Evidence for enhancing childhood obesity treatment from a dual-process perspective: A systematic literature review

Eva Kemps, Lien Goossens, Jasmine M. Petersen, Sandra Verbeken, Leentje Vervoort, Caroline Braet

Research output: Contribution to journalReview article

1 Citation (Scopus)

Abstract

Impulsivity contributes to poor outcomes of existing childhood obesity treatments. Conceptualised within dual-process models, this self-regulation failure reflects the operation of strong automatic processing (heightened food responsivity) and/or weak regulatory processing (poor self-control). This systematic literature review examined the evidence for the self-regulation failure hypothesis from a dual-process models perspective to evaluate its potential for enhancing childhood obesity treatment. Searches were conducted from six databases. Eligibility criteria included: (1) recruited a child or adolescent sample; (2) measured or manipulated one or more automatic and/or regulatory processes (attentional bias, approach bias, working memory, inhibitory control, executive function); (3) used a cross-sectional, longitudinal or experimental design; and (4) included a primary outcome measure that was eating/weight related and/or pertained to the underlying process(es). The search identified 147 eligible studies. Despite methodological variations and inconsistent findings across studies, evidence points to a stronger impact of automatic processes and in particular a reduced capacity for regulatory processing in youngsters with overweight/obesity. Emerging evidence suggests that these processes can be altered by targeted training to curtail food intake and associated weight gain. An intervention protocol based on the dual-process framework has the potential to enhance current childhood obesity treatments. Recommendations for future research are provided.

Original languageEnglish
Article number101840
JournalClinical Psychology Review
Volume77
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Childhood obesity
  • Dual-process models
  • Automatic and regulatory processing

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