Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/37646
Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
Title: Barriers and enablers toward healthy eating and weight gain among pregnant women in Vietnam: A qualitative study with analysis informed by the theoretical domains framework and COM-B model
Author(s): Nguyen-Hoang, Anh
Eades, Claire
Harris, Fiona M
Cheyne, Helen
Contact Email: c.e.eades@stir.ac.uk
Keywords: Vietnamese women
Pregnancy
Nutrition
COM-B model
Gestational weight gain
Barriers
Digital health intervention
Theoretical domains framework
Issue Date: 1-Dec-2024
Date Deposited: 2-Dec-2024
Citation: Nguyen-Hoang A, Eades C, Harris FM & Cheyne H (2024) Barriers and enablers toward healthy eating and weight gain among pregnant women in Vietnam: A qualitative study with analysis informed by the theoretical domains framework and COM-B model. <i>Appetite</i>, 203, Art. No.: 107710. https://doi.org/10.1016/j.appet.2024.107710
Abstract: Undernutrition and insufficient gestational weight gain can negatively affect maternal and infant health short- and long-term. In Vietnam, 50% of pregnant women lack essential nutrients, and 75% do not gain enough weight. Current interventions have limited success due to a gap in understanding their determinants. This study aimed to identify barriers and facilitators to healthy eating and weight gain among pregnant Vietnamese women. This qualitative study collected data from 20 pregnant Vietnamese women via virtual focus groups. Discussions were audio-recorded and translated into English for thematic analysis. The study utilized the Capability, Opportunity, Motivation to Behaviour (COM-B) model and Theoretical Domains Framework (TDF) to map the identified themes. Fifteen themes mapped onto nine of the 14 theoretical TDF domains, providing a comprehensive understanding of barriers and enablers to healthy eating and gestational weight gain within the COM-B model.
DOI Link: 10.1016/j.appet.2024.107710
Rights: © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

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