Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum
Name
s41746-022-00579-9.pdf
Description
Published version
Size
945.03 KB
Format
Adobe PDF
Checksum (MD5)
1b53f216524417e84c662096f64ebc14
Author(s)
Goodday, SM
Karlin, E
Brooks, A
Chapman, C
Karlin, DR
Foschini, L
Kipping, E
Wildman, M
Francis, M
Greenman, H
Date Issued
December 2022
Journal
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Citation
Goodday, SM, Karlin, E, Brooks, A, Chapman, C, Karlin, DR et al. 2022. "Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum." npj Digital Medicine, 5 (1).
Version
Final published version
Abstract
AbstractThe Better Understanding the Metamorphosis of Pregnancy (BUMP) study is a longitudinal feasibility study aimed to gain a deeper understanding of the pre-pregnancy and pregnancy symptom experience using digital tools. The present paper describes the protocol for the BUMP study. Over 1000 participants are being recruited through a patient provider-platform and through other channels in the United States (US). Participants in a preconception cohort (BUMP-C) are followed for 6 months, or until conception, while participants in a pregnancy cohort (BUMP) are followed into their fourth trimester. Participants are provided with a smart ring, a smartwatch (BUMP only), and a smart scale (BUMP only) alongside cohort-specific study apps. Participant centric engagement strategies are used that aim to co-design the digital approach with participants while providing knowledge and support. The BUMP study is intended to lay the foundational work for a larger study to determine whether participant co-designed digital tools can be used to detect, track and return multimodal symptoms during the perinatal window to inform individual level symptom trajectories.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41746-022-00579-9
