Question · asked by 1 review
Responsive complementary feeding (RCF) / other caregivers' feeding practices vs Other models of complementary feeding (traditional/non-responsive CF): Type 2 diabetes (later in life)
- Population
- Healthy, term-born, normal-birth-weight infants aged 4-24 months (breastfed and/or formula-fed) in Western industrialized countries
- Intervention
- Responsive complementary feeding (RCF) / other caregivers' feeding practices
- Comparison
- Other models of complementary feeding (traditional/non-responsive CF)
- Outcome
- Type 2 diabetes (later in life)
No eligible studies examined the influence of complementary feeding practices on later type 2 diabetes; this is an evidence gap.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Responsive complementary feeding (RCF) / other caregivers' feeding practices vs Other models of complementary feeding (traditional/non-responsive CF) Type 2 diabetes (later in life) · Healthy, term-born, normal-birth-weight infants aged 4-24 months (breastfed and/or formula-fed) in Western… · Later in life, Outpatient care in Western industrialized (European/Anglo-Saxon) countries No eligible studies examined the influence of complementary feeding practices on later type 2 diabetes; this is an evidence gap. Complementary Feeding Caregivers' Practices and Growth, Risk of Overweight/Obesity, and Other… (2022) All outcomes | No studies on the influence of caregivers' feeding practices during complementary feeding on type 2 diabetes later in life were identified. | 0 studies | No studies found |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Complementary Feeding Caregivers' Practices and Growth, Risk of Overweight/Obesity, and Other Non-Communicable Diseases: A Systematic… 2022 All outcomes | Updated to September 2021 | 24 | Low / Critically Low |
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.