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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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 & 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 & aim The aim of this study was to describe a decrease in resting energy expenditure during weight loss that is larger than expected based on changes in body composition, called adaptive thermogenesis (AT), in overweight and obese older adults. Methods Multiple studies were combined to assess AT in younger and older subjects. Body composition and resting energy expenditure (REE) were measured before and after weight loss. Baseline values were used to predict fat free mass and fat mass adjusted REE after weight loss. AT was defined as the difference between predicted and measured REE after weight loss. The median age of 55 y was used as a cutoff to compare older with younger subjects. The relation between AT and age was investigated using linear regression analysis. Results In this study 254 (M = 88, F = 166) overweight and obese subjects were included (BMI: 31.7 ± 4.4 kg/m2, age: 51 ± 14 y). The AT was only significant for older subjects (64 ± 185 kcal/d, 95% CI [32, 96]), but not for younger subjects (19 ± 152 kcal/d, 95% CI [−9, 46]). The size of the AT was significantly higher for older compared to younger adults (β = 47, p = 0.048), independent of gender and type and duration of the weight loss program. Conclusions We conclude that adaptive thermogenesis is present only in older subjects, which might have implications for weight management in older adults. A reduced energy intake is advised to counteract the adaptive thermogenesis.
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Rationale: Obesity is a risk factor for type 2 diabetes (DM2), however not all obese people develop DM2. We explored differences in energy intake and expenditure between obese older adults with and without DM2. Methods: Baseline data from 2 lifestyle interventions with a total of 202 obese older adults were included in the analyses. Obesity was defined as BMI > 30.0, or >27.0 with waist circumference >88 (women) or >102 cm (men). DM2 was confirmed by use of diabetes medication. Subjects were between 55 and 85 years old and 45% was female. Energy intake (EI) was measured by 3-day food diary and physical activity level (PAL) by 3-day movement diary. Resting energy expenditure (REE) was measured using indirect calorimetry and total energy expenditure (TEE) was calculated as REE x PAL. Between group differences were analysed with independent samples T-tests. Results: The obese group with DM2 (n = 117) had more males (67.5% vs 37.6% p < 0.001) and similar BMI (33.3 vs 33.0 kg/m2) compared to the group without DM2 (n = 85). Analyses of males and females separately showed lower PAL in males with DM2 (vs without DM2; 1.37 vs 1.45, p = 0.015), without differences in EI (2055 vs 1953 kcal/d), REE (1970 vs 1929 kcal/d), and TEE (2699 vs 2830 kcal/d). In females with DM2, both PAL (1.38 vs 1.47, p = 0.014) and EI (1543 vs 1839 kcal/d, p = 0.008) were significantly lower, whereas REE (1592 vs 1598 kcal/d) and TEE (2220 vs 2318 kcal/d) did not differ significantly from obese females without DM2. Conclusion: In both males and females, obese older adults with type 2 diabetes showed similar resting and total energy expenditure but lower physical activity level compared to those without DM2. Females with DM2 showed lower energy intake. On average, subjects seem to have a negative energy balance, which is probably due to a combination of underreporting of intake and over-reporting of activity.
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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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OBJECTIVE: To evaluate if using surface neuromuscular electrical stimulation (NMES) for paralyzed lower-limb muscles results in an increase in energy expenditure and if the number of activated muscles and duty cycle affect the potential increase.DESIGN: Cross-sectional study.RESULTS: Energy expenditure during all NMES protocols was significantly higher than the condition without NMES (1.2 ± 0.2 kcal/min), with the highest increase (+ 51%; +0.7 kcal/min, 95% CI: 0.3 - 1.2) for the protocol with more muscles activated and the duty cycle with a shorter rest period. A significant decrease in muscle contraction size during NMES was found with a longer stimulation time, more muscles activated or the duty cycle with a shorter rest period.CONCLUSION: Using NMES for paralyzed lower-limb muscles can significantly increase the energy expenditure compared to sitting without NMES with the highest increase for the protocol with more muscles activated and the duty cycle with a shorter rest period. Muscle fatigue occurred significantly with the more intense NMES protocols which might cause a lower energy expenditure in a longer protocol. Future studies should further optimize the NMES parameters and investigate the long-term effects of NMES on weight management in people with SCI.
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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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Introduction: It has been suggested that physical education (PE) can make a meaningful contribution to children's physical activity (PA) levels. The amount of moderate-to-vigorous physical activity (MVPA) in PE has been quantified in various manners, including heart rate monitoring and direct observation (Fairclough & Stratton, 2005). However, data on the contribution of PE to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). However, the fairly recent emergence of combined sensing methodology allows for low-invasive measurement of PAEE in free-living conditions. In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. We assessed the contribution of PE and other school-related activity to PAEE and MVPA. Methods: Nineteen secondary school students (16 ± 0,7 yrs, BMI 22 ± 4) were included after they and their parents had consented. All had 100 minutes of scheduled PE per week. Actiheart monitors (CamNtech, Cambridge, UK) were used to determine PAEE on four weekdays and two weekend days consecutively. Actiheart monitors combine a heart rate monitor and an uniaxial accelerometer in a single 10 gram unit, that is applied to the chest with electrodes. Using a step test, an individual heart rate-energy expenditure relationship was determinded in each subject. Through a validated branched equation model (Brage, S. et al., 2007), energy expenditure was calculated. In addition, subjects kept an activity diary for the same six-day period. They recorded predefined activities including PE and active transport. These activities were then retraced to the Actiheart data by visual inspection. Results: Table 1 shows the (contribution of) PE, and school-related active transport to PAEE, while table 2 shows similar data for MVPA. Data are mean (± SD). Table 1: PAEE for PE, and active transport (AT). Table 2: MVPA for PE and active transport (AT). PAEE (KJ) % of total % of school PE 805(474) 5(4) 16(7) AT 1698(1033) 11(6) 31(11) MVPA (min) % of total % of school PE 36(19) 9(8) 22(11) AT 90(56) 20(11) 48(14) Over all six days, the physical activity level (PAL, which is total EE/Resting EE) was 1,54 ± 0,12; total MVPA was 472 min ± 179, and total PAEE 16262 KJ ± 5267. PAEE at school (4 days, including AT) was 5311 ± 3065 KJ, amounting to 34 % of total PAEE during the six measurement days. Students accumulated 179 ± 77 minutes of MVPA at school, which was 38% of total MVPA. Discussion: To our knowledge, this is the first study to present data on PE's contribution to total physical activity energy expenditure. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE. This was substantially less than the amount of energy expended for active transport to and from school. However, it should be noted that in the Netherlands, the vast majority of secondary school students cycle to school. And while PE was scheduled on one day per week in all of the measured students, active transport takes place on all school days. The total amount of MVPA accumulated at school was 179 minutes. With adolescent physical activity guidelines generally recommending 60 min of MVPA per day, i.e. 420 minutes per week, this means that school-related PA covered ~43% of this. PE provided 36 minutes to this total, all on one day. It could be argued that daily PE could potentially provide a substantial amount of MVPA. But with current time allocated to PE in the curriculum, its contribution to physical activity guidelines and PAEE is quite modest. The preliminary data presented here reflect a small subsample of a larger study that is still in progress. Therefore, care should be taken not to interpret these outcomes as representative for the whole of the Netherlands. However, they do provide a first indication for the order of magnitude of the contribution of PE and school-related activity to total PAEE. References: Fairclough, S. J. & Stratton, G. (2005) Physical Activity Levels in Middle and High School Physical Education: A Review. Pediatric Exercise Science, 17, 217. Welk, G. J. (2002) Physical activity assessments for health-related research, Champaign, Ill.; United States, Human Kinetics. Brage, S., Ekelund, U., Brage, N. Hennings, M.A., Froberg, K., Franks, P.W., Wareham. N.J. (2007). Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. J Appl Physiol, 103, (682-692)
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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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