Dynamic Time Warp (DTW) as a scalable, data-efficient, and clinically relevant
analysis of dynamic processes in patients with psychiatric disorders: a tutorial
#MMPMID41121445
Kopland MCG
; Giltay EJ
J Eat Disord
2025[Oct]; 13
(1
): 230
PMID41121445
show ga
Dynamic Time Warping (DTW) is an emerging analytic technique that offers a
flexible approach to modeling symptom dynamics in psychological and psychiatric
research. Unlike traditional network models, which often rely on linear
associations, DTW aligns symptom trajectories even when changes unfold at
slightly different speeds or time intervals. This tutorial offers a brief
introduction into DTW and demonstrates how to apply DTW to panel or time series
data. We illustrate the workflow using clinical case data from patients with
eating disorders, to capture temporal patterns that cannot be detected with
conventional network analysis techniques, as these require more intensive
time-series data. Key advantages include its applicability to non-stationary
data, flexibility in handling irregular time intervals, and reduced reliance on
frequent assessments, which patients often cannot maintain due to the burden. We
also discuss some of the limitations such as noise, scaling decisions and lack of
Granger causality associations. Finally, we outline directions for future
research. By expanding the methodological toolkit available for studying therapy
processes, DTW holds promise for advancing both research and clinical practice in
personalized mental health care.
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