Optimization of Protocols Using Neuromuscular Electrical Stimulation for Paralyzed Lower-Limb Muscles to Increase Energy Expenditure in People With Spinal Cord Injury.

OBJECTIVE: The aim of this study was to evaluate whether using surface neuromuscular electrical stimulation (NMES) for paralyzed lower-limb muscles results in an increase in energy expenditure and whether the number of activated muscles and duty cycle affect the potential increase. DESIGN: This was a 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% confidence interval, 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 energy expenditure compared with 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.

Predicting resting energy expenditure in people with chronic spinal cord injury.

STUDY DESIGN: Cross-sectional study. OBJECTIVES: The aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI. SETTING: University, the Netherlands. METHODS: Forty-eight community-dwelling men and women with SCI were recruited (age: 18-75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis. RESULTS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: -11%; Nightingale and Gorgey: -11%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45-0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA, ±23%). CONCLUSIONS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.

Bio-Electrical Impedance Analysis: A Valid Assessment Tool for Diagnosis of Low Appendicular Lean Mass in Older Adults?

Background: The diagnosis of sarcopenia is essential for early treatment of sarcopenia in older adults, for which assessment of appendicular lean mass (ALM) is needed. Multi-frequency bio-electrical impedance analysis (MF-BIA) may be a valid assessment tool to assess ALM in older adults, but the evidences are limited. Therefore, we validated the BIA to diagnose low ALM in older adults. Methods: ALM was assessed by a standing-posture 8 electrode MF-BIA (Tanita MC-780) in 202 community-dwelling older adults (age ≥ 55 years), and compared with dual-energy X-ray absorptiometry (DXA) (Hologic Inc., Marlborough, MA, United States; DXA). The validity for assessing the absolute values of ALM was evaluated by: (1) bias (mean difference), (2) percentage of accurate predictions (within 5% of DXA values), (3) the mean absolute error (MAE), and (4) limits of agreement (Bland-Altman analysis). The lowest quintile of ALM by DXA was used as proxy for low ALM (< 22.8 kg for men, < 16.1 kg for women). Sensitivity and specificity of diagnosing low ALM by BIA were assessed. Results: The mean age of the subjects was 72.1 ± 6.4 years, with a BMI of 25.4 ± 3.6 kg/m2, and 71% were women. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 1.2 kg. The percentage of accurate predictions was 54% with a MAE of 1.1 kg, and limits of agreement were -3.0 to + 1.8 kg. The sensitivity for ALM was 80%, indicating that 80% of subjects who were diagnosed as low ALM according to DXA were also diagnosed low ALM by BIA. The specificity was 90%, indicating that 90% of subjects who were diagnosed as normal ALM by DXA were also diagnosed as normal ALM by the BIA. Conclusion: This comparison showed a poor validity of MF-BIA to assess the absolute values of ALM, but a reasonable sensitivity and specificity to recognize the community-dwelling older adults with the lowest muscle mass.

Accuracy of bioelectrical impedance analysis and skinfold thickness in the assessment of body composition in people with chronic spinal cord injury.

STUDY DESIGN: Cross-sectional study. OBJECTIVES: This study: (1) investigated the accuracy of bioelectrical impedance analysis (BIA) and skinfold thickness relative to dual-energy X-ray absorptiometry (DXA) in the assessment of body composition in people with spinal cord injury (SCI), and whether sex and lesion characteristics affect the accuracy, (2) developed new prediction equations to estimate fat free mass (FFM) and percentage fat mass (FM%) in a general SCI population using BIA and skinfolds outcomes. SETTING: University, the Netherlands. METHODS: Fifty participants with SCI (19 females; median time since injury: 15 years) were tested by DXA, single-frequency BIA (SF-BIA), segmental multi-frequency BIA (segmental MF-BIA), and anthropometry (height, body mass, calf circumference, and skinfold thickness) during a visit. Personal and lesion characteristics were registered. RESULTS: Compared to DXA, SF-BIA showed the smallest mean difference in estimating FM%, but with large limits of agreement (mean difference = -2.2%; limits of agreement: -12.8 to 8.3%). BIA and skinfold thickness tended to show a better estimation of FM% in females, participants with tetraplegia, or with motor incomplete injury. New equations for predicting FFM and FM% were developed with good explained variances (FFM: R2 = 0.94; FM%: R2 = 0.66). CONCLUSIONS: None of the measurement techniques accurately estimated FM% because of the wide individual variation and, therefore, should be used with caution. The accuracy of the techniques differed in different subgroups. The newly developed equations for predicting FFM and FM% should be cross-validated in future studies.

The effects of a preschool-based intervention for Early Childhood Education and Care teachers in promoting healthy eating and physical activity in young children: A cluster randomised controlled trial.

The need for excess weight gain prevention in disadvantaged young children is widely recognised. Early Childhood Education and Care teachers are potential key actors in early interventions to prevent overweight and obesity. This study examines the effects of a preschool-based intervention for teachers in promoting healthy eating and physical activity in young children. A cluster randomised controlled trial was conducted at 41 preschools in a deprived area of Amsterdam, The Netherlands. The intervention consisted of 2 programmes that were applied in succession: A Healthy Start and PLAYgrounds for TODdlers. The study period was 9 months. Primary outcomes were assessed via questionnaires and included teachers' knowledge, attitude, food/activity-related practices, and level of confidence in promoting healthy behaviours. Secondary outcomes in this study were teachers' and children's BMI (z-score), body composition, dietary intake and physical activity level. Intention-to-treat analyses were performed using linear mixed models. In total, 115 teachers and 249 children (mean age 3.0 (0.2) years) were included. A positive effect on teachers' knowledge about the Dutch dietary guidelines was found after the programme A Healthy Start (difference = 1.38; 1-sided 95% CL = 0.29; p = 0.02). This effect was not sustained at 9 months (difference = 0.34; 1-sided 95% CL = -0.76; p = 0.31). The overall intervention had a positive effect on 3 of the 5 attitude statements regarding a healthy lifestyle (difference ranged from 0.34 to 0.55) and on the practice scale Activity-related-Modelling (difference = 0.16; 1-sided 95% CL = 0.06; p = 0.01). No intervention effects were observed on food-related practice scales and the level of confidence in promoting healthy behaviours. At this stage, no effects were seen on teachers' and children's BMI (z-score). This study contributes to the professional development of Early Childhood Education and Care teachers and addresses the call for interventions to prevent overweight/obesity and to minimise health inequalities in young children.

Beyond maternal education: Socio-economic inequalities in children's diet in the ABCD cohort.

OBJECTIVE: We examined whether the role of maternal education in children's unhealthy snacking diet is moderated by other socio-economic indicators. METHODS: Participants were selected from the Amsterdam Born Children and their Development cohort, a large ongoing community-based birth cohort. Validated Food Frequency Questionnaires (FFQ) (n = 2782) were filled in by mothers of children aged 5.7±0.5yrs. Based on these FFQs, a snacking dietary pattern was derived using Principal Component Analysis. Socio-economic indicators were: maternal and paternal education (low, middle, high; based on the highest education completed) household finance (low, high; based on ability to save money) and neighbourhood SES (composite score including educational level, household income and employment status of residents per postal code). Cross-sectional multivariable linear regression analysis was used to assess the association and possible moderation of maternal education and other socio-economic indicators on the snacking pattern score. Analyses were adjusted for children's age, sex and ethnicity. RESULTS: Low maternal education (B 0.95, 95% CI 0.83;1.06), low paternal education (B 0.36, 95% CI 0.20;0.52), lower household finance (B 0.18, 95% CI 0.11;0.26) and neighbourhood SES (B -0.09, 95% CI -0.11;-0.06) were independently associated with higher snacking pattern scores (p<0.001). The association between maternal education and the snacking pattern score was somewhat moderated by household finance (p = 0.089) but remained strong. Children from middle-high educated mothers (B 0.44, 95% CI 0.35;0.52) had higher snacking pattern scores when household finance was low (B 0.49, 95% CI 0.33;0.65). CONCLUSIONS: All socio-economic indicators were associated with increased risk of unhealthy dietary patterns in young children, with low maternal education conferring the highest risk. Yet, within the group of middle-high educated mothers, lower household finance was an extra risk factor for unhealthy dietary patterns. Intervention strategies should therefore focus on lower educated mothers and middle-high educated mothers with insufficient levels of household finance.

Weight development between age 5 and 10 years and its associations with dietary patterns at age 5 in the ABCD cohort.

BACKGROUND: Social inequalities in bodyweight start early in life and track into adulthood. Dietary patterns are an important determinant of weight development in children, towards both overweight and underweight. Therefore, we aimed to examine weight development between age 5 and 10 years by ethnicity, SES and thereafter by BMI category at age 5, to explore its association with dietary patterns at age 5. METHODS: Participants were 1765 children from the Amsterdam Born Children and their Development (ABCD) cohort that had valid data on BMI at age 5 and 10 and diet at age 5. Linear mixed model analysis was used to examine weight development between age 5 and 10 years and to assess if four previously identified dietary patterns at age 5 (snacking, full-fat, meat and healthy) were associated with weight development. Analyses were adjusted for relevant confounders, stratified by ethnicity and SES and thereafter stratified per BMI category at age 5. RESULTS: Overall, weight decreased in Dutch and high SES children and increased in non-Dutch and low/middle SES children. Across the range of bodyweight categories at age 5, we observed a conversion to normal weight, which was stronger in Dutch and high SES children but less pronounced in non-Dutch and low/middle SES children. Overall, the observed associations between weight development and dietary patterns were mixed with some unexpected findings: a healthy dietary pattern was positively associated with weight development in most groups, regardless of ethnicity and SES (e.g. Dutch B 0.084, 95% CI 0.038;0.130 and high SES B 0.096, 95% CI 0.047;0.143) whereas the full-fat pattern was negatively associated with weight development (e.g. Dutch B -0.069, 95% CI -0.114;-0.024 and high SES B -0.072, 95% CI -0.119;-0.026). CONCLUSIONS: We observed differential weight development per ethnic and SES group. Our results indicate that each ethnic and SES group follows its own path of weight development. Associations between dietary patterns and weight development showed some unexpected findings; follow-up research is needed to understand the association between dietary patterns and weight development.

Determinants of dietary behaviour in wheelchair users with spinal cord injury or lower limb amputation: Perspectives of rehabilitation professionals and wheelchair users.

OBJECTIVE: This study aims to identify determinants of dietary behaviour in wheelchair users with spinal cord injury or lower limb amputation, from the perspectives of both wheelchair users and rehabilitation professionals. The findings should contribute to the field of health promotion programs for wheelchair users. METHODS: Five focus groups were held with wheelchair users (n = 25), and two with rehabilitation professionals (n = 11). A thematic approach was used for data analysis in which the determinants were categorized using an integrated International Classification of Functioning, Disability and Health and Attitude, Social influence and self-Efficacy model. RESULTS: Reported personal factors influencing dietary behaviour in wheelchair users were knowledge, boredom, fatigue, stage of life, habits, appetite, self-control, multiple lifestyle problems, intrinsic motivation, goal setting, monitoring, risk perception, positive experiences, suffering, action planning, health condition, function impairments, attitude and self-efficacy. Reported environmental factors influencing dietary behaviour in wheelchair users were unadjusted kitchens, monitoring difficulties, eating out, costs, unfavourable food supply, nutrition education/counselling, access to simple healthy recipes, eating together, cooking for others, and awareness and support of family and friends. CONCLUSIONS: Important modifiable determinants of dietary behaviour in wheelchair users that might be influenced in lifestyle interventions are knowledge, fatigue, habits, self-control, intrinsic motivation, risk perception, attitude and self-efficacy. It is recommended to involve relatives, since they appear to significantly influence dietary behaviour.

A preschool-based intervention for Early Childhood Education and Care (ECEC) teachers in promoting healthy eating and physical activity in toddlers: study protocol of the cluster randomized controlled trial PreSchool@HealthyWeight.

BACKGROUND: Interventions to prevent overweight and obesity in toddlers are needed to minimize health inequalities, especially in migration and lower socio-economic groups. Preschools are identified as important environments for interventions to prevent overweight and obesity. Early Childhood Education and Care (ECEC) teachers in preschools are potential key actors in promoting healthy eating and physical activity. This paper describes the research design of a Dutch preschool-based intervention for ECEC teachers in promoting healthy eating and physical activity in toddlers. METHODS: PreSchool@HealthyWeight concerns a cluster randomized controlled trial on preschools in Amsterdam Nieuw-West, Netherlands. This city district is characterised by inhabitants with a migration background and low socio-economic status. Forty-one preschools, with 115 ECEC teachers and 249 toddlers/parents, were randomly allocated to an intervention or control group. An intervention for teachers will be carried out on intervention locations and consists of modified versions of 2 existing programs: 'A Healthy Start' and 'PLAYgrounds'. In 'A Healthy Start', ECEC teachers learn to provide a healthy and active environment for toddlers. The 'PLAYgrounds for Toddlers' program, coaches ECEC teachers to stimulate physical activity in the playgrounds of preschools. PreSchool@HealthyWeight aims to evaluate the effectiveness of the intervention after 9 months. Primary outcomes are the teachers' knowledge, attitude and practices concerning healthy eating and physical activity, and consequently the level of confidence of ECEC teachers in promoting healthy eating and physical activity in toddlers. Secondary outcomes include the Body Mass Index, body composition, dietary intake and physical activity level of teachers and toddlers. In addition, the activating role of ECEC teachers and the physical activity of toddlers on the playgrounds will be evaluated. Lastly, the knowledge, attitude and practices of parents concerning healthy eating and physical activity will be assessed. DISCUSSION: It is hypothesized that this preschool-based intervention for ECEC teachers improves the knowledge, attitude and practices regarding healthy eating and physical activity, and consequently the level of confidence of ECEC teachers in promoting healthy eating and physical activity of toddlers. The intervention addresses the call for early intervention to prevent overweight and obesity and to minimize health inequalities. TRIAL REGISTRATION: Netherlands Trial Register (NTR): NL5850 . Date registered: August 26, 2016.

Ethnicity and socioeconomic status are related to dietary patterns at age 5 in the Amsterdam born children and their development (ABCD) cohort.

BACKGROUND: Health inequalities are already present at young age and tend to vary with ethnicity and socioeconomic status (SES). Diet is a major determinant of overweight, and studying dietary patterns as a whole in relation to overweight rather than single nutrients or foods has been suggested. We derived dietary patterns at age 5 and determined whether ethnicity and SES were both related to these dietary patterns. METHODS: We analysed 2769 validated Food Frequency Questionnaires filled in by mothers of children (5.7 ± 0.5y) in the Amsterdam Born Children and their Development (ABCD) cohort. Food items were reduced to 41 food groups. Energy adjusted intake per food group (g/d) was used to derive dietary patterns using Principal Component Analysis and children were given a pattern score for each dietary pattern. We defined 5 ethnic groups (Dutch, Surinamese, Turkish, Moroccan, other ethnicities) and 3 SES groups (low, middle, high, based on maternal education). Multivariate ANOVA, with adjustment for age, gender and maternal age, was used to test potential associations between ethnicity or SES and dietary pattern scores. Post-hoc analyses with Bonferroni adjustment were used to examine differences between groups. RESULTS: Principal Component Analysis identified 4 dietary patterns: a snacking, full-fat, meat and healthy dietary pattern, explaining 21% of the variation in dietary intake. Ethnicity was related to the dietary pattern scores (p < 0.01): non-Dutch children scored high on snacking and healthy pattern, whereas Turkish children scored high on full-fat and Surinamese children on the meat pattern. SES was related to the snacking, full-fat and meat patterns (p < 0.01): low SES children scored high on the snacking and meat pattern and low on the full-fat pattern. CONCLUSIONS: This study indicates that both ethnicity and SES are relevant for dietary patterns at age 5 and may enable more specific nutrition education to specific ethnic and low socioeconomic status target groups.

Reduction in energy expenditure during weight loss is higher than predicted based on fat free mass and fat mass in older adults.

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 (ß = 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.

Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients.

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.

Ventilator-derived carbon dioxide production to assess energy expenditure in critically ill patients: proof of concept.

INTRODUCTION: Measurement of energy expenditure (EE) is recommended to guide nutrition in critically ill patients. Availability of a gold standard indirect calorimetry is limited, and continuous measurement is unfeasible. Equations used to predict EE are inaccurate. The purpose of this study was to provide proof of concept that EE can be accurately assessed on the basis of ventilator-derived carbon dioxide production (VCO2) and to determine whether this method is more accurate than frequently used predictive equations. METHODS: In 84 mechanically ventilated critically ill patients, we performed 24-h indirect calorimetry to obtain a gold standard EE. Simultaneously, we collected 24-h ventilator-derived VCO2, extracted the respiratory quotient of the administered nutrition, and calculated EE with a rewritten Weir formula. Bias, precision, and accuracy and inaccuracy rates were determined and compared with four predictive equations: the Harris-Benedict, Faisy, and Penn State University equations and the European Society for Clinical Nutrition and Metabolism (ESPEN) guideline equation of 25 kcal/kg/day. RESULTS: Mean 24-h indirect calorimetry EE was 1823 ± 408 kcal. EE from ventilator-derived VCO2 was accurate (bias +141 ± 153 kcal/24 h; 7.7 % of gold standard) and more precise than the predictive equations (limits of agreement -166 to +447 kcal/24 h). The 10 % and 15 % accuracy rates were 61 % and 76 %, respectively, which were significantly higher than those of the Harris-Benedict, Faisy, and ESPEN guideline equations. Large errors of more than 30 % inaccuracy did not occur with EE derived from ventilator-derived VCO2. This 30 % inaccuracy rate was significantly lower than that of the predictive equations. CONCLUSIONS: In critically ill mechanically ventilated patients, assessment of EE based on ventilator-derived VCO2 is accurate and more precise than frequently used predictive equations. It allows for continuous monitoring and is the best alternative to indirect calorimetry.

Resting energy expenditure prediction in recreational athletes of 18-35 years: confirmation of Cunningham equation and an improved weight-based alternative.

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. METHODS: 90 (53 M, 37 F) adult athletes, exercising on average 9.1 ± 5.0 hours a week and 5.0 ± 1.8 times a week, were included. REE was measured using indirect calorimetry (Vmax Encore n29), FFM and FM were measured using air displacement plethysmography. Multiple linear regression analysis was used to develop a new FFM-based and weight-based REE predictive equation. The percentage accurate predictions (within 10% of measured REE), percentage bias, root mean square error and limits of agreement were calculated. Results: The Cunningham equation and the new weight-based equation REE(kJ / d) = 49.940* weight(kg) + 2459.053* height(m) - 34.014* age(y) + 799.257* sex(M = 1,F = 0) + 122.502 and the new FFM-based equation REE(kJ / d) = 95.272*FFM(kg) + 2026.161 performed equally well. De Lorenzo's equation predicted REE less accurate, but better than the other generally used REE predictive equations. Harris-Benedict, WHO, Schofield, Mifflin and Owen all showed less than 50% accuracy. CONCLUSION: For a population of (Dutch) recreational athletes, the REE can accurately be predicted with the existing Cunningham equation. Since body composition measurement is not always possible, and other generally used equations fail, the new weight-based equation is advised for use in sports nutrition.

Effect of the Go4it multidisciplinary group treatment for obese adolescents on health related quality of life: a randomised controlled trial.

BACKGROUND: We developed a Dutch outpatient multidisciplinary group treatment (Go4it) for obese adolescents, including cognitive behavioural therapy and education on healthy dietary and physical activity behaviour. This study examined the effect of Go4it on Health Related Quality of Life (HRQoL). METHODS: At our outpatient paediatric obesity clinic, obese adolescents (n = 122, 11-18 years) were randomly assigned to 1) Go4it, 7 sessions with an interval of 2 weeks or 2) current regular care consisting of referral to a dietician in the home care setting (controls). Linear mixed model analysis was performed to evaluate the intervention effects on HRQoL at 6 and 18-month follow-ups. HRQoL indicators included the Child Health Questionnaire, the Paediatric Quality of Life Inventory™ Version 4.0 (PedsQL™4.0), and the Body Esteem Scale (BES). RESULTS: In total, 95 adolescents (Go4it 57, controls 38) were included in the current analysis with a mean age of 14.5 ± 1.7 and mean BMI-SDS of 2.9 ± 0.5. At baseline, all participants experienced lower levels of physical and psychosocial well-being compared to a normal weight reference group. At the 18 month follow-up, we found small but beneficial intervention effects on all subscales of the PedsQL™4.0 and BES questionnaires. Two subscales improved significantly; i.e., physical health (between group difference 5.4; 95%CI: 0.3; 10.6), and school functioning (between group difference 7.4; 95%CI: 1.6; 13.2). CONCLUSION: Obese adolescents experienced lower HRQoL than their healthy peers. The Go4it intervention had small beneficial effects on HRQoL compared to the current regular care practices for obese adolescents. TRIAL REGISTRATION: Netherlands Trial Register: ISRCTN27626398, METC number: 05.134 (WMO, monocenter).

Resting energy expenditure in malnourished older patients at hospital admission and three months after discharge: predictive equations versus measurements.

BACKGROUND: Predicting resting energy expenditure (REE) in malnourished hospitalized older patients is important for establishing optimal goals for nutritional intake. Measuring REE by indirect calorimetry is hardly feasible in most clinical settings. OBJECTIVE: To study the most accurate and precise REE predictive equation for malnourished older patients at hospital admission and again three months after discharge. DESIGN: Twenty-three equations based on weight, height, gender, age, fat free mass (FFM) and/or fat mass (FM) and eleven fixed factors of kcal/kg were compared to measured REE. REE was measured by indirect calorimetry. Accuracy of REE equations was evaluated by the percentage patients 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: REE was measured in 194 patients at hospital admission (mean 1473 kcal/d) and again three months after hospital discharge in 107 patients (mean 1448 kcal/d). The best equations predicted 40% accuracy at hospital admission (Lazzer, FAO/WHO-wh and Owen) and 63% three months after discharge (FAO/WHO-wh). Equations combined with FFM, height or illness factor predicted slightly better. Fixed factors produce large RMSE's. All predictive equations showed proportional bias, with overestimation of low REE values and underestimation of high REE values. Correction by regression analysis did not improve results. CONCLUSIONS: The REE predictive equations are not adequate to predict REE in malnourished hospitalized older patients. There is an urgent need for either a new accurate REE predictive equation, or accurate easy-to-use equipment to measure REE in clinical practice.

Resting energy expenditure in head and neck cancer patients before and during radiotherapy.

BACKGROUND & AIMS: Weight loss is a frequently observed problem in patients with head and neck cancer (HNC) during radiotherapy. It is still to be assessed whether hypermetabolism is contributing to this problem. The aim of this study was to investigate hypermetabolism before radiotherapy, and changes in resting energy expenditure (REE) in HNC patients during radiotherapy. METHODS: REE was measured by indirect calorimetry in 71 patients with HNC before radiotherapy, after 3 and 6 weeks of radiotherapy, and 3 months after radiotherapy. The association between REE and tumour stage, CRP, and prior tumour surgery was analyzed by linear regression analyses. Forty healthy control subjects were one-to-one matched to 40 patients by gender, age and fat free mass (FFM) index to compare REE. RESULTS: Before radiotherapy, REE was not significantly different between patients and controls, neither in absolute values (1568 ± 247 vs. 1619 ± 244 kcal/d; p = 0.29), nor after weight-adjustment (22.1 ± 3.5 vs. 21.5 ± 3.3 kcal/kg, p = 0.42) or FFM-adjustment (31.5 ± 4.9 vs. 30.7 ± 4.5 kcal/kg, p = 0.38). REE was independent of tumour stage, CRP, and prior tumour surgery. REE (kcal/d) decreased during radiotherapy and thereafter by 9% from pre-radiotherapy (p < 0.01). Weight and FFM also decreased significantly over time (p < 0.001). REE adjusted for FFM decreased in the first 3 weeks of radiotherapy with 4% (B = -1.39 kcal/kg FFM, p < 0.01), increased at the end of radiotherapy and decreased again 3 months after radiotherapy (B = -1.31 kcal/kg FFM, p = 0.04). CONCLUSIONS: Head and neck cancer patients had normal REE before radiotherapy. During radiotherapy, REE decreased continuously with ongoing weight loss. However, weight loss is not the only explaining factor, since REE expressed per kg FFM showed a much more divergent course which is currently unexplained.

Validation of predictive equations for resting energy expenditure in obese adolescents.

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, fat-free 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 (+/-SD) body mass index SD score of 2.93 +/- 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.

Validity of predictive equations for resting energy expenditure in Belgian normal weight to morbid obese women.

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 173kcal/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 45kg/m(2), 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 40kg/m(2), however, should be improved.

Prevalence of undernutrition in Dutch hospital outpatients.

BACKGROUND: The prevalence of undernutrition in hospital inpatients is high. Earlier detection and treatment in the hospital outpatient clinic may help to reduce these numbers. The purpose of this study was to assess the prevalence of undernutrition in hospital outpatients in the Netherlands, to determine high risk departments, and to determine the percentage of patients receiving dietetic treatment. METHODS: This cross-sectional multicenter study was conducted in nine hospitals. Patients who visited the outpatient clinic on one of the screening days in the period March-May 2008 received a short questionnaire and were weighed. Patients were classified as severely undernourished, moderately undernourished or not undernourished. RESULTS: 2288 patients were included in the study, of which 5% were severely undernourished and 2% were moderately undernourished. The prevalence of severe undernutrition was highest in the outpatient departments of oral maxillofacial surgery (17%), oncology (10%), rehabilitation (8%), gastroenterology (7%) and pulmonology (7%). Only 17% of all severely undernourished and 4% of all moderately undernourished patients reported to receive dietetic treatment. CONCLUSION: The prevalence of undernutrition in hospital outpatients is generally low but largely undertreated. Future screening should focus on high risk departments.