The roles of sleep and eating patterns in adiposity gain among preschool-aged children.

BACKGROUND: Short sleep durations are related to risks for obesity in preschool children. However, the underlying mechanism or mechanisms are not clear. OBJECTIVES: We evaluated the relationships between sleep characteristics and body composition, energetics, and weight-regulating behaviors in preschool-aged children, as well as the longitudinal associations between children's sleep and eating patterns and body composition at a 1-year follow-up. METHODS: Data were drawn from a longitudinal study of 118 children aged 3-5 years. Sleep (duration, midpoint, regularity) and physical activity (PA) were measured by accelerometry over 6 consecutive days; total energy expenditure (TEE) was measured using the doubly labeled water method; body composition (fat mass, fat-free mass, and percent body fat) was measured by DXA; and dietary intake (energy intake, timing) was measured using two 24-hour recalls. Multivariable regression was used to estimate interindividual associations of sleep parameters with body composition, PA, TEE, and dietary outcomes and to examine the relationships between sleep and dietary behaviors and body composition 1 year later. RESULTS: Cross-sectionally, later sleep midpoint is associated with having a greater fat mass (0.33; 95% CI: 0.05, 0.60) and a higher percent body fat (0.92; 95% CI: 0.15, 1.70). Later sleep midpoint was associated with delayed morning mealtimes (0.51; 95% CI: 0.28, 0.74) and evening mealtimes (0.41; 95% CI: 0.29, 0.53), higher nighttime energy intakes (45.6; 95% CI: 19.7, 71.4), and lower morning energy intakes (-44.8; 95% CI: -72.0, -17.6). Longitudinally, shorter sleep duration (-0.02; 95% CI: -0.03, 0.00) and later meal timing (0.83; 95% CI: 0.24, 1.42) were associated with higher percent body fat measurements 1 year later. CONCLUSIONS: Shorter sleep duration and later meal timing are associated with adiposity gains in preschoolers.

Impact of changes in maternal body composition on birth weight and neonatal fat mass in dichorionic twin pregnancies.

Background: Although the impact of gestational weight gain (GWG) on birth weight in twin pregnancies has been demonstrated, the specific components of GWG have not been delineated for twin gestations. Fetal body composition has been shown to be modifiable in singleton gestations based on nutritional intervention strategies and may prove to have similar modifications in twin gestations. Objective: We aimed to determine the relation of maternal body composition changes to birth weight, birth length, and neonatal fat mass (FM) in dichorionic-diamniotic twin pregnancies. Design: This is a prospective study of 20 women with twin gestations. Comparisons were made between body composition variables during each trimester and for the entire pregnancy and compared with the outcomes of birth weight, neonatal fat percentage, and birth length. Results: GWG within or above compared with below the IOM recommendations was associated with higher birth weights (P = 0.03, P = 0.04, respectively), but also with higher postpartum weight retention (P = 0.001). Total maternal protein gain over the pregnancy was positively associated with birth weight (P = 0.03). Changes in maternal fat-free mass (FFM), total body water (TBW), and FM from the first to the third trimester were not associated with either birth weight or neonatal FM percentage. However, maternal FM change from the second to the third trimester was significantly correlated to neonatal FM percentage (P = 0.02). Third trimester GWG and total protein gain were positively correlated with neonatal birth length (P = 0.02 and 0.03, respectively). Maternal FFM over all 3 trimesters showed a positive relation with neonatal birth length (P = 0.01). Conclusions: Significant increases in maternal protein are associated with greater birth weight and neonatal birth length. Protein accretion, in contrast to TBW and FM gains, may be the most critical component of maternal GWG in dichorionic twin gestations.

Estimated energy requirements increase across pregnancy in healthy women with dichorionic twins.

Background: Estimated energy requirement (EER) has not been defined for twin pregnancy. This study was designed to determine the EER of healthy women with dichorionic-diamniotic (DCDA) twin pregnancies. Objectives: We aimed to estimate energy deposition from changes in maternal body protein and fat; to measure resting energy expenditure (REE), physical activity level (PAL), and total energy expenditure (TEE) throughout pregnancy and postpartum; and to define the EER based on the sum of TEE and energy deposition for twin gestation. Design: This is a prospective study of 20 women with DCDA twin gestations. Maternal EER, energy deposition, REE, TEE, and PAL were obtained during the first, second, and third trimesters of pregnancy and immediately postpartum. A mixed-effects linear regression model for repeated measures with random intercept was used to test for the effects of BMI groups and time. Results: Gains in total body protein (mean ± SD: 2.1 ± 0.7 kg) and fat mass (5.9 ± 2.8 kg) resulted in total energy deposition of 67,042 ± 25,586 kcal between 0 and 30-32 weeks of gestation. REE increased 26% from 1392 ± 162 to 1752 ± 172 kcal/d across the 3 trimesters, whereas TEE increased 17% from 2141 ± 283 to 2515 ± 337 kcal/d. Physical activity decreased steadily throughout pregnancy. Reductions in physical activity did not compensate for the rise in REE and energy deposition, thus requiring an increase in dietary energy intake as pregnancy progressed. EER increased 29% from 2257 ± 325 kcal/d in the first trimester to 2941 ± 407 kcal/d in the second trimester, and stayed consistent at 2906 ± 350 kcal/d in the third trimester. Conclusion: Increased energy intake, on average ∼700 kcal/d in the second and third trimesters when compared with the first trimester, is required to support gestational weight gain and the rise in energy expenditure of DCDA twin pregnancies.

Efficacy of a Community- Versus Primary Care-Centered Program for Childhood Obesity: TX CORD RCT.

OBJECTIVE: This randomized controlled trial was conducted to determine comparative efficacy of a 12-month community-centered weight management program (MEND2-5 for ages 2-5 or MEND/CATCH6-12 for ages 6-12) against a primary care-centered program (Next Steps) in low-income children. METHODS: Five hundred forty-nine Hispanic and black children (BMI ≥ 85th percentile), stratified by age groups (2-5, 6-8, and 9-12 years), were randomly assigned to MEND2-5 (27 contact hours)/MEND/CATCH6-12 (121.5 contact hours) or Next Steps (8 contact hours). Primary (BMI value at the 95th percentile [%BMIp95 ]) and secondary outcomes were measured at baseline, 3 months (Intensive Phase), and 12 months (Transition Phase). RESULTS: For age group 6-8, MEND/CATCH6-12 resulted in greater improvement in %BMIp95 than Next Steps during the Intensive Phase. Effect size (95% CI) was -1.94 (-3.88, -0.01) percentage points (P = 0.05). For age group 9-12, effect size was -1.38 (-2.87, 0.16) percentage points for %BMIp95 (P = 0.07). MEND2-5 did not differentially affect %BMIp95 . Attendance averaged 52% and 22% during the Intensive and Transition Phases. Intervention compliance was inversely correlated to change in %BMIp95 during the Intensive Phase (P < 0.05). In the Transition Phase, %BMIp95 was maintained or rebounded in both programs (P < 0.05). CONCLUSIONS: MEND/CATCH6-12 was more efficacious for BMI reduction at 3 months but not 12 months compared to Next Steps in underserved children. Intervention compliance influenced outcomes, emphasizing the need for research in sustaining family engagement in low-income populations.

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.

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.

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.

Effectiveness of the modified progressive aerobic capacity endurance run test for assessing aerobic fitness in Hispanic children who are obese.

The purpose of this study was to evaluate the effectiveness of the progressive aerobic capacity endurance run (PACER) and a newly designed modified PACER (MPACER) for assessing aerobic fitness in Hispanic children who are obese. Thirty-nine (aged 7-12 years) children who were considered obese (≥ 95 th body mass index [BMI] percentile) and 16 children who were considered normal weight (<85th BMI percentile) participated in this study. Performance outcomes included test duration (in minutes) and exercise heart rate (HR) (first-stage and peak HR) for each test. Ninety-five percent confidence intervals and independent t-tests were used to assess differences in primary outcomes. Mean PACER test duration was 1.6 ± 0.6 and 3.1 ± 1.3 minutes for children who were obese and normal weight, respectively. Modified PACER duration was higher than 3 minutes for the obese (3.6 ± 0.6 minutes) and normal weight (5.3 ± 1.2 minutes) groups. Children first-stage HR, expressed as a percent of peak HR, was above the predicted anaerobic threshold during the PACER, but below the anaerobic threshold during the MPACER. Relative first-stage HR was not significantly different between groups for the PACER, but they were significantly different between groups for the MPACER. In conclusion, the MPACER was a better alternative than the PACER for assessing aerobic fitness in Hispanic children who were normal weight and obese. When validated, this modified field test could be used to assess aerobic fitness in Hispanic children, particularly those who are overweight or obese. Additionally, the study provides evidence in which physical educators, personal trainers, and others most apt to assess aerobic fitness in children who are obese, should modify tests originally designed for the population who are normal weight.

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.

Predicting metabolic rate across walking speed: one fit for all body sizes?

We formulated a "one-size-fits-all" model that predicts the energy requirements of level human walking from height, weight, and walking speed. Our three-component model theorizes that the energy expended per kilogram per stride is independent of stature at mechanically equivalent walking speeds. We measured steady-state rates of oxygen uptake of 78 subjects who spanned a nearly twofold range of statures (1.07-2.11 m) and sevenfold range of body masses (16-112 kg) at treadmill speeds from 0.4 to 1.9 m/s. We tested the size independence of the model by deriving best-fit equations in the form of the model on four stature groups (n ≥ 15): short, moderately short, moderately tall, and tall. The mean walking metabolic rates predicted by these four independently derived equations for the same set of reference subjects (n = 16; stature range: 1.30-1.90 m) agreed with one another to within an average of 5.2 ± 3.7% at the four intermediate speeds in our protocol. We next evaluated the model's gross predictive accuracy by dividing our 78 subjects into 39 stature-matched pairs of experimental and validation group subjects. The model best-fit equation derived on the experimental group subjects predicted the walking metabolic rates of the validation group subjects to within an average of 8.1 ± 6.7% (R(2) = 0.90; standard error of estimate = 1.34 ml O2·kg(-1)·min(-1)). The predictive error of the American College of Sports Medicine equation (18.0 ± 13.1%), which does not include stature as a predictor, was more than twice as large for the same subject group. We conclude that the energy cost of level human walking can be accurately predicted from height, weight, and walking speed.

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.

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.

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.

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry.

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observations on the same individual, may be advantageous for prediction of EE. CSTS models for prediction of minute-by-minute EE and, hence, total EE (TEE) from heart rate (HR), physical activity (PA) measured by accelerometry, and observable subject variables were developed in 109 children and adolescents by use of Actiheart and 24-h room respiration calorimetry. CSTS models based on HR, PA, time-invariant covariates, and interactions were developed. These dynamic models involve lagged and lead values of HR and lagged values of PA for better description of the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1- and 2-min lagged and lead values of HR, HR(2), and PA and 1- and 2-min lagged values of PA, PA(2), age, age(2), sex, weight, height, minimum HR, sitting HR, HR x height, HR x weight, HR x age, PA x weight, and PA x sex interactions (P < 0.001). Prediction error for TEE was 0.9 +/- 10.3% (mean +/- SD). Errors were not correlated with age, weight, height, or body mass index. CSTS modeling provides a useful predictive model for EE and, hence, TEE in children and adolescents on the basis of HR and PA and other observable explanatory subject characteristics of age, sex, weight, and height.