Interventions and public health nutrition

Clustering of lifestyle behaviours and relation to body composition in European children. The IDEFICS study




Dietary patterns, physical activity (PA) and sedentary behaviours are some of the main behavioural determinants of obesity; their combined influence in children has been addressed in a limited number of studies.


Children (16 228) aged 2–9 years old from eight European countries participated in the baseline survey of the IDEFICS study. A subsample of 11 674 children (50.8% males) were included in the present study. Children’s food and beverage consumption (fruit and vegetables (F&V) and sugar-sweetened beverages (SSBs)), PA and sedentary behaviours were assessed via parental questionnaires. Sex-specific cluster analysis was applied to identify behavioural clusters. Analysis of covariance and logistic regression were applied to examine the association between behavioural clusters and body composition indicators (BCIs).


Six behavioural clusters were identified (C1–C6) both in boys and girls. In both sexes, clusters characterised by high level of PA (C1 and C3) included a large proportion of older children, whereas clusters characterised by low SSB consumption (C5 and C6) included a large proportion of younger children. Significant associations between derived clusters and BCI were observed only in boys; those boys in the cluster with the highest time spent in sedentary activities and low PA had increased odds of having a body mass index z-score (odds ratio (OR)=1.33; 95% confidence interval (CI)=(1.01, 1.74)) and a waist circumference z-score (OR=1.41; 95%CI=(1.06, 1.86)) greater than one.


Clusters characterised by high sedentary behaviour, low F&V and SSB consumption and low PA turned out to be the most obesogenic factors in this sample of European children.


Changes in multiple lifestyle behaviours contributing to energy imbalance are required in successful multi-factorial approaches of obesity prevention.1 Dietary patterns, physical activity (PA) and sedentary behaviours are some of these behaviours,2, 3, 4 which often coexist and interrelate,2 and are established at an early age.5 Individual patterns of health behaviour have shown to be independently associated with increased obesity in children and adolescents.6 Currently, limited evidence exists that points to the potential benefits of examining the synergetic effects of multiple obesity-related behaviours as opposed to single behaviours, which have been shown to be clustered within individuals and certain subgroups.7 For example, a recent study by Bel et al.8 examined the relationship between lifestyle clusters and cardiovascular disease (CVD) risk factor, showing that low levels of sedentary behaviours combined with low sugar-sweetened beverage (SSB) consumption may result in a healthier CVD profile.

Several studies conducted in adult and adolescent populations8, 9, 10, 11, 12, 13 have reported on clusters of lifestyle behaviours but only few in young populations. In adolescents, identified clusters were associated with body mass index z-scores (BMIz) and higher odds of obesity incidence and prevalence in females.7, 14 In addition, findings from the HELENA study showed that only a small proportion of adolescents had high scores in health-related behaviour clusters, that is, 18%.15 The current study offers the opportunity to examine clustering patterns of health behaviours and their association with body composition in a large sample of young European children where we especially consider: dietary-related habits (consumption of fruits and vegetables (F&V) and SSBs), PA and sedentary behaviours.

Therefore, the aims of this paper are as follows: (1) to identify clustered lifestyle behaviours based on dietary, PA and sedentary indicators and (2) to examine the association between such clustering patterns (dietary, PA and sedentary behaviours) and body composition in children aged 2–9 years participating in a large multi-centre European study.

Participants and Methods


The IDEFICS (Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infants) study is a setting-based, community-oriented intervention aiming to prevent obesity in children from eight European countries. Findings from the baseline survey of this multi-centre cross-sectional study are the focus of the current paper. All measurements were taken following a highly standardised examination programme summarised in a detailed operation manual. Full details on the study procedures are described elsewhere.16, 17, 18


Between September 2007 and June 2008, 16 864 children from pre-schools and primary schools of selected regions in Italy, Estonia, Cyprus, Belgium, Sweden, Hungary, Germany and Spain were recruited. The sampling frame was based on country-specific intervention and control regions comparable in terms of infrastructural, socio-demographic and socio-economic characteristics not intended to generate a representative sample for each country.17 In total, 16 228 (96%) of these children fulfilled the inclusion criteria (complete information on sex, height and weight) and comprised the final study population. The study’s response rate was ~71.9%. Of the 16 228 participants, only those with complete information on diet, PA, sedentary behaviours and body composition (n=11 674) were included in the current analysis. Approximately half of the subset was male (50.8%). The study was approved by the local Ethics Committees in each of the involved centres. Parents provided written informed consent for all examinations and children were asked for oral consent before each examination.

Socio-economic factors

Information on socio-demographic factors including sex, age and parental educational level were collected by means of a standardised self-reported parental questionnaire. Parental educational level was classified according to the International Standard Classification of Education19 and used as a proxy indicator of socio-economic status (SES).



F&V and SSB consumption were assessed using the proxy-administered food frequency section of the Children’s Eating Habits Questionnaire (CEHQ-FFQ).20, 21, 22 Parents reported the number of times the child had eaten or drunk these items included in the questionnaire during a typical week in the previous month. Responses included eight frequency categories of consumption as follows: (1) never/less than once a week, (2) 1–3 times per week, (3) 4–6 times per week, (4) once per day, (5) 2 times per day, (6) 3 times per day, (7) 4 or more times per day and (8) I have no idea. Frequencies of consumption were then converted into times per week ranging from 0 to 30. The following conversion factors were applied to obtain estimates of weekly consumption: category 1=0 times per week, 2=2 times per week, 3=5 times per week, 4=7 times per week, 5=14 times per week, 6=21 times per week, 7=30 times per week and 8=missing. From the 43 food groups included in the questionnaire only five were used: cooked vegetables, potatoes and beans; raw vegetables; fresh or raw fruits; fresh or raw fruits with added sugar and SSB. According to the purpose of the present analysis, F&V were combined into one group.

Sedentary behaviours

A standardised self-reported parental questionnaire was used to obtain information on children’s PA and sedentary behaviour. Parents reported hours of television (TV)/DVD/video viewing separately for weekday and weekends. Response categories included as follows: (0) not at all, (1)½ h per day, (2)1 h per day, (3) between 1 and <2 h per day, (4) between 2 and <3 h per day, (5)3 h per day. Total sedentary minutes per day were calculated as follows: category 0=0 min, 1=15 min, 2=45 min, 3=90 min, 4=150 min and 5=220 min, respectively. Total weekly sedentary time was calculated by considering the mean time in the selected response category, separately for weekdays and weekends, and then by multiplying each mean with the corresponding number of days divided by seven, that is, (((weekdays × 5)+(weekends × 2))/60))/7.

In this analysis, screen-time behaviour was used as an indicator of children’ sedentary behaviour TV viewing considered to be the dominant sedentary behaviour during leisure time in young children.23, 24

Physical activity

Proxies reported hours and minutes of participation in sports club activities per week (indicator of children’s PA used in this analysis). Total PA weekly time (hours) was calculated using the following formula: (hours+(minutes/60)). This question was chosen as children's daily time spent on PA, as assessed by the question, was significantly positively correlated with accelerometer-derived moderate-to-vigorous PA within the IDEFICS study.25 Further details on the assessment of PA are described elsewhere.18, 26

Body composition

Weight, height and skinfold thickness of the children were measured by trained researchers.26 Weight was recorded to the nearest 0.1 kg using a digital scale (type TANITA BC 420 SMA) and height to the nearest 0.1 cm, using a telescopic height measuring instrument (type SECA 225). Waist circumference was measured using an inelastic tape (type SECA 200), precision 0.1 cm, range 1–150 cm after previous landmarking with the subject in a standing position in the midpoint between the top of the iliac crest and the lower costal border (10th rib). Skinfold thicknesses were measured after previous landmarking using skinfold calliper (type Holtain Tanner/Whitehouse Lt., Crosswell, UK). Skinfolds measurements were taken at the following sites: (1) triceps, halfway between the acromion and the olecranon process at the back of the arm; and (2) subscapular about 20 mm below the tip of the scapula, at an angle of 45° to the lateral side of the body. Only light indoor clothing was worn. The reliability of the body composition measurements was studied in 298 children (mean age of 5.4 (±1.2) years). Intra- and inter-observer reliability was >0.95 and 0.88, respectively.27 Body mass index (BMI; kg/m2) was calculated thereafter. In addition, all skinfolds were summed up. On the basis of these measures, sex- and age-specific BMIz, waist circumference z-scores (WCz) and sum of skinfolds z-scores (SSz) were calculated. Excess of BMI, waist circumference and sum of skinfolds was assumed if z-scores were >1.

Statistical analyses

The Predictive Analytics SoftWare version 18.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data. All analysis was stratified by sex because of observed significant differences in sedentary behaviours and food and beverage consumption patterns between male and female participants. Food and drink consumption, sport participation and TV/DVD/video indicators were considered as continuous standardised variables. Cluster analysis was performed to identify group- and sex-specific clusters of lifestyle behaviours. The analysis was split into two steps in which a combination of hierarchical and non-hierarchical clustering was applied.28 In the first step, a hierarchical cluster analysis was carried out using Ward’s method based on Euclidean distances. To reduce the sensitivity of the Ward’s method to outliers, univariate outliers (values with >3 s.d. smaller or higher to the respective mean) and multivariate outliers (those with high Mahalanobis values distance) for any of the four variables investigated were removed before the analysis. At this stage, a comparison of several possible cluster solutions was performed. Using the resulting centroids, a non-hierarchical k-means cluster analysis was performed to further improve the preliminary hierarchical cluster solution. To examine the stability of the derived cluster solutions, the sample was randomly split into two halves and the full two-step procedure (Ward, followed by k-means) was then applied to each half. The elements of each half of the sample were assigned to a new cluster based on their Euclidean distances to the clusters centres of the other half of the sample. These new clusters were then compared for agreement with the original clusters with Cohen’s kappa (κ). Agreement was excellent (0.985 and 0.983 in boys and girls).29

One-way analysis of variance was used to compare characteristics between clusters. Differences in z-score body composition indicators (BMIz, WCz and SSz) were examined using one-way analysis of covariance for each cluster solution adjusted for SES and age. Bonferroni correction was used for a post hoc multiple comparisons test. All remaining tests were not adjusted for multiplicity. Binary logistic regression analysis was applied to estimate odds ratio (confidence interval 95%) for the z-scores of each body composition indicator (dichotomisation based on >1 s.d. above the mean) for each cluster solution adjusted for SES and age.


Cluster analysis resulted in a six clusters C1–C6 for both sexes (Figure 1) and their characteristics are indicated by low or high z-scores. Differences between cluster solution characteristics in terms of age, SES and crude BMI, and lifestyle behaviour reported in times or hours per week (mean±s.d.) are described in Table 1. In both sexes, C1 comprised of children with a high level of PA, whereas C2 consisted of children with a high level of sedentary activities. Children in cluster 3 were characterised by high level of PA and sedentary behaviours. Children in cluster 4 were characterised by high SSB consumption, whereas individuals in C5 were characterised by low SSB consumption and low level of sedentary activities. High consumption of F&V and low consumption of SSB and low level of sedentary behaviours characterised children in C6. Those clusters characterised by high levels of PA (C1 and C3) included a high proportion of children between 6 and 9 years. In addition, those clusters characterised by low beverage consumption (C5 and C6) included a high proportion of children between 2 and 6 years. A high proportion of children with low SES were found in the cluster with the highest SSB consumption (C4). Sex-specific clusters differed significantly with respect to age, SES and crude BMI.

Figure 1

Cluster solutions and mean z-scores of obesity-related behaviours in boys and girls.

Table 1 Socio-demographic characteristics and behavioural factors by cluster solution in boys and girls participating in the IDEFICS study

Table 2 presents means and standard errors of BMIz, WCz and SSz by sex-specific cluster solutions. The children in the clusters with the highest sedentary time, namely C2 and C3, had statistically significant higher BMIz, WCz and SSz (in both boys and girls; P<0.05) compared with children in the remaining clusters (C1, C4–C6).

Table 2 Analysis of covariance of body composition (BMI, waist circumference and sum skinfolds) z-scores by cluster solution in boys and girls participating in the IDEFICS study

Table 3 presents the results of the logistic regression analyses with estimated odds of having BMIz, WCz and SSz equal or >1. None of the clusters showed positive scores for all behavioural determinants; for this reason, C6 was chosen as the reference cluster scoring positive in three behaviours (high F&V, low SSB and low sedentary time). Significant associations between derived clusters and body composition indicators were observed only in boys; those boys in the cluster with the highest time spent in sedentary activities and low PA had increased odds of having a BMIz (odds ratio=1.33; 95% confidence interval=(1.01,1.74)) and a WCz (odds ratio=1.41; 95% confidence interval=(1.06, 1.86)) greater than one. No significant effect was observed for z-scores indicators in girls. No significant effect was observed in SSz indicators for both sexes.

Table 3 Binary logistic regression analyses between clusters of obesity-related behaviours and excessive BMI,a abdominal adiposityb and fatc in boys and girls participating in the IDEFICS study


This study examined the association between clusters of lifestyle behaviours and indicators of body composition in a sample of European children. Despite their acknowledged independent influence, little evidence exists on the synergetic effect of lifestyle factors in various populations. This is one of the first studies addressing such associations in European children.

The four obesity-related behaviours that were considered in the cluster analysis reflect the targeted IDEFICS intervention behaviours.30 Adherence to F&V recommendations (five portions per day) in our sample was low (17% in both sexes).31 No recommendations for SSB exists, but the European Society for Pediatric Gastroenterology, Hepatology and Nutrition suggests water as the main source of fluids for children instead of SSB to prevent obesity in children.32 In our study, only half of the children showed a satisfactory water intake.33 During the last years, the consumption of SSB has increased in several developed countries.34, 35, 36 Our results are in line with such findings and suggest that 9% of children usually drink nearly one SSB per day. Sedentary time remained slightly below the established threshold for media time (2 h per day) in all clusters.37 A combination of several sedentary variables may be considered as more appropriate to capture sedentary lifestyle; however, this analysis focussed on TV viewing because it was the predominant sedentary behaviour in the IDEFICS study and reflected one of the aims of the IDEFICS intervention.23, 24 Individuals in all cluster solutions, even those characterised by high PA, did not meet the recommendations for moderate-to-vigorous PA (1 h per day) by far.38

Six stable clustering patterns were found in both sexes, although correlations between the considered lifestyle factors were low (see Supplementary Table A in the Supplementary Appendix). This means that low correlations between health-related behavioural factors should not be interpreted such that these behaviours do not coexist in the same individual. Our study provides an assessment of several clusters from early ages and their association with body composition determinants such as BMI, waist circumference or skinfolds. To the authors’ knowledge, this is the first study to report clusters of all four obesity-related behaviours in children aged between 2 and 9 years and addressing the association between them and indicators of body composition.

In our study, the healthiest cluster (C6) as defined earlier, was observed only in 17% of children (both sexes), where participants had high F&V and low SSB consumption, low time spent in sedentary behaviours and at the same time low participation in sport activities. The same proportion of European individuals assigned to a healthy lifestyle cluster was reported by Sabbe et al.10 and Ottevaere et al.15 However, others failed to identify and report a healthy cluster.39

Children in clusters with the highest PA levels (C1 and C3) were older, indicating increases in structured activities and PA with age. This is in line with Landsberg et al.39 who reported the same findings in German and Swedish children, for both sexes. In addition, a high proportion of children with low SES was observed in the cluster characterised by high beverage consumption (C4).39 In our study, the cluster characterised by high sedentary activities, low PA, low F&V and low SSB consumption (C2) was the only cluster significantly associated with high BMIz and WCz, as compared with C6 used as the reference cluster, characterised by high F&V consumption, low SSB consumption, low PA and low sedentary time. Boys in C2 were those with the highest risk to have excess BMIz and WCz. Our findings are in contrast to those of Ottevaere et al.15 and Sabbe et al.10 who reported no significant differences among clusters by BMI. Moreover, children practicing specific health-related behaviours are not necessarily involved in other specific health behaviours, that is, children in C3 had high levels of PA and sedentary behaviours at the same time, in concordance with previous studies.10, 40

The importance of examining the joint influence of energy balance-related behaviours is highlighted by a recent study in adolescents showing that sedentary behaviours were related to higher odds of SSB and lower odds of fruit consumption.41 It is reasonable to assume that having a diet rich in F&V and low sedentary behaviours are components related to a healthier body composition profile. The combined influence of the three behaviours could be the key in formulating further research questions. Similarly to our findings, several studies11, 39 indicated that sedentary behaviours are not necessarily barriers for PA, and could coexist in the same population.42 Regarding the potential effects on body composition, findings of a longitudinal study showed that individuals with the highest levels of TV viewing during childhood had the greatest increases in body fat over time.42 In summary, the joint influence of low F&V consumption, high sedentary behaviour and low PA levels, despite low SSB consumption, is linked with excessive body fat. This is of great relevance as such synergetic effects observed in this study should be taken into consideration in obesity prevention intervention strategies.

Strengths and limitations

This study is not without limitations. The cross-sectional nature of the data does not allow any causal conclusions to be drawn. It should also be considered that data from diet, PA and sedentary behaviours are based on parental-reported questionnaires. The fact that the assessment of energy balance-related behaviours has often shown to be difficult and complex in young children may have further impaired our data quality.43 However, the questionnaires used in this study have been tested and validated before.20, 22, 25 Moreover, the use of TV viewing as an indicator of sedentary behaviour may not necessarily reflect overall sedentary time. Other indicators of sedentary behaviour as using computers, videogames or internet are becoming more dominant and should be considered in future studies. One of the strengths of the IDEFICS study is the broad range of examinations of obesity-related behaviours on a European scale. Cluster analysis is an exploratory method that provides answers, which might be useful in the development of tailored interventions. To address robustness of the cluster solutions, we replicated these clusters in a 50% internal random sample and obtained excellent stability.


It is important to consider multiple lifestyle-related health factors when classifying individuals. In boys, high sedentary time, unhealthy PA (low levels) and dietary patterns (low F&V and SSB consumption) can be considered as the most obesogenic and deleterious combination. The current findings provide evidence to support international recommendation for energy balance-related behaviours. Moreover, longitudinal studies are needed to assess the effect of energy balance-related behaviours on body composition indicators. Adherence to recommendations (<2 h per day of screen time, >60 min per day of M-VPA and >5 portions per day of F&V intake) should be promoted to avoid the development of obesity. Thus, our research supports the key messages of the primary prevention programme developed in course of the IDEFICS study30 that focused on lowering sedentary time and increasing PA and F&V consumption as main targets of the intervention.


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We thank all members of the study teams and especially the children and their parents for their participation in the study. This work was carried out as part of the IDEFICS Study and is published on behalf of its European Consortium ( We gratefully acknowledge the financial support of the European Community within the Sixth RTD Framework Program, Contract No. 016181 (FOOD). In addition, AMS-P received financial support by Fundación Cuenca Villoro (Spain), and was partially supported by grants from the Spanish Carlos III Health Institute: RD12/0026/0009 (Red SAMID: Maternal, Child Health and Development Research Network). The information in this document reflects the authors’ view and is provided as it is. No guarantee or warranty is given that the information is fit for any particular purpose. The reader, therefore, uses the information at its sole risk and liability.

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Correspondence to A M Santaliestra-Pasías.

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Santaliestra-Pasías, A., Mouratidou, T., Reisch, L. et al. Clustering of lifestyle behaviours and relation to body composition in European children. The IDEFICS study. Eur J Clin Nutr 69, 811–816 (2015).

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