A path model of psychosocial and health behaviour change predictors of excessive gestational weight gain

This study aimed to evaluate a conceptual model of psychosocial, behaviour change, and behavioural predictors of excessive gestational weight gain (GWG). Background: Excessive GWG can place women and their babies at risk of poor health outcomes, including obesity. Models of psychosocial and behaviour change predictors of excessive GWG have not been extensively explored; understanding the mechanisms leading to excess GWG will provide crucial evidence towards the development of effective interventions. Method: Two hundred and eighty-eight pregnant women (≤18 weeks gestation) were recruited to a prospective study. Demographic, psychosocial, health behaviour change, and behavioural factors were assessed at 17 (Time 1, T1) and 33 weeks (Time 2, T2) gestation. Pre-pregnancy and final pregnancy weight were obtained and women were classified with/without excessive GWG. Logistic regressions refined the list of predictors of excessive GWG; variables with p < .1 were included in a path analysis. Results: Age, family income, T2 depression, T2 pregnancy-specific coping, T1 buttocks dissatisfaction, T2 GWG-specific self-efficacy, T1 dietary readiness, T1 dietary importance, and T1 vegetable intake predicted excessive GWG in the logistic regressions and were included in the path model. The baseline path model demonstrated poor fit. Once statistically and theoretically plausible paths were added, adequate model fit was achieved (χ² = 21.61(9), p < .05; RMSEA = .07; CFI = .93); this revised model explained 19.5% of the variance in excessive GWG. Women with high T1 buttocks dissatisfaction were more likely to exhibit low levels of dietary readiness. Women with low dietary readiness were more likely to have a lower vegetable intake, which predicted excessive GWG. Women with higher T2 depressive symptoms were more likely to report lower GWG self-efficacy and gain excessively. Conclusion: Future behavioural GWG trials should consider combining psychosocial and health behaviour change factors to optimise GWG.