Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data

Nissen M, Barrios Campo N, Flaucher M, Jäger K, Titzmann A, Blunck D, Fasching P, Engelhardt V, Eskofier B, Leutheuser H (2023)

Publication Language: English

Publication Type: Journal article

Publication year: 2023

Journal

npj Digital Medicine Nature Research

Book Volume: 6

Article Number: 189

Journal Issue: 1

DOI: 10.1038/s41746-023-00935-3

Abstract

During pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms. To date, the resulting data are primarily used from a commercial rather than a scientific perspective. In this work, we aim to examine symptom occurrence, course, and their correlation throughout pregnancy. Self-reported app data of a pregnancy symptom tracker is used. In this context, we present methods to handle noisy real-world app data from commercial applications to understand the trajectory of user and patient-reported data. We report real-world evidence from patient-reported outcomes that exceeds previous works: 1,549,186 tracked symptoms from 183,732 users of a smartphone pregnancy app symptom tracker are analyzed. The majority of users track symptoms on a single day. These data are generalizable to those users who use the tracker for at least 5 months. Week-by-week symptom report data are presented for each symptom. There are few or conflicting reports in the literature on the course of diarrhea, fatigue, headache, heartburn, and sleep problems. A peak in fatigue in the first trimester, a peak in headache reports around gestation week 15, and a steady increase in the reports of sleeping difficulty throughout pregnancy are found. Our work highlights the potential of secondary use of industry data. It reveals and clarifies several previously unknown or disputed symptom trajectories and relationships. Collaboration between academia and industry can help generate new scientific knowledge.

Authors with CRIS profile

Michael Nissen Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Madeleine Flaucher Machine Learning and Data Analytics Lab Katharina Jäger Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Adriana Titzmann Department of Obstetrics and Gynaecology Dominik Blunck Lehrstuhl für Betriebswirtschaftslehre, insbesondere Gesundheitsmanagement Peter Fasching Professur für Translationale Frauenheilkunde und Geburtshilfe Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Heike Leutheuser Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Involved external institutions

Keleya Digital-Health Solutions GmbH DE Germany (DE)

How to cite

APA:

Nissen, M., Barrios Campo, N., Flaucher, M., Jäger, K., Titzmann, A., Blunck, D.,... Leutheuser, H. (2023). Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data. npj Digital Medicine, 6(1). https://doi.org/10.1038/s41746-023-00935-3

MLA:

Nissen, Michael, et al. "Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data." npj Digital Medicine 6.1 (2023).

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