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Open AccessArticle

Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling

1
Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Avda, Vicent Sos Baynat s/n, 12071 Castellon de la Plana, Spain
2
Instituto de Investigación Sanitaria de Aragón, C/ San Juan Bosco, 13, 50009 Zaragoza, Spain
3
Department of Psychology and Sociology, Universidad de Zaragoza, C/ Atarazanas, 4, 44003 Teruel, Spain
4
Nursing Department, Jaume I University, Avda, Vicent Sos Baynat s/n, 12071 Castellon de la Plana, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(22), 8445; https://doi.org/10.3390/ijerph17228445
Received: 23 September 2020 / Revised: 27 October 2020 / Accepted: 12 November 2020 / Published: 14 November 2020
(This article belongs to the Special Issue Maternal Perinatal Mental Health)
The prediction of postpartum depression (PPD) should be conceptualized from a biopsychosocial perspective. This study aims at exploring the longitudinal contribution of a set of biopsychosocial factors for PPD in perinatal women. A longitudinal study was conducted, assessment was made with a website and included biopsychosocial factors that were measured during pregnancy (n = 266, weeks 16–36), including age, affective ambivalence, personality characteristics, social support and depression. Depression was measured again at postpartum (n = 101, weeks 2–4). The analyses included bivariate associations and structural equation modeling (SEM). Age, affective ambivalence, neuroticism, positive, and negative affect at pregnancy were associated with concurrent depression during pregnancy (all p < 0.01). Age, affective ambivalence, positive affect, and depression at pregnancy correlated with PPD (all p < 0.05). Affective ambivalence (β = 1.97; p = 0.003) and positive (β = −0.29; p < 0.001) and negative affect (β = 0.22; p = 0.024) at pregnancy remained significant predictors of concurrent depression in the SEM, whereas only age (β = 0.27; p = 0.010) and depression (β = 0.37; p = 0.002) at pregnancy predicted PPD. Biopsychosocial factors are clearly associated with concurrent depression at pregnancy, but the stability of depression across time limits the prospective contribution of biopsychosocial factors. Depression should be screened early during pregnancy, as this is likely to persist after birth. The use of technology, as in the present investigation, might be a cost-effective option for this purpose. View Full-Text
Keywords: pregnancy; postpartum; depressive symptoms; risk factors; biopsychosocial; longitudinal studies; information and communication technologies pregnancy; postpartum; depressive symptoms; risk factors; biopsychosocial; longitudinal studies; information and communication technologies
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MDPI and ACS Style

Martínez-Borba, V.; Suso-Ribera, C.; Osma, J.; Andreu-Pejó, L. Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling. Int. J. Environ. Res. Public Health 2020, 17, 8445. https://doi.org/10.3390/ijerph17228445

AMA Style

Martínez-Borba V, Suso-Ribera C, Osma J, Andreu-Pejó L. Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling. International Journal of Environmental Research and Public Health. 2020; 17(22):8445. https://doi.org/10.3390/ijerph17228445

Chicago/Turabian Style

Martínez-Borba, Verónica; Suso-Ribera, Carlos; Osma, Jorge; Andreu-Pejó, Laura. 2020. "Predicting Postpartum Depressive Symptoms from Pregnancy Biopsychosocial Factors: A Longitudinal Investigation Using Structural Equation Modeling" Int. J. Environ. Res. Public Health 17, no. 22: 8445. https://doi.org/10.3390/ijerph17228445

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