MOM2B - A perinatal health project using a smartphone application and machine learning algorithms

PI: Alkistis Skalkidou
Colleagues:  Fotis PapadopoulosEmma FranssonUlf ElofssonAyesha-Mae BilalMengyu Zhong, Konstantina Pagoni

Tell us more about your research project?
Mom2B is a unique research project about peripartum depression and preterm birth. It explores the possibility to use active and passive data gathered via a smartphone application for the early detection of women with high risk to develop complications during pregnancy and after childbirth.

Peripartum depression (PPD) refers to a depressive episode with onset during pregnancy or after childbirth. In Sweden, 16 000 women suffer from PPD, while four commit suicide in the perinatal period. Despite national guidelines for universal screening and the efforts of healthcare professionals to follow up women during pregnancy and after childbirth, only 1 of 3 women receive some sort of treatment.

The Mom2B study is based on a smartphone application which women can use in order to answer to questionnaires, record their voice and share data from the different phone sensors. Participants can choose which data they want to share with the possibility to at any time change their consent.

The aim of this project is to predict which women are at high risk to later in the perinatal period develop depression; this will allow for the implementation of personalized follow-up and preventive measures, which as cost-effective in this setting.  

What do you hope the impact of this project to be?
It is estimated that every year 16.000 women suffer from peripartum depression but only 30% of them receiving some type of care. With this study, analyzing the active and passive data from the Mom2B mobile application with machine learning methods, we hope to be able to create an algorithm for the early detection of women with high risk to develop mental ill-health during the peripartum period.

Other information, references and links:
Study website:

Bilal, A. M., Fransson, E., Bränn, E., Eriksson, A., Zhong, M., Gidén, K., Elofsson, U., Axfors, C., Skalkidou, A., & Papadopoulos, F. C. (2022). Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ open12(4), e059033.

Alkistis Skalkidou

Professor at Department of Women's and Children's Health, Obstetric and Reproductive Health Research

Last modified: 2022-11-01