Abstract
Background: The application of latent class growth analysis (LCGA) has been limited in behavioral studies on high-cardiovascular-risk populations.
Aim: The current study aimed to identify distinct health behavior trajectories in high-cardiovascular-risk populations using LCGA. We also examined the baseline individual characteristics associated with different health behavior trajectories and determined which trajectory is associated with improved cardiovascular risk outcomes at 52 weeks.
Methods: This secondary analysis of a clinical trial included 200 patients admitted to primary care clinics. Latent class growth analysis was conducted to identify the trajectories of physical activity and dietary intake; these were measured at 4 different time points during a 52-week study period. Analysis of variance/[chi]2 test was used to assess the associations between baseline individual characteristics and trajectories, and logistic regression analysis was used to identify associations between trajectories and cardiovascular risk outcomes at 52 weeks.
Results: Three trajectories were identified for physical activity (low-, moderate-, and high-stable). Risk perception, patient activation, and depressive symptoms predicted the trajectories. High-stable trajectory for physical activity was associated with better cardiovascular risk outcomes at the 52-week follow-up. Two trajectories (low-stable and high-decreasing) were identified for percent energy from fat, but the factors that can predict trajectories were limited.
Conclusions: Interventions are needed to target patients who begin with a lower physical activity level, with the goal of enhanced cardiovascular health. The predictors identified in the study may facilitate earlier and more tailored interventions.