Energy Expenditure Prediction Equations Research Articles

Accuracy of resting energy expenditure predictive equations in coronavirus disease 2019 (COVID-19) survivors

Accuracy of resting energy expenditure predictive equations in coronavirus disease 2019 (COVID-19) survivors

Novel resting energy expenditure prediction equation for hospitalised older adults with polymorbity in Singapore

Novel resting energy expenditure prediction equation for hospitalised older adults with polymorbity in Singapore

Assessment and Prediction of Energy Expenditure: One-Mile Walks and Runs Among African American

Among ethnicities, African Americans exhibit the highest prevalence of obesity. A more comprehensive grasp of energy expenditure while walking and running can be instrumental in managing and averting obesity. Regrettably, there is a scarcity of research dedicated to scrutinizing energy expenditure in African Americans during walking and running. Consequently, the primary objective of this study was to contrast energy expenditure disparities during a one-mile walk and run between African Americans of normal-weight and those classified as overweight. Additionally, the secondary aim was to formulate and validate a predictive equation for energy expenditure tailored specifically to African Americans. A total of 68 African American participants were involved in this study, comprising 21 individuals classified as normal-weight walkers, 27 as overweight walkers, and 20 as runners. Energy expenditure was assessed using indirect calorimetry. To compare energy expenditure across these groups, an Analysis of Variance (ANOVA) was conducted, followed by a post hoc Scheffe test. Linear regression analysis was employed for predicting energy expenditure. Additionally, a dependent t-test and a Chow test were utilized for cross-validating the predictive equation. The findings revealed that runners exhibited significantly higher energy expenditure compared to normal-weight walkers. When energy expenditure was normalized to body weight, runners expended significantly more energy than both normal-weight and overweight walkers. However, when expressed relative to fat-free mass, normal-weight walkers expended less energy than runners and overweight walkers. A prediction equation tailored specifically for African Americans was formulated as follows: EE = 1.012 BW − 9.233 Gender (M = 1, F = 2) + 47.188. The results from cross-validation tests confirmed the validity of this equation. Consequently, we recommend the use of this energy expenditure prediction equation for calculating energy expenditure during one-mile walks or runs for African American adults, whether they fall within the normal-weight or overweight categories.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BackgroundPredicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. ObjectivesTo generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. MethodsTEE derived from “gold standard” methods from n = 1657 (n = 1019 females, age range 65–90 y), was used to generate PAL values. PAL ranged 1.28–2.05 for males and 1.26–2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. ResultsEquations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = −5680.17 − 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = −5290.72 − 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. ConclusionsThese new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study.

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland–Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Validation of Resting Energy Expenditure Equations in Older Adults with Obesity

It is unclear which energy expenditure prediction equation should guide weight loss interventions in older adults with obesity. We ascertained the validity of four equations commonly used in practice in a series of weight loss studies of adults aged ≥65 with a body mass index ≥30kg/m2 using indirect calorimetry data. Diagnostic accuracy was defined as <10% discrepancy between predicted and measured resting metabolic rate (RMR). Mean was 73.4 years. RMR using the ReeVue was 1,643 kCal. With 59.0% accuracy, the WHO equation demonstrated the highest accuracy while the Harris-Benedict yielded 53.5% accuracy. The Owens equation demonstrated the least variability (21.5% overprediction, 27.8% underprediction) with 50.7% accuracy. A SECA bioimpedance analyzer noted the second lowest accuracy of 49.6%. Only 43.1% of measurements were within 10% of the gold-standard indirect calorimetry value using the Mifflin equation. All equations demonstrated <60% accuracy suggesting a great need for estimating energy needs.

Energy expenditure prediction equations in paediatric population: a systematic review

Rationale: The determination of energy requirements is of vital importance in the pediatric population, currently various equations have been designed and modified for their prediction. In this study, the equations, their components and metric characteristics that have been designed for the prediction of energy expenditure at baseline and at rest in healthy and sick pediatric patients were identified.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12-month weight-loss intervention.

Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention. Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m2 ) enrolled in a randomized clinical weight-loss trial. All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01). Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

The nutritional management of people living with amyotrophic lateral sclerosis: A national survey of dietitians.

People living with amyotrophic lateral sclerosis (ALS) face many challenges with respect to taking adequate nutrition. Growing evidence links weight loss with negative prognostic outcomes. We aimed to explore the practice of dietitians in the UK with regard to the nutritional management of ALS. A national online survey was disseminated via professional groups, social media and newsletters to UK healthcare professionals between September and November 2018. The survey examined the nutritional management of ALS. Dietitian responses are reported in the present study. In total, 130 dietitians responded to the survey. Two-thirds reported that ALS comprised less than 20% of their total patient caseload. Forty-two percent reported that nutritional screening took place in their organisation. One-half of dietitians reported that patients were referred for dietetic assessment at 'about the right time', although 44% reported referrals were made too late. The majority (83%) of dietitians used resting energy expenditure predictive equations not validated in ALS. When setting weight goals, dietitians reported most frequently recommending weight maintenance if the patient's body mass index was 18.5-25kgm-2 (72%), 25-30kgm-2 (98%), and >30kgm-2 (79%). In addition, 43% reported that people with ALS were not weighed sufficiently frequently. Although the importance of early nutritional assessment is recognised, the timeliness of dietetic input and on-going monitoring of nutritional status in ALS care might not currently be ideal. Dietitians report using energy requirement predictive equations and setting weight goals that may not promote positive outcomes. Further research is required to understand the optimal nutritional management of ALS.

The agreement between resting energy expenditure predictive equations in patients with breast cancer

The agreement between resting energy expenditure predictive equations in patients with breast cancer

Energy expenditure in a Syme’s amputee triathlete

Energy expenditure prediction equations developed for able-bodied populations may be inaccurate for para-athletes, a population characterized by different types of impairments. Sufficient data do not exist currently to generate or validate energy expenditure predictive equations for para-athletes to establish dietary recommendations to cover the demands of sport. The purpose of this observational study was to assess energy expenditure and substrate usage from a trained para-athlete at rest and while performing the athlete’s specific mode of exercise. One para-athlete recruited from the Challenged Athletes Foundation (female; age 32) participated in the study. The participant was a left leg Syme’s amputee triathlete. Resting metabolic rate was measured for the triathlete. Energy expenditure was also measured at moderate (64-76% of age-predicted HRmax) and vigorous intensity exercise (77-95% of age-predicted HRmax) on a stationary cycle ergometer on a separate day. The triathlete’s energy expenditure during exercise was higher than that predicted by the metabolic equivalent of task (MET) matched by exercise type at a moderate (measured: 7.0 kcal/min, predicted: 5.0 kcal/min) and vigorous intensity (measured: 9.3 kcal/min, predicted: 7.0 kcal/min). The Revised Harris-Benedict and the Mifflin-St Jeor equations underestimated resting metabolic rate in the triathlete (measured: 2,194 kcal/day, predicted: 1,348 ± 39 kcal/day). This study indicates that equations developed for able-bodied populations underestimate energy expenditure in a Syme’s amputee when compared to the measured energy expenditure values assessed by indirect calorimetry at rest and during a workout on both a stationary cycle ergometer and while running on the track.

Validity Of Accelerometry In Ambulatory Children And Young Adults With Cerebral Palsy

PURPOSE: This study aimed to validate five published ActiGraph (AG) cut-off points for the measurements of physical activity (PA) and sedentary time (ST) in ambulatory children and young adults with cerebral palsy (CP). Additionally, four energy expenditure (EE) prediction equations based on AG counts and activPAL (AP) steps were examined in this population, using oxygen uptake (VO2) as the criterion. METHODS: Four male and six female participants with CP (GMFCS levels I-III, ages 9-21 years) completed seven activities while simultaneously wearing an AG, AP monitor and indirect calorimetry unit. VO2 was measured on a breath-by-breath basis using the indirect calorimetry and was converted into EE using metabolic equivalents. AG counts were classified as sedentary, light PA (LPA) or moderate-to-vigorous PA (MVPA) using five cut-off points: Puyau, Evenson, Romanzini, Clanchy and Baque. The predicted EE was computed using three AG-based equations (Freedson, Trost and Treuth) and an AP step-based equation. The classification accuracies of the five AG cut-off points were assessed using Spearman correlation (r) and kappa (κ) coefficients. Agreements between measured and predicted EE values were assessed using paired-t tests, mean differences (95% confidence interval) and Bland-Altman plots. RESULTS: Of the five AG cut-off points, Baque (r = 0.896, κ = 0.773) and Clanchy (r = 0.935, κ = 0.721) classified PA and ST most accurately. All the equations overestimated EE during sitting activities and underestimated EE during rapid walking. Across all activities, the mean bias and 95% limits of agreement for the Freedson, Trost, Treuth and AP prediction equations were -0.05 METs (-2.15, 2.05), -0.28 kcal·min-1 (-2.18, 2.74), -0.54 METs (-2.37, 1.29) and 0.04 METs (-2.60, 2.68), respectively. The Freedson, Treuth and AP equations exhibited systematic bias during rapid walking, as their differences from the criterion measure increased progressively with increasing activity intensity. CONCLUSION: The AG accurately classified PA and ST when the Baque and Clanchy cut-off points were used. However, none of the available AG or AP equations accurately predicted the EE during PA and ST in children and young adults with CP. Further development is needed to ensure that both devices can estimate EE accurately in this population.

Validity of accelerometry for predicting physical activity and sedentary time in ambulatory children and young adults with cerebral palsy

Background/Objectives: This study aimed to validate five published ActiGraph (AG) cut-off points for the measurements of physical activity (PA) and sedentary time (ST) in ambulatory children and young adults with cerebral palsy (CP). Additionally, four energy expenditure (EE) prediction equations based on AG counts and activPAL (AP) steps were examined in this population, using oxygen uptake (VO2) as the criterion. MethodsFour male and six female participants with CP (Gross Motor Function Classification System levels I–III, ages 9–21 years) completed seven activities while simultaneously wearing an AG, AP monitor and indirect calorimetry unit. VO2 was measured on a breath-by-breath basis using the indirect calorimetry and was converted into EE using metabolic equivalents. AG counts were classified as sedentary, light PA (LPA) or moderate-to-vigorous PA (MVPA) using five cut-off points: Puyau, Evenson, Romanzini, Clanchy and Baque. The predicted EE was computed using three AG-based equations (Freedson, Trost and Treuth) and an AP step-based equation. ResultsBased on 1920 available data points from the 10 participants, Baque (r = 0.896, κ = 0.773) and Clanchy (r = 0.935, κ = 0.721) AG cut-off points classified PA and ST most accurately. All the equations overestimated EE during sitting activities and underestimated EE during rapid walking. The Freedson, Treuth and AP equations exhibited systematic bias during rapid walking, as their differences from the criterion measure increased progressively with increasing activity intensity. ConclusionsThe AG accurately classified PA and ST when the Baque and Clanchy cut-off points were used. However, none of the available AG or AP equations accurately predicted the EE during PA and ST in children and young adults with CP. Further development is needed to ensure that both devices can estimate EE accurately in this population.

Estimating energy expenditure from accelerometer data in healthy adults and patients with type 2 diabetes.

The aim of this study was to develop specific prediction equations based on acceleration data measured at three body sites for estimating energy expenditure (EE) during static and active conditions in middle-aged and older adults with and without type 2 diabetes (T2D). Forty patients with T2D (age: 40-74yr, body mass index (BMI): 21-29.4kg·m-2) and healthy participants (age: 47-79yr, BMI: 20.2-29.8kg·m-2) completed trials in both static conditions and treadmill walking. For all trials, gas exchange was monitored using indirect calorimetry and vector magnitude was calculated from acceleration data measured using inertial measurement units placed to the participant's center of mass (CM), hip and ankle. Stepwise multiple regression analyses were conducted to select relevant variables to include in the three EE prediction equations, and three Monte Carlo cross-validation procedures were used to evaluate each separate equation. Vector magnitude (p<0.0001) and personal data (gender, diabetes status and BMI; p<0.0001) were used to develop three linear prediction equations to estimate EE during static conditions and walking. Cross-validation revealed similar robust coefficients of determination (R2: 0.81 to 0.85) and small bias (mean bias: 0.008 to -0.005kcal·min-1) for all three equations. However, the equation based on CM acceleration exhibited the lowest root mean square error (0.60kcal·min-1 vs. 0.65 and 0.69kcal·min-1 for the hip and ankle equations, respectively; p<0.001). The three equations based on acceleration data and participant characteristics accurately estimated EE during sedentary conditions and walking in middle-aged and older adults, with or without diabetes.

A systematic review and quantitative analysis of resting energy expenditure prediction equations in healthy overweight and obese children and adolescents.

Resting energy expenditure (REE) estimates are often needed in young people and can be predicted using prediction equations based on body weight. However, these equations may perform poorly in those who are obese and overweight. The aim of this systematic review was to identify equations based on simple anthropometric and demographic variables that provide the most accurate and precise estimates of REE in healthy obese and overweight young people. Systematic searches for relevant studies in healthy obese and overweight young people aged ≤18years were undertaken using PubMed, Scopus, Cinahl, OpenGrey and Cochrane Library (completed January 2018). Search terms included metabolism, calorimetry, obesity and prediction equation. Data extraction, study appraisal and synthesis followed PRISMA guidelines. From 390 screened titles, 13 studies met inclusion criteria. The most accurate REE predictions (least biased) were provided by Schofield equations [+0.8% (3-18years); 0% (11-18years); +1.1% (3-10years)]. The most precise REE estimations (percentage of predictions±10% of measured) for 11-18years were provided by Mifflin equations (62%) and, for 7-18years, by the equations of Schmelzle (57%), Henry (56%) and Harris Benedict (54%). Precision of Schofield predictions was 43% in both age groups. No accuracy data were available for those <3years or for precision for those <7years. No single equation provided accurate and precise REE estimations in this population. Schofield equations provided the most accurate REE predictions so are useful for groups. Mifflin equations provided the most precise estimates for individuals aged 11-18years but tended to underestimate REE.

Establishment of energy expenditure prediction equation for breaststroke and front crawl swimming by tri-axial-accelerometer(actigraph GT3X＋)

Establishment of energy expenditure prediction equation for breaststroke and front crawl swimming by tri-axial-accelerometer(actigraph GT3X＋)

Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers.

BackgroundUsing wearable inertial sensors to accurately estimate energy expenditure (EE) during an athletic training process is important. Due to the characteristics of inertial sensors, however, the positions in which they are worn can produce signals of different natures. To understand and solve this issue, this study used the heart rate reserve (HRR) as a compensation factor to modify the traditional empirical equation of the accelerometer EE sensor and examine the possibility of improving the estimation of energy expenditure for sensors worn in different positions.MethodsIndirect calorimetry was used as the criterion measure (CM) to measure the EE of 90 healthy adults on a treadmill (five speeds: 4.8, 6.4, 8.0, 9.7, and 11.3 km/h). The measurement was simultaneously performed with the ActiGraph GT9X-Link (placed on the wrist and waist) with the Polar H10 Heart Rate Monitor.ResultsAt the same exercise intensity, the EE measurements of the GT9X on the wrist and waist had significant differences from those of the CM (p < 0.05). By using multiple regression analysis—utilizing values from vector magnitudes (VM), body weight (BW) and HRR parameters—accuracy of EE estimation was greatly improved compared to traditional equation. Modified models explained a greater proportion of variance (R2) (wrist: 0.802; waist: 0.805) and demonstrated a good ICC (wrist: 0.863, waist: 0.889) compared to Freedson’s VM3 Combination equation (R2: wrist: 0.384, waist: 0.783; ICC: wrist: 0.073, waist: 0.868).ConclusionsThe EE estimation equation combining the VM of accelerometer measurements, BW and HRR greatly enhanced the accuracy of EE estimation based on data from accelerometers worn in different positions, particularly from those on the wrist.

Validity Of Existing Energy Expenditure Prediction Algorithms Using Wearable Devices For Wheelchair Users

This study aims to validate the accuracy of predictive Energy Expenditure equations which are based on Actigraph activity monitors worn on the wrist of Manual Wheelchair Users with Spinal Cord Injury.

Accuracy of Resting Energy Expenditure Predictive Equations in Patients With Cancer.

Our purpose was to assess the accuracy of resting energy expenditure (REE) equations in patients with newly diagnosed stage I-IV non-small cell lung, rectal, colon, renal, or pancreatic cancer. In this cross-sectional study, REE was measured using indirect calorimetry and compared with 23 equations. Agreement between measured and predicted REE was assessed via paired t-tests, Bland-Altman analysis, and percent of estimations ≤ 10% of measured values. Accuracy was measured among subgroups of body mass index (BMI), stage (I-III vs IV), and cancer type (lung, rectal, and colon) categories. Fat mass (FM) and fat-free mass (FFM) were assessed using dual x-ray absorptiometry. Among 125 patients, most had lung, colon, or rectal cancer (92%, BMI: 27.5±5.6kg/m2 , age: 61±11years, REE: 1629±321kcal/d). Thirteen (56.5%) equations yielded REE values different than measured (P<0.05). Limits of agreement were wide for all equations, with Mifflin-St. Jeor equation having the smallest limits of agreement, -21.7% to 11.3% (-394 to 203 kcal/d). Equations with FFM were not more accurate except for one equation (Huang with body composition; bias, limits of agreement: -0.3±11.3% vs without body composition: 2.3±10.1%, P<0.001). Bias in body composition equations was consistently positively correlated with age and frequently negatively correlated with FM. Bias and limits of agreement were similar among subgroups of patients. REE cannot be accurately predicted on an individual level, and bias relates to age and FM.