Question · asked by 1 review

Baby-led weaning (BLW) vs Traditional complementary feeding (TCF): Overweight/obesity risk

Population
Healthy, term-born, normal-birth-weight infants aged 4-24 months (breastfed and/or formula-fed) in Western industrialized countries
Intervention
Baby-led weaning (BLW)
Comparison
Traditional complementary feeding (TCF)
Outcome
Overweight/obesity risk

Among observational studies, baby-led weaning was associated with lower odds of overweight/obesity than other feeding models (OR 0.37, 95% CI 0.25 to 0.55), but certainty is very low.

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Baby-led weaning (BLW) vs Other models of complementary feeding (traditional CF)
Overweight/obesity risk · Healthy, term-born, normal-birth-weight infants aged 4-24 months (breastfed and/or formula-fed) in Western… · Follow-up interval 18-78 months, Outpatient care in Western industrialized (European/Anglo-Saxon) countries, BLW; observational studies
Among observational studies, baby-led weaning was associated with lower odds of overweight/obesity than other feeding models (OR 0.37, 95% CI 0.25 to 0.55), but certainty is very low.
Complementary Feeding Caregivers' Practices and Growth, Risk of Overweight/Obesity, and Other… (2022) All outcomes
OR 0.37 [0.25, 0.55]3 studies · 969 participantsVery low

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Complementary Feeding Caregivers' Practices and Growth, Risk of Overweight/Obesity, and Other Non-Communicable Diseases: A Systematic…
2022 All outcomes
Updated to September 202124Low / Critically Low

The same intervention and outcome, asked in another population or against another comparator.

Studies behind the findings

The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.

How reviews are matched to this question

Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.