Literature DB >> 27297611

Personalised estimation of a woman's most fertile days.

Daniel Li1, Leslie Heyer2, Victoria H Jennings3, Colin A Smith2, David B Dunson4.   

Abstract

OBJECTIVES: We propose a new, personalised approach of estimating a woman's most fertile days that only requires recording the first day of menses and can use a smartphone to convey this information to the user so that she can plan or prevent pregnancy.
METHODS: We performed a retrospective analysis of two cohort studies (a North Carolina-based study and the Early Pregnancy Study [EPS]) and a prospective multicentre trial (World Health Organization [WHO] study). The North Carolina study consisted of 68 sexually active women with either an intrauterine device or tubal ligation. The EPS comprised 221 women who planned to become pregnant and had no known fertility problems. The WHO study consisted of 706 women from five geographically and culturally diverse settings. Bayesian statistical methods were used to design our proposed method, Dynamic Optimal Timing (DOT). Simulation studies were used to estimate the cumulative pregnancy risk.
RESULTS: For the proposed method, simulation analyses indicated a 4.4% cumulative probability of pregnancy over 13 cycles with correct use. After a calibration window, this method flagged between 11 and 13 days when unprotected intercourse should be avoided per cycle. Eligible women should have cycle lengths between 20 and 40 days with a variability range less than or equal to 9 days.
CONCLUSIONS: DOT can easily be implemented by computer or smartphone applications, allowing for women to make more informed decisions about their fertility. This approach is already incorporated into a patent-pending system and is available for free download on iPhones and Androids.

Entities:  

Keywords:  Fertility awareness; health; personalised medicine

Mesh:

Year:  2016        PMID: 27297611     DOI: 10.1080/13625187.2016.1196485

Source DB:  PubMed          Journal:  Eur J Contracept Reprod Health Care        ISSN: 1362-5187            Impact factor:   1.848


  6 in total

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Authors:  Richard J Fehring; Qiyan Mu
Journal:  Linacre Q       Date:  2017-03-10

2.  Fecundability in relation to use of mobile computing apps to track the menstrual cycle.

Authors:  Joseph B Stanford; Sydney K Willis; Elizabeth E Hatch; Kenneth J Rothman; Lauren A Wise
Journal:  Hum Reprod       Date:  2020-10-01       Impact factor: 6.918

3.  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

4.  Assessing the Efficacy of an App-Based Method of Family Planning: The Dot Study Protocol.

Authors:  Rebecca G Simmons; Dominick C Shattuck; Victoria H Jennings
Journal:  JMIR Res Protoc       Date:  2017-01-18

5.  Lessons From the Dot Contraceptive Efficacy Study: Analysis of the Use of Agile Development to Improve Recruitment and Enrollment for mHealth Research.

Authors:  Liya T Haile; Rebecca G Simmons; Dominick Shattuck
Journal:  JMIR Mhealth Uhealth       Date:  2018-04-20       Impact factor: 4.773

6.  User profile and preferences in fertility apps for preventing pregnancy: an exploratory pilot study.

Authors:  Mary Summer Starling; Zosha Kandel; Liya Haile; Rebecca G Simmons
Journal:  Mhealth       Date:  2018-06-30
  6 in total

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