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Nutritional Epidemiology

Dietary and Physical Activity Patterns in French Children Are Related to Overweight and Socioeconomic Status^1,2

³ French Food Safety Agency, Dietary Survey Unit-Nutritional Epidemiology, F-94700 Maisons-Alfort, France; ⁴ Doctoral School ABIES-AgroParisTech, F-75231 Paris, France; and ⁵ Institut de Recherche pour le Développement, UR106 Nutrition, Food, Societies (WHO Collaborating Center for Nutrition), F-34394 Montpellier, France

^* To whom correspondence should be addressed. E-mail: maire{at}mpl.ird.fr.

	ABSTRACT

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
Sedentary behavior (SED) has already been identified as a risk factor of childhood overweight (OW) but less is known about the dietary patterns related to adiposity. Our objective was to investigate if lifestyle patterns combining overall diet and physical activity were associated with childhood OW and if they were involved in the reverse association between socioeconomic status (SES) and OW. Dietary intake was assessed using a 7-d food record in 748 French children aged 3–11 y from the 1998–1999 cross-sectional French Enquête Individuelle et Nationale sur les Consommations Alimentaires national food consumption survey. Weight and height, leisure time physical activity, SED (television viewing), and SES were reported by parents or children by answering questionnaires. Scores for lifestyle patterns were assessed with factor analysis and their relationship with OW was explored by logistic regression analysis. Two similar lifestyle patterns were identified in children aged 3–6 y and 7–11 y: "snacking and sedentary" and "varied food and physically active." The snacking and sedentary pattern was positively associated with OW in the youngest children (P-trend = 0.0161) and partly mediated the negative association of SES to OW. The varied food and physically active pattern was inversely correlated with OW in the eldest children only (P-trend = 0.0401). A third pattern called "big eaters at main meals" was derived in children aged 7–11 y and was positively correlated with OW (P-trend = 0.0165). From a public health perspective, the combinations of identifiable dietary and physical activity behaviors may be useful as a basis for recommendations on preventing OW.

	Introduction

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

This method has been used to not only study dietary patterns in adult populations (4) but also to examine specific health outcomes, such as obesity and cardiovascular disease (4–7). Few studies concerned with food patterning have included similar analyses in a child population (8–10). To our knowledge, no study has been performed on children using a similar PCA patterning method and combining food intake and other lifestyle factors, such as physical activity.

The prevalence and severity of childhood OW is also on the increase in France (11), particularly in population groups with lower socioeconomic status (SES) (12). Given this background, the present study used data from the French Enquête Individuelle et Nationale sur les Consommations Alimentaires (INCA1) national food consumption survey to describe lifestyle patterns in French children aged 3–11 y. In this work, lifestyle patterns were identified based on the combination of overall food intake, physical activity, and sedentary behavior (SED). We then assessed the relationship between childhood OW and lifestyle patterns and investigated whether lifestyle patterns were involved in the relationship of SES to OW.

	Subjects and Methods

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
Subjects

The French INCA1 national food consumption survey was performed between August 1998 and June 1999 by the Research Center for the Study and the Observation of Way of Life and the French Food Safety Agency. This cross-sectional survey was primarily designed to assess the food intake of French children and adults. A complex sampling design was used to obtain a nationally representative sample of people living in French households. Sampling procedures and response rates have been described in more detail elsewhere (12,13). This study focused on children aged 3–11 y (n = 748) who were separated into 2 age groups of similar size, 3–6 y (n = 340) and 7–11 y (n = 408), which correspond to the preschool and primary school levels in France. This stratification was based on the assumption that children may have different lifestyle patterns depending on their age and school environments.

Measurements

A 7-d record was used to collect information on all food and drink intake during the week of the survey. The other variables, i.e. behavioral, anthropometrical, and sociodemographic variables, were reported in self-reported and face-to-face questionnaires. These documents were delivered to the child's home and checked for accuracy by a trained and certified investigator. If the child was <10 y old, parents or caregivers completed both documents together with the child. The older children self-reported their food intake and most of the individual variables (behaviors, weight, and height) but could be helped by their parents or caregivers if necessary.

    Dietary data. In the 7-d record, subjects reported the type of eating occasion at which each food or beverage was consumed, i.e. meals and snacks. Participants estimated portion sizes using the supplémentation en vitamines et en minéraux antioxydants photographic booklet (14). Macronutrient intake was evaluated with the Centre d'information sur la qualité des aliments food composition tables (15). In this study, we assessed mean daily energy (in kcal/d)⁷ and food intakes (in g/d) and the contribution of both fats and each eating occasion (i.e. breakfast, lunch, dinner, and snacks) to total energy intake (EI). An additional variable was related to the regularity of eating breakfast. Because a relatively low proportion of children ate fewer than 6 breakfasts during the week of the survey (i.e. 2.7% of the children aged 3–6 y and 6.2% of children aged 7–11 y), a child was considered to have skipped breakfast if at least 1 breakfast per week was not eaten.

Thirty-two major food groups were established based on similar source characteristics (Table 1). Energy density (ED) of both food and beverage intakes was also estimated at the individual level, weighting the composition of each item consumed (energy in kcal) by its effective consumption during the week (in g). ED was calculated independently for foods and drinks in kcal/100 g consumed.

    SES. The head of household's occupation determined the child's SES, which was divided into low, middle, and high. High was assigned to executive, top management, and professional categories; middle to middle professions (employees, technicians, and similar); and low to the others (including unemployed people).

    OW status. Weight and height were used to calculate the child BMI (weight/height² in kg/m²). OW and obesity were then estimated according to the International Obesity Task Force age- and gender-specific child BMI cut-off points (19).

Statistical analysis

    Lifestyle patterns assessment. Distinct lifestyle patterns were identified for each age category by conducting factor analysis (PCA) with Varimax rotation on the 32 food groups and on the 2 behavioral variables describing physical activity, i.e. LTPA and SED. All 34 variables were standardized. The number of components were selected considering both the interpretability of the factor loadings (which represent the correlations of each variable with 1 given component) and the scree plot (20). To interpret the data, we considered the items most strongly related to the factor, i.e. those for which the loading coefficient was >0.2 or <–0.2. Labels were allocated according to the most significant items associated with the components. For each child, the factor score for each pattern was calculated by summing the observed behavioral components (intake per food group, LTPA and SED) weighted by the factor loadings. These scores were categorized (tertiles), as is often done in dietary epidemiological studies that relate food patterns to health outcomes when there is no a priori knowledge of the function that best fits the data (4,21).

    Association between SES, lifestyle patterns, and OW (including obesity). We used logistic regression analysis stratified on the 2 age categories to calculate OR and 95% CI to compare OW status (dependent variable) according to lifestyle patterns. All multivariate models were adjusted for sex, age (introduced as a continuous variable to eliminate any remaining potentially confounding effect of age within each age category), and mutually adjusted for all lifestyle patterns. Another set of multivariate logistic regression analyses stratified on the 2 age groups was carried out to investigate the potential mediating role of each lifestyle pattern regarding the relationship of SES to OW (dependent variable). Model 1 included sociodemographic variables: SES, sex, and age (continuous). Model 2 included 1 lifestyle pattern in addition to the variables from Model 1.

To limit potential misreporting, we excluded 5 children from the data set whose log-transformed value of EI was out of the range of the mean (±3 SD) within the age classes. Statistical tests were 2-sided and the accepted significance level was 5%. Analyses were performed using SAS software (version 8.2, 1999, SAS Institute).

	Results

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
    Characteristics of the dietary patterns. Of the 748 children included in the study, 29 observations were eliminated in the analyses because of incomplete data (behaviors, n = 4; anthropometry, n = 20) or potential misreporting (n = 5). Taken together, these children represented 3.6% of the initial sample and did not differ from the others in age, sex, or SES. We identified 2 main lifestyle patterns in children aged 3–6 y and 3 patterns in children aged 7–11 y. The factor-loading matrix between food groups, physical behaviors, and the patterns is given in Table 1. In children aged 3–6 y, pattern 1 was positively and mainly correlated with the consumption of meat, vegetables, fruits, cheese, bread, and fats such as butter and other fats (mainly oils). This pattern was significantly and positively associated with LTPA, EI, the percentage of EI ascribed to fats, and the contribution of lunch to total EI and inversely associated with ED of food intake and the contribution of snacks to total EI (Table 2). A positive but nonsignificant relationship was observed between this pattern and SES (P = 0.0849) and an inverse but nonsignificant association was observed with skipping breakfast (P = 0.0737). It was named "varied food and physically active." Pattern 2 was mainly characterized by high consumption of French fries, carbonated soft drinks, noncarbonated sweetened beverages, and high SED, as well as by low consumption of yogurt/cottage cheese, jam/honey, and water. Pattern 2 was also significantly and positively correlated with LTPA and ED of food intake and inversely associated with SES and contribution of breakfast to EI (Table 2). A positive but nonsignificant relationship was observed between this pattern and the contribution of dinner to the daily EI (P = 0.0657). It was labeled "snacking and sedentary."

	Discussion

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
    Assessment of lifestyle patterns. Childhood OW has reached epidemic proportions worldwide. France is also affected, particularly in this age range where rates of OW are close to 17% (12). The statistical methods classically used in nutritional epidemiology to define dietary patterns based on collected dietary information include a posteriori approaches, such as cluster and factor analyses, and the a priori dietary index approach. We chose to use exploratory statistical analyses based on factor analysis to provide new insights into some combined lifestyle risk factors associated with OW. The patterns labeled varied food and physically active and snacking and sedentary were observed in both age categories, i.e. 3–6 y and 7–11 y, suggesting that both shared similar lifestyles in the late 1990s. Adherence to the varied food and physically active lifestyle patterns was higher in the upper SES categories than in the snacking and sedentary lifestyle patterns. In children aged 7–11 y, the lifestyle pattern labeled big eaters at main meals was mainly characterized by diet, because loadings for LTPA or SED were close to 0.

Comparison with other studies is not straightforward due to the data-driven approach to pattern analysis, with, notably, differences in dietary assessment methods, the redistribution of the primary items (e.g. foods) into groups, treatment of these variables (e.g. standardization), the number of groups identified for entry into the analysis, the methods of rotation of axes, if any, the number of patterns retained for description and/or further etiological analyses, and the statistical analysis techniques used. Despite these variations in methodology, Newby et al. (4) reported in their review that some reproducibility is observed between most studies that derived dietary patterns in adults. We identified some similarities with our results in 3 other studies performed in children. In Spanish children aged 3–14 y, Aranceta et al. (8) derived 2 patterns from factor analysis based on overall diet labeled snacky and healthy. The snacky component was characterized by higher consumption of snack foods such as candy and soft drinks. Children whose mother had a low level of education and those who spent >2 h/d watching TV were more likely to follow this dietary pattern. Higher intakes of fruit, vegetables, and fish were associated with the healthy component, which was positively associated with the level of education of the mother and inversely associated with the time spent watching TV. Northstone et al. (10) also showed similar characteristics in 2 dietary patterns called junk and health conscious, identified using factor analysis in British children 4 and 7 y old. As far as we know, only 1 study has been performed on children (French children aged 12 y) that investigates lifestyle patterns combining some dietary habits, SED, and physical activity (22). Two distinct profiles were derived using multiple correspondence analysis. The first was characterized by physical activity and the consumption of fruit, vegetables, and fruit juices. This profile was not associated with SES. The other lifestyle pattern, inversely correlated with SES, was defined by SED, the consumption of French fries or potato chips, sweetened drinks as the most usual drink, and snacking while watching TV.

There is a growing body of evidence based on more traditional individual food approaches to suggest that 1 of the most powerful influences on children's food preferences (both directly and through parents or peers) is watching TV (23). Not only do children who watch TV frequently request and consume advertised foods, which are mostly convenience foods such as salty or sweet snacks and fast foods, but they are inactive and often eat while watching (24). Conversely, beneficial associations between physical activity and healthy food choices such as fruits and vegetables have been described in studies in adults (25) and young people (26).

    Association between SES, lifestyle patterns, and OW. In children aged 3–6 y, the snacking and sedentary pattern was positively associated with OW, confirming that the association of TV viewing with snack foods should be considered as an early indicator of higher risk of childhood OW. Although this result is not strictly comparable to other findings, studies based either on consumption of snack foods (27) or screen behavior (28,29) have also shown positive relationships between each of these patterns taken separately and child OW. Snack foods tend to be high in fat, sucrose, and therefore in ED. They can displace less energy-dense and more nutrient-dense foods from the diet, such as fruit and vegetables or dairy products, as already described in Australian children (30). In association with more inactive time spent watching TV, this lifestyle pattern is likely to contribute to a positive energy balance and, therefore, OW.

This significant relationship between the snacking and sedentary lifestyle pattern and OW was no longer observed in children aged 7–11 y, in contrast to both the varied food and physically active and the big eaters at main meals patterns, which were associated with OW in this specific age range. These differential findings on preschool and elder children thus corroborated our initial decision to stratify the analyses on age category. The varied food and physically active pattern was negatively associated with OW in children aged 7–11 y, suggesting that a balanced lifestyle combining structured meals, regular breakfast, foods with low ED, and LTPA is an indicator of lower risk of OW in this age category. Conversely, the big eaters at main meals pattern was relatively less balanced in terms of contribution of meals to daily EI, particularly breakfast, which contributed relatively less to the daily EI than in the previous pattern. Consequently, dairy products and other foods typical of the French breakfast (i.e. bread, butter, and jam/honey) did not contribute to characterizing this lifestyle factor. Neither LTPA nor ED was correlated with this pattern in contrast to total EI and the percentage of EI ascribed to fats. Consequently, this lifestyle pattern, which is rather unbalanced in meal events and structure, may also contribute to a positive energy balance and therefore OW. It should be noted that other studies have shown positive relationships between childhood OW and both skipping breakfast (or lesser amount of EI at breakfast) (31,32) and contribution of dinner to EI (33).

Finally, we found that among all the lifestyle patterns described here, only the snacking and sedentary lifestyle pattern played a partial mediation role in the relationship of SES to OW in children aged 3–6 y. Other intermediate factors probably include patterns of physical activity and other aspects of SED that were not taken into account in the present analysis. Additional limitations must be acknowledged in the interpretation of the findings of this study. Although the INCA1 survey collected comprehensive variables involved in the energy balance equation, precision and accuracy were not optimal for all measurements. Not all variables known to be risk factors for childhood OW, such as maternal obesity and age of adiposity rebound, could be measured in this single survey and, as a result, could not be controlled for in the multivariate approach. In addition, because of the cross-sectional design of the study, inferences regarding cause and effect must be made with caution and cannot be conclusive. Finally, data-driven patterning methods are characterized by inherent subjectivity resulting from the empirical decisions made by the investigators. In the context of etiological research on associations between lifestyle patterns and OW, other statistical approaches such as structural equation modeling could provide complementary information or at least confirm hypotheses derived from exploratory statistical models.

Despite these limitations, this study illustrates that it is worth deriving lifestyle patterns combining not only overall diet but also indicators of physical activity and sedentariness to assess composite and correlated behaviors associated with childhood OW. Among the lifestyle patterns we identified, some were associated with either higher or lower risk of OW in childhood and this has major implications, because these patterns are likely to track into adulthood (34,35). In addition, from a public health perspective, the combinations of identifiable dietary and physical activity behaviors may be useful as a basis for recommendations on preventing OW. The lifestyle patterns derived from these national data were not equally shared by all SES categories and although longitudinal studies are needed to confirm our findings, considering SES may help better target the prevention programs aimed at decreasing child OW rates.

	FOOTNOTES

 
¹ Supported by the French Food Safety Agency (AFSSA).

² Author disclosures: S. Lioret, M. Touvier, L. Lafay, J.-L. Volatier, and B. Maire, no conflicts of interest.

⁶ Abbreviations used: ED, energy density; EI, energy intake; INCA1, French Enquête Individuelle et Nationale sur les Consommations Alimentaires; LTPA, leisure time physical activity; OW, overweight; PCA, principal component analysis; SED, sedentary behavior; SES, socioeconomic status; TV, television.

⁷ 1 kcal = 4.184 kJ.

Manuscript received 1 August 2007. Initial review completed 29 August 2007. Revision accepted 17 October 2007.

	LITERATURE CITED

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 

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