Translation of the Weight-Related Behaviours Questionnaire into a Short-Form Psychosocial Assessment Tool for the Detection of Women at Risk of Excessive Gestational Weight Gain

Shanna Fealy, Lucy Leigh, Michael Hazelton, John Attia, Maralyn Foureur, Christopher Oldmeadow, Clare E. Collins, Roger Smith and Alexis J. Hure
Additional contact information
Shanna Fealy: School of Nursing, Paramedicine, and Healthcare Sciences, Faculty of Science and Health, Charles Sturt University, 7 Major Innes Road, Port Macquarie, NSW 2444, Australia
Lucy Leigh: Hunter Medical Research Institute, Lot 1 Kookaburra Circuit, New Lambton Heights, NSW 2305, Australia
Michael Hazelton: School of Nursing, Paramedicine, and Healthcare Sciences, Faculty of Science and Health, Charles Sturt University, 7 Major Innes Road, Port Macquarie, NSW 2444, Australia
John Attia: School of Medicine and Public Health, College of Health and Wellbeing, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
Maralyn Foureur: School of Nursing and Midwifery, College of Health and Wellbeing, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
Christopher Oldmeadow: Hunter Medical Research Institute, Lot 1 Kookaburra Circuit, New Lambton Heights, NSW 2305, Australia
Clare E. Collins: Hunter Medical Research Institute, Lot 1 Kookaburra Circuit, New Lambton Heights, NSW 2305, Australia
Roger Smith: School of Medicine and Public Health, College of Health and Wellbeing, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
Alexis J. Hure: School of Medicine and Public Health, College of Health and Wellbeing, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia

IJERPH, 2021, vol. 18, issue 18, 1-13

Abstract: The identification and measurement of psychosocial factors that are specific to pregnancy and relevant to gestational weight gain is a challenging task. Given the general lack of availability of pregnancy-specific psychosocial assessment instruments, the aim of this study was to develop a short-form psychosocial assessment tool for the detection of women at risk of excessive gestational weight gain with research and clinical practice applications. A staged scale reduction analysis of the weight-related behaviours questionnaire was conducted amongst a sample of 159 Australian pregnant women participating in the Women and Their Children’s Health (WATCH) pregnancy cohort study. Exploratory factor analysis, univariate logistic regression, and item response theory techniques were used to derive the minimum and most predictive questions for inclusion in the short-form assessment tool. Of the total 49 questionnaire items, 11 items, all 4 body image items, n = 4 attitudes towards weight gain, and n = 3 self-efficacy items, were retained as the strongest predictors of excessive gestational weight gain. These within-scale items were highly correlated, exhibiting high item information function value statistics, and were observed to have high probability ( p < 0.05) for excessive gestational weight gain, in the univariate analysis. The short-form questionnaire may assist with the development of tailored health promotion interventions to support women psychologically and physiologically to optimise their pregnancy weight gain. Confirmatory factor analysis is now required.

Keywords: psychosocial; gestational weight gain; body image; weight gain attitudes; self-efficacy; pregnancy care; public health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/18/9522/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/18/9522/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:18:p:9522-:d:632309

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().