Aim and method: To examine in obese people the potential effectiveness of a six-week, two times weekly aquajogging program on body composition, fitness, health-related quality of life and exercise beliefs. Fifteen otherwise healthy obese persons participated in a pilot study. Results: Total fat mass and waist circumference decreased 1.4 kg (p = .03) and 3.1 cm (p = .005) respectively. The distance in the Six-Minute Walk Test increased 41 meters (p = .001). Three scales of the Impact of Weight on Quality of Life-Lite questionnaire improved: physical function (p = .008), self-esteem (p = .004), and public distress (p = .04). Increased perceived exercise benefits (p = .02) and decreased embarrassment (p = .03) were observed. Conclusions: Aquajogging was associated with reduced body fat and waist circumference, and improved aerobic fitness and quality of life. These findings suggest the usefulness of conducting a randomized controlled trial with long-term outcome assessments.
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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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Objective: This study evaluated the effect of an after-school group-based medium-intensity multicomponent behavioural intervention programme for children aged 8–12 years classified as overweight, obese or at risk for overweight on body mass index standard deviation score (BMI SDS). In accordance with standardized protocols body weight and height were measured in 195 participants (88 boys, 107 girls) at baseline and at the end of the programme. A total of 166 children derived from a school-based monitoring system served as control group. Multivariate regression analyses examined the effect of the intervention and the independent factors associated with better outcomes in the intervention group. Results: Analysis of covariance showed a significant intervention effect on BMO SDS in favour of the intervention group (b-coefficient - 0.13 ± 0.03; p < 0.01) compared with the control group. Change in BMI SDS between baseline and follow-up in the intervention group was associated with baseline age (b-coefficient 0.03 ± 0.02; p=0.04) but was independent from gender, ethnicity, baseline BMI SDS, time between baseline and follow-up, school year and attendance rate.
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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: 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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Gebrek aan lichaamsbeweging is een onafhankelijke risicofactor voor diverse aandoeningen die met overgewicht geassocieerd zijn, zoals hart- en vaatziekten, diabetes en artrose. De ondergrens 'gezond bewegen' wordt door de minderheid van de Westerse volwassenen gehaald en bij personen met obesitas is dat aantal nog kleiner. Het belangrijkste doel van een bewegingsprogramma is niet om tot gewichtsverlies te komen, maar om ondersteuning te bieden voor een actieve leefstijl die levenslang vol te houden is. Om mensen met obesitas als bewegingsdeskundige te begeleiden effectief en duurzaam meer te bewegen, is daarom inzicht nodig in de complexe etiologie van obesitas en de manier waarop verregaande simplificatie daarvan stigmatisering in de hand werkt. Fysieke training bij patiënten met obesitas vereist kennis van trainingsprincipes en geschikte bewegingsprogramma's, maar ook van achterliggende en psychologische en sociale processen. Dat zijn bijvoorbeeld ideeën en overtuigingen (cognities) van obese patiënten die motivatie en therapietrouw kunnen beïnvloeden. In deze voordracht wordt kort ingegaan op de etiologie van obesitas en het belang van bewegen. Factoren die bevorderend of belemmerend werken bij het beginnen met een sport of bewegingsprogramma, en factoren die een rol spelen bij het volhouden daarvan, komen aan bod. Vanuit de theorie worden handreikingen gedaan voor elementen die bepalend zijn voor een duurzaam programma: bij aanvang van een trainingsprogramma is een inventarisatie van de verwachtingen en de motivatie van de patiënt belangrijk. Gedurende het programma dient de autonomie van de patiënt leidend te zijn en heeft de begeleider (bijvoorbeeld fysiotherapeut) naast een expertrol, ook een rol als coach. Door 'empowering' van de patiënt en het verbeteren van zijn of haar competentiegevoel en motivatie, ontstaat een verbetering van het zelfregulerende vermogen, met een duurzaam effect.
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RATIONALE: Currently there is no consensus on protein requirements for obese older adults during weight loss. Here we explore the potential use of a new method for assessment of protein requirements based on changes in appendicular muscle mass during weight loss.METHODS: 60 obese older adults were subjected to 13 wk weight loss program, including hypocaloric diet and resistance training. Assessment of appendicular muscle mass was performed by DXA at baseline and after 13 wk challenge period, and the difference calculated as muscle mass change. Protein intake (g/kg body weight and g/kg fat free mass (FFM)) at 13wks was used as marker of protein intake during 13 wk period. 30 subjects received 10 times weekly 20 g protein supplement throughout the 13 week hypocaloric phase which is included in the calculation of total protein intake. Receiver operating characteristic (ROC) curve analysis was used to explore the optimal cutoff point for protein intake (g/kg) versus increase in appendicular muscle mass of more than 250 g over 13 wks (y/n). Subsequently, logistic regression analysis was performed for protein intake cutoff and muscle mass accretion, adjusted for sex, age, baseline BMI, and training compliance.RESULTS: ROC curve analysis provided a protein intake level per day of 1.2 g/kg bw and 1.9 g/kg FFM as cutoff point. Presence of muscle mass accretion during 13 wk challenge period was significantly higher with protein intake higher than 1.2 g/kg bw (OR 5.4, 95%CI 1.4-20.6, p = 0.013) or higher than 1.9 g/kg FFM (OR 8.1, 95%CI 2.1-31.9, p = 0.003). Subjects with a protein intake higher than 1.2 g/kg had significantly more often muscle mass accretion, compared to subjects with less protein intake (10/14 (72%) vs 15/46 (33%), p = 0.010). For 1.9 g/kg FFM this was 70% vs 28% (p = 0.002).CONCLUSION: This exploratory study provided a level of at least 1.2 g/kg body weight or 1.9 g/kg fat free mass as optimal daily protein intake for obese older adults under these challenged conditions of weight loss, based on muscle mass accretion during the challenge.TRIAL REGISTRATION: Dutch Trial Register under number NTR2751.
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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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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: The number of obese older adults with diabetes type 2 is increasing worldwide. Weight loss treatment in this group seems beneficial for cardio-metabolic and other health outcomes, but it might reduce muscle mass and bone mineral density (BMD). The association between obesity and BMD is controversial, and the role of muscle mass and dietary protein intake is not fully clear. This study explores the association between body weight, muscle mass, dietary protein intake, and physical activity level on BMD in obese older adults with diabetes type 2. Methods: For this cross-sectional analysis we used baseline data of a 13-week randomized trial evaluating the effect of a multi-modal intervention on muscle preservation and insulin sensitivity during a weight loss program in obese older adults (55-80y) with diabetes type 2 (PROBE). Body weight was measured using a calibrated scale (Life Measurement), appendicular lean mass (ALM) was used as a proxy for muscle mass and was measured by dual-energy X-ray absorptiometry (DXA, Hologic Discovery A), dietary protein intake was estimated by a 3-day food record, Physical Activity Level (PAL) was estimated by a 3-day activity record, and hip BMD was assessed by DXA. After determination of Pearson’s correlation coefficients for body weight, ALM, protein intake, and PAL with BMD, linear regression analysis was performed with significantly correlating determinants (body weight [kg], ALM [kg], protein intake [g/kg/d], and/or PAL [-]) and hip BMD (g/cm2) as outcome variable. Results: Mean age of the 122 included subjects was 67±6y, with a BMI of 33±4kg/m2. 65% of subjects were male. Body weight and ALM correlated significantly with BMD (r=0.34, p<0.001; r=0.43, p<0.001) whereas protein intake and PAL did not (r=0.02, p=0.84; r=0.005, p=0.95). Linear regression analysis with the two determinants body weight and ALM identified ALM as being significantly associated with BMD, whereas body weight was not. Beta for ALM was +0.011 g/cm2 (95% CI: 0.004 – 0.017; p<0.01), meaning that a 1 kg increase in ALM is associated with a +0.011 g/cm2 increase in BMD. Conclusion: In this explorative cross-sectional analysis appendicular muscle mass is positively associated with BMD, rather than body weight, protein intake, and physical activity level.
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