Wang Y, Park J, Zhang C Y, Jukic A M Z, Baird D D, Coull B A, Hauser R, Mahalingaiah S, Zhang S, Curry C L
Apple Inc. Health, Cupertino, CA, USA.
Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.
Hum Reprod. 2025 Mar 1;40(3):469-478. doi: 10.1093/humrep/deaf005.
Can algorithms using wrist temperature, available on compatible models of iPhone and Apple Watch, retrospectively estimate the day of ovulation and predict the next menses start day?
Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.
Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events.
STUDY DESIGN, SIZE, DURATION: We conducted a prospective cohort study of 262 menstruating females (899 menstrual cycles) aged 14 and older who logged their menses, performed urine LH testing to define day of ovulation, recorded daily basal body temperature (BBT), and collected overnight wrist temperature. Participants contributed between 2 and 13 menstrual cycles.
PARTICIPANTS/MATERIALS, SETTING, METHODS: Algorithm performance was evaluated for three algorithms: one for retrospective ovulation day estimate in ongoing cycles (Algorithm 1), one for retrospective ovulation day estimate in completed cycles (Algorithm 2), and one for prediction of next menses start day (Algorithm 3). Each algorithm's performance was evaluated under multiple scenarios, including for participants with all typical cycle lengths (23-35 days) and those with some atypical cycle lengths (<23, >35 days), in cycles with the temperature change of ≥0.2°C typically associated with ovulation, and with any temperature change included.
Two hundred and sixty participants provided 889 cycles. Algorithm 1 provided a retrospective ovulation day estimate in 80.5% of ongoing menstrual cycles of all cycle lengths with ≥0.2°C wrist temperature signal with a mean absolute error (MAE) of 1.59 days (95% CI 1.45, 1.74), with 80.0% of estimates being within ±2 days of ovulation. Retrospective ovulation day in an ongoing cycle (Algorithm 1) was estimated in 81.9% (MAE 1.53 days, 95% CI 1.35, 1.70) of cycles for participants with all typical cycle lengths and 77.7% (MAE 1.71 days, 95% CI 1.42, 2.01) of cycles for participants with atypical cycle lengths. Algorithm 2 provided a retrospective ovulation day estimate in 80.8% of completed menstrual cycles with ≥0.2°C wrist temperature signal with an MAE of 1.22 days (95% CI 1.11, 1.33), with 89.0% of estimates being within ±2 days of ovulation. Wrist temperature provided the next menses start day prediction (Algorithm 3) at the time of ovulation estimate (89.4% within ±3 days of menses start) with an MAE of 1.65 (95% CI 1.52, 1.79) days in cycles with ≥0.2°C wrist temperature signal.
LIMITATIONS, REASONS FOR CAUTION: There are several limitations, including reliance on LH testing to identify ovulation, which may mislabel some cycles. Additionally, the potential for false retrospective ovulation estimates when no ovulation occurred reinforces the idea that this estimate should not be used in isolation.
Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.
STUDY FUNDING/COMPETING INTEREST(S): Apple is the funding source for this manuscript. Y.W., C.Y.Z., J.P., S.Z., and C.L.C. own Apple stock and are employed by Apple. S.M. has research funding from Apple for a separate study, the Apple Women's Health Study, including meeting and travel support to present research findings related to that separate study. A.M.Z.J., D.D.B., B.A.C., and J.P. had no conflicts of interest.
NCT05852951.
使用iPhone和Apple Watch兼容型号上的手腕温度算法，能否回顾性地估计排卵日并预测下一次月经开始日？
使用手腕温度的算法可以为月经周期长度正常或异常的个体提供回顾性排卵估计和下一次月经开始日预测。
手腕皮肤温度受与月经周期相关的激素变化影响，可用于估计周期事件的时间。
研究设计、规模、持续时间：我们对262名14岁及以上的月经女性（899个月经周期）进行了一项前瞻性队列研究，她们记录了自己的月经情况，进行尿液促黄体生成素（LH）检测以确定排卵日，记录每日基础体温（BBT），并收集夜间手腕温度。参与者贡献了2至13个月经周期的数据。
参与者/材料、设置、方法：评估了三种算法的性能：一种用于正在进行的周期中回顾性排卵日估计（算法1），一种用于已完成周期中回顾性排卵日估计（算法2），一种用于预测下一次月经开始日（算法3）。每种算法的性能在多种情况下进行评估，包括所有月经周期长度正常（23 - 35天）的参与者以及一些月经周期长度异常（<23天，>35天）的参与者，在通常与排卵相关的温度变化≥0.2°C的周期中，以及包括任何温度变化的情况下。
260名参与者提供了889个周期的数据。算法1在所有周期长度且手腕温度信号≥0.2°C的正在进行的月经周期中，80.5%能提供回顾性排卵日估计，平均绝对误差（MAE）为1.59天（95%置信区间1.45，1.74），80.0%的估计在排卵日的±2天范围内。对于所有月经周期长度正常的参与者，算法1在81.9%的周期中估计正在进行的周期中的回顾性排卵日（MAE 1.53天，95%置信区间1.35，1.70），对于月经周期长度异常的参与者，在77.7%的周期中（MAE 1.71天，95%置信区间1.42，2.01）。算法2在手腕温度信号≥0.2°C的已完成月经周期中，80.8%能提供回顾性排卵日估计，MAE为1.22天（95%置信区间1.11，1.33），89.0%的估计在排卵日的±2天范围内。在手腕温度信号≥0.2°C的周期中，在估计排卵时，手腕温度能提供下一次月经开始日预测（算法3），89.4%在月经开始日的±3天范围内，MAE为1.65（95%置信区间1.52，1.79）天。
局限性、谨慎原因：存在几个局限性，包括依赖LH检测来识别排卵，这可能会错误标记一些周期。此外，在未发生排卵时可能出现错误的回顾性排卵估计，这强化了不应单独使用该估计的观点。
使用手腕温度的算法可以为月经周期长度正常或异常的个体提供回顾性排卵估计和下一次月经开始日预测。
研究资金/竞争利益：苹果是本手稿的资金来源。Y.W.、C.Y.Z.、J.P.、S.Z.和C.L.C.拥有苹果股票并受雇于苹果。S.M.从苹果获得了另一项单独研究（苹果女性健康研究）的研究资金，包括会议和旅行支持以展示与该单独研究相关的研究结果。A.M.Z.J.、D.D.B.、B.A.C.和J.P.没有利益冲突。
NCT05852951。
