This study explored developmental trajectory patterns of BMI and associated factors. Participants included 1,609 students who were followed from age 7 to 12 years. Data collection involved annual self-administered questionnaires and records of height and weight. An ecological model was used to identify the factors associated with BMI trajectories. Group-based trajectory models and multinomial logit models were used in the statistical analysis. There were gender differences in BMI trajectories. Among boys, four BMI trajectories were normal or slightly underweight, persistently normal weight, overweight becoming obese, and persistently obese. Among girls, four BMI trajectories were persistently slightly underweight, persistently normal weight, persistently overweight, and persistently obese. The mean BMI in each trajectory group demonstrated an upward trend over time. In boys, BMI trajectories were significantly associated with after-school exercise, academic performance, family interactions, overweight parents, and father’s education level. In girls, BMI trajectories were significantly associated with television viewing or computer use, family interactions, peer interactions, and overweight parents. Children under age 7 years who are already overweight or obese are an important target for interventions. The different factors associated with BMI trajectories can be used for targeting high risk groups.
Childhood overweight and obesity is increasing worldwide [
Currently, our understanding of developmental trajectories of childhood overweight is still limited. Longitudinal research examining trajectories of overweight and obesity in children is extremely important as childhood is the key developmental period during which risk factors for obesity arise. Moreover, a growing number of children are presenting with overweight or a rapid increase in body mass index (BMI) which if it continues into adolescence and adulthood will result in overweight [
It is essential to consider the influence of an obesogenic environment when considering factors associated with the development of childhood overweight and obesity [
Therefore, the main aims of our study were (1) to describe BMI developmental trajectories in our sample from grade 1 to grade 6 and to determine the presence of any sex differences; and (2) to determine factors associated with BMI developmental trajectories in children at the individual, family, school, and community level.
Our study analyzes data from the child and adolescent behaviors in long-term evolution (CABLE) project, a cohort study that commenced in 2001 [
The baseline CABLE sample comprised 2215 children. The present study includes those participants who were followed from grade one (2001) to grade six (2006) and had complete data for height and weight. We also excluded participants with missing data for father’s and mother’s height and weight, resulting in a final sample of 1609 children for our analysis (50.34% boys and 49.66% girls). The proportion of boys and girls in our sample was not significantly different from that at baseline (51.20% boys and 48.40% girls).
The dependent variable in our analysis was the BMI developmental trajectory groups. BMI (weight/height2) was calculated using height and weight data collected as part of the annual school health check from 2001 to 2006. These annual estimations of BMI were then used to examine BMI trajectories.
Independent variables were taken from the child questionnaires (data from 2001 to 2006 were taken) and school survey. Multilevel variables included (1) the individual factors including residential location, self-perceived academic performance (data from 2003 to 2006 were taken), and time-varying behavioral factors including eating breakfast, eating fruit and vegetables, eating fast food, drinking sugary drinks, participating in after-school exercise, television viewing or computer use patterns, and staying up late. We measured the frequency of these behaviors in the past week (data from 2001 to 2006 were analyzed); (2) family factors included family interactions (data from 2001 to 2006 was taken), parent’s behavioral supervision (data from 2003 to 2006 was taken), and peer interactions (data from 2005 to 2006 was taken); (3) school factors included the presence of a consumer’s cooperative and recreational facilities; and (4) community factors included perceived neighborhood interactions and perceived neighborhood safety (data from 2005 to 2006 was taken). Control variables included level of pubertal development, parent’s overweight, household monthly income, father’s education level, and mother’s education level. Parent’s overweight was estimated by using the self-reported height and weight from the parent’s questionnaire to calculate BMI. The presence of overweight was then determined based on national standardized BMI cut-points. The reliability and validity of the scales for family interactions were assessed. Reliability was measured by internal consistency and validity was assessed by exploratory factor analysis.
Based on our study aim of examining trajectories in BMI in our sample over 6 years, we incorporated the 6 annual measurements of BMI into a group-based trajectory model. Group-based trajectory model is a semiparametric model by Nagin et al. [
The statistical model is described as follows:
It assumes that distinct trajectory groups exist as opposed to a heterogeneous individual distribution of long-term trajectories in the population. During analysis, the sample was divided into a number of groups and distinct groups of BMI developmental trajectories were selected based on model fit indices. The most appropriate model was selected based on the highest BIC value (2ΔBIC > 10), a posterior probability of greater than 0.70 and a trajectory group size of at least 5% of the sample.
In this study a number of factors associated with BMI trajectories change over time. These factors include eating breakfast, eating fruit and vegetables, drinking sugary drinks, eating fast food, participating in after-school exercise, television viewing and computer use patterns, staying up late, self-perceived academic performance, family interactions, parent’s behavioral supervision, and peer interactions. Therefore, we included data on changes in these variables from grade one to six and divided them into different groups when comparing different trajectory groups.
A multinomial logit model was used to examine factors associated with BMI developmental trajectories in the sample. The dependent variable was BMI trajectory groups. We examined differences in factors associated with particular BMI trajectory groups compared to the reference trajectory group among boys and girls, respectively.
Baseline demographic characteristics of the study sample are shown in Table
Distribution of demographic characteristics in the study sample.
Characteristic | Boys |
Girls |
Total |
---|---|---|---|
|
|
|
|
Parent’s overweight | |||
Neither | 232 (28.64) | 239 (29.91) | 471 (29.27) |
Father | 350 (43.21) | 353 (44.18) | 703 (43.69) |
Mother | 75 (9.26) | 64 (8.01) | 139 (8.64) |
Both | 153 (18.89) | 143 (17.90) | 296 (18.40) |
Household monthly income | |||
Low | 123 (15.19) | 120 (15.02) | 243 (15.10) |
Medium | 286 (35.31) | 301 (37.67) | 587 (36.48) |
High | 401 (49.51) | 378 (47.31) | 799 (48.42) |
Father’s education level | |||
Junior high school and below | 63 (7.78) | 60 (7.51) | 123 (7.64) |
Senior high school | 256 (31.60) | 267 (33.42) | 532 (32.50) |
College and above | 491 (60.62) | 472 (59.07) | 963 (59.85) |
Mother’s education level | |||
Junior high school and below | 60 (7.41) | 63 (7.88) | 123 (7.64) |
Senior high school | 362 (44.69) | 353 (44.18) | 715 (44.44) |
College and above | 388 (47.90) | 383 (47.93) | 771 (47.91) |
BMI values over the six years of follow up were used to create the BMI developmental trajectories. Trajectories were selected based on the largest BIC value, 2ΔBIC greater than 10, a posterior probability of greater than 0.70, and a trajectory group size of at least 5% of the study sample. This resulted in four distinct BMI trajectory groups. The mean BMI for each age group and the mean BMI of each trajectory group increased over time. The chi-squared test demonstrated a statistically significant sex difference in the distribution of BMI trajectory groups (
We used group-based trajectory modeling to analyze the sample, using the abovementioned criteria for model selection. BMI developmental trajectories were separated by sex and the mean BMI for each trajectory group was compared with national standard cut-points for overweight and obesity. The trajectory groups were then named accordingly. Table
Mean BMI by BMI trajectory group and age in boys.
Trajectory group | Group 1 | Group 2 | Group 3 | Group 4 | ||||
---|---|---|---|---|---|---|---|---|
(normal or slightly underweight) | (persistently normal weight) | (overweight becoming obese) | (persistently obese) | |||||
|
|
|
| |||||
Age (years) | Mean | (95% CI) | Mean | (95% CI) | Mean | (95% CI) | Mean | (95% CI) |
7.0 | 15.08 | (14.89–15.19) | 16.71 | (16.51–16.89) | 18.83 | (18.51–19.01) | 23.47 | (22.93–23.66) |
8.0 | 15.28 | (15.22–15.43) | 17.38 | (17.20–17.53) | 20.38 | (20.22–20.60) | 25.11 | (24.71–25.20) |
9.0 | 15.59 | (15.54–15.77) | 18.21 | (18.09–18.44) | 21.64 | (21.61–22.02) | 26.10 | (26.08–26.59) |
10.0 | 16.12 | (15.93–16.17) | 19.28 | (19.05–19.42) | 23.06 | (22.79–23.20) | 27.35 | (27.23–27.76) |
11.0 | 16.48 | (16.36–16.61) | 20.14 | (19.90–20.30) | 24.08 | (23.73–24.13) | 28.51 | (28.12–28.65) |
12.0 | 16.97 | (16.80–17.15) | 20.66 | (20.46–20.94) | 24.53 | (24.37–24.89) | 28.91 | (28.62–29.40) |
Mean BMI by BMI trajectory group and age in girls.
Trajectory group | Group 1 | Group 2 | Group 3 | Group 4 | ||||
---|---|---|---|---|---|---|---|---|
(persistently slightly underweight) | (persistently normal weight) | (persistently overweight) | (persistently obese) | |||||
|
|
|
| |||||
Age (years) | Mean | (95% CI) | Mean | (95% CI) | Mean | (95% CI) | Mean | (95% CI) |
7.0 | 14.41 | (14.26–14.57) | 16.01 | (15.83–16.13) | 18.01 | (17.94–18.28) | 21.68 | (21.36–22.08) |
8.0 | 14.57 | (14.41–14.63) | 16.46 | (16.34–16.58) | 18.99 | (18.85–19.14) | 22.99 | (22.73–23.25) |
9.0 | 14.75 | (14.64–14.88) | 16.95 | (16.86–17.13) | 19.93 | (19.75–20.01) | 24.46 | (24.24–24.71) |
10.0 | 15.13 | (15.00–15.24) | 17.64 | (17.46–17.75) | 20.95 | (20.63–20.90) | 25.76 | (25.68–26.16) |
11.0 | 15.54 | (15.49–15.74) | 18.23 | (18.13–18.42) | 21.70 | (21.49–21.81) | 27.18 | (26.86–27.38) |
12.0 | 16.26 | (16.07–16.41) | 19.03 | (18.82–19.18) | 22.37 | (22.34–22.73) | 28.00 | (27.48–28.21) |
BMI developmental trajectory groups in boys and girls from ages 7 to 12 years (upper graph is boys and the lower graph is girls).
Multinomial logit modeling was used to examine the relationship between individual, family, school, community, behavioral factors, and BMI developmental trajectories in the study sample. The persistently normal weight trajectory group was taken as the reference group, against which the other trajectory groups were compared. The various multilevel, behavioral, and control variables were entered into the model and those that remained statistically significant were retained in the final multinomial logit model. The results are stated below.
We found that boys with an overweight father (
We found that boys with fathers with a college or higher level of education were less likely (
Factors associated with BMI development trajectory groups in boys and girls.
Boys1 | Girls2 | ||||
---|---|---|---|---|---|
Variable | Overweight becoming obese | Persistently obese | Variable | Persistently overweight | Persistently obese |
OR | OR | OR | OR | ||
After school exercise | Television viewing or computer use | ||||
Low becoming high/persistently high | 1.08 | 2.47 | Persistently medium/persistently low level | 1.41 | 1.96 |
Persistently low/persistently high | 1.14 | 3.76** | Persistently high level/persistently low level | 2.26* | 4.03* |
Self-perceived academic performance | Family interactions | ||||
Low satisfaction/high satisfaction | 1.36 | 2.30* | Persistently medium level/persistently low level | 1.72* | 1.21 |
Persistently high level/persistently low level | 1.39 | 1.06 | |||
Family interactions | Peer interactions | ||||
Low becoming medium/persistently low | 1.41 | 2.14 | Medium becoming high/persistently high | 1.02 | 1.49 |
Medium becoming low/persistently low | 1.52 | 0.66 | High becoming low/persistently high | 1.23 | 2.22* |
Persistently high/persistently low | 2.32* | 2.02 | |||
Parent’s overweight | Parent’s overweight | ||||
Father overweight/neither parent overweight | 1.71 | 4.23* | Father overweight/neither parent overweight | 1.75* | 1.42 |
Mother overweight/neither parent overweight | 2.56 | 4.68* | Mother overweight/neither parent overweight | 1.55 | 1.11 |
Both parents overweight/neither parent overweight | 1.53 | 8.22** | Both parents overweight/neither parent overweight | 2.31** | 6.84*** |
Father’s education level | |||||
Senior high school/junior high school and below | 0.68 | 0.70 | |||
College and above/junior high school and below | 0.44* | 0.50 |
2The OR in girls has been adjusted for television viewing or computer use, family interactions, peer interactions, and parent’s overweight. Reference group: persistently normal weight.
We found that girls whose parents were both overweight were more likely to have a persistently obese BMI trajectory (
We found that girls whose fathers were overweight (
In this study we used a longitudinal data to classify children by their long-term BMI status and examined associated factors in school children. We found that there were four types of BMI trajectory and the mean BMI in each group increased over time. Our key finding was that BMI status persisted from grade 1 to 6. In addition, BMI trajectory types differed between boys and girls. Boys had a high possibility going from overweight to obesity than girls. Using an ecological model, we included a wide range of variables over multiple levels. There were differences in the factors associated with the patterns between boys and girls. In boys “overweight becoming obese” was associated with higher family interactions and lower father’s education level, whereas “persistently obese” was associated with lower frequency after-school exercise, lower self-perceived academic performance, and parent’s overweight. In girls, persistently higher television viewing or computer use and parent’s overweight were associated with an overweight or obesity trajectories. In addition, “persistently overweight” was associated with persistent medium family interactions and “persistently obese” was associated with unstable peer interactions.
In this study we found that the mean BMI of school children increased over time from grade one to grade six in both boys and girls. Similar to the present study, a previous study has also found distinct BMI trajectory groups in girls [
In contrast to previous research, the use of longitudinal BMI data in our study enabled us to demonstrate distinct BMI developmental trajectory groups in boys and girls. Past research has used BMI values to directly divide the study sample into overweight or obese categories and then examined patterns of developmental trajectories. These past researches did not found any gender differences in developmental trajectories [
We also incorporated a wide range of multilevel variables associated with BMI trajectories in the present study. The majority of past research investigating factors associated with BMI trajectories has focused on early life factors such as birth weight and mother’s weight at delivery. In addition, the majority of studies have used baseline values or mean values when examining relationships with BMI trajectories. In contrast, the present study included individual behaviors as well as family, school, and community factors; we also considered changes over time in these variables in the multilevel model, which investigated factors associated with BMI trajectory groups. We found that boys who had persistently low levels of participation after-school exercise were more likely to have a persistently obese trajectory. One past study reported no relationship between an overweight trajectory and after-school exercise. However, they found that those who were less physically active were more likely to be in a everoverweight group (versus never overweight) [
Girls who had a high frequency of television viewing or computer use were more likely to have a persistently overweight or persistently obese BMI trajectory patterns. O’Brien et al. also found that a greater frequency of television viewing was associated with a greater likelihood of being in the elementary overweight group or in a constantly overweight group [
We found that girls whose frequency of peer interactions changed from high to low levels were more likely to have a persistently obese BMI trajectory compared to girls with persistently high peer interactions. Cross-sectional research has found that overweight children are more likely to experience psychological stress or be worried about their weight. Associations have also been observed between BMI and being made fun of by peers and girls report experiencing more psychological stress than boys [
We found that boys with persistently high family interactions were more likely to have an overweight becoming obese BMI trajectory compared to boys with persistently low family interactions. In girls, those with a persistently medium level of family interactions were more likely to have a persistently overweight trajectory compared to girls with persistently low family interactions. Zabinski et. al found that household dietary rules and family support influence the intake of dietary fat and fruit and vegetables by children [
We found that parent’s overweight was associated with BMI trajectories in their children. Moreover, we found that the greatest risk of a persistently obese trajectory occurred when both parents were overweight. The influence of parent’s overweight on their child’s BMI could be due to a combination of genetic and environmental factors. Overweight in very young children may be more related to genetic factors, whereas environmental factors may be more important for overweight following puberty [
This study has several limitations. The presence of overweight or obesity in parents was based on the calculation of BMI from self-reported height and weight. As there was no direct measurement of parents’ actual height or weight this may have led to some inaccuracies. In addition, as there were only two years of data available for the community variables of self-perceived neighborhood safety and neighborhood interactions, we were unable to look at the change in these variables over six years. We also did not have any data on social environmental factors such as the density of fast food outlets or the amount of green space in the local area, so we were unable to include these variables in our analysis.
In conclusion, we found that there were distinct BMI trajectory groups and associated factors from grade 1 to 6. We suggested attention should be given to the time before grade 1 for prevention of overweight and obesity. In addition to parent’s overweight and father’s education level, other factors such as individual behaviors, family, and school factors also play an important role. Boys with overweight parents, fathers with a lower level of education, and lower self-perceived academic performance are in need of particular attention. Girls with overweight parents and unstable peer interaction are in need of particular attention. The different factors associated with BMI trajectories in boys and girls can be used for targeting high risk groups. We suggested the interventions for boys should promote persistence higher exercise time after class, whereas, for girls, it is to reduce the amount of time spent on watching television or using the computer. The focus on food as a part of family and peer gatherings should be reduced with more emphasis on activities involving movement so that children develop good habits at an early age.
The authors declare that there is no conflict of interests.
The authors would like to thank the Division of Preventive Medicine and Health Services Research, National Health Research Institutes, the Graduate Institute of Health Policy and Management, National Taiwan University, and the Bureau of Health Promotion, Department of Health, for carrying out the Child and Adolescent Behaviors in Long-Term Evolution Project, which provided data for our study.