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      Conceptualizing and Measuring Appetite Self-Regulation Phenotypes and Trajectories in Childhood: A Review of Person-Centered Strategies

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          Abstract

          This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is person-centered provides strategies that increase the possibilities for investigating ASR phenotypes. We first examine the utility of examining underlying phenotypes using latent profile/class analysis drawing on cross-sectional data. The use of trajectory analysis to investigate developmental change is then discussed, with attention to phenotypes using trajectories of individual behaviors as well as phenotypes based on multi-trajectory modeling. Data analysis strategies and measurement approaches from recent examples of these person-centered approaches to the conceptualization and investigation of appetite self-regulation and its development in childhood are examined. Where relevant, examples from older children as well as developmental, clinical and educational psychology are drawn on to discuss when and how person-centered approaches can be used. We argue that there is scope to incorporate recent advances in biological and psychoneurological knowledge about appetite self-regulation as well as fundamental processes in the development of general self-regulation to enhance the examination of phenotypes and their trajectories across childhood (and beyond). The discussion and conclusion suggest directions for future research and highlight the potential of person-centered approaches to progress knowledge about the development of appetite self-regulation in childhood.

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          Most cited references120

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          Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

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            Group-based trajectory modeling in clinical research.

            Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.
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              Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.

              Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.
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                Author and article information

                Contributors
                Journal
                Front Nutr
                Front Nutr
                Front. Nutr.
                Frontiers in Nutrition
                Frontiers Media S.A.
                2296-861X
                22 December 2021
                2021
                : 8
                : 799035
                Affiliations
                [1] 1College of Education, Psychology and Social Work, Flinders University , Bedford Park, SA, Australia
                [2] 2School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition (IPAN), Deakin University , Geelong, VIC, Australia
                Author notes

                Edited by: Jena Shaw Tronieri, University of Pennsylvania, United States

                Reviewed by: Jeffrey Liew, Texas A&M University, United States; Lori Anne Francis, The Pennsylvania State University (PSU), United States

                *Correspondence: Catherine G. Russell georgie.russell@ 123456deakin.edu.au

                This article was submitted to Eating Behavior, a section of the journal Frontiers in Nutrition

                Article
                10.3389/fnut.2021.799035
                8727374
                35004827
                bad66d84-ab52-4e11-bec6-a709f27bbeff
                Copyright © 2021 Russell, Leech and Russell.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 October 2021
                : 30 November 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 120, Pages: 11, Words: 10229
                Funding
                Funded by: National Health and Medical Research Council, doi 10.13039/501100000925;
                Funded by: Deakin University, doi 10.13039/501100001778;
                Categories
                Nutrition
                Review

                phenotypes,appetite regulation,mixture models,developmental trajectories,latent class analysis,unobserved (or underlying) heterogeneity,eating behavior,child

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