Page 1 of 10 (page number not for citation purposes)
BMC Public Health
Changes in health risk behaviors of elementary school students in
northern Taiwan from 2001 to 2003: results from the child and
adolescent behaviors in long-term evolution study
, Hsing-Yi Chang1
, Lee-Lan Yen*2
and Tony Szu-Hsien Lee3
Address: 1Center for Health Policy Research and Development, National Health Research Institutes. No. 35, Keyan Road, Zhunan Town, Miaoli
County 350, Taiwan, R.O.C, 2Institute of Health Policy and Management, College of Public Health, National Taiwan University. Rm. 623, No. 17,
Xu-Zhou Road, Taipei 100, Taiwan, R.O.C and 3Department of Health Education, National Taiwan Normal University. 162 Sec. 1 Ho-Ping East
Road, Taipei 106, Taiwan, R.O.C
Email: Wen-Chi Wu - email@example.com; Hsing-Yi Chang - firstname.lastname@example.org; Lee-Lan Yen* - email@example.com; Tony Szu-Hsien Lee - firstname.lastname@example.org
* Corresponding author
Background: Previous research has indicated that children's behaviors have long-term effects on
later life. Hence it is important to monitor the development of health risk behaviors in childhood. This study examined the changes in health risk behaviors in fourth- to sixth-grade students in northern Taiwan from 2001 to 2003.
Methods: The Child and Adolescent Behaviors in Long-Term Evolution (CABLE) study collected
data from 1,820 students from 2001 to 2003 (students were 9 or 10 years old in 2001). Exploratory factor analysis was used to determine the aggregation of health risk behaviors. A linear growth curve model was used to determine whether health risk behaviors changed over time.
Results: Of the 13 behaviors, staying up late and eating snacks late at night were the most
prevalent (82.3% of subjects in 2001, 81.8% in 2002, 88.5% in 2003) and second most prevalent (68.7%, 67.4%, 71.6%) behaviors, respectively, from 2001 to 2003. The three least prevalent health risk behaviors were chewing betel nut (1.0%, 0.4%, 0.2%), smoking (1.4%, 1.0%, 0.8%), and drinking alcohol (8.5%, 6.0%, 5.2%). The frequencies of swearing and staying up late showed the greatest significant increases with time. On the other hand, suppressing urination and drinking alcohol decreased over time. Using exploratory factor analysis, we aggregated the health risk behaviors into three categories: unhealthy habits, aggressive behaviors, and substance use. Although students did not display high levels of aggressive behavior or experimentation with substances, the development of these behaviors in a small proportion of students should not be ignored. The results of the linear growth curve model indicated that unhealthy habits and aggressive behaviors increased over time. However, substance use slightly decreased over time.
Conclusion: We found that some health risk behaviors increased with time while others did not.
Unhealthy habits and aggressive behaviors increased, whereas substance use slightly decreased during this period. Educational professionals should pay attention to the different patterns of change in these behaviors in elementary school students.
Published: 12 November 2007
BMC Public Health 2007, 7:323 doi:10.1186/1471-2458-7-323
Received: 9 April 2007 Accepted: 12 November 2007
This article is available from: http://www.biomedcentral.com/1471-2458/7/323 © 2007 Wu et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Health risk behaviors such as smoking, alcohol abuse, unhealthy dietary patterns, sedentary lifestyle, unsafe behaviors, and aggressive behaviors have been found to have an important influence on morbidity and mortality [1-3]. These behaviors not only influence individuals' health but also create burdens for the nation and society as a whole. It is well documented that behaviors devel-oped during childhood influence health in adolescence and adulthood [4-6]. Furthermore, one longitudinal study reported that behaviors of seventh graders, includ-ing physical activity, food preference behaviors, and smoking, consolidated early . Thus, helping children establish healthy lifestyles and avoid developing health risk behaviors is crucial and should be started before these behaviors are firmly established. In other words, because children's behaviors have long-term effects on later life, it is important to monitor the development of children's health risk behaviors earlier, in order to design health pro-motion programs for children.
Health risk behaviors, which develop over time, often cor-relate with each other [8-11]. Some researchers have fur-ther found that people with multiple risk behaviors are at greater risk of developing chronic diseases and suffering injuries than those with only one risk behavior [12,13]. However, very few researchers have monitored the devel-opment of aggregated risk behaviors. Cohort studies can assess relationships among the outcomes and variables under study, many of which may be temporal in nature, thus leading to causal models. However, longitudinal research is often difficult to conduct and adequate statisti-cal methods have been lacking. Time trends of the differ-ent types of risk behaviors starting from early childhood have rarely been reported. Nevertheless, longitudinal studies provide essential information about the develop-ment of different types of risk behaviors . Recent developments in multilevel analysis of hierarchical data in longitudinal studies have provided efficient methods to estimate the change over time [14,15]. We are fortunate to have developed a student cohort. The main purpose of this study was to determine whether health risk behaviors of elementary school students changed from 2001 to 2003.
Data were extracted from the Child and Adolescent Behav-iors in Long-Term Evolution (CABLE) study , which was initiated in 2001. Samples for the CABLE study were chosen from 9 public elementary schools in Taipei City (representing a metropolitan area) and 9 in Hsinchu County (representing a rural area). The schools were selected by randomized cluster sampling based on school size. There were 152 elementary schools in Taipei City and
79 in Hsinchu County in 2000. Based on the number of students, schools were divided into small, medium-sized, and large. Six small schools, 2 medium-sized schools, and 1 large school were selected from each location. The details of CABLE's study design and sampling method have been reported previously [11,16].
The CABLE study was approved by the international review board of the National Health Research Institutes in Taiwan. All parents of students in the study were asked to sign a consent form if their children agreed to participate in the study . The CABLE cohort consisted of 2,075 fourth graders in 2001. We analyzed data on the 1,820 sixth-grade students in 2003 who had completed the pre-vious two surveys. In other words, we followed partici-pants from age 9 or 10 years (fourth grade) to age 11 or 12 years (sixth grade). The follow-up rate was 87.7%. Losses to follow up were due to absence from school due to ill-ness, transferring to another school, school reorganiza-tion and refusal to participate. There were no significant differences between the original sample and the sample who completed follow up in demographic characteristics. Hence these losses are likely to be random in nature and should not have significantly biased the results. The study sample included 943 boys (51.81%) and 877 girls (48.19%); 965 (53.02%) were from Taipei City and 855 (46.98%) were from Hsinchu County.
The 13 health risk behaviors of interest to us in our study included (1) staying up late (past 10:00 p.m.), (2) eating snacks late at night (that is, before sleep), (3) eating fast food (such as carry-out lunches, fried chicken, and ham-burgers), (4) suppressing urination (having the urge to urinate but not passing urine), (5) playing video games for prolonged periods (more than 2 consecutive hours), and (6) watching television for prolonged periods (more than 2 consecutive hours). In addition, the following behaviors were also considered: (7) swearing, (8) throw-ing ththrow-ings when angry, (9) hittthrow-ing others, (10) vandalism, (11) smoking, (12) drinking alcohol and (13) chewing betel nut. We asked students to report occurrences of health risk behaviors 1 through 6 that they had engaged in during the week before the interview, and occurrences of the remaining 7 health risk behaviors engaged in during the month before the interview. Behavioral performance was measured using a four-point scale: never, once or twice, many times, and almost every day. (Additional file 1.)
Most of these behaviors were selected based on previous studies [17-20], such as watching TV, playing video games, hitting others, smoking and drinking alcohol, and one behavior instrument , such as lack of sleep, swear-ing, throwing things, and vandalism. Eating fast food and
BMC Public Health 2007, 7:323 http://www.biomedcentral.com/1471-2458/7/323
Page 3 of 10 (page number not for citation purposes) late night snacks were included because they are both
related to childhood obesity in Taiwan and Japan [22,23]. Suppressing urination was included as due to the tradi-tional educatradi-tional style in Taiwan; some students do not ask permission to go to the bathroom during class as they are embarrassed about raising their hands. This suppres-sion of urination may be detrimental to their bladder. Chewing betel nut is a prevalent local custom in Taiwan and was selected based on its relationship with oral cancer . All health risk behaviors were related to health out-comes, not only physical but also mental and social. For example, swearing, one of the verbal aggressive behaviors, is related to self-concept damage, hurt feelings, relational deterioration and even physical aggression . Hence, swearing can be harmful to mental and social health. Stay-ing up late could lead to lack of sleep and result in exter-nalizing behaviors and attention and social problems . Particular health outcomes may result from more than one risk behavior. For example, eating fast food, short sleep duration, and watching TV for long periods are all related to child overweight . The CABLE study questionnaire has been carefully refined for reliability and validity [11,16]. Ten experts, including a psychologist, sociologist, behavioral scientist, health educator, and ele-mentary school teacher, were invited to give suggestions on improving validity. A pilot study was conducted on 84 fourth graders to ensure that wording was appropriate. The validity and reliability of the questionnaire were also analyzed using pilot data.
Percentages of students with health risk behaviors were calculated for each year by adding up percentages for the occurrences once or twice, many times, and almost every day. Behaviors were then ranked from highest to lowest prevalence for each year. Linear trend analysis was used to determine whether each behavior varied with time. Exploratory factor analysis was carried out to examine aggregating characteristics of the 13 health risk behaviors based on 2001 data . The statistical software SAS 8.02 was used to perform exploratory factor analysis using principal component analysis and varimax rotation to estimate the pattern coefficient . If the latent structure of health risk behaviors can be demonstrated, we can con-clude that the health risk behaviors can be aggregated in childhood. Different latent factors extracted through fac-tor analysis indicated different types of health risk behav-iors.
To confirm the results of the factor analysis, we added up the original points of the behaviors under each factor to derive total scores for each student over the 3 year period. All items were assigned 0 to 3 points indicating never, once or twice, many times, and almost every day. Higher
scores in particular factors indicated that the student per-formed higher levels of this particular type of risk behav-ior in that year. We used these scores of different types of health risk behaviors as the dependent variable in the fol-lowing longitudinal analysis.
Considering the variances between individuals and time points, we analyzed the longitudinal data using a linear growth curve model [29-31]. The statistical software MLwiN 2.0 was used . We use iterative generalized least squares to estimate parameters after applying the lin-earization. Because the study sample structure consisted of measurements of 3 years nested within students, the units of this research sample lay at two hierarchical levels. (Actually, the students were nested within schools. How-ever, the variances in school level were not statistical sig-nificant; hence we omitted the differences in school level.) We modeled a two-level random intercept model to fit measures repeating three times. A time variable was included in the model to examine the time trends for health risk behaviors. A positive parameter indicated that the particular type of behavior increased over time. The statistical model was as follows:
Yij = β0 + β1Time + u0j + eij
where i represents the time point, j represents the study subject, Yij represents the level of expression of the health risk behavior by subject j at time i, β0 is the total mean, β1 is the coefficient for time trend of the risk behaviors over time, u0j is the variation between students, and eij is the variation within students. β0 and β1 are fixed effects and do not vary between individuals or over time; u0j and eij are random effects and vary between individuals or over time. Furthermore, in the multiple regression models, we added residential area and gender as control variables.
Distribution and time trend of each health risk behavior in study participants
Table 1 shows the distribution of health risk behaviors in the subjects from 2001 (grade 4) to 2003 (grade 6). Per-centages of students ever having health risk behaviors were calculated by adding up percentages of the occur-rences once or twice, many times, and almost every day. The most prevalent and second most prevalent health risk behaviors for each year were staying up late and eating snacks late at night, respectively. In 2001, the third most prevalent behavior was watching television for prolonged periods. In 2002, the third most prevalent behavior was eating fast food, and in 2003, it was swearing. The two least prevalent behaviors over the 3-year period were chewing betel nut and smoking. Compared to the smok-ing rate (over 20% of 9th to 12th graders in the US
BM C Public Health 2 007, 7:323 http://ww w.b iomed central.com/1 471 -2 458 Pa ge 4 of (page nu mber not for cit a tion pur
Behavior Grade 4 (2001) Grade 5 (2002) Grade 6 (2003)
once/twice many times every day ever Rank once/twice many times every day ever Rank once/twice many times every day ever Rank
Staying up late 45.13 22.43 14.73 82.29 1 45.19 20.89 15.67 81.75 1 39.82 29.59 19.03 88.44 1
Eating snacks at night 45.38 13.63 9.73 68.74 2 46.43 11.76 9.18 67.37 2 47.28 15.52 8.75 71.55 2
Watching TV for prolonged periods 36.73 19.83 11.58 68.14 3 33.02 17.78 10.18 60.98 4 36.03 20.74 12.38 69.15 4
Eating fast food 56.77 5.94 1.82 64.53 4 57.64 5.00 1.32 63.96 3 54.87 5.72 1.10 61.69 5
Swearing 46.37 9.02 3.69 59.08 5 46.08 10.33 4.03 60.44 5 48.16 17.01 6.05 71.22 3
Hitting others 43.53 8.98 1.82 54.33 6 31.98 4.29 1.98 38.25 7 39.45 7.53 2.47 49.45 6
Suppressing urination 44.56 7.01 2.43 54.00 7 41.26 3.41 1.48 46.15 6 41.42 5.23 0.94 47.59 7
Playing video games for prolonged periods 29.04 8.26 5.95 43.25 8 21.37 6.77 6.17 34.31 8 22.78 12.30 9.05 44.13 8
Throwing things when angry 23.19 2.65 1.82 27.66 9 18.02 2.7 1.32 22.04 9 22.16 3.52 1.59 27.27 9
Drinking alcohol 7.2 0.93 0.33 8.46 10 5.39 0.55 0.06 6.00 11 4.18 0.66 0.38 5.22 11
Vandalism 6.35 0.55 0.17 7.07 11 6.27 0.66 0.33 7.26 10 7.20 0.88 0.22 8.30 10
Smoking 1.16 0.17 0.06 1.39 12 0.82 0.11 0.05 0.98 12 0.71 0.11 0.00 0.82 12
BMC Public Health 2007, 7:323 http://www.biomedcentral.com/1471-2458/7/323
Page 5 of 10 (page number not for citation purposes) smoked in the 30 days preceding the survey in 2001 and
2003) reported by YRBS , in this sample fewer than 1% of 4th to 6th graders smoked in the same period. However, further follow up is required to see whether the smoking rate will catch up to the rate in the US when the students reach high school.
According to the results of linear trend analysis, behaviors that increased significantly over the 3-year period were swearing, staying up late and playing video games for pro-longed periods (Table 2). The behaviors are ranked by t-value to show the extent of increasing or decreasing with time. The behavior with the largest increase was swearing (t value is 11.87). These behaviors showed identical trends across gender. Behaviors that decreased signifi-cantly were suppressing urination, drinking alcohol, chewing betel nut, hitting others and eating fast food. However, for girls, there were no linear trends for declines in drinking alcohol, chewing betel nut, hitting others and eating fast food. Among boys, eating snacks at night increased and smoking decreased. Among girls, both van-dalism and watching TV increased. Therefore, it can be seen that some behaviors showed different time trends across gender. Nevertheless, the mean score of each health risk behavior was still higher for boys than girls.
The frequencies of some behaviors decreased in the sec-ond year, namely 5th grade, such as playing video games
for prolonged periods, watching TV for prolonged peri-ods, eating snacks at night, throwing things when angry, and hitting others. Possible reasons for this are discussed further below.
Latent structure of health risk behaviors
Exploratory factor analysis was used to look at the general patterns of the 13 behaviors and to extract the latent struc-ture of subjects' health risk behaviors in 2001 (grade 4), as shown in Table 3. Using an eigenvalue > 1 as the criterion, the 13 behaviors could be grouped into three factors: (1) aggressive behaviors (swearing, throwing things when angry, hitting others, and vandalism), (2) substance use behaviors (smoking, drinking alcohol, and chewing betel nut), and (3) unhealthy habits (staying up late, eating snacks late at night, eating fast food, suppressing urina-tion, playing video games for prolonged periods, and watching television for prolonged periods). The percent-age of variance explained by these three factors was more than 40%. The behaviors under each factor may be affected by some similar correlates. Further, we analyzed the factor structures of these behaviors separately for rural and urban areas. The data showed no differences in pat-terns between these areas. Hence, we can say that the health risk behaviors can be aggregated into three types for the period beginning when the students were in the fourth grade.
Description and longitudinal trends of aggregated health risk behaviors
To analyze longitudinal trends in the aggregated behav-iors, we calculated the total scores for each type of health risk behavior by adding up the points for behaviors under each factor. Participants' patterns of aggregated health risk behaviors from 2001 (grade 4) to 2003 (grade 6) are shown in Table 4. The range of possible points for aggres-sive behaviors was 4–16, that for substance use behaviors was 3–12, and that for unhealthy habits was 6–24. Gener-ally, the means of aggressive behaviors and unhealthy habits increased from 2001 to 2003, with a slight decrease in the second year. Substance use behaviors decreased slightly from 2001 to 2003. Given the range of possible points for these three types of behaviors, the means indi-cate that the students in this study exhibited mild or mod-erate health risk behaviors.
The results of the linear growth curve model are shown in Table 5. Analysis was carried out after adding the time var-iable , so that any changes in the performance of health risk behaviors over time could be confirmed. The results show that the coefficients of time for unhealthy habits and aggressive behaviors were positive, meaning that these types of health risk behaviors increased over time. The coefficient of time for substance use behaviors was small and negative, meaning that this type of behav-ior decreased slightly over time. Comparing model 2 to model 1, the effect of time was unchanged after control-ling for sex and location. Boys expressed more health risk behavior than girls for all three types of behavior and stu-dents who lived in Hsinchu (rural) expressed more health risk behaviors than those who lived in Taipei (urban).
We observed changes in health risk behaviors of school children in Taiwan from 2001 to 2003. In terms of single behaviors, behaviors that increased with time were swear-ing, staying up late, and playing video games for pro-longed periods, and behaviors that decreased with time were suppressing urination, drinking alcohol, chewing betel nut, hitting others and eating fast food. As a whole, the 13 health risk behaviors in elementary school students could be aggregated into three latent factors: unhealthy habits, aggressive behaviors, and substance use behaviors. The results are similar to findings in previous studies [8-13]. After controlling for residential area and gender, we found that unhealthy habits and aggressive behaviors increased from 2001 to 2003, but that substance use behaviors decreased slightly.
The two leading health risk behaviors for students since fourth grade were staying up late and eating snacks at night (Table 1). The least prevalent health risk behavior was chewing betel nut. The prevalences of these behaviors
BM C Public Health 2 007, 7:323 http://ww w.b iomed central.com/1 471 -2 458 Pa ge 6 of (page nu mber not for cit a tion pur
Behaviors Total Boys Girls
Grade 4 (2001) Grade 5 (2002) Grade 6 (2003) Linear trend analysis Grade 4 (2001) Grade 5 (2002) Grade 6 (2003) Linear trend analysis Grade 4 (2001) Grade 5 (2002) Grade 6 (2003) Linear trend analysis
Mean SD Mean SD Mean SD T value Sig. Mean SD Mean SD Mean SD T value Sig. Mean SD Mean SD Mean SD T value Sig.
1. Swearing 1.75 0.77 1.79 0.78 2.00 0.84 11.87 *** 1.86 0.81 1.91 0.83 2.11 0.83 8.56 *** 1.65 0.71 1.66 0.71 1.89 0.83 8.24 ***
2. Staying up late 2.34 0.93 2.34 0.95 2.56 0.93 8.56 *** 2.38 0.95 2.38 0.99 2.57 0.93 5.18 *** 2.30 0.92 2.29 0.90 2.55 0.92 7.03 ***
3. Playing video games for prolonged periods
1.63 0.87 1.53 0.87 1.75 0.99 4.61 *** 1.86 0.97 1.80 1.00 1.99 1.08 3.30 ** 1.39 0.66 1.25 0.57 1.48 0.80 3.29 **
4. Vandalism 1.08 0.31 1.09 0.33 1.10 0.34 1.67 - 1.12 0.38 1.12 0.40 1.13 0.39 0.34 - 1.04 0.19 1.05 0.24 1.06 0.28 2.68 **
5. Watching TV for
prolonged periods 2.11 0.98 1.99 0.99 2.15 1.00 1.35 - 2.18 1.02 2.05 1.04 2.17 1.02 -0.28 - 2.04 0.94 1.92 0.93 2.12 0.97 2.36 *
6. Eating snacks at night 2.02 0.92 1.97 0.90 2.05 0.89 1.10 - 2.01 0.94 2.03 0.94 2.09 0.92 2.14 * 2.03 0.89 1.92 0.85 2.00 0.85 -0.71
-7. Throwing things when angry
1.34 0.62 1.27 0.58 1.34 0.62 -0.01 - 1.40 0.68 1.33 0.64 1.39 0.67 -0.57 - 1.28 0.55 1.22 0.50 1.29 0.57 0.73
-8. Smoking 1.02 0.15 1.01 0.13 1.01 0.11 -1.77 - 1.03 0.20 1.02 0.17 1.01 0.12 -2.28 * 1.00 0.08 1.00 0.07 1.01 0.09 0.61
-9. Eating fast food 1.74 0.65 1.72 0.62 1.70 0.63 -2.42 * 1.77 0.67 1.74 0.64 1.73 0.65 -1.61 - 1.71 0.63 1.69 0.59 1.66 0.59 -1.85
-10. Hitting others 1.67 0.71 1.46 0.67 1.62 0.73 -2.47 * 1.72 0.74 1.46 0.67 1.58 0.72 -4.76 *** 1.61 0.68 1.47 0.68 1.66 0.75 1.45
-11. Chewing betel nut 1.02 0.17 1.00 0.09 1.00 0.06 -3.57 *** 1.03 0.22 1.01 0.12 1.00 0.08 -3.14 ** 1.00 0.08 1.00 0.03 1.00 0.00 -1.85
-12. Drinking alcohol 1.10 0.36 1.07 0.28 1.07 0.31 -3.62 *** 1.13 0.41 1.09 0.33 1.07 0.34 -3.95 *** 1.07 0.28 1.04 0.20 1.06 0.28 -0.78
-13. Suppressing urination
1.66 0.71 1.53 0.64 1.55 0.64 -5.94 *** 1.65 0.74 1.50 0.62 1.52 0.63 -4.77 *** 1.67 0.69 1.55 0.65 1.58 0.65 -3.57 ***
BMC Public Health 2007, 7:323 http://www.biomedcentral.com/1471-2458/7/323
Page 7 of 10 (page number not for citation purposes) were relatively stable over the 3 years. However, the
aggressive behavior of swearing jumped from being the fifth to being the third most common behavior from 2001 to 2003. The result of time trend analysis also showed that the frequency of swearing had the most significant increase with time. Compared to the rate of seeing other 13–15 year olds swearing, insulting or making nasty com-ments at least once or twice a month reported by a research group in the US , the frequency of swearing in this study sample (59.08%~71.22%) was higher than the US sample (14%~18%). Even though the participants in the US were older and reported what they observed rather than their own situation, the rate in our sample was almost three-fold that in the US. Therefore, grade 5 could be the appropriate time for preventive education targeting aggressive behaviors, especially verbal aggression in Northern Taiwan.
It is important not to ignore substance use behaviors, as once they have developed they can be difficult to break.
When looking at individual behavioral items, two of the three substance use behaviors decreased from 2001 to 2003 (Table 5). The total score of aggregated substance use behaviors also decreased slightly. We used stricter def-initions (student took one puff, took one sip of alcohol, and chewed one piece of betel nut) for substance use behaviors. We found that more children had drunk alco-hol than smoked or chewed betel nut (Table 1). One pos-sible reason is that during celebrations or feasts in Taiwan, parents often let children have a taste of alcohol. Although our study did not find high rates of substance use behav-iors, the results still indicate that a small proportion of children have tried using these substances at a young age. Furthermore, the decrease in each substance use behavior was only found among boys, and girls did not show the same trends (Table 2). Research indicates that gender dif-ferences exist in health-related beliefs and health behavior [35-37]. We found that boys and girls had different sub-stance use behavior patterns. Although boys engaged in substance use more frequently than girls, girls did not Table 4: Means and standard deviations of aggregated health risk behaviors over 3 years
Aggressive behaviors Substance use behaviors Unhealthy habits
Grade 4 Grade 5 Grade 6 Grade 4 Grade 5 Grade 6 Grade 4 Grade 5 Grade 6 Mean 5.85 5.61 6.06 3.13 3.08 3.08 11.50 11.08 11.74 Standard
1.70 1.66 1.79 0.54 0.38 0.36 2.77 2.83 2.87
Table 3: Rotated factor structure of health risk behaviors of subjects in grade 4 in 2001
Factor 1 Factor 2 Factor 3 Behavior aggressive behaviors substance use behaviors unhealthy habits
Hitting others 0.751 0.006 0.081
Swearing 0.702 0.000 0.057
Throwing things when angry 0.645 0.118 0.190
Vandalism 0.434 0.215 0.026
Smoking 0.066 0.829 0.017
Chewing betel nut 0.021 0.797 0.017
Drinking alcohol 0.149 0.678 0.070
Eating fast food 0.065 --0.009 0.583
Eating snacks at night --0.045 0.055 0.578
Staying up late --0.030 0.033 0.544
Watching TV for prolonged periods 0.290 0.003 0.520 Playing video games for prolonged periods 0.295 0.100 0.498 Suppressing urination 0.309 --0.041 0.393
Eigenvalue 2.689 1.682 1.141
Explained variance (%) 0.207 0.129 0.088 Cumulative explained variance (%) 0.207 0.336 0.424
Cronbach's α 0.5935 0.6638 0.5200
show the same pattern of decline as boys. Hence, atten-tion still needs to be given to girls' substance using behav-iors.
The three types of health risk behaviors showed different time trends. Unhealthy behavior and aggressive behavior increased with time, whereas substance use behavior decreased with time (Table 5). However, comparing the results of time trend analysis for the aggregated behaviors to those for single behaviors (Table 2), we can see that not all of the single behaviors followed the general time pat-tern. Staying up late and playing video games for pro-longed periods showed the greatest contribution to the increasing pattern of unhealthy behavior. Likewise, swear-ing played a major role in the increase in aggressive behav-ior. For substance use behavior, there was more consistency between changes in single behaviors and the total score. From these results we can see that although a health risk behavior type may have a general time pattern as a whole, each single behavior may still have its own unique time pattern. For example, for the six behaviors that were grouped as unhealthy habits, the first behavior to increase could be staying up late. For aggressive behav-ior, swearing may be the first behavior in this category. Therefore, as health educators, although we can develop strategies targeting groups of related behaviors, we still need to pay attention to the different development time courses of each behavior in the group. As a result, two dif-ferent strategies would be necessary for health education, namely those focusing on single behaviors as well as those focused on groups of behaviors.
The mean scores of aggressive behaviors and substance use behaviors suggest that these types of behaviors were quite infrequent and that only a small proportion of stu-dents experiment with substances at this young age (Table 3). This finding is probably due to the fact that the partic-ipants came from the general population. However, Mill-stein et al.  indicated that an extremely important and
currently neglected area in professional education is knowledge about the general adolescent population. Our study is one of the few studies to fill this gap.
Furthermore, we unexpectedly found that some behaviors decreased in the 5th grade, as well as the mean scores of the
three behavioral groups. This is probably because students in Taiwan are rearranged into new classes in the 5th grade and therefore, most of the students will be with new class-mates at this time. They are too unfamiliar with each other to undertake risk behaviors together such as playing video games, throwing things when angry, and hitting others. They may also feel too stressed to relax on their own by doing things such as watching TV, eating snacks late at night and fast food. However, we have no evidence to sup-port this hypothesis and further research is needed to solve this puzzle.
Laaksonen et al.  showed that having three or four health risk behaviors concurrently is related to age: having such behaviors was most common in those aged 20–34, less common in those aged 35–49, and least common in those aged 50–64. According to our results, health risk behaviors are engaged in as early as elementary school. Millstein et al.  suggested that we should stop viewing young adolescents as naive children and begin to view them as participants in a changing social environment. As a result, they should be educated about healthy behaviors and encouraged to develop healthy behaviors in child-hood.
As shown in Table 5, substance use behaviors slightly decreased over time. The decrease in substance use behav-iors is possibly due to implementation of anti-smoking legislation and education with an emphasis on drugs and smoking. Such legislation and education have strength-ened the formation of negative social norms and restric-tive attitudes about these kinds of behaviors. One possible reason for the increase in unhealthy habits and aggressive Table 5: Time trends of aggregated health risk behaviors of subjects from grade 4 to grade 6
Unhealthy habits Aggressive behavior Substance using behavior Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Fixed effects Coef. SD. Coef. SD. Coef. SD. Coef. SD. Coef. SD. Coef. SD.
Intercept 10.680* 0.113 10.676* 0.113 5.321* 0.069 5.321* 0.069 3.097* 0.017 3.097* 0.017 Time 0.119* 0.036 0.119* 0.036 0.104* 0.022 0.104* 0.022 -0.027* 0.006 -0.027* 0.006 Boy vs. Girl 0.758* 0.102 0.419* 0.062 0.072* 0.014 Hsinchu vs. Taipei 0.287* 0.102 0.194* 0.062 0.037* 0.014 Random effects Var. SD. Var. SD. Var. SD. Var. SD. Var. SD. Var. SD. Individual variability 3.156* 0.161 3.156* 0.161 1.135* 0.059 1.135* 0.059 0.039* 0.003 0.039* 0.003 Time variability 4.709* 0.110 4.709* 0.110 1.789* 0.042 1.789* 0.042 0.146* 0.003 0.146* 0.003 * p: < 0.05
BMC Public Health 2007, 7:323 http://www.biomedcentral.com/1471-2458/7/323
Page 9 of 10 (page number not for citation purposes) behaviors over time is maturity. As students move from
grade 4 to grade 6, not only are their bodies developing and maturing, but they are also undergoing psychological development. As these students become more independ-ent and develop a stronger sense of self, they are less will-ing to accept the restrictions placed on them by parents and schools. In addition, they are influenced by the media, television programs, and computer and video games. The increase in staying up late is likely due to greater amounts of homework, and the reason for the increase in swearing could be peer pressure or the increas-ing acceptability of swearwords. This project will continue to collect information on the students to learn more about the time trends of health risk behaviors and their effects. The participants in this study represent only students of public schools in Taipei City and Hsinchu County in northern Taiwan. In addition, the study focused on health risk behaviors over only 3 years. These limitations restrict the generalizability of our findings. We suggest that future studies involve different populations and monitor behav-iors for longer durations to more closely examine the issue of health risk behaviors in childhood.
We found that some health risk behaviors (swearing, stay-ing up late and playstay-ing video games for prolonged peri-ods) increased with time, while some others (suppressing urination, drinking alcohol, chewing betel nut, hitting others and eating fast food) did not decrease or stayed sta-ble. In general, health risk behaviors of children can be aggregated into three types; unhealthy habits, aggressive behaviors and substance use. Although the frequency of aggressive behaviors and substance use was low, after con-trolling for gender and area of residence, the frequencies of unhealthy habits and aggressive behaviors increased significantly from 2001 to 2003, whereas substance use slightly decreased. Educational professionals should pay attention to the different patterns of health risk behaviors in elementary school students and preventive measures for behaviors that increase during this period should be initiated earlier in childhood.
The author(s) declare that they have no competing inter-ests.
WCW wrote the paper and conducted statistical analyses. HYC revised the article and gave statistical advice. LLY contributed to the study design and led the CABLE research team. TSHL made suggestions regarding the Dis-cussion section. All authors read and approved the final manuscript.
The study analyzed part of the 2001–2003 data obtained through the National Health Research Institutes' CABLE project (HP-090-SG03). We extend our appreciation to the education departments of Taipei City and Hsinchu County for administrative support, the 18 participating schools for providing venues and time for the surveys, the children and their parents who were involved in the survey, and the interviewers and supervisors who conducted the survey.
1. McGinnis JM, Foege WH: Actual causes of death in the United States. JAMA 1993, 270:2207-2212.
2. Mokdad AH, Marks JS, Stroup DF, Gerberding JL: Actual causes of death in the United States, 2000. JAMA 2004, 291:1238-1245.
3. The burden of chronic diseases and their risk factors:
national and state perspectives 2004 Centers for Disease Control
and Prevention 2004 [http://www.cdc.gov/nccdphp/burdenbook2004].
4. Guo SS, Huang C, Maynard LM, Demerath E, Towne B, Chumlea WC, Siervogel RM: Body mass index during childhood, adolescence and young adulthood in relation to adult overweight and adi-posity: the Fels Longitudinal Study. Int J Obes 2000, 24:1628-1635.
5. Hancox DJ, Milne BJ, Poulton R: Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study. Lancet 2004, 364:257-262.
6. Pierce JP, Gilpin E: How long will today's new adolescent smoker be addicted to cigarettes? Am J Public Health 1996, 86:253-256.
7. Kelder SH, Perry CL, Klepp KI, Lytle LL: Longitudinal tracking of adolescent smoking, physical activity, and food choice behav-iors. Am J Public Health 1994, 84:1121-1126.
8. Yen LL, Wen HC, Tseng TS, Sun YC: A study on health-risk behaviors of youth: the current status among junior high school students in Taipei City [in Chinese]. Public Health 1999, 26:75-90.
9. Koivusilta J, Honkala S, Honkala E, Rimpela A: Tooth brushing as part of the adolescent lifestyle predicts education level. J
Dent Res 2003, 82:361-366.
10. Bongers IL, Koot HM, ven der Ende J, Verhulst FC: Developmental trajectories of externalizing behaviors in childhood and ado-lescence. Child Dev 2004, 75:1523-1537.
11. Yen LL, Chiu CJ, Wu WC: Aggregation of health behaviors among fourth graders in northern Taiwan. J Adolesc Health 2006, 39:435-442.
12. Pickett W, Garner M, Biyce WF, King MA: Gradients in risk for youth injury associated with multiple-risk behaviours: a study of 11,329 Canadian adolescents. Soc Sci Med 2002, 55:1055-1068.
13. Laaksonen M, Prattaila R, Lahelma E: Sociodemographic determi-nants of multiple unhealthy behaviours. Scand J Public Health 2003, 31:37-43.
14. Anstey KJ, Hofer SM: Longitudinal designs, methods and analy-sis in psychiatric research. Aust N Z J Psychiatry 2004, 38:93-104. 15. Boyle MH, Willms JD: Multilevel modelling of hierarchical data in developmental studies. J Child Psychol Psychiatry 2001, 42:141-162.
Additional file 1
Appendix A. Wording of health risk behaviors in the questionnaire. The appendix shows the wording and the scales for measuring the 13 health risk behaviors.
Click here for file
Publish with BioMed Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime."
Sir Paul Nurse, Cancer Research UK Your research papers will be:
available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours — you keep the copyright
Submit your manuscript here:
16. Yen LL, Chen L, Lee SH, Hsiao C, Pan LY: Child and Adolescent Behaviour in Long-Term Evolution (CABLE): a school-based health lifestyle study. Promot Educ 2002:33-40.
17. Currie C, Samdal O, Boyce W, Smith B: Health Behavior in School-Aged
Children: a World Health Organization Cross-National Study: Research Pro-tocol for the 2001/02 Survey Edinburgh, UK: Child and Adolescent
Health Research Unit, University of Edinburgh; 2001.
18. National Longitudinal Survey of Children: overview of
sur-vey instruments Statistics Canada [http://www.statcan.ca/bsolc/ english/bsolc?catno=89F0078X].
19. The National Longitudinal Study of Adolescent Health:
study design [http://www.cpc.unc.edu/projects/addhealth/design] 20. Brener ND, Kann L, Kinchen SA, Grunbaum JA, Whalen L, Eaton D,
Hawkins J, Ross JG: Methodology of the youth risk behavior surveillance system. MMWR Recomm Rep 2004, 53(RR-12):1-13. 21. Archenbach TM, Edelbrock C: Manual for the Child Behavior Checklist
and Revised Child Behavior Profile Burlington, VT: University of
Ver-mont, Department of Psychiatry; 1983.
22. Wu FL, Yu S, Wei LL, Yin TJC: Weight-control behavior among obese children: association with family-related factors. J Nurs
Res 2003, 11:19-29.
23. Sugimori H, Yoshida K, Izuno T, Miyakawa M, Suka M, Sekine M, Yamagami T, Kagamimori S: Analysis of factors that influence body mass index from ages 3 to 6 years: A study based on the Toyama cohort study. Pediatr Int 2004, 46:302-310.
24. Lin YS, Jen YM, Wang BB, Lee JC, Kang BH: Epidemiology of oral cavity cancer in Taiwan with emphasis on the role of betel nut chewing. ORL J Otorhinolaryngol Relat Spec 2005, 67:230-236. 25. Roberto AJ, Meyer G, Boster FJ, Roberto HL: Adolescents'
deci-sions about verbal and physical aggression: An application of the Theory of Reasoned Action. Hum Commun Res 2003, 29:137-147.
26. Aronen ET, Paavonen EJ, Fjällberg M, Soininen M, Törrönen J: Sleep and psychiatric symptoms in school-age children. J Am Acad
Child Adolesc Psychiatry 2000, 39:502-508.
27. Pohlmann JT: Use and interpretation of factor analysis in the Journal of Educational Research: 1992–2002. J Educ Res 2004, 98:14-22.
28. SAS Institute Inc: SAS/STAT User's Guide, Version 8 Cary, NC: SAS Insti-tute Inc.; 1999.
29. Duncan C, Jones K, Moon G: Health-related behaviour in con-text: a multilevel modelling approach. Soc Sci Med 1996, 42:817-830.
30. Duncan C, Jones K, Moon G: Context, composition and hetero-geneity: using multilevel models in health research. Soc Sci
Med 1998, 46:97-117.
31. Snijders TAB, Bosker RJ: Multilevel Analysis: An Introduction to Basic and
Advanced Multilevel Modeling Thousand Oaks, CA: Sage; 1999.
32. Rasbash J, Steele F, Browne W: A User's Guide to MLwiN Version 2.0 London: Centre for Multilevel Modelling, Institute of Education, Uni-versity of London; 2003.
33. YRBSS National Youth Risk behavior Survey: 1991–2005. Trends in the prevalence of selected risk behaviors. [http:// www.cdc.gov/HealthyYouth/yrbs/trends.htm].
34. Akin CK, Smith SW, Roberto AJ, Fediuk T, Wagner T: Correlates of verbally aggressive communication in adolescents. J Appl
Commun Res 2002, 30:251-268.
35. Bailey KR: The girls are the ones with the pointy nails London, Ontario: Althouse Press; 1993.
36. Centers for Disease Control and Prevention: Youth RiskBehavior Surveillance – 2005. MMWR Surveill Summ 2006, 55(SS-5):1-108. 37. Courtenay WH: Constructions of masculinity and their influ-ence on men's well-being: a theory of gender and health.
Social Science & Medicine 50(2000):1385-1401.
38. Millstein SG, Nightingale EO, Petersen AC, Mortimer AM, Hamburg DA: Promoting the healthy development of adolescents.
JAMA 1993, 269:1413-1415.
39. Millstein SG, Irwin CE Jr, Adler NE, Cohn LD, Kegeles SM, Dolcini MM: Health-risk behaviors and health concerns among young adolescents. Pediatrics 1992, 89:422-428.
The pre-publication history for this paper can be accessed here: