Health behaviors, waist circumference and waist-to-height ... - Nature

European Journal of Clinical Nutrition (2011) 65, 841–848

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ORIGINAL ARTICLE

Health behaviors, waist circumference and waist-to-height ratio in children R Lehto1, C Ray1,2, M Lahti-Koski3 and E Roos1,2 1 Folkha¨lsan Research Center, Paasikivenkatu 4, Helsinki, Finland; 2Hjelt Institute, the University of Helsinki, Helsinki, Finland and 3The Finnish Heart Association, Helsinki, Finland

Background: Waist circumference (WC) and waist-to-height ratio (WHtR) begin to gain attention as measures of adiposity and as important cardiometabolic disease risk factors also among children. Still, little research has been done on behavioral determinants of WC and WHtR in children. The purpose of this study was to examine associations between health behaviors, WC and WHtR in children. Methods: The study was a cross-sectional study conducted in Swedish-speaking schools in Helsinki region in 2006. In all, 1146 children were recruited, from which 55 % took part in the study. A total of 604 9–11-year-old children (312 girls, 292 boys) were measured by research staff and completed a study questionnaire on their health behaviors, including breakfast intake, TV viewing, sleep duration and physical activity, and a 16-item food frequency questionnaire. Covariance analysis was used as the statistical analysis method. Results: When controlling for other health behaviors, for example, irregular breakfast (B-coefficient 2.49 CI, 0.64–4.34; Po0.01), TV viewing (B-coefficient 0.89 CI, 0.17–1.61; Po0.05), a TV in child’s room (B-coefficient 2.30 CI, 0.73-3.86; Po0.01) and physical inactivity during school breaks (B-coefficient 0.78 CI, 0.19–1.37; Po0.01) were associated with larger WC. Results were similar with WHtR. Conclusions: Many health behaviors were related to children’s WC and WHtR. Determinants were associated to both WC and WHtR similarly.

European Journal of Clinical Nutrition (2011) 65, 841–848; doi:10.1038/ejcn.2011.49; published online 13 April 2011 Keywords: health behavior; waist circumference; waist-to-height ratio; children; physical activity; food habits

Background Although children’s overweight and obesity, based on body mass index (BMI), has increased significantly in recent decades, children’s waist circumference (WC) has increased even more (Moreno et al., 2001; McCarthy et al., 2003). In some studies, WC has been found to be an even stronger indicator of cardiometabolic disease risk factors than BMI in children (Lee et al., 2006; Sung et al., 2007; Reinehr and Wunsch, 2010). However, a recent systematic review concluded that WC and BMI were equally useful in identifying cardiometabolic disturbances in children (Reilly et al., 2010). One shortcoming of WC as an indicator of obesity is that it does not take height into account. To solve this problem,

Correspondence: Dr E Roos, Folkha¨lsan Research Center, Paasikivenkatu 4, Helsinki 00250, Finland. E-mail: [email protected] Received 28 October 2010; revised 1 February 2011; accepted 9 March 2011; published online 13 April 2011

waist-to-height ratio (WHtR) has been used (McCarthy and Ashwell, 2006; Cossio et al., 2009; Nambiar et al., 2009). In adults, WHtR has been shown to be a stronger factor in identifying cardiometabolic disease risk factors compared with WC, waist-hip ratio and BMI, although the differences were small (Lee et al., 2008). A few studies have shown similar results in children (Kahn et al., 2005; Cossio et al., 2009). Although many studies have examined the relationship between health behaviors and BMI in children, studies on health behaviors and WC are few. In addition to our knowledge, no studies have examined the relationship between health behaviors and WHtR in children. In most of the previous studies on physical activity and WC, physical activity has been found to be associated with smaller WC (Klein-Platat et al., 2005; Delmas et al., 2007; Ortega et al., 2007; Lazarou and Soteriades, 2010). Sedentary behavior, that is TV viewing or total sedentary behavior, has been associated with larger WC (Klein-Platat et al., 2005;

Health behaviors and central adiposity in children R Lehto et al

842 Ortega et al., 2007; Lazarou and Soteriades, 2010). The presence of a TV in a child’s bedroom was found to be associated with larger WC and other obesity measures, but only in boys (Delmas et al., 2007). Shorter sleep duration was related to larger WC, at least in girls in two studies (Yu et al., 2007; Hitze et al., 2009). Only few studies have reported results on food habits and WC in children. In a study on Cypriot children, an index describing adherence to a Mediterranean diet was not associated with WC (Lazarou and Soteriades, 2010). In another study on American children, intake of some foods was associated with WC (Bradlee et al., 2010). Few previous studies done on breakfast intake and WC have shown that skipping breakfast is associated with larger WC (Isacco et al., 2010; Smith et al., 2010). As BMI, WC and WHtR correlate strongly with each other, the factors related to each of them are probably similar. Nevertheless, differences may occur, especially as WC is growing more rapidly than BMI among children. In addition, these measures are indicators of different kinds of fat distribution. Therefore, it is important to get more knowledge about factors that are related to WC and WHtR. The aim of this study was to examine the associations between health behaviors, WC and WHtR among school children in Finland. Studied health behaviors were food consumption, the regularity of breakfast, physical activity, screen time and sleep duration. In addition, we investigated the possible associations with and without adjustment for general obesity, defined by BMI, to see if health behaviors are associated with WC and WHtR independently of BMI.

Methods Participants This study was performed as a part of a project called Ha¨lsoverkstaden (Health workshop), which studies the health behaviors of 9–11-year-old children (Ray et al., 2009; Westerlund et al., 2009; Lehto et al., 2010). The study material was cross-sectional, and was collected in Swedishspeaking elementary schools in the capital region of Finland during 2006. All 44 Swedish-speaking schools with more than 50 pupils in the region were asked to take part in the study. The headmasters in 27 schools decided that their school would participate. The participating and not participating schools did not vary according to the socio-economic status of the neighborhood. The study was approved by the ethical committee of the Department of Public Health of the University of Helsinki. In the spring 2006, all children in the grades three and four (n ¼ 1146) and their parents in the participating schools were contacted. In all, 677 children and their parents gave their informed consent for participation in the study. Data were collected during two school visits. In total, 630 children were measured and weighed by the research staff in the spring. The 47 children who gave their consent but weren’t European Journal of Clinical Nutrition

measured, were either absent during the measurements or declined to be measured. In the fall, 604 of those children who were measured completed a questionnaire on their health behavior. The questionnaire was administered in a classroom setting, and a member of the research staff was always present. These children (n ¼ 604) form the sample of our study, representing a participation rate of 53% of the children who were first contacted.

Anthropometrics The children’s height was measured without shoes to the nearest 0.5 cm with the same study measure. WC was measured on top of a t-shirt to the nearest 1 cm midway between iliac crest and the lowest rib. WHtR was calculated as WC (cm) divided by height (cm). The children were weighed with the same study scale to the nearest 0.1 kg wearing only underwear and a t-shirt. The measurements were always carried out before lunch-time.

The children’s health behaviors The children’s research questionnaire included a 16-item food frequency questionnaire (FFQ), and questions on meal patterns, sleep duration, sedentary behavior and physical activity. All questions on health behavior, except for the question on school break activities, were taken from the WHO’s Health Behavior in School-aged Children study questionnaire (Currie et al., 2001). We only used data on breakfast frequency, sleep duration and screen time during weekdays, because in our view weekday activities represent routines of everyday life. The 16-item FFQ measured habitual intake of certain foods and food groups. The answer choices in the FFQ were as follows: never, less than once a week, once a week, 2–4 times per week, 5–6 times per week, once a day and several times a day. These were then scored afterwards by researchers as 0, 0.5, 1, 3, 5.5, 7 and 14 to represent average intake occasions per week. Principal component analysis was also used to form food indices from the FFQ. This has been described in detail previously (Westerlund et al., 2009). Two factors were found from which sum variables were formed by adding up the weekly intake occasions. The first sum variable consisted of pizza; hamburgers, hot dogs and meat pastry; potato chips and popcorn; cookies; ice cream; sweets and chocolate; and cola and other soft drinks. It was named the energy-dense food index. The other sum variable consisted of fresh vegetables, cooked vegetables, fruits and berries, and dark bread and it was named the nutrient-dense food index. The frequency of eating breakfast at home during the school week was asked with answer alternatives ranging from zero to five. A regular breakfast was then defined as having breakfast usually on all 5 days during school week. Less than 5 days was defined as an irregular breakfast. Sleep duration was calculated as the difference between bed time and waking time for school days. Alternative


843 answer choices for bed time when next day was school day included every half an hour starting from ‘at 8 pm at the latest’ and ending with ‘midnight or later.’ For waking time alternative answers included every half an hour between ‘at 6 am at the latest’ and ‘8.30 am or later’. Total television viewing and computer screen time per day during the school week was calculated through two questions, one on TV, video and DVD viewing time and the other on time spent using a computer or playing with game consoles. The seven alternative answers ranged from ‘not at all’ to ‘approximately 5 h per day or more’. The children were also asked two questions about the presence of a TV and a computer or a game console in their room, with the answer alternatives of yes or no. The amount of free time physical activity per week in a sports club or on one’s own was asked with six answer alternatives ranging from ‘not at all’ to ‘7 h or more’. These answer alternatives were then converted to hours equaling 7, 5, 2.5, 1, 0.5 and 0 h. Physical activity during school breaks was assessed with five statements about school break activities. The statements were: ‘I am physically active when I play,’ ‘I play ball games,’ ‘I walk,’ ‘I talk with friends,’ and ‘I stand still.’ The alternative answer choices were ‘almost every break,’ ‘during most breaks,’ ‘seldom,’ and ‘never during breaks’ and they were given points from 1 to 4. Principal component analysis (with varimax rotation) was employed to extract factors from the school break activity statements (Stevens, 1992). Two components had eigenvalues over 1, the first of which consisted of the statements ‘I walk,’ ‘I talk with friends,’ and ‘I stand still’ (load over 0.5), and the second of the two statements, ‘I am physically active when I play’ and ‘I play ball games’ (load over 0.5). Two sum variables were formed based on these factors by adding up the points from each statement. The first variable was named physical inactivity

during school breaks and the second physical activity during school breaks.

Statistical methods Gender differences in WC, other anthropometrics and health behaviors were tested with the t-test and w2-test. Covariance analysis was used to examine the association of health behaviors with WC and WHtR. Free time physical activity, physical inactivity during school breaks (quartiles), physical activity during school breaks (quartiles), sleep duration, TV viewing and the energy-dense food index (quartiles) were all used as continuous variables in these analyses, as they appeared to have a linear association with WC. The nutrient-rich food index and computer/game console time were used as categorical variables, as they did not appear to have a linear association with WC. SPSS for Windows 17.0 was used for the analyses (SPSS Inc., Chicago, IL, USA). Three different models were used. The first model was adjusted only for age and gender, and the second for the aforementioned plus all other health behaviors. In the third model, BMI was added to the model 2.

Results A description of the study sample is seen in Table 1. The average WC was larger in the boys than in the girls. The same applied to WHtR, whereas no gender differences in BMI were found. A larger proportion of the boys than the girls had a TV or a computer/game console in their room, and the boys also reported spending more time on a computer or playing with a game console than the girls. The boys exercised more than the girls during their free time and they were physically

Table 1 Description of the study sample Girls (N ¼ 312)

Age (years)a WCb WHtRb Height (cm) Weight (kg) BMI (m2/kg) TV viewing (h/school day) Computer/game console use (h/school day)b Sleep duration (h/school day) Physical activity (hours/week)b Regular breakfast (yes) (%) TV in child’s room (yes) (%)b Computer/game console in child’s room (%)b

Boys (N ¼ 292)

Total (N ¼ 604)

Mean

s.d.

Mean

s.d.

Mean

s.d.

9.6 64.5 0.449 144 36.4 17.5 1.3 0.8 9.7 4.3 88 33 28

0.6 7.2 0.04 7.9 8.0 2.6 1.0 0.8 0.7 2.0

9.7 66.2 0.459 144 36.8 17.5 1.3 1.3 9.7 4.7 85 46 53

0.6 7.4 0.04 6.5 7.3 2.5 1.1 1.1 0.8 2.1

9.6 65.3 0.454 144 36.6 17.5 1.3 1 9.7 4.5 87 39 40

0.6 7.3 0.04 7.3 7.7 2.6 1.0 1.0 0.7 2.1

Abbreviations: BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference. a During anthropometric measurements. b Significant difference between boys and girls (P ¼ o0.05), t-test and w2-test.

European Journal of Clinical Nutrition


844 more active also during school breaks. There were no gender differences in the intake of energy-dense and nutrientrich foods. In the first model, adjusted only for age and gender, irregular breakfast, a TV in the child’s room, a computer or game console in the child’s room, more TV viewing, less frequent intake of energy-dense foods, less physical activity in free time and during school breaks, shorter sleep duration and more physical inactivity during school breaks were associated with larger WC (Table 2). All of these variables, except for physical inactivity during school breaks, were also associated with WHtR. Frequency of intake of nutrient-rich foods and time spent on a computer or playing with a game console were not associated with WC or WHtR. In the second model, when health behavior variables were adjusted for each other, irregular breakfast, TV viewing, less frequent intake of energy-dense foods, a TV in the child’s room, more physical activity and less inactivity during school breaks remained associated with larger WC and WTHR (Table 2). In the third model, when BMI was added to the model, physical activity during free time was inversely related to both WC and WHtR (Table 2). In addition, physical inactivity during school breaks was associated with larger WC and physical activity during school breaks with smaller WHtR. Other associations were no longer significant.

Discussion In this study on 9–11-year-old Finnish children, many health behaviors were related to children’s WC and WHtR. When adjusted with other health behaviors, irregular breakfast, less frequent intake of energy-dense foods, more TV viewing, the presence of a TV in the child’s room, more physical inactivity and less physical activity during school breaks were associated with larger WC. All of these variables except for physical inactivity during school breaks were also associated with larger WHtR. After controlling for BMI, only variables concerning physical activity were associated with WC and WHtR. In concordance with our results, the association between free time physical activity and WC has been found in other studies (Ortega et al., 2007). In a large sample of 12-year-old French children, the association also persisted after controlling for BMI (Klein-Platat et al., 2005). The reason for physical activity and WC/WHtR associations might be that physical activity may be more strongly associated with measures of central adiposity than with BMI, because physical activity might increase fat-free mass, which would increase BMI but not WC. In concordance with other studies on having breakfast and WC (Isacco et al., 2010; Smith et al., 2010), we found that an irregular pattern of eating breakfast was related to larger WC and WHtR, but this association was no longer significant after adjusting for BMI. A number of studies have also shown European Journal of Clinical Nutrition

that skipping breakfast is associated with higher BMI and overweight in children and adolescents both in crosssectional (Dubois et al., 2009; Szajewska and Ruszczynski, 2010) and prospective settings (Albertson et al., 2007; Timlin et al., 2008). A reason for this can be that breakfast eaters have consistently been reported to have healthier diets than breakfast skippers (Rampersaud et al., 2005). Breakfast eating can also be an indicator of an overall healthy lifestyle (Vereecken et al., 2009). Contrary to our findings, in an American study, (Bradlee et al., 2010) reported that the consumption of dairy, grains, and fruits and vegetables was associated with smaller WC in 12–16-year-old adolescents. These results are not fully comparable, as in Bredlee’s study the measure of diet was 24 h recall while we used a FFQ, and instead of separate food groups we used a sum variable in our study. In a Cypriot study, no association was found between adherence to a Mediterranean diet and children’s WC (Lazarou and Soteriades, 2010). Problems in finding a valid and reliable method to measure diet and eliciting reliable information from the study subjects might be reasons for inconsistent findings. Similar to our results, other studies have also found that overweight children report eating sweets less often than normal weight children (Andersen et al., 2005; Janssen et al., 2005). One explanation for these odd findings can be more underreporting by bigger children (Lanctot et al., 2008; Singh et al., 2009). However, it is possible that children with larger WC and WHtR truly do consume energy-dense foods less often due to their own or a parental restriction. In our study, sleep duration was related to WC and WHtR only in the first model, which was adjusted for age and gender. This can be due to a small statistical power or the fact that meal patterns, TV viewing and food habits, which all have been associated with sleep duration (Ray et al., 2007; Hitze et al., 2009; Westerlund et al., 2009) were mediators of this association. In two other studies, sleep duration has been related to larger WC, but mostly only among girls (Yu et al., 2007; Hitze et al., 2009). In many recent studies, shorter sleep duration has been found to be associated with overweight in children (Patel and Hu, 2008). Similar to our study, associations have been found between TV viewing or sedentary behavior and WC in previous studies, but in most studies only among one gender (Klein-Platat et al., 2005; Delmas et al., 2007; Ortega et al., 2007; Lazarou and Soteriades, 2010). Ortega et al. (2007) also found that the negative impact of TV viewing on WC can be attenuated with more vigorous physical activity. However, TV viewing’s association with childhood obesity after controlling for physical activity has been stated a number of times (Vandewater and Huang, 2006; Jackson et al., 2009). In our study as well, the results were controlled for physical activity. In accordance with our study, the presence of a TV in a child’s room was associated with larger WC and other obesity measures in a sample of 12-year-old French school children, but only in boys (Delmas et al., 2007). The mechanism by


845 Table 2 The associations of health behaviors with WC and WHtR. Covariance analysis, B-coefficients and 95% confidence intervals (CI) WC

WHtR

Model

B

1 2 3

0 2.47** 2.49** 0.59

Energy-dense food index (quartiles as a continuous variable)

1 2 3

1.50*** 1.45*** 0.16

2.09 to (0.90) 2.04 to (0.86) 0.49–0.17

Nutrient-rich food index (quartiles as a categorical variable, least often vs most often)

1

0.76

2.32–0.81

0.001

0.009–0.010

2 3

1.71 0.32

3.43–0.01 1.26–0.62

0.005 0.003

0.016–0.006 0.003–0.010

Sleep duration (h)

1 2 3

0.95* 0.61 0.21

1.77 to (0.14) 1.50 to 0.28 0.69–0.28

0.005* 0.002 0.001

0.01–0.00 0.007–0.004 0.003–0.004

TV viewing (h)

1 2 3

0.64* 0.89* 0.08

0.07–1.2 0.17–1.61 0.48–0.31

0.004* 0.006** 0.000

0.001–0.007 0.002–0.011 0.002–0.003

0.67–3.80 0.73–3.86 0.22–1.49

0 0.013** 0.014** 0.004

0.0030.023 0.0040.024 0.002–0.010

Regular breakfast (no vs yes)

TV in child’s room (no vs yes) 1 2 3 Computer/game console time (tertiles as a categorical variable, least vs most)

Physical activity during school breaks (quartiles as a continuous variable)

Physical inactivity during school breaks (quartiles as a continuous variable)

0.60–4.35 0.64–4.34 0.43–1.62

0 0.89 0.00 0.11

3.14–1.36 2.66–2.65 1.55–1.33

1 2 3

0 1.33* 0.53 0.21

0.09–2.57 1.08–2.14 1.08–0.66

1 2 3

0.31* 0.21 0.26**

1 2 3

0 0.71* 0.68* 0.27

1 2 3

0.81** 0.78** 0.33*

B 0 0.017*** 0.015** 0.004 0.006*** 0.009*** 0.001

95% CI

0.007–0.027 0.004–0.027 0.003–0.011 0.009 to (0.003) 0.013 to (0.005) 0.003–0.001

0

1 2 3 Computer/game console in child’s room (no vs yes)

Physical activity (h/week)

0 2.24** 2.30** 0.63

95% CI

0.002 0.004 0.005

0.015–0.012 0.021–0.013 0.015–0.005

0 0.009* 0.004 0.000

0.001–0.016 0.006–0.014 0.006–0.005

0.59 to (0.03) 0.53–0.11 0.43–(0.09)

0.002* 0.001 0.002**

0.004–0.000 0.003–0.001 0.003–0.000

1.28 to (0.14) 1.29 to (0.07) 0.60–0.06

0 0.005** 0.005** 0.003*

0.008 to (0.002) 0.009 to (0.001) 0.005–0.000

0.25–1.37 0.19–1.37 0.01–0.65

0.003 0.003 0.000

0.000–0.007 0.000–0.007 0.0020.002

Abbreviations: WHtR, waist-to-height ratio; WC, waist circumference. Models: adjusted for: model 1: age, gender; model 2: model 1 þ all other health behaviors; model 3: model 2 þ BMI. *P ¼ o0.05; **P ¼ o0.01; ***P ¼ o0.001.

which a TV in a child’s room could be associated with larger WC is the duration of TV viewing time. It has been found that a TV in child’s room is associated with TV viewing time (Gorely et al., 2004; Delmas et al., 2007), although that was not the case in our study (data not shown). Thus, it is surprising that a TV in a child’s room is associated with a

larger WC even after controlling for TV viewing time. This might be because children misreport TV viewing time and children with a TV in their room in particular underestimate their TV viewing. One strength of our study was that we measured the anthropometrics of the study children. Self-reported or European Journal of Clinical Nutrition


846 parent-reported anthropometrics would have been prone to errors and underreporting (Goodman et al., 2000; Sherry et al., 2007). Many lifestyle-related health behaviors were studied, and therefore, we were able to study the association between many health behaviors and WC or WHtR simultaneously. The weaknesses of this study relate mostly to the study sample. The study sample was selective as it represented a language minority in the capital region of Finland. The children came mostly from high socio-economic status families. In that sense, it is possible that more associations would have been found with a larger heterogeneity in relation to children’s socio-economic status. Because the study sample was quite small, covariance analyses were not run separately for boys and girls. In any case, larger and more representative studies are needed to confirm our results. The response rate was quite low, because 20% of the contacted parents and children did not fill in the consent form and due to time limitations no reminder was sent. Even though the ability of children of this age to answer questions about their health behavior could be questioned, 10–12-year-old children are considered to be able to fill in FFQs (Livingstone and Robson, 2000), and the FFQ used in our study has been validated with 11–12-year-old children (Vereecken and Maes, 2003). Most of the questions were also used in WHO’s Health Behavior in School-aged Children study with 11–15-year-old children (Currie et al., 2001), but to our knowledge the questions have not been validated in this age group. However, the questions on physical activity have been validated among 13–18-year old adolescents (Rangul et al., 2008), showing that these questions had good validity. In our view, it would have been problematic to use parents’ reports on their child’s health behavior, because parents do not automatically know about their child’s food intake, TV viewing, computer use or physical activity that well. In Finland both parents usually work full-time. All children also get a warm lunch at school, and therefore parents are not that well aware of what their child eats at lunch. Thus, as children of this age spend a large part of the day without their parents being present, parents’ ability to report for example, food intake of their child has been reported to be limited (Livingstone and Robson, 2000). In a subsample, we have data on the children’s health behaviors reported by the parents as well. When comparing children’s and parents’ reports on the children’s health behavior, it was found that the reports were quite similar, but children reported more screen time and less sleep (Roos et al., 2009). By using a model including both BMI and WC, we wanted to find out if health behaviors are associated with measures of central obesity when BMI is constant. We are aware that this is problematic, as the correlation between BMI and WC was high (0.87). However, similar models have been used in several other studies (Rexrode et al., 1998; Halkjaer et al., 2004; Klein-Platat et al., 2005; Wildman et al., 2005). The cross-sectional design of our study does not permit any conclusions to be made of causality. It can be, as it is European Journal of Clinical Nutrition

proposed in many cross-sectional studies on BMI and physical activity, that children with larger WC have become more sedentary as a consequence of their size. The same thing applies to other results, such as the intake frequency of energy-dense foods. In addition, the 6-month delay in the administration of the study questionnaire after the anthropometrics was measured, poses additional challenges. To overcome these problems, prospective studies on the matter are needed.

Conclusions In this study, we have examined multiple health behaviors with regard to WC and WHtR among 9–11-year-old children. Controlling for other health behaviors and later for BMI gave us new knowledge about the independent contribution of different factors to the associations with WC and WHtR. We found that many health behaviors were associated with children’s WC and WHtR. After controlling for BMI, measures of physical activity were still associated with WC and WHtR. Our study suggests that physical activity can have a special association with WC and WHtR beyond BMI, but further and especially prospective studies on the matter are needed.

Conflict of interest The authors declare no conflict of interest.

Acknowledgements We would like to thank all the study schools, children and their parents for the participation in the study. This study was supported by Juho Vainio Foundation, Pa¨ivikki and Sakari Sohlberg Foundation, Signe and Ane Gyllenberg ¨ dsfo ¨ rening Liv och Ha¨lsa. Foundation, Medicinska understo

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