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
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
Wen-Chi Wu 2
Hsing-Yi Chang 2
Lee-Lan Yen 1
Tony Szu-Hsien Lee 0
0 Department of Health Education, National Taiwan Normal University. 162 Sec. 1 Ho-Ping East Road, Taipei 106 , Taiwan, R.O.C
1 Institute 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
2 Center for Health Policy Research and Development, National Health Research Institutes. No. 35, Keyan Road, Zhunan Town, Miaoli County 350 , Taiwan, R.O.C
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.
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
developed during childhood influence health in adolescence
and adulthood [4-6]. Furthermore, one longitudinal
study reported that behaviors of seventh graders,
including 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
promotion programs for children.
Health risk behaviors, which develop over time, often
correlate with each other [8-11]. Some researchers have
further 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
development 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
statistical methods have been lacking. Time trends of the
different types of risk behaviors starting from early childhood
have rarely been reported. Nevertheless, longitudinal
studies provide essential information about the
development 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
Data were extracted from the Child and Adolescent
Behaviors 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
previous two surveys. In other words, we followed
participants 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
illness, transferring to another school, school
reorganization 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
hamburgers), (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)
throwing things when angry, (9) hitting 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
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,
swearing, throwing things, and vandalism. Eating fast food and
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
traditional educational 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
suppression 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
outcomes, 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.
Staying up late could lead to lack of sleep and result in
externalizing 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
elementary 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
conclude that the health risk behaviors can be aggregated in
childhood. Different latent factors extracted through
factor analysis indicated different types of health risk
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
performed higher levels of this particular type of risk
behavior in that year. We used these scores of different types of
health risk behaviors as the dependent variable in the
following 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
linearization. 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.
However, the variances in school level were not statistical
significant; 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:
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
Table 1 shows the distribution of health risk behaviors in
the subjects from 2001 (grade 4) to 2003 (grade 6).
Percentages of students ever having health risk behaviors
were calculated by adding up percentages of the
occurrences 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
smoking rate (over 20% of 9th to 12th graders in the US
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
prolonged periods (Table 2). The behaviors are ranked by
tvalue 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
significantly 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
vandalism 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
second year, namely 5th grade, such as playing video games
for prolonged periods, watching TV for prolonged
periods, eating snacks at night, throwing things when angry,
and hitting others. Possible reasons for this are discussed
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
structure 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
urination, playing video games for prolonged periods, and
watching television for prolonged periods). The
percentage 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
patterns 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
Description and longitudinal trends of aggregated health
To analyze longitudinal trends in the aggregated
behaviors, 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
aggressive behaviors was 4–16, that for substance use behaviors
was 3–12, and that for unhealthy habits was 6–24.
Generally, 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
indicate that the students in this study exhibited mild or
moderate health risk behaviors.
The results of the linear growth curve model are shown in
Table 5. Analysis was carried out after adding the time
variable , 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
behavior decreased slightly over time. Comparing model 2 to
model 1, the effect of time was unchanged after
controlling for sex and location. Boys expressed more health risk
behavior than girls for all three types of behavior and
students 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
swearing, staying up late, and playing video games for
prolonged 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
. 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
/3Table 2: Time trends of health risk behaviors in subjects by grade (n = 1,820)
-4285 Behaviors Total
/1o4m Grade 4 (2001) Grade 5 (2002) Grade 6 (2003) Linear trend analysis
l.trcen Mean SD Mean SD Mean SD T value Sig.
i.bom1. Swearing 1.75 0.77 1.79 0.78 2.00 0.84 11.87 ***
/:/www 2. Staying up late 2.34 0.93 2.34 0.95 2.56 0.93 8.56 ***
tthp f3o.rPplaryoi nlognvgieddeopegraimodess 1.63 0.87 1.53 0.87 1.75 0.99 4.61 ***
4. Vandalism 1.08 0.31 1.09 0.33 1.10 0.34 1.67
5p.roWloantgcehdinpgeTrVio dfosr 2.11 0.98 1.99 0.99 2.15 1.00 1.35
6. Eating snacks at night 2.02 0.92 1.97 0.90 2.05 0.89 1.10
7w.hTehnraonwgirnyg things 1.34 0.62 1.27 0.58 1.34 0.62 -0.01
8. Smoking 1.02 0.15 1.01 0.13 1.01 0.11 -1.77
9. Eating fast food 1.74 0.65 1.72 0.62 1.70 0.63 -2.42 *
10. Hitting others 1.67 0.71 1.46 0.67 1.62 0.73 -2.47 *
:3327 11. Chewing betel nut 1.02 0.17 1.00 0.09 1.00 0.06 -3.57 ***
,0207 12. Drinking alcohol 1.10 0.36 1.07 0.28 1.07 0.31 -3.62 ***
lthea u1r3i.nSautipopnressing 1.66 0.71 1.53 0.64 1.55 0.64 -5.94 ***
uP *: p < 0.05, **: p < 0.01, ***: P < 0.001.
Grade 4 (2001)
Grade 5 (2002)
Grade 6 (2003)
Linear trend analysis
Grade 4 (2001)
Grade 5 (2002)
Grade 6 (2003)
Linear trend analysis
substance use behaviors
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
comments 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
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
definitions (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
alcohol than smoked or chewed betel nut (Table 1). One
possible 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
behaviors, 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
differences exist in health-related beliefs and health behavior
[35-37]. We found that boys and girls had different
substance use behavior patterns. Although boys engaged in
substance use more frequently than girls, girls did not
Substance use behaviors
* p: < 0.05
show the same pattern of decline as boys. Hence,
attention still needs to be given to girls' substance using
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.
Model 1 Model 2
Coef. SD. Coef.
Model 1 Model 2
Coef. SD. Coef. SD.
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
pattern. Staying up late and playing video games for
prolonged periods showed the greatest contribution to the
increasing pattern of unhealthy behavior. Likewise,
swearing played a major role in the increase in aggressive
behavior. 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
behavior, 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
different 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
students experiment with substances at this young age (Table
3). This finding is probably due to the fact that the
participants came from the general population. However,
Millstein et al.  indicated that an extremely important and
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
classmates 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
support 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
As shown in Table 5, substance use behaviors slightly
decreased over time. The decrease in substance use
behaviors is possibly due to implementation of anti-smoking
legislation and education with an emphasis on drugs and
smoking. Such legislation and education have
strengthened the formation of negative social norms and
restrictive attitudes about these kinds of behaviors. One possible
reason for the increase in unhealthy habits and aggressive
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
independent and develop a stronger sense of self, they are less
willing 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
increasing 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
behaviors for longer durations to more closely examine the issue
of health risk behaviors in childhood.
We found that some health risk behaviors (swearing,
staying up late and playing video games for prolonged
periods) increased with time, while some others (suppressing
urination, drinking alcohol, chewing betel nut, hitting
others and eating fast food) did not decrease or stayed
stable. 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
controlling 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
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
Discussion section. All authors read and approved the final
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
Click here for file
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.
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