IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10535-d896123.html
   My bibliography  Save this article

Parent–Child Interaction Therapy Supports Healthy Eating Behavior in Child Welfare-Involved Children

Author

Listed:
  • Emma R. Lyons

    (Pediatric Mental Health Institute, Children’s Hospital Colorado, Aurora, CO 80045, USA)

  • Akhila K. Nekkanti

    (Center for Innovation and Research on Choice-Filled Lives, Choice-Filled Lives, Inc., Atlanta, GA 30305, USA)

  • Beverly W. Funderburk

    (Department of Developmental and Behavioral Pediatrics, Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA)

  • Elizabeth A. Skowron

    (Department of Psychology, University of Oregon, Eugene, OR 97403, USA)

Abstract

Objective: We tested the efficacy of standard Parent–Child Interaction Therapy (PCIT), a live-coached, behavioral parent-training program, for modifying problematic eating behaviors in a larger effectiveness trial of PCIT for children involved in the child welfare system. Method: Children ages 3–7 years and their parents were randomly assigned to PCIT intervention ( n = 120) or services as the usual control (SAU; n = 84) groups in a randomized clinical trial. Children’s eating behaviors were assessed pre- and post-intervention via the Child Eating Behaviors Questionnaire (CEBQ). Intention-to-treat analyses were conducted, followed by per-protocol analyses, on treatment-engaging families only. Results: PCIT led to reductions in child welfare-involved children’s food responsiveness, speed of food consumption, and tendency to engage in emotional overeating relative to children in the services-as-usual control condition. Standard PCIT may be an effective intervention to promote healthy child eating behaviors in families involved with child welfare, even when food-related behaviors are not directly targeted by the intervention. Public Health Significance: This clinical trial provides evidence that child welfare-involved children who received PCIT experienced significant reductions in maladaptive eating-related behaviors, namely food responsiveness, emotional overeating, and speed of eating. These findings were observed in relation to children in a comparison control group who had access to child welfare services-as-usual.

Suggested Citation

  • Emma R. Lyons & Akhila K. Nekkanti & Beverly W. Funderburk & Elizabeth A. Skowron, 2022. "Parent–Child Interaction Therapy Supports Healthy Eating Behavior in Child Welfare-Involved Children," IJERPH, MDPI, vol. 19(17), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10535-:d:896123
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10535/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/17/10535/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Domoff, Sarah E. & Niec, Larissa N., 2018. "Parent-child interaction therapy as a prevention model for childhood obesity: A novel application for high-risk families," Children and Youth Services Review, Elsevier, vol. 91(C), pages 77-84.
    3. Skowron, Elizabeth A. & Funderburk, Beverly W., 2022. "In vivo social regulation of high-risk parenting: A conceptual model of Parent-Child Interaction Therapy for child maltreatment prevention," Children and Youth Services Review, Elsevier, vol. 136(C).
    4. Kimberley A. Baxter & Smita Nambiar & Tsz Hei Jeffrey So & Danielle Gallegos & Rebecca Byrne, 2022. "Parental Feeding Practices in Families Experiencing Food Insecurity: A Scoping Review," IJERPH, MDPI, vol. 19(9), pages 1-43, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    6. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    7. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    8. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    9. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    10. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    11. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    12. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    13. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    14. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    15. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    16. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    17. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    18. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    19. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    20. Debra Javeline & Tracy Kijewski-Correa & Angela Chesler, 2019. "Does it matter if you “believe” in climate change? Not for coastal home vulnerability," Climatic Change, Springer, vol. 155(4), pages 511-532, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10535-:d:896123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.