Ovulation day varies considerably for any given menstrual cycle length, thus it is not possible for calendar/app methods that use cycle-length information alone to accurately predict the day of ovulation. National Clinical Trial Code: NCT01577147. Registry website: www.clinicaltrials.gov .

Background: Period tracking applications (apps) allow women to track their menstrual cycles and receive a prediction for their period dates. The majority of apps also provide predictions of ovulation day and the fertile window. Research indicates apps are basing predictions on assuming women undergo a textbook 28-day cycle with ovulation occurring on day 14 and a fertile window between days 10 and 16. Objective: To determine how the information period tracker apps give women on their period dates, ovulation day and fertile window compares to expected results from big data. Methods: Five women’s profiles for 6 menstrual cycles were created and entered into 10 apps. Cycle length and ovulation day for the sixth cycle were Woman 1—Constant 28 day cycle length, ovulation day 16; Woman 2—Average 23 day cycle length, ovulation day 13; Woman 3—Average 28 day cycle length, ovulation day 17; Woman 4—Average 33 day cycle length, ovulation day 20; and Woman 5—Irregular, average 31 day cycle length, ovulation day 14. Results: The 10 period tracker apps examined gave conflicting information on period dates, ovulation day and the fertile window. For cycle length, the apps all predicted woman 1’s cycles correctly but for women 2–5, the apps predicted 0 to 8 days shorter or longer than expected. For day of ovulation, for women 1–4, of the 36 predictions, 3 (8%) were exactly correct, 9 predicted 1 day too early (25%) and 67% of predictions were 2–9 days early. For woman 5, most of the apps predicted a later day of ovulation. Conclusion: Period tracker apps should ensure they only give women accurate information, especially for the day of ovulation and the fertile window which can only be predicted if using a marker of ovulation, such as basal body temperature, ovulation sticks or cervical mucus.

Background Lutenising hormone (LH) and human chorionic gonadotropin (hCG) hormone are useful biochemical markers to indicate ovulation and embryonic implantation, respectively. We explored “point‐of‐care” LH and hCG testing using a digital home‐testing device in a cohort trying to conceive. Objective To determine conception and spontaneous pregnancy loss rates, and to assess whether trends in LH‐hCG interval which are known to be associated with pregnancy viability could be identified with point‐of‐care testing. Methods We recruited healthy women aged 18‐44 planning a pregnancy. Participants used a home monitor to track LH and hCG levels for 12 menstrual cycles or until pregnancy was conceived. Pregnancy outcomes (viable, clinical miscarriage, or biochemical pregnancy loss) were recorded. Monitor data were analysed by a statistician blinded to pregnancy outcome. Results From 387 recruits, there were 290 pregnancies with known outcomes within study timeline. Adequate monitor data for analysis were available for 150 conceptive cycles. Overall spontaneous first‐trimester pregnancy loss rate was 30% with clinically recognised miscarriage rate of 17%. The difference to LH‐hCG interval median had wider spread for biochemical losses (0.5‐8.5 days) compared with clinical miscarriage (0‐5 days) and viable pregnancies (0‐6 days). Fixed effect hCG profile change distinguished between pregnancy outcomes from as early as day‐2 post‐hCG rise from baseline. Conclusions The risk of first‐trimester spontaneous pregnancy loss in our prospective cohort is comparable to studies utilising daily urinary hCG collection and laboratory assays. A wider LH‐hCG interval range is associated with biochemical pregnancy loss and may relate to late or early implantation. Although early hCG changes discriminate between pregnancies that will miscarry from viable pregnancies, this point‐of‐care testing model is not sufficiently developed to be predictive.

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Background Human chorionic gonadotrophin is a marker of early pregnancy. This study sought to determine the possibility of being able to distinguish between healthy and failing pregnancies by utilizing patient-associated risk factors and daily urinary human chorionic gonadotrophin concentrations. Methods Data were from a study that collected daily early morning urine samples from women trying to conceive (n = 1505); 250 of whom became pregnant. Data from 129 women who became pregnant (including 44 miscarriages) were included in these analyses. A longitudinal model was used to profile human chorionic gonadotrophin, a Cox proportional hazards model to assess demographic/menstrual history data on the time to failed pregnancy, and a two-stage model to combine these two models. Results The profile for log human chorionic gonadotrophin concentrations in women suffering miscarriage differs to that of viable pregnancies; rate of human chorionic gonadotrophin rise is slower in those suffering a biochemical loss (loss before six weeks, recognized by a rise and fall of human chorionic gonadotrophin) and tends to plateau at a lower log human chorionic gonadotrophin in women suffering an early miscarriage (loss six weeks or later), compared with viable pregnancies. Maternal age, longest cycle length and time from luteinizing hormone surge to human chorionic gonadotrophin reaching 25 mIU/mL were found to be significantly associated with miscarriage risk. The two-stage model found that for an increase of one day in the time from luteinizing hormone surge to human chorionic gonadotrophin reaching 25 mIU/mL, there is a 30% increase in miscarriage risk (hazard ratio: 1.30; 95% confidence interval: 1.04, 1.62). Conclusion Rise of human chorionic gonadotrophin in early pregnancy could be useful to predict pregnancy viability. Daily tracking of urinary human chorionic gonadotrophin may enable early identification of some pregnancies at risk of miscarriage.