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Findings from a mobile application–based cohort are consistent with established knowledge of the menstrual cycle, fertile window, and conception
Fertility and Sterility ( IF 6.6 ) Pub Date : 2019-09-01 , DOI: 10.1016/j.fertnstert.2019.05.008
Louis Faust , Dani Bradley , Erin Landau , Katie Noddin , Leslie V. Farland , Alex Baron , Adam Wolfberg

OBJECTIVE To investigate the validity of self-reported fertility data generated by a mobile application-based cohort in comparison with data collected by traditional clinical methodologies. DESIGN Data were collected from July 2013 to July 2018 through a mobile application designed to track fertility. Bayesian hierarchical models were used to assess day-specific pregnancy probabilities. Descriptive statistics were used to estimate differences in day of ovulation and lengths of menstrual phases and to assess changes in the cervix and ovulation-related symptoms drawing closer to the day of ovulation. SETTING Not applicable. PATIENT(S) Data consisted of 225,596 menstrual cycles from 98,903 women. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) Day-specific probabilities of pregnancy, variability in lengths of the follicular and luteal phases, trends in prevalence of symptoms and cervix changes across the fertile window. RESULT(S) Analyses were consistent with established clinical knowledge. Probability of conception was highest during the 5 days before and day of ovulation, with the highest probability occurring the day before ovulation. The average cycle length was 29.6 days, and average lengths of the follicular and luteal phases were 15.8 and 13.7 days, respectively. Closer to day of ovulation, women were more likely to report changes in the cervix corresponding to fluid consistency, feel, position, and openness and symptoms associated with ovulation, including pelvic pain, tender breasts, increased sex drive, and cramps. CONCLUSION(S) Components of the menstrual cycle and fertile window, when re-evaluated with a mobile application-based cohort, were found to be consistent with established clinical knowledge, suggesting an agreement between traditional and modern data collection methodologies.

中文翻译:

来自基于移动应用程序的队列的结果与对月经周期、受孕期和受孕的既定知识一致

目的 与传统临床方法收集的数据相比,调查基于移动应用程序的队列生成的自我报告生育数据的有效性。设计数据是从 2013 年 7 月至 2018 年 7 月通过旨在跟踪生育率的移动应用程序收集的。贝叶斯分层模型用于评估特定日期的怀孕概率。描述性统计用于估计排卵日和月经期长度的差异,并评估接近排卵日的宫颈和排卵相关症状的变化。设置 不适用。患者数据包括来自 98,903 名女性的 225,596 个月经周期。干预措施 无。主要结果测量(S)特定日的怀孕概率,卵泡期和黄体期长度的变异性,整个生育期症状和宫颈变化的流行趋势。结果分析与既定的临床知识一致。受孕概率在排卵前5天和排卵当天最高,排卵前一天的概率最高。平均周期长度为 29.6 天,卵泡期和黄体期的平均长度分别为 15.8 天和 13.7 天。接近排卵日,女性更有可能报告与液体稠度、感觉、位置和开放度相对应的子宫颈变化以及与排卵相关的症状,包括盆腔疼痛、乳房触痛、性欲增强和痉挛。结论(S)当使用基于移动应用程序的队列重新评估时,月经周期和受孕期的组成部分,
更新日期:2019-09-01
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