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Association between screen time and metabolic syndrome in children and adolescents in Korea: The 2005 Korean National Health and Nutrition Examination Survey
diabetes research and clinical practice 89 (2010) 72–78
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Diabetes Research and Clinical Practice jou rna l hom ep ag e: w ww.e lse v ier .com/ loca te /d iab res
Association between screen time and metabolic syndrome in children and adolescents in Korea: The 2005 Korean National Health and Nutrition Examination Survey Hee-Taik Kang a, Hye-Ree Lee a, Jae-Yong Shim a, Youn-Ho Shin b, Byoung-Jin Park a, Yong-Jae Lee a,* a b
Department of Family Medicine, College of Medicine, Yonsei University, Republic of Korea Department of Pediatrics, College of Medicine, Pochon CHA University, Republic of Korea
article info
abstract
Article history:
Objective: To examine the association between screen time and the metabolic syndrome
Received 22 December 2009
(MetS) in a nationally representative sample of children and adolescents.
Received in revised form
Methods: A cross-sectional survey was conducted in 845 children and adolescents (10–18
18 February 2010
years of age) from the 2005 Korean National Health and Nutrition Examination Surveys
Accepted 22 February 2010
(KNHANES). Screen time was defined as TV time + computer time. The definition of MetS
Published on line 29 March 2010
was based on the modified criteria used by the National Cholesterol Education ProgramAdult Treatment Panel III, using age- and sex-specific values for some of the criteria.
Keywords:
Results: In comparison with the children and adolescents in the ST-Q1 (16 h for a week),
Screen time
the odds ratio (95% confidence interval) for MetS of subjects in the ST-Q4 (35 h for a week)
Metabolic syndrome
was 2.23 (95% CI, 1.02–4.86) after the adjustment for age, household income, and residence
Cardiovascular disease
area. Moreover, screen time for a weekend day was also strongly associated with the
Prevention
prevalence of MetS, but not for a weekday. Conclusions: Screen time was independently associated with an increased prevalence risk of MetS in children and adolescents in Korea. Public health intervention to reduce screen time particularly for a weekend may be needed to prevent pediatric MetS in Korea. # 2010 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Reaven and his colleagues first described metabolic syndrome (MetS) in 1998 [1], and now MetS represents a cluster of cardiometabolic risk factors. Subjects with MetS are more susceptible to cardiovascular disease (CVD), type 2 diabetes mellitus (DM), and some cancers [2–4]. Although MetS is particularly important in adults, the clustering of CVD risk factors has also been observed in children and adolescents and these risk factors continue on into adulthood [5–7]. Thus, the early identification of MetS in children and adolescents is
important in order to prevent the morbidity and mortality that is often caused by MetS. The prevalence of childhood obesity and MetS is increasing worldwide as a global epidemic. In Korea, the prevalence of MetS in children and adolescents increased from 6.8% in 1998 to 9.2% in 2001 [8]. The rapid socioeconomic growth of Korea resulted in profound lifestyle changes such as the introduction of Westernized diets and sedentary behaviors. The amount of time spent in sedentary behaviors, such as watching TV, and using computers and playing computer games, is known as screen time. Screen time is a stereotype of major sedentary
* Corresponding author. Department of Family Medicine, College of Medicine, Yonsei University, 23 Yongmun-ro, Yongin city, Gyeonggi-do, Republic of Korea. Tel.: +82 31 331 8710; fax: +82 31 331 5551. E-mail address: [email protected] (Y.-J. Lee). 0168-8227/$ – see front matter # 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2010.02.016
diabetes research and clinical practice 89 (2010) 72–78
behaviors [9,10]. The adverse impact of excessive TVwatching and computer-using on pediatric health and well-being led to the public health and clinical guidelines regarding screen time duration. For instance, the American Academy of Pediatrics (AAP) has recommended watching TV for no more than 2 h a day in order to prevent the negative health effects that may result from TV viewing [11]. Several previous studies have reported that screen time, particularly TV viewing, is associated with MetS in adults, but there have only been a few studies that focused on children and adolescents [12,13] and no pediatric data regarding screen time exists for East Asian populations. Therefore, this study aimed to examine the association between screen time and MetS in a nationally representative sample of children and adolescents in Korea.
2.
Materials and methods
2.1.
Study population
This study was based on data that was obtained from the 2005 Korean National Health and Nutrition Examination Survey (KNHANES), which is a cross-sectional and nationally representative survey that was conducted by the Korean Ministry of Health and Welfare in 2005. The target population of the survey was non-institutionalized civilians in Korea that were at least 1 year in age. Sampling units were households that were selected through a stratified, multistage, probability-sampling design that was based on geographic area, sex, and age using household registries. There were 246,097 primary sampling units, each of which contained approximately 60 households. Six-hundred sampling frames, consisting of 13,345 households from the primary sampling units, were randomly sampled. Of these, 12,001 households (89.9%) were included in the study. Weights indicating the probability of being sampled were assigned to each participant, which enabled the results from this study to represent the entire Korean population. Participants completed four parts of a questionnaire, the Health Interview Survey, Health Behavior Survey, Health Examination Survey, and the Nutrition Survey. The Health Examination Survey was completed by 7597 (70.2%) of the 10,816 selected individuals who had taken part in the Health Interview Survey. Blood tests were performed on the 6412 individuals that were at least 10 years of age. After excluding 5559 adults that were older than 19 years of age and persons without anthropometric data, the data from 853 children and adolescents that were between the ages of 10–18 years were selected for this study. Of these 853 children and adolescents, eight subjects were excluded from the study because they had not fasted for at least 12 h prior to their blood sampling. A total of 845 children and adolescents were included in the final analysis.
2.2.
Data collection
At the time the 2005 KNHANES was conducted, citizens were informed that they had been randomly selected as a household to voluntarily participate in the nationally representative
73
survey conducted by the Korean Ministry of Health and Welfare. All citizens were given the right to refuse to participate in accordance with the National Health Enhancement Act that is supported by the National Statistics Law of Korea. The participants and their parents provided their written informed consent to participate in the study. The Korea Centers for Disease Control and Prevention also obtained written informed consent to use blood samples from their participants for further analyses. This study was approved by the Institutional Review Board of the Yonsei University College of Medicine, Seoul, Korea. Physical examinations were performed by trained medical staff following a standardized procedure. Body weight and height were measured in light indoor clothing without shoes to the nearest 0.1 kg and 0.1 cm, respectively. Waist circumference was measured at the narrowest point between the lower border of the rib cage and the iliac crest. Body mass index (BMI) was calculated as the ratio of weight (kg)/height2 (m2). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the right arm using a standard mercury sphygmomanometer (Baumanometer, USA). The average of two systolic and diastolic blood pressure readings, which were recorded at an interval of five minutes, was used for analysis. Dietary intake was collected using the 24-h recall method. All subjects were instructed to maintain their usual dietary habits. Daily calorie intake was calculated with the Can-Pro 2.0, nutrient intake assessment software that was developed by the Korean Nutrition Society. After 12 h of overnight fasting, blood samples were obtained from the antecubital veins of the study subjects. Fasting plasma glucose, total cholesterol, triglyceride, and HDL-cholesterol levels were measured using a Hitachi 7600-110 chemistry analyzer (Hitachi, Tokyo, Japan).
2.3.
Definition of metabolic syndrome
Blood pressure and anthropometric variables such as height, weight, and waist circumference tend to vary with age and stage of pubertal development. Consequently, age- and sexspecific values must be considered when defining MetS in children and in adolescents. Since there is little agreement on the definition of MetS, we used the same modified criteria for MetS that was used by the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) [14]. In this study, MetS was defined as having three or more of the following criteria: waist circumference 90th percentile for age and sex; systolic or diastolic blood pressure 90th percentile for age, sex, and height; fasting plasma glucose (FPG) 100 mg/dL; triglyceride level 110 mg/dL; or a HDLcholesterol level 40 mg/dL. The cut-off values for waist circumference and blood pressure were based on the growth charts that were published by the Korean Pediatric Society in 2005. The percentile values for the MetS component were used within each age grouping of 0.5 years in order to determine which subjects had each specific MetS component. If the 90th percentile value of the MetS component surpassed the adult cut-off values for waist circumference and blood pressure, the adult cut-off value was used instead (each cut-off value for waist circumference and blood pressure was described on the Appendix 1).
74
2.4.
diabetes research and clinical practice 89 (2010) 72–78
Definition of screen time
In the Health Behavior Survey, there were four questions that were used to assess screen time [TV time (time spent watching TV) + computer time (time spent using a computer, computer game, or internet)]: TV time for a weekday, ‘‘Over the past week, on average how many hours per weekday did you watch TV?’’; TV time for a weekend day, ‘‘Over the past week, on average how many hours per weekend day did you watch TV?’’; computer time for a weekday, ‘‘Over the past week, on average how many hours per weekday did you use a computer, computer game, or internet?’’; and computer time for a weekend day, ‘‘Over the past week, on average how many hours per weekend day did you use a computer, computer game, or internet?’’. We defined ‘screen time’ as time spent watching TV and using a computer, playing a computer game, and browsing the internet. Total screen time was defined as follows: ‘screen time for five weekdays’ + ‘screen time for two weekend days’. We divided the time spent watching TV, using a computer, and the total screen time into 4 quartile groups, which each of these groups referred to as TV time quartile (TVQ), computer time quartile (COM-Q), and screen time quartile (ST-Q). The four quartiles of TV time were categorized as follows; TV-Q1: 0–8 h, TV-Q2: 9–13 h, TV-Q3: 14–20 h, and TVQ4: 21 h. The four quartiles of computer time were expressed as COM-Q1: 0–5 h, COM-Q2: 6–8 h, COM-Q3: 9–15 h, and COMQ4: 16 h. The four quartiles of screen time were categorized as follows; ST-Q1: 0–16 h, ST-Q2: 17–24 h, ST-Q3: 25–34 h, and ST-Q4: 35 h.
2.5.
Statistical analysis
The characteristics of the study population were summarized using either the independent two sample t-test or the one-way ANOVA test for continuous variables and the chisquare test for categorical variables. The prevalence of MetS
according to each quartile of TV time, computer time and screen time for 1 week was calculated. Hours of TV time, computer time, and screen time in a 1-week period were compared between populations with or without MetS using independent two sample t-test. The odds ratios (ORs) and 95% confidence intervals (95% CIs) for MetS were calculated using logistic regression analysis after the adjustment for confounding variables across the screen time quartiles. In order to examine the different roles of screen time for a weekday and for a weekend day, another multivariate logistic regression analysis was also conducted after the stratification of weekday and weekend day were made. All statistical analyses were conducted using SAS statistical software, version 9.1 (SAS Institute Inc, Cary, NC, USA). All statistical tests were 2-sided and statistical significance was determined at P-value <0.05.
3.
Results
Table 1 shows the subject characteristics of the 845 children and adolescents (449 boys and 396 girls). The mean age of boys was 13.5 years and that of girls was 13.4 years. The mean BMI, waist circumference, SBP and DBP, FPG, and total energy intake were significantly higher in boys than in girls, whereas the total cholesterol and HDL-cholesterol levels were higher in girls than in boys. In the present study, the overall prevalence of MetS according to the modified NCEP-ATP III criteria was 7.3%, with a higher prevalence in boys (9.6%) than in girls (4.9%). Table 2 shows the subject characteristics according to the quartiles of screen time (TV time + computer time). The mean BMI, waist circumference, SBP, total cholesterol, and triglyceride levels increased as the quartile of screen time increased. On the contrary, household income was inversely correlated with the quartile of screen time.
Table 1 – Characteristics of the study population.
n (%) MetS (%) Age (years) BMI (kg/m2) WC (cm) SBP (mm Hg) DBP (mm Hg) FPG (mg/dL) Total cholesterol (mg/dL) Triglyceride (mg/dL) HDL-cholesterol (mg/dL) Total energy intake (kcal) Household income (US dollard) Residence in rural area (%)
All
Boys
Girls
845 (100) 7.3 13.4 2.5 20.6 3.7 69.2 10.0 105.8 10.9 67.8 9.4 87.8 6.3 156.1 26.2 91.4 50.0 44.2 9.0 2160.2 808.2 2239.2 1291.2 59.2
449 (53.1) 9.6 13.5 2.4 21.0 3.9 71.6 10.6 108.0 11.2 68.6 10.0 88.4 6.4 151.8 25.4 90.6 54.0 42.7 8.8 2314.8 857.1 2286.4 1331.2 56.8
396 (46.9) 4.9 13.4 2.5 20.2 3.5 66.5 8.5 103.3 10.0 66.8 8.5 87.1 6.1 161.0 26.3 92.3 44.9 45.8 9.0 1984.5 710.2 2185.6 1244.0 61.9
P-valuea NAb 0.008c 0.41 0.002 <0.001 <0.001 0.007 0.003 <0.001 0.63 <0.001 <0.001 0.26 0.13
Abbreviations: BMI; body mass index, WC; waist circumference, SBP; systolic blood pressure, DBP; diastolic blood pressure, FPG; fasting plasma glucose, HDL; high-density lipoprotein. a P-value except for MetS was calculated to compare between boys and girls by independent t-test. b Not applicable. c P-value for MetS was calculated by chi-square test. d One US dollar was calculated as 1250 Korean won.
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diabetes research and clinical practice 89 (2010) 72–78
Table 2 – Characteristics of the study population according to quartile of screen time.
n (boy %) Age (years) BMI (kg/m2) WC (cm) SBP (mm Hg) DBP (mm Hg) FPG (mg/dL) Total cholesterol (mg/dL) Triglyceride (mg/dL) HDL-cholesterol (mg/dL) Total energy intake (kcal) Household income (US dollarb) Residence in rural area (%)
All
ST-Q1 (16 h)
845 (53.1) 13.4 2.5 20.6 3.7 69.2 10.0 105.8 10.9 67.8 9.4 87.8 6.3 156.1 26.2 91.4 50.0 44.2 9.0 2160.2 808.2 2239.2 1291.2 59.2
205 (48.8) 13.6 2.5 20.5 3.6 69.0 9.6 106.0 10.3 68.3 9.2 86.9 6.7 155.3 26.4 89.7 51.3 43.7 8.5 2235.9 778.5 2498.4 1277.6 60.0
ST-Q2 (17–24 h) ST-Q3 (25–34 h) 200 (49.0) 13.3 2.5 20.7 4.0 69.5 10.1 104.0 11.9 66.8 8.7 87.6 6.0 160.6 27.7 97.8 54.2 44.3 9.2 2121.2 821.4 2382.4 1379.2 62.5
215 (53.0) 12.9 2.5 19.9 3.2 67.4 9.2 105.5 10.0 66.9 10.1 88.1 6.2 154.8 22.8 83.6 32.9 45.4 9.0 2113.9 820.1 2244.8 1292.8 56.3
ST-Q4 (35 h)
P-valuea
225 (60.9) 13.8 2.3 21.2 4.0 70.9 10.9 107.6 11.1 68.9 9.3 88.5 6.2 154.2 27.6 94.8 56.9 43.4 9.3 2172.5 811.4 1876.0 1140.0 58.2
0.04 <0.001 0.004 0.003 0.009 0.05 0.05 0.05 0.02 0.09 0.45 <0.001 0.61
Abbreviations: BMI; body mass index, BMI-SDS; BMI standard deviation score, WC; waist circumference, SBP; systolic blood pressure, DBP; diastolic blood pressure, FPG; fasting plasma glucose, HDL; high-density lipoprotein. ST-Q means screen time (TV time + computer time) divided by quartile percentage for a week (ST-Q1; 0–16 h, ST-Q2; 17–24 h, ST-Q3; 25–34 h, and ST-Q4 35 h). a P-value was calculated by ANOVA test. b One US dollar was calculated as 1250 Korean won.
Fig. 1 shows the proportion of MetS according to the time quartiles of TV time, computer time, and screen time for 1 week. The proportion of MetS was highest in the 4th quartile for each of these categories. The P-values for TV-Q, Com-Q, and ST-Q are 0.71, 0.086, and 0.036, respectively. Fig. 2 illustrates TV time, computer time and screen time for 1 week among subjects with and without MetS. The mean TV time, computer time and screen time for a 1-week period were higher among the subject groups with MetS than for the groups without MetS. Table 3 shows the results of the logistic regression analyses designed to examine the relationship between each quartile of screen time and MetS. In comparison with the children and adolescents that were categorized in the ST-Q1 (16 h for a week), the odds ratio for MetS of children and adolescents that
were categorized in the ST-Q4 (35 h for a week) was 2.23 (95% CI, 1.02–4.86) after the adjustment for age, sex, household income, and residence area. To further evaluate the predictive role of screen time for a weekday and a weekend day, we conducted a multivariate logistic regression analysis that was stratified by weekday and weekend day. Screen time for a weekend day (WE-ST-Q) was strongly associated with the prevalence of MetS. The OR (95% CI) of MetS in children and adolescents that were categorized into the 4th quartile of WE-ST-Q4 (7 h for weekend day) was 2.62 (95% CI, 1.16–5.90), when compared with subjects in 1st quartile of WE-ST-Q1 (3 h) after the adjustment for age, sex, household income, and residence area. However, there was no significant association between the screen time during a weekday and the prevalence of MetS. After additionally
Fig. 1 – The prevalence of MetS according to each time quartile for 1 week [TV-Q means time to watch TV for a week, divided by quartile percentage (TV-Q1; 0–8 h, TVQ2; 9–13 h, TV-Q3; 14–20 h, TV-Q4; I21 h). Com-Q means time to use computer, computer game, and internet for a week, divided by quartile percentage (Com-Q1; 0–5 h, Com-Q2; 6–8 h, Com-Q3; 9–15 h, Com-Q4; I16 h). ST-Q means screen time (TV time + computer time), divided by quartile percentage (ST-Q1; 0–16 h, ST-Q2; 17–24 h, ST-Q3; 25–34 h, and ST-Q4 I35 h)]. The P-values for TV-Q, Com-Q, and ST-Q are 0.71, 0.086, and 0.036, respectively.
Fig. 2 – Mean time for 1 week among subjects with or without MetS (TV time, computer time, and screen time mean total time to watch TV, time to use computer, computer game, and internet, and time to spend both TV and computer for 1 week. P-value was calculated by independent two sample t-test. *P-value <0.05, **P-value <0.01).
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diabetes research and clinical practice 89 (2010) 72–78
Table 3 – Odds ratios and 95% confidence intervals for MetS according to quartile of screen time.
Week screen time Model 1a Model 2b Model 3c
Weekday screen time Model 1a Model 2b Model 3c
Weekend screen time Model 1a Model 2b Model 3c
ST-Q1 (16 h)
ST-Q2 (17–24 h)
ST-Q3 (25–34 h)
1.00 1.00 1.00
1.70 (0.75–3.83) 1.72 (0.76–3.88) 1.75 (0.77–3.99)
1.15 (0.49–2.73) 1.19 (0.50–2.82) 1.16 (0.49–2.78)
W-ST-Q1 (0–1 h)
W-ST-Q2 (2 h)
W-ST-Q3 (3 h)
0.57 (0.23–1.42) 0.59 (0.24–1.48) 0.58 (0.23–1.47)
0.59 (0.24–1.47) 0.62 (0.25–1.54) 0.64 (0.25–1.62)
1.00 1.00 1.00
WE-ST-Q1 (0–3 h)
WE-ST-Q2 (4 h)
WE-ST-Q3 (5–6 h)
1.00 1.00 1.00
1.59 (0.58–4.33) 1.60 (0.59–4.39) 1.47 (0.53–4.05)
1.88 (0.79–4.44) 1.91 (0.81–4.53) 1.71 (0.72–4.09)
ST-Q4 (35 h) 2.33 (1.09–5.00) 2.31 (1.08–4.96) 2.23 (1.02–4.86)
W-ST-Q4 (4 h) 1.36 (0.69–2.65) 1.38 (0.93–1.15) 1.37 (0.69–2.75)
WE-ST-Q4 (7 h) 2.90 (1.30–6.48) 2.90 (1.30–6.50) 2.62 (1.16–5.90)
ST-Q means screen time (TV time + computer time) for a week. W-ST-Q means screen time (TV time + computer time) for a weekday. WE-ST-Q means screen time (TV time + computer time) for a weekend day. a Model 1: unadjusted. b Model 2: adjusted for age. c Model 3: adjusted for age, sex, household income, and residence area.
adjusting for age, sex, household income, residence area, dietary intake and physical activity, children and adolescents who were categorized into the only highest quartile of screen time for a weekend day (WE-ST-Q4, 7 h), but not for a week and a weekday, had the significant odds ratio of 2.88 (95% CI, 1.04–7.98) (data not shown).
4.
Discussion
In this cross-sectional study, we examined the prevalence of MetS and its association with screen time in a representative sample of children and adolescents in Korea. The worldwide prevalence of MetS in children and adolescents ranges widely from 3 to 11% depending on the ethnic group, lifestyle, urbanization, and diagnostic criteria [14–18]. In the current study, we used the age-modified NCEP-ATP III criteria that was proposed by Ford et al. [14]. The estimated prevalence of MetS in children and adolescents in Korea was 7.3% (9.6% in boys, 4.9% in girls) in our study. When the recent prevalence of MetS (8.6–10.6%) of US adolescents from National Health and Nutrition Examination Surveys (NHANES) between the years 1999 and 2006 was compared to that of our study [17,18], the results showed that the overall prevalence of MetS in Korea is relatively low. Several longitudinal studies have suggested that the clustering of risk factors for CVD should begin in childhood and track into adulthood. Results from the Bogalusa Heart Study revealed that childhood obesity was a strong predictor of MetS in adulthood [6]. Similarly, Morrison et al. reported that the pediatric measures of waist circumference and triglyceride level were important sentinels for the development of MetS in adults [19]. Considering the adverse health consequences such as CVD mortality, early identification of
MetS in children and adolescents in specific populations is important in public health planning. Another major finding in this current study was that screen time was found to be independently associated with the presence of MetS in children and adolescents in Korea. This result is consistent with the emerging evidence that a sedentary lifestyle is strongly linked to obesity, insulin resistance, and MetS. Increased screen time may also lead individuals to have a greater energy intake, by the increased consumption of snacks and soft drinks, and intake less of vegetables and fresh fruits [20–22]. Moreover, a greater amount of screen time may also displace physical activity [23,24]. However, there was no correlation of screen time with energy intake or physical activity in the present study (data not shown). While some epidemiologic studies have shown that screen time is associated with MetS and insulin resistance, there has been little research on the association in children and adolescents, particularly in East Asian countries. In a study of adolescents between the ages of 12–19 years that took the 1994–2004 US NHANES, Mark et al. has shown that screen time is associated with an increased possibility of MetS in a dosedependent manner [25]. As shown in Table 3, the Korean children and adolescents in the 4th quartile of screen time were also more likely to have more than twice the probability of the MetS, after the adjustments for age, sex, household income, and residence area were made. Viner et al. also illustrated that weekend TV viewing could influence obesity in adulthood [26]. We also conducted a multivariate logistic regression analysis that was stratified by weekday and weekend day to further evaluate the predictive role of screen time that occurs on a weekday and a weekend day. Screen time for a weekend day was also strongly associated with the prevalence of MetS,
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diabetes research and clinical practice 89 (2010) 72–78
but not for a weekday. The 4th quartile of screen time (WE-STQ4) on a weekend had statistical significance (OR = 2.62, 95% CI = 1.16–5.90). These findings indicate that there may be a closer interrelationship between MetS and screen time on a weekend day than a weekday. There are some limitations that should be considered when interpreting the findings from this study. Firstly, it is difficult to explain a causal relationship between screen time and increased risk for the MetS from this cross-section study design. Secondly, TV time and computer time were selfreported by the study participants. Self-reported measures of socially undesirable behaviors, such as TV watching and computer use, tend to be underestimated. Although screen time (TV time + computer time) is generally considered to be underestimated, a previous study in middle school-aged children found that the difference between self-reporting and actual time spent was only 0.09 h per week in TV time and 0.68 h per week in computer time [27]. Thirdly, we could not fully exclude the effect on the information bias, because this study was based on the questionnaire survey and screen time was calculated from this survey. Finally, the questions do not distinguish between leisure screen time usage such as surfing the internet, playing computer games, or watching TV and productive screen time such as doing homework, research, or watching an educational TV program. Since an increase in productive time is associated with higher levels of physical activity [28], the measure of total screen time that does not distinguishing between leisure screen time from productive screen time, could influence the results regarding the risk of MetS. In conclusion, we confirmed a clear and significant relationship between screen time and the prevalence of MetS in a representative sample of children and adolescents in Korea. We also suggest that screen time on a weekend day may be more closely associated with the prevalence of MetS in children and adolescents. Public health intervention to reduce screen time particularly for a weekend may be needed to prevent pediatric MetS in Korea.
Conflict of interest There are no conflicts of interest.
Appendix A Cut-off points of each MetS criteria based on the growth charts that were published by the Korean Pediatric Society in 2005.
10-year 10-year 11-year 11-year 12-year 12-year 13-year 13-year 14-year
boy girl boy girl boy girl boy girl boy
WC (cm)
SBP (mm Hg)
DBP (mm Hg)
79.2 73.4 82.1 76.7 85.2 77.3 87.1 77.8 89.5
119 117 121 119 124 119 126 120 129
77 76 77 76 79 78 79 78 80
14-year 15-year 15-year 16-year 16-year 17-year 17-year 18-year 18-year
girl boy girl boy girl boy girl boy girl
78.5 89.7 78.8 88.4 79.0 90.0 79.9 89.0 80.0
121 130 122 130 123 130 124 130 126
79 81 79 81 80 82 81 85 82
references
[1] G.M. Reaven, Banting Lecture 1988: role of insulin resistance in human disease, Diabetes 37 (1988) 1595–1607. [2] D.E. Moller, K.D. Kaufman, Metabolic syndrome: a clinical and molecular perspective, Annu. Rev. Med. 56 (2005) 45–62. [3] S. Cowey, R.W. Hardy, The metabolic syndrome: a high-risk state for cancer? Am. J. Pathol. 169 (2006) 1505–1522. [4] A. Russo, M. Autelitano, L. Bisanti, Metabolic syndrome and cancer risk, Eur. J. Cancer 44 (2008) 293–297. [5] P.T. Katzmarzyk, L. Pe´russe, R.M. Malina, J. Bergeron, J.P. Despre´s, C. Bouchard, Stability of indicators of the metabolic syndrome from childhood and adolescence to young adulthood: the Quebec Family Study, J. Clin. Epidemiol. 54 (2001) 190–195. [6] S.R. Srinivasan, L. Myers, G.S. Berenson, Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study, Diabetes 51 (2002) 204–209. [7] W.H. Dietz, Health consequences of obesity in youth: childhood predictors of adult disease, Pediatrics 101 (1998) 518–525. [8] H.M. Kim, J. Park, H.S. Kim, D.H. Kim, Prevalence of the metabolic syndrome in Korean adolescents aged 12–19 years from the Korean National Health and Nutrition Examination Survey 1998 and 2001, Diabetes Res. Clin. Pract. 75 (2007) 111–114. [9] T. Tammelin, U. Ekelund, J. Remes, S. Na¨yha¨, Physical activity and sedentary behaviors among Finnish youth, Med. Sci. Sports Exerc. 39 (2007) 1067–1074. [10] T. Sugiyama, G.N. Healy, D.W. Dunstan, J. Salmon, N. Owen, Is television viewing time a marker of a broader pattern of sedentary behavior? Ann. Behav. Med. 35 (2008) 245–250. [11] American Academy of Pediatrics, Committee on public education, American Academy of Pediatrics: children, adolescents, and television, Pediatrics 107 (2001) 423–426. [12] P.E. Pardee, G.J. Norman, R.H. Lustig, D. Preud’homme, J.B. Schwimmer, Television viewing and hypertension in obese children, Am. J. Prev. Med. 33 (2007) 439–443. [13] R.E. Andersen, C.J. Crespo, S.J. Bartlett, L.J. Cheskin, M. Pratt, Relationship of physical activity and television watching with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey, JAMA 279 (1998) 938–942. [14] E.S. Ford, C. Li, S. Cook, H.K. Choi, Serum concentrations of uric acid and the metabolic syndrome among US children and adolescents, Circulation 115 (2007) 2526–2532. [15] Y. Li, X. Yang, F. Zhai, F.J. Kok, W. Zhao, J. Piao, et al., Prevalence of the metabolic syndrome in Chinese adolescents, Br. J. Nutr. 99 (2008) 565–570. [16] R. Singh, A. Bhansali, R. Sialy, A. Aggarwal, Prevalence of metabolic syndrome in adolescents from a north Indian population, Diabet. Med. 24 (2007) 195–199. [17] W.D. Johnson, J.J. Kroon, F.L. Greenway, C. Bouchard, D. Ryan, P.T. Katzmarzyk, Prevalence of risk factors for
78
[18]
[19]
[20]
[21]
[22]
diabetes research and clinical practice 89 (2010) 72–78
metabolic syndrome in adolescents: National Health and Nutrition Examination Survey (NHANES), 2001–2006, Arch. Pediatr. Adolesc. Med. 163 (2009) 371–377. K.B. Daratha, R.C. Bindler, Effects of individual components, time, and sex on prevalence of metabolic syndrome in adolescents, Arch. Pediatr. Adolesc. Med. 163 (2009) 365–370. J.A. Morrison, L.A. Friedman, W.R. Harlan, L.C. Harlan, B.A. Barton, G.B. Schreiber, et al., Development of the metabolic syndrome in black and white adolescent girls: a longitudinal assessment, Pediatrics 116 (2005) 1178–1182. N. Stroebele, J.M. de Castro, Television viewing is associated with an increase in meal frequency in humans, Appetite 42 (2004) 111–113. S.M. Phillips, L.G. Bandini, E.N. Naumova, H. Cyr, S. Colclough, W.H. Dietz, et al., Energy-dense snack food intake in adolescence: longitudinal relationship to weight and fatness, Obes. Res. 12 (2004) 461–472. R. Lowry, H. Wechsler, D.A. Galuska, J.E. Fulton, L. Kann, Television viewing and its associations with overweight, sedentary lifestyle, and insufficient consumption of fruits and vegetables among US high school students: differences by race, ethnicity, and gender, J. Sch. Health 72 (2002) 413–421.
[23] J.P. Rey-Lo´pez, G. Vicente-Rodrı´guez, M. Biosca, L.A. Moreno, Sedentary behaviour and obesity development in children and adolescents, Nutr. Metab. Cardiovasc. Dis. 18 (2008) 242–251. [24] D.B. Panagiotakos, C. Pitsavos, C. Chrysohoou, J. Skoumas, D. Tousoulis, M. Toutouza, et al., Impact of lifestyle habits on the prevalence of the metabolic syndrome among Greek adults from the ATTICA study, Am. Heart J. 147 (2004) 106–112. [25] A.E. Mark, I. Janssen, Relationship between screen time and metabolic syndrome in adolescents, J. Public Health 30 (2008) 153–160. [26] R.M. Viner, T.J. Cole, Television viewing in early childhood predicts adult body mass index, J. Pediatr. 147 (2005) 429–435. [27] K.H. Schimitz, L. Harnack, J.E. Fulton, D.R. Jacobs Jr., S. Gao, L.A. Lytle, et al., Reliability and validity of a brief questionnaire to assess television viewing and computer use by middle school children, J. Sch. Health 74 (2004) 370–377. [28] D.E. Feldman, T. Barnett, I. Shrier, M. Rossignol, L. Abenhaim, Is physical activity differentially associated with different types of sedentary pursuits? Arch. Pediatr. Adolesc. Med. 157 (2003) 797–802.
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Diabetes Research and Clinical Practice jou rna l hom ep ag e: w ww.e lse v ier .com/ loca te /d iab res
Association between screen time and metabolic syndrome in children and adolescents in Korea: The 2005 Korean National Health and Nutrition Examination Survey Hee-Taik Kang a, Hye-Ree Lee a, Jae-Yong Shim a, Youn-Ho Shin b, Byoung-Jin Park a, Yong-Jae Lee a,* a b
Department of Family Medicine, College of Medicine, Yonsei University, Republic of Korea Department of Pediatrics, College of Medicine, Pochon CHA University, Republic of Korea
article info
abstract
Article history:
Objective: To examine the association between screen time and the metabolic syndrome
Received 22 December 2009
(MetS) in a nationally representative sample of children and adolescents.
Received in revised form
Methods: A cross-sectional survey was conducted in 845 children and adolescents (10–18
18 February 2010
years of age) from the 2005 Korean National Health and Nutrition Examination Surveys
Accepted 22 February 2010
(KNHANES). Screen time was defined as TV time + computer time. The definition of MetS
Published on line 29 March 2010
was based on the modified criteria used by the National Cholesterol Education ProgramAdult Treatment Panel III, using age- and sex-specific values for some of the criteria.
Keywords:
Results: In comparison with the children and adolescents in the ST-Q1 (16 h for a week),
Screen time
the odds ratio (95% confidence interval) for MetS of subjects in the ST-Q4 (35 h for a week)
Metabolic syndrome
was 2.23 (95% CI, 1.02–4.86) after the adjustment for age, household income, and residence
Cardiovascular disease
area. Moreover, screen time for a weekend day was also strongly associated with the
Prevention
prevalence of MetS, but not for a weekday. Conclusions: Screen time was independently associated with an increased prevalence risk of MetS in children and adolescents in Korea. Public health intervention to reduce screen time particularly for a weekend may be needed to prevent pediatric MetS in Korea. # 2010 Elsevier Ireland Ltd. All rights reserved.
1.
Introduction
Reaven and his colleagues first described metabolic syndrome (MetS) in 1998 [1], and now MetS represents a cluster of cardiometabolic risk factors. Subjects with MetS are more susceptible to cardiovascular disease (CVD), type 2 diabetes mellitus (DM), and some cancers [2–4]. Although MetS is particularly important in adults, the clustering of CVD risk factors has also been observed in children and adolescents and these risk factors continue on into adulthood [5–7]. Thus, the early identification of MetS in children and adolescents is
important in order to prevent the morbidity and mortality that is often caused by MetS. The prevalence of childhood obesity and MetS is increasing worldwide as a global epidemic. In Korea, the prevalence of MetS in children and adolescents increased from 6.8% in 1998 to 9.2% in 2001 [8]. The rapid socioeconomic growth of Korea resulted in profound lifestyle changes such as the introduction of Westernized diets and sedentary behaviors. The amount of time spent in sedentary behaviors, such as watching TV, and using computers and playing computer games, is known as screen time. Screen time is a stereotype of major sedentary
* Corresponding author. Department of Family Medicine, College of Medicine, Yonsei University, 23 Yongmun-ro, Yongin city, Gyeonggi-do, Republic of Korea. Tel.: +82 31 331 8710; fax: +82 31 331 5551. E-mail address: [email protected] (Y.-J. Lee). 0168-8227/$ – see front matter # 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2010.02.016
diabetes research and clinical practice 89 (2010) 72–78
behaviors [9,10]. The adverse impact of excessive TVwatching and computer-using on pediatric health and well-being led to the public health and clinical guidelines regarding screen time duration. For instance, the American Academy of Pediatrics (AAP) has recommended watching TV for no more than 2 h a day in order to prevent the negative health effects that may result from TV viewing [11]. Several previous studies have reported that screen time, particularly TV viewing, is associated with MetS in adults, but there have only been a few studies that focused on children and adolescents [12,13] and no pediatric data regarding screen time exists for East Asian populations. Therefore, this study aimed to examine the association between screen time and MetS in a nationally representative sample of children and adolescents in Korea.
2.
Materials and methods
2.1.
Study population
This study was based on data that was obtained from the 2005 Korean National Health and Nutrition Examination Survey (KNHANES), which is a cross-sectional and nationally representative survey that was conducted by the Korean Ministry of Health and Welfare in 2005. The target population of the survey was non-institutionalized civilians in Korea that were at least 1 year in age. Sampling units were households that were selected through a stratified, multistage, probability-sampling design that was based on geographic area, sex, and age using household registries. There were 246,097 primary sampling units, each of which contained approximately 60 households. Six-hundred sampling frames, consisting of 13,345 households from the primary sampling units, were randomly sampled. Of these, 12,001 households (89.9%) were included in the study. Weights indicating the probability of being sampled were assigned to each participant, which enabled the results from this study to represent the entire Korean population. Participants completed four parts of a questionnaire, the Health Interview Survey, Health Behavior Survey, Health Examination Survey, and the Nutrition Survey. The Health Examination Survey was completed by 7597 (70.2%) of the 10,816 selected individuals who had taken part in the Health Interview Survey. Blood tests were performed on the 6412 individuals that were at least 10 years of age. After excluding 5559 adults that were older than 19 years of age and persons without anthropometric data, the data from 853 children and adolescents that were between the ages of 10–18 years were selected for this study. Of these 853 children and adolescents, eight subjects were excluded from the study because they had not fasted for at least 12 h prior to their blood sampling. A total of 845 children and adolescents were included in the final analysis.
2.2.
Data collection
At the time the 2005 KNHANES was conducted, citizens were informed that they had been randomly selected as a household to voluntarily participate in the nationally representative
73
survey conducted by the Korean Ministry of Health and Welfare. All citizens were given the right to refuse to participate in accordance with the National Health Enhancement Act that is supported by the National Statistics Law of Korea. The participants and their parents provided their written informed consent to participate in the study. The Korea Centers for Disease Control and Prevention also obtained written informed consent to use blood samples from their participants for further analyses. This study was approved by the Institutional Review Board of the Yonsei University College of Medicine, Seoul, Korea. Physical examinations were performed by trained medical staff following a standardized procedure. Body weight and height were measured in light indoor clothing without shoes to the nearest 0.1 kg and 0.1 cm, respectively. Waist circumference was measured at the narrowest point between the lower border of the rib cage and the iliac crest. Body mass index (BMI) was calculated as the ratio of weight (kg)/height2 (m2). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the right arm using a standard mercury sphygmomanometer (Baumanometer, USA). The average of two systolic and diastolic blood pressure readings, which were recorded at an interval of five minutes, was used for analysis. Dietary intake was collected using the 24-h recall method. All subjects were instructed to maintain their usual dietary habits. Daily calorie intake was calculated with the Can-Pro 2.0, nutrient intake assessment software that was developed by the Korean Nutrition Society. After 12 h of overnight fasting, blood samples were obtained from the antecubital veins of the study subjects. Fasting plasma glucose, total cholesterol, triglyceride, and HDL-cholesterol levels were measured using a Hitachi 7600-110 chemistry analyzer (Hitachi, Tokyo, Japan).
2.3.
Definition of metabolic syndrome
Blood pressure and anthropometric variables such as height, weight, and waist circumference tend to vary with age and stage of pubertal development. Consequently, age- and sexspecific values must be considered when defining MetS in children and in adolescents. Since there is little agreement on the definition of MetS, we used the same modified criteria for MetS that was used by the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) [14]. In this study, MetS was defined as having three or more of the following criteria: waist circumference 90th percentile for age and sex; systolic or diastolic blood pressure 90th percentile for age, sex, and height; fasting plasma glucose (FPG) 100 mg/dL; triglyceride level 110 mg/dL; or a HDLcholesterol level 40 mg/dL. The cut-off values for waist circumference and blood pressure were based on the growth charts that were published by the Korean Pediatric Society in 2005. The percentile values for the MetS component were used within each age grouping of 0.5 years in order to determine which subjects had each specific MetS component. If the 90th percentile value of the MetS component surpassed the adult cut-off values for waist circumference and blood pressure, the adult cut-off value was used instead (each cut-off value for waist circumference and blood pressure was described on the Appendix 1).
74
2.4.
diabetes research and clinical practice 89 (2010) 72–78
Definition of screen time
In the Health Behavior Survey, there were four questions that were used to assess screen time [TV time (time spent watching TV) + computer time (time spent using a computer, computer game, or internet)]: TV time for a weekday, ‘‘Over the past week, on average how many hours per weekday did you watch TV?’’; TV time for a weekend day, ‘‘Over the past week, on average how many hours per weekend day did you watch TV?’’; computer time for a weekday, ‘‘Over the past week, on average how many hours per weekday did you use a computer, computer game, or internet?’’; and computer time for a weekend day, ‘‘Over the past week, on average how many hours per weekend day did you use a computer, computer game, or internet?’’. We defined ‘screen time’ as time spent watching TV and using a computer, playing a computer game, and browsing the internet. Total screen time was defined as follows: ‘screen time for five weekdays’ + ‘screen time for two weekend days’. We divided the time spent watching TV, using a computer, and the total screen time into 4 quartile groups, which each of these groups referred to as TV time quartile (TVQ), computer time quartile (COM-Q), and screen time quartile (ST-Q). The four quartiles of TV time were categorized as follows; TV-Q1: 0–8 h, TV-Q2: 9–13 h, TV-Q3: 14–20 h, and TVQ4: 21 h. The four quartiles of computer time were expressed as COM-Q1: 0–5 h, COM-Q2: 6–8 h, COM-Q3: 9–15 h, and COMQ4: 16 h. The four quartiles of screen time were categorized as follows; ST-Q1: 0–16 h, ST-Q2: 17–24 h, ST-Q3: 25–34 h, and ST-Q4: 35 h.
2.5.
Statistical analysis
The characteristics of the study population were summarized using either the independent two sample t-test or the one-way ANOVA test for continuous variables and the chisquare test for categorical variables. The prevalence of MetS
according to each quartile of TV time, computer time and screen time for 1 week was calculated. Hours of TV time, computer time, and screen time in a 1-week period were compared between populations with or without MetS using independent two sample t-test. The odds ratios (ORs) and 95% confidence intervals (95% CIs) for MetS were calculated using logistic regression analysis after the adjustment for confounding variables across the screen time quartiles. In order to examine the different roles of screen time for a weekday and for a weekend day, another multivariate logistic regression analysis was also conducted after the stratification of weekday and weekend day were made. All statistical analyses were conducted using SAS statistical software, version 9.1 (SAS Institute Inc, Cary, NC, USA). All statistical tests were 2-sided and statistical significance was determined at P-value <0.05.
3.
Results
Table 1 shows the subject characteristics of the 845 children and adolescents (449 boys and 396 girls). The mean age of boys was 13.5 years and that of girls was 13.4 years. The mean BMI, waist circumference, SBP and DBP, FPG, and total energy intake were significantly higher in boys than in girls, whereas the total cholesterol and HDL-cholesterol levels were higher in girls than in boys. In the present study, the overall prevalence of MetS according to the modified NCEP-ATP III criteria was 7.3%, with a higher prevalence in boys (9.6%) than in girls (4.9%). Table 2 shows the subject characteristics according to the quartiles of screen time (TV time + computer time). The mean BMI, waist circumference, SBP, total cholesterol, and triglyceride levels increased as the quartile of screen time increased. On the contrary, household income was inversely correlated with the quartile of screen time.
Table 1 – Characteristics of the study population.
n (%) MetS (%) Age (years) BMI (kg/m2) WC (cm) SBP (mm Hg) DBP (mm Hg) FPG (mg/dL) Total cholesterol (mg/dL) Triglyceride (mg/dL) HDL-cholesterol (mg/dL) Total energy intake (kcal) Household income (US dollard) Residence in rural area (%)
All
Boys
Girls
845 (100) 7.3 13.4 2.5 20.6 3.7 69.2 10.0 105.8 10.9 67.8 9.4 87.8 6.3 156.1 26.2 91.4 50.0 44.2 9.0 2160.2 808.2 2239.2 1291.2 59.2
449 (53.1) 9.6 13.5 2.4 21.0 3.9 71.6 10.6 108.0 11.2 68.6 10.0 88.4 6.4 151.8 25.4 90.6 54.0 42.7 8.8 2314.8 857.1 2286.4 1331.2 56.8
396 (46.9) 4.9 13.4 2.5 20.2 3.5 66.5 8.5 103.3 10.0 66.8 8.5 87.1 6.1 161.0 26.3 92.3 44.9 45.8 9.0 1984.5 710.2 2185.6 1244.0 61.9
P-valuea NAb 0.008c 0.41 0.002 <0.001 <0.001 0.007 0.003 <0.001 0.63 <0.001 <0.001 0.26 0.13
Abbreviations: BMI; body mass index, WC; waist circumference, SBP; systolic blood pressure, DBP; diastolic blood pressure, FPG; fasting plasma glucose, HDL; high-density lipoprotein. a P-value except for MetS was calculated to compare between boys and girls by independent t-test. b Not applicable. c P-value for MetS was calculated by chi-square test. d One US dollar was calculated as 1250 Korean won.
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diabetes research and clinical practice 89 (2010) 72–78
Table 2 – Characteristics of the study population according to quartile of screen time.
n (boy %) Age (years) BMI (kg/m2) WC (cm) SBP (mm Hg) DBP (mm Hg) FPG (mg/dL) Total cholesterol (mg/dL) Triglyceride (mg/dL) HDL-cholesterol (mg/dL) Total energy intake (kcal) Household income (US dollarb) Residence in rural area (%)
All
ST-Q1 (16 h)
845 (53.1) 13.4 2.5 20.6 3.7 69.2 10.0 105.8 10.9 67.8 9.4 87.8 6.3 156.1 26.2 91.4 50.0 44.2 9.0 2160.2 808.2 2239.2 1291.2 59.2
205 (48.8) 13.6 2.5 20.5 3.6 69.0 9.6 106.0 10.3 68.3 9.2 86.9 6.7 155.3 26.4 89.7 51.3 43.7 8.5 2235.9 778.5 2498.4 1277.6 60.0
ST-Q2 (17–24 h) ST-Q3 (25–34 h) 200 (49.0) 13.3 2.5 20.7 4.0 69.5 10.1 104.0 11.9 66.8 8.7 87.6 6.0 160.6 27.7 97.8 54.2 44.3 9.2 2121.2 821.4 2382.4 1379.2 62.5
215 (53.0) 12.9 2.5 19.9 3.2 67.4 9.2 105.5 10.0 66.9 10.1 88.1 6.2 154.8 22.8 83.6 32.9 45.4 9.0 2113.9 820.1 2244.8 1292.8 56.3
ST-Q4 (35 h)
P-valuea
225 (60.9) 13.8 2.3 21.2 4.0 70.9 10.9 107.6 11.1 68.9 9.3 88.5 6.2 154.2 27.6 94.8 56.9 43.4 9.3 2172.5 811.4 1876.0 1140.0 58.2
0.04 <0.001 0.004 0.003 0.009 0.05 0.05 0.05 0.02 0.09 0.45 <0.001 0.61
Abbreviations: BMI; body mass index, BMI-SDS; BMI standard deviation score, WC; waist circumference, SBP; systolic blood pressure, DBP; diastolic blood pressure, FPG; fasting plasma glucose, HDL; high-density lipoprotein. ST-Q means screen time (TV time + computer time) divided by quartile percentage for a week (ST-Q1; 0–16 h, ST-Q2; 17–24 h, ST-Q3; 25–34 h, and ST-Q4 35 h). a P-value was calculated by ANOVA test. b One US dollar was calculated as 1250 Korean won.
Fig. 1 shows the proportion of MetS according to the time quartiles of TV time, computer time, and screen time for 1 week. The proportion of MetS was highest in the 4th quartile for each of these categories. The P-values for TV-Q, Com-Q, and ST-Q are 0.71, 0.086, and 0.036, respectively. Fig. 2 illustrates TV time, computer time and screen time for 1 week among subjects with and without MetS. The mean TV time, computer time and screen time for a 1-week period were higher among the subject groups with MetS than for the groups without MetS. Table 3 shows the results of the logistic regression analyses designed to examine the relationship between each quartile of screen time and MetS. In comparison with the children and adolescents that were categorized in the ST-Q1 (16 h for a week), the odds ratio for MetS of children and adolescents that
were categorized in the ST-Q4 (35 h for a week) was 2.23 (95% CI, 1.02–4.86) after the adjustment for age, sex, household income, and residence area. To further evaluate the predictive role of screen time for a weekday and a weekend day, we conducted a multivariate logistic regression analysis that was stratified by weekday and weekend day. Screen time for a weekend day (WE-ST-Q) was strongly associated with the prevalence of MetS. The OR (95% CI) of MetS in children and adolescents that were categorized into the 4th quartile of WE-ST-Q4 (7 h for weekend day) was 2.62 (95% CI, 1.16–5.90), when compared with subjects in 1st quartile of WE-ST-Q1 (3 h) after the adjustment for age, sex, household income, and residence area. However, there was no significant association between the screen time during a weekday and the prevalence of MetS. After additionally
Fig. 1 – The prevalence of MetS according to each time quartile for 1 week [TV-Q means time to watch TV for a week, divided by quartile percentage (TV-Q1; 0–8 h, TVQ2; 9–13 h, TV-Q3; 14–20 h, TV-Q4; I21 h). Com-Q means time to use computer, computer game, and internet for a week, divided by quartile percentage (Com-Q1; 0–5 h, Com-Q2; 6–8 h, Com-Q3; 9–15 h, Com-Q4; I16 h). ST-Q means screen time (TV time + computer time), divided by quartile percentage (ST-Q1; 0–16 h, ST-Q2; 17–24 h, ST-Q3; 25–34 h, and ST-Q4 I35 h)]. The P-values for TV-Q, Com-Q, and ST-Q are 0.71, 0.086, and 0.036, respectively.
Fig. 2 – Mean time for 1 week among subjects with or without MetS (TV time, computer time, and screen time mean total time to watch TV, time to use computer, computer game, and internet, and time to spend both TV and computer for 1 week. P-value was calculated by independent two sample t-test. *P-value <0.05, **P-value <0.01).
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diabetes research and clinical practice 89 (2010) 72–78
Table 3 – Odds ratios and 95% confidence intervals for MetS according to quartile of screen time.
Week screen time Model 1a Model 2b Model 3c
Weekday screen time Model 1a Model 2b Model 3c
Weekend screen time Model 1a Model 2b Model 3c
ST-Q1 (16 h)
ST-Q2 (17–24 h)
ST-Q3 (25–34 h)
1.00 1.00 1.00
1.70 (0.75–3.83) 1.72 (0.76–3.88) 1.75 (0.77–3.99)
1.15 (0.49–2.73) 1.19 (0.50–2.82) 1.16 (0.49–2.78)
W-ST-Q1 (0–1 h)
W-ST-Q2 (2 h)
W-ST-Q3 (3 h)
0.57 (0.23–1.42) 0.59 (0.24–1.48) 0.58 (0.23–1.47)
0.59 (0.24–1.47) 0.62 (0.25–1.54) 0.64 (0.25–1.62)
1.00 1.00 1.00
WE-ST-Q1 (0–3 h)
WE-ST-Q2 (4 h)
WE-ST-Q3 (5–6 h)
1.00 1.00 1.00
1.59 (0.58–4.33) 1.60 (0.59–4.39) 1.47 (0.53–4.05)
1.88 (0.79–4.44) 1.91 (0.81–4.53) 1.71 (0.72–4.09)
ST-Q4 (35 h) 2.33 (1.09–5.00) 2.31 (1.08–4.96) 2.23 (1.02–4.86)
W-ST-Q4 (4 h) 1.36 (0.69–2.65) 1.38 (0.93–1.15) 1.37 (0.69–2.75)
WE-ST-Q4 (7 h) 2.90 (1.30–6.48) 2.90 (1.30–6.50) 2.62 (1.16–5.90)
ST-Q means screen time (TV time + computer time) for a week. W-ST-Q means screen time (TV time + computer time) for a weekday. WE-ST-Q means screen time (TV time + computer time) for a weekend day. a Model 1: unadjusted. b Model 2: adjusted for age. c Model 3: adjusted for age, sex, household income, and residence area.
adjusting for age, sex, household income, residence area, dietary intake and physical activity, children and adolescents who were categorized into the only highest quartile of screen time for a weekend day (WE-ST-Q4, 7 h), but not for a week and a weekday, had the significant odds ratio of 2.88 (95% CI, 1.04–7.98) (data not shown).
4.
Discussion
In this cross-sectional study, we examined the prevalence of MetS and its association with screen time in a representative sample of children and adolescents in Korea. The worldwide prevalence of MetS in children and adolescents ranges widely from 3 to 11% depending on the ethnic group, lifestyle, urbanization, and diagnostic criteria [14–18]. In the current study, we used the age-modified NCEP-ATP III criteria that was proposed by Ford et al. [14]. The estimated prevalence of MetS in children and adolescents in Korea was 7.3% (9.6% in boys, 4.9% in girls) in our study. When the recent prevalence of MetS (8.6–10.6%) of US adolescents from National Health and Nutrition Examination Surveys (NHANES) between the years 1999 and 2006 was compared to that of our study [17,18], the results showed that the overall prevalence of MetS in Korea is relatively low. Several longitudinal studies have suggested that the clustering of risk factors for CVD should begin in childhood and track into adulthood. Results from the Bogalusa Heart Study revealed that childhood obesity was a strong predictor of MetS in adulthood [6]. Similarly, Morrison et al. reported that the pediatric measures of waist circumference and triglyceride level were important sentinels for the development of MetS in adults [19]. Considering the adverse health consequences such as CVD mortality, early identification of
MetS in children and adolescents in specific populations is important in public health planning. Another major finding in this current study was that screen time was found to be independently associated with the presence of MetS in children and adolescents in Korea. This result is consistent with the emerging evidence that a sedentary lifestyle is strongly linked to obesity, insulin resistance, and MetS. Increased screen time may also lead individuals to have a greater energy intake, by the increased consumption of snacks and soft drinks, and intake less of vegetables and fresh fruits [20–22]. Moreover, a greater amount of screen time may also displace physical activity [23,24]. However, there was no correlation of screen time with energy intake or physical activity in the present study (data not shown). While some epidemiologic studies have shown that screen time is associated with MetS and insulin resistance, there has been little research on the association in children and adolescents, particularly in East Asian countries. In a study of adolescents between the ages of 12–19 years that took the 1994–2004 US NHANES, Mark et al. has shown that screen time is associated with an increased possibility of MetS in a dosedependent manner [25]. As shown in Table 3, the Korean children and adolescents in the 4th quartile of screen time were also more likely to have more than twice the probability of the MetS, after the adjustments for age, sex, household income, and residence area were made. Viner et al. also illustrated that weekend TV viewing could influence obesity in adulthood [26]. We also conducted a multivariate logistic regression analysis that was stratified by weekday and weekend day to further evaluate the predictive role of screen time that occurs on a weekday and a weekend day. Screen time for a weekend day was also strongly associated with the prevalence of MetS,
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diabetes research and clinical practice 89 (2010) 72–78
but not for a weekday. The 4th quartile of screen time (WE-STQ4) on a weekend had statistical significance (OR = 2.62, 95% CI = 1.16–5.90). These findings indicate that there may be a closer interrelationship between MetS and screen time on a weekend day than a weekday. There are some limitations that should be considered when interpreting the findings from this study. Firstly, it is difficult to explain a causal relationship between screen time and increased risk for the MetS from this cross-section study design. Secondly, TV time and computer time were selfreported by the study participants. Self-reported measures of socially undesirable behaviors, such as TV watching and computer use, tend to be underestimated. Although screen time (TV time + computer time) is generally considered to be underestimated, a previous study in middle school-aged children found that the difference between self-reporting and actual time spent was only 0.09 h per week in TV time and 0.68 h per week in computer time [27]. Thirdly, we could not fully exclude the effect on the information bias, because this study was based on the questionnaire survey and screen time was calculated from this survey. Finally, the questions do not distinguish between leisure screen time usage such as surfing the internet, playing computer games, or watching TV and productive screen time such as doing homework, research, or watching an educational TV program. Since an increase in productive time is associated with higher levels of physical activity [28], the measure of total screen time that does not distinguishing between leisure screen time from productive screen time, could influence the results regarding the risk of MetS. In conclusion, we confirmed a clear and significant relationship between screen time and the prevalence of MetS in a representative sample of children and adolescents in Korea. We also suggest that screen time on a weekend day may be more closely associated with the prevalence of MetS in children and adolescents. Public health intervention to reduce screen time particularly for a weekend may be needed to prevent pediatric MetS in Korea.
Conflict of interest There are no conflicts of interest.
Appendix A Cut-off points of each MetS criteria based on the growth charts that were published by the Korean Pediatric Society in 2005.
10-year 10-year 11-year 11-year 12-year 12-year 13-year 13-year 14-year
boy girl boy girl boy girl boy girl boy
WC (cm)
SBP (mm Hg)
DBP (mm Hg)
79.2 73.4 82.1 76.7 85.2 77.3 87.1 77.8 89.5
119 117 121 119 124 119 126 120 129
77 76 77 76 79 78 79 78 80
14-year 15-year 15-year 16-year 16-year 17-year 17-year 18-year 18-year
girl boy girl boy girl boy girl boy girl
78.5 89.7 78.8 88.4 79.0 90.0 79.9 89.0 80.0
121 130 122 130 123 130 124 130 126
79 81 79 81 80 82 81 85 82
references
[1] G.M. Reaven, Banting Lecture 1988: role of insulin resistance in human disease, Diabetes 37 (1988) 1595–1607. [2] D.E. Moller, K.D. Kaufman, Metabolic syndrome: a clinical and molecular perspective, Annu. Rev. Med. 56 (2005) 45–62. [3] S. Cowey, R.W. Hardy, The metabolic syndrome: a high-risk state for cancer? Am. J. Pathol. 169 (2006) 1505–1522. [4] A. Russo, M. Autelitano, L. Bisanti, Metabolic syndrome and cancer risk, Eur. J. Cancer 44 (2008) 293–297. [5] P.T. Katzmarzyk, L. Pe´russe, R.M. Malina, J. Bergeron, J.P. Despre´s, C. Bouchard, Stability of indicators of the metabolic syndrome from childhood and adolescence to young adulthood: the Quebec Family Study, J. Clin. Epidemiol. 54 (2001) 190–195. [6] S.R. Srinivasan, L. Myers, G.S. Berenson, Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study, Diabetes 51 (2002) 204–209. [7] W.H. Dietz, Health consequences of obesity in youth: childhood predictors of adult disease, Pediatrics 101 (1998) 518–525. [8] H.M. Kim, J. Park, H.S. Kim, D.H. Kim, Prevalence of the metabolic syndrome in Korean adolescents aged 12–19 years from the Korean National Health and Nutrition Examination Survey 1998 and 2001, Diabetes Res. Clin. Pract. 75 (2007) 111–114. [9] T. Tammelin, U. Ekelund, J. Remes, S. Na¨yha¨, Physical activity and sedentary behaviors among Finnish youth, Med. Sci. Sports Exerc. 39 (2007) 1067–1074. [10] T. Sugiyama, G.N. Healy, D.W. Dunstan, J. Salmon, N. Owen, Is television viewing time a marker of a broader pattern of sedentary behavior? Ann. Behav. Med. 35 (2008) 245–250. [11] American Academy of Pediatrics, Committee on public education, American Academy of Pediatrics: children, adolescents, and television, Pediatrics 107 (2001) 423–426. [12] P.E. Pardee, G.J. Norman, R.H. Lustig, D. Preud’homme, J.B. Schwimmer, Television viewing and hypertension in obese children, Am. J. Prev. Med. 33 (2007) 439–443. [13] R.E. Andersen, C.J. Crespo, S.J. Bartlett, L.J. Cheskin, M. Pratt, Relationship of physical activity and television watching with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey, JAMA 279 (1998) 938–942. [14] E.S. Ford, C. Li, S. Cook, H.K. Choi, Serum concentrations of uric acid and the metabolic syndrome among US children and adolescents, Circulation 115 (2007) 2526–2532. [15] Y. Li, X. Yang, F. Zhai, F.J. Kok, W. Zhao, J. Piao, et al., Prevalence of the metabolic syndrome in Chinese adolescents, Br. J. Nutr. 99 (2008) 565–570. [16] R. Singh, A. Bhansali, R. Sialy, A. Aggarwal, Prevalence of metabolic syndrome in adolescents from a north Indian population, Diabet. Med. 24 (2007) 195–199. [17] W.D. Johnson, J.J. Kroon, F.L. Greenway, C. Bouchard, D. Ryan, P.T. Katzmarzyk, Prevalence of risk factors for
78
[18]
[19]
[20]
[21]
[22]
diabetes research and clinical practice 89 (2010) 72–78
metabolic syndrome in adolescents: National Health and Nutrition Examination Survey (NHANES), 2001–2006, Arch. Pediatr. Adolesc. Med. 163 (2009) 371–377. K.B. Daratha, R.C. Bindler, Effects of individual components, time, and sex on prevalence of metabolic syndrome in adolescents, Arch. Pediatr. Adolesc. Med. 163 (2009) 365–370. J.A. Morrison, L.A. Friedman, W.R. Harlan, L.C. Harlan, B.A. Barton, G.B. Schreiber, et al., Development of the metabolic syndrome in black and white adolescent girls: a longitudinal assessment, Pediatrics 116 (2005) 1178–1182. N. Stroebele, J.M. de Castro, Television viewing is associated with an increase in meal frequency in humans, Appetite 42 (2004) 111–113. S.M. Phillips, L.G. Bandini, E.N. Naumova, H. Cyr, S. Colclough, W.H. Dietz, et al., Energy-dense snack food intake in adolescence: longitudinal relationship to weight and fatness, Obes. Res. 12 (2004) 461–472. R. Lowry, H. Wechsler, D.A. Galuska, J.E. Fulton, L. Kann, Television viewing and its associations with overweight, sedentary lifestyle, and insufficient consumption of fruits and vegetables among US high school students: differences by race, ethnicity, and gender, J. Sch. Health 72 (2002) 413–421.
[23] J.P. Rey-Lo´pez, G. Vicente-Rodrı´guez, M. Biosca, L.A. Moreno, Sedentary behaviour and obesity development in children and adolescents, Nutr. Metab. Cardiovasc. Dis. 18 (2008) 242–251. [24] D.B. Panagiotakos, C. Pitsavos, C. Chrysohoou, J. Skoumas, D. Tousoulis, M. Toutouza, et al., Impact of lifestyle habits on the prevalence of the metabolic syndrome among Greek adults from the ATTICA study, Am. Heart J. 147 (2004) 106–112. [25] A.E. Mark, I. Janssen, Relationship between screen time and metabolic syndrome in adolescents, J. Public Health 30 (2008) 153–160. [26] R.M. Viner, T.J. Cole, Television viewing in early childhood predicts adult body mass index, J. Pediatr. 147 (2005) 429–435. [27] K.H. Schimitz, L. Harnack, J.E. Fulton, D.R. Jacobs Jr., S. Gao, L.A. Lytle, et al., Reliability and validity of a brief questionnaire to assess television viewing and computer use by middle school children, J. Sch. Health 74 (2004) 370–377. [28] D.E. Feldman, T. Barnett, I. Shrier, M. Rossignol, L. Abenhaim, Is physical activity differentially associated with different types of sedentary pursuits? Arch. Pediatr. Adolesc. Med. 157 (2003) 797–802.