According to the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) conducted in 2006, 15·0 % of all children and adolescents (aged 3–17 years) in Germany are overweight, including 6·3 % obese(Reference Kurth and Schaffrath-Rossario1). Compared with the German reference data(Reference Kromeyer-Hauschild, Wabitsch and Kunze2) based on studies from 1985 to 1999, the prevalence of overweight rose by about 50 % and that of obesity doubled(Reference Kurth and Schaffrath-Rossario1). In recent years, the prevalence of overweight and obesity among children and adolescents has risen dramatically worldwide(Reference Lobstein and Frelut3, Reference Ebbeling, Pawlak and Ludwig4). This increase has led to many studies postulating potential determinants and prevention strategies(Reference Flodmark, Marcus and Britton5). Most people become obese during adulthood, but there are significant correlations between childhood and adolescence BMI and obesity as well as obesity-associated morbidity in adults(Reference Parsons, Power, Logan and Summerbell6).
Many different variables are considered as risk factors for childhood obesity, ranging from the conditions of the home environment, including overweight parents and nutritional patterns, to physical activity and dietary factors, as well as behavioural and psychological factors(Reference Ebbeling, Pawlak and Ludwig4, Reference Swinburn, Egger and Raza7). The current literature suggests that profound changes in the quantity and quality of diets, combined with a decrease in levels of physical activity as well as an increase in sedentary activities (e.g. watching television), have led to an increase in the prevalence of overweight. Whereas numerous studies have examined the role of changes in dietary habits and the effect of high-energy, nutrient-poor foods on the development of overweight and obesity, recent studies have highlighted the role of eating patterns in terms of e.g. meal frequency and increasing portion sizes. Some researchers found an inverse relationship between meal frequency and the prevalence of overweight and obesity in adults(Reference Kant, Schatzkin, Graubard and Ballard-Barbash8) as well as in adolescents(Reference Franko, Striegel-Moore, Thompson, Affenito, Schreiber, Daniels and Crawford9), while others failed to detect significant associations(Reference Kant, Schatzkin, Graubard and Ballard-Barbash8, Reference Nicklas, Yang, Baranowski, Zakeri and Berenson10). The association of increasing meal frequency with decreasing BMI might be due to metabolic advantage(Reference Jenkins, Wolever and Vuksan11), e.g. increased thermogenesis, and/or higher insulin metabolism(Reference Franko, Striegel-Moore, Thompson, Affenito, Schreiber, Daniels and Crawford9, Reference Toschke, Kuchenhoff, Koletzko and von Kries12). In addition, subjects reporting higher meal frequencies often exercised more and made overall healthier food choices(Reference Nicklas, Yang, Baranowski, Zakeri and Berenson10, Reference Berkey, Rockett, Gillman, Field and Colditz13). Additionally, ‘skipping’ meals (breakfast) is associated with negative lifestyle factors(Reference Sjoberg, Hallberg, Hoglund and Hulthen14). Lower meal frequencies and breakfast skipping also seem to be more common among children of families with lower socio-economic status(Reference Moore, Tapper, Murphy, Lynch, Raisanen, Pimm and Moore15). Moore et al.(Reference Moore, Tapper, Murphy, Lynch, Raisanen, Pimm and Moore15) found a positively association between the level of deprivation and breakfast skipping as well as the quality of breakfasts consumed.
While numerous studies exist concerning the impact of breakfast consumption, little is known about other nutritional behaviours as well as the extent to which parental characteristics mediate these behaviours. The present paper focuses on different meal patterns (breakfast consumption, meal frequency, family meals, school lunch participation) of children and adolescents and their association with children’s weight status and parental characteristics (parental weight status, education and employment status).
Methods
The survey of Jena schoolchildren was initiated in October 2005 and lasted until February 2006. Jena is a university town in East Germany with ∼100 000 inhabitants. Relative to Germany as a whole and also to the population in East Germany, it is characterized by a higher proportion of upper middle-class families with an academic background(Reference Kromeyer-Hauschild, Zellner, Jaeger and Hoyer16).
Primary aims of the study were to describe the prevalence of overweight and obesity among schoolchildren in Jena as well as to analyse several risk factors considered as determinants of childhood overweight. Following previous surveys in Jena(Reference Zellner, Kromeyer and Jaeger17), the selected design was a cross-sectional study. Study approval was obtained from the Friedrich-Schiller-University ethics committee and from school authorities. Children participated voluntarily, and written consent was obtained from all parents on behalf of their children.
Subjects
A total of 2054 schoolchildren aged 7–14 years (1071 boys, 983 girls) were randomly recruited and measured. Additional parental characteristics were collected for 1571 children (810 boys and 761 girls, response rate 76·5 %). There were no significant differences between the BMI standard deviation score (BMI-SDS) of responders and non-responders. (χ 2 test: P = 0·072 in total population, P = 0·242 in boys, P = 0·173 in girls), indicating no bias. For a better comparison with previous studies of the Jena schoolchildren, children without German nationality were excluded from the analyses.
Instruments
Anthropometric measurements were performed by the research staff of the Institute of Human Genetics and Anthropology in Jena as well as undergraduates at the local schools. The staff were well trained and routinely controlled to ensure data quality. Height was measured with a Martin anthropometer according to the guidelines of Martin and Saller(Reference Martin and Saller18) and weight was measured with an electronic scale which was calibrated regularly. Height and weight were measured in light clothing (underwear) and without shoes; no adjustments were made for clothing.
In addition, a questionnaire for assessing parental characteristics and meal patterns was offered to all parents. The questionnaire was a shortened version of the form used in 2001(Reference Kromeyer-Hauschild19). The reliability of the altered questionnaire was tested by means of the split half procedure, and no significant differences were observed (P varied between 0·076 and 0·824).
The variables included in the present analysis are each child’s sex, age and BMI-SDS. Age ranged from 7 to 14 years. Individual BMI was calculated from measured height and weight (weight divided by the square of height; kg/m2) and classified into two groups according to sex- and age-specific percentiles: non-overweight, BMI ≤ 90th percentile; and overweight/obese, BMI > 90th percentile (German reference values)(Reference Kromeyer-Hauschild, Wabitsch and Kunze2). For the analyses, all individual BMI data were also converted into standard deviation scores (BMI-SDS) using the LMS method(Reference Cole and Green20).
BMI values for the parents were based on self-reported heights and weights, gathered from the questionnaire. The classification for parental weight status was BMI < 25 kg/m2 for non-overweight and BMI ≥ 25 kg/m2 for overweight/obese individuals(21).
The following parental characteristics were assessed.
1. Parental education (based on school attendance stated by each parent), categorized as ‘high’ for 12 years of school attendance, ‘middle’ for 10 years of school attendance and ‘low’ for 9 years or less of school attendance.
2. Paternal employment, classified as ‘no employment’, ‘working part-time’ and ‘working full-time’.
3. Groups for parental employment (refer to both parents together) were ‘both full-time workers’, ‘one full-time worker’, ‘both part-time or one full-time and the other jobless’ and ‘at least one working part-time or both jobless’.
Variables used to describe the meal patterns of the children were the following.
1. School lunch participation, designated as ‘daily’, ‘1–4× per week’ and ‘no participation’.
2. Breakfast consumption, designated as ‘2× breakfast’, ‘1× breakfast’ and ‘no breakfast’. The 2× breakfast denotes an early breakfast usually taken at home and a late breakfast usually taken at school.
3. Meal frequencies, ranging from 2 to 5. The parents had to decide which of the given traditional meals their children usually consume.
4. Family meals, the frequency of main meals (breakfast, lunch, dinner) eaten together with all family members, ranging from 0 to 3.
Only data from weekdays were analysed with regard to these meal patterns.
Statistical analysis
The statistical analyses were performed using the SPSS statistical software package version 15·0 (SPSS Inc., Chicago, IL, USA). The distributions of parental variables based on the weight status of their children were examined, and the χ 2 test was used to compare the frequencies of categorical variables. Meal patterns and their distribution among sex and age groups were assessed and differences were proven by means of the χ 2 test. To identify factors related to BMI-SDS in children, an age- and sex-adjusted linear regression analysis was performed. Linear regression analysis adjusted for sex was used to show variables that were strongly associated with meal frequencies. The backward stepwise procedure ensured that variables not significantly explaining or contributing to the fit of the model were excluded. In the multicollinearity statistics, a tolerance above 0·5 suggests the absence of significant correlations between the respective covariates. Statistical tests were two-sided and the accepted significance level was set at P < 0·05 (highly significant: P < 0·01).
Results
The prevalence of overweight and obesity in Jena schoolchildren aged 7–14 years was 10·0 % in boys (2·9 % obese) and 9·0 % in girls (2·2 % obese). This sex difference was not statistically significant (χ 2 test, P = 0·427).
Parental characteristics
Table 1 presents the distribution of the parental characteristics relative to the children’s weight status. Several factors differed significantly between overweight and non-overweight children. Overweight and obesity were highly associated with parental weight status, parental education and parental employment status. Children more often were overweight or obese if they had one or two overweight parents. An inverse association was observed regarding parental education and parental employment status. Overweight and obese children belonged more frequently to families with low educated parents or parents working part-time or with no employment, respectively.
Table 1 Distribution of parental characteristics in weight status categoriesFootnote *: schoolchildren aged 7–14 years, Jena, south-east Germany, 2005–6
* Weight status of the children according to national BMI percentilesReference Kromeyer-Hauschild, Wabitsch and Kunze(2).
† Parental weight status according to WHO guidelines(21).
Meal patterns
Table 2 summarises the children’s meal patterns by sex. There were no statistically significant differences between the sexes for school lunch participation (χ 2 test, P = 0·473), breakfast consumption (χ 2 test, P = 0·210), meal frequencies (χ 2 test, P = 0·322) and eating meals together with all members of the family (χ 2 test, P = 0·579). The distribution of meal patterns among the age groups showed significant differences. As shown in Fig. 1, which presents the distribution for all age classes, there was a strong relationship between school lunch participation rate and age group (χ 2 test, P < 0·001). Younger children ate school lunch more often than older children. This fact corresponds to the observed decrease in meal frequencies (Fig. 2), as well as the decrease in breakfast consumption and in number of meals eaten with the family as children grow older (χ 2 test, P < 0·001).
Table 2 Distribution of meal patterns according to sex: schoolchildren aged 7–14 years, Jena, south-east Germany, 2005–6
Fig. 1 School lunch participation rate (█, daily; ▒, 1–4× per week; ␣, no participation) according to age: schoolchildren aged 7–14 years, Jena, south-east Germany, 2005–6
Fig. 2 Meal frequencies (␣, 2; ░, 3; ▒, 4; █, 5) according to age: schoolchildren aged 7–14 years, Jena, south-east Germany, 2005–6
Factors related to weight status of Jena schoolchildren
Linear regression analysis was performed to investigate the factors associated with the children’s BMI-SDS, including parental characteristics as well as the variables describing meal patterns. Table 3 presents the remaining sex- and age-adjusted effects of those variables that contributed significantly or at least with explaining contribution to the prediction of BMI-SDS in Jena schoolchildren. The variance explained by the chosen model was 7·0 % (R 2). Greater meal frequency was related to decreasing BMI-SDS. Parental weight status and father’s education were also significantly related to children’s BMI-SDS, corresponding to the bivariate analyses. Breakfast consumption, family meals and parental employment contributed to explain children’s BMI, although there were no significant associations.
Table 3 Factors associated with BMI standard deviation score (sex- and age-adjusted): schoolchildren aged 7–14 years, Jena, south-east Germany, 2005–6
B, unstandardized coefficient; β, standardized coefficient.
*Excluded variables in the model: school lunch participation, mother’s educational status.
†Collinearity statistics.
Factors related to meal frequency among Jena schoolchildren
Table 4 demonstrates the sex-adjusted effects of those variables that contributed independently to the prediction of meal frequencies by means of linear regression analysis. The explained variance attained was 52·9 % (R 2). A higher participation rate in school lunch and breakfast consumption was positively associated with meal frequency. Meal frequency was also higher among children whose parents had lower employment. As can be seen in the table, the older the children were, the lower the frequency of meals consumed became. Family meals contributed to explain meal frequencies, although there were no significant associations.
Table 4 Factors associated with meal frequencies (sex-adjusted): schoolchildren aged 7–14 years, Jena, south-east Germany, 2005–6
B, unstandardized coefficient; β, standardized coefficient.
*Excluded variables in the model: mother’s weight status, father’s weight status, mother’s educational status, father’s educational status.
†Collinearity statistics.
Discussion
Factors found in the present study to be significantly associated with BMI (SDS) in 7–14-year-old schoolchildren from Jena were weight status of the parents, frequency of meal consumption and education of the father. Breakfast consumption, parental employment and number of family meals also contributed to the degree of BMI (Table 3). The observation that parental weight status is a major determinant of weight status in children includes genetic as well as environmental components. Twin, adoption and family studies suggest that genetic factors have a major impact on weight (see literature review by Maes et al.(Reference Maes, Neale and Eaves22)). Otherwise it was demonstrated frequently that the lifestyle of obese parents (food habits, meal patterns and physical activity) increases the risk of a child being overweight or obese.
As already shown previously(Reference Moore, Tapper, Murphy, Lynch, Raisanen, Pimm and Moore15, Reference Siega-Riz, Popkin and Carson23), the present study confirms the inverse relationship between meal frequency and BMI in children. Skipping meals is associated with higher BMI values. The meal frequency itself depends on breakfast consumption, age of the children, participation in school lunch and parental employment, found in the present study. Franko et al.(Reference Franko, Striegel-Moore, Thompson, Affenito, Schreiber, Daniels and Crawford9) similarly demonstrated the age dependency of meal frequency. Skipping meals was most typical for adolescents and breakfast was most commonly missed. Additionally, girls were found to be more likely to skip meals(Reference Jenkins, Wolever and Vuksan11). Kersting et al.(Reference Kersting, van Oost and Stolley24) already ascertained in 1975 that many German children do not have breakfast. The Health Behaviour in School-aged Children (HBSC) survey of 2001–2, conducted by the WHO in European children aged 11–15 years, showed that 40 % of girls and 39 % of boys did not regularly eat breakfast on school days. Between the ages of 11 and 15 years, breakfast consumption decreased by 9 % among boys and 17 % among girls(Reference Currie, Roberts, Morgan, Smith, Settertobulte, Samdal and Rasmussen25).
A number of studies have shown a relationship between socio-economic status (e.g. measured by parental educational level) and meal patterns(Reference Franko, Striegel-Moore, Thompson, Affenito, Schreiber, Daniels and Crawford9, Reference Neumark-Sztainer, Hannan, Story, Croll and Perry26), similar to the association between parental employment and meal frequencies found here in the Jena children. In our children, the number of meals was significantly reduced when both parents were working full-time. Neumark-Sztainer et al.(Reference Neumark-Sztainer, Hannan, Story, Croll and Perry26) also observed lower meal frequencies when mothers worked full-time. Parental factors such as the parents’ level of education or employment may reflect also the ability of parents to arrange a healthy lifestyle.
The causal linkages that explain our findings according the associations between meal patterns, parental characteristics and weight status may be complex. We found that the relationship between meal frequency and BMI-SDS remained even when social variables were taken into account.
A positive metabolic effect of an increased meal frequency, in addition to the amount of food and diet quality, might explain the inverse relationship with BMI(Reference Jenkins, Wolever and Vuksan11). A reasonable frequency of meals is also essential for an equal energy distribution throughout the day(Reference Siega-Riz, Popkin and Carson23). Regularity in breakfast consumption is associated with higher overall diet quality and less impulsive snack intakes at the rest of the day(Reference Berkey, Rockett, Gillman, Field and Colditz13). Studies have also found a relationship between meal frequency and portion sizes. Reduced meal frequency is usually associated with larger portions(Reference Hill and Davies27, Reference Livingstone, Robson and Wallace28). Even in 1964, Fábry et al.(Reference Fabry, Hejl, Fodor, Braunt and Zvolankova29) showed that larger food portions at longer intervals between consumption led to a number of adaptive changes in metabolism that suggest biological explanations. The group with the lowest meal frequency had the lowest energy intake, but higher fat mass. The authors assumed that the ability to form metabolic reserves during times of fasting might explain this association. Infrequent consumption of large meals appears to favour fat deposition, whereas frequent consumption of small servings that add up to the same total energy intake does not. This adaptation may have been biologically and ecologically useful in the past, but it could be pathogenic and lead to obesity today(Reference Nicklas, Yang, Baranowski, Zakeri and Berenson10).
Another interesting aspect of the meal patterns was the frequency of family meals. According to Neumark-Sztainer et al.(Reference Neumark-Sztainer, Hannan, Story, Croll and Perry26), frequent family meals are positively associated with intakes of fruits, vegetables, grains and Ca-rich foods and negatively associated with soft drink consumption. Family meals go along with the development of ‘regular’ meal patterns and the positive psychosocial development of adolescents, as well as decreased disordered eating(Reference Livingstone, Robson and Wallace28–Reference Neumark-Sztainer, Wall, Story and Fulkerson30).
Some remarks should be made with regard to the methodology of our study. Due to the volunteer nature and sample size of the present study, the results are not representative of the nation as a whole, although our study was larger than most regionally specific studies. The high return rate of the questionnaire and the lack of significant differences in BMI between the responders and non-responders suggest valid analyses.
Other limitations are the self-reported heights and weights of the parents, which might have resulted in some inaccuracies. A further major problem in nutritional research is reporting bias(Reference Hill and Davies27, Reference Livingstone, Robson and Wallace28, Reference Westerterp and Goris31). Several studies indicate that non-overweight as well as overweight individuals may under-report their food intake (e.g. Pryer et al.(Reference Pryer, Vrijheid, Nichols, Kiggins and Elliott32)), whereas other investigations have shown that under-reporting is more prevalent among the obese than the non-overweight(Reference Heitmann and Lissner33, Reference Bandini, Schoeller, Cyr and Dietz34).
Due to the cross-sectional nature of our study design, it is not possible to ensure whether the differences in determinants precede or follow changes in BMI-SDS or healthy eating behaviour. In the present study, only associations are stated, and no causality can be assumed. Prospective studies are needed to prove causality.
The findings in our study explain only a small amount of the variability in BMI-SDS. The remaining variance might be due to both genetic and environmental factors. Our study focused only on meal patterns, whereas energy intake, energy expenditure and food consumption details, e.g. macronutrient composition of the diet, were not considered.
The present study clearly indicates that more frequent meal consumption predicts a healthier BMI, and that meal frequency decreases with increasing age. Therefore one preventive strategy might be to encourage greater meal frequency. Age differences in meal frequency suggest that age-specific prevention programmes should be targeted to children and adolescents. The close involvement of parents is essential in any strategy for improving children’s (families’) diets.
Acknowledgements
The authors received no funding for this work. None of the authors had a personal or financial conflict of interests. A.W. contributed to data collection, performed statistical analyses and wrote the manuscript by incorporating critical inputs from all authors. K.Z. conducted the study and contributed to the data collection. K.K.-H. contributed to the study design and to the data collection and assisted in the statistical analyses. All authors read and approved the final manuscript. We especially thank the children and adolescents of Jena, as well as their parents, for their participation, without whom this work could not have been accomplished.
