Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain

Fuller-Tyszkiewicz, Matthew, Skouteris, Helen, Hill, Briony, Teede, Helena and McPhie, Skye 2016, Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain, Midwifery, vol. 32, pp. 38-44, doi: 10.1016/j.midw.2015.10.007.

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Title Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain
Author(s) Fuller-Tyszkiewicz, MatthewORCID iD for Fuller-Tyszkiewicz, Matthew orcid.org/0000-0003-1145-6057
Skouteris, Helen
Hill, BrionyORCID iD for Hill, Briony orcid.org/0000-0003-4993-3963
Teede, Helena
McPhie, Skye
Journal name Midwifery
Volume number 32
Start page 38
End page 44
Total pages 7
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-01
ISSN 1532-3099
Keyword(s) BMI
Classification and regression tree analysis
Gestational weight gain
Pregnancy
Psychosocial risk factors
Science & Technology
Life Sciences & Biomedicine
Nursing
RANDOMIZED CONTROLLED-TRIAL
POSTNATAL DEPRESSION SCALE
SLEEP QUALITY INDEX
BODY ATTITUDES
WOMEN
OBESITY
HEALTH
MODEL
SYMPTOMS
Summary OBJECTIVE: overweight/obese weight status during pregnancy increases risk of a range of adverse health outcomes for mother and child. Whereas identification of those who are overweight/obese pre-pregnancy and in early pregnancy is straightforward, prediction of who will experience excessive gestational weight gain (EGWG), and thus be at greater risk of becoming overweight or obese during pregnancy is more challenging. The present study sought to better identify those at risk of EGWG by exploring pre-pregnancy BMI as well as a range of psychosocial risk factors identified as risk factors in prior research. METHODS: 225 pregnant women completed self-reported via postal survey measures of height, weight, and psychosocial variables at 16-18 weeks gestation, and reported their weight again at 32-34 weeks to calculate GWG. Classification and regression tree analysis (CART) was used to find subgroups in the data with increased risk of EGWG based on their pre-pregnancy BMI and psychosocial risk factor scores at Time 1. FINDINGS: CART confirmed that self-reported BMI status was a strong predictor of EGWG risk for women who were overweight/obese pre-pregnancy. Normal weight women with low motivation to maintain a healthy diet and who reported lower levels of partner support were also at considerable risk of EGWG. IMPLICATIONS FOR PRACTICE: present findings offer support for inclusion of psychosocial measures (in addition to BMI) in early antenatal visits to detect risk of EGWG. However, these findings also underscore the need for further consideration of effect modifiers that place women at increased or decreased risk of EGWG. Proposed additional constructs are discussed to direct further theory-driven research.
Language eng
DOI 10.1016/j.midw.2015.10.007
Field of Research 170106 Health, Clinical and Counselling Psychology
170110 Psychological Methodology, Design and Analysis
111716 Preventive Medicine
1110 Nursing
1117 Public Health And Health Services
1114 Paediatrics And Reproductive Medicine
Socio Economic Objective 920408 Health Status (e.g. Indicators of Well-Being)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079935

Document type: Journal Article
Collection: School of Psychology
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