Methods Commentary: Uncovering Unobserved Data Patterns With Latent Variable Mixture Modeling

In a recent article in theJournal of Pediatric Psychology entitled “Latent Profiles of Physical Activity and Sedentary Behavior in Elementary School-Age Youth: Associations With Health-Related Quality of Life,”Mitchell and Steele (2017) used latent variable mixture modeling (LVMM) to empirically uncover three patterns of physical activity and sedentary behavior (SB) based on accelerometer data. These three patterns, also known as latent classes, were subgroups of youth who were classified as Active, Moderate, or Inactive in terms of their physical activity and SB. More specifically, these patterns were based on the average proportion of time in moderate-to-vigorous physical activity (MVPA) and SB and average proportion of time in MVPA and SB bouts over the course of 3 weekdays and 2 weekend days. Male gender and younger age were found to increase the odds of being a member of the active profile (relative to the moderate and inactive profiles), which in turn was associated with higher psychosocial health-related quality of life (HRQOL), above and beyond the direct effects of gender and age on this outcome.

Source: Journal of Pediatric Psychology - June 8, 2018 Category: Pediatrics Source Type: research