activity type classification (i.e., sedentary, lifestyle, and ambulatory) and activity intensity classification (i.e., sedentary, light, moderate, and vigorous), and regression models were built to predict VO2 (Aim 2). Following the laboratory visit, participants simultaneously wore an accelerometer at each of the three locations for three days of free-living data collection. The site-specific algorithms developed in Aim 2 were compared for equivalence (Aim 3). Aim 1 Results: Analysis of variance indicated that accelerometer output differed between the NDW, DW, and hip for all activities completed, except for lying supine. Differences were expected; thus, Pearson correlation coefficients were calculated between the NDW, DW, and hip, and compared across activity type and intensity classifications. For activity type, the relationships between all wear locations were different for all activity types (i.e., sedentary, lifestyle PA, and ambulatory PA). For activity intensity, the relationships between the wrists were significantly different between sedentary and light activities. Additionally, relationships between the NDW, DW, and hip differed between light and moderate, and light and vigorous PA for all wear locations. The disparate correlations indicated that accelerometer signals do not just increase in magnitude as intensity increases; rather they increase differentially by wear location and activity type. Aim 2 Results: Site-specific random forest algorithms were developed to predict activity type and intensity classification. The algorithms utilized 10-15 features of the accelerometer signal related to variability, location, and central tendency. The hip had prediction accuracies of 84.9% for activity type classification and 80.2% for activity intensity classification. The dominant wrist had activity type prediction accuracy of 83.6% and intensity prediction accuracy of 78.9%. The non-dominant wrist had prediction accuracies of 83.1% and 78.0% for activity type and intensity, respectively. The VO2 prediction algorithms had Mean Absolute Errors of 2.96 ml/kg/min for the hip, 3.34 ml/kg/min for the NDW, and 3.49 ml/kg/min for the DW. This equates to an average error of 0.93 metabolic equivalents (METs); algorithms currently used in practice yield errors of 0.89 to 2.00 METs. Aim 3 Results: The site-specific prediction algorithms were applied to free-living data. Using the random forest algorithms, activity type classification estimates differed by 2 to 82 minutes/day, and activity intensity classification estimates differed by 0 to 83 minutes/day; however, these differences were not significantly different. The VO2 prediction models provided estimates of PA within 0 to 57 minutes/day of one another. The hip provided the lowest estimates of MVPA, while the NDW provided the highest estimates, however the VO2 estimates from all wear locations were statistically equivalent to one another. Conclusion: The differential relationships among accelerometer outputs from the NDW, DW, and hip indicate that output differs based on activity type and intensity. This non-systematic error prevents scaling or comparing data collected at different wear locations and supports the need for site-specific analysis methods. Site-specific prediction algorithms provided comparable to improved performance over currently-utilized analysis methods in PA research, and the PA estimates were equivalent across wear locations. This research provides a more nuanced understanding of the impact of wear location on accelerometer output and alternative methods for analysis. Importantly, the algorithms created allow for comparisons to be made among data collected at the NDW, DW, and hip, which has not previously been possible.

Background: The greatest increase in obesity rates have been seen with young adults in college due to their unhealthy dietary habits and behaviors. Interventions at this life stage may reduce the development of obesity related health concerns. There is an evident need for nutrition and lifestyle interventions yet there is limited research on the implementation of comprehensive nutrition programs for college students. Objective: This research study has a dual focus. We aim (1) to describe nutrition services offered through the Georgia State University Student Recreation Center to include nutrition assessment in a college population. Specifically, measured energy needs (Korr ReeVue™ Portable Indirect Calorimeter) were compared with estimated energy needs (predictive equations) and nutrition software (BioEx Nutrition Maker Software© Plus 2.0). In addition, this study aims (2) to survey what types of nutrition services are offered by campus recreation centers that are part of the Urban 13 research-sharing institutions. Methods: (1) Twenty-three healthy weight (n=8) and overweight/obese (n=15) 18 -37 year old males (n=11) and females (n=12) that were members of the Georgia State University student recreation center and had undergone indirect calorimetry. Paired samples t-tests were used to compare the means of measured resting metabolic rate (RMR) with RMR estimated from the Harris-Benedict and Mifflin-St. Jeor equations. Measured and estimated RMR were used to calculate total energy expenditure (TEE). Paired sample t-tests were also used to compare the means of each calculated TEE. A p-value ≤ 0.05 defined significance. (2) The 21 campus recreation centers of the Urban 13 were asked to complete a five-question survey by email or phone. Survey results were tabulated based on result frequencies. Results: (1) A statistical significance (p < 0.003) was found when comparing the means of measured RMR (1627 ± 393 kcal/day) with RMR estimated with the Harris Benedict equation (1781 ± 321 kcal/day). Significant differences (p < 0.001) were found between TEE calculated via measured RMR (2153 ± 534 kcal/day) and TEE calculated with estimated RMR via the Harris-Benedict equation (2354 ± 420 kcal/day) and Nutrition Maker© Plus 2.0 (2623 ± 582 kcal/day). Results were not statistically significant when comparing the means (p=0.308) of measured RMR (1626 ± 393 kcal/day) with RMR estimated with the Mifflin-St. Jeor equation (1677 ± 287 kcal/day), or between the means (p=0.317) of calculated TEE from measured RMR (2153 ± 534 kcal/day) and the Mifflin St-Jeor equation (2218 ± 381 kcal/day). (2) Of the 21 schools evaluated, seven stated nutrition services were provided through the campus recreation center. Of the seven schools, five staff an RD at the recreation center, and two schools, including Georgia State University, staff an RD and offer indirect calorimetry. Conclusions: To our knowledge, this is the first study to describe nutrition services offered at a campus recreation center. In the absence of indirect calorimetry, the Mifflin-St. Jeor equation is the best method to estimate energy needs for a college population. Nutrition services provided by an RD are limited at campus recreation centers of the Urban 13. Furthermore, only two campuses, including Georgia State University, provide nutrition assessment via indirect calorimetry.

The nutritional care and management of patients includes provision of adequate nutrition support to ensure that they attain and maintain a desirable body weight, improve nutritional status and avoid negative outcomes associated with over- or underfeeding. The success of nutrition support relies on accurately estimating energy requirements so that adequate energy and nutrients can be provided to the patient. Energy requirements are most accurately determined by measurement of energy expenditure. Most methods for doing so however are expensive, time-consuming, require trained technicians to perform them and are therefore impractical in the clinical setting. As such, prediction equations, which are easy to use, inexpensive and universally available, are commonly used to estimate the energy requirements of hospitalised patients. The accuracy of these equations however is questionable. Recently, a new portable hand-held indirect calorimeter (MedGem(tm), HealtheTech, USA), which has been promoted for its ease of use and relatively short measurement time, has been validated in healthy subjects but is yet to be validated in patients with illnesses. Weight loss and malnutrition occur commonly in patients with cancer and are often thought to be associated with disturbances in energy metabolism caused by the tumour. Minimising weight loss is an important goal for the nutritional care of patients with cancer. The ability to accurately determine the energy requirements of these patients is therefore essential for the provision of optimal nutrition support. This research project proceeded in two phases. Phase 1 aimed to determine current methods used by dietitians for estimating adult patients' energy requirements using a descriptive study. Results of this study informed phase 2, which aimed to investigate differences in energy expenditure of cancer patients compared to healthy control subjects and to compare different methods for determining energy requirements of people with cancer in the clinical setting. To address phase 1 a national cross-sectional survey of dietitians working in acute care adult hospitals was undertaken to determine their usual dietetic practice with respect to estimating patients' energy requirements. Responses to the survey (n=307, 66.2%) indicated a large variation in dietitians' practice for estimating energy requirements particularly with respect to the application of methods involving injury factors. When applied to a case study, these inconsistencies resulted in an extremely wide range for the calculated energy requirement, suggesting that there is error inherent in the use of prediction methods, which may be associated with negative consequences associated with under- or overfeeding. The types of patients for whom dietitians estimate energy requirements appears to be heavily influenced by feeding method. Initial dietetic education was identified as the main influencing factor in the choice of method for estimation of energy requirements. Phase 2 was addressed using four studies based on the same study population - a case-control study, two clinical validation studies and a measurement methods study. Patients had histologically proven solid tumours, excluding tumours of the breast, prostate and brain, and were undergoing anti-cancer therapy (n=18). Healthy control subjects were group matched to cancer patients by gender, age, height and weight from a purposive sample (n=17). Resting energy expenditure (REE) was measured by respiratory gas exchange using a traditional indirect calorimeter (VMax 229) and the MedGem indirect calorimeter. A measurement methods side-study established that steady state defined as a three-minute period compared to a five-minute period measured REE within clinically acceptable limits. REE was also predicted from a range of prediction equations. Analyses of available data found that REE in cancer patients was not significantly different from healthy subjects, with only a 10% higher REE observed in this sample of cancer patients when adjusted for fat free mass. For both cancer patients and healthy subjects the portable MedGem indirect calorimeter and all prediction equations did not measure or estimate individual REE within clinically acceptable limits compared to the VMax 229 (limits of agreement of approximately -40% to 30% for both the MedGem and prediction equations). Collectively, the results of this research project have indicated that current practical methods for determining patients' energy requirements in a clinical setting do not accurately predict the resting energy expenditure of individual subjects, healthy or with cancer. Greater emphasis should therefore be placed on ensuring intake meets requirements. For this to occur, dietetic practice should be focused on directly monitoring both patients' actual energy intake and patient outcomes, such as weight, body composition and nutritional status, to determine whether energy requirements are being met. This research has led to multiple recommendations for dietetic practice, focusing on the standardisation of education practices. Recommendations for future research address methodological improvements.

Objetivos: Quantificar o gasto energético de crianças e adolescentes com bronquiolite obliterante pós-infecciosa acompanhados em ambulatório de pneumologia pediátrica e comparar com crianças e adolescentes hígidos. Metodologia: Estudo transversal com grupo controle incluindo 72 crianças e adolescentes de 8 a 18 anos. Comparou-se dois grupos de 36 indivíduos, um com diagnóstico de BO e outro hígido, os quais foram pareados pelo gênero, idade e classificação do IMC. Para avaliação nutricional utilizou-se a antropometria e a composição corporal. O gasto energético foi medido pela calorimetria indireta; o fator atividade pelo recordatório 24h de atividades físicas e a ingestão energética pelos inquéritos alimentares. Resultados: Os resultados a seguir são apresentados, respectivamente, para o grupo BO e para o grupo controle: idade (11,8 ± 2,7) e (12,3 ± 2,8); índice de massa corporal (18,9 ± 4,0 Kg/m2) e (18,8 ± 3,4 Kg/m2); gasto energético de repouso (GER) (1717,6 ± 781,5) e (2019,9 ± 819); gasto energético total (GET) (2677,5 ± 1514,0 Kcal/dia) e (3396,1 ± 1557,9Kcal/dia); estimativa da ingestão energética (2294,1 ± 746,7Kcal/dia) e (2116,5 ± 612,1Kcal/dia). O GER e GET não foram estatisticamente diferentes entre os grupos (p= 0,102; p=0,051). O GER não foi estatisticamente diferente nem quando ajustado para massa magra (p=0,116). O GER está associado a MM (r=0,490; p0,001), sendo que quanto maior a MM, maior o gasto energético. Não houve diferenças estatisticamente significativas entre o GET e o consumo energético no grupo BO (p=0,202). O grupo controle consumiu, em média, 1.279,6Kcal a menos do que o previsto pelo GET (p<0,001). Houve diferença estatisticamente significante entre o gasto energético medido pela calorimetria indireta e o estimado pelas fórmulas de predição. Todas as fórmulas de predição subestimaram a necessidade energética. Conclusão: O GER e o GET foram semelhantes entre os grupos. A estimativa da ingestão energética das crianças e adolescentes hígidos foi menor que o GET. Os resultados sugerem que todas as fórmulas de predição utilizadas subestimaram as necessidades energéticas quando comparadas com a calorimetria indireta. / Objectives: To quantify energy expenditure in children and adolescents with post-infectious obliterans bronchiolitis (OB) receiving outpatient pediatric pulmonology care and compare them with healthy children and adolescents. Methods: Cross-sectional study with control group including 72 children and adolescents aged 8-18 years. Two groups of 36 individuals were compared – one diagnosed with OB and another healthy – which were paired as to gender, age, and body mass index classification. Anthropometry and body composition were used for nutritional assessment. Energy expenditure was measured by indirect calorimetry, activity factor was assessed through 24-hour recall, and energy intake was measured by nutrition surveys. Results: The following results are respectively presented to the OB group and to the control group: age (11.8 ± 2.7) and (12.3 ± 2.8); body mass index (18.9 ± 4.0 Kg/m2) and (18.8 ± 3.4 Kg/m2); resting energy expenditure (REE) (1717.6 ± 781.5) and (2019.9 ± 819); total energy expenditure (TEE) (2677.5 ± 1514.0 Kcal/day) and (3396.1 ± 1557.9 Kcal/day); energy intake estimate (2294.1 ± 746.7 Kcal/day) and (2116.5 ± 612.1 Kcal/day). REE and TEE were not statistically different between groups (p=0.102; p=0.051). REE was not statistically different even when adjusted for lean mass (p=0.116). REE is associated with lean mass (r=0.490; p<0.001) – the higher the lean mass, the higher the energy expenditure. There were no statistically significant differences between TEE and energy expenditure in the OB group (p=0.202). The control group had an average intake 1279.6 Kcal lower than that predicted by TEE (p<0.001). There was a statistically significant difference between energy expenditure measured by indirect calorimetry and that estimated by prediction equations. All prediction equations underestimated energy needs. Conclusion: REE and TEE weresimilar between groups. Energy intake estimate in the healthy children and adolescents was lower than TEE. Results suggest that all prediction equations used in this study underestimated the energy needs when compared with indirect calorimetry.

Introdução: As mudanças ocorridas no estilo de vida nas últimas décadas têm levado a um aumento na prevalência de excesso de peso. Isso afeta tanto países desenvolvidos como aqueles em desenvolvimento e também todas as faixas etárias. Diversas intervenções para reverter essa situação vêm sendo testadas, porém, em crianças e adolescentes o melhor manejo para reverter esse quadro ainda não está completamente elucidado. Objetivo: Avaliar as mudanças ocorridas no escore-z do índice de massa corporal (zIMC), circunferência do braço (CB), prega cutânea tricipital (PCT) e subescapular (PCSE), circunferência muscular do braço (CMB), percentual de gordura corporal (GC), circunferência da cintura (CC) e exames bioquímicos em crianças e adolescentes após uma intervenção exclusivamente dietoterápica ajustada pela taxa metabólica basal (TMB) obtida através do exame de calorimetria indireta (CI). Procedimentos Metodológicos: Após randomização, os participantes alocados para essa pesquisa participaram de encontros mensais individuais com a nutricionista responsável pelo projeto, pelo período de 1 ano. Os participantes tinham entre 8 e 15 anos, todos com excesso de peso (zIMC≥+1). As medidas antropométricas e de GC foram avaliadas na inclusão, no 6° e no 12° mês, após cada criança ou adolescente ser submetido ao exame de CI. A GC foi medida através do equipamento de bioimpedância elétrica. A coleta de sangue para as análises bioquímicas foi realizada após a CI. Cada participante recebeu uma dieta ajustada pelo valor do exame de CI. Usamos ANOVA para medidas repetidas, e teste de Friedman e Cochran’s Q para avaliar os resultados. Para as análises estatísticas foi utilizado o PASW 18.0 para Windows e valores-P<0,05 foram considerados estatisticamente significativos. Resultados: Completaram as três avaliações 27 participantes, com média de idade de 12±2 anos, 51,9% do sexo feminino. A TMB não apresentou mudança significativa ao final do estudo (P=0,233), enquanto o zIMC reduziu -0,19±0,04 nos primeiros 6 meses e -0,17±0,05 (P=0,014) ao final do seguimento. A CB aumentou em 12 meses e a PCSE nos últimos 6 meses, permanecendo estável ao final da pesquisa. O percentual de GC reduziu inicialmente, mas nos últimos 6 meses apresentou aumento, ainda que mais da metade dos participantes tenha reduzido a GC em 12 meses. O colesterol total (CT) e a lipoproteína de baixa densidade (LDL-c) reduziram nos primeiros 6 meses (-11,5±2,7;P=0,001/-8,1±2,5;P=0,011), mas essa redução não se manteve significativa ao final da intervenção. A maior parte das crianças e adolescentes reduziu os valores de triglicerídeos, LDL-c (88,9%) e CT (66,7%) ao final do estudo. Conclusões: O manejo dietoterápico da obesidade infanto-juvenil realizado por um nutricionista, com encontros mensais, é efetivo após 12 meses de intervenção e, especialmente em 6 meses. / Background: The changes in lifestyle over the past decades have led to an increase in the prevalence of overweight. This affects both developed countries and developing ones as well as all age groups. Several interventions to reverse this situation have been tested, however, in children and adolescents the best management to reverse this situation is not yet fully elucidated. Objective: To evaluate the changes in the z-score of body mass index (zBMI), arm circumference (AC), triceps skinfold (TSF) and subscapular (SSF), arm muscle circumference (AMC), body fat percentage (BF), waist circumference (WC) and biochemical tests in children and adolescents after an intervention only dietotherapeutic adjusted for basal metabolic rate (BMR) was obtained through examination of indirect calorimetry (IC). Methodological Procedures: After randomization, participants allocated to this research participated in monthly and individual meetings with the nutritionist responsible for the project for a period of one year. Participants aged between 8 and 15 years, all overweight (≥ zBMI+1). Anthropometric measurements and BF were evaluated at inclusion in the 6th and 12th month after each child or adolescent be submitted to the examination of IC. The BF was measured by bioelectrical impedance equipment. Blood samples for biochemical analysis was performed after IC. Each participant received a diet adjusted by the value of IC exam. We used repeated measures ANOVA and Friedman and Cochran's Q test to evaluate the results. The statistical analysis was used PASW 18.0 for Windows and P-values <0.05 were considered statistically significant. Results: Completed all three assessments 27 participants, mean age 12±2 years, 51.9% were female. The BMR showed no significant change at the end of the study (P=0.233), while the Zimc reduced -0.19 0.04 in the first 6 months and -0.17±0.05 (P=0.014) at the end of follow-up. The AC increased by 12 months and SSF in the last six months, remaining stable at the end of the study. The BF percentage was initially reduced, but in the last six months had increased, although more than half of the participants has reduced BF in 12 months. The total cholesterol (TC) and low density lipoprotein (LDL-c) reduced in the first 6 months (-11.5±2.7;P=0.001/-8.1±2.5;P=0.011), but this significant reduction was not maintained to the end of the intervention. Most children and adolescents reduced the values of triglycerides, LDL-c (88.9%) and TC (66.7%) at the end of the study. Conclusions: The dietary management of obesity in children and adolescents conducted by a nutritionist, with monthly meetings, is effective after 12 months of intervention, and especially in six months.

Objetivos: Medir o gasto e estimar a ingestão energética de adolescentes asmáticos com excesso de peso e comparar com asmáticos eutróficos e não asmáticos com excesso de peso. Metodologia: Estudo transversal incluindo 69 adolescentes de 10 a 18 anos. Foram comparados três grupos que foram pareados. Para avaliação nutricional utilizaram-se medidas antropométricas e de composição corporal. O gasto energético foi medido por calorimetria indireta e a ingestão energética estimada por inquéritos alimentares. Resultados: Cada grupo era composto por 23 adolescentes, sendo 13 do sexo masculino, com média de idade 12,39±2,40 anos. Os resultados a seguir são apresentados, respectivamente, para os grupos: asmáticos com excesso de peso; asmáticos eutróficos e não asmáticos com excesso de peso: índice de massa corporal (24,83 ± 2,73Kg/m2), (19,01 ± 2,10 Kg/m2) e (25,35 ± 3,66Kg/m2); gasto energético de repouso (GER) (1550,24±547,23Kcal/dia), (1540,82±544,22Kcal/dia) e (1697,24 ± 379,84Kcal/dia); estimativa da ingestão energética (2068,75± 516,66Kcal/dia), (2174,05± 500,55Kcal/dia) e (1673,17 ± 530,68Kcal/dia). O GER não foi estatisticamente diferente entre os grupos, mesmo quando ajustado pela massa magra e massa gorda (f=0,186; p=0,831). Somente nos grupos dos adolescentes asmáticos, a estimativa da ingestão energética foi maior que o GER. Conclusão: O GER foi semelhante entre os grupos. A estimativa da ingestão energética dos adolescentes asmáticos foi maior que o GER. / Objectives: To measure resting energy expenditure and calculate caloric intake of asthmatic adolescents with excess body weight and compare results with those of groups of adolescents eutrophic asthmatics and no-asthmatic adolescents with excess body weight. Methods: Cross-sectional study with 69 adolescents aged 10 to 18 years divided into three matched groups. Nutritional status was assessed using anthropometric and body composition measurements. Indirect calorimetry was used to measure energy expenditure; caloric intake was estimated from dietary recalls. Results: In each group, there were 23 adolescents (10 girls) aged 12.39±2.40 years. Results for each group (asthmatic adolescents with excess body weight; adolescents eutrophic asthmatics; and no-asthmatic adolescents with excess body weight) were, respectively: Body mass index = 24.83±2.73 kg/m2, 19.01±2.10 kg/m2, and 25.35±3.66 kg/m2; resting energy expenditure (REE) = 1550.24±547.23 kcal/day, 1540.82±544.22 kcal/day, and 1697.24±379.84 kcal/day; estimated caloric intake = 2068.75±516.66 kcal/day, 2174.05±500.55 kcal/day, and 1673.17±530.68 kcal/day. REE between groups was not statistically different, not even after correction for lean mass and fat mass (F=0.186; P=0.831). Estimated caloric intake was greater than REE only in the group of adolescents with asthma. Conclusion: REE was not significantly different between groups. Estimated caloric intake was greater than REE in the group of adolescents with asthma.

Objetivo: O objetivo do presente estudo foi determinar o Gasto Energético Basal (GEB) através da Calorimetria Indireta (CI) em pacientes com carcinoma epidermóide de esôfago (CEE). Métodos: Estudo transversal com 30 pacientes internados com diagnóstico de CEE submetidos à CI antes de iniciar a terapia oncológica. A avaliação nutricional foi realizada a partir de parâmetros antropométricos (Índice de Massa Corporal, Circunferência do Braço, Dobra Cutânea Triciptal, Circunferência Muscular do Braço e Percentual de Perda de Peso), parâmetros bioquímicos (albumina, transferrina e Proteína C Reativa) e bioimpedância tetrapolar. Além disso, foram determinados a capacidade pulmonar e o estadiamento clínico. A CI foi realizada depois de uma noite de jejum. Valores de p < 0,05 foram considerados estatisticamente significativos. Resultados: A média do GEB pela CI foi de 1.421,8 ± 348,2 kcal e pela Equação de Harris e Benedict (EHB) de 1.310,6 ± 215,1 kcal. A EHB subestimou o GEB comparado com a CI (p= 0,014). Foi encontrada diferença significativa no GEB entre os pacientes desnutridos (1.181,7 ± 278,1 kcal) e bem nutridos (1.509,1 ± 334,1 kcal) pelo IMC (p=0,020). Pelo %PP não foram encontradas diferenças significativas entre o GEB dos pacientes com PP significativa e não significativa (p=0,526). Entre os pacientes que apresentavam o percentual de massa magra abaixo do esperado, foi encontrada GEB de 1.408,9 ± 364,3 kcal, enquanto que os que tinham o percentual de massa magra adequado o GEB foi de 1.538,4 ± 97,5 kcal (p=0,550). Não houve associação entre o GEB pela CI e o estadiamento (p=0,255) e o Índice de Tiffeneau (p=0,946). Na associação entre os exames laboratoriais e o GEB pela CI, não foram encontradas associações significativas entre os que tinham alteração e os que não a tinham (p= 0,364, 0,309 e 0,780, respectivamente). Conclusões: O GEB de pacientes com CEE foi subestimado pela EHB sem fator injúria e superestimado pela EHB com fator injúria de 1,3 quando comparado ao GEB medido pela CI. / Expenditure (BEE) of patients with squamous cell carcinoma (SCC) of the esophagus by indirect calorimetry (IC). Methods: Cross-sectional study involving 30 patients admitted with a diagnosis of SCC who underwent IC before starting cancer therapy. Nutritional assessment was conducted using anthropometric parameters (body mass index, arm circumference, triceps skinfold thickness, arm muscle circumference and percentage weight loss), biochemical parameters (albumin, transferrin and Creactive protein) and tetrapolar bioimpedance analysis. Additionally, lung capacity and clinical staging were determined. IC was carried out after overnight fasting. P values <0.05 were considered statistically significant. Results: The mean of the BEE for IC and Harris-Benedict equation were 1421.8 ± 348.2 kcal/day and 1310.6 ± 215.1 kcal/day, respectively. The HBE underestimated the BEE when compared with IC (p=0.014). A significant difference was found in the BEE between malnourished (1181.7 ± 278.1 kcal/day) and well-nourished (1509.1 ± 334.1 kcal/day) patients by BMI (p=0.020). In terms of percentage weight loss, no significant difference was found in BEE between patients with significant or non-significant weight loss (p=0.526). BEE for patients presenting with a lower than expected lean body mass was 1408.9 ± 364.3 kcal/day, whereas for those with an adequate lean body mass it was 1538.4 ± 97.5 kcal/day (p=0.550). No association was found between BEE measured by IC and clinical staging (p=0.255) or the Tiffeneau Index (p=0.946). Additionally, when comparing laboratory examinations with BEE measured by IC, no significant association was found between those with and those without alterations (p=0.364, 0.309 and 0.780 respectively). Conclusion: When compared to BEE measured by IC of patients with SCC, it was found that the HBE without injury factor underestimated, whereas the HBE with an injury factor of 1.3 overestimated BEE.

Introdução: A prevalência da obesidade infanto juvenil tem aumentado, sendo importante que mais profissionais estejam habilitados para o manejo. Objetivo: Comparar o efeito, após 12 meses, de três intervenções: programa de atividade física (AF), manejo dietoterápico (D) e atendimento ambulatório de referência em um hospital terciário (AMO) sobre o índice de massa corporal (IMC), composição corporal, taxa metabólica (TMB) e perfil metabólico de crianças e adolescentes com obesidade. Procedimentos Metodológicos: Ensaio clínico randomizado em 82 sujeitos de 8 a 15 anos, com obesidade. No grupo D, recebia dieta ajustada a partir da TMB por calorimetria indireta; no grupo AF, educador físico orientava prática de AF no domicílio e, no grupo AMO, orientações para introdução e manutenção de hábitos saudáveis. O acompanhamento foi mensal e os dados antropométricos, composição corporal, taxa metabólica basal e exames laboratoriais foram determinados no início, aos 6 e 12 meses de seguimento. O projeto foi aprovado pelo Comitê de Ética e Pesquisa do HCPA número 10-0011 e registrado www.clinicaltrials.gov NCT012973774. Resultados: Foram avaliados 82 sujeitos. A diferença (percentual) entre os grupos AF, D e AMO, respectivamente, foi de: escore Z do IMC -7,1% (-10,8 a -3,3), -5,0% (-8,4 a -1,5) e –15,2%(- 19,5 a -10,9); p=0,001; Peso (Kg) massa gorda 9,2% (4,3 a 14,2), 10,4% (4,8 a 15,9) e 4,3% (-1,7 a 10,4), p=0,248; Peso(Kg) massa livre de gordura 10,5%(7,5 a 13,4), 7,0% (4,1 a 10,0) e 6,7% (2,8 a 10,7) p=0,203; TMB 50,3%(2,2 a 232), 31,6%(-23,7 a 147), 38,6%(-17,3 a 232), p=0,669. As alterações laboratoriais mais frequentes no início do estudo nos grupos AMO, AF e D foram, respectivamente, valor HDL baixo (82,1%, 77,8%, 63%), insulina jejum 15 μUI/mL (71,4%, 81,5% e 66,7%) e homeostasis model assessment insulin resistence índex alterado (67,9%, 81,5%, 63%). O diagnóstico de síndrome metabólica (SM) foi feito em 20% das crianças obesas. No grupo AMO houve redução maior significativa do escore Z do IMC, da circunferência da cintura (CC), aumento do valor do HDL, redução da insulina de jejum e do HOMA-IR, redução da pressão sistólica e no número de componentes da SM. No grupo AF houve uma redução maior no colesterol total, no valor do LDL e dos triglicerídeos (TG). O grupo D também mostrou melhora no escore Z do IMC, redução do valor do colesterol total (CT), do LDL e dos TG. Apesar da redução dos indivíduos com diagnóstico de síndrome metabólica no grupo AMO (de 7 para 2) e AF (de 4 para 1) não houve diferença significativa quando comparados os 3 grupos. Conclusões: As três intervenções foram efetivas no tratamento da obesidade de crianças e adolescentes. Este estudo comprova a necessidade de estratégias combinadas e a longo prazo no manejo da obesidade na criança e adolescente para reduzir as alterações cardio metabólicas presentes nesta população e evitar a progressão dos fatores de risco para doenças cardiovasculares, metabólicas e morte prematura. Portanto, estas 3 estratégias podem ser utilizadas no manejo da obesidade infanto juvenil. / Background: The prevalence of paediatric obesity has increased in recent years, and health care workers must be trained to treat these conditions. Objective: To compare the effect of three year-long interventions: physical activity (PA) programme, dietary intervention (D) and treatment in an outpatient obesity clinic (OOC), on the body mass index (BMI), body composition, basal metabolic rate (BMR) and biochemical measurements of children and adolescents with obesity. Methodological Procedures: Randomized clinical trial of 82 participants aged between 8 and 15, diagnosed with obesity. Participants in the PA group received individualised home-based physical activity programmes from a physical education teacher. In the D group, participants received dietary counselling based on BMRs as measured by indirect calorimetry. In the OOC group, participants were given instructions on how to adopt and maintain healthy lifestyle habits. Participants had monthly appointments with physical educators, nutritionists or health care workers, depending on participant group. Anthropometric data, body composition, BMR and biochemical measurements were assessed at baseline, and after 6 and 12 months of treatment. Project approved by Comitê de Ética e Pesquisa do HCPA número 10-0011 and registred www.clinicaltrials.gov NCT012973774. Results: Differences between the PA, D and OOC groups over the course of the study were as follows: BMI Z score -7.1% (-10.8 to -3.3), -5.0% (-8.4 to -1.5) and –15.2%(-19.5 to -10.9); p=0.001; Fat mass (Kg) 9,2% (4,3 a 14,2), 10,4% (4,8 a 15,9) and 4,3% (-1,7 a 10,4), p=0,248; Fat-free mass weight(Kg) 10.5%(7.5 to 13.4), 7.0% (4.1 to 10.0) and 6.7% (2.8 to 10.7) p=0.203; BMR 50.3%(2.2 to 232), 31.6%(-23.7 to 147), 38.6%(-17.3 to 232), p=0.669. The most frequent laboratory anormalities at baseline in groups OOC, PA and D were, respectively, value low HDL (82.1%, 77.8%, 63%), fasting insulin 15 μUI / mL (71.4% , 81.5% and 66.7%) and high homeostasis model assessment insulin resistence index (67.9%, 81.5%, 63%). The diagnosis of metabolic syndrome was made in 20% of obese children. OOC group was significantly greater reduction in BMI Z score, waist circumference, total cholesterol, increasing the value of HDL, reduced fasting insulin and HOMA-IR, systolic blood pressure reduction and the number of metabolic syndrome components. In the PA group, there was a greater reduction in total cholesterol in the amount of LDL and TG. Group D also showed improvement in BMI Z score, reducing the value of total cholesterol, LDL and TG. Despite the reduction of individuals diagnosed with metabolic syndrome in the group OOC (7 to 2) and PA (4 to 1) no significant difference when comparing the three groups. Conclusions: All three interventions led to a reduction in BMI Z score, and increased fat free mass, BMR and biochemical measurements over the course of a year. This study demonstrates the need for combined strategies and long-term management of obesity in children and adolescents to reduce cardio metabolic changes present in this population and prevent the progression of risk factors for cardiovascular and metabolic diseases and premature death. Therefore, all three interventions are suitable treatments for paediatric obesity.