Clustering individuals’ temporal patterns of affective states, hunger, and food craving by latent class vector-autoregression

Abstract

Background: Eating plays an important role in mental and physical health and is influenced by affective (e.g., emotions, stress) and appetitive (i.e., food craving, hunger) states, among others. Yet, substantial temporal variability and marked individual differences in these relationships have been reported. Exploratory data analytical approaches that account for variability between and within individuals might benefit respective theory development and subsequent confirmatory studies. Methods: Across two weeks, 115 individuals (83% female) reported on momentary affective states, hunger, and food craving six times a day. Based on these ecological momentary assessment (EMA) data we investigated whether latent class vector-autoregression (LCVAR) can identify different clusters of participants based on similarities in their temporal associations between these states. Results: LCVAR allocated participants into three distinct clusters. Within clusters, we found both positive and negative associations between affective states and hunger/food craving, which further varied temporally. Clusters differed on eating-related traits such as stress-eating and food craving as well as on EMA completion rates. Discussion: LCVAR provides novel opportunities to analyse time-series data in affective science and eating behaviour research and uncovers that traditional models of affect-eating relationships might be overly simplistic. To account for the high degree of variability, future research may benefit from considering individuality, daytime as well as specific affective states. Methodological implications for EMA research are discussed.