Perinatal depression screening using smartphone technology: Exploring uptake, engagement and future directions for the MGH Perinatal Depression Scale (MGHPDS).
Rachel C VanderkruikEdwin RaffiMarlene P FreemanRebecca WalesLee CohenPublished in: PloS one (2021)
Women may experience new-onset or worsening depressive disorders during pregnancy and the postpartum. If untreated, there may be detrimental consequences to the health and wellbeing of the woman and to her baby. There is a need for improved tools and approaches that can be easily and broadly implemented to effectively detect depression during the perinatal period. Early identification of depression during pregnancy is an important first step towards connecting women to treatment and preventing continued depression into the postpartum or beyond. This report provides preliminary findings from a pilot study of a digital screening app for perinatal depression expiring potential for app reach, engagement, and user demographics and mental health symptoms. With mainly passive recruitment efforts, we collected cross-sectional mental health data on over 700 women during the perinatal period, including women across over 30 countries. We report on mean depression scores among women during pregnancy and the postpartum as well as on constructs that are commonly comorbid with depression, including anxiety, sleep dysregulation, and perceived stress. Over half of the women during pregnancy and over 70% of women in the postpartum had a depression score indicative of clinical depression. Future research directions for this work and potential for public health impact are discussed, including longitudinal data collection and analyses of symptomology over time and embedding evidence-based digital therapeutics into the app as a means to increase access to mental health services.
Keyphrases
- sleep quality
- depressive symptoms
- mental health
- polycystic ovary syndrome
- public health
- pregnant women
- pregnancy outcomes
- cervical cancer screening
- social support
- adipose tissue
- current status
- machine learning
- climate change
- social media
- risk assessment
- metabolic syndrome
- type diabetes
- electronic health record
- artificial intelligence
- big data
- replacement therapy
- quality improvement
- heat stress
- data analysis