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• W4384338667 abstract "<sec> <title>BACKGROUND</title> Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events. </sec> <sec> <title>OBJECTIVE</title> The objective of this study was evaluation of three algorithms available on compatible models of iPhone and Apple Watch which use wrist temperature to retrospectively estimate the day of ovulation and to predict next menses start day. </sec> <sec> <title>METHODS</title> We conducted a prospective cohort study of menstruating females aged 14 and older who logged their menses, performed urine leutinizing hormone testing to define day of ovulation, recorded daily basal body temperature (BBT) and collected overnight wrist temperature. Algorithm performance was evaluated for three algorithms: one for retrospective ovulation day estimate in ongoing cycles (Algorithm 1), one for retrospective ovulation estimate in completed cycles (Algorithm 2), and one for prediction of next menses start day (Algorithm 3). Cycles considered to have likely tracking errors as well as those missing temperature data for 30% or more of days were excluded from analysis. 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. </sec> <sec> <title>RESULTS</title> Two-hundred 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 next menses start day prediction (Algorithm 3) at the time of ovulation estimate (89.4% within +/- 3 days of menses start) with a MAE of 1.65 (95% CI 1.52, 1.79) days in cycles with ≥ 0.2°C wrist temperature signal. </sec> <sec> <title>CONCLUSIONS</title> Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths. Inclusion of wrist skin temperature, along with other physiologic measurements, can aid in menstrual cycle tracking and understanding cycle patterns. </sec> <sec> <title>CLINICALTRIAL</title> NCT05852951 </sec>" @default.
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• W4384338667 date "2023-07-13" @default.
• W4384338667 modified "2023-09-27" @default.
• W4384338667 title "Performance of Algorithms Using Wrist Temperature for Retrospective Ovulation Day Estimate and Next Menses Start Day Prediction: Prospective Cohort Study (Preprint)" @default.
• W4384338667 doi "https://doi.org/10.2196/preprints.50821" @default.
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