Abstract
Peripartum depression (PPD) is a significant public health problem, yet many women who experience PPD do not receive adequate treatment. In many cases, this is due to social stigmas surrounding PPD that prevent women from disclosing their symptoms to their providers. Examples of these are fear of being labeled a “bad mother,” or having misinformed expectations regarding motherhood. Online forums dedicated to PPD can provide a practical setting where women can better manage their mental health in the peripartum period. Data from such forums can be systematically analyzed to understand the technology and information needs of women experiencing PPD. However, deeper insights are needed on how best to translate information derived from online forum data into digital health features. In this study, we aim to adapt a digital health development framework, Digilego, toward translation of our results from social media analysis to inform digital features of a mobile intervention that promotes PPD prevention and self-management. The first step in our adaption was to conduct a user need analysis through semi-automated analysis of peer interactions in two highly popular PPD online forums: What to Expect and BabyCenter. This included the development of a machine learning pipeline that allowed us to automatically classify user post content according to major communication themes that manifested in the forums. This was followed by mapping the results of our user needs analysis to existing behavior change and engagement optimization models. Our analysis has revealed major themes being discussed by users of these online forums- family and friends, medications, symptom disclosure, breastfeeding, and social support in the peripartum period. Our results indicate that Random Forest was the best performing model in automatic text classification of user posts, when compared to Support Vector Machine, and Logistic Regression models. Computerized text analysis revealed that posts had an average length of 94 words, and had a balance between positive and negative emotions. Our Digilego-powered theory mapping also indicated that digital platforms dedicated to PPD prevention and management should contain features ranging from educational content on practical aspects of the peripartum period to inclusion of collaborative care processes that support shared decision making, as well as forum moderation strategies to address issues with cyberbullying.
Highlights
Peripartum depression (PPD) is a condition which affects ∼1 in 10 pregnant women and new mothers in the U.S every year [1, 2]
We evaluated the performance of three machine learning (ML) classifiers using our manually labeled dataset: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)
There is scarce research work which has leveraged data from online forums exclusively dedicated to the condition of PPD. This data can offer unique insights into how women manage their mental health during the peripartum period, as online forums are a unique setting where some women can feel more comfortable disclosing their stories than in a face-to-face setting
Summary
Peripartum depression (PPD) is a condition which affects ∼1 in 10 pregnant women and new mothers in the U.S every year [1, 2]. PPD can result in adverse health outcomes such as longer depression episodes for women and later cognitive and behavioral problems for the infant [8, 9]. A common instrument used for screening is the Edinburg Postnatal Depression Scale (EPDS). It is a self-reporting instrument of ten questions that can be completed in minutes and that has been shown to have good reliability [11]. Others do not receive treatment due to social stigmas; for example, fear of being seen as an unfit parent or even losing custody of their child can keep women from the important step of disclosing their mental health struggles [13]
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