ObjectivesTo assess if nutritional interventions informed by indirect calorimetry (IC), compared to predictive equations, show greater improvements in achieving weight goals, muscle mass, strength, physical and functional performance.DesignQuasi-experimental study.Setting and ParticipantsGeriatric rehabilitation inpatients referred to dietitian.Intervention and MeasurementsPatients were allocated based on admission ward to either the IC or equation (EQ) group. Measured resting metabolic rate (RMR) by IC was communicated to the treating dietitian for the IC group but concealed for the EQ group. Achieving weight goals was determined by comparing individualised weight goals with weight changes from inclusion to discharge (weight gain/loss: >2% change, maintenance: ≤2%). Muscle mass, strength, physical and functional performance were assessed at admission and discharge. Food intake was assessed twice over three-days at inclusion and before discharge using plate waste observation.ResultsFifty-three patients were included (IC n=22; EQ n=31; age: 84.3±8.4 years). The measured RMR was lower than the estimated RMR within both groups [mean difference IC −282 (95%CI −490;−203), EQ −273 (−381;−42) kcal/day)] and comparable between-groups (median IC 1271 [interquartile range 1111;1446] versus EQ 1302 [1135;1397] kcal/day, p=0.800). Energy targets in the IC group were lower than the EQ group [mean difference −317 (95%CI −479;−155) kcal/day]. There were no between-group differences in energy intake, achieving weight goals, changes in muscle mass, strength, physical and functional performance.ConclusionsIn geriatric rehabilitation inpatients, nutritional interventions informed by IC compared to predictive equations showed no greater improvement in achieving weight goals, muscle mass, strength, physical and functional performance. IC facilitates more accurate determination of energy targets in this population. However, evidence for the potential benefits of its use in nutrition interventions was limited by a lack of agreement between patients’ energy intake and energy targets.
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Background & aims: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women.Methods: Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE.Results: Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18–71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris–Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias −1.7, −0.5, +1.1, +2.2, and −1.8%, RMSE 168, 170, 163, 167, and 173 kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45 kg/m2, the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group.Conclusions: In Belgian women, the original Harris–Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5–50). Estimations for the BMI range between 30 and 40 kg/m2, however, should be improved.
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Rationale: It is well established that resting energy expenditure (REE) decreases with age. Data derived from indirect calorimetry (IC) are still limited with respect to the number of high aged individuals, BMI groups and health conditions. Therefore, IC generated REE of the BASAROT sample and those calculated according to the Harris-Benedict (HB) equation were used to re-evaluate the proposed association between REE and age. Methods: The IC-BASAROT sample combines the result of IC performed in 2622 individuals from 10 centers (7 Germany, 2 Italy, 1 Netherlands) done under strictly standardized conditions (e.g. at least 8h of fasting) in free-living, mostly healthy adults aged 18 to 100 years including all BMI ranges. IC was performed by canopy technique (Cosmed Quark RMR/Sensor Medics Vmax29) in 96.5% of cases and by face mask (Cosmed Fitmate) in 3.5%. Weight was measured by calibrated scales and height was determined to the nearest of 1mm. Results: REE in the total sample (BMI: 26.9±9.1 kg/m², 43.7±17.6 y) correlated more positively with body weight than with BMI (r=0.768; p<0.001 vs. r=0.571; p<0.001). Gender+body weight explained 75% of REE variance, gender+BMI 69% and gender+age only 28%. To reduce confounding by body weight we performed age-related analysis in the subgroup of women weighing 50-79 kg (n=780, BMI: 23.4±3.4 kg/m², 41.4±18.5 y) and men weighing 60-89 kg (n=500, BMI: 24.9±3.0 kg/m², 47.5±19.3 y) and compared results with REEHB (tab. 1). IC results from 18 to 100 y showed an approximately 50% lower decrease in REE than HB in women (-129 kcal/d vs. - 257 kcal/d) and in men (-200 kcal/d vs. -406 kcal/d, tab. 1). REEIC (n=1280) did not correlate with age (r=-0.042; p=0.132). In line, we observed a significant overestimation of REE by HB up to 39 y in both sexes and an underestimation in men 60 y of age and older. Conclusion: Age-related decline in REE appears to be lower than expected and might due to changes in body composition both in the younger and older generation. No indication of the often proposed systematic overestimation of HB in women was seen. Overall, findings should be considered in future models for estimating REE.
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BACKGROUND: Ambulatory children with Spina Bifida (SB) often show a decline in physical activity leading to deconditioning and functional decline. Therefore, assessment and promotion of physical activity is important. Because energy expenditure during activities is higher in these children, the use of existing pediatric equations to predict physical activity energy expenditure (PAEE) may not be valid. AIMS: (1) To evaluate criterion validity of existing predictions converting accelerocounts into PAEE in ambulatory children with SB and (2) to establish new disease-specific equations for PAEE. METHODS: Simultaneous measurements using the Actical, the Actiheart, and indirect calorimetry took place to determine PAEE in 26 ambulatory children with SB. DATA ANALYSIS: Paired T-tests, Intra-class correlations limits of agreement (LoA), and explained variance (R2) were used to analyze validity of the prediction equations using true PAEE as criterion. New equations were derived using regression techniques. RESULTS: While T-tests showed no significant differences for some models, the predictions developed in healthy children showed moderate ICC’s and large LoA with true PAEE. The best regression models to predict PAEE were: PAEE = 174.049 + 3.861 × HRAR – 60.285 × ambulatory status (R2 = 0.720) and PAEE = 220.484 + 0.67 × Actical counts – 60.717 × ambulatory status (R2 = 0.681). CONCLUSIONS: Existing equations to predict PAEE are not valid for use in children with SB for the individual evaluation of PAEE. The best regression model was based on HRAR in combination with ambulatory status, followed by a new model for the Actical monitor. A benefit of HRAR is that it does not require the use of expensive accelerometry equipment. Further cross-validation of these models is still needed.
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Background: When the resting energy expenditure (REE) of overweight and obese adolescents cannot be measured by indirect calorimetry, it has to be predicted with an equation. Objective: The aim of this study was to examine the validity of published equations for REE compared with indirect calorimetry in overweight and obese adolescents. Design: Predictive equations based on weight, height, sex, age, fatfree mass (FFM), and fat mass were compared with measured REE. REE was measured by indirect calorimetry, and body composition was measured by dual-energy X-ray absorptiometry. The accuracy of the REE equations was evaluated on the basis of the percentage of adolescents predicted within 10% of REE measured, the mean percentage difference between predicted and measured values (bias), and the root mean squared prediction error (RMSE). Results: Forty-three predictive equations (of which 12 were based on FFM) were included. Validation was based on 70 girls and 51 boys with a mean age of 14.5 y and a mean (6SD) body mass index SD score of 2.93 6 0.45. The percentage of adolescents with accurate predictions ranged from 74% to 12% depending on the equation used. The most accurate and precise equation for these adolescents was the Molnar equation (accurate predictions: 74%; bias: –1.2%; RMSE: 174 kcal/d). The often-used Schofield-weight equation for age 10–18 y was not accurate (accurate predictions: 50%; bias: +10.7%; RMSE: 276 kcal/d). Conclusions: Indirect calorimetry remains the method of choice for REE in overweight and obese adolescents. However, the sex-specific Molnar REE prediction equation appears to be the most accurate for overweight and obese adolescents aged 12–18 y. This trial was registered at www.trialregister.nl with the Netherlands Trial Register as ISRCTN27626398.
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BACKGROUND: When indirect calorimetry is not available, predictive equations are used to estimate resing energy expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese inpatients and outpatients by comparison with indirect calorimetry.METHODS: Equations were included when based on weight, height, age, and/or gender. REE was measured with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate. The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression analysis and tested.RESULTS: 513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients. Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and 2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs 45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on weight and height performed best in all categories except from the obese subgroup. The original Harris and Benedict (HB) equation was best for obese patients.CONCLUSIONS: REE predictive equations are only accurate in about half the patients. The WHO equation is advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional support in hospital inpatients and outpatients with different degrees of malnutrition.
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Rationale: Predictive equations for resting energy expenditure (REE) are used in the treatment of overweight and obesity, but the validity of these equations in overweight older adults is unknown. This study evaluates which predictive REE equation is the best alternative to indirect calorimetry in overweight older adults with and without diabetes. Methods: In total 273 adults aged ≥55 years with a BMI of ≥25 kg/m2 were included. REE (by indirect calorimetry), body weight, body height, age, gender, and fat-free and fat mass (from air-displacement plethysmography) were measured. The measured REE was used as a reference and compared with 28 existing REE equations. The accuracy of the equations was evaluated by the percentage accurate predictions (within 10% of REE measured), the root mean squared error (RMSE), and the mean percentage difference (bias) between predicted and measured REE. Subgroup analyses were performed for type 2 diabetics (T2D) and non-T2D. Results: Mean age was 64 ± (SD 6) years, 42% had T2D (n = 116), and mean BMI was 32.8 ± (SD 4.5) with range 25–54 kg/m2. The adjusted Harris & Benedict (1984) provided the highest percentage accurate predictions in all adults (70%) and in T2D (74%), and second best in non-T2D (67%). RMSE was 184, 175 and 191 kcal/day, and bias −1.2%, −1.5% and −1.0% for all adults, T2D and non-T2D, respectively. Conclusion: For Dutch overweight older adults with and without diabetes the adjusted Harris–Benedict (1984) predictive equation for REE seems to be the best alternative to indirect calorimetry.
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Physical activity (PA) and sedentary behavior (SB) have important implications for health benefits. A growing number of people use consumer available wearables such as activity trackers, which claim to objectively monitor PA and SB in free-living conditions. These devices could provide essential information to understand the influence of behavior on health. This understanding assumes that available consumer products correctly monitor PA in the everyday life. A general approach in science is to validate such activity devices in a controlled environment. The classical procedure to investigate criterion validity is to examine new devices based on the gold standard. To our knowledge, the resulting validation data are not often analyzed and shared with manufacturers to further develop and improve the activity device. The current study can be seen as a validation study to check the criterion validity of a consumer-level activity device. The novelty of this study was the application of a stepwise approach to optimize the calculations of a consumer available activity device (i.e., Activ8; www.activ8all.com/product/activ8-professionalactivity-monitor/) for estimating energy expenditure (EE) in walking and running. Forty adults (27 males and 13 females) participated in three substudies. Each substudy consisted of several walking and running activities in which EE was simultaneously measured with indirect calorimetry (as reference value) and the Activ8 activity device. EE values at each walking and running speed were compared to identify the accuracy of the Activ8 device. After completion of the first and second substudies, the results were shared and discussed with the manufacturer of Activ8. Next, the calculations for EE were adapted to the indirect calorimetry values to improve accuracy. In the second and third substudies, the modifications were tested, and results were used to further optimize the calculation of EE. The results of this study show an improved correlation between EE measured by indirect calorimetry and the Activ8 activity device (R2 from 0.91 to 0.95); a decrease in differences between substudy A and substudy B considering EE measured (indirect calorimetry) and calculated (Activ8 calculation) was observed. The second modification in the calculation showed a further increase in correlation (R2 from 0.95 to 0.97) between the measured and calculated EE; however, the absolute difference between the two values increased. The results from a validation study are valuable to use for further adaptation of accelerometer device calculations. A stepwise science-industry collaboration can improve the calculation accuracy and may be a practical approach for validation studies in which human movement scientists and technology manufacturers work together to successfully improve the validity and accuracy of consumer-based activity devices.
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Using stable isotope techniques, this study shows that plasma free fatty acid oxidation is not impaired during exercise in non-obese type II diabetic patients.
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