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The Datafication of Fertility and Reproductive Health: Menstrual Cycle Tracking Apps and Ovulation Detection Algorithms

The Datafication of Fertility and Reproductive Health: Menstrual Cycle Tracking Apps and... With growing evidence of the datafication of fertility and reproductive health, there is a pivotal need for comprehending menstrual cycle tracking apps and ovulation detection algorithms. In this research, previous findings were cumulated showing that mobile apps advanced to monitor menstrual cycle and symptoms are fashionable for birth control purposes and fertility awareness, and contribute to the literature by indicating that period and fertility trackers as digital health technologies configure users’ empowerment by use of robust data. Throughout May 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “female reproductive health apps,” “menstrual cycle tracking apps,” “period tracking apps,” and “fertility tracking apps.” As research published between 2019 and 2021 was inspected, only 176 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 22 mainly empirical sources. Subsequent analyses should develop on cutting-edge digital technologies and apps designed for handling fertility, menstrual cycles, and reproduction. Future research should thus investigate fertility tracking apps providing predictions as regards fertile days. Attention should be directed to the precision of predicting the fertile window during menstrual cycle tracking. Keywords: menstrual cycle; tracking app; datafication; fertility; reproductive health http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Research in Gender Studies Addleton Academic Publishers

The Datafication of Fertility and Reproductive Health: Menstrual Cycle Tracking Apps and Ovulation Detection Algorithms

Michael Morrison
The Journal of Research in Gender Studies , Volume 11 (2): 13 – Jan 1, 2021
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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
2164-0262
eISSN
2378-3524
Publisher site
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Abstract

With growing evidence of the datafication of fertility and reproductive health, there is a pivotal need for comprehending menstrual cycle tracking apps and ovulation detection algorithms. In this research, previous findings were cumulated showing that mobile apps advanced to monitor menstrual cycle and symptoms are fashionable for birth control purposes and fertility awareness, and contribute to the literature by indicating that period and fertility trackers as digital health technologies configure users’ empowerment by use of robust data. Throughout May 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “female reproductive health apps,” “menstrual cycle tracking apps,” “period tracking apps,” and “fertility tracking apps.” As research published between 2019 and 2021 was inspected, only 176 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 22 mainly empirical sources. Subsequent analyses should develop on cutting-edge digital technologies and apps designed for handling fertility, menstrual cycles, and reproduction. Future research should thus investigate fertility tracking apps providing predictions as regards fertile days. Attention should be directed to the precision of predicting the fertile window during menstrual cycle tracking. Keywords: menstrual cycle; tracking app; datafication; fertility; reproductive health

Journal

The Journal of Research in Gender Studies – Addleton Academic Publishers

Published: Jan 1, 2021

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