Abstract
Background:
Our aim was to determine adult's trajectories of meeting 150 minutes per week of physical activity (PA) and assess associations between environmental features (and changes in these) with trajectory membership.
Methods:
We used data from the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) cohort study, Australia. Data on PA were assessed in 2007, 2009, 2011, 2013, and 2016 among individuals aged 45-60 years. At each survey, data on length of bike paths, area of parks, residential density, street connectivity, land use mix, and number of street lights were measured. PA trajectories were determined using group-based trajectory modeling. Group differences were analyzed based on median and interquartile range (IQR) of environmental features and associations were assessed through multinomial logistic regression.
Results:
We identified 4 trajectories of meeting PA guidelines: low (14.4%), decreasing (13.7%), increasing (30.4%), and high (41.5%). Median (IQR) values of environmental features were very similar among trajectory groups (P > .05) with relatively little change between initial (2007) and final (2016) timepoints. Associations with trajectory membership were observed for bike path length among participants in the mid-tertile of the low trajectory group compared with the high trajectory (odds ratio [OR] = 0.55; 95% confidence interval [CI], 0.36-0.84); and for street connectivity in the low (OR = 0.67; 95% CI, 0.51-0.87), and increasing trajectory groups (OR = 0.76; 95% CI, 0.61-0.94).
Conclusion:
In the context of this "livable city," environmental features did not thoroughly predict patterns of PA among mid-age adults. Environmental predictors of PA deserve further attention, especially in low-income and middle-income countries, as active friendly environments have important connotations for various types of PA.
Our aim was to determine adult's trajectories of meeting 150 minutes per week of physical activity (PA) and assess associations between environmental features (and changes in these) with trajectory membership.
Methods:
We used data from the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) cohort study, Australia. Data on PA were assessed in 2007, 2009, 2011, 2013, and 2016 among individuals aged 45-60 years. At each survey, data on length of bike paths, area of parks, residential density, street connectivity, land use mix, and number of street lights were measured. PA trajectories were determined using group-based trajectory modeling. Group differences were analyzed based on median and interquartile range (IQR) of environmental features and associations were assessed through multinomial logistic regression.
Results:
We identified 4 trajectories of meeting PA guidelines: low (14.4%), decreasing (13.7%), increasing (30.4%), and high (41.5%). Median (IQR) values of environmental features were very similar among trajectory groups (P > .05) with relatively little change between initial (2007) and final (2016) timepoints. Associations with trajectory membership were observed for bike path length among participants in the mid-tertile of the low trajectory group compared with the high trajectory (odds ratio [OR] = 0.55; 95% confidence interval [CI], 0.36-0.84); and for street connectivity in the low (OR = 0.67; 95% CI, 0.51-0.87), and increasing trajectory groups (OR = 0.76; 95% CI, 0.61-0.94).
Conclusion:
In the context of this "livable city," environmental features did not thoroughly predict patterns of PA among mid-age adults. Environmental predictors of PA deserve further attention, especially in low-income and middle-income countries, as active friendly environments have important connotations for various types of PA.
| Original language | English |
|---|---|
| Pages (from-to) | 1-11 |
| Number of pages | 11 |
| Journal | Journal of Physical Activity and Health |
| DOIs | |
| Publication status | E-pub ahead of print - 8 Oct 2025 |
