Physical activity (PA) levels and dietary habits are considered some of the most important factors associated with obesity. The present study aimed to examine the association between PA level and food and beverage consumption in European children (2–10 years old).

A sample of 7229 children (49·0 % girls) from eight European countries participating in the IDEFICS (Identification and prevention of Dietary and lifestyle induced health EFfects In Children and infantS) study was included. Moderate-to-vigorous PA (MVPA) was assessed objectively with accelerometers. FFQ was used to register dietary habits. ANCOVA and binary logistic regression were applied.

Boys who spent less time in MVPA reported lower consumption of vegetables, fruits, cereals, yoghurt, milk, bread, pasta, candies and sugar-sweetened beverages (SSB) than boys who spent more time in MVPA (P<0·05). Moreover, boys who spent less time in MVPA were more likely to consume fast foods and water than those in the highest MVPA tertile (P<0·05). Girls who spent less time in MVPA reported lower consumption frequencies of vegetables, pasta, bread, yoghurt, candies, jam/honey and SSB than girls in the highest MVPA tertile (P<0·05). Also, girls in the lowest MVPA tertile were more likely to consume fast foods and water than those with high levels of MVPA (P<0·05).

Food intake among European children varied with different levels of daily MVPA. Low time spent in MVPA was associated with lowest consumption of both high- and low-energy-dense foods and high fast-food consumption.

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward’s method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data.

The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice.

The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward’s methods, the performance of k-means was better in 64–100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a ‘non-processed’ cluster with a high consumption of fruits, vegetables and wholemeal bread, a ‘balanced’ cluster with only slight preferences of single foods and a ‘junk food’ cluster.

The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

To assess the relationship between parental education level and the consumption frequency of obesity-related foods in European children.

The analysis was based on data from the cross-sectional baseline survey of a prospective cohort study. The effects of parental education on food consumption were explored using analysis of covariance and logistic regression.

Primary schools and pre-schools of selected regions in Italy, Estonia, Cyprus, Belgium, Sweden, Hungary, Germany and Spain.

Participants (n 14 426) of the IDEFICS baseline cohort study aged 2 to 9 years.

Parental education level affected the intake of obesity-related foods in children. Children in the low and medium parental education level groups had lower odds of more frequently eating low-sugar and low-fat foods (vegetables, fruits, pasta/noodles/rice and wholemeal bread) and higher odds of more frequently eating high-sugar and high-fat foods (fried potatoes, fruits with sugar and nuts, snacks/desserts and sugared beverages; P < 0·001). The largest odds ratio differences were found in the low category (reference category: high) for vegetables (OR = 0·56; 95 % CI 0·47, 0·65), fruits (OR = 0·56; 95 % CI 0·48, 0·65), fruits with sugar and nuts (OR = 2·23; 95 % CI 1·92, 2·59) and sugared beverages (OR = 2·01; 95 % CI 1·77, 2·37).

Low parental education level was associated with intakes of sugar-rich and fatty foods among children, while high parental education level was associated with intakes of low-sugar and low-fat foods. These findings should be taken into account in public health interventions, with more targeted policies aiming at an improvement of children's diet.