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Updated: Apr 14, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Area Between Trajectories: Insights into Optimal Group Selection and Trajectory Heterogeneity in Group-Based

Yi-Chen Hsiao1,2, Chun-Yuan Chen3,4, Mei-Fen Tang5,6,7

  • 1Institute of Health and Welfare Policy, School of Medicine, National Yang-Ming University, Taipei, Taiwan.

Prevention Science : the Official Journal of the Society for Prevention Research
|April 12, 2026
PubMed
Summary

Group-based trajectory modeling (GBTM) helps identify health patterns in older adults. A new method, area between trajectories (ABT), quantifies differences between these patterns for better clinical relevance.

Keywords:
Area between trajectoriesGroup-based trajectory modelingLongitudinal patternsOptimal number of groupsTrajectory heterogeneity

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Area of Science:

  • Gerontology
  • Biostatistics
  • Health Outcomes Research

Background:

  • Group-based trajectory modeling (GBTM) is a key statistical method for analyzing longitudinal health data in older adults.
  • Determining the optimal number of distinct trajectories is critical but often relies on statistical criteria alone.
  • Increasingly, clinical relevance is sought to ensure identified patterns reflect meaningful health changes, yet objective measures are lacking.

Purpose of the Study:

  • To introduce and evaluate the Area Between Trajectories (ABT) as a quantitative measure for assessing clinical relevance in GBTM.
  • To demonstrate the application of ABT using a simulated dataset for sleep quality trajectories.
  • To explore the utility and limitations of ABT in understanding trajectory heterogeneity.

Main Methods:

  • Group-based trajectory modeling (GBTM) was employed to analyze a simulated sleep quality dataset.
  • Models with varying numbers of groups were generated and compared.
  • The Area Between Trajectories (ABT) metric was calculated and visualized to quantify differences between identified trajectory groups.

Main Results:

  • GBTM successfully identified distinct longitudinal patterns in simulated sleep quality.
  • The ABT metric provided a quantitative assessment of the separation and differences between these trajectory groups.
  • Visualizations and calculations demonstrated the practical application of ABT in evaluating trajectory distinctness.

Conclusions:

  • The Area Between Trajectories (ABT) offers a novel, quantitative approach to assess the clinical relevance of trajectory groups identified by GBTM.
  • ABT can supplement visual inspection, providing objective insights into the heterogeneity of health outcomes over time.
  • Further research is warranted to refine ABT and explore its broader applications in longitudinal health research.