Mobile app helps predict future depression in pregnant people

A simple survey administered during the first trimester via the digital pregnancy support app MyHealthyPregnancy predicted with a high degree of accuracy which mothers would develop moderate to severe depression. according to to a new one Archives of Women’s Mental Health study led by researchers from the University of Pittsburgh and UPMC.

“Depression is a major complication during pregnancy, with approximately 15% of patients reporting symptoms at some point during their pregnancy,” said lead author Tamar Krishnamurti, Ph.D., associate professor of general internal medicine at Pitt and researcher at Magee-Womens . Research Institute.

“We already have great screening tools for active depression, but our approach is unique because it predicts who is likely to develop depression in the future. If we can identify people early, before symptoms emerge, we may be able to tailor preventative care and provide tools and support to address the underlying triggers of depression.”

Krishnamurti and her team, who had previously developed the MyHealthyPregnancy app, analyzed data from 944 patients who used the app as part of a larger study and had no history of depression. During the first trimester, participants completed a survey with questions about demographics, medical history, psychosocial factors, such as stress and feelings of sadness, and pregnancy-specific stressors, such as concerns about labor and delivery. A subset of patients also completed optional questions about health-related social factors, such as food insecurity. All participants completed a verified depression screening once per trimester.

After using 80% of the data to train six different machine learning models, the researchers used the remaining 20% ​​to test how well they could predict the onset of depression later in pregnancy. The best model was 89% accurate in predicting future depression and used only 14 of 55 possible variables, including anxiety history, partner status, psychosocial factors and pregnancy-specific stressors.

As part of the research, the team worked with healthcare providers and perinatal individuals to review and refine the model so that it reflected their professional and lived experiences.

When the researchers included health-related social factors from the subset of participants who answered these questions, food insecurity emerged as a major risk factor for depression. When this variable was included in the model, race and income ceased to be important, and the model’s accuracy increased to 93%, with only nine variables in total.

“We can ask people a small number of questions and get a good idea of ​​whether they are likely to become depressed,” says Krishnamurti. “Strikingly, many risk factors for future depression are things that are modifiable – such as sleep quality, concerns about childbirth and, most importantly, access to food – meaning we can and should do something about them.”

Perinatal depression is linked to poor outcomes for mother and baby, including higher rates of preterm birth, delayed infant development and problems with mother-infant bonding. Although a history of depression is a strong predictor of perinatal depression, this tool may help identify others who become depressed for the first time during pregnancy.

Now Krishnamurti and her team are developing approaches to integrate these screening questions into clinical workflows and identifying the best ways for doctors to talk to patients about depression risk.

“We want to think carefully about how to talk to patients about the risk of depression, as opposed to the active experience of depression,” she explained. “To ensure that this information is empowering and not frightening, it is important that it is easy to understand and actionable. Our focus now is not only on refining our ability to predict depression, but also on improving and personalizing interventions so that they are most effective. for a particular individual.”

Such interventions could include connecting people to resources in their area, recommending in-person maternal support groups that address pregnancy-related stressors, or offering virtual, app-based therapy options.

Other study authors included Samantha Rodriguez, MS, Priya Gopalan, MD, and Hyagriv Simhan, MD, MS, all of Pitt of UPMC; and Bryan Wilder, Ph.D., of Carnegie Mellon University.