This study showed that TV watching was positively related to fatness in a dose-dependent manner. Strong, dose-dependent associations between fatness and soft drink consumption (positive relationship), breakfast consumption (inverse relationship) and after-school physical activity (inverse relationship) were observed. These associations were independent of ethnicity, sex and age. Along with their effect sizes, a highlight of our study was that differences in lifestyle obesity risk factors were associated with percentage differences in body composition variables that were greatest for TFM, followed by either BMIz or %BF and then BMI.
The positive TV watching- and soft drink-fatness associations and the inverse breakfast consumption- and physical activity-fatness relationships we observed have previously been reported in studies carried out internationally [2–7]. However, our study differs from those studies in that we adjusted for dieting intention and quantified %BF and TFM.
Of particular interest was the size of the associations. In the present study, compared to the low-exposure categories, BMI in the high-exposure categories was between 0.23 and 0.75 kg/m2 higher (Tables 2, 3, 4 and 5) or between 1.1 to 3.5% higher (Figure 2). These effect sizes are larger than those reported in meta-analyses and several previous studies [2, 3, 5]. This difference may reflect strengths of our study (discussed below) – in particular, the large variation in fatness level and exposure to lifestyle factors (Table 1), together with accounting for dieting intention in analyses.
Change in the level of exposure to lifestyle obesity risk factors, such as an increase in soft drink consumption, alters fat stores through change in energy intake or expenditure. BMIz, %BF and, in particular, BMI have a limited ability to reflect these changes in body composition as they are influenced by total fat-free mass. In addition, increases in TFM will be 'underestimated’ by the changes in %BF since the latter is based on the ratio of TFM and body weight, both of which increase with increasing TFM. Consistent with this, the present study showed that lifestyle variables were most strongly related to TFM and had weaker associations with BMIz, %BF and BMI. To our knowledge, this is the first study to show this in children and adolescents; the relative strength of associations of these four body composition variables with lifestyle factors has not been formally assessed by previous work.
Across ethnic groups, TV watching and soft drink consumption associations were consistently positive, while the breakfast consumption and after-school physical activity associations were consistently inverse. TV watching relationships were dose-dependent, while soft drink consumption, breakfast consumption and after-school physical activity relationships were strong plus dose-dependent. Of note, associations were especially strong for soft drink consumption and after-school physical activity, with TFM percentage differences between highest and lowest exposure categories exceeding 11% (Figure 2). In the light of successful intervention studies [6, 14, 15], the consistent, strong and dose-dependent nature of these associations gives evidence that they may well be causal.
In the Pacific region (defined in this paper as Australia, New Zealand and other Pacific Island countries), the prevalence of obesity is among the highest in the world . Due to the geographical location of our participants, this study has the ability to provide particularly relevant evidence for youth in the Pacific region. Previous studies similar to ours have a limited ability to do so, especially since they were small, used BMI alone as a fatness measure, did not adjust for dieting intention or studied mainly primary-school aged children [16, 18, 20–44]. Given that our study addressed these drawbacks, it provides stronger evidence of whether lifestyle factors contribute to obesity in Pacific region youth. In addition, our study defines a type of physical activity (after-school physical activity) that is predictive of fatness, which is important because this helps to specify an appropriate physical activity intervention target. Few studies have done this [18, 41]; nearly all have measured overall physical activity (such as daily step counts) instead.
This study has a unique ability to explain ethnic disparities in fatness levels that exist among adolescents in the Pacific region (Table 1, ). Of note, fatness levels are especially high among New Zealand Pacific Island and Maori youth (Table 1, ), indicating a need for obesity interventions targeted at these groups. These disparities are partly due to the fact that high TV watching, high soft drink consumption and breakfast skipping are more prevalent in these groups (Table 1, ). Breakfast skipping is a particularly important explanation as its association with fatness is strongest in these groups (Table 4 and Figure 1). This may be attributed to ethnic differences in factors that determine the level of consumption of unhealthy (energy-dense) food outside of home – a plausible mediator in the breakfast skipping-fatness association. These factors include: 1) healthiness and accessibility of school food options outside of home (in school canteens and shops) and, 2) spending money allocated for school food purchase.
This study is the largest that we are aware of carried out in children and adolescents in the Pacific region [16, 18, 20–44] and larger than most of those performed outside the Pacific region [2–4, 7]. Other study strengths are that physical measurements were objectively measured (not self-reported), the use of validated %BF and TFM measures, the large variation in fatness level and exposure to lifestyle factors (Table 1), and the ethnically and geographically diverse nature of the sample. Furthermore, serving size was included in the assessment of soft drink consumption; this is often not the case . Finally, the large number of sub-samples (8 ethnic groups) and homogenous measurements across these facilitated assessment of consistency of associations. Studies that have examined consistency have utilised fewer sub-groups or have pooled together results of different studies with heterogenous measurements.
A limitation of this study is error inherent in the measurement of the lifestyle variables. Random measurement error associated with the lifestyle variables – resulting from day-to-day variation in lifestyle habits and imperfect memory to recall these – would weaken associations. Therefore, the associations may well be stronger than we observed. Being cross-sectional, this study is unable to rule out the possibility of reverse causation. However, this possibility and any influence of reverse causation were reduced for some reasons. Firstly, analysis was restricted to those who said they were not trying to change weight, so the trigger for reverse causality (trying to lose weight) would have been minimal. Secondly, lifestyle factors were most strongly related to TFM, followed by %BF and then BMI, and this hierarchical pattern would fit a forward causation (changes in energy intake or expenditure leading to changes in TFM, as discussed above) but a reverse-causation mechanism would be unlikely to produce this hierarchy of strength of relationships. In addition, if reverse causation did account for the relationships between physical activity and fatness, fear of being teased may act as a mediator . However, when a measure of this mediator was adjusted for, the physical activity-fatness associations remained significant (data not shown), which gives some evidence to suggest that reverse causation did not fully explain these associations.
The fact that analysis was largely restricted to those who were not trying to change weight would have limited the ability to extrapolate findings to those trying to change weight. Because of the improvement in internal validity it provided (as discussed in Measurements), given that a notable fraction of participants were trying to change weight (Additional file 1), this restriction was considered to be – by us and Rothman et al. – important and justifiable. However, our findings have at least some applicability to the “change weight” group because individuals from this group probably would have previously made no attempts to change weight, which is supported from epidemiological evidence that weight-control attempts are less prevalent in childhood than in adolescence . In other words, our results suggest that lifestyle factors may well have contributed to weight gain of individuals before they tried to change weight.
With regard to SES confounding, the participants were recruited from schools with similar SES and there was low variation in personal SES in the areas sampled from in New Zealand . Further, in Australia, analyses showed that inclusion of SEIFA scores in statistical models did not alter our conclusions (data not shown). These factors reduce the possibility of confounding by SES.
We did not measure pubertal status, which may have been a covariate worth controlling for in statistical models. However, maturational stage is correlated with age and sex, and may have varied with ethnicity in our dataset . Thus, at least some adjustment for puberty would have been provided through the inclusion of age, sex and ethnicity in models. Further, any correlation between pubertal stage and lifestyle factors might be mediated by weight-control attempt , but we accounted for the latter in the analyses.
The self-reported nature of the lifestyle data collection raises the possibility of there being social desirability bias associated with the measurement of the lifestyle variables. For instance, obese adolescents may have under-reported their intake of soft drinks because of their unhealthy connotation. However, any influence on the results from this source of error was minimised by indicating to the students that all collected data were confidential and having each student answer survey questions alone.