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

Matthew Fuller-Tyszkiewicz, Helen Skouteris, Briony Hill, Helena Teede, Skye McPhie

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)38-44
Number of pages7
JournalMidwifery
Volume32
DOIs
Publication statusPublished - Jan 2016

Keywords

  • BMI
  • Classification and regression tree analysis
  • gestational weight gain
  • pregnancy
  • psychosocial risk factor

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