Couple to Couple League, Cincinnati, OH Georgetown School of Medicine, Washington, D.C..
Fertility Appreciative Collaborative to Teach the Science & Adjunct Associate Professor Georgetown University School of Medicine.
Contraception. 2022 Nov;115:12-16. doi: 10.1016/j.contraception.2022.07.007. Epub 2022 Jul 25.
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.
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.
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.
Natural Cycles designated a higher proportion of cycles days as infertile within the estimated physiologic fertile window than CycleProGo.
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.
自然周期应用程序通过专有算法利用每日基础体温来定义易孕期，临床证明其在避孕方面非常有效。我们试图（1）比较自然周期应用程序定义的易孕期与使用 BBT 和宫颈粘液来定义易孕期的 CycleProGo 应用程序，以及（2）比较应用程序定义的易孕期与估计的生理易孕期。
从 CycleProGo 数据库中随机选择 20 名月经周期规律且至少有 12 个完整周期的女性，将其每日基础体温输入到自然周期中。确定周期中具有相同（±1 个周期日）易孕期开始和易孕期结束的比例。然后使用峰值粘液来估计排卵，将应用程序定义的易孕期与估计的生理易孕期进行比较。
57%（136/238）的周期具有相同的易孕期开始日期，36%（72/181）的周期具有相同的易孕期结束日期。与 CycleProGo 相比，自然周期的平均总易孕期长度为 12.8 天，而 CycleProGo 为 15.1 天（p<0.001）。自然周期算法宣布 12%至 30%的周期易孕期开始日期和 13%至 38%的周期易孕期结束日期在估计的生理易孕期内。CycleProGo 算法宣布 4%至 14%的周期易孕期开始日期和没有周期易孕期结束日期在估计的生理易孕期内。
自然周期在估计的生理易孕期内将更多的周期天数指定为非易孕期，而 CycleProGo 则不然。
除了 BBT 之外，使用宫颈粘液可能会提高识别易孕期的准确性。使用其他排卵和易孕期标志物的进一步研究将为应用程序定义的易孕期差异的临床意义提供更多的见解。
