Acceptability of 12 fortified balanced energy protein supplements - Insights from Burkina Faso.
Leslie JonesBrenda de KokKatie MooreSaskia DePeeJuliet BedfordKatrien VanslambrouckLaeticia Celine ToeCarl K LachatNathalie De CockMoctar OuedraogoRasmané GanabaPatrick KolsterenSheila IsanakaPublished in: Maternal & child nutrition (2020)
Poor maternal nutrition contributes to poor birth outcomes, including low birth weight and small for gestational age births. Fortified balanced energy protein (BEP) supplements may be beneficial, although evidence is limited. This mixed method study, conducted among pregnant women in Burkina Faso, is part of a larger clinical trial that seeks to understand the impact of fortified BEP supplements on pregnancy outcomes and child growth. The formative research reported here, a single-meal rapid assessment of 12 product formulations, sought to understand product preferences for provision of BEP supplements and contextual factors that might affect product acceptability and use. Results indicate a preference for products perceived as sweet rather than salty/savoury and for products perceived as familiar, as well as a sensitivity to product odours. Women expressed a willingness and intention to use the products even if they did not like them, because of the health benefits for their babies. Data also indicate that household food sharing practices may impact supplement use, although most women denied any intention to share the products. Sharing behaviour should therefore be monitored, and strategies to avoid sharing should be developed during the succeeding parts of the research.
Keyphrases
- pregnancy outcomes
- gestational age
- preterm birth
- birth weight
- low birth weight
- pregnant women
- health information
- mental health
- physical activity
- clinical trial
- social media
- preterm infants
- healthcare
- social support
- depressive symptoms
- polycystic ovary syndrome
- primary care
- human milk
- public health
- protein protein
- palliative care
- binding protein
- adipose tissue
- metabolic syndrome
- machine learning
- electronic health record
- randomized controlled trial
- artificial intelligence
- small molecule
- amino acid
- glycemic control
- deep learning
- weight gain