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Relationships between physical activity variables and Kg were examined using Pearson correlation analysis.
Table 1 shows the polychoric correlations (correlations between ordinal variables) between physical activity variables for males and females.
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We also performed a number of different multicollinearity tests between the physical activity variables and separately for the socioeconomic status variables.
Possible interactions were tested between the two physical activity variables, each of the physical activity variables and all the other variables and gender and all the other variables.
Table 3 presents the associations between our health and physical activity variables and the distances travelled by active travel, by motorised travel and overall.
Associations between independent mobility and physical activity variables were analysed using generalized linear models, accounting for clustered sampling, Actiheart wear time, socio-demographics, and assessing interactions by sex.
A complex set of interrelationships occur between lifestyle, anthropometric, psychological and physical activity variables [ 27].
Furthermore, it was stated that self-reported physical activity that captures specific domains of activity allow for tests of association between conceptually matched environmental and physical activity variables.
Data for the participants of the two countries were pooled, since there were no significant differences between the groups for the physical activity variables.
The associations between the latent variable and the observed physical activity variables were always stronger than the associations between the representative reported physical activity variable and the remaining reported variables (results not shown).
ICCs for clinical and physical activity variables ranged between 0 0.08 in three cluster trials of residential health care.
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