The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China.
Reprod Biol Endocrinol. 2022 Aug 13;20(1):118. doi: 10.1186/s12958-022-00993-4.
Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators.
This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Participants used an ear thermometer to assess BBT and wore the Huawei Band 5 to record HR. Ovarian ultrasound and serum hormone levels were used to determine the ovulation day. Menstruation was self-reported by women. We used linear mixed models to assess changes in physiological parameters and developed probability function estimation models to predict the fertile window and menses with machine learning.
We included data from 305 and 77 qualified cycles with confirmed ovulations from 89 regular menstruators and 25 irregular menstruators, respectively. For regular menstruators, BBT and HR were significantly higher during fertile phase than follicular phase and peaked in the luteal phase (all P < 0.001). The physiological parameters of irregular menstruators followed a similar trend. Based on BBT and HR, we developed algorithms that predicted the fertile window with an accuracy of 87.46%, sensitivity of 69.30%, specificity of 92.00%, and AUC of 0.8993 and menses with an accuracy of 89.60%, sensitivity of 70.70%, and specificity of 94.30%, and AUC of 0.7849 among regular menstruators. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 72.51%, 21.00%, 82.90%, and 0.5808 respectively, for fertile window prediction and 75.90%, 36.30%, 84.40%, and 0.6759 for menses prediction.
By combining BBT and HR recorded by the Huawei Band 5, our algorithms achieved relatively ideal performance for predicting the fertile window and menses among regular menstruators. For irregular menstruators, the algorithms showed potential feasibility but still need further investigation.
ChiCTR2000036556. Registered 24 August 2020.
生育力的自我感知和经期预测对于提高生育能力和健康管理非常重要。先前的研究使用生理参数，如基础体温（BBT）和心率（HR），来预测排卵期和经期。然而，它们的准确性还远远不能令人满意。此外，很少有研究人员研究不规则经期者。因此，我们旨在为规则和不规则经期者开发排卵期和经期预测算法。
这是一项在中国上海国际和平妇幼保健院进行的前瞻性观察队列研究。参与者于 2020 年 8 月至 2020 年 11 月招募，并至少随访 4 个月经周期。参与者使用耳温计评估 BBT，并佩戴华为手环 5 记录 HR。卵巢超声和血清激素水平用于确定排卵日。女性通过自我报告来记录经期。我们使用线性混合模型来评估生理参数的变化，并使用机器学习开发概率函数估计模型来预测排卵期和经期。
我们纳入了 89 名规则经期者和 25 名不规则经期者的 305 个和 77 个有排卵的合格周期的数据。对于规则经期者，BBT 和 HR 在排卵期明显高于卵泡期，在黄体期达到峰值（均 P<0.001）。不规则经期者的生理参数也呈现出类似的趋势。基于 BBT 和 HR，我们开发了预测排卵期的算法，其准确率为 87.46%，灵敏度为 69.30%，特异性为 92.00%，AUC 为 0.8993，预测经期的准确率为 89.60%，灵敏度为 70.70%，特异性为 94.30%，AUC 为 0.7849。对于不规则经期者，预测排卵期的准确率、灵敏度、特异性和 AUC 分别为 72.51%、21.00%、82.90%和 0.5808，预测经期的准确率、灵敏度、特异性和 AUC 分别为 75.90%、36.30%、84.40%和 0.6759。
通过结合华为手环 5 记录的 BBT 和 HR，我们的算法在预测规则经期者的排卵期和经期方面取得了相对理想的效果。对于不规则经期者，该算法具有潜在的可行性，但仍需要进一步研究。
ChiCTR2000036556。注册于 2020 年 8 月 24 日。
