Identifying Multiple Knee Pain Trajectories and the Prediction of Opioid and NSAID Medication Used: A Latent Class Growth Approach

Pain Practice ◽  
2021 ◽  
Author(s):  
Sayyed M. Haybatollahi ◽  
Richard J. E. James ◽  
Gwen Fernandes ◽  
Ana Valdes ◽  
Michael Doherty ◽  
...  
Keyword(s):  
Knee Pain ◽  
Latent Class ◽  
Pain Trajectories ◽  
Growth Approach ◽  
Latent Class Growth

scholarly journals Preschool Intervention Can Promote Sustained Growth in the Executive-Function Skills of Children Exhibiting Early Deficits

2017 ◽  
Vol 28 (12) ◽  
pp. 1719-1730 ◽  
Author(s):  
Tyler R. Sasser ◽  
Karen L. Bierman ◽  
Brenda Heinrichs ◽  
Robert L. Nix
Keyword(s):  
Executive Function ◽  
Head Start ◽  
Latent Class ◽  
Intervention Group ◽  
Third Grade ◽  
Control Group ◽  
Social Emotional ◽  
Usual Practice ◽  
Preschool Intervention ◽  
Latent Class Growth

This study examined the effects of the Head Start Research-Based, Developmentally Informed (REDI) preschool intervention on growth in children’s executive-function (EF) skills from preschool through third grade. Across 25 Head Start centers, each of 44 classrooms was randomly assigned either to an intervention group, which received enhanced social-emotional and language-literacy components, or to a “usual-practice” control group. Four-year-old children ( N = 356; 25% African American, 17% Latino, 58% European American; 54% girls) were followed for 5 years, and EF skills were assessed annually. Latent-class growth analysis identified high, moderate, and low developmental EF trajectories. For children with low EF trajectories, the intervention improved EF scores in third grade significantly more ( d = 0.58) than in the control group. Children who received the intervention also demonstrated better academic outcomes in third grade than children who did not. Poverty often delays EF development; enriching the Head Start program with an evidence-based curriculum and teaching strategies can reduce early deficits and thereby facilitate school success.

scholarly journals Social Maladjustment and Criminal Behavior Pattern Changes in Adolescents in Conflict with the Law

2020 ◽  
Vol 30 ◽  
Author(s):  
Lais Sette Galinari ◽  
Rafaelle Carolynne Santos Costa ◽  
André Vilela Komatsu ◽  
Marina Rezende Bazon
Keyword(s):  
Criminal Behavior ◽  
Latent Class ◽  
Behavior Pattern ◽  
Social Maladjustment ◽  
Continuity And Change ◽  
Jesness Inventory ◽  
The Social ◽  
The Law ◽  
Student’S T ◽  
Latent Class Growth

Abstract Personality aspects that present a risk for criminal conducts are susceptible to changes. This study aimed to identify the profile of adolescents in conflict with the law based on the Social Maladjustment (SM) construct, to describe patterns of criminal conducts, and to verify the continuity and change on these variables, in a longitudinal prospective study. A sample of 78 adolescents answered to the Jesness Inventory - revised in Brazil and to the Questionnaire of Youth Behaviors, at two collection times (W1 and W2). The profiles were identified with latent class growth analysis and the behavior patterns were compared with Student’s t test. Two classes were obtained: High SM and Normative SM. At W1, SM high scores were associated to high frequency in the perpetration of crimes and both classes had lower SM at W2. The results point to the possibility of changes in SM and in conduct over time.

scholarly journals Latent Class Growth Analysis and Growth Mixture Modeling using R: A tutorial for two R-packages and a comparison with Mplus.

2020 ◽  
Author(s):  
Klaas J Wardenaar
Keyword(s):  
Latent Class ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Trajectories ◽  
Growth Mixture ◽  
Software Packages ◽  
Model Configuration ◽  
R Packages ◽  
Latent Class Growth ◽  
Growth Analyses

Latent Class Growth Analyses (LCGA) and Growth Mixture Modeling (GMM) analyses are used to explain between-subject heterogeneity in growth on an outcome, by identifying latent classes with different growth trajectories. Dedicated software packages are available to estimate these models, with Mplus (Muthén & Muthén, 2019) being widely used . Although this and other available commercial software packages are of good quality, very flexible and rich in options, they can be costly and fit poorly into the analytical workflow of researchers that increasingly depend on the open-source R-platform. Interestingly, although plenty of R-packages to conduct mixture analyses are available, there is little documentation on how to conduct LCGA/GMM in R. Therefore, the current paper aims to provide applied researchers with a tutorial and coding examples for conducting LCGA and GMM in R. Furthermore, it will be evaluated how results obtained with R and the modeling approaches (e.g., default settings, model configuration) of the used R-packages compare to each other and to Mplus.

scholarly journals The course of social media use problems in secondary school adolescents: A latent class growth analysis

2021 ◽  
Author(s):  
Maartje Boer ◽  
gonneke stevens ◽  
Catrin Finkenauer ◽  
Regina van den Eijnden
Keyword(s):  
Social Media ◽  
Secondary School ◽  
Growth Analysis ◽  
Latent Class ◽  
Media Use ◽  
Latent Class Growth Analysis ◽  
Social Media Use ◽  
Immigrant Background ◽  
Low Levels ◽  
Latent Class Growth

Little is known about how addiction-like social media use (SMU) problems evolve over time. Using four waves of longitudinal data collected in 2015-2019 from 1,414 adolescents (Mage = 12.5, 46.0% girl, 21.9% immigrant background), this study aimed to identify adolescents’ trajectories of SMU problems in parallel with their trajectories of SMU intensity. Latent class growth analysis identified two subgroups with persistently high levels of SMU problems, of which one with high (24.7%) and one with average SMU intensity (14.8%), and two subgroups with persistently low levels of SMU problems, of which one with low (22.3%) and one with high SMU intensity (38.2%). Compared to the largest subgroup, the two subgroups with high levels of SMU problems showed more problematic profiles.

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