Literature DB >> 32328534

Real-life insights on menstrual cycles and ovulation using big data.

I Soumpasis1, B Grace1, S Johnson1.   

Abstract

STUDY QUESTION: What variations underlie the menstrual cycle length and ovulation day of women trying to conceive? SUMMARY ANSWER: Big data from a connected ovulation test revealed the extent of variation in menstrual cycle length and ovulation day in women trying to conceive. WHAT IS KNOWN ALREADY: Timing intercourse to coincide with the fertile period of a woman maximises the chances of conception. The day of ovulation varies on an inter- and intra-individual level. STUDY DESIGN SIZE DURATION: A total of 32 595 women who had purchased a connected ovulation test system contributed 75 981 cycles for analysis. Day of ovulation was determined from the fertility test results. The connected home ovulation test system enables users to identify their fertile phase. The app benefits users by enabling them to understand their personal fertility information. During each menstrual cycle, users input their perceived cycle length into an accessory application, and data on hormone levels from the tests are uploaded to the application and stored in an anonymised cloud database. This study compared users' perceived cycle characteristics with actual cycle characteristics. The perceived and actual cycle length information was analysed to provide population ranges. PARTICIPANTS/MATERIALS SETTING
METHODS: This study analysed data from the at-home use of a commercially available connected home ovulation test by women across the USA and UK. MAIN RESULTS AND THE ROLE OF CHANCE: Overall, 25.3% of users selected a 28-day cycle as their perceived cycle length; however, only 12.4% of users actually had a 28-day cycle. Most women (87%) had actual menstrual cycle lengths between 23 and 35 days, with a normal distribution centred on day 28, and over half of the users (52%) had cycles that varied by 5 days or more. There was a 10-day spread of observed ovulation days for a 28-day cycle, with the most common day of ovulation being Day 15. Similar variation was observed for all cycle lengths examined. For users who conducted a test on every day requested by the app, a luteinising hormone (LH) surge was detected in 97.9% of cycles. LIMITATIONS REASONS FOR CAUTION: Data were from a self-selected population of women who were prepared to purchase a commercially available product to aid conception and so may not fully represent the wider population. No corresponding demographic data were collected with the cycle information. WIDER IMPLICATIONS OF THE
FINDINGS: Using big data has provided more personalised insights into women's fertility; this could enable women trying to conceive to better time intercourse, increasing the likelihood of conception. STUDY FUNDING/COMPETING INTERESTS: The study was funded by SPD Development Company Ltd (Bedford, UK), a fully owned subsidiary of SPD Swiss Precision Diagnostics GmbH (Geneva, Switzerland). I.S., B.G. and S.J. are employees of the SPD Development Company Ltd.
© The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology.

Entities:  

Keywords:  connected ovulation test systems; menstrual cycle; menstrual cycle length; menstrual cycle variability; ovulation

Year:  2020        PMID: 32328534      PMCID: PMC7164578          DOI: 10.1093/hropen/hoaa011

Source DB:  PubMed          Journal:  Hum Reprod Open        ISSN: 2399-3529


  25 in total

1.  Monitoring the menstrual cycle: Comparison of urinary and serum reproductive hormones referenced to true ovulation.

Authors:  Judith Roos; Sarah Johnson; Sarah Weddell; Erhard Godehardt; Julia Schiffner; Günter Freundl; Christian Gnoth
Journal:  Eur J Contracept Reprod Health Care       Date:  2015-05-27       Impact factor: 1.848

2.  Development of the first urinary reproductive hormone ranges referenced to independently determined ovulation day.

Authors:  Sarah Johnson; Sarah Weddell; Sonya Godbert; Guenter Freundl; Judith Roos; Christian Gnoth
Journal:  Clin Chem Lab Med       Date:  2015-06       Impact factor: 3.694

3.  The Accuracy of Web Sites and Cellular Phone Applications in Predicting the Fertile Window.

Authors:  Robert Setton; Christina Tierney; Tony Tsai
Journal:  Obstet Gynecol       Date:  2016-07       Impact factor: 7.661

4.  Low fertility awareness in United States reproductive-aged women and medical trainees: creation and validation of the Fertility & Infertility Treatment Knowledge Score (FIT-KS).

Authors:  Rashmi Kudesia; Elizabeth Chernyak; Beth McAvey
Journal:  Fertil Steril       Date:  2017-09-11       Impact factor: 7.329

5.  Can apps and calendar methods predict ovulation with accuracy?

Authors:  Sarah Johnson; Lorrae Marriott; Michael Zinaman
Journal:  Curr Med Res Opin       Date:  2018-05-25       Impact factor: 2.580

6.  Characteristics of the urinary luteinizing hormone surge in young ovulatory women.

Authors:  Susanna J Park; Laura T Goldsmith; Joan H Skurnick; Andrea Wojtczuk; Gerson Weiss
Journal:  Fertil Steril       Date:  2007-04-16       Impact factor: 7.329

7.  Length and variation in the menstrual cycle--a cross-sectional study from a Danish county.

Authors:  K Münster; L Schmidt; P Helm
Journal:  Br J Obstet Gynaecol       Date:  1992-05

8.  Levels of urinary human chorionic gonadotrophin (hCG) following conception and variability of menstrual cycle length in a cohort of women attempting to conceive.

Authors:  Sarah R Johnson; Fernando Miro; Sophie Barrett; Jayne E Ellis
Journal:  Curr Med Res Opin       Date:  2009-03       Impact factor: 2.580

9.  National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys.

Authors:  Maya N Mascarenhas; Seth R Flaxman; Ties Boerma; Sheryl Vanderpoel; Gretchen A Stevens
Journal:  PLoS Med       Date:  2012-12-18       Impact factor: 11.069

10.  Should home-based ovulation predictor kits be offered as an additional approach for fertility management for women and couples desiring pregnancy? A systematic review and meta-analysis.

Authors:  Ping Teresa Yeh; Caitlin E Kennedy; Sheryl Van der Poel; Thabo Matsaseng; Laura Bernard; Manjulaa Narasimhan
Journal:  BMJ Glob Health       Date:  2019-04-25
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  9 in total

1.  A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction.

Authors:  Iñigo Urteaga; Kathy Li; Amanda Shea; Virginia J Vitzthum; Chris H Wiggins; Noémie Elhadad
Journal:  Proc Mach Learn Res       Date:  2021-08

2.  Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: A Comprehensive Review of the Literature.

Authors:  Joelle S Schantz; Claudia S P Fernandez; Z Jukic Anne Marie
Journal:  Curr Epidemiol Rep       Date:  2021-02-20

Review 3.  The Impact of Menstrual Cycle Phase on Athletes' Performance: A Narrative Review.

Authors:  Mikaeli Anne Carmichael; Rebecca Louise Thomson; Lisa Jane Moran; Thomas Philip Wycherley
Journal:  Int J Environ Res Public Health       Date:  2021-02-09       Impact factor: 4.614

4.  Predictive Factors of Conception and the Cumulative Pregnancy Rate in Subfertile Couples Undergoing Timed Intercourse With Ultrasound.

Authors:  So Hyun Ahn; Inha Lee; SiHyun Cho; Hye In Kim; Hye Won Baek; Jae Hoon Lee; Yun Jeong Park; Heeyon Kim; Bo Hyon Yun; Seok Kyo Seo; Joo Hyun Park; Young Sik Choi; Byung Seok Lee
Journal:  Front Endocrinol (Lausanne)       Date:  2021-04-15       Impact factor: 5.555

5.  A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.

Authors:  Kathy Li; Iñigo Urteaga; Amanda Shea; Virginia J Vitzthum; Chris H Wiggins; Noémie Elhadad
Journal:  J Am Med Inform Assoc       Date:  2021-12-28       Impact factor: 4.497

6.  Period tracker applications: What menstrual cycle information are they giving women?

Authors:  Lauren Worsfold; Lorrae Marriott; Sarah Johnson; Joyce C Harper
Journal:  Womens Health (Lond)       Date:  2021 Jan-Dec

7.  EndoTime: non-categorical timing estimates for luteal endometrium.

Authors:  Julia Lipecki; Andrew E Mitchell; Joanne Muter; Emma S Lucas; Komal Makwana; Katherine Fishwick; Joshua Odendaal; Amelia Hawkes; Pavle Vrljicak; Jan J Brosens; Sascha Ott
Journal:  Hum Reprod       Date:  2022-04-01       Impact factor: 6.918

8.  The ABC of reproductive intentions: a mixed-methods study exploring the spectrum of attitudes towards family building.

Authors:  B Grace; J Shawe; S Johnson; N O Usman; J Stephenson
Journal:  Hum Reprod       Date:  2022-05-03       Impact factor: 6.353

Review 9.  The real-world applications of the symptom tracking functionality available to menstrual health tracking apps.

Authors:  Tatheer Adnan; Brent A Coull; Anne Marie Jukic; Shruthi Mahalingaiah
Journal:  Curr Opin Endocrinol Diabetes Obes       Date:  2021-12-01       Impact factor: 3.243

  9 in total

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