There is good evidence that poor fetal growth, commonly measured by low birth weight for gestational age, is associated with an increased risk of coronary heart disease (4), hypertension (2,3,6), and diabetes (2,13) in later life. These associations are hypothesized to be due to fetal growth restriction and in utero nutritional factors that may initiate physiological and anatomical responses, leading to remodeling of the vascular system, which is adaptive in fetal life but maladaptive after birth (2,8,15).
Moreover, shorter birth length and smaller head circumference are also markers of in utero growth restriction and are associated with higher blood pressure, small vessel disease, and metabolic syndrome (7,14,22). Hence, given the associations between birth length and head circumference with cardiovascular risk factors, it is reasonable to ask whether the effects of these birth parameters are mediated through physical inactivity and/or sedentariness. To our best knowledge, no population-based study has assessed these relationships. In addition, there is no epidemiological data on whether birth parameters influence time spent in screen viewing activities during adolescence. Although some studies have shown that screen time is largely uncorrelated with physical activity (19,29), it has not been conclusively established whether physical inactivity (i.e., not meeting physical activity recommendations) is a consequence of spending too much time in recreational screen viewing or other sedentary pursuits (21). Hence, as low birth weight infants spend less time in physical activity in later life (1), it is plausible that this could lead to more time being spent in screen viewing activities.
A better understanding of the causal pathway between fetal growth restriction and activity behaviors could have potential clinical or public health implications. Therefore, we aimed to investigate whether indicators of poor in utero growth (lower birth weight, shorter birth length, and smaller head circumference at birth) influence time spent in physical activity (outdoor and indoor) and screen time (TV viewing, videogame, and computer usage) among schoolchildren age 12 yr at baseline and age 17–18 yr at a 5-yr follow-up. A unique feature of the present study not previously assessed is the link between birth parameters and participation in the different types of physical activity and screen time and whether this association persists during adolescence.
The Sydney Childhood Eye Study is a longitudinal population-based survey of eye conditions and other health outcomes in school children living within the Sydney Metropolitan Area, Australia. It was approved by the Human Research Ethics Committee, University of Sydney, the Department of Education and Training, and the Catholic Education Office, New South Wales, Australia (23). We obtained informed written consent from at least one parent of each child and verbal assent from each child before the survey. Study methods have been previously described (23). Briefly, students in a stratified random cluster sample of 21 high schools across Sydney (attending school year 7) were eligible to participate. Stratification was based on socioeconomic status data from the Australian Bureau of Statistics. The sample included a proportional mix of public, private, or religious high schools. Data for the 12-yr-old cohort were collected during 2004–2005. Of the 3144 eligible 12-yr-old children, 2367 were given parental permission to participate and 2353 were surveyed (74.9%); their mean age was 12.7 yr. At the 5-yr follow-up study (during 2009–2011), 1213 of baseline participants (51.7%) were resurveyed at age 17–18 yr. The study characteristics of participants followed up (n = 1213) and those lost to follow-up (n = 1140) were compared. Participants versus nonparticipants were more likely to be East Asian (68.1% vs 31.9%) and Southeast Asian (63.0% vs 37.0%, P < 0.0001), older (12.74 vs 12.67 yr, P = 0.0003), have lower body mass index (BMI; 22.7 vs 22.8, P = 0.01), and spent less time in total physical activity (2.4 vs 2.6 h·wk−1, P = 0.001) and videogame usage (0.5 vs 0.6 h·d−1, P < 0.0001).
Assessment of birth parameters.
At baseline, parents were sent a comprehensive 193-item questionnaire to complete or had this administered at a telephone interview. The parents of all Australian children are provided with a health record booklet at birth (“blue book”) in which health professionals accurately record birth parameters, specifically birth weight, birth length, head circumference, gestational age, and mode of delivery. As reported elsewhere, we asked parents to extract this information from their child’s health record booklet (23). The distribution of birth weight in the present sample agrees well with published Australian figures (25). We defined very low, low, normal, and high birth weight as <2000, 2001–2499, 2500–4000, and >4000 g, respectively, and premature birth as <37 wk of gestation, in keeping with the World Health Organization definition (10,22,28).
Assessment of physical activity and sedentary behaviors.
Adolescents self-reported the time usually spent in various physical activity and screen time in an average week. The questions relating to sporting activity comprised a list of nine sporting activities in which school-age children participated in the following: (a) dancing, gymnastics, and calisthenics; (b) athletics; (c) swimming; (d) football, soccer, rugby league, and Australian football; (e) netball and basketball; (f) tennis; (g) Kanga cricket (modified Australian version of cricket for children); (h) skating, riding a scooter, and rollerblading; and (i) baseball and softball. The physical activities listed are those most commonly engaged in by this age group and represent those activities that are classified as having an energy expenditure of moderate-to-vigorous intensity, which is associated with health benefits. Students recorded the number of hours per week they spent in each of these activities (i.e., wrote in a response) and whether the activity was outdoors/indoors. The time spent in each activity was summated, and the average hours per week were calculated separately for total (i.e., all outdoor and indoor activities) outdoor and indoor physical activities.
For screen time, students were asked to think about an average week and to report the number of hours usually spent daily watching TV, playing video games, and using a computer (for fun and/or homework). The response categories were not at all, less than 1 h·d−1, 1–2 h·d−1, and 3 h·d−1 or more. Total screen time was the sum of time spent watching TV, playing video games, and using a computer.
Collection of other information.
Sociodemographic information was provided by parents and included ethnicity, country of birth, education, occupation, employment status, home ownership, parental age, and smoking status. Anthropometric information on adolescents was collected during school visits. Height was measured with shoes off using a freestanding SECA height rod (Model 220, Hamburg, Germany). Weight (kg) was measured using a standard portable scale, after removing any heavy clothing. BMI was calculated as weight (kg) divided by height (m) squared (kg·m−2).
Statistical analyses were performed using SAS (version 9.1; SAS Institute, Cary, NC) including t-tests, χ 2 tests, and linear regression. Linear regression models were constructed to examine possible associations between birth parameters (independent variable) with physical activity and screen time (dependent variables). General linear model was used to calculate adjusted means, which were then compared across quartiles/categories of birth parameters. Total physical activity and total screen time were analyzed separately, as was each type of physical activity and screen medium. Birth parameters were analyzed as categorical variables either as quartiles or as low, normal, and high birth weight categories. Associations between birth parameters and time spent in activity behaviors were first adjusted for age and sex and then further adjusted for ethnicity, BMI, parental education, home ownership, exposure to passive smoking, and gestational age. Mixed models (PROC MIXED) was used to adjust for school clustering effect in all analyses.
Of the 2353 12-yr-old schoolchildren surveyed, 1794 had complete birth parameter and activity data and were included for analyses. Participants versus nonparticipants were more likely to be Caucasian (64.4% vs 45.3%, P < 0.0001), have parents who own their home (75.4% vs 61.5%, P < 0.0001), and are less likely to have been exposed to passive smoking (21.3% vs 32.0%, P < 0.0001). Table 1 shows the study characteristics of those included for analyses; 12-yr-old schoolchildren in the very low or low compared with the normal and high birth weight category had significantly lower birth length, head circumference at birth, BMI, and gestational age; were significantly shorter and heavier; and were more likely to be East Asian. The range of birth weight, birth length, head circumference, and BMI is 920–5670 g, 34.0–61.1 cm, 26.5–39.5 cm, and 12.5–47.2 kg·m−2, respectively.
In 12-yr-old children, after adjusting for age, sex, and BMI, birth weight was significantly associated with total physical activity (P trend = 0.01). After adjustment for other covariates, a significant increase in time spent in both total (∼56 min·wk−1) and outdoor physical activity (∼1 h·wk−1) was observed with increasing birth weight (from the lowest to highest quartile), P trend = 0.02 and P trend = 0.02, respectively (Table 2). After multivariable adjustment, each SD (1 SD = 573.5 g) increase in birth weight was associated with a 15- and 20-min·wk−1 increase in total physical activity (P = 0.05) and outdoor physical activity (P = 0.01), respectively. Similarly, 12-yr-old children in the high birth weight group (>4000 g) compared with the very low birth weight group (<2000 g) spent approximately 1.3 h more in outdoor physical activity per week (P trend = 0.02; Table 3). Birth weight (analyzed as quartiles) was not significantly associated with total screen time, TV viewing, videogame, or computer usage (multivariable-adjusted P trend = 0.77, 0.36, 0.36, and 0.41, respectively). Significant associations were not observed with head circumference or birth length (see Tables, Supplemental Digital Content 1 and 2, http://links.lww.com/MSS/A197 and http://links.lww.com/MSS/A198 showing associations between head circumference and birth length with time spent in physical activity in 12-yr-old children, respectively).
Of those examined at baseline, 752 participants (32%) with complete physical activity, screen time, and birth parameter data were reexamined 5 yr later at age 17–18 yr. Participants compared with nonparticipants were more likely to be female (53.3% vs 44.5%, P = 0.03), Caucasian (62.1% vs 48.7%, P < 0.0001), and have parents who owned their home (83.4% vs 70.0%, P < 0.0001) but less likely to be exposed to passive smoking (15.9% vs 21.8%, P = 0.01). We also compared the study characteristics of these adolescents stratified by birth weight category. Those in the very low (<2000 g) versus high birth weight (>4000) category had significantly lower gestational age (32 vs 39 wk, P < 0.0001) and were taller (172.1 vs 170.1 cm, P = 0.04). Significant differences in other characteristics were not observed between the different birth weight categories in the 17- to 18-yr-old study sample (data not shown). Table 4 shows that among 17- to 18-yr-old adolescents, increasing birth weight (lowest to highest quartile) was associated with greater time spent in outdoor activity (∼1 h·wk−1, P trend = 0.04). After multivariable adjustment, each SD (1 SD = 573.5 g) increase in birth weight was nonsignificantly associated with a 12.6- and 18-min·wk−1 increase in total physical activity (P = 0.26) and outdoor physical activity (P = 0.08), respectively. Significant associations were not observed with screen time among adolescents, that is, birth weight (analyzed as quartiles) was not significantly associated with total screen time, TV viewing, videogame, or computer usage (multivariable-adjusted P trend = 0.48, 0.15, 0.56, and 0.78, respectively). Significant associations were also not observed with head circumference or birth length among adolescents (see Tables, Supplemental Digital Content 3 and 4, http://links.lww.com/MSS/A199 and http://links.lww.com/MSS/A200 showing associations between head circumference and birth length with time spent in physical activity in adolescents, respectively).
To our best knowledge, this is the first epidemiological study to examine the association between various birth parameters and both physical activity and screen time among adolescents. We show that increasing birth weight was associated with increasing time spent in total and outdoor physical activity among preadolescents. Moreover, 12-yr-old children who had high birth weight (>4000 g) compared with their very low birth weight peers (<2000 g) spent an additional hour in physical activity each week. The significant association between birth weight and outdoor physical activity persisted 5 yr later during late adolescence. These associations were independent of potential confounders including sex, ethnicity and other socioeconomic factors, BMI, and gestational age.
In our study, adjusting for gestational age in the analyses only marginally attenuated the association between birth weight and time spent in physical activity. Moreover, gestational age was not a significant covariate in the final, multivariable model. This finding contrasts with data from a Nordic cohort study of adolescents and adults (1), in that it suggests that intrauterine growth pattern (i.e., birth weight for gestational age) is not a stronger predictor of physical activity than birth weight per se. Similarly, other birth parameters such as birth length and head circumference were not significant predictors of physical activity. Given that ours is the first study to examine the relation between these parameters and activity behaviors, we are unable to explain these null findings. However, we speculate that genetic factors could be a potential underlying reason. For instance, some reports have documented that head circumference could be highly influenced by genes and that environmental factors are less important for variation in head circumference than birth weight (16,17). Nevertheless, these null findings could be due to chance, and further childhood studies are warranted to confirm our observations.
Birth weight had limited influence on adolescents’ screen time. These findings suggest that although adolescents in the low birth weight category tend to lead an inactive lifestyle, they are not necessarily spending more time in front of the screen than their age peers, which concurs with the notion that physical and sedentary activities are distinct behaviors and not just the reverse of each other (19,29). Comparisons are difficult, as no other population-based study has explored the relation between birth parameters and screen time. We hypothesize that the observed nonsignificant association could be due to other environmental and/or lifestyle factors exerting a greater influence on screen time than birth weight per se. Specifically, factors such as eating meals in front of the screen and having a TV in the bedroom are strong determinants of screen time (30) during adolescence, possibly more so than early life factors.
There are several strengths of this study, including its longitudinal design, random cluster sample of a relatively large number of school-age children, and documented data on birth parameters. A limitation, however, is that we used self-reported rather than an objective measurement of time spent in physical activity and screen time. The use of self-reported questionnaires in large cohort studies is however common practice (31) given the high costs, logistics, and expertise required to use criterion measures such as accelerometers. Further, our findings need to be interpreted with caution as measures of physical activity and screen time were not validated. Second, although we adjusted for several important confounders, we cannot exclude the possibility that unmeasured or residual confounding, for example from genetic factors, dietary parameters, parenting skills, and other societal factors, could have influenced the observed associations in our study. Third, dependant variables (i.e., activity behaviors) did not have normally distributed residuals; hence, observed significance levels need to be interpreted with caution. Finally, we cannot discount the possibility of selection bias, as approximately 50% of baseline participants were lost to follow-up and significant differences in study characteristics between participants and nonparticipants were observed, which could have influenced our findings.
In summary, our findings suggest that physical activity during adolescence, particularly outdoor physical activity could be influenced by birth weight. Although these findings support the hypothesis that behavior might be predetermined in the early critical window periods (12), further prospective studies of adolescents are still required to confirm or reject our observed associations between birth weight and physical activity participation. If our results are confirmed, these findings could have possible clinical implications; for example, physiotherapists and pediatricians could target children of lower birth weight and focus on increasing their activity levels to at least partly mitigate their future disease risk.
The Sydney Childhood Eye Study was supported by the Australian National Health and Medical Research Council (grant nos. 253732 and 512530); the Westmead Millennium Institute, University of Sydney; and the Vision Co-operative Research Centre, University of New South Wales, Sydney, Australia.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
The authors declare no conflict of interest.
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ADOLESCENCE; BIRTH WEIGHT; PHYSICAL ACTIVITY; SCREEN TIME; SYDNEY CHILDHOOD EYE STUDY
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© 2013 American College of Sports Medicine