A comparison of app-defined fertile days from two fertility tracking apps using identical cycle data

Abstract

Objective: The Natural Cycles app employs daily basal body temperature to define the fertile window via a proprietary algorithm and is clinically established effective in preventing pregnancy. We sought to (1) compare the app-defined fertile window of Natural Cycles to that of CycleProGo, an app that uses BBT and cervical mucus to define the fertile window and (2) compare the app-defined fertile windows to the estimated physiologic fertile window.

Study design: Daily BBT were entered into Natural Cycles from 20 randomly selected regularly cycling women with at least 12 complete cycles from the CycleProGo database. The proportion of cycles with equivalent (±1 cycle day) fertile-window starts and fertile-window ends was determined. The app-defined fertile windows were then compared to the estimated physiologic fertile window using Peak mucus to estimate ovulation.

Results: Fifty seven percent of cycles (136/238) had equivalent fertile-window starts and 36% (72/181) had equivalent fertile-window end days. The mean overall fertile-window length from Natural Cycles was 12.8 days compared to 15.1 days for CycleProGo (p < 0.001). The Natural Cycles algorithm declared 12% to 30% of cycles with a fertile-window start and 13% to 38% of cycles with a fertile-window end within the estimated physiologic fertile window. The CycleProGo algorithm declared 4% to 14% of cycles with a fertile-window start and no cycles with a fertile-window end within the estimated physiologic fertile window.

Conclusions: Natural Cycles designated a higher proportion of cycles days as infertile within the estimated physiologic fertile window than CycleProGo.

Implications: Use of cervical mucus in addition to BBT may improve the accuracy of identifying the fertile window. Additional studies with other markers of ovulation and the fertile window would give additional insight into the clinical implications of app-defined fertile window differences.

Keywords: Family planning; Femtech; Mobile app; Ovulation tracking; Sympto-thermal method.