Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure.

Obesity is caused by a prolonged positive energy balance1,2. Whether reduced energy expenditure stemming from reduced activity levels contributes is debated3,4. Here we show that in both sexes, total energy expenditure (TEE) adjusted for body composition and age declined since the late 1980s, while adjusted activity energy expenditure increased over time. We use the International Atomic Energy Agency Doubly Labelled Water database on energy expenditure of adults in the United States and Europe (n = 4,799) to explore patterns in total (TEE: n = 4,799), basal (BEE: n = 1,432) and physical activity energy expenditure (n = 1,432) over time. In males, adjusted BEE decreased significantly, but in females this did not reach significance. A larger dataset of basal metabolic rate (equivalent to BEE) measurements of 9,912 adults across 163 studies spanning 100 years replicates the decline in BEE in both sexes. We conclude that increasing obesity in the United States/Europe has probably not been fuelled by reduced physical activity leading to lowered TEE. We identify here a decline in adjusted BEE as a previously unrecognized factor.

Variability in energy expenditure is much greater in males than females.

In mammals, trait variation is often reported to be greater among males than females. However, to date, mainly only morphological traits have been studied. Energy expenditure represents the metabolic costs of multiple physical, physiological, and behavioral traits. Energy expenditure could exhibit particularly high greater male variation through a cumulative effect if those traits mostly exhibit greater male variation, or a lack of greater male variation if many of them do not. Sex differences in energy expenditure variation have been little explored. We analyzed a large database on energy expenditure in adult humans (1494 males and 3108 females) to investigate whether humans have evolved sex differences in the degree of interindividual variation in energy expenditure. We found that, even when statistically comparing males and females of the same age, height, and body composition, there is much more variation in total, activity, and basal energy expenditure among males. However, with aging, variation in total energy expenditure decreases, and because this happens more rapidly in males, the magnitude of greater male variation, though still large, is attenuated in older age groups. Considerably greater male variation in both total and activity energy expenditure could be explained by greater male variation in levels of daily activity. The considerably greater male variation in basal energy expenditure is remarkable and may be explained, at least in part, by greater male variation in the size of energy-demanding organs. If energy expenditure is a trait that is of indirect interest to females when choosing a sexual partner, this would suggest that energy expenditure is under sexual selection. However, we present a novel energetics model demonstrating that it is also possible that females have been under stabilizing selection pressure for an intermediate basal energy expenditure to maximize energy available for reproduction.

Total energy expenditure is repeatable in adults but not associated with short-term changes in body composition.

Low total energy expenditure (TEE, MJ/d) has been a hypothesized risk factor for weight gain, but repeatability of TEE, a critical variable in longitudinal studies of energy balance, is understudied. We examine repeated doubly labeled water (DLW) measurements of TEE in 348 adults and 47 children from the IAEA DLW Database (mean ± SD time interval: 1.9 ± 2.9 y) to assess repeatability of TEE, and to examine if TEE adjusted for age, sex, fat-free mass, and fat mass is associated with changes in weight or body composition. Here, we report that repeatability of TEE is high for adults, but not children. Bivariate Bayesian mixed models show no among or within-individual correlation between body composition (fat mass or percentage) and unadjusted TEE in adults. For adults aged 20-60 y (N = 267; time interval: 7.4 ± 12.2 weeks), increases in adjusted TEE are associated with weight gain but not with changes in body composition; results are similar for subjects with intervals >4 weeks (N = 53; 29.1 ± 12.8 weeks). This suggests low TEE is not a risk factor for, and high TEE is not protective against, weight or body fat gain over the time intervals tested.

Daily energy expenditure through the human life course.

Total daily energy expenditure ("total expenditure") reflects daily energy needs and is a critical variable in human health and physiology, but its trajectory over the life course is poorly studied. We analyzed a large, diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 years. Total expenditure increased with fat-free mass in a power-law manner, with four distinct life stages. Fat-free mass-adjusted expenditure accelerates rapidly in neonates to ~50% above adult values at ~1 year; declines slowly to adult levels by ~20 years; remains stable in adulthood (20 to 60 years), even during pregnancy; then declines in older adults. These changes shed light on human development and aging and should help shape nutrition and health strategies across the life span.

Behavior Modification of Diet and Parent Feeding Practices in a Community- Vs Primary Care-Centered Intervention for Childhood Obesity.

OBJECTIVE: To evaluate behavior modification of diet and parent feeding practices in childhood obesity interventions. DESIGN: Secondary analysis of randomized, controlled trial comparing Mind, Exercise, Nutrition … Do It! (MEND2-5 and MEND/Coordinated Approach to Child Health [CATCH6-12]) vs Next Steps at baseline and 3 and 12 months. SETTING: Austin and Houston, TX. PARTICIPANTS: A total of 549 Hispanic and black children randomized to programs by age groups (2-5, 6-8, and 9-12 years) INTERVENTIONS: Twelve-month MEND2-5 and MEND/CATCH6-12 vs Next Steps. MAIN OUTCOME MEASURE(S): Diet (MEND-friendly/unfriendly food groups and Healthy Eating Index-2010) and parent feeding practices (parental overt control, discipline, limit setting, monitoring, reinforcement, modeling, and covert control; and food neophobia). ANALYSIS: Mixed-effects linear regression. RESULTS: Changes in diet quality, consumption of MEND-unfriendly foods, and parent feeding practices did not differ between programs. In both interventions, MEND-unfriendly vegetables, grains, dairy and protein, added fat and desserts/sugar-sweetened beverages declined in 2-5- and 6-8-year-olds (P < .001). Healthy Eating Index-2010 improved in 2-5- (treatment; P = .002) and 6-8-year-olds (P = .001). Parental overt control decreased and limit setting, discipline, monitoring, reinforcement, and covert control increased with both interventions in 2-5- and 6-8-year-olds (P < 0.01-0.001). CONCLUSIONS: Diet quality, consumption of MEND-unfriendly foods, and parent feeding practices were altered constructively in 2 pediatric obesity interventions, especially in 2-5- and 6-8-year-olds.

Energy Cost Expression for a Youth Compendium of Physical Activities: Rationale for Using Age Groups.

PURPOSE: This study compared the accuracy of physical activity energy expenditure (PAEE) prediction using 2 methods of accounting for age dependency versus 1 standard (single) value across all ages. METHODS: PAEE estimates were derived by pooling data from 5 studies. Participants, 6-18 years (n = 929), engaged in 14 activities while in a room calorimeter or wearing a portable metabolic analyzer. Linear regression was used to estimate the measurement error in PAEE (expressed as youth metabolic equivalent) associated with using age groups (6-9, 10-12, 13-15, and 16-18 y) and age-in-years [each year of chronological age (eg, 12 = 12.0-12.99 y)] versus the standard (a single value across all ages). RESULTS: Age groups and age-in-years showed similar error, and both showed less error than the standard method for cycling, skilled, and moderate- to vigorous-intensity activities. For sedentary and light activities, the standard had similar error to the other 2 methods. Mean values for root mean square error ranged from 0.2 to 1.7 youth metabolic equivalent across all activities. Error reduction ranged from -0.2% to 21.7% for age groups and -0.23% to 18.2% for age-in-years compared with the standard. CONCLUSIONS: Accounting for age showed lower errors than a standard (single) value; using an age-dependent model in the Youth Compendium is recommended.

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.

BMI z-Scores are a poor indicator of adiposity among 2- to 19-year-olds with very high BMIs, NHANES 1999-2000 to 2013-2014.

OBJECTIVE: Although the Centers for Disease Control and Prevention (CDC) growth charts are widely used, BMI-for-age z-Scores (BMIz) are known to be uninformative above the 97th percentile. This study compared the relations of BMIz and other BMI metrics (%BMIp95 , percent of 95th percentile, and ΔBMIp95 , BMI minus 95th percentile) to circumferences, skinfolds, and fat mass. We were particularly interested in the differences among children with severe obesity (%BMIp95 ≥ 120). METHODS: Data was used from 30,003 2- to 19-year-olds who were examined from 1999-2000 through 2013-2014 in the National Health and Nutrition Examination Survey (NHANES). RESULTS: The theoretical maximum BMIz based on the growth charts varied by more than threefold across ages. The BMI metrics were strongly intercorrelated, but BMIz was less strongly related to the adiposity measures than were ΔBMIp95 and %BMIp95 . Among children with severe obesity, circumferences and triceps skinfold showed almost no association with BMIz (r ≤ 0.10), whereas associations with %BMIp95 and ΔBMIp95 ranged from r = 0.32 to 0.79. Corresponding associations with fat mass ÷ height2 ranged from r = 0.40 (BMIz) to r =0.82 (%BMIp95 ) among 8- to 19-year-olds. CONCLUSIONS: Among children with severe obesity, BMIz is only weakly associated with other measures of body fatness. Very high BMIs should be expressed relative to the CDC 95th percentile, particularly in studies that evaluate obesity interventions.

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.

Energy Intake and Energy Expenditure for Determining Excess Weight Gain in Pregnant Women.

OBJECTIVE: To conduct a secondary analysis designed to test whether gestational weight gain is the result of increased energy intake or adaptive changes in energy expenditures. METHODS: In this secondary analysis, energy intake and energy expenditure of 45 pregnant women (body mass index [BMI] 18.5-24.9 [n=33] and BMI 25 or greater [n=12]) were measured preconceptionally and at 22 and 36 weeks of gestation. Energy intake was calculated as the sum of total energy expenditure measured by doubly-labeled water and energy deposition determined by the four-compartment body composition model. Measurements of weight, body composition, and basal metabolic rate were completed preconceptionally and 9, 22, and 36 weeks of gestation. Basal metabolic rate was measured by indirect calorimetry in a room calorimeter and activity energy expenditure by doubly-labeled water. RESULTS: Energy intake from 22 to 36 weeks of gestation was significantly higher in high gainers (n=19) (3,437±99 kcal per day) compared with low+ideal gainers (n=26) (2,687±110, P<.001) within both BMI categories. Basal metabolic rate increased in proportion to gestational weight gain; however, basal metabolic rate adjusted for body composition changes with gestational weight gain was not significantly different between high gainers and low+ideal gainers (151±33 compared with 129±36 kcal per day; P=.66). Activity energy expenditure decreased throughout pregnancy in both groups (low+ideal gainers: -150±70 kcal per day; P=.04 and high gainers: -230±92 kcal per day; P=.01), but there was no difference between high gainers and low+ideal gainers (P=.49). CONCLUSION: Interventions designed to increase adherence to the Institute of Medicine guidelines for weight gain in pregnancy may have increased efficacy if focused on limiting energy intake while increasing nutrient density and maintaining levels of physical activity.

Exploring Metrics to Express Energy Expenditure of Physical Activity in Youth.

BACKGROUND: Several approaches have been used to express energy expenditure in youth, but no consensus exists as to which best normalizes data for the wide range of ages and body sizes across a range of physical activities. This study examined several common metrics for expressing energy expenditure to determine whether one metric can be used for all healthy children. Such a metric could improve our ability to further advance the Compendium of Physical Activities for Youth. METHODS: A secondary analysis of oxygen uptake (VO2) data obtained from five sites was completed, that included 947 children ages 5 to 18 years, who engaged in 14 different activities. Resting metabolic rate (RMR) was computed based on Schofield Equations [Hum Nutr Clin Nut. 39(Suppl 1), 1985]. Absolute oxygen uptake (ml.min-1), oxygen uptake per kilogram body mass (VO2 in ml.kg-1.min-1), net oxygen uptake (VO2 - resting metabolic rate), allometric scaled oxygen uptake (VO2 in ml.kg-0.75.min-1) and YOUTH-MET (VO2.[resting VO2] -1) were calculated. These metrics were regressed with age, sex, height, and body mass. RESULTS: Net and allometric-scaled VO2, and YOUTH-MET were least associated with age, sex and physical characteristics. For moderate-to-vigorous intensity activities, allometric scaling was least related to age and sex. For sedentary and low-intensity activities, YOUTH-MET was least related to age and sex. CONCLUSIONS: No energy expenditure metric completely eliminated the influence of age, physical characteristics, and sex. The Adult MET consistently overestimated EE. YOUTH-MET was better for expressing energy expenditure for sedentary and light activities, whereas allometric scaling was better for moderate and vigorous intensity activities. From a practical perspective, The YOUTH-MET may be the more feasible metric for improving of the Compendium of Physical Activities for Youth.

Longitudinal effects of parental, child and neighborhood factors on moderate-vigorous physical activity and sedentary time in Latino children.

BACKGROUND: Moderate-vigorous physical activity (%MVPA) confers beneficial effects on child musculoskeletal health, cardiovascular fitness, and psychosocial well-being; in contrast, sedentary time (%SED) is emerging as a risk factor for health. This study aimed to identify parental, child and neighborhood factors influencing longitudinal assessments of body mass index (BMI) and activity patterns among Latino children, and to estimate lagged and cross-lagged effects between child BMI, %MVPA and %SED. METHODS: A longitudinal design with assessments at baseline, 1 and 2 years follow-up (FU) was used to evaluate the effects of maternal and paternal factors (BMI, age, education level, acculturation, household income and household size), child factors (gender, age, BMI, pubertal status) and neighborhood factors (disorder, victimization) on child BMI, %MVPA and %SED, expressed as a percent of awake time, in 282 Latino children ages 8-10 y and their parents. This study was restricted to families with a mother and biological father or father figure in the child's life. RESULTS: Across time, total daily accelerometer counts (p = 0.04) and steps decreased (p = 0.0001), %SED increased (p = 0.0001), and %MVPA decreased (p = 0.02). Moderate lagged effects or tracking was seen for %MVPA and %SED (p = 0.001). %MVPA varied by gender (5.5% higher in boys than girls, p = 0.0001); child age (-0.4% per year, p = 0.03), and child BMI in boys only (-0.22%, p = 0.0002). Negative effects of paternal age, maternal education and maternal changes in BMI on %MVPA also were seen. %SED increased with child age (2.5% higher per year, p = 0.0001). Positive effects of paternal acculturation, maternal change in BMI, paternal age, and negative effects of household size on %SED were observed. A cross-lagged positive effect of BMI at FU1 on %SED at FU2 was observed for boys and girls (p = 0.03). Neighborhood disorder and victimization were not significant predictors of child BMI, %MVPA or %SED. CONCLUSION: The major child determinants of physical activity (age, gender and BMI) and minor parental influences (maternal BMI and education, paternal age and acculturation) should be considered in designing interventions to promote %MVPA and reduce %SED among Latino children as they approach adolescence.

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.

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.

Demographic and socioeconomic correlates of adiposity assessed with dual-energy X-ray absorptiometry in US children and adolescents.

BACKGROUND: Associations between demographic-socioeconomic characteristics and childhood obesity are complex in the United States. OBJECTIVE: We examined associations between demographic-socioeconomic characteristics (age, sex, race-ethnicity, family income, household size, and birthplace) and adiposity measured by dual-energy X-ray absorptiometry in US children. DESIGN: Data were from 8-19-y-old US children enrolled in NHANES 2001-2004 (n = 5436). Adiposity was expressed as the fat mass index (FMI = fat mass/height(2); in kg/m(2)), percentage body fat (%BF), and prevalence of normal, overfat, and excess fat corresponding to %BF <25%, 25-29.9%, and ≥30% in boys and <30%, 30-34.9%, and ≥35% in girls, respectively. We used sex-specific linear and multinomial logistic regression models adjusted for demographic-socioeconomic factors. RESULTS: The prevalence of excess fat was higher in Mexican American (36%) than in white (28%; P ≤ 0.10) and black (21%; P < 0.05) boys; higher in Mexican American (44%) than in white (36%; P ≤ 0.10) and black (35%; P < 0.05) girls; and higher in US-born (38%) than in foreign-born (29%; P ≤ 0.10) Mexican American boys. In boys, %BF was higher in Mexican Americans and lower in blacks than in whites (P < 0.05). Adiposity was negatively associated with family income in white boys and girls and in Mexican American girls (P < 0.05). Racial-ethnic disparities in adiposity persisted (P < 0.001) after control for demographic-socioeconomic factors available in NHANES. The R(2) for sex-specific models of %BF or FMI regressed on age, race-ethnicity, family income, household size, and birthplace ranged from 2% to 11%. CONCLUSION: The association between demographic-socioeconomic factors and adiposity among US children varied substantially by age, sex, and race-ethnicity.

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.

The mass-specific energy cost of human walking is set by stature.

The metabolic and mechanical requirements of walking are considered to be of fundamental importance to the health, physiological function and even the evolution of modern humans. Although walking energy expenditure and gait mechanics are clearly linked, a direct quantitative relationship has not emerged in more than a century of formal investigation. Here, on the basis of previous observations that children and smaller adult walkers expend more energy on a per kilogram basis than larger ones do, and the theory of dynamic similarity, we hypothesized that body length (or stature, L(b)) explains the apparent body-size dependency of human walking economy. We measured metabolic rates and gait mechanics at six speeds from 0.4 to 1.9 m s(-1) in 48 human subjects who varied by a factor of 1.5 in stature and approximately six in both age and body mass. In accordance with theoretical expectation, we found the most economical walking speeds measured (J kg(-1) m(-1)) to be dynamically equivalent (i.e. similar U, where U=velocity(2)/gravity · leg length) among smaller and larger individuals. At these speeds, stride lengths were directly proportional to stature whereas the metabolic cost per stride was largely invariant (2.74±0.12 J kg(-1) stride(-1)). The tight coupling of stature, gait mechanics and metabolic energy expenditure resulted in an inverse relationship between mass-specific transport costs and stature (E(trans)/M(b)∝L(b)(-0.95), J kg(-1) m(-1)). We conclude that humans spanning a broad range of ages, statures and masses incur the same mass-specific metabolic cost to walk a horizontal distance equal to their stature.

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.

Evidence that multiple genetic variants of MC4R play a functional role in the regulation of energy expenditure and appetite in Hispanic children.

BACKGROUND: Melanocortin-4-receptor (MC4R) haploinsufficiency is the most common form of monogenic obesity; however, the frequency of MC4R variants and their functional effects in general populations remain uncertain. OBJECTIVE: The aim was to identify and characterize the effects of MC4R variants in Hispanic children. DESIGN: MC4R was resequenced in 376 parents, and the identified single nucleotide polymorphisms (SNPs) were genotyped in 613 parents and 1016 children from the Viva la Familia cohort. Measured genotype analysis (MGA) tested associations between SNPs and phenotypes. Bayesian quantitative trait nucleotide (BQTN) analysis was used to infer the most likely functional polymorphisms influencing obesity-related traits. RESULTS: Seven rare SNPs in coding and 18 SNPs in flanking regions of MC4R were identified. MGA showed suggestive associations between MC4R variants and body size, adiposity, glucose, insulin, leptin, ghrelin, energy expenditure, physical activity, and food intake. BQTN analysis identified SNP 1704 in a predicted micro-RNA target sequence in the downstream flanking region of MC4R as a strong, probable functional variant influencing total, sedentary, and moderate activities with posterior probabilities of 1.0. SNP 2132 was identified as a variant with a high probability (1.0) of exerting a functional effect on total energy expenditure and sleeping metabolic rate. SNP rs34114122 was selected as having likely functional effects on the appetite hormone ghrelin, with a posterior probability of 0.81. CONCLUSION: This comprehensive investigation provides strong evidence that MC4R genetic variants are likely to play a functional role in the regulation of weight, not only through energy intake but through energy expenditure.