Beyond the mean: Sequence analysis methods for clustering ordinal EMA data

2026-04-26

AI Summary

Traditional ecological momentary assessment (EMA) analyses often rely on simplified metrics such as means, which inadequately capture individual longitudinal dynamics. This study moves beyond mean-based approaches by integrating sequence analysis, principal component analysis, and K-means clustering to directly identify latent subgroups exhibiting similar behavioral patterns from full time-series data, while effectively accommodating heterogeneity in sample size and observation frequency. Validated against latent class analysis (LCA) and latent transition analysis (LTA), the proposed method successfully uncovers distinct stress trajectory subgroups in real-world EMA stress data and substantially enhances explanatory power for variability in cognitive performance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

Ecological momentary assessment (EMA) ratings are widely used in studies of behavioral and psychological phenomena to capture real-time data in subjects' real-world environments. Because the data are collected repeatedly over the study period, they provide rich longitudinal rating profiles for each individual. However, the number of observations per subject is often large, while both sample size and sampling intensity can vary substantially across individuals, which complicates the analysis. In some settings, simplified summaries of individual profiles, such as averages computed across the study period, are used for downstream analyses, including regression-style modeling. Although such summaries can be convenient, they may fail to fully capture dynamic temporal patterns present in the complete longitudinal profiles. To address this, we borrow measures from sequence analysis that capture individual-level patterns over time and then applied principal component analysis (PCA) followed by $K$-means clustering to identify unobserved latent groups of individuals with similar profiles. We test our approach using simulated data from a categorical functional regression model and compare its performance with two commonly used methods for detecting unobserved group structures: latent class analysis (LCA), and latent transition analysis (LTA). Using EMA stress observations from a large sample of U.S. adults (Newman et al., 2024, 2025), we identify distinct latent stress profile groups and show that they improve characterization of the impact on cognitive performance.

Problem

• Ecological Momentary Assessment
• sequence analysis
• latent groups
• ordinal longitudinal data
• temporal patterns

Innovation

• sequence analysis
• ecological momentary assessment
• latent clustering
• longitudinal patterns
• ordinal data