A groundbreaking mobile app could help predict if a pregnant woman will develop depression in the later stages of pregnancy, according to new research. By collecting survey responses during their first trimester, researchers have identified various risk factors, such as sleep quality and food insecurity, that contribute to the likelihood of developing depression.
“We can ask people a small set of questions and get a good sense of whether they’ll become depressed,” said lead author Tamar Krishnamurti, an associate professor of general internal medicine at the University of Pittsburgh. “Strikingly, a lot of risk factors for future depression are things that are modifiable — such as sleep quality, concerns about labour and delivery, and importantly, access to food — meaning that we can and should do something about them,” Krishnamurti added.
Identifying women vulnerable to depression in the early stages of pregnancy could help tailor preventive care and offer support to address underlying causes, the researchers emphasized. For the study, they analyzed the survey responses of 944 pregnant women who used the app as part of a larger study and did not have a history of depression.
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In their first trimester, the women answered questions about their demographics and medical history, as well as their stress levels and feelings of sadness. Some participants also responded to optional questions on social health factors, such as food insecurity.
All participants were screened for depression once every trimester. Researchers developed six machine-learning models using the collected data. The most accurate model predicted depression in pregnant women with 89 percent accuracy. When responses to optional questions on health-related social factors were included, the model’s accuracy rose to 93 percent.
A machine learning algorithm, a form of artificial intelligence, learns from past data to make predictions. This study highlights the potential of using technology to identify and support pregnant women at risk of depression, ultimately improving maternal health outcomes.
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