{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T07:47:26Z","timestamp":1787212046043,"version":"build-2736575974"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"Abstract<\/jats:title>\n \n STUDY QUESTION<\/jats:title>\n 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?<\/jats:p>\n <\/jats:sec>\n \n SUMMARY ANSWER<\/jats:title>\n Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.<\/jats:p>\n <\/jats:sec>\n \n WHAT IS KNOWN ALREADY<\/jats:title>\n Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events.<\/jats:p>\n <\/jats:sec>\n \n STUDY DESIGN, SIZE, DURATION<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n PARTICIPANTS\/MATERIALS, SETTING, METHODS<\/jats:title>\n 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\u2019s performance was evaluated under multiple scenarios, including for participants with all typical cycle lengths (23\u201335\u2009days) and those with some atypical cycle lengths (&lt;23, &gt;35\u2009days), in cycles with the temperature change of \u22650.2\u00b0C typically associated with ovulation, and with any temperature change included.<\/jats:p>\n <\/jats:sec>\n \n MAIN RESULTS AND ROLE OF CHANCE<\/jats:title>\n 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 \u22650.2\u00b0C wrist temperature signal with a mean absolute error (MAE) of 1.59\u2009days (95% CI 1.45, 1.74), with 80.0% of estimates being within \u00b12\u2009days of ovulation. Retrospective ovulation day in an ongoing cycle (Algorithm 1) was estimated in 81.9% (MAE 1.53\u2009days, 95% CI 1.35, 1.70) of cycles for participants with all typical cycle lengths and 77.7% (MAE 1.71\u2009days, 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 \u22650.2\u00b0C wrist temperature signal with an MAE of 1.22\u2009days (95% CI 1.11, 1.33), with 89.0% of estimates being within \u00b12\u2009days of ovulation. Wrist temperature provided the next menses start day prediction (Algorithm 3) at the time of ovulation estimate (89.4% within \u00b13\u2009days of menses start) with an MAE of 1.65 (95% CI 1.52, 1.79) days in cycles with \u22650.2\u00b0C wrist temperature signal.<\/jats:p>\n <\/jats:sec>\n \n LIMITATIONS, REASONS FOR CAUTION<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n WIDER IMPLICATIONS OF THE FINDINGS<\/jats:title>\n Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.<\/jats:p>\n <\/jats:sec>\n \n STUDY FUNDING\/COMPETING INTEREST(S)<\/jats:title>\n 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\u2019s 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.<\/jats:p>\n <\/jats:sec>\n \n TRIAL REGISTRATION NUMBER<\/jats:title>\n NCT05852951.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/humrep\/deaf005","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T15:13:12Z","timestamp":1737126792000},"page":"469-478","source":"Crossref","is-referenced-by-count":11,"title":["Performance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study"],"prefix":"10.1093","volume":"40","author":[{"given":"Y","family":"Wang","sequence":"first","affiliation":[{"name":"Apple Inc. Health , Cupertino, CA,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J","family":"Park","sequence":"additional","affiliation":[{"name":"Apple Inc. 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