Data used in the current analysis were derived from the IDEFICS (‘Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infantS’) study. A total of 16,224 children aged 2 to 9 years were recruited during the baseline survey, which was conducted between 2007 and 2008 in 8 European countries (Italy, Estonia, Cyprus, Belgium, Sweden, Germany, Hungary, Spain) . All participants met the general IDEFICS inclusion criteria: age group 2 to 9 years, available data on body mass and height, and completion of the parental questionnaire. From the total sample of 16,224 children, a subset of 12,134 had valid data for age, body mass, height, body mass index (BMI) and blood sample parameters. As accelerometry was measured only in a subset from every center due to availability of accelerometers, when the objective measurement of PA was included in the analyses the sample size was reduced. For the purposes of the current analyses, only subjects (n = 3,019) with a complete set of data that included total triglycerides (TG), total cholesterol (TC), HDL-c, glucose, insulin, systolic blood pressure (SBP), sum of two skinfold thickness measurements, exposure (PA intensities) and confounding variables were included. No differences with respect to mean age, body mass and Z score BMI were observed between individuals in the subset with complete data and the rest of the sample. The study was conducted according to the standards of the Declaration of Helsinki. (Edinburgh 2000 revision), the Good Clinical Practice, and the legislation about clinical research in humans. All applicable institutional and governmental regulations pertaining to the ethical use of human volunteers were followed during this research. Approval by the appropriate ethics committees was obtained by each of the eight participating centers carrying out the fieldwork (Belgium: Ethics Committee, University Hospital, Gent; Cyprus: Cyprus National Bioethics Committee; Estonia: Tallinn Medical Research Ethics Committee; Germany: Ethics Committee, University of Bremen; Hungary: Egészségügyi Tudományos Tanács, Pécs; Italy: Comitato Etico, ASL Avellino; Spain: Comité Ético de Investigación, Clínica de Aragón (CEICA); Sweden: Regional Ethics Review Board, University of Gothenburg). Written informed consent was obtained from the parents (or guardian) of each child participating in the study.
For quality management, all measurements followed detailed standard operating procedures that were laid down in the general survey manual and finalized after the pretest of all survey modules . Field personnel from each study center participated in the central training and organized local training sessions thereafter. The coordinating center conducted site visits to each study location during both field surveys to check adherence of field .
Socioeconomic status (SES)
SES was estimated using the International Standard Classification of Education. A score was calculated from the highest education and qualification levels of both parents. Five groups were defined using a scale from 0 to 6: level 1 (0 and 1), level 2 (2), level 3 (3), level 4 (4), and level 5 (5 and 6); the lower the score, the lower SES.
Body mass was measured in light clothing to the nearest 0.1 kg with an electronic scale (TANITA BC 420 SMA, Tokyo, Japan). Height was measured without shoes to the nearest 0.1 cm using a stadiometer (Seca 225; Seca, Hamburg, Germany). Skinfold thicknesses were measured with a Holtain caliper (Holtain Ltd., Croswell, UK) at the triceps and subscapular sites. Blood pressure was measured with an electronic sphygmomanometer (Welch Allyn 4200B-E2; Welch Allyn, Aston Abbotts, UK)  preferably in the right arm with the child seated and in a calm environment. Two measurements were taken at 2-minute intervals and, if they differed by >5%, a third measurement was taken. The mean of the two (or three) measurements was used in all statistical analyses.
The uniaxial Actigraph accelerometer (Actigraph MTI, model GT1M; Manufacturing Technology Inc., Fort Walton Beach, FL, USA) and the ActiTrainer (http://www.actitrainer.com) were used to measure PA. The ActiTrainer technology is based on the ActiGraph accelerometer with additional functions (heart rate). The rationale to use the ActiGraph in younger and ActiTrainer in older children was to record, when possible, the heart rate. However, in the current study only data from accelerometers were used and crossvalidation was not necessary as both accelerometers are essentially the same model of ActiGraph. Prior to data collection, parents were instructed in the correct positioning of the accelerometer; that is, to attach the accelerometer to the right hip of the child during their waking day by means of an elastic belt adjusted to ensure close contact with the body. The accelerometer needed to be worn all day over 4 to 5 days, except during water-based activities and during sleep. Recordings were for at least 6 h/day for at least 3 days (2 weekdays and 1 day of the weekend or holiday) in accord with the results of the reliability analysis indicating a minimum duration of 6 h per day of monitoring to achieve 80% reliability . The sampling interval (epoch) was set at 15 s. Non-wear time was excluded from the data by means of an automated method that uses an algorithm developed using R (version R 2.9.0.; R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org). Thus, periods of 20 minutes or more consecutive zero counts were replaced by missing data code before further analysis . A measure of average total volume of activity (hereafter called total PA) was expressed as the sum of recorded counts divided by total daily registered time expressed in minutes (counts/minute; cpm). The cut-offs to define the PA intensity categories were derived from previously-validated cut-offs , with time spent in light PA (minutes) defined as the sum of time-per-day in which counts per epoch were 26 to 573 cpm. The time engaged in moderate PA was calculated based upon a cut-off of 574 to 1,002 cpm per epoch. The time engaged in vigorous PA was calculated based upon a cut-off of ≥1,003 cpm per epoch. In addition, the time spent at the ‘effective’ intensity level was calculated as the sum of time spent in moderate + vigorous PA (MVPA).
Fitness was measured by the progressive 20-m shuttle run test . This test required subjects to run back and forth between two lines set 20 m apart at a pace determined by audio signals. The initial speed was set at 8.5 km/h increasing by 0.5 km/h every minute (1 minute equals 1 stage). The test was completed when the child failed to reach the end lines in time with the audio signals on two consecutive occasions. The final score was computed as the number of stages completed (precision of 0.5 stages). Stages completed were used to estimate the VO2max.
A detailed description of the blood sampling procedures has been published elsewhere . Briefly, blood samples were obtained after an overnight fast and previous confirmation by questionnaire of achievement this criterion. Blood glucose, TC, HDL-c and TG were assessed on site at each study center by point-of-care analysis using a Cholestech LDX analyzer (Cholestech, Hayward, CA, USA) . Serum insulin concentrations were determined by luminescence immunoassay in a central laboratory using an AUTO-GA Immulite 2000, Siemens, Eschborn, Germany. To derive a measure of insulin resistance we used the homeostasis model assessment (HOMA-IR)  using fasting glucose and plasma insulin according to the following formula: HOMA-IR = [fasting insulin (pmol/l)/6.945] × [fasting glucose (mmol/l)/22.5].
Cardiovascular risk score
According to Andersen et al.  a continuous score clustering CVD risk factors (CRFs) was computed using the following variables: SBP, TG, TC/HDL-c ratio, HOMA-IR, and sum of two skinfolds (score CRFs). Since the 20-m shuttle run test was only performed in children >6 years of age, a second CVD risk score was obtained for older children containing the score CRFs + the cardiorespiratory fitness variable using the total number of stages (termed score CRFs + fit). Z scores were calculated for each risk factor variable by age and gender, followed by a summing of individual Z scores to create the two clustered risk scores. Cardiorespiratory fitness Z score was multiplied by -1 to indicate higher metabolic risk with increasing value. The lower the CVD risk the better the overall CVD risk factor profile.
Predictive Analytics SoftWare (PASW, version 18; SPSS Inc., Chicago, IL, USA) was used to perform the analyses. Statistical significance was set at P <0.05. The data are presented as mean ± standard deviation (SD) unless otherwise stated. Mean and SD for CVD risk were calculated for age and gender groupings of the children who had a complete set of measurements. Age groups were recorded as younger children (between 2 to 6 years) and older children (between 6 to 9 years). The distributions of PA were observed to be skewed and so to achieve normality of distributions, moderate PA, vigorous PA, and MVPA were transformed to the natural logarithm values. Individuals >1 SD away from the mean in the clustered risk scores were defined as being ‘at risk’. For descriptive variables, the Student’s t test was used to test the differences between genders. To examine the association between PA intensities and CVD risk scores, partial correlation analyses adjusted for country were conducted in both age groups.
Age and gender-specific quintiles were created for each PA intensity. One-way analysis of covariance (ANCOVA) was used to test the differences in CVD risk scores (dependent variables) among quintiles of PA (fixed factor) segregated by age and gender and adjusted for country (dummy variable) and SES.
Logistic regression models were used to calculate the odds ratios (OR) for having clustered risk score (dichotomous variable; Z score above 1 SD) across quintiles of different PA intensities (quintile 5 as reference) segregated by age and gender. Country (dummy variable) and SES were included as covariates. Moreover, descriptive analyses were performed to stand out the mean, SD and range of time corresponding at each quintile among the different PA intensities segregated by age and gender. Finally, the mean, SD and range at the highest quintile (Q5) of PA was selected as potential recommendation.