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Digital contraceptives based on basal body temperature measurements
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-2718-0262
2019 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 52, p. 141-151Article in journal (Refereed) Published
Abstract [en]

Digital contraceptives and fertility awareness products are currently offered as convenient smartphone applications. The first legitimate contraceptive smartphone app was recently introduced on the European market, with the digital processing based on measurements of the female user's basal body temperature (BBT). According to recent pilot market data, at some Swedish hospitals, up to 5–10% of women seeking abortion had become involuntarily pregnant while using the product. This fact motivates a review of the research on fertility determination based on BBT measurements. This paper provides the first estimation theoretical review and evaluation of BBT-based ovulation detection. From an engineering perspective, it is concluded that the available detection algorithms have similar performance and that the performance is rather insensitive to a one- or two-decimal resolution of the employed thermometer. Further, we highlight that when using the output from proposed ovulation detection algorithms, one must consider not only the uncertainty in the relative time difference of the detected temperature shift and the ovulation, but also the statistical uncertainty of the detection methods due to noisy measurements.

Place, publisher, year, edition, pages
Elsevier Ltd , 2019. Vol. 52, p. 141-151
Keywords [en]
Basal body temperature method, Digital contraceptives, Fertility detection, Medical information systems, Ovulation detection, Commerce, mHealth, Physiology, Signal detection, Smartphones, Temperature measurement, Basal body temperatures (BBT), Body temperature, Body temperature measurements, Detection algorithm, Engineering perspective, Smart-phone applications, Statistical uncertainty, Uncertainty analysis
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-252467DOI: 10.1016/j.bspc.2019.04.019ISI: 000473381100015Scopus ID: 2-s2.0-85064463556OAI: oai:DiVA.org:kth-252467DiVA, id: diva2:1337463
Note

QC 20190715

Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-07-29Bibliographically approved

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Händel, Peter

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CiteExportLink to record
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  • apa
  • harvard1
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  • Other locale
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Output format
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