Cumulative effect of obesogenic behaviours on adiposity in spanish children and adolescents

© 2017 The Author(s) Published by S. Karger GmbH, Freiburg Original Article Obes Facts 2017;10:584–596 Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents Helmut Schröder a, b Rowaedh Ahmed Bawaked a, c Lourdes Ribas-Barba d, e Maria Izquierdo-Pulido e, f Blanca Roman-Viñas d, e Montserrat Fíto a, e Lluis Serra-Majem d, e, g a Cardiovascular Risk and Nutrition Research Group (CARIN), IMIM (Hospital del Mar Medical Research Institute), Barcelona , Spain; b CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid , Spain; c Biomedicine PhD program, Pompeu Fabra University, Barcelona , Spain; d Fundación para la Investigación Nutricional (Nutrition Research Foundation), Barcelona , Spain; e CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid , Spain; f Department of Nutrition, Food Sciences and Gastronomy, University of Barcelona, Barcelona , Spain; g Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, Las Palmas , Spain Keywords Childhood obesity · Physical activity · Diet · Obesogenic behaviours Abstract Objective: Little is known about the cumulative effect of obesogenic behaviours on childhood obesity risk. We determined the cumulative effect on BMI z-score, waist-to-height ratio (WHtR), overweight and abdominal obesity of four lifestyle behaviours that have been linked to obesity. Methods: In this cross-sectional analysis, data were obtained from the EnKid sudy, a representative sample of Spanish youth. The study included 1,614 boys and girls aged 5–18 years. Weight, height and waist circumference were measured. Physical activity (PA), screen time, breakfast consumption and meal frequency were self-reported on structured questionnaires. Obesogenic behaviours were defined as <1 h PA/day, ≥ 2 h/day screen time, skipping breakfast and <3 meals/day. BMI z -score was computed using ageand sex-specific reference values from the World Health Organization (WHO). Overweight including obesity was defined as a BMI > 1 SD from the mean of the WHO reference population. Abdominal obesity was defined as a WHtR ≥ 0.5. Results: High screen time was the most prominent obesogenic beReceived: February 8, 2017 Accepted: August 17, 2017 Published online: December 6, 2017 Dr. Helmut Schröder Cardiovascular Risk and Nutrition Research Group (CARIN), Program of Research in Epidemiology and Public Health IMIM (Institut Hospital del Mar d’Investigacions Mèdiques) Parc de Recerca Biomèdica de Barcelona Doctor Aiguader, 88, 08003 Barcelona, Spain hschroeder @ imim.es www.karger.com/ofa DOI: 10.1159/000480403 This article is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND) (http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes as well as any distribution of modified material requires written permission. Helmut Schröder and Rowaedh Ahmed Bawaked contributed equally to the study, and each can be considered first authors of this article. 585 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg haviour (49.7%), followed by low physical activity (22.4%), low meal frequency (14.4%), and skipping breakfast (12.5%). Although 33% of participants were free of all 4 obesogenic behaviours, 1, 2, and 3 or 4 behaviours were reported by 44.5%, 19.3%, and 5.0%, respectively. BMI z-score and WHtR were positively associated (p < 0.001) with increasing numbers of concurrent obesogenic behaviours. The odds of presenting with obesogenic behaviours were significantly higher in children who were overweight (OR 2.68; 95% CI 1.50; 4.80) or had abdominal obesity (OR 2.12; 95% CI 1.28; 3.52); they reported more than 2 obesogenic behaviours. High maternal and parental education was inversely associated (p = 0.004 and p < 0.001, respectively) with increasing presence of obesogenic behaviours. Surrogate markers of adiposity increased with numbers of concurrent presence of obesogenic behaviours. The opposite was true for high maternal and paternal education. © 2017 The Author(s) Published by S. Karger GmbH, Freiburg Introduction The childhood obesity epidemic is among the world’s most challenging public health problems, with an especially high prevalence of overweight and obesity in South European countries. At present, 38.6% of Spanish children and adolescents are overweight or obese [1] , and 16.5% have abdominal obesity [2] . The high obesity prevalence is of concern because premature onset of related illnesses such as diabetes, hypercholesterolaemia and non-alcoholic fatty liver disease is increased among obese children [3, 4] . Therefore, there is an urgent need to develop effective intervention programmes to curb this troubling epidemic. Behaviours such as low physical activity, high screen time, skipping breakfast and low meal frequency are prevalent in young populations [5–9] , and all of them have been linked to an increased risk of childhood obesity and therefore defined as obesogenic behaviours [5, 6, 10–12] . Most studies investigated the individual impact of these behaviours on obesity risk [5, 6, 10–12] . However, clustering of multiple obesity risk behaviours is well known in young populations [13] . Furthermore, findings indicate that co-occurrence of obesogenic behaviours is associated with lower parental socio-economic status [14, 15] . Less is known, however, about the cumulative effect of these behaviours on childhood obesity risk [16] . It is important to determine whether adding a second unhealthy lifestyle behaviour significantly increases the effect size of the association with weight gain or obesity. The cumulative effect of the concurrent presence of low physical activity, skipping breakfast, high screen time and low meal frequency would imply a need for multilevel intervention programmes addressing these modifiable behaviours. In this context it is crucial to know if the risk of co-occurrence of multiple obesity risk behaviours differs with parental socioeconomic level. We hypothesized an increase in obesity risk estimates with the co-occurrence of one or more obesogenic behaviours and a relationship between parental socio-economic status and the presences of more than one of these behaviours. The aim of this study was to determine the individual and cumulative effect of low physical activity, skipping breakfast, high screen time and low meal frequency on BMI, waistto-height ratio (WHtR), overweight and abdominal obesity in a nationwide representative sample of Spanish children and adolescents. A secondary objective was to analyse the association between the concurrent presence of these obesity-related behaviours and parental educational level. 586 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg Material and Methods Study Design and Subjects The EnKid study on nutritional status and food habits of Spanish children and young people, which was conducted between 1998 and 2000, was a cross-sectional survey of the Spanish population aged 2–24 years, selected by multistage random sampling procedures based on a population census. The objective of the EnKid study was two-fold: i) to establish the prevalence of micronutrient deficiencies in the population aged 2–24 years and ii) to analyse the association of these micronutrients with group membership (gender and age groups). The sample size was calculated according to i) the estimated prevalence of most micronutrients with 95% confidence interval and accuracy of 5% of the average value of the micronutrient and ii) a statistical power of 80% to detect significant differences between two groups > 10% of the mean of the micronutrients (setting the alpha error at p = 0.05). The calculated sample size of 3,850 individuals was over-estimated by 30%, resulting in a theoretical sample size of 5,500 individuals. The final sample size of the EnKid study was 3,534 individuals. The final study sample consisted of 1,614 individuals aged 5–18 years. Data were collected by trained dietitians during a personal interview with each participant or, for those younger than 8 years, with the mother or the person responsible for feeding the child. All field workers received training prior to data collection. Parental written consent was obtained on behalf of each participant under 18 years. The study protocol was approved by the ethics committee of the Spanish Society of Community Nutrition. Anthropometric Measurements For each participant, the following anthropometric measurements were assessed according to standard protocols: Body weight height, and waist circumference were measured on the day of the interview, with the subject in underwear without shoes, using an electronic scale (to the nearest 100 g), a portable Kawe stadiometer (to the nearest 1 mm) and a Hoechst metric tape (to the nearest 1 mm). Using a flexible non-stretch tape measure, waist circumference was measured by trained interviewers in the narrowest zone between the lower costal rib and iliac crest, in the supine decubitus and horizontal positions. Excessive abdominal fat was defined as WHtR > 0.50. BMI z -score was computed using ageand sex-specific reference values from the World Health Organization (WHO) [17] . Overweight and obesity were defined as BMI > 1 SD < 2 SD and BMI >2 SD, respectively, from the mean of the WHO reference population. Physical Activity, Screen Time and Dietary Habits Physical activity was recorded by a structured questionnaire including one question about frequency and duration of active games during free time after the school or during the weekend, one question about frequency and duration of physical education class at school, three questions about frequency and duration of walking, and six questions about sport activities outside the school. The questions about sports activities were derived from a validated questionnaire [18] . Physical activity-induced energy expenditure was calculated using the following algorithm: Physical activity-induced energy expenditure = physical activity (in min/day) × metabolic equivalent of task (MET) × resting metabolic rate [19] × body weight (in kg). We assigned the MET value of 4.2 to all participants because detailed data on the intensity of each physical activity were not available. Data on regular breakfast consumption and overall meal frequency were recorded. Questions on screen time included watching television, DVDs/videos, using a computer for fun and playing computer or video games. Obesogenic Behaviour Score To determine the cumulative effect of obesogenic behaviours, we calculated a composite obesogenic behaviour score. One point was allocated to low physical activity, defined as <1 h/day, high screen time (>2 h/day of television and computer use), no daily breakfast consumption and low meal frequency (<3 eating occasions/day). The final score ranged from 0 to 4 points, with higher scores indicating cumulative unhealthy obesogenic behaviours. We combined participants with 3 and 4 obesogenic behaviours as one group because only 0.7% presented with all 4 obesity-related behavioural risk factors. Energy Intake and Misreporting Energy and macronutrient consumption was assessed from two 24-hour recalls. The 2nd 24-hour recall was completed in a random sample of 25% of the participants at an independent non-consecutive day. To 587 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg avoid bias brought on by day-to-day intake variability, the questionnaires were administered homogeneously from Monday to Sunday. We included this sentence in the method section of the manuscript. The administration of the 2nd questionnaire allowed for the adjustment of intakes for random intra-individual variation using the method described by Liu et al. [20] . Information was collected at the home of the participant, using household measures to estimate portion sizes. Basal metabolic rate (BMR) was estimated using Schofield’s equations based on sex, age, weight and height [19] . Implausible reporters of energy intake were identified by replacing Goldberg’s single cut-off [21] with ageand sex-specific cut-offs. The cut-off values were the 95% confidence limits of the agreement between physical activity level (PAL) and the ratio of energy intake to BMR. The following formula was used: ( 1 ) where (2) Intra-individual variations of energy intake (CV 2 wEI ) and BMR (CV 2 wBMR ) and inter-individual variation in physical activity level (CV 2 wtP ) were calculated using sexand age-specific reference values [22–24] . The single Goldberg PAL of 1.55 was replaced by sexand age-dependent PAL for adolescents. Socio-Economic Status Determination of socio-economic status was based on maternal and paternal educational level. A high level of education was defined as studying beyond secondary education. Statistical Analysis General linear models were used to define mean values of socio-demographic and lifestyle variables according to numbers of obesogenic behaviours. To define the p value for linear trends, we used polynomial contrast for continuous normal distributed variables, chi-square test for categorical variables and KruskalWallis H test for non-parametric variables. Additionally, we fitted confounder-adjusted general linear models to determine the association of the cumulative effect of obesogenic behaviours with BMI z-score and WHtR. A Bonferroni correction was used to correct for multiple comparisons. Logistic regression was performed to assess the effect of the cumulative obesogenic behaviours on obesity, abdominal obesity, and maternal and paternal education educational level adjusted for potential confounders. Taking the minimum value as the reference (0 obesogenic risk factors), odds ratios (ORs) were estimated using the spline technique, which gives smoothed estimates when large differences were unlikely to be observed between OR estimates corresponding to two consecutive exposure values. Cubic spline analysis was performed using the ‘gam’ package in R version 3.0.2. Statistical analysis was performed using SPSS version 18.0 (SPSS Inc. Chicago, IL, USA). Results Overall, 31% of the children did not report any of the four obesogenic behaviours and 44.5% reported engaging in just one, while 19.3% and 5.0% reported three and all four behaviours, respectively. The reported behaviours and combinations (<4) were distributed as follows: 1 behaviour – high screen time (28.4%), low physical activity (8.4%), low meal frequency (4.3%), and skipping breakfast (3.4%); 2 behaviours – low physical activity + high screen time (9.1%), high screen time + low meal frequency (4.1%), skipping breakfast + low meal frequency (1.1%), low physical activity + skipping breakfast (0.7%), and low physical activity + low meal frequency (0.7%); 3 behaviours – low physical activity+ high screen time+ skipping breakfast (0.8%), low physical activity+ high screen time+ low meal frequency (1.4%), low physical activity + skipping breakfast + low meal frequency (0.6%) and high screen time +skipping breakfast + low meal frequency (1.6%). High screen time was the most 588 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg Table 1. Demographic and lifestyle characteristics of participants according to the cumulative effect of obesogenic behaviours1 Obesogenic behaviours 0 (n = 502) 1 (n = 719) 2 (n = 312) 3–4 (n = 81) p value2 Age, years 11.4 (3.68) 12.5 (3.60) 13.4 (3.63) 14.8 (2.80) <0.001 Girls, n (%) 249 (49.6) 346 (48.1) 161 (51.6) 55 (67.9) 0.032 BMI z-score 0.49 (1.12) 0.49 (1.10) 0.65 (1.18) 0.71 (0.99) 0.016 WHtR 0.45 (0.05) 0.45 (0.05) 0.46 (0.06) 0.46 (0.06) 0.047 Overweight, n (%) 143 (28.5) 223 (31.0) 117 (37.5) 27 (66.7) 0.019 Abdominal obesity, n (%) 76 (15.1) 122 (17.0) 64 (20.5) 14 (17.3) 0.102 Maternal education3, n (%) 135 (26.9) 139 (19.3) 46 (14.7) 13 (16.0) <0.001 Paternal education3, n (%) 163 (32.5) 186 (25.9) 65 (20.8) 13 (16.0) <0.001 Physical activity, min/day 144 (96; 204) 132 (7) 54 (24; 144) 40 (16; 77) <0.001 PAEE, kcal/day 542 (355; 814) 520 (280; 885) 227 (107; 668) 166 (82; 375) <0.001 Low physical activity4, n (%) 0 (0.00) 136 (18.9) 170 (54.5) 55 (67.9) <0.001 High screen time5, n (%) 0 (0.00) 458 (63.7) 272 (87.2) 72 (88.9) <0.001 Skipping breakfast6, n (%) 0 (0.00) 55 (7.65) 87 (27.9) 59 (72.8) <0.001 Low meal frequency7, n (%) 0 (0.00) 70 (9.74) 95 (30.4) 68 (84.0) <0.001 Energy, kcal/day 2,158 (730) 2,153 (800) 2,075 (725) 1,744 (632) <0.001 Carbohydrate, % E846.0 45.9 46.0 46.0 0.779 Protein, % E814.9 15.9 15.4 16.3 0.165 Fat, % E839.1 38.2 39.6 38.7 0.317 EI/EEPA 3.7 (2.5; 5.7) 3.8 (2.2; 6.9) 7.3 (2.8; 15.3) 8.7 (3.5; 18.9) <0.001 Energy underreporting, n (%) 58 (11.6) 118 (16.4) 49 (15.7) 23 (28.4) 0.001 Community size, n (%) 0.920 <10,000 inhabitants 113 (22.5) 168 (23.4) 77 (24.7) 25 (30.9) 10,000–49,999 inhabitants 146 (29.1) 207 (28.8) 66 (21.2) 15 (18.5) 50,000–350,000 inhabitants 126 (25.1) 176 (24.5) 100 (32.1) 19 (23.5) >350,000 inhabitants 117 (23.3) 168 (23.4) 69 (22.1) 22 (27.2) Region of Spain, n (%) <0.001 Centre 103 (20.5) 156 (21.7) 66 (21.2) 17 (21.0) Northeast 151 (30.1) 169 (23.5) 53 (17.0) 16 (19.8) North 132 (26.3) 166 (23.1) 81 (26.0) 23 (28.4) South 60 (12.0) 121 (16.8) 52 (16.7) 13 (16.0) East 47 (9.4) 89 (12.4) 47 (15.1) 5 (6.2) Canary Islands 9 (1.8) 18 (2.5) 13 (4.2) 7 (8.6) EI:PAEE = Energy intake: physical activity energy expenditure; PAEE = physical activity energy expenditure; WHtR = waistto-height ratio. 1General linear models were used to determine the association of sociodemographic and lifestyle variables according to numbers of obesogenic behaviours. Values are expressed as mean (standard deviation), median (interquartile range) and number (%). 2p values were obtained by ANOVA, Kruskal-Wallis, and Pearson’s chi square for normal continuous, non-normal continuous, and categorical variables, respectively. 3University degree. 4Physical activity <60 min/day. 5Screen time >2 h/day. 6No daily breakfast. 7Fewer than 4 meals per day. 8Percentage of energy consumption. 589 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg prominent obesogenic behaviour (49.7%) followed by low physical activity (22.4%), low meal frequency (14.4%) and skipping breakfast (12.5%). Low physical activity / high screen time and high screen time / skipping breakfast / low meal frequency were the most prevalent combinations within clusters of 2 behaviours and more than 2 behaviours, respectively. Estimation of the effect of different combinations of obesogenic behaviours on the association with the risk of overweight and abdominal obesity was not possible due to the limited statistical power. The accumulation of obesogenic risk factors was positively associated with age, BMI z-score, the ratio between energy intake and physical activity-induced energy expenditure and WHtR, whereas the opposite was true for physical activity and energy intake ( table 1 ). The proportion of girls and of children with obesity, abdominal obesity and low/ medium level of parental education increased with co-occurrence of obesogenic behaviours. A northeast to south gradient was observed for the accumulation of obesogenic behaviours, with the Canary Island as the region with the highest risk. Multivariate analysis of the association between the cumulative effects of obesogenic behaviours and BMI z-score or WHtR revealed a difference of 0.50 SD for BMI z-score and of 0.025 for WHtR between participants with no obesogenic risk behaviours and those with Table 2. Adjusted general linear models of the association of obesogenic behaviours (OB) and BMI z-score and waist-to-height ratio (WHtR) of 1614 boys and girls of the EnKid study1 n (%) BMI z-score p value WHtR p value OB ab sent OB present OB absent OB present Individual OB Low physical activity2361 (22.4) 0.47 (0.41; 0.53) 0.74 (0.63; 0.85) <0.001 0.449 (0.447; 0.452) 0.46.3 (0.458; 0.468) <0.001 High screen time3802 (44.5) 0.49 (0.41; 0.56) 0.58 (0.51; 0.65) 0.077 0.451 (0.447; 0.454) 0.454 (0.451; 0.457) 0.223 Skipping breakfast4201 (12.5) 0.52 (0.46; 0.57) 0.63 (0.48; 0.78) 0.100 0.451 (0.448; 0.453) 0.465 (0.458; 0.472) 0.001 Low meal frequency5233 (14.4) 0.51 (0.45; 0.57) 0.67 (0.56; 0.80) 0.041 0.452 (0.450; 0.455) 0.453 (0.447; 0.460) 0.796 Cumulative OB None 502 (31.1) 0.43 (0.34; 0.53) 0.447 (0.442; 0.451) 1 OB 719 (44.5) 0.48 (0.41; 0.56) 0.451 (0.448; 0.455) 2 OB 312 (19.3) 0.73 (0.61; 0.85) 0.461 (0.455; 0.466) 3/4 OB 81 (5) 0.87 (0.63; 1.11) 0.467 (0.457; 0.478) P for linear trend6<0.001 <0.001 1 Values are expressed as mean (95% confidence interval) and adjusted for sex, age, region, community size, maternal education, energy, and energy over and underreporting. 2Physical activity < 1 h/day. 3Screen time > 2 h/day. 4No daily breakfast. 5Less than 4 meals per day. 6Polynomial contrast. Bonferroni correction was used to correct for multiple comparisons. 590 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg three or more ( table 2 ). At the individual level, all obesogenic behaviours, with the exception of high screen time, were significantly related to increased levels of at least one surrogate marker of adiposity ( table 2 ). All models were adjusted for age, sex, region, community size, maternal education, energy intake and energy underand over-reporting. Multivariate logistic models adjusted for sex, age, region, community size, energy intake and energy underand over-reporting showed higher odds of overweight and abdominal obesity for children with increasing numbers of obesogenic behaviours, compared to those having none of the risk behaviours ( table 3 ). In participants with at least 3 obesogenic behaviours, the odds of being overweight and having abdominal obesity increased by 168% and 112% respectively, compared to those with none. Low physical activity was associated with significantly higher odds of overweight and abdominal obesity, whereas skipping breakfast increased the odds for abdominal obesity. The odds of parents having higher education levels decreased as the number of obesogenic behaviours increased ( fig. 1 ). Participants with 3 or more obesogenic behaviours had 52.0% and 59.0% higher odds of low maternal and paternal education, respectively, compared to their peers without obesogenic behaviours ( fig. 1 ). To determine the robustness of our data, we performed sensitivity analysis, fitting multivariate models of the association between two main outcomes (BMI z-score and WHtR) and categories of obesogenic behaviours stratified by sex and age group (children aged 5–12 years vs. adolescents aged 13–17 years) ( table 4 ). The effect sizes of the associations were similar in boys and girls but stronger in adolescents. Table 3. Adjusted* logistic regression analysis of the association of individual and cumulative obesogenic behaviours (OB) with overweight and abdominal obesity of 1614 boys and girls of the EnKid study1 N (%) Overweight/obesity Abdominal obesity OR (95% CI) OR (95% CI) Individual OB Low physical activity2361 (22.4) 1.87 (1.31; 2.68) 1.69 (1.24; 2.32) High screen time3802 (44.5) 1.36 (0.99; 1.88) 1.13 (0.85; 1.49) Skipping breakfast4201 (12.5) 1.23 (0.77; 1.98) 1.52 (1.02; 2.28) Low meal frequency5233 (14.4) 1.05 (0.64; 1.72) 0.86 (0.55; 1.33) Cumulative OB None 502 (31.1) 1 1 1 OB 719 (44.5) 1.39 (1.14; 1.69) 1.28 (1.08; 1.52) 2 OB 312 (19.3) 1.93 (1.31; 2.85) 1.65 (1.18; 2.31) 3/4 OB 81 (5) 2.68 (1.50; 4.80) 2.12 (1.28; 3.52) Linear trend <0.001 0.004 1 Logistic regression models adjusted for sex, age, region, community size, maternal education, energy, and energy over and underreporting. Individual OB were mutually adjusted. 2Physical activity < 1 h/day. 3Screen time > 2 h/day. 4No daily breakfast. 5Less than 4 meals per day. 591 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg Fig. 1. Odds ratio (OR) of the association between cumulative obesogenic behaviours (OB) and high maternal ( A ) and paternal ( B ) education adjusted for sex, age, region, community size, energy and energy underand over-reporting. 592 Obes Facts 2017;10:584–596 DOI: 10.1159/000480403 Schröder et al.: Cumulative Effect of Obesogenic Behaviours on Adiposity in Spanish Children and Adolescents www.karger.com/ofa © 2017 The Author(s). Published by S. Karger GmbH, Freiburg Discussion The risk of overweight and abdominal obesity in Spanish children and adolescents increased with the number of concurrent obesogenic behaviours analysed – including low physical activity, high screen time, skipping breakfast, and low meal frequency. Additionally, BMI z-score and WHtR increased with number of these obesogenic behaviours. Higher parental educational level was less prevalent in the group reporting multiple obesogenic behaviours. In the present study, we observed the highest risk for obesogenic behaviours in the Canary Islands, which corresponds to a report of 2000 [25] that Gran Canary (the largest island) had the highest prevalence of childhood obesity in Spain. Further research is needed to establish the cause for the high prevalence of obesogenic behaviours, especially considering that cardiovascular mortality in the Canary Islands was among the highest in Spain at the same time [26] , an unfortunate distinction with an increasing trend according to a 2009 white paper for the 2015–2017 Health Plan for the Canary Islands [27] . The report emphasised that the community had the highest mortality rates related to ischaemic heart disease and diabetes in Spain and presented the highest rates of obesity, both overall and in children and youth, making actions to combat sedentary lifestyles and overweight a public health priority. A strong positive association has been established between obesity and fatal coronary heart disease [28] . Individual lifestyle behaviours identified as a potential cause of the obesity epidemic in children and adolescents, i.e. obesogenic behaviours, are highly prevalent in European counTable 4. Adjusted general linear models of the association of cumulative obesogenic behaviors and BMI z-score and WHtR stratified by sex and age1 n (%) None 1 OB 2 OB 3/4 OB p for trend2 BMI z-score Boys 803 (49.8) 0.56 (0.42; 0.71) 0.60 (0.48; 0.72) 0.96 (0.77; 1.14) 1.30 (0.85; 1.74) <0.001 Girls 811 (50.2) 0.30 (0.18; 0.42) 0.38 (0.28; 0.48) 0.50 (0.35; 0.65) 0.56 (0.30; 0.83) 0.049 WHtR Boys 803 (49.8) 0.458 (0.452; 0.465) 0.461 (0.456; 0.466) 0.476 (0.467; 0.484) 0.486 (0.467; 0.51) 0.003 Girls 811 (50.2) 0.435 (0.429; 0.440) 0.442 (0.437; 0.446) 0.447 (0.440; 0.454) 0.453 (0.440; 0.465) 0.007 BMI z-score Children 662 (41.0) 0.68 (0.52; 0.80) 0.72 (0.58; 0.85) 1.02 (0.51; 1.82) 1.17 (0.51; 1.82) 0.076 Adolescents 952 (59.0) 0.28 (0.15; 0.41) 0.32 (0.22; 0.41) 0.54 (0.41; 0.67) 0.70 (0.46; 0.94) 0.001 WHtR Children 662 (41.0) 0.461 (0.456; 0.467) 0.468 (0.462; 0.473) 0.475 (0.466; 0.485) 0.468 (0.442; 0.495) 0.500 Adolescents 952 (59.0) 0.437 (0.431; 0.444) 0.440 (0.435; 0.444) 0.450 (0.443; 0.457) 0.459 (0.447; 0.471) <0.001 WHtR = Waist-to-height ratio. 1Values are expressed as mean (95% confidence interval). Adjusted for sex, age, region, community size, maternal education, energy, and energy over and underreporting. 2Polynomial contrast. Bonferroni correction was used to correct for multiple comparisons.