Personalised estimation of a woman's most fertile days.
OBJECTIVE
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.
a The Ohio State University College of Medicine , Columbus , OH , USA ;
Daniel
D
Li
b Cycle Technologies , Washington , DC , USA ;
Leslie
L
Heyer
c Department of Obstetrics and Gynecology , Georgetown University , Washington , DC , USA ;
Victoria H
VH
Jennings
b Cycle Technologies , Washington , DC , USA ;
Colin A
CA
Smith
d Department of Statistical Science , Duke University , Durham , NC , USA.
David B
DB
Dunson
D001499:Bayes Theorem; D005260:Female; D005298:Fertility / Q000502:physiology*; D006801:Humans; D008597:Menstrual Cycle / Q000502:physiology*; D063731:Mobile Applications; D012252:Natural Family Planning Methods / Q000379:methods*; D000068997:Smartphone
Fertility awareness; health; personalised medicine