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      Group‐based trajectory modeling of body mass index and body size over the life course: A scoping review

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          Abstract

          Background

          Group‐based trajectory modeling has been applied to identify distinct trajectories of growth across the life course. Objectives of this study were to describe the methodological approaches for group‐based modeling of growth across the life course and to summarize outcomes across studies.

          Methods

          A scoping review with a systematic search of Medline, EMBASE, CINAL, and Web of Science was conducted. Studies that used a group‐based procedure to identify trajectories on any statistical software were included. Data were extracted on trajectory methodology, measures of growth, and association with outcomes.

          Results

          A total of 59 studies were included, and most were published from 2013 to 2020. Body mass index was the most common measure of growth ( n = 43). The median number of identified trajectories was 4 (range: 2–9). PROC TRAJ in SAS was used by 33 studies, other procedures used include TRAJ in STATA, lcmm in R, and Mplus. Most studies evaluated associations between growth trajectories and chronic disease outcomes ( n = 22).

          Conclusions

          Group‐based trajectory modeling of growth in adults is emerging in epidemiologic research, with four distinct trajectories observed somewhat consistently from all studies. Understanding life course growth trajectories may provide further insight for population health interventions.

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          Most cited references74

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          The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

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            Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm

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              An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models.

              Pediatric psychologists are often interested in finding patterns in heterogeneous longitudinal data. Latent variable mixture modeling is an emerging statistical approach that models such heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of the second of a 2-article set is to offer a nontechnical introduction to longitudinal latent variable mixture modeling.
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                Author and article information

                Contributors
                LN.Anderson@mcmaster.ca
                Journal
                Obes Sci Pract
                Obes Sci Pract
                10.1002/(ISSN)2055-2238
                OSP4
                Obesity Science & Practice
                John Wiley and Sons Inc. (Hoboken )
                2055-2238
                29 September 2020
                February 2021
                : 7
                : 1 ( doiID: 10.1002/osp4.v7.1 )
                : 100-128
                Affiliations
                [ 1 ] Department of Health Research Methods, Evidence, and Impact McMaster University Hamilton Ontario Canada
                [ 2 ] Applied Health Research Centre Li Ka Shing Knowledge Institute St. Michael's Hospital University of Toronto Toronto Ontario Canada
                [ 3 ] Division of Biostatistics Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada
                [ 4 ] Child Health Evaluative Sciences The Hospital for Sick Children Research Institute Toronto Ontario Canada
                Author notes
                [*] [* ] Correspondence

                Laura N. Anderson, Department of Health Research Methods, Evidence, and Impact, McMaster University, CRL‐221, 1280 Main St W, Hamilton, ON L8S 4K1, Canada.

                Email: LN.Anderson@ 123456mcmaster.ca

                Author information
                https://orcid.org/0000-0002-6106-5073
                Article
                OSP4456
                10.1002/osp4.456
                7909593
                0538acff-127f-4ba4-90fa-3c0d445bb0fb
                © 2020 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 August 2020
                : 08 September 2020
                : 11 September 2020
                Page count
                Figures: 2, Tables: 4, Pages: 29, Words: 10371
                Funding
                Funded by: Canadian Institutes of Health Research , open-funder-registry 10.13039/501100000024;
                Categories
                Review
                Review
                Custom metadata
                2.0
                February 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.9 mode:remove_FC converted:26.02.2021

                body weight,growth mixture modeling,latent class growth analysis,life course

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