Semin Reprod Med 2024; 42(02): 081-089
DOI: 10.1055/s-0044-1793829
Review Article

Innovative Approaches to Digital Health in Ovulation Detection: A Review of Current Methods and Emerging Technologies

1   Quanovate Tech, San Francisco, California
› Author Affiliations

Abstract

Ovulation is a vital sign, as significant as body temperature, heart rate, respiratory rate, and blood pressure, in assessing overall health and identifying potential health issues. Ovulation is a key event of the menstrual cycle that provides insights into the hormonal and reproductive health aspects. Affected by the orchestra of hormones, namely thyroid, prolactin, and androgens, disruptions in ovulation can indicate endocrinological conditions and lead to gynecological problems, such as heavy menstrual bleeding, irregular periods, amenorrhea, dysmenorrhea, and difficulties in getting pregnant. Monitoring ovulation and detecting disruptions can aid in the early detection of health issues, extending beyond reproductive health concerns. It can help identify underlying causes of symptoms like excessive fatigue and abnormal hair growth. The integration of digital health technologies, such as mobile apps using machine learning algorithms, wearables tracking temperature, heart rate, breath rate, and sleep patterns, and devices measuring reproductive hormones in urine or saliva samples, offers a wealth of opportunities in family planning, early health issue diagnosis, treatment adjustment, and tracking menstrual cycles during assisted reproductive techniques. These advancements provide a comprehensive approach to health monitoring, addressing both reproductive and overall health concerns.



Publication History

Article published online:
21 November 2024

© 2024. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 
  • References

  • 1 Bull JR, Rowland SP, Scherwitzl EB, Scherwitzl R, Danielsson KG, Harper J. Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles. NPJ Digit Med 2019; 2: 83
  • 2 He M, Zhang T, Yang Y, Wang C. Mechanisms of oocyte maturation and related epigenetic regulation. Front Cell Dev Biol 2021; 9: 654028
  • 3 Encyclopedia of Endocrine Diseases 2nd ed. Danielle Monniaux; 2019: 377-398
  • 4 Holesh JE, Bass AN, Lord M. Physiology, Ovulation. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2023
  • 5 Buhi WC, Alvarez IM, Kouba AJ. Oviductal regulation of fertilization and early embryonic development. J Reprod Fertil Suppl 1997; 52: 285-300
  • 6 Homburg R. Global Library of Women's Medicine (ISSN: 1756–2228) 2014
  • 7 Stamatiades GA, Carroll RS, Kaiser UB. GnRH - a key regulator of FSH. Endocrinology 2019; 160 (01) 57-67
  • 8 Clarke IJ, Cummins JT. The temporal relationship between gonadotropin releasing hormone (GnRH) and luteinizing hormone (LH) secretion in ovariectomized ewes. Endocrinology 1982; 111 (05) 1737-1739
  • 9 Smith KM, Dinh DT, Akison LK. et al. Intraovarian, isoform-specific transcriptional roles of progesterone receptor in ovulation. Cells 2022; 11 (09) 1563
  • 10 Park CJ, Lin PC, Zhou S. et al. Progesterone receptor serves the ovary as a trigger of ovulation and a terminator of inflammation. Cell Rep 2020; 31 (02) 107496
  • 11 McNeilly AS, Crawford JL, Taragnat C, Nicol L, McNeilly JR. The differential secretion of FSH and LH: regulation through genes, feedback and packaging. Reprod Suppl 2003; 61: 463-476
  • 12 Reed BG, Carr BR. The Normal Menstrual Cycle and the Control of Ovulation. [Updated 2018 Aug 5]. In: Feingold KR, Anawalt B, Blackman MR. et al., eds. Endotext [Internet]. South Dartmouth, MA: MDText.com, Inc.; 2000
  • 13 Maman E, Adashi EY, Baum M, Hourvitz A. Prediction of ovulation: new insight into an old challenge. Sci Rep 2023; 13 (01) 20003
  • 14 Holesh JE, Bass AN, Lord M. Physiology, Ovulation. [Updated 2023 May 1]. In: StatPearls [Internet]. Treasure Island, FL: StatPearls Publishing; 2024
  • 15 Temporal relationships between ovulation and defined changes in the concentration of plasma estradiol-17 beta, luteinizing hormone, follicle-stimulating hormone, and progesterone. I. Probit analysis. World Health Organization, Task Force on Methods for the Determination of the Fertile Period, Special Programme of Research, Development and Research Training in Human Reproduction. Am J Obstet Gynecol 1980; 138 (04) 383-390
  • 16 Lyzwinski L, Elgendi M, Menon C. Innovative approaches to menstruation and fertility tracking using wearable reproductive health technology: systematic review. J Med Internet Res 2024; 26: e45139
  • 17 Zhu TY, Rothenbühler M, Hamvas G. et al. The accuracy of wrist skin temperature in detecting ovulation compared to basal body temperature: prospective comparative diagnostic accuracy study. J Med Internet Res 2021; 23 (06) e20710
  • 18 Goodale BM, Shilaih M, Falco L, Dammeier F, Hamvas G, Leeners B. Wearable sensors reveal menses-driven changes in physiology and enable prediction of the fertile window: observational study. J Med Internet Res 2019; 21 (04) e13404
  • 19 Baker FC, Lee KA. Menstrual cycle effects on sleep. Sleep Med Clin 2018; 13 (03) 283-294
  • 20 Jacobs N, Evers J. Ethical perspectives on Femtech: moving from concerns to capability-sensitive designs. Bioethics 2023; 37 (05) 430-439
  • 21 Erickson J, Yuzon JY, Bonaci T. What you do not expect when you are expecting: privacy analysis of Femtech. IEEE Trans Technol Soc 2022; 3: 121-131
  • 22 Grenfell P, Tilouche N, Shawe J, French RS. Fertility and digital technology: narratives of using smartphone app ‘Natural Cycles’ while trying to conceive. Sociol Health Illn 2021; 43 (01) 116-132
  • 23 Pearson JT, Chelstowska M, Rowland SP. et al. Natural Cycles app: contraceptive outcomes and demographic analysis of UK users. Eur J Contracept Reprod Health Care 2021; 26 (02) 105-110
  • 24 Behre HM, Kuhlage J, Gassner C. et al. Prediction of ovulation by urinary hormone measurements with the home use ClearPlan Fertility Monitor: comparison with transvaginal ultrasound scans and serum hormone measurements. Hum Reprod 2000; 15 (12) 2478-2482
  • 25 Lotan Y, Diamant YZ. The value of simple tests in the detection of human ovulation. Int J Gynaecol Obstet 1978-1979; 16 (04) 309-313
  • 26 Enenbach M, Kochmann M, Haworth C, Hawkins C. “When Am I Fertile?”: A Pilot Study Comparing Ovulation Prediction Accuracy of Menstrual Tracking Apps Versus LH Home Ovulation Kits. Council on Clinical Information Technology Program; 2021
  • 27 Shilaih M, Goodale BM, Falco L, Kübler F, De Clerck V, Leeners B. Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle. Biosci Rep 2018; 38 (06) BSR20171279
  • 28 Ceric F, Silva D, Vigil P. Ultrastructure of the human periovulatory cervical mucus. J Electron Microsc (Tokyo) 2005; 54 (05) 479-484
  • 29 Schmalenberger KM, Eisenlohr-Moul TA, Jarczok MN. et al. Menstrual cycle changes in vagally-mediated heart rate variability are associated with progesterone: evidence from two within-person studies. J Clin Med 2020; 9 (03) 617
  • 30 Hamidovic A, Davis J, Wardle M, Naveed A, Soumare F. Periovulatory subphase of the menstrual cycle is marked by a significant decrease in heart rate variability. Biology (Basel) 2023; 12 (06) 785
  • 31 Brunelli R, Papi M, Arcovito G. et al. Globular structure of human ovulatory cervical mucus. FASEB J 2007; 21 (14) 3872-3876
  • 32 Melnick H, Goudas VT. The detection of a salivary ferning pattern using the Knowhen ovulation monitoring system as an indication of ovulation. J Womens Health Care 2015; 4: 235
  • 33 Su HW, Yi YC, Wei TY, Chang TC, Cheng CM. Detection of ovulation, a review of currently available methods. Bioeng Transl Med 2017; 2 (03) 238-246
  • 34 Ersyari R, Wihardja R, Dardjan M. Determination of ovulation in women using saliva ferning test. Padjadjaran Journal of Dentistry. 2014; 26
  • 35 Leiva R, DiRienzo L. Combination of home-based hormonal and mobile technology for virtual monitoring of menstrual cycles. Ann Fam Med 2021; 19 (02) 180
  • 36 Mu Q, Fehring RJ. A comparison of two hormonal fertility monitoring systems for ovulation detection: a pilot study. Medicina (Kaunas) 2023; 59 (02) 400
  • 37 Hart R, D'Hooghe T, Dancet E. et al. P–593 Self-monitoring of hormones via a urine-based hormonal assay—a topical endeavour into telemedicine in medically-assisted reproduction (MAR). Hum Reprod 2021; 36 (Suppl. 01) deab130.592
  • 38 Li H, Chen J, Overstreet JW, Nakajima ST, Lasley BL. Urinary follicle-stimulating hormone peak as a biomarker for estimating the day of ovulation. Fertil Steril 2002; 77 (05) 961-966
  • 39 Nakhuda GS, Li N, Yang Z, Kang S. At-home urine estrone-3-glucuronide quantification predicts oocyte retrieval outcomes comparably with serum estradiol. F S Rep 2023; 4 (01) 43-48
  • 40 Vladimirov IK, Vladimirov M, Tacheva DM. A new protocol for Controlled Ovarian Stimulation Monitoring by Self-Determination of Estrone-3-Glucuronide and Single Ultrasound (COSSESU). Open J Obstet Gynecol 2021
  • 41 Cunningham AC, Pal L, Wickham AP. et al. Chronicling menstrual cycle patterns across the reproductive lifespan with real-world data. Sci Rep 2024; 14 (01) 10172
  • 42 Meyers M, Fehring RJ, Schneider M. Case reports from women using a quantitative hormone monitor to track the perimenopause transition. Medicina (Kaunas) 2023; 59 (10) 1743
  • 43 Bouchard TP, Schweinsberg K, Smith A, Schneider M. Using quantitative hormone monitoring to identify the postpartum return of fertility. Medicina (Kaunas) 2023; 59 (11) 2008
  • 44 Bouchard TP. Using quantitative hormonal fertility monitors to evaluate the luteal phase: proof of concept case study. Medicina (Kaunas) 2023; 59 (01) 140
  • 45 Karim JL, Talhouk A. Person-generated health data in women's health: protocol for a scoping review. JMIR Res Protoc 2021; 10 (05) e26110 [published correction appears in JMIR Res Protoc. 2021 Oct 18;10(10):e34211]