A Youth Compendium of Physical Activities: Activity Codes and Metabolic Intensities.

PURPOSE: A Youth Compendium of Physical Activities (Youth Compendium) was developed to estimate the energy costs of physical activities using data on youth only. METHODS: On the basis of a literature search and pooled data of energy expenditure measurements in youth, the energy costs of 196 activities were compiled in 16 activity categories to form a Youth Compendium of Physical Activities. To estimate the intensity of each activity, measured oxygen consumption (V˙O2) was divided by basal metabolic rate (Schofield age-, sex-, and mass-specific equations) to produce a youth MET (METy). A mixed linear model was developed for each activity category to impute missing values for age ranges with no observations for a specific activity. RESULTS: This Youth Compendium consists of METy values for 196 specific activities classified into 16 major categories for four age-groups, 6-9, 10-12, 13-15, and 16-18 yr. METy values in this Youth Compendium were measured (51%) or imputed (49%) from youth data. CONCLUSION: This Youth Compendium of Physical Activities uses pediatric data exclusively, addresses the age dependency of METy, and imputes missing METy values and thus represents advancement in physical activity research and practice. This Youth Compendium will be a valuable resource for stakeholders interested in evaluating interventions, programs, and policies designed to assess and encourage physical activity in youth.

Functional data analysis of sleeping energy expenditure.

Adequate sleep is crucial during childhood for metabolic health, and physical and cognitive development. Inadequate sleep can disrupt metabolic homeostasis and alter sleeping energy expenditure (SEE). Functional data analysis methods were applied to SEE data to elucidate the population structure of SEE and to discriminate SEE between obese and non-obese children. Minute-by-minute SEE in 109 children, ages 5-18, was measured in room respiration calorimeters. A smoothing spline method was applied to the calorimetric data to extract the true smoothing function for each subject. Functional principal component analysis was used to capture the important modes of variation of the functional data and to identify differences in SEE patterns. Combinations of functional principal component analysis and classifier algorithm were used to classify SEE. Smoothing effectively removed instrumentation noise inherent in the room calorimeter data, providing more accurate data for analysis of the dynamics of SEE. SEE exhibited declining but subtly undulating patterns throughout the night. Mean SEE was markedly higher in obese than non-obese children, as expected due to their greater body mass. SEE was higher among the obese than non-obese children (p<0.01); however, the weight-adjusted mean SEE was not statistically different (p>0.1, after post hoc testing). Functional principal component scores for the first two components explained 77.8% of the variance in SEE and also differed between groups (p = 0.037). Logistic regression, support vector machine or random forest classification methods were able to distinguish weight-adjusted SEE between obese and non-obese participants with good classification rates (62-64%). Our results implicate other factors, yet to be uncovered, that affect the weight-adjusted SEE of obese and non-obese children. Functional data analysis revealed differences in the structure of SEE between obese and non-obese children that may contribute to disruption of metabolic homeostasis.

Energy Cost of Activities in Preschool-Aged Children.

BACKGROUND: The absolute energy cost of activities in children increase with age due to greater muscle mass and physical capability associated with growth and developmental maturation; however, there is a paucity of data in preschool-aged children. Study aims were 1) to describe absolute and relative energy cost of common activities of preschool-aged children in terms of VO2, energy expenditure (kilocalories per minute) and child-specific metabolic equivalents (METs) measured by room calorimetry for use in the Youth Compendium of Physical Activity, and 2) to predict METs from age, sex and heart rate (HR). METHODS: Energy expenditure (EE), oxygen consumption (VO2), HR, and child-METs of 13 structured activities were measured by room respiration calorimetry in 119 healthy children, ages 3 to 5 years. RESULTS: EE, VO2, HR, and child-METs are presented for 13 structured activities ranging from sleeping, sedentary, low-, moderate- to high-active. A significant curvilinear relationship was observed between child-METs and HR (r2 = .85; P = .001). CONCLUSION: Age-specific child METs for 13 structured activities in preschool-aged children will be useful to extend the Youth Compendium of Physical Activity for research purposes and practical applications. HR may serve as an objective measure of MET intensity in preschool-aged children.

Role of physical activity and sleep duration in growth and body composition of preschool-aged children.

OBJECTIVE: The impact of physical activity patterns and sleep duration on growth and body composition of preschool-aged children remains unresolved. Aims were (1) to delineate cross-sectional associations among physical activity components, sleep, total energy expenditure (TEE), and body size and composition; and (2) to determine whether physical activity components, sleep, and TEE predict 1-year changes in body size and composition in healthy preschool-aged children. METHODS: Anthropometry, body composition, accelerometry, and TEE by doubly labeled water were measured at baseline; anthropometry and body composition were repeated 1 year later (n = 111). RESULTS: Cross-sectionally, positive associations between sedentary activity and weight and fat-free mass (FFM) (P = 0.009-0.047), and a negative association between moderate-vigorous physical activity (MVPA) and percent fat mass (FM) (P = 0.015) were observed. TEE and activity energy expenditure (AEE) were positively associated with weight, body mass index (BMI), FFM, and FM (P = 0.0001-0.046). Prospectively, TEE, AEE, physical activity level, and MVPA, but not sedentary activity, were positively associated with changes in BMI (P = 0.0001-0.051) and FFM (P = 0.0001-0.037), but not percent FM. Sleep duration inversely predicted changes in FM (P = 0.005) and percent FM (P = 0.006). CONCLUSIONS: Prospectively, MVPA, TEE, AEE, and physical activity level promote normal growth and accretion of FFM, whereas sleep duration inversely predicts changes in adiposity in preschool-aged children.

Global metabolomic profiling targeting childhood obesity in the Hispanic population.

BACKGROUND: Metabolomics may unravel important biological pathways involved in the pathophysiology of childhood obesity. OBJECTIVES: We aimed to 1) identify metabolites that differ significantly between nonobese and obese Hispanic children; 2) collapse metabolites into principal components (PCs) associated with obesity and metabolic risk, specifically hyperinsulinemia, hypertriglyceridemia, hyperleptinemia, and hyperuricemia; and 3) identify metabolites associated with energy expenditure and fat oxidation. DESIGN: This trial was a cross-sectional observational study of metabolomics by using gas chromatography-mass spectrometry and ultrahigh-performance liquid chromatography-tandem mass spectrometry analyses performed on fasting plasma samples from 353 nonobese and 450 obese Hispanic children. RESULTS: Branched-chained amino acids (BCAAs) (Leu, Ile, and Val) and their catabolites, propionylcarnitine and butyrylcarnitine, were significantly elevated in obese children. Strikingly lower lysolipids and dicarboxylated fatty acids were seen in obese children. Steroid derivatives were markedly higher in obese children as were markers of inflammation and oxidative stress. PC6 (BCAAs and aromatic AAs) and PC10 (asparagine, glycine, and serine) made the largest contributions to body mass index, and PC10 and PC12 (acylcarnitines) made the largest contributions to adiposity. Metabolic risk factors and total energy expenditure were associated with PC6, PC9 (AA and tricarboxylic acid cycle metabolites), and PC10. Fat oxidation was inversely related to PC8 (lysolipids) and positively related to PC16 (acylcarnitines). CONCLUSIONS: Global metabolomic profiling in nonobese and obese children replicates the increased BCAA and acylcarnitine catabolism and changes in nucleotides, lysolipids, and inflammation markers seen in obese adults; however, a strong signature of reduced fatty acid catabolism and increased steroid derivatives may be unique to obese children. Metabolic flexibility in fuel use observed in obese children may occur through the activation of alternative intermediary pathways. Insulin resistance, hyperleptinemia, hypertriglyceridemia, hyperuricemia, and oxidative stress and inflammation evident in obese children are associated with distinct metabolomic profiles.

Individual differences in susceptibility to large portion sizes among obese and normal-weight children.

OBJECTIVE: Determine the association of children's susceptibility to large food portion sizes with appetite regulation and obesity. METHODS: Normal-weight and obese non-Hispanic black children (n = 100) aged 5-6 years were observed in four dinner conditions of varying portion size; portions of all foods (except milk) offered were: 100% (677 kcal), 150% (1015 kcal), 200% (1353 kcal), or 250% (1691 kcal) of those in the reference condition (100%). Condition order was randomly assigned to 2-4 children who ate together at each meal. Child height and weight were measured and caregiver reports of child appetite were obtained. Hierarchical growth curve models were used to estimate associations of meal energy intake with portion size condition, child weight status, and appetite regulation traits, controlling for demographics. RESULTS: Total energy intake increased across conditions of increasing food portion size (P < 0.001). The effect of portion size condition on total energy intake varied with food responsiveness (P = 0.05) and satiety responsiveness (P < 0.05), but not weight status (P = 0.682). Children with lower satiety responsiveness and greater food responsiveness showed greater increases in meal energy across conditions. CONCLUSIONS: Children with poorer appetite regulation may be more vulnerable to obesogenic dietary environments offering large food portions than other children.

Energetic adaptations persist after bariatric surgery in severely obese adolescents.

OBJECTIVE: Energetic adaptations induced by bariatric surgery have not been studied in adolescents or for extended periods postsurgery. Energetic, metabolic, and neuroendocrine responses to Roux-en-Y gastric bypass (RYGB) surgery were investigated in extremely obese adolescents. METHODS: At baseline and at 1.5, 6, and 12 months post-baseline, 24-h room calorimetry, body composition, and fasting blood biochemistries were measured in 11 obese adolescents relative to five matched controls. RESULTS: In the RYGB group, mean weight loss was 44 ± 19 kg at 12 months. Total energy expenditure (TEE), activity EE, basal metabolic rate (BMR), sleep EE, and walking EE significantly declined by 1.5 months (P = 0.001) and remained suppressed at 6 and 12 months. Adjusted for age, sex, fat-free mass, and fat mass, EE was still lower than baseline (P = 0.001). Decreases in serum insulin, leptin, and triiodothyronine (T3), gut hormones, and urinary norepinephrine (NE) paralleled the decline in EE. Adjusted changes in TEE, BMR, and/or sleep EE were associated with decreases in insulin, homeostatic model assessment, leptin, thyroid stimulating hormone, total T3, peptide YY3-36, glucagon-like peptide-2, and urinary NE and epinephrine (P = 0.001-0.05). CONCLUSIONS: Energetic adaptations in response to RYGB-induced weight loss are associated with changes in insulin, adipokines, thyroid hormones, gut hormones, and sympathetic nervous system activity and persists 12 months postsurgery.

Revision of Dietary Reference Intakes for energy in preschool-age children.

BACKGROUND: Dietary Reference Intakes (DRI) for energy aim to balance energy expenditure at a level of physical activity consistent with health and support adequate growth in children. DRIs were derived from total energy expenditure (TEE) measured by using the doubly labeled water (DLW) method; however, the database was limited in the 3-5-y-old range. OBJECTIVE: We reexamined the DRI for energy for preschool-age children. DESIGN: Ninety-seven healthy, normal-weight, preschool-age children (mean ± SD age: 4.5 ± 0.9 y) completed a 7-d DLW protocol while wearing accelerometer and heart rate-monitoring devices. RESULTS: Mean TEE and physical activity level (PAL) averaged 1159 ± 171 kcal/d and 1.34 ± 0.14, respectively. TEE predicted by DRI equations agreed with observed TEE (+34 kcal/d or 3%) if the sedentary PAL category was assumed but was overestimated by using the low active (+219 kcal/d or 19%), active (398 kcal/d or 34%), and very active (593 kcal/d or 51%) PAL categories. PAL categories were redefined on the basis of the narrower PAL range observed in preschoolers (range: 1.05-1.70) compared with older children and adults (range: 1.0-2.5). Sex-specific nonlinear regression models were newly developed to predict TEE from age, weight, height, and new PAL categories. The mean absolute error of TEE prediction equations was 0.00 ± 35 kcal/d or 0.1 ± 3%. Ancillary measures, such as total accelerometer counts and total daily steps, that were significantly correlated (P = 0.01-0.05) with TEE (r = 0.26-0.38), TEE per kilogram (r = 0.31-0.41), and PAL (r = 0.36-0.48) may assist in the classification of preschoolers into PAL categories. CONCLUSIONS: Current DRIs for energy overestimate energy requirements of preschool-age children because of the erroneous classification of children into PAL categories. New TEE prediction equations that are based on DLW and appropriate PAL categories are recommended for preschool-age children. This trial was registered at clinicaltrials.gov as H12067.

Prediction of energy expenditure and physical activity in preschoolers.

PURPOSE: Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) for the prediction of EE using room calorimetry and doubly labeled water (DLW) and established accelerometry cut points for PA levels. METHODS: Fifty preschoolers, mean ± SD age of 4.5 ± 0.8 yr, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+), and HR (Actiheart). Free-living 105 children, ages 4.6 ± 0.9 yr, completed the 7-d DLW procedure while wearing the devices. AC cut points for PA levels were established using smoothing splines and receiver operating characteristic curves. RESULTS: On the basis of calorimetry, mean percent errors for EE were -2.9% ± 10.8% and -1.1% ± 7.4% for CSTS models and -1.9% ± 9.6% and 1.3% ± 8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. On the basis of DLW, mean percent errors were -0.5% ± 9.7% and 4.1% ± 8.5% for CSTS models and 3.2% ± 10.1% and 7.5% ± 10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut points were determined: 41, 449, and 1297 cpm for Actiheart x-axis; 820, 3908, and 6112 cpm for ActiGraph vector magnitude; and 240, 2120, and 4450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA), respectively. On the basis of confusion matrices, correctly classified rates were 81%-83% for sedentary PA, 58%-64% for light PA, and 62%-73% for MVPA. CONCLUSIONS: The lack of bias and acceptable limits of agreement affirms the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut points are satisfactory for the classification of sedentary, light, and moderate/vigorous levels of PA in preschoolers.

Support vector machines classifiers of physical activities in preschoolers.

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a supervised protocol of physical activities while wearing a triaxial accelerometer. Accelerometer counts, steps, and position were obtained from the device. We applied K-means clustering to determine the number of natural groupings presented by the data. We used MLR and SVM to classify the six activity types. Using direct observation as the criterion method, the 10-fold cross-validation (CV) error rate was used to compare MLR and SVM classifiers, with and without sleep. Altogether, 58 classification models based on combinations of the accelerometer output variables were developed. In general, the SVM classifiers have a smaller 10-fold CV error rate than their MLR counterparts. Including sleep, a SVM classifier provided the best performance with a 10-fold CV error rate of 24.70%. Without sleep, a SVM classifier-based triaxial accelerometer counts, vector magnitude, steps, position, and 1- and 2-min lag and lead values achieved a 10-fold CV error rate of 20.16% and an overall classification error rate of 15.56%. SVM supersedes the classical classifier MLR in categorizing physical activities in preschool-aged children. Using accelerometer data, SVM can be used to correctly classify physical activities typical of preschool-aged children with an acceptable classification error rate.

Multivariate path analysis of serum 25-hydroxyvitamin D concentration, inflammation, and risk of type 2 diabetes mellitus.

BACKGROUND AND AIMS: Despite growing interest in the protective role that vitamin D may have in health outcomes, little research has examined the mechanisms underlying this role. This study aimed to test two hypotheses: (1) serum 25-hydroxyvitamin D [25(OH)D] is inversely associated with type 2 diabetes mellitus (T2DM) and elevated hemoglobin A1c; (2) these associations are mediated by serum C-reactive protein (CRP). METHODS: Participants aged 20 and older in 2001-2006 National Health and Nutrition Examination Surveys (n = 8,655) with measures of serum 25(OH)D, CRP, hemoglobin A1c, and other important covariates were included in the present study. Logistic regression and path analysis methods were applied to test the study hypotheses. RESULTS: Decreased serum 25(OH)D concentration was significantly associated with increased odds of T2DM. In males, an estimated 14.9% of the association between 25(OH)D and hemoglobin A1c was mediated by serum CRP. However, this mediation effect was not observed in females. CONCLUSION: Using a nationally representative sample, the present study extends previous research and provides new evidence that the effect of decreased serum vitamin D concentration on T2DM may proceed through increased systemic inflammation in males. Longitudinal studies and randomized control trials are needed to confirm the present findings.

Modeling energy expenditure in children and adolescents using quantile regression.

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5-18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children.

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers.

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and maturation, age-specific EE prediction equations are required. We used advanced technology (fast-response room calorimetry, Actiheart and Actigraph accelerometers, and miniaturized HR monitors) and sophisticated mathematical modeling [cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS)] to develop models for the prediction of minute-by-minute EE in 69 preschool-aged children. CSTS and MARS models were developed by using participant characteristics (gender, age, weight, height), Actiheart (HR+AC_x) or ActiGraph parameters (AC_x, AC_y, AC_z, steps, posture) [x, y, and z represent the directional axes of the accelerometers], and their significant 1- and 2-min lag and lead values, and significant interactions. Relative to EE measured by calorimetry, mean percentage errors predicting awake EE (-1.1 ± 8.7%, 0.3 ± 6.9%, and -0.2 ± 6.9%) with CSTS models were slightly higher than with MARS models (-0.7 ± 6.0%, 0.3 ± 4.8%, and -0.6 ± 4.6%) for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. Predicted awake EE values were within ±10% for 81-87% of individuals for CSTS models and for 91-98% of individuals for MARS models. Concordance correlation coefficients were 0.936, 0.931, and 0.943 for CSTS EE models and 0.946, 0.948, and 0.940 for MARS EE models for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. CSTS and MARS models should prove useful in capturing the complex dynamics of EE and movement that are characteristic of preschool-aged children.

Validation of uniaxial and triaxial accelerometers for the assessment of physical activity in preschool children.

PURPOSE: Given the unique physical activity (PA) patterns of preschoolers, wearable electronic devices for quantitative assessment of physical activity require validation in this population. Study objective was to validate uniaxial and triaxial accelerometers in preschoolers. METHODS: Room calorimetry was performed over 3 hours in 64 preschoolers, wearing Actical, Actiheart, and RT3 accelerometers during play, slow, moderate, and fast translocation. Based on activity energy expenditure (AEE) and accelerometer counts, optimal thresholds for PA levels were determined by piecewise linear regression and discrimination boundary analysis. RESULTS: Established HR cutoffs in preschoolers for sedentary/light, light/moderate and moderate/vigorous levels were used to define AEE (0.015, 0.054, 0.076 kcal·kg-1·min-1) and PA ratio (PAR; 1.6, 2.9, 3.6) thresholds, and accelerometer thresholds. True positive predictive rates were 77%, 75%, and 76% for sedentary; 63%, 61%, and 65% for light; 34%, 52%, and 49% for moderate; 46%, 46%, and 49% for vigorous levels. Due to low positive predictive rates, we combined moderate and vigorous PA. Classification accuracy was improved overall and for the combined moderate-to-vigorous PA level (69%, 82%, 79%) for Actical, Actiheart, and RT3, respectively. CONCLUSION: Uniaxial and triaxial accelerometers are acceptable devices with similar classification accuracy for sedentary, light, and moderate-to-vigorous levels of PA in preschoolers.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water.

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant characteristics, heart rate (HR), and accelerometer counts (AC) for prediction of minute-by-minute EE, and hence 24-h total EE (TEE), against a 7-d doubly labeled water (DLW) method in children and adolescents. Our secondary aim was to demonstrate the utility of CSTS and MARS to predict awake EE, sleep EE, and activity EE (AEE) from 7-d HR and AC records, because these shorter periods are not verifiable by DLW, which provides an estimate of the individual's mean TEE over a 7-d interval. CSTS and MARS models were validated in 60 normal-weight and overweight participants (ages 5-18 y). The Actiheart monitor was used to simultaneously measure HR and AC. For prediction of TEE, mean absolute errors were 10.7 +/- 307 kcal/d and 18.7 +/- 252 kcal/d for CSTS and MARS models, respectively, relative to DLW. Corresponding root mean square error values were 305 and 251 kcal/d for CSTS and MARS models, respectively. Bland-Altman plots indicated that the predicted values were in good agreement with the DLW-derived TEE values. Validation of CSTS and MARS models based on participant characteristics, HR monitoring, and accelerometry for the prediction of minute-by-minute EE, and hence 24-h TEE, against the DLW method indicated no systematic bias and acceptable limits of agreement for pediatric groups and individuals under free-living conditions.

Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents.

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in heat production or energy expenditure (EE). Multivariate adaptive regression splines (MARS) is a nonparametric method that estimates complex nonlinear relationships by a series of spline functions of the independent predictors. The specific aim of this study is to construct MARS models based on heart rate (HR) and accelerometer counts (AC) to accurately predict EE, and hence 24-h total EE (TEE), in children and adolescents. Secondarily, MARS models will be developed to predict awake EE, sleep EE, and activity EE also from HR and AC. MARS models were developed in 109 and validated in 61 normal-weight and overweight children (ages 5-18 yr) against the criterion method of 24-h room respiration calorimetry. Actiheart monitor was used to measure HR and AC. MARS models were based on linear combinations of 23-28 basis functions that use subject characteristics (age, sex, weight, height, minimal HR, and sitting HR), HR and AC, 1- and 2-min lag and lead values of HR and AC, and appropriate interaction terms. For the 24-h, awake, sleep, and activity EE models, mean percent errors were -2.5 +/- 7.5, -2.6 +/- 7.8, -0.3 +/- 8.9, and -11.9 +/- 17.9%, and root mean square error values were 168, 138, 40, and 122 kcal, respectively, in the validation cohort. Bland-Altman plots indicated that the predicted values were in good agreement with the observed TEE, and that there was no bias with increasing TEE. Prediction errors for 24-h TEE were not statistically associated with age, sex, weight, height, or body mass index. MARS models developed for the prediction of EE from HR monitoring and accelerometry were demonstrated to be valid in an independent cohort of children and adolescents, but require further validation in independent, free-living populations.

Are energy dense diets also nutrient dense?

OBJECTIVE: Some beverages are nutrient dense, but they are often excluded from nutrient density calculations. The purpose of this study was to assess whether the energy-nutrient association changed when beverages were included in these calculations. DESIGN: Applying a cross-sectional design, a 24-hour dietary recall was collected on each participant. Subjects/ SETTING: 440 young adults (ages 19-28 years) in Bogalusa, Louisiana participated in this study. STATISTICAL ANALYSIS: Mean nutrient intakes and food group consumption were examined across the energy density (ED) tertiles using two calculation methods: one with food and all beverages (excluding water) (ED1) and one including food and only energy containing beverages (ED2). Regression models were used and multiple comparisons were performed using the Tukey-Kramer procedure. A p-value < 0.05 was considered to be significant. RESULTS: With increasing ED, there was a significant increase in the consumption of total meats (ED1 p < 0.05; ED2 p < 0.01). In contrast, there was a significant decrease in consumption of fruits/juices (ED1 p < 0.01; ED2 p < 0.0001), vegetables (ED1 p < 0.01; ED2 p < 0.05), beverages (both p < 0.0001) and total sweets with increasing ED (both p < 0.0001). There was a significantly higher mean intake of total protein (grams) (ED2 p < 0.0001), amino acids (ED1 histidine/leucine p < 0.05; ED2 p < 0.0001), and total fat (grams) (ED1 p < 0.0001; ED2 p < 0.0001) with higher ED compared to lower ED. The percent energy from protein (ED1 p < 0.05; ED2 p < 0.0001), total fat (both p < 0.001) and saturated fatty acids (both p < 0.0001) significantly increased and the percent energy from carbohydrate (both p < 0.0001) and sucrose (both p < 0.0001) significantly decreased with increasing ED. CONCLUSION: This study suggests that ED may influence the ND of the diet depending on whether energy containing beverages are included or excluded in the analysis.

Metabolic and behavioral predictors of weight gain in Hispanic children: the Viva la Familia Study.

BACKGROUND: Despite the high prevalence of overweight among Hispanic children in the United States, definitive predictors of weight gain have not been identified in this population. OBJECTIVE: The study objective was to test sociodemographic, metabolic, and behavioral predictors of 1-y weight gains in a large cohort of Hispanic children studied longitudinally. DESIGN: Subjects (n = 879) were siblings from 319 Hispanic families enrolled in the Viva la Familia Study. Families were required to have at least one overweight child aged 4-19 y. One-year changes in weight and body composition by dual-energy X-ray absorptiometry were measured. Data were from parental interviews, birth certificates, multiple-pass 24-h dietary recalls, 3-d accelerometry, 24-h respiration calorimetry, measurements of eating in the absence of hunger, and measurement of fasting blood biochemistry indexes by radioimmunoassay. Generalized estimating equations and principal component analysis were applied. RESULTS: Weight gain increased with age (P = 0.001), peaking at approximately 10 y of age in girls and approximately 11 y of age in boys. Mean (+/-SD) weight gain was significantly higher in overweight (7.5 +/- 3.7 kg/y) than in nonoverweight (4.4 +/- 2.4 kg/y) children and in boys than in girls. When adjusted for age, age squared, sex, and Tanner stage, the final model indicated a child's body mass index (BMI; kg/m2) status, maternal BMI, energy expenditure (total energy expenditure, basal metabolic rate, and sleeping metabolic rate), and fasting blood biochemistry indexes (total triiodothyronine, insulin, leptin, and ghrelin) as independent, positive predictors of weight gain (P = 0.01-0.001). CONCLUSION: Knowledge of the metabolic and behavioral predictors of weight gain in Hispanic children will inform prevention and treatment efforts to address this serious public health problem in the United States.

Food-purchasing patterns for home: a grocery store-intercept survey.

OBJECTIVES: To identify the most common frequency of food-purchasing patterns and relate this pattern to characteristics of individuals and families. DESIGN: A customer-intercept survey was conducted in the greater Houston area, Texas, USA, in 2002. The frequency of food shopping at supermarkets, convenience stores and restaurants to buy food for eating at home was assessed. SUBJECTS: A total of 823 adults (78.5% female; mean age 37.4 years) who went to any of several grocery or convenience stores, including European, Hispanic and African Americans, and Asian/Pacific Islanders. RESULTS: Major food-shopping patterns were a weekly big trip with a few small trips (34.9%), biweekly big trips with a few small trips (21.9%), no big shopping trips (15.4%), a weekly big trip without small trips (13.9%), a monthly big trip (8.3%), and biweekly big trips without small trips (6.4%). While 61.1% of participants never went to convenience stores to buy fruit and vegetables (F&V) for eating at home, 67% went to restaurants for F&V. African American families shopped for food least frequently, while Asian American families shopped for food most frequently. Educational level was negatively associated with the use of convenience stores and positively associated with take-away from restaurants. CONCLUSIONS: There is substantial variability in the frequency of food shopping. Future research on food shopping should incorporate this variable.

Relationships between maternal and child cardiovascular risk factors: ethnic differences and lack of influence of physical activity.

OBJECTIVES: To obtain information about the relationship between cardiovascular and diabetic risk factors of mothers and their children and to determine whether these relationships differ by physical activity or ethnicity. DESIGN: Prospective study. SETTING: The Texas site of the Studies of Child Activity and Nutrition Program. PATIENTS: A 1986 to 1989 triethnic sample (European American, African American, and Hispanic) of 133 mothers and their 6- to 7-year-old children. MAIN OUTCOME MEASURES: Maternal and child fasting insulin levels; total, high-density lipoprotein, and low-density lipoprotein cholesterol levels; triglyceride levels; waist circumference; systolic and diastolic blood pressures; height; weight; and body mass index. Maternal physical activity was assessed by means of a 7-day recall. Child physical activity was assessed by heart rate monitoring. Correlational methods were used to describe the relationships among metabolic risk factors and physical activity; chi2 tests of independence were used to examine the relationships between ethnic groups. RESULTS: Body mass index and waist circumference were significantly (P<.05) associated among Hispanic mothers and their children, but not in other ethnic groups. Insulin, high-density lipoprotein, and low-density lipoprotein levels were significantly associated among African American mothers and their children, but not in other ethnic groups. Maternal and child physical activity were not significantly associated with any of the risk variables or each other. CONCLUSION: The relationships between the risk factors of mothers and children differed by ethnicity, but not by physical activity.