Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

Samantha Bothwell; Alex Kaizer; Ryan Peterson; Danielle Ostendorf; Victoria Catenacci; Julia Wrobel

doi:10.1111/biom.13773

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Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

Journal article

Peer reviewed

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

Samantha Bothwell, Alex Kaizer, Ryan Peterson, Danielle Ostendorf, Victoria Catenacci and Julia Wrobel

Biometrics, Vol.79(3), pp.2719-2731

09/01/2023

DOI: 10.1111/biom.13773

PMCID: PMC10393286

PMID: 36217829

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https://pmc.ncbi.nlm.nih.gov/articles/PMC10393286/pdf/nihms-1913730.pdfView

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Abstract

"Smart"-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.

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Details

Title: Subtitle: Pattern-based clustering of daily weigh-in trajectories using dynamic time warping
Creators: Samantha Bothwell - University of Colorado Anschutz Medical Campus
Alex Kaizer - Univ Colorado, Colorado Sch Publ Hlth, Dept Biostat & Informat, Anschutz Med Campus, Aurora, CO 80045 USA
Ryan Peterson - University of Colorado Anschutz Medical Campus
Danielle Ostendorf - University of Colorado Anschutz Medical Campus
Victoria Catenacci - University of Colorado Anschutz Medical Campus
Julia Wrobel - Colorado School of Public Health
Resource Type: Journal article
Publication Details: Biometrics, Vol.79(3), pp.2719-2731
DOI: 10.1111/biom.13773
PMID: 36217829
PMCID: PMC10393286
NLM abbreviation: Biometrics
ISSN: 0006-341X
eISSN: 1541-0420
Publisher: Oxford Univ Press
Number of pages: 13
Language: English
Date published: 09/01/2023
Academic Unit: Biostatistics; Internal Medicine
Record Identifier: 9984914018102771

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