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Pediatrics

October 1, 2026 Β· via Europe PMC

Behavioral Interventions in Pediatric Type 1 Diabetes Management: An Updated Meta-Analysis.

Monzon AD, Hoover JC, Pierce JS, Wasserman R, Ulysse S, Patton SR.

Journal ArticleAbstract only β€” full text under license

AI summary β€” for doctors, generated from the abstract only

Behavioural interventions continue to modestly improve HbA1c in youth with type 1 diabetes, with multicomponent family-focused approaches most effective.

What was studied

A meta-analysis of 46 RCTs (2009-2025) of adherence-promoting behavioural interventions for type 1 diabetes in youth and young adults up to 25 years.

Key findings

  • Pooled effect on HbA1c small (g=0.12)
  • Pilot RCTs showed larger effects (g=0.36) than full RCTs (g=0.07)
  • Multicomponent interventions g=0.19 vs direct 0.08 and indirect 0.15
  • Family-focused g=0.29 vs youth-focused 0.05 and parent-focused 0.04

Clinical relevance

This supports family-centred, multicomponent behavioural support as an adjunct to diabetes technology in youth.

Who should care

Pediatrics

Limitations

Limited to published RCTs; effects are small and smaller in full-scale trials.

Family-focused behavioural support still helps youth with type 1 diabetes, though modestly.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates β€” verify against the original source before acting on it.

Abstract

Context

The last meta-analysis of behavioral interventions regarding pediatric type 1 diabetes (T1D) management was conducted over a decade ago.

Objective

This meta-analysis aimed to (1) provide an updated review of adherence-promoting behavioral interventions and their effect on youths' hemoglobin A1C (HbA1C) and (2) evaluate whether multicomponent interventions targeting both direct and indirect processes of T1D management continue to yield greater HbA1C improvements.

Data sources

We conducted a systematic search in PubMed and Scopus for articles published between April 2009 and February 2025.

Study selection

Included studies were randomized controlled trials (RCTs) evaluating behavioral interventions targeting T1D self-management in youth and young adults up to age 25Β years.

Data extraction

Extracted data included study characteristics, intervention content and duration, and glycemic outcomes for intervention and comparison groups.

Results

Of the 62 RCTs identified, 46 reported sufficient data for quantitative analysis. The pooled effect size demonstrated a small benefit of adherence-promoting interventions on HbA1C (g = 0.12). Pilot RCTs showed larger effects (g = 0.36) than full RCTs (g = 0.07). Multicomponent interventions yielded greater improvements (g = 0.19) than direct (g = 0.08) or indirect (g = 0.15) interventions. Family-focused interventions demonstrated stronger effects (g = 0.29) than youth- (g = 0.05) or parent-focused (g = 0.04) interventions.

Limitations

Findings are limited to published RCTs.

Conclusions

Despite significant advancements in diabetes technology over the last decade, behavioral interventions continue to modestly improve HbA1C in youth with T1D. Multicomponent, family-focused strategies appear to be most effective. Findings highlight the need for rigorous trial designs to advance care for youth living with T1D.

Article summary

Updated meta-analysis shows behavioral interventions modestly improve HbA1C in youth with T1D, with multicomponent, family-focused approaches outperforming single-component, youth- or parent-focused strategies overall.

DOI

10.1542/peds.2025-075333

PMID

42778184

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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