Modeling the Effects of Personal Characteristics on Longitudinal Cognitive Performance and Its Variability: An application to the Multicultural Healthy Diet Study

Abstract

Advances in digital data collection technologies have greatly improved researchers' ability to collect intensive longitudinal cognition data in real-world environments. Ecological momentary assessment (EMA), as one of such enhancements, involves repeated measures of ultra-brief cognitive tasks that obtains ecologically valid data via smartphones, thus allowing for less measurement error than traditional lab-based assessments. More sophisticated analytical approaches are required to disentangle the complex structure of this data. This thesis considers EMA cognitive data from a culturally diverse sample in the Multicultural Healthy Diet (MHD) study and provides an exploratory analysis using a multilevel mixed-effects model to better understand their associations with demographic and cultural factors, modeling both the mean and variability in cognitive performance. We start by providing the theoretical background of linear mixed-effects models as a powerful tool and a flexible multilevel modeling framework for longitudinal data analysis in Chapter 2. Chapter 3 presents a preliminary analysis on day-level averages to inform any potential learning effects and assess covariate influences across days. In Chapter 4, we extend our exploration of covariates effects to session-level data with an emphasison within- and between-person variance in relation to covariates. We conclude this thesis in Chapter 5 by summarizing these findings and discuss potential future work. Overall, this thesis offers a deeper insight into within-day and day-to-day fluctuations in cognitive performance among people from different cultural backgrounds.