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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Background & aims: Sarcopenia is defined as the age-related loss in muscle quantity and quality which is associated with physical disability. The assessment of muscle quantity plays a role in the diagnosis of sarcopenia. However, the methods used for this assessment have many disadvantages in daily practice and research, like high costs, exposure to radiation, not being portable, or doubtful reliability. Ultrasound has been suggested for the estimation of muscle quantity by estimating muscle mass, using a prediction equation based on muscle thickness. In this systematic review, we aimed to summarize the available evidence on existing prediction equations to estimate muscle mass and to assess whether these are applicable in various adult populations. Methods: The databases PubMed, PsycINFO, and Web of Science were used to search for studies predicting total or appendicular muscle mass using ultrasound. The methodological quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies, version 2 (QUADAS-2) and the quality assessment checklist (QA) designed by Pretorius and Keating (2008). Results: Twelve studies were included in this systematic review. The participants were between 18 and 79 years old. Magnetic Resonance Imaging and dual-energy X-ray absorptiometry were used as reference methods. The studies generally had low risk of bias and there were low concerns regarding the applicability (QUADAS-2). Nine out of eleven studies reached high quality on the QA. All equations were developed in healthy adults. Conclusions: The ultrasound-derived equations in the included articles are valid and applicable in a healthy population. For a Caucasian population we recommend to use the equation of Abe et al., 2015. While for an Asian population, we recommend to use the equation of Abe et al., 2018, for the South American population, the use of the equation of Barbosa-Silva et al., 2021 is the most appropriate.
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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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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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Since 2016, it is mandatory for all future students at the department of Media, Information and Communication, to participate in a study choice test (SCT), prior their enrollment. However, the outcome is not binding and students are still entitled to enter the first year after receiving negative advice. With the help of a structural model, built for my PhD research, the predictive value of the SCT is tested by comparing the time it takes the students to finish all first year exams, their average grade point and attrition, against the results of the SCT. By using the structural model, various background variables are also measured, such as engagement, effort and commitment are also measured. By using the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA), the fit of the model is established. In addition, a comparison of the direct and indirect influence of the SCT will provide more knowledge about the correlations between the different variables, the SCT and ultimately student success.
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Abstract: INTRODUCTION: Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation.
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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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First year undergraduate students at the Amsterdam University of Applied Sciences of two different departments, were asked to join self-reported surveys in two succeeding years. Along with background variables, effort and commitment, the surveys asked elements of engagement. The later was analysed with factor analysis. The data of the surveys together with the results of the exams from the first year, were investigated to find out if the mandatory study choice test (SCT) taken before entering the faculty, had any predictive effect on their success. Not only basic statistical analysis like correlation was performed, but also more advanced analysis such as structural equation modelling (SEM) was used to uncover the value of the SCT. After a model was built, the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA), were used to determine the fit of the model. By comparing the influence of the variables on the SCT, the benefits of the latter will be determined and ultimately enhance the knowledge about influences upon student success in higher education.
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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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Objectives The aim of this study was to examine the reciprocal association between work–family conflict and depressive complaints over time. Methods Cross-lagged structural equation modeling (SEM) was used and three-wave follow-up data from the Maastricht Cohort Study with six years of follow-up [2416 men and 585 women at T1 (2008)]. Work–family conflict was operationalized by distinguishing both work–home interference and home–work interference, as assessed with two subscales of the Survey Work–Home Interference Nijmegen. Depressive complaints were assessed with a subscale of the Hospital Anxiety and Depression scale. Results The results showed a positive cross-lagged relation between home–work interference and depressive complaints. The results of the χ 2 difference test indicated that the model with cross-lagged reciprocal relationships resulted in a significantly better fit to the data compared to the causal (Δχ 2 (2)=9.89, P=0.001), reversed causation model (Δχ 2 (2)=9.25, P=0.01), and the starting model (Δχ 2 (4)=16.34, P=0.002). For work–home interference and depressive complaints, the starting model with no cross-lagged associations over time had the best fit to the empirical data. Conclusions The findings suggest a reciprocal association between home–work interference and depressive complaints since the concepts appear to affect each other mutually across time. This highlights the importance of targeting modifiable risk factors in the etiology of both home–work interference and depressive complaints when designing preventive measures since the two concepts may potentiate each other over time.
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