OBJECTIVES:To identify the major risk factors of overweight and obesity in prepubertal children. DESIGN: Cross-sectional study. SETTING: In all, 32 primary schools in Kiel (248 000 inhabitants), northwest Germany. SUBJECTS: A total of 2631 5-7-y-old German children and their parents. MAIN OUTCOME MEASURES: Weight status, socio-economic status (SES), parental overweight, dietary intake, activity, inactivity and further determinants (birth weight, breast feeding, nutritional status of siblings) of the children. RESULTS: The prevalence of overweight (Z90th BMI percentile of reference) was 9.2% in boys and 11.2% in girls, respectively. Considered univariately, family-, environment-and development-related determinants showed some relations to overweight and obesity. In multivariate analyses parental overweight, a low SES as well as a high birth weight were the strongest independent risk factors of overweight and obesity in children. Additionally, there were sex-specific risk factors: parental smoking and single households were risk factors in boys, whereas a low activity was associated with obesity in girls. Birth weight was associated with obesity, but not with overweight. The prevalence of obesity reached 29.2% in boys and 33.4% in girls with all the three main risk factors. CONCLUSIONS: Overweight families of low SES have the highest risk of overweight and obese children. Future prevention programmes must also take into account sex-specific risk factors.

The purpose of the present study was to assess different aspects of physical activity and fitness in order to develop a basis for sport programmes for overweight and obese children. Eighty-eight prepubertal children (49 boys, 39 girls, 4.8-11.4 years old, 61% obese, 14% overweight and 25% normal weight) were examined. Body composition was assessed by combined use of anthropometrics and bioelectrical impedance analysis. Resting energy expenditure (REE) and total energy expenditure (TEE) were measured by indirect calorimetry (IC) and individually calibrated 24-h heart rate (HR) monitoring, respectively. Activity-related energy expenditure (AEE) and physical activity level (PAL) were calculated from TEE and REE. Fitness [assessed by O2-pulse, respiratory exchange ratio (RER) at submaximal work intensities] was determined by ergometry. The maximal isometric muscle strength of the legs (m. quadriceps, Fa max, m. ischiocruralis, Fb max) was measured by computer tensiometry. Children were grouped according to their nutritional state, AEE, O2-pulse and muscle strength. When compared with normal weight children, obese and overweight children had increased fat mass (FM), fat-free mass (FFM), waist-to-hip ratio and REE, but no group differences were observed for TEE, AEE, and PAL. Obese and overweight children spent more hours per day watching TV. After correction for body weight and FFM, no group differences in REE were observed, but normal weight children had a higher O2-pulse than overweight and obese children. By contrast, RER was increased in the latter group. The fittest group had the lowest body weight, BMI, FM and FFM. Children with a low O2-pulse spent more hours per day watching TV. Grouping children according to their degree of muscle strength, younger children (4-7.5 years) did not show group differences in nutritional state, energy expenditure, physical activity and fitness. However, in the group of 7.6- to 11-year-old children, those with the greatest muscle strength and FFM had reduced BMI, skin folds, FM and FFM. FM correlated inversely with O2-pulse, but was not associated with TEE, AEE, PAL or muscle strength. By contrast TV consumption was positively associated with FM. To summarize, overweight and obese children were less fit and watched more TV than their normal weight counterparts. FM did not correspond to TEE, AEE or PAL. Muscle strength was not associated with FM in young children, but was inversely associated with FM in older children. Our cross-sectional data are consistent with the idea that increased fitness and reduced physical inactivity may prevent children from being overweight.

The objective of the present study was to investigate the contribution of intra-individual variance of resting energy expenditure (REE) to interindividual variance in REE. REE was measured longitudinally in a sample of twenty-three healthy men using indirect calorimetry. Over a period of 2 months, two consecutive measurements were done in the whole group. In subgroups of seventeen and eleven subjects, three and four consecutive measurements were performed over a period of 6 months. Data analysis followed a standard protocol considering the last 15 min of each measurement period and alternatively an optimised protocol with strict inclusion criteria. Intra-individual variance in REE and body composition measurements (CV intra ) as well as interindividual variance (CV inter ) were calculated and compared with each other as well as with REE prediction from a population-specific formula. Mean CV intra for measured REE and fat-free mass (FFM) ranged from 5·0 to 5·6 % and from 1·3 to 1·6 %, respectively. CV intra did not change with the number of repeated measurements or the type of protocol (standard v. optimised protocol). CV inter for REE and REE adjusted for FFM (REE adj ) ranged from 12·1 to 16·1 % and from 10·4 to 13·6 %, respectively. We calculated total error to be 8 %. Variance in body composition (CV intra FFM) explains 19 % of the variability in REE adj , whereas the remaining 81 % is explained by the variability of the metabolic rate (CV intra REE). We conclude that CV intra of REE measurements was neither influenced by type of protocol for data analysis nor by the number of repeated measurements. About 20 % of the variance in REE adj is explained by variance in body composition.Resting energy expenditure: Intra-individual variance: Interindividual variance: Resting energy expenditure prediction Individuals vary in their resting energy expenditure (REE). The majority of interindividual variance in REE (CV inter ) is explained by fat-free mass (FFM), fat mass (FM), age and sex, leaving only 19 % unexplained (Ravussin et al. 1986). Unexplained variance is mainly due to composition of FFM, genetic factors and thyroid hormone levels (Müller et al. 2002). In comparison with CV inter , intraindividual variance in REE (CV intra ) is reported to be low (2-10 %; Soares & Shetty, 1986;Weststrate, 1993). However, CV intra could partly explain the interindividual variance in REE observed in different studies by contributing to between-group differences (i.e. between normal-weight and overweight subjects). Intra-individual variance in REE is explained by biological and methodological variability in REE. Since FFM is the major determinant of REE, the biological and methodological variance in FFM adds to the variance in REE adjusted for FFM. Intra-individual variance in REE may also contribute to inaccuracies of REE prediction by both limiting the accuracy of databases for the generation of prediction formulas as well as the implementation of such a formula on the individual level. Applying established REE prediction equa...

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ZusammenfassungDer Mensch verbraucht ständig Energie. Der tägliche oder 24−Stunden−Energieverbrauch (= 24−h−EE, 24 h energy expenditu− re) ist die Summe des Ruheenergieverbrauchs (= REE, resting energy expenditure), der für körperliche Aktivitäten aufzuwen− denden Energien (sog. arbeitsinduzierte Thermogenese, = AEE, activity energy expenditure) und der nahrungsinduzierten Ther− mogenese (DIT, diet−induced thermogenesis oder TEF, thermic ef− fect of food). Der REE erklärt 60 ± 70 % des 24−h−EE, die DIT beträgt 5 ± 10 %. Bei einem inaktiven Lebensstil erklärt die AEE 20 ± 30 % des 24−h−EE, das Verhältnis AEE/REE beträgt heute im Mittel der Bevölkerung etwa 0,5. Das Prinzip der Messungen geht auf den ersten Hauptsatz der Thermodynamik zurück, wonach in einem geschlossenen System Energie weder erzeugt noch vernichtet werden kann. Indirekte und direkte Kalorimetrie, Isotopendilu− tion, 24−Stunden−Herzfrequenzmessung und Bewegungsmesser sind für die Erfassung der verschiedenen Komponenten des Energieverbrauchs geeignet. Die intra− und interindividuellen Varianzen der verschiedenen Methoden zeigen erhebliche Un− terschiede (VKintra von 5 % für den REE bis zu 22,7 % für Bewe− gungsmessungen; VKinter von 4,5 % für Messungen des 24−h−EE in einer Respirationskammer bis zu 69,2 % für den AEE unter All− tagsbedingungen). Neben den technisch methodischen Vorraus− setzungen müssen bei der Wahl der Methoden die Zielgrößen, die Machbarkeit und die gewünschte Genauigkeit beachtet wer− den. Aufgrund der heute verfügbaren und an jeweils größeren Bevölkerungsgruppen erhobenen Messwerte wurden neue Prä− Abstract 24 hour energy expenditure (24h−EE) is the sum of resting ener− gy expenditure (REE), activity energy expenditure (AEE) and diet− or food−induced thermogenesis (DIT). REE explains 60 ± 70 % of 24h−EE, DIT is about 5 ± 10 %. AEE is highly variable. In a sendentary lifestyle AEE contributes to 20 ± 30 % of 24h−EE. Methods of assessment of energy expenditure follow the first law of thermodynamics and include direct and indirect calori− metry, isotope dilution techniques (ie doubly−labelled water or NaH 13 CO 2 −turnover), 24h−heart rate monitoring, and measure− ment of movement (eg accelerometry). Intra− and inter−individ− ual variance of the different components of energy expenditure is variable (cv intra of 5 % for REE and up to 22.7 % for movement, cv inter of 4.5 % for measurements of 24h−EE within a respiration chamber up to 69.2 for AEE under real life conditions). Suitable methods should be selected based on feasibility, the variable of interest, methodological know how and equipment as well as the precision and accuracy of the method. There are new pre− diction formulas for energy expenditure in humans.

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