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

The relationship of sleep duration and quality to energy expenditure and physical activity in children

BACKGROUND

Shorter sleep duration has been linked to the risk for obesity in children. The pathways linking sleep duration and quality to the risk of obesity are unclear, particularly the effect of sleep on energetics.

OBJECTIVE

We investigated the relationship between sleep duration, quality and timing in children, to the basal metabolic rate (BMR), total energy expenditure (TEE) and physical activity (PA).

METHODS

Fifty nine children in two age-groups (5-11 and 12-18 years) underwent evaluation of body composition (DXA), BMR in a room calorimeter, free-living TEE by doubly labelled water method, sleep and PA (7-day Actiheart monitor) during school break.

RESULTS

Sleep duration contributed to the variance in BMR (β = 0.11, P = .009) after adjusting for age-group, sex, lean and fat mass, but not to the variance in TEE. Late sleep timing was related to lower PA. In the younger age-group, children who met recommended sleep duration on ≥50% of the 7 days had higher light PA (P = .03) and lower sedentary time (P = .009).

CONCLUSION

Suboptimal sleep is associated with lower BMR, lower PA, and higher sedentary behaviours in young children. Prospective studies are needed to confirm if insufficient sleep duration or late sleep timing contribute to obesity risk by increasing sedentary behaviours and decreasing BMR.

Evaluation of the impact of body mass index on venous thromboembolism risk factors

In this paper, we investigate the interaction impacts of body mass index (BMI) on the other important risk factors for venous thromboembolism (VTE), using deep venous thrombosis (DVT) patient data from the International Warfarin Pharmacogenetics Consortium (IWPC). We apply eight machine learning techniques, including naive Bayes classifier (NB), support vector machine (SVM), elastic net regression (ENET), logistic regression (LR), lasso regression (LAR), multivariate adaptive regression splines (MARS), boosted regression tree (BRT) and random forest model (RF). The RF method is selected as the best model for classification. Out of 33 features considered in this study, we identify 12 variables as relatively important risk factors for VTE. Finally, we examine the interaction impacts of BMI on these important VTE risk factors. We conclude that the impacts of risk factors on VTE incidence are varying across different BMI groups, and the variations are different for different risk factors. Therefore the interaction impacts of BMI on the other risk factors have to be taken into account in order to better understand the incidence of VTE.

Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation

Machine learning models to predict energy expenditure (EE) from accelerometer data have traditionally been trained on data from laboratory-based activity trials. However, accuracy is typically attenuated when implemented in free-living scenarios. Currently, no studies involving preschool children have evaluated the accuracy of EE prediction models trained on laboratory (LAB) under free-living conditions.

Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors

Physical inactivity is responsible for 7-10% of all premature deaths worldwide. Thus, valid, reliable and unobtrusive methods for monitoring activities of daily living (ADL) to predict total energy expenditure (TEE) is desired. Multiple methods exist to quantify TEE, but microelectromechanical systems (MEMSs) are the only method, which has shown promising results and are applicable for long-term monitoring in the field. However, no perfect method exists for predicting TEE on a daily basis. The present study evaluates TEE estimation based on a MEMS (Xsens Link system) taking gender and heart rate into account. Fifteen individuals performed seven ADL wearing the Xsens Link system, a heart rate belt and an oxygen mask. Multiple linear regression models were established for sedentary and dynamic activities and evaluated by leave-one-out cross-validation and compared with indirect calorimetry. The linear regression model showed better prediction for dynamic activities (adjusted R² 0.95±0.16) compared to sedentary activities (adjusted R² 0.61±0.19). The root-mean-square error for the TEE estimation ranged between 0.02 and 0.08 kJ/min/kg for the sedentary and dynamic models, respectively. The study showed a viable approach to predict TEE in ADL compared to previously published results. Further studies are warranted to reduce the number of sensors in the estimation of TEE.

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL.

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.

Comparison of total energy expenditure assessed by two devices in controlled and free-living conditions

The objective of this study was to evaluate the validity of total energy expenditure (TEE) provided by Actiheart and Armband. Normal-weight adult volunteers wore both devices either for 17 hours in a calorimetric chamber (CC, n = 49) or for 10 days in free-living conditions (FLC) outside the laboratory (n = 41). The two devices and indirect calorimetry or doubly labelled water, respectively, were used to estimate TEE in the CC group and FLC group. In the CC, the relative value of TEE error was not significant (p > 0.05) for Actiheart but significantly different from zero for Armband, showing TEE underestimation (-4.9%, p < 0.0001). However, the mean absolute values of errors were significantly different between Actiheart and Armband: 8.6% and 6.7%, respectively (p = 0.05). Armband was more accurate for estimating TEE during sleeping, rest, recovery periods and sitting-standing. Actiheart provided better estimation during step and walking. In FLC, no significant error in relative value was detected. Nevertheless, Armband produced smaller errors in absolute value than Actiheart (8.6% vs. 12.8%). The distributions of differences were more scattered around the means, suggesting a higher inter-individual variability in TEE estimated by Actiheart than by Armband. Our results show that both monitors are appropriate for estimating TEE. Armband is more effective than Actiheart at the individual level for daily light-intensity activities.

Examination of wrist and hip actigraphy using a novel sleep estimation procedure ☆

Objective

Improving and validating sleep scoring algorithms for actigraphs enhances their usefulness in clinical and research applications. The MTI^® device (ActiGraph, Pensacola, FL) had not been previously validated for sleep. The aims were to (1) compare the accuracy of sleep metrics obtained via wrist- and hip-mounted MTI^® actigraphs with polysomnographic (PSG) recordings in a sample that included both normal sleepers and individuals with presumed sleep disorders; and (2) develop a novel sleep scoring algorithm using spline regression to improve the correspondence between the actigraphs and PSG.

Methods

Original actigraphy data were amplified and their pattern was estimated using a penalized spline. The magnitude of amplification and the spline were estimated by minimizing the difference in sleep efficiency between wrist- (hip-) actigraphs and PSG recordings. Sleep measures using both the original and spline-modified actigraphy data were compared to PSG using the following: mean sleep summary measures; Spearman rank-order correlations of summary measures; percent of minute-by-minute agreement; sensitivity and specificity; and Bland–Altman plots.

Results

The original wrist actigraphy data showed modest correspondence with PSG, and much less correspondence was found between hip actigraphy and PSG. The spline-modified wrist actigraphy produced better approximations of interclass correlations, sensitivity, and mean sleep summary measures relative to PSG than the original wrist actigraphy data. The spline-modified hip actigraphy provided improved correspondence, but sleep measures were still not representative of PSG.

Discussion

The results indicate that with some refinement, the spline regression method has the potential to improve sleep estimates obtained using wrist actigraphy.

Validation of SenseWear Armband and ActiHeart monitors for assessments of daily energy expenditure in free-living women with chronic obstructive pulmonary disease

To provide individually adapted nutritional support to patients with chronic obstructive pulmonary disease (COPD), objective and reliable methods must be used to assess patient energy requirements. The aim of this study was to validate the use of SenseWear Armband (SWA) and ActiHeart (AH) monitors for assessing total daily energy expenditure (TEE) and activity energy expenditure (AEE) and compare these techniques with the doubly labeled water (DLW) method in free-living women with COPD. TEE and AEE were measured in 19 women with COPD for 14 days using SWAs with software version 5.1 (TEESWA5, AEESWA5) or 6.1 (TEESWA6, AEESWA6) and AH monitors (TEEAH, AEEAH), using DLW (TEEDLW) as the criterion method. The three methods were compared using intraclass correlation coefficient (ICC) and Bland-Altman analyses. The mean TEE did not significantly differ between the DLW and SWA5.1 methods (-21 ± 726 kJ/day; P = 0.9), but it did significantly differ between the DLW and SWA6.1 (709 ± 667 kJ/day) (P < 0.001) and the DLW and AH methods (709 ± 786 kJ/day) (P < 0.001). Strong agreement was observed between the DLW and TEESWA5 methods (ICC = 0.76; 95% CI 0.47-0.90), with moderate agreements between the DLW and TEESWA6 (ICC = 0.66; 95% CI 0.02-0.88) and the DLW and TEEAH methods (ICC = 0.61; 95% CI 0.05-0.85). Compared with the DLW method, the SWA5.1 underestimated AEE by 12% (P = 0.03), whereas the SWA6.1 and AH monitors underestimated AEE by 35% (P < 0.001). Bland-Altman plots revealed no systematic bias for TEE or AEE. The SWA5.1 can reliably assess TEE in women with COPD. However, the SWA6.1 and AH monitors underestimate TEE. The SWA and AH monitors underestimate AEE.

[Food consumption in children and youth: effect of sedentary activities]

Sedentary behavior has progressed with modern society, generating very low levels of energy expenditure and subsequent body weight disorders (obesity). There is also evidence that the absence of physical activity associated with short sleep time and watching television or playing video games leads to poor eating habits and favors high-energy intake. These findings have generally been reported in adults, with a few studies including data on children and adolescents. This brief review summarizes the current literature regarding the impact of such activities on food consumption and eating behavior in children and adolescents. There appears to be an uncoupling effect dissociating these activities from the sensation of hunger and thus energy intake. Children and adolescents seem to increase their energy intake during and after such activities without any alteration of their subjective appetite. In addition to considering the impact of sedentary behavior and physical activity level, future public health recommendations should also focus on associated nutritional adaptations (energy balance).

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.

Tri-axial high-resolution acceleration for oxygen uptake estimation: Validation of a multi-sensor device and a novel analysis method

We validated a multi-sensor chest-strap against indirect calorimetry and further introduced the total-acceleration-variability (TAV) method for analyzing high-resolution accelerometer data. Linear regression models were developed to predict oxygen uptake from the TAV-processed multi-sensor data. Individual correlations between observed and TAV-predicted oxygen uptake (V̇O2) were strong (mean r = 0.94) and bias low (1.5 mL·min(-1)·kg(-1), p < 0.01; 95% confidence interval: 8.7 mL·min(-1)·kg(-1); -5.8 mL·min(-1)·kg(-1)); however, caution should be taken when a single-model value is used as a surrogate for V̇O2.

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.

The use of Bayesian nonlinear regression techniques for the modelling of the retention behaviour of volatile components of Artemisia species

The main aim of this work was to assess the ability of Bayesian multivariate adaptive regression splines (BMARS) and Bayesian radial basis function (BRBF) techniques for modelling the gas chromatographic retention indices of volatile components of Artemisia species. A diverse set of molecular descriptors was calculated and used as descriptor pool for modelling the retention indices. The ability of BMARS and BRBF techniques was explored for the selection of the most relevant descriptors and proper basis functions for modelling. The results revealed that BRBF technique is more reproducible than BMARS for modelling the retention indices and can be used as a method for variable selection and modelling in quantitative structure-property relationship (QSPR) studies. It is also concluded that the Markov chain Monte Carlo (MCMC) search engine, implemented in BRBF algorithm, is a suitable method for selecting the most important features from a vast number of them. The values of correlation between the calculated retention indices and the experimental ones for the training and prediction sets (0.935 and 0.902, respectively) revealed the prediction power of the BRBF model in estimating the retention index of volatile components of Artemisia species.

Relation between holiday weight gain and total energy expenditure among 40- to 69-y-old men and women (OPEN study)

BACKGROUND

A significant proportion of the average annual body weight (BW) gain in US adults (~0.5-1 kg/y) may result from modest episodes of positive energy balance during the winter holiday season.

OBJECTIVE

We tested whether holiday BW gain was reduced in participants with high baseline total energy expenditure (TEE) or whether it varied by BMI (in kg/m(2)).

DESIGN

In a secondary analysis of previously published data, ΔBW normalized over 90 d from mid-September/mid-October 1999 to mid-January/early March 2000 was analyzed by sex, age, and BMI in 443 men and women (40-69 y of age). TEE was measured by doubly labeled water. High or low energy expenditure was assessed as residual TEE after linear adjustment for age, height, and BW.

RESULTS

No correlations between ΔBW and TEE or TEE residuals were found. Sixty-five percent of men and 58% of women gained ≥0.5 kg BW, with ~50% of both groups gaining ≥1% of preholiday BW. Obese men (BMI ≥30) gained more BW than did obese women.

CONCLUSIONS

A high preholiday absolute TEE or residual TEE did not protect against BW gain during the winter holiday quarter. It is not known whether higher than these typical TEE levels would protect against weight gain or if the observed gain may be attributed to increased food consumption and/or reduced physical activity during the holiday quarter.

Advances in physical activity monitoring and lifestyle interventions in obesity: a review

Obesity represents a strong risk factor for developing chronic diseases. Strategies for disease prevention often promote lifestyle changes encouraging participation in physical activity. However, determining what amount of physical activity is necessary for achieving specific health benefits has been hampered by the lack of accurate instruments for monitoring physical activity and the related physiological outcomes. This review aims at presenting recent advances in activity-monitoring technology and their application to support interventions for health promotion. Activity monitors have evolved from step counters and measuring devices of physical activity duration and intensity to more advanced systems providing quantitative and qualitative information on the individuals' activity behavior. Correspondingly, methods to predict activity-related energy expenditure using bodily acceleration and subjects characteristics have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can monitor modes of sedentary behavior as well as the engagement in specific activity types that helps to evaluate the effectiveness of lifestyle interventions. In conclusion, advances in activity monitoring have the potential to support the design of response-dependent physical activity recommendations that are needed to generate effective and personalized lifestyle interventions for health promotion.

Validation of the Actiheart activity monitor for measurement of activity energy expenditure in children and adolescents with chronic disease

BACKGROUND/OBJECTIVES

The purpose of this study was to develop an activity energy expenditure (AEE) prediction equation for the Actiheart activity monitor for use in children with chronic disease.

SUBJECTS/METHODS

In total, 63 children, aged 8-18 years with different types of chronic disease (juvenile arthritis, hemophilia, dermatomyositis, neuromuscular disease, cystic fibrosis or congenital heart disease) participated in an activity testing session, which consisted of a resting protocol, working on the computer, sweeping, hallway walking, steps and treadmill walking at three different speeds. During all activities, actual AEE was measured with indirect calorimetry and the participants wore an Actiheart on the chest. Resting EE and resting heart rate were measured during the resting protocol and heart rate above sleep (HRaS) was calculated.

RESULTS

Mixed linear modeling produced the following prediction equation: This equation results in a nonsignificant mean difference of 2.1 J/kg/min (limits of agreement: -144.2 to 148.4 J/kg/min) for the prediction of AEE from the Actiheart compared with actual AEE.

CONCLUSIONS

The Actiheart is valid for the use of AEE determination when using the new prediction equation for groups of children with chronic disease. However, the prediction error limits the use of the equation in individual subjects.

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.