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Do Healthy Dietary Interventions Improve Pediatric Depressive Symptoms? A Systematic Review and Meta-Analysis.

Susan C CampisiClare ZasowskiShailja ShahGlyneva Bradley-RidoutSheri MadiganPeter SzatmariDaphne J Korczak
Published in: Advances in nutrition (Bethesda, Md.) (2022)
Dietary recommendations have been proposed as a way of impacting current and future mental health. There exists a limited body of conflicting literature related to pediatric nutritional interventions and depression. This meta-analysis aims to determine the efficacy of child and adolescent dietary interventions on depression. Systematic searches in electronic databases and gray literature were conducted. After screening 6725 citations, 17 studies were included in this systematic review. Quality assessment was performed using the Cochrane risk-of-bias tool and the Joanna Briggs Institute Critical Appraisal Tool for Quasi-Experimental Studies. A meta-analysis of Hedges g values was calculated using the Hartung-Knapp-Sidik-Jonkman method. Publication bias was assessed with funnel plots and the Egger test. The results of the meta-analysis of the RCTs (k = 7) demonstrated a nonsignificant effect of dietary intervention (g = 0.05; 95% CI: -0.25, 0.35; P = 0.70) whereas the results of the pre-post intervention studies (k = 9) demonstrated a significant small-to-medium effect favoring dietary intervention for reducing depression (g = -0.45; 95% CI: -0.64, -0.27; P = 0.001). Publication bias was not detected by the Egger test or by funnel plot asymmetry. The current meta-analysis demonstrates that "healthy" dietary interventions for children or adolescents in the community have little impact on nonclinical depression. Confusion will persist until better-designed studies in pediatric nutritional psychiatry research focusing on adolescents with depressive illness are conducted.
Keyphrases
  • systematic review
  • depressive symptoms
  • mental health
  • physical activity
  • young adults
  • case control
  • meta analyses
  • randomized controlled trial
  • sleep quality
  • healthcare
  • mental illness
  • big data
  • artificial intelligence