Monitoring Energy Metabolism with Indirect Calorimetry: Instruments, Interpretation, and Clinical Application

Metabolic Costs of Walking with Weighted Vests

INTRODUCTION

The US Army Load Carriage Decision Aid (LCDA) metabolic model is used by militaries across the globe and is intended to predict physiological responses, specifically metabolic costs, in a wide range of dismounted warfighter operations. However, the LCDA has yet to be adapted for vest-borne load carriage, which is commonplace in tactical populations, and differs in energetic costs to backpacking and other forms of load carriage.

PURPOSE

The purpose of this study is to develop and validate a metabolic model term that accurately estimates the effect of weighted vest loads on standing and walking metabolic rate for military mission-planning and general applications.

METHODS

Twenty healthy, physically active military-age adults (4 women, 16 men; age, 26 ± 8 yr old; height, 1.74 ± 0.09 m; body mass, 81 ± 16 kg) walked for 6 to 21 min with four levels of weighted vest loading (0 to 66% body mass) at up to 11 treadmill speeds (0.45 to 1.97 m·s -1 ). Using indirect calorimetry measurements, we derived a new model term for estimating metabolic rate when carrying vest-borne loads. Model estimates were evaluated internally by k -fold cross-validation and externally against 12 reference datasets (264 total participants). We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured walking metabolic rate. Estimation accuracy, precision, and level of agreement were also evaluated by the bias, standard deviation of paired differences, and concordance correlation coefficient (CCC), respectively.

RESULTS

Metabolic rate estimates using the new weighted vest term were statistically equivalent ( P < 0.01) to measured values in the current study (bias, -0.01 ± 0.54 W·kg -1 ; CCC, 0.973) as well as from the 12 reference datasets (bias, -0.16 ± 0.59 W·kg -1 ; CCC, 0.963).

CONCLUSIONS

The updated LCDA metabolic model calculates accurate predictions of metabolic rate when carrying heavy backpack and vest-borne loads. Tactical populations and recreational athletes that train with weighted vests can confidently use the simplified LCDA metabolic calculator provided as Supplemental Digital Content to estimate metabolic rates for work/rest guidance, training periodization, and nutritional interventions.

A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.

The Caloric Necessities of Critical Care Patients During the First Week of Admission

INTRODUCTION

 The nutritional needs of critically ill patients have been the subject of intense controversy. In accordance with international guidelines, it is advocated to optimize a nutritional intake based on the following recommendation: 25-30 kcal/kg body weight per day. However, there still are authors who recommend permissive underfeeding in the first week of hospitalization. Nevertheless, energy expenditure (EE) and necessity are influenced by the catabolic phase of critical illness, which may vary over time on a patient and from patient to patient.

OBJECTIVE

The objective of this study is to assess if the energy needs of critically ill patients admitted in our intensive care unit (ICU) in the first week of hospitalization are in line with those recommended by the European Society for Clinical Nutrition and Metabolism (ESPEN) international guidelines.

METHODS

A prospective cross-sectional study was carried out from September to December 2019. The energy needs were evaluated by indirect calorimetry and by the Harris-Benedict equation. Stress variables were evaluated, namely, the type of pathology, hemodynamic support, sedation, temperature, sequential organ failure assessment (SOFA) score, and state at discharge.

RESULTS

Forty-six patients were included in this study, with an average energy expenditure by indirect calorimetry of 19.22 ± 4.67 kcal/kg/day. The energy expenditure was less than 20 kcal/kg/day in 63% of the measurements. The concordance rate did not show the relationship between the Harris-Benedict equation and the values of indirect calorimetry. Stress variables were analyzed, with the SOFA score as the only variable with values close to statistical significance.

CONCLUSION

In our ICU, the energy needs of critically ill patients in the first week of hospitalization are lower than the intake recommended by the guidelines.

Modeling the Metabolic Costs of Heavy Military Backpacking

Introduction

Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter.

Purpose

This study aimed to develop and validate an equation for estimating metabolic rates during heavy backpacking for the US Army Load Carriage Decision Aid (LCDA), an integrated software mission planning tool.

Methods

Thirty healthy, active military-age adults (3 women, 27 men; age, 25 ± 7 yr; height, 1.74 ± 0.07 m; body mass, 77 ± 15 kg) walked for 6–21 min while carrying backpacks loaded up to 66% body mass at speeds between 0.45 and 1.97 m·s⁻¹. A new predictive model, the LCDA backpacking equation, was developed on metabolic rate data calculated from indirect calorimetry. Model estimation performance was evaluated internally by k-fold cross-validation and externally against seven historical reference data sets. We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured metabolic rate. Estimation accuracy and level of agreement were also evaluated by the bias and concordance correlation coefficient (CCC), respectively.

Results

Estimates from the LCDA backpacking equation were statistically equivalent (P < 0.01) to metabolic rates measured in the current study (bias, −0.01 ± 0.62 W·kg⁻¹; CCC, 0.965) and from the seven independent data sets (bias, −0.08 ± 0.59 W·kg⁻¹; CCC, 0.926).

Conclusions

The newly derived LCDA backpacking equation provides close estimates of steady-state metabolic energy expenditure during heavy load carriage. These advances enable further optimization of thermal-work strain monitoring, sports nutrition, and hydration strategies.

Sensing leg movement enhances wearable monitoring of energy expenditure

Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.

Development of a Portable Respiratory Gas Analyzer for Measuring Indirect Resting Energy Expenditure (REE)

Objective

A rapidly growing home healthcare market has resulted in the development of many portable or wearable products. Most of these products measure, estimate, or calculate physiologic signals or parameters, such as step counts, blood pressure, or electrocardiogram. One of the most important applications in home healthcare is monitoring one's metabolic state since the change of metabolic state could reveal minor or major changes in one's health condition. A simple and noninvasive way to measure metabolism is through breath monitoring. With breath monitoring by breath gas analysis, two important indicators like the respiratory quotient (RQ) and resting energy exposure (REE) can be calculated. Therefore, we developed a portable respiratory gas analyzer for breath monitoring to monitor metabolic state, and the performance of the developed device was tested in a clinical trial. Approach. The subjects consisted of 40 healthy men and women. Subjects begin to measure exhalation gas using Vmax 29 for 15 minutes. After that, subjects begin to measure exhalation gas via the developed respiratory gas analyzer. Finally, the recorded data on the volume of oxygen (VO₂), volume of carbon dioxide (VCO₂), RQ, and REE were used to validate correlations between Vmax 29 and the developed respiratory gas analyzer.

Results

The results showed that the root-mean-square errors (RMSE) values of VCO₂, VO₂, RQ, and REE are 0.0315, 0.0417, 0.504, and 0.127. Bland-Altman plots showed that most of the VCO₂, VO₂, RQ, and REE values are within 95% of the significance level.

Conclusions

We have successfully developed and tested a portable respiratory gas analyzer for home healthcare. However, there are limitations of the clinical trial; the number of subjects is small in size, and the age and race of subjects are confined. The developed portable respiratory gas analyzer is a cost-efficient method for measuring metabolic state and a new application of home healthcare.

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.

Energy Expenditure in Mechanically Ventilated Korean Children: Single-Center Evaluation of a New Estimation Equation

OBJECTIVES

Accurate assessments of energy expenditure are vital for determining optimal nutritional support, especially in critically ill children. We evaluated current methods for energy expenditure prediction, in comparison with indirect calorimetry, and developed a new estimation equation for mechanically ventilated, critically ill Korean children.

DESIGN

Single-center retrospective study.

SETTING

Fourteen-bed pediatric medical ICU in a tertiary care children's hospital.

PATIENTS

Pediatric patients admitted to the PICU between October 2017 and September 2019 with a measured energy expenditure by indirect calorimetry.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total 95 pediatric patients (70 in derivation cohort for development of a new predictive equation and 25 in validation cohort) were included. Mean measured energy expenditure of group A was 66.20 ± 15.35 kcal/kg/d. All previously established predictive equations underestimated the predicted energy expenditure, compared with the measured energy expenditure, except the Food and Agriculture/World Health Organization/United Nations University equation. The Schofield-Height and Weight equation showed the best performance among the tested predictive equations for the entire cohort (least bias, -68.58 kcal/d; best percentage, 108.46% ± 33.60%) compared with the measured energy expenditure. It was also the best performing predictive equation in subgroup analysis by age, sex, nutritional status, and organ failure. Because some discrepancies remained between the measured energy expenditure and predicted energy expenditures, we developed a new estimation equation using multiple regression analysis and those variables significantly associated with our current measured energy expenditures: Energy expenditure = -321.264 + 72.152 × (body weight, kg)-1.396 × (body weight) + 5.668 × height (cm) + organ dysfunction* (*hematologic, 76.699; neurologic, -87.984). This new estimation equation showed the least bias and best percentage compared with previous predictive equations (least bias, 15.51 kcal/d; best percentage, 102.30% ± 28.10%).

CONCLUSIONS

There are significant disparities between measured and calculated energy expenditures. We developed a new estimation equation based on measured energy expenditure data that shows better performance in mechanically ventilated Korean children than other equations. This new estimation equation requires further prospective validation in pediatric series with a range in body habitus.

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking

BACKGROUND

Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure using correlation to data from wearable sensors, such as heart rate monitors or accelerometers, their accuracy has not been evaluated for activity conditions or subjects not included in the correlation process. The goal of our study was to develop data-driven models to estimate energy expenditure at intervals of approximately one second and demonstrate their ability to predict energetic cost for new conditions and subjects. Model inputs were muscle activity and vertical ground reaction forces, which are measurable by wearable electromyography electrodes and pressure sensing insoles.

METHODS

We developed models that estimated energy expenditure while walking (1) with ankle exoskeleton assistance and (2) while carrying various loads and walking on inclines. Estimates were made each gait cycle or four second interval. We evaluated the performance of the models for three use cases. The first estimated energy expenditure (in Watts) during walking conditions for subjects with some subject specific training data available. The second estimated all conditions in the dataset for a new subject not included in the training data. The third estimated new conditions for a new subject.

RESULTS

The mean absolute percent errors in estimated energy expenditure during assisted walking conditions were 4.4%, 8.0%, and 8.1% for the three use cases, respectively. The average errors in energy expenditure estimation during inclined and loaded walking conditions were 6.1%, 9.7%, and 11.7% for the three use cases. For models not using subject-specific data, we evaluated the ability to order the magnitude of energy expenditure across conditions. The average percentage of correctly ordered conditions was 63% for assisted walking and 87% for incline and loaded walking.

CONCLUSIONS

We have determined the accuracy of estimating energy expenditure with data-driven models that rely on ground reaction forces and muscle activity for three use cases. For experimental use cases where the accuracy of a data-driven model is sufficient and similar training data is available, standard indirect calorimetry could be replaced. The models, code, and datasets are provided for reproduction and extension of our results.

Are Predictive Energy Expenditure Equations Accurate in Cirrhosis?

Malnutrition is associated with significant morbidity and mortality in cirrhosis. An accurate nutrition prescription is an essential component of care, often estimated using time-efficient predictive equations. Our aim was to compare resting energy expenditure (REE) estimated using predictive equations (predicted REE, pREE) versus REE measured using gold-standard, indirect calorimetry (IC) (measured REE, mREE). We included full-text English language studies in adults with cirrhosis comparing pREE versus mREE. The mean differences across studies were pooled with RevMan 5.3 software. A total of 17 studies (1883 patients) were analyzed. The pooled cohort was comprised of 65% men with a mean age of 53 ± 7 years. Only 45% of predictive equations estimated energy requirements to within 90⁻110% of mREE using IC. Eighty-three percent of predictive equations underestimated and 28% overestimated energy needs by ±10%. When pooled, the mean difference between the mREE and pREE was lowest for the Harris⁻Benedict equation, with an underestimation of 54 (95% CI: 30⁻137) kcal/d. The pooled analysis was associated with significant heterogeneity (I² = 94%). In conclusion, predictive equations calculating REE have limited accuracy in patients with cirrhosis, most commonly underestimating energy requirements and are associated with wide variations in individual comparative data.

An original piecewise model for computing energy expenditure from accelerometer and heart rate signals

OBJECTIVE

Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals.

APPROACH

The proposed model, which consists of an original branched model, uses heart rate signals for computing EE on moderate to vigorous physical activities and a linear combination of heart rate and counts per minute for computing EE on light to moderate physical activities. Model parameters were estimated from a given data set composed of 53 subjects performing 25 different physical activities (light-, moderate- and vigorous-intensity), and validated using leave-one-subject-out. A different database (semi-controlled in-city circuit), was used in order to validate the versatility of the proposed model. Comparisons are done versus linear and nonlinear models, which are also used for computing EE from accelerometer and/or HR signals.

MAIN RESULTS

The proposed piecewise model leads to more accurate EE estimations ([Formula: see text], [Formula: see text] and [Formula: see text] J kg^-1 min^-1 and [Formula: see text], [Formula: see text], and [Formula: see text] J kg^-1 min^-1 on each validation database).

SIGNIFICANCE

This original approach, which is more conformable and less expensive than the reference methods, allows accurate EE estimations, in real time (minute-by-minute), during a large variety of physical activities. Therefore, this model may be used on applications such as computing the time that a given subject spent on light-intensity physical activities and on moderate to vigorous physical activities (binary classification accuracy of 0.8155).

Expression of Metabolic Syndrome in Women with Severe Obesity

BACKGROUND

The prevalence of metabolic syndrome (MetS) generally rises with increasing adiposity, but tends to plateau at the highest levels of body mass index (BMI) with some individuals, even with severe obesity, expressing few or no components of MetS. We examined factors associated with the expression of MetS in severely obese women participating in a large observational study.

METHODS

Anthropometrics, including Heath equation-adjusted bioimpedance-determined fat-free mass (FFM) and fat mass (FM), lipids and related laboratory measurements, resting energy expenditure (REE), and respiratory quotient (RQ), were studied in 949 women with severe obesity.

RESULTS

Even though the mean BMI was 45.7 kg/m² and all participants met MetS criteria for increased waist circumference, 30% of subjects did not have MetS. Unadjusted FM (P = 0.0011), FFM (P < 0.0001), and REE (P < 0.0001) were greater in the women with MetS. Surprisingly, in multivariate logistic regression FFM was positively associated with MetS (P = 0.0002), while FM was not (P = 0.89). Moreover, FFM, not FM, was significantly associated with all five components of MetS except for triglyceride levels. REE and RQ were higher in those with MetS, and REE was strongly associated with multiple components of MetS.

CONCLUSIONS

In women with severe obesity, higher FFM and REE were paradoxically associated with increased rather than decreased risk of MetS, while FFM-adjusted FM was unrelated to MetS.

Effects of Hypercaloric Feeding on Nutrition Status and Carbon Dioxide Production in Patients With Long-Term Mechanical Ventilation

BACKGROUND

To clarify clinical arguments regarding nutrition support in patients with long-term mechanical ventilation, we investigated the effects of hypercaloric feeding on nutrition status and carbon dioxide production.

METHODS

Twenty-eight mechanically ventilated, clinically stable patients with nasogastric tube feeding were recruited and randomly divided into the control and hypercaloric groups, which were provided with 1.2- and 1.8-fold of resting energy expenditure (REE), respectively. The arterial and venous blood samples were collected, the anthropometric measurements were determined, the serum concentrations of nutrition-related proteins were measured, and the parameters on the ventilator and indirect calorimeter were recorded on weeks 0, 2, and 4.

RESULTS

There were no significant changes in anthropometric measurements, blood gas tensions, and REE between the control and hypercaloric groups during the experimental period (mixed model with repeated measures analysis, p < .05). After adjusted for values on week 0 and time, patients with hypercaloric feeding had significantly increased levels in white blood cells, hemoglobin, and hematocrit. However, the control group had significantly decreased and the hypercaloric group had significantly increased serum concentrations of prealbumin and transferrin, rate of carbon dioxide production, and respiratory quotient (RQ) from week 0 to week 4.

CONCLUSION

Our results suggest that 4 weeks of hypercaloric feeding may significantly increase the production of carbon dioxide but may not significantly alter the clinical outcomes in patients with long-term mechanical ventilation. The adverse effects of hypercaloric feeding may easily be overlooked, and the appropriateness of nutrition support should be carefully monitored in patients with mechanical ventilation.

Predicting Energy Expenditure in Extremely Obese Women

BACKGROUND

The most common clinical method for resting energy expenditure (REE) assessment is prediction equations. The purpose of this study was to elucidate which prediction equation is most accurate for REE assessment in extremely obese women.

METHODS

Fourteen extremely obese women (mean +/- SD body mass index: 49.8 +/- 6.2 kg/m(2); age: 49 +/- 10 years) were measured for height and weight and REE via indirect calorimetry (IC) by a metabolic cart system. Predicted REE was evaluated by several equations, including Harris-Benedict with actual body weight, Harris-Benedict with several adjustments to body weight, Cunningham, Mifflin-St Jeor, Owen, World Health Organization (WHO), and Bernstein equations. Accuracy was determined by mean difference data (IC REE - equation REE; Student's paired t-test), correlation coefficients, and agreement between methods by Bland-Altman plots. Accuracy was also evaluated on an individual basis, defined by the percentage of individuals within +/-10% of IC REE.

RESULTS

The Mifflin-St Jeor, Harris-Benedict with actual body weight, and the WHO equations were the most accurate in terms of mean predicted REE. The mean predicted REE values by all other equations were different from the IC REE values (p < .1). According to the individual data, the Mifflin-St Jeor was most accurate (14% outside +/-10% IC REE). The Harris-Benedict with actual body weight and WHO equations were less accurate on individual terms, with 29% and 42% of the predicted REE values, respectively, falling outside +/-10% of IC REE.

CONCLUSIONS

The Mifflin-St Jeor equation was most accurate method for REE assessment in extremely obese women.

Assessing resting energy expenditure in overweight and obese adolescents in a clinical setting: validity of a handheld indirect calorimeter

BACKGROUND

Accurately determining energy requirements is key for nutritional management of pediatric obesity. Recently, a portable handheld indirect calorimeter, MedGem (MG) has become available to measure resting energy expenditure (REE). Our work aims to determine the clinical validity and usefulness of MG to measure REE in overweight and obese adolescents.

METHODS

Thirty-nine overweight and obese adolescents (16 male (M): 23 female (F), 15.2 ± 1.9 y, BMI percentile: 98.6 ± 2.2%) and 15 normal weight adolescents (7M: 8F, age 15.2 ± 2.0 y, BMI percentile: 39.2 ± 20.9%) participated. REE was measured with both MG and standard indirect calorimeter (VMax) in random order.

RESULTS

MG REE (1,600 ± 372 kcal/d) was lower than VMax REE (1,727 ± 327 kcal/) in the overweight and obese adolescents. Bland Altman analysis (MG -VMax) showed a mean bias of -127 kcal/d (95% CI = -72 to -182 kcal/d, P < 0.001), and a proportional bias existed such that lower measured REE by VMax was underestimated by MG, and higher measured REE by VMax were overestimated by MG.

CONCLUSION

MG systematically underestimates REE in the overweight and adolescent population, thus the MG portable indirect calorimeter is not recommended for routine use. Considering that it is a systematic underestimation of REE, MG may be clinically acceptable, only if used with caution.

Effect of paracentesis on metabolic activity in patients with advanced cirrhosis and ascites

OBJECTIVE

Patients with decompensated cirrhosis often suffer from malnutrition. To enable appropriate nutritional supplementation a correct estimation of resting energy expenditure (REE) is needed. It is, however, unclear whether the volume of ascites should be included or not in the calculations of the REE.

MATERIAL AND METHODS

In 19 patients with cirrhosis and ascites, measurements of REE by indirect calorimetry were performed before paracentesis, after paracentesis, and four weeks after paracentesis. Moreover, handgrip strength (HGS), dual X-ray absorptiometry (DXA), and biochemistry were assessed.

RESULTS

Calculated and measured REE differed more than 10% in 63% of the patients at baseline. By including the weight of ascites in the calculation of REE, the REE was overestimated by 283 (-602-1381) kJ/day (p = 0.69). By subtracting the weight of ascites in the calculation of REE, it was underestimated by -379 (-1915 - 219) kJ/day, (p  = 0.06). Patients in whom measured REE decreased after paracentesis had higher middle arterial pressure (MAP) (p = 0.02) and p-sodium (p = 0.02) at baseline. Low HGS (M: <30 kg; W < 20 kg) was evident in 68% of the patients. T-scores revealed osteopenia and osteoporosis in 58% and 16%, respectively. Reduced vitamin D levels (<50 nmol/l) were found in 68%.

CONCLUSIONS

The presence of ascites seems to increase REE, why we suggest that when REE is calculated, the weight of ascites should be included. Indirect calorimetry is, however, preferable for REE estimation. More than two-third of patients with ascites suffer from muscle weakness and/or osteopenia.

VO2 and VCO2 variabilities through indirect calorimetry instrumentation

The aim of this paper is to understand how to measure the VO2 and VCO2 variabilities in indirect calorimetry (IC) since we believe they can explain the high variation in the resting energy expenditure (REE) estimation. We propose that variabilities should be separately measured from the VO2 and VCO2 averages to understand technological differences among metabolic monitors when they estimate the REE. To prove this hypothesis the mixing chamber (MC) and the breath-by-breath (BbB) techniques measured the VO2 and VCO2 averages and their variabilities. Variances and power spectrum energies in the 0-0.5 Hertz band were measured to establish technique differences in steady and non-steady state. A hybrid calorimeter with both IC techniques studied a population of 15 volunteers that underwent the clino-orthostatic maneuver in order to produce the two physiological stages. The results showed that inter-individual VO2 and VCO2 variabilities measured as variances were negligible using the MC while variabilities measured as spectral energies using the BbB underwent 71 and 56% (p < 0.05), increase respectively. Additionally, the energy analysis showed an unexpected cyclic rhythm at 0.025 Hertz only during the orthostatic stage, which is new physiological information, not reported previusly. The VO2 and VCO2 inter-individual averages increased to 63 and 39% by the MC (p < 0.05) and 32 and 40% using the BbB (p < 0.1), respectively, without noticeable statistical differences among techniques. The conclusions are: (a) metabolic monitors should simultaneously include the MC and the BbB techniques to correctly interpret the steady or non-steady state variabilities effect in the REE estimation, (b) the MC is the appropriate technique to compute averages since it behaves as a low-pass filter that minimizes variances, (c) the BbB is the ideal technique to measure the variabilities since it can work as a high-pass filter to generate discrete time series able to accomplish spectral analysis, and (d) the new physiological information in the VO2 and VCO2 variabilities can help to understand why metabolic monitors with dissimilar IC techniques give different results in the REE estimation.

Energy expenditure in acute posttraumatic amputation: comparison of four methods for assessment

BACKGROUND

Adequate energy intake is a component of successful recovery after injury, yet little is known about the energy requirements in the acute period following traumatic amputation. The purpose of this study was to compare the clinical applicability of resting energy expenditure (REE) measured by a handheld calorimeter and estimated by 3 different predictive equations to that measured by the gold standard, indirect calorimetry using a metabolic cart, during the acute postamputation period of inpatient hospitalization.

MATERIALS AND METHODS

Indirect calorimetry measured using both a metabolic cart and handheld calorimeter and predicted by 3 equations were used to assess energy needs of eligible subjects admitted to Brooke Army Medical Center with traumatic amputation(s). REE measured by the handheld calorimeter and estimated using 3 predictive equations (Mifflin St. Jeor, Ireton-Jones 1992, and the American College of Chest Physicians [ACCP]) were compared to the gold standard metabolic cart. Each measure was assessed for significant differences and level of clinical acceptability defined as ± 10% REE by metabolic cart.

RESULTS

Thirteen male service members with traumatic amputation(s) were included. The majority of subject's measurements using the handheld calorimeter (n = 9, 69%) and 3 predictive equations (Mifflin St. Jeor [n = 7, 54%], Ireton-Jones 1992 [n = 8, 62%], ACCP [n = 7, 54%]) fell outside of the ± 10% range of clinical acceptability.

CONCLUSION

Use of the metabolic cart for measuring energy needs remains optimal in this population.

Total daily energy expenditure is increased following a single bout of sprint interval training

REGULAR ENDURANCE EXERCISE IS AN EFFECTIVE STRATEGY FOR HEALTHY WEIGHT MAINTENANCE, MEDIATED VIA INCREASED TOTAL DAILY ENERGY EXPENDITURE (TDEE), AND POSSIBLY AN INCREASE IN RESTING METABOLIC RATE (RMR: the single largest component of TDEE). Sprint interval training (SIT) is a low-volume alternative to endurance exercise; however, the utility of SIT for healthy weight maintenance is less clear. In this regard, it is feasible that SIT may evoke a thermogenic response above and beyond the estimates required for prevention of weight gain (i.e., >200-600 kJ). The purpose of these studies was to investigate the hypotheses that a single bout of SIT would increase RMR and/or TDEE. Study 1: RMR (ventilated hood) was determined on four separate occasions in 15 healthy men. Measurements were performed over two pairs of consecutive mornings; each pair was separated by 7 days. Immediately following either the first or third RMR measurement (randomly assigned) subjects completed a single bout of SIT (cycle ergometer exercise). RMR was unaffected by a single bout of SIT (7195 ± 285 kJ/day vs. 7147 ± 222, 7149 ± 246 and 6987 ± 245 kJ/day (mean ± SE); P = 0.12). Study 2: TDEE (whole-room calorimeter) was measured in 12 healthy men, on two consecutive days, one of which began with a single bout of SIT (random order). Sprint exercise increased TDEE in every research participant (9169 ± 243 vs. 10,111 ± 260 kJ/day; P < 0.0001); the magnitude of increase was 946 ± 62 kJ/day (∼10%). These data provide support for SIT as a strategy for increasing TDEE, and may have implications for healthy body weight maintenance.

Resting energy expenditure assessment in anorexia nervosa: comparison of indirect calorimetry, a multisensor monitor and the Müller equation

The aim of this study was to compare the estimations provided by three different means of measuring the resting energy expenditure (REE) in anorexia nervosa (AN) patients. REE was measured, after 24 h of refeeding, using a portable multisensor body monitor [SenseWear Pro2 Armband (SWA)], FitMate™ method and the Müller equation for individuals with body mass index < 18.5, the latter being based on dual-energy X-ray absorptiometry assessment of body composition. The mean differences between REE values estimated by SWA and those provided by the Müller equation and the FitMate™ method were significantly different from zero in both cases. In contrast, the mean differences between FitMate™ method and Müller equation were weakly significantly different from zero, and a significant correlation was noted between these two methods. In conclusion, the SWA does not appear to be an alternative to FitMate™ and Müller equation methods for assessing REE in AN patients.

Negative effects of obesity analyzed through bioimpedance, indirect calorimetry, the sympathovagal index and the orthoclinostatic test

Early analysis of the negative effects of obesity is important to prevent the development of chronic diseases related to this condition. There is a need to monitor these effects through simple instrumentation that measures fat-free mass (FFM) catabolism. Obesity leads to a decrease in the FFM energy expenditure and to an increase in the autonomic nervous system (ANS) activity. Thus, the measurement of FFM dynamic catabolism can provide information regarding the effects of obesity. The hypothesis is that this increased ANS activity produces an increase of energy expenditure of carbohydrates and fats when the subjects are under stress; in this case after an 8-hour fast and while they are undergoing an orthoclinostatic test. A pilot study was conducted on 29 volunteers, 16 women and 13 men. The results show significant statistical differences (p&#60;0.1) in fat and carbohydrate utilization during the orthoclinostatic tests: A move from the clinostatic to the orthostatic positions produced the following: Fat metabolism varied from 97.2 to 105.9 gr/day of fat for women and 24.9 to 35.7 gr/day of fat for men; carbohydrate metabolism changed from 38 to 39 gr/day for women and 239 to 277 gr/day for men; FFM averages were 47 Kg for women and 57.6 Kg for men; changes in the sympathovagal index (SVI) averages were 0.4 to 1.8 for women and 0.8 to 2.7 for men. The conclusions show that the methodology's sensitivity is such that gender differences can be used as a model to prove FFM metabolic differences. We believe that further studies will lead to the development of a robust methodology for the early detection of the negative effects of obesity.

Provision of protein and energy in relation to measured requirements in intensive care patients

BACKGROUND & AIMS

Adequacy of nutritional support in intensive care patients is still a matter of investigation. This study aimed to relate mortality to provision, measured requirements and balances for energy and protein in ICU patients.

DESIGN

Prospective observational cohort study of 113 ICU patients in a tertiary referral hospital.

RESULTS

Death occurred earlier in the tertile of patients with the lowest provision of protein and amino acids. The results were confirmed in Cox regression analyses which showed a significantly decreased hazard ratio of death with increased protein provision, also when adjusted for baseline prognostic variables (APACHE II, SOFA scores and age). Provision of energy, measured resting energy expenditure or energy and nitrogen balance was not related to mortality. The possible cause-effect relationship is discussed after a more detailed analysis of the initial part of the admission.

CONCLUSION

In these severely ill ICU patients, a higher provision of protein and amino acids was associated with a lower mortality. This was not the case for provision of energy or measured resting energy expenditure or energy or nitrogen balances. The hypothesis that higher provision of protein improves outcome should be tested in a randomised trial.

Resting Energy Expenditure in Anorexia Nervosa: Measured versus Estimated

Introduction. Aim of this study was to compare the resting energy expenditure (REE) measured by the Douglas bag method with the REE estimated with the FitMate method, the Harris-Benedict equation, and the Müller et al. equation for individuals with BMI < 18.5 kg/m(2) in a severe group of underweight patients with anorexia nervosa (AN). Methods. 15 subjects with AN participated in the study. The Douglas bag method and the FitMate method were used to measure REE and the dual energy X-ray absorptiometry to assess body composition after one day of refeeding. Results. FitMate method and the Müller et al. equation gave an accurate REE estimation, while the Harris-Benedict equation overestimated the REE when compared with the Douglas bag method. Conclusion. The data support the use of the FitMate method and the Müller et al. equation, but not the Harris-Benedict equation, to estimate REE in AN patients after short-term refeeding.

Active metabolic weight estimation using bioimpedance, indirect calorimetry and the clino-ortho maneuver

The resting energy expenditure (REE) and substrate utilization are computed by indirect calorimetry technique (ICT). The REE represents 80-85% of the total energy expenditure (TEE) but only accounts for the 7% of the actual body weight (ABW). The TEE is produced by the organs plus muscles, whereas the REE accounts only for the main organs. An important problem comes up when the REE is computed throughout the fat free mass (FFM) computation or anthropometric measurements because they do not explain the tremendous catabolic variability by ICT when subjects show the same body composition. Therefore, the aim of this work is to develop a method to compute the metabolic active weight (MAW) as a new form that may help to understand the catabolic activity of the body composition. The premise was the clino-ortho maneuver can split the ABW in two parts: one in which the MAW reflects the FFM catabolism while the second part was not considered since there is not energy requirement in it. The experiment design studied 37 young volunteers undergoing the clino-ortho maneuver during fast and postprandial conditions. The results showed REE increments of 21% during phase I (fast), while in phase II (postprandial) only 14% was achieved in ortho-postprandial. Therefore, the computed MAWs were 65.5Kg and 58Kg, respectively, when the ABW average was 70 Kg and the FFM was 50 Kg. One first conclusion was that the 15.5 Kg of the MAW above the FFM could explain a catabolic equivalence which can be exclusively related to the fast-ortho position which can help to classify exclusively the dynamic over activity of the FFM.

Mechanical ventilation mode (volume × pressure) does not change the variables obtained by indirect calorimetry in critically ill patients

PURPOSE

The aim of the study was to analyze the difference between the results obtained by indirect calorimetry (IC) using volume-controlled and pressure-controlled mechanical ventilation in 2 different ventilators and to characterize the variables achieved by IC after well-defined changes in minute volume (Vm).

MATERIALS AND METHODS

Prospective study of 20 critically ill patients under volume-controlled (n = 15) or pressure-controlled (n = 5) mechanical ventilation. Three IC measurements of 45 minutes each were taken; values of oxygen consumption (Vo(2)), carbon dioxide production (Vco(2)), Vm, resting energy expenditure (REE), and respiratory quotient (RQ) were obtained. For the last measurement, Vm was set at 20% above the baseline.

RESULTS

There were no differences between the results obtained by IC during volume-controlled and pressure-controlled mechanical ventilation. The most relevant changes in the variables obtained by IC before and after intervention in Vm were a significant increase in Vco(2) (from 165 to 177 mL·min(-1); P < .01), a decrease in Paco(2) (from 38.49 to 28.46 mm Hg; P < .01), and a rise in pH (from 7.41 to 7.49; P < .01). There were no alterations in Vo(2), REE, or RQ.

CONCLUSIONS

Ventilators and ventilation modes do not influence the IC measurements. The observed changes have no clinical effects and are reversible, provided that increased Vm is maintained for no longer than 45 minutes.

Study of the metabolic status by complex indirect calorimetry and bioimpedometry

Evaluation of the metabolic status by combined use of indirect calorimetry and body composition by the parameters of protein and fat utilization is proposed.

Harris-Benedict equation for critically ill patients: are there differences with indirect calorimetry?

PURPOSE

The aim of this study was to compare the measured energy expenditure (EE) and the estimated basal EE (BEE) in critically ill patients.

MATERIALS AND METHODS

Seventeen patients from an intensive care unit were randomly evaluated. Indirect calorimetry was performed to calculate patient's EE, and BEE was estimated by the Harris-Benedict formula. The metabolic state (EE/BEE x 100) was determined according to the following criteria: hypermetabolism, more than 130%; normal metabolism, between 90% and 130%; and hypometabolism, less than 90%. To determine the limits of agreement between EE and BEE, we performed a Bland-Altman analysis.

RESULTS

The average EE of patients was 6339 +/- 1119 kJ/d. Two patients were hypermetabolic (11.8%), 4 were hypometabolic (23.5%), and 11 normometabolic (64.7%). Bland-Altman analysis showed a mean of -126 +/- 2135 kJ/d for EE and BEE. Only one patient was outside the limits of agreement between the 2 methods (indirect calorimetry and Harris-Benedict).

CONCLUSIONS

The calculation of energy needs can be done with the equation of Harris-Benedict associated with lower values of correction factors (approximately 10%) to avoid overfeeding, with constant monitoring of anthropometric and biochemical parameters to assess the nutritional changing and adjust the infusion of energy.

Improved estimation of energy expenditure by artificial neural network modeling

Estimation of energy expenditure in daily living conditions can be a tool for clinical assessment of health status, as well as a self-measure of lifestyle and general activity levels. Criterion measures are either prohibitively expensive or restricted to laboratory settings. Portable devices (heart rate monitors, pedometers) have gained recent popularity, but accuracy of the prediction equations remains questionable. This study applied an artificial neural network modeling approach to the problem of estimating energy expenditure with different dynamic inputs (accelerometry, heart rate above resting (HRar), and electromyography (EMG)). Nine feed-forward back-propagation models were trained, with the goal of minimizing the mean squared error (MSE) of the training datasets. Model 1 (accelerometry only) and model 2 (HRar only) performed poorly and had significantly greater MSE than all other models (p < 0.001). Model 3 (combined accelerometry and HRar) had overall performance similar to EMG models. Validation of all models was performed by simulating untrained datasets. MSE of all models increased when tested with validation data. While models 1 and 2 again performed poorly, model 3 MSE was lower than all but 2 EMG models. Squared correlation coefficients of measured and predicted energy expenditure for models 3 to 9 ranged from 0.745 to 0.817. Analysis of mean error within specific movement categories indicates that EMG models may be better at predicting higher-intensity energy expenditure, but combined accelerometry and HRar provides an economical solution, with sufficient accuracy.

Recent advances in free-living physical activity monitoring: a review

It has become clear recently that the epidemic of type 2 diabetes sweeping the globe is associated with decreased levels of physical activity and an increase in obesity. Incorporating appropriate and sufficient physical activity into one's life is an essential component of achieving and maintaining a healthy weight and overall health, especially for those with type II diabetes mellitus. Regular physical activity can have a positive impact by lowering blood glucose, helping the body to be more efficient at using insulin. There are other substantial benefits for patients with diabetes, including prevention of cardiovascular disease, hyperlipidemia, hypertension, and obesity. Several complications of utilizing a self-care treatment methodology involving exercise include (1) patients may not know how much activity that they engage in and (2) health-care providers do not have objective measurements of how much activity their patients perform. However, several technological advances have brought a variety of activity monitoring devices to the market that can address these concerns. Ranging from simple pedometers to multisensor devices, the different technologies offer varying levels of accuracy, comfort, and reliability. The key notion is that by providing feedback to the patient, motivation can be increased and targets can be set and aimed toward. Although these devices are not specific to the treatment of diabetes, the importance of physical activity in treating the disease makes an understanding of these devices important. This article reviews these physical activity monitors and describes the advantages and disadvantages of each.

Nutritional assessment

Hospital-based malnutrition continues to be an important comorbidity affecting clinical outcomes. Knowledge of performing an appropriate nutrition assessment and implementing a rational nutrition therapy should be part of any patient's hospital plan of care. Familiarity with nutrition assessment scoring systems and nutrition assessment tools should be part of any gastroenterologist's expertise. Assessment of a patient's caloric and protein needs should be part of any hospital patient's clinical evaluation.

Accurate Determination of Energy Needs in Hospitalized Patients

OBJECTIVE

To evaluate the accuracy of seven predictive equations, including the Harris-Benedict and the Mifflin equations, against measured resting energy expenditure (REE) in hospitalized patients, including patients with obesity and critical illness.

DESIGN

A retrospective evaluation using the nutrition support service database of a patient cohort from a similar timeframe as those used to develop the Mifflin equations.

SUBJECTS/SETTING

All patients with an ordered nutrition assessment who underwent indirect calorimetry at our institution over a 1-year period were included.

INTERVENTION

Available data was applied to REE predictive equations, and results were compared to REE measurements.

MAIN OUTCOME MEASURES

Accuracy was defined as predictions within 90% to 110% of the measured REE. Differences >10% or 250 kcal from REE were considered clinically unacceptable.

STATISTICAL ANALYSES PERFORMED

Regression analysis was performed to identify variables that may predict accuracy. Limits-of-agreement analysis was carried out to describe the level of bias for each equation.

RESULTS

A total of 395 patients, mostly white (61%) and African American (36%), were included in this analysis. Mean age+/-standard deviation was 56+/-18 years (range 16 to 92 years) in this group, and mean body mass index was 24+/-5.6 (range 13 to 53). Measured REE was 1,617+/-355 kcal/day for the entire group, 1,790+/-397 kcal/day in the obese group (n=51), and 1,730+/-402 kcal/day in the critically ill group (n=141). The most accurate prediction was the Harris-Benedict equation when a factor of 1.1 was multiplied to the equation (Harris-Benedict 1.1), but only in 61% of all the patients, with significant under- and over-predictions. In the patients with obesity, the Harris-Benedict equation using actual weight was most accurate, but only in 62% of patients; and in the critically ill patients the Harris-Benedict 1.1 was most accurate, but only in 55% of patients. The bias was also lowest with Harris-Benedict 1.1 (mean error -9 kcal/day, range +403 to -421 kcal/day); but errors across all equations were clinically unacceptable.

CONCLUSIONS

No equation accurately predicted REE in most hospitalized patients. Without a reliable predictive equation, only indirect calorimetry will provide accurate assessment of energy needs. Although indirect calorimetry is considered the standard for assessing REE in hospitalized patients, several predictive equations are commonly used in practice. Their accuracy in hospitalized patients has been questioned. This study evaluated several of these equations, and found that even the most accurate equation (the Harris-Benedict 1.1) was inaccurate in 39% of patients and had an unacceptably high error. Without knowing which patient's REE is being accurately predicted, indirect calorimetry may still be necessary in difficult to manage hospitalized patients.

Indirect calorimetry: methodology, instruments and clinical application

PURPOSE OF REVIEW

This review aims to identify the basic methods for accurately measuring a patient's energy expenditure in clinical nutrition practice by indirect calorimetry, and the impact upon a disease state of applying the results obtained.

RECENT FINDINGS

The open-circuit method is the most widely used in the majority of classical instruments for measuring energy consumption. Advances in gas exchange measurement have made this technique readily and precisely available at the bedside. Nevertheless, it is important to understand its intricate primary methodology for safe and correct application. The stress and activity factors should be carefully and specifically applied, and the respiratory quotient abandoned, for tailoring a patient's daily nutrition regimens. Caloric expenditure measured by indirect calorimetry coupled with the doubly labeled water technique introduced the concept of physical activity energy expenditure, which added to resting energy expenditure results in total daily energy expenditure. Compact modular and handheld devices have been introduced into the market, together with similar technology for evaluating exercise energy expenditure, making utilization easier, safer and precise. In the critically ill population, which is exposed to medical and surgical interventions, indirect calorimetry has greatly changed the practice of caloric administration, significantly reducing the total daily amount.

SUMMARY

In conclusion, one has to be careful when choosing devices, and understanding and clinically applying the results obtained by indirect calorimetry, bearing in mind that measured resting energy expenditure should be the daily caloric goal in order to diminish clinical morbidity.

Nutrition Assessment and Management of Left Ventricular Assist Device Patients

Nutrition evaluation and support is an integral component of left ventricular assist device (LVAD) therapy. Malnutrition in the LVAD patient contributes to a host of post-operative problems, such as infection and limited functional capacity, which compromise long-term outcomes. Comprehensive pre-operative evaluation of the LVAD patient should include a nutrition assessment and formalized plan to initiate and advance nutrition support while addressing the metabolic imbalances associated with heart failure. An interdisciplinary approach, including a nutrition support team, is desirable to manage these patients effectively. This article reviews essential aspects regarding nutrition management of these patients.

Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice?

PURPOSE OF REVIEW

To recognize the fundamental factors that alter energy expenditure on a daily basis and the impact they have on the measurement of caloric consumption by the human body, through respiratory indirect calorimetry, and thus to try to determine which predictive equation best correlates with total energy expenditure generated from energy measurements.

RECENT FINDINGS

The most important compartment of the body, for its metabolic activity and influence upon resting metabolic rate, is fat-free mass. Other variables affecting energy expenditure are sex, weight, height, age, body surface area, fat mass and ethnicity. Metabolic and activity factors such as the thermic effect of nutrients, facultative thermogenesis, anabolism/growth and physical activity, also contribute, comprising total daily energy expenditure. Following the pioneering work of Harris and Benedict for the estimation of energy expenditure, several authors turned their experimental interest to this area, and various recent predictive formulae were derived. These are useful and easy to apply in daily clinical nutrition practice. However, because of the cited variables upon energy expenditure, the final daily caloric estimates show inherent errors ranging from -23.5 to +22.5% upon measured caloric expenditure. These are particularly remarkable in critically ill patients who are exposed to medical and surgical interventions.

SUMMARY

One has to be careful in choosing, understanding and clinically applying the results from predictive equations, bearing in mind that the original population from which the equation was derived does not always correspond to that currently being evaluated.