Physical activity level-specific analysis

These results can be found in Table 4.3.5 (active), Table 4.3.6 (moderately active), and Table 4.3.7 (inactive). Disaggregating the sample by physical activity index revealed

119 scores predicted BMI in the low-BMI component, with DQI and HEI being associated with decreasing BMI in both components (although only at a 10% significance level in the low-BMI component). The moderately active subgroup exhibited a large difference in effect of diet quality between the two components, with no statistically significant effect in the low-BMI component but a large, strongly significant effect of decreasing BMI in the high-BMI component. In the inactive subgroup, however, none of the diet quality indices significantly predicted BMI. In comparison, the full-sample analysis showed a non-significant relationship between BMI and diet quality in the low-BMI component, and a highly significant inverse relationship in the high-BMI component – a pattern similar to that seen in the moderately-active subgroup. Glycemic index was not associated with BMI in either component in any of the PAI-specific subgroups.

The association between age and BMI appeared to weaken with increasing physical activity: in the active subgroup, age was not significantly associated with BMI in any of the models. In the moderately active subgroup, it was only significant in the low-BMI components, and in the inactive subgroup, age was strongly predictive of decreased BMI in the low-BMI component and weakly predictive in the high-BMI component. The quadratic age term was only significantly associated with decreased BMI in the low-BMI component of the inactive subgroup, with the turning point in the relationship between age and BMI occurring at around age 57. In the full sample, both age and its squared term were statistically significant in both components.

120 subgroup, however, gender was not significantly associated with BMI in the high-BMI component.

Energy intake was strongly associated with decreased BMI in the low-BMI components of all three subgroups; for the high-BMI component, it was only significant in the moderately active subgroup, in which it was associated with increased BMI. A similar pattern was seen in the full-sample analysis, in which energy intake was significantly associated with decreased BMI in the low-BMI component and weakly significantly associated with increased BMI in the high-BMI component.

Percentage of calories consumed at home was not significantly associated with BMI in the inactive subgroup; it did predict lower BMI in the low-BMI component of moderately active subgroup, and it was slightly significantly associated with decreased BMI in the high-BMI component of the active subgroup. In comparison, this variable was weakly significantly associated (10% only) with BMI in the low-BMI component of the full sample, and moderately significantly associated with BMI in the full sample high-BMI component.

Leisure energy expenditure was associated with decreased BMI in the low-BMI

component of active subgroup and in the high-BMI component of inactive subgroup. It was not significantly predictive of BMI in the moderately active respondents. In

comparison, the full sample analysis showed that leisure energy expenditure was inversely associated with BMI in the low-BMI component only. Since physical activity index is defined by thresholds of leisure energy expenditure, there may be low variation

121 in leisure energy expenditure in the moderately active subgroup because its leisure energy expenditure range is bounded at both ends (see section 3.2.8.2 on page 78).

The associations between province and BMI were shown to be limited to inactive subgroup. Among them, being from a Maritime province strongly predicted (at less than 1% significance) increased BMI in the low-BMI component and being from

Manitoba/Saskatchewan weakly predicted (only at 10% significance) increased BMI in both components. The full sample analysis displayed similar associations in the low-BMI component, but did not show any significant associations between province and BMI in the high-BMI component.

Urban/rural residence was not a significant indicator of BMI in the inactive subgroup. In the active subgroup, it appeared that compared to living in urban cores, living in urban areas outside of urban cores was associated with increased BMI in the low-BMI

component (especially in urban areas outside CMAs and CAs) and decreased BMI in the high-BMI component (especially the urban fringe areas). Rural living was associated with increased BMI in the high-BMI component of the active subgroup, especially in rural fringe areas inside CMAs and CAs. In the moderately active subgroup, living in urban fringe areas appeared to be associated with increased BMI in the low-BMI component. The effect of living in a rural area was limited to the high-BMI component and appeared to depend on whether the respondent’s residence was inside or outside CMAs and CAs: the rural dwellers inside CMAs and CAs had significantly decreased BMI, while those outside CMAs and CAs had significantly increased BMI. In the full

122 Marital status had similar associations with BMI in each of the three subgroups, although they were only significant in the active and inactive subgroups. In the full sample, being married was significantly associated with increased BMI in the low-BMI component and decreased BMI in the high-BMI component. The positive association in the low-BMI component was significant in the active and inactive subgroups but not in the moderately active subgroup. The negative association in the high-BMI component was not significant in any of the subgroups. As well, there was a weakly significant negative association between BMI and being widowed/separated/divorced in the high-BMI component of the full sample; this was not apparent in any of the PAI-specific subgroups.

Education level was not significant in either active or moderately active subgroups, but post-secondary graduation was associated with decreased BMI in the low-BMI

component of the inactive subgroup. In the full sample analysis, education was generally not significantly related to BMI.

Similarly, income adequacy was only strongly associated with BMI in the inactive subgroup, with increasing income adequacy being associated with increased BMI in the low-BMI components. The full sample analysis showed a similar trend to that seen in the inactive subgroup.

Being unemployed was significantly associated with decreased BMI in the low-BMI component of the active subgroup; it was not significant in the other subgroups. Full-time student status was related to lower BMI in the high-BMI class in moderately active and inactive subgroups, but not in the active subgroup. In the full sample, only the association

123 The association between high stress and BMI was limited to the moderately active

subgroup, in which high stress level was associated with increased BMI in the low-BMI class. This association was also seen in the full sample.

When disaggregated by activity level, the effect of being a non-recent immigrant was not significant in any of the subgroups (whereas in the full sample, it was significantly associated with decreased BMI in the low-BMI component). Being a recent immigrant, which was strongly associated with decreased BMI in all components in the full-sample analysis, also strongly predicted lower BMI in the high-BMI class of all three activity levels. In the low-BMI class it only significantly predicted lower BMI in active and moderately active subgroups.

Smoking was generally not significantly related to BMI in active and moderately active subgroups, with daily smoking only weakly predicting lower BMI in high-BMI

component active subgroup. In the inactive subgroup, however, daily smoking

significantly predicted lower BMI in the low-BMI component. In comparison, in the full sample daily smoking significantly predicted decreased BMI in both components.

Having provided a weekend recall was generally not significantly associated with BMI in active or moderately active subgroups, but was significantly associated with lower BMI in the high-BMI component of inactive respondents. Interestingly, this contrasts the results from the full sample analysis, which showed a significant negative association in only the low-BMI component.

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