Stress and sleep cross lagged dynamic panel data with DSEM

Background The onset of the COVID-19 pandemic disrupted and changed sleep as well as elevated stress levels worldwide. Previous research has demonstrated a bidirectional relationship between stress and sleep, in that stress contributes to poorer sleep and poor sleep leads to higher stress. It is hypothesized that perseverative cognition (i.e., worry, racing thoughts) is a key cognitive mechanism in this relationship. In addition, little is known about whether health behaviors such as physical activity could moderate the daily relationships between stress and sleep. The goal of our study was to examine the relationships between stress and sleep during a major global stressor, testing key cognitive and behavioral factors that may influence this relationship. Method 191 adults aged 18 and above were recruited to complete a text-message survey twice per day for 3 distinct weeks spread over a 4-month period. Sleep duration and efficiency during the previous night and evening/overnight perseverative cognition were measured in the morning survey, daily stress levels were measured in the evening survey. Physical activity was measured by the International Physical Activity Questionnaire (IPAQ). Results were analyzed using a DSEM or dynamic structural equations model adjusted for age, gender and race/ethnicity. In traditional SEM analysis, measured variables have an intercept/mean that is a function of an indicator variable a_y. A latent variable/factor of measured variables Y1-4 shares the indicator function a but is allowed to assume its own intercept as a unique function g_F1(a) as well as its own residual D_1. DSEM is a methodological advancement for intensive longitudinal Data, combining three well-established modeling techniques: Time Series Analysis to account for lagged time points within data; Multilevel Modeling for simultaneous analysis of multiple clusters, as well as within and between person effects providing a framework to analyze these quantitative differences and implement proper correlation structures; and Structural Equation modeling for further analysis of these effects through path and factor analysis; together providing a framework through which to analyze cross-lagged variables then standardize and compare them.