Oura Ring commercial users can choose to self-report the results of home luteinizing hormone (LH) tests through the Oura Ring app. Positive LH test results served as the benchmark reference for algorithm performance testing. The reference ovulation date was defined as the day after the last positive LH test in the menstrual cycle [19,33].

Participants were selected for the study if they self-reported a positive LH test result between January 1, 2019, and April 15, 2024. Inclusion criteria required the positive LH test date to be logged within a complete menstrual cycle, such that menses start and end dates were both logged. The self-reported menses and reference LH test dates were required to demarcate biologically plausible cycle phase lengths. Acceptable ranges were 10-90 days for the follicular phase and 8-20 days for the luteal phase [33]. If a single user had multiple complete menstrual cycles containing an LH test reference, these were included as long as the algorithm input window did not overlap. These inclusion criteria resulted in 121 users with 2 or more observations, for a total of 1209 ovulatory menstrual cycles from 1051 unique users.

We excluded menstrual cycles from the analysis based on insufficient physiology data, hormone use, or self-reported pregnancy. Insufficient data were defined as more than 40% of missing physiology data in the last 60 days. Hormone use was self-reported by a questionnaire, where participants were asked to report any hormone intake that could influence ovulation, including hormonal birth control, hormone replacement therapy, or fertility medications. Of these exclusion criteria, 47 cycles were excluded due to insufficient physiology data, 6 due to hormone use, and 1 due to pregnancy. The resulting dataset contained 1155 ovulatory cycles from 1007 unique participants.

The study sample consisted of 964 female participants. Participants had a mean age of 32.8 (SD 5.5) years and a mean self-reported cycle length of 29.1 (SD 3.4) days.

The physiology method from Oura Ring correctly detected 1113 (96.4%) of 1155 ovulatory cycles observed (95% CI 95.3-97.4%). The estimated ovulation date MAE was 1.26 (SEM 0.02) days. Of the total 1155 ovulatory cycles, 1097 (95%) of ovulations were detected within 3 days, 1015 (87.9%) were detected within 2 days, and 785 (68%) were detected within 1 day. By comparison, the calendar method’s ovulation MAE was 3.44 (SEM 0.07) days. In total, the calendar method detected 768 (66.5%) of ovulations within 3 days, 585 (50.7%) within 2 days, and 371 (32.2%) within 1 day. The physiology method estimated ovulation with significantly improved accuracy compared with the calendar method (U₁₁₅₄=904942, P<.001, Cliff δ=0.46). Refer to Figure 2 for a breakdown of the algorithm’s performance by cycle length, cycle variability, and age.

Ovulation detection rate was impacted by cycle length. The Fisher test for detection rates suggests the odds of detecting ovulation were 3.56 times higher in medium cycle lengths compared with short cycle lengths (odds ratio 3.56, 95% CI 1.65-8.06, P=.008). Long (P=.99) and abnormally long (P=.59) cycle lengths did not statistically differ relative to medium cycle lengths. Accuracy did not significantly differ across regular cycle lengths (short, medium, or long) when using the physiology method (short vs medium: P=.41; long vs medium: P=.99). However, abnormally long cycle lengths ≥36 days) were associated with increased error (U=22,383, P=.03, δ=−0.17) with an MAE of 1.7 (SEM .09) days compared with 1.18 (SEM .02) days. The calendar method also performed significantly worse in abnormally long cycles (U=21,184, P<.001, δ=−0.30), with an MAE of 7.32 (SEM .54) days compared with 3.02 (SEM .07) days.

Neither detection rate nor accuracy differed significantly based on cycle variability when using the physiology method (for detection rate, moderate vs regular: P=.33 and irregular vs regular: P=.07; for accuracy, moderate vs regular: P=.99 and irregular vs regular: P=.15). However, the calendar method was associated with significantly worse accuracy for moderately irregular cyclers (U=54,093.5, P<.001, δ=−0.17) and even worse accuracy in irregular cycles (U=21,643, P<.001, δ=−0.43) when compared with typical cycle variability.

Neither the physiology method nor the calendar method was associated with differences across age in ovulation detection rate or estimated ovulation date error.

Finger temperature collected from Oura Ring was formulated as a physiology-based method of estimating the ovulation date. To assess the performance of this algorithm, we compare the physiology method with the calendar method in 1155 ovulatory cycles. The physiology method correctly detected ovulation in 1113 (96.4%) of 1155 cycles with an MAE of 1.26 days. This method was significantly more precise than the calendar method, which exhibited an MAE of 3.44 days, representing a 2.7-fold increase in error. In practical applications, Oura Ring primarily uses the physiology-based method to estimate a cycle’s ovulation date. However, the calendar method is used as a secondary approach when the physiology-based method cannot estimate the ovulation date, for example, in instances of excessive missing data. Thus, a thorough understanding of the strengths and limitations of both methods is critical. We also assessed if subgroups including age, cycle variation, and cycle length impact the accuracy of ovulation date estimation.

Age did not influence the accuracy of estimated ovulation dates within the assessed age range of 18-52 years, using either physiology-based or calendar methods. This finding aligns with the existing literature suggesting that age should not significantly affect the performance of the calendar method, as the luteal phase length remains consistent across age groups, and the calendar method estimates ovulation day by subtracting the luteal phase length from the user’s typical cycle length [31]. Although it was hypothesized that the physiology method may be less accurate across age due to variations in the magnitude of the postovulatory temperature rise [32,41], our analysis indicates that age did not significantly impact performance. However, ovulation estimation using Oura Ring has not yet been tested in individuals younger than 18 years and thus accuracy should not be assumed for adolescents.

Cycle variability significantly impacted the performance of the calendar method, but not the physiology method. Specifically, participants with irregular cycles experienced poorer performance using the calendar method, with an MAE of 6.63 days. This is a 2.6-fold decrease in accuracy relative to regular cyclers using the calendar method, and a 4.5-fold decrease in accuracy relative to the physiology-based method in irregular cyclers. When framing this as a window of days where the ovulation is 95% likely to have occurred, in irregular cyclers, that window is reduced from 14.2 days to 3.4 days. The superior accuracy of the physiology method likely stems from its independence from follicular phase length, which predominantly drives cycle variability and thus increases error in the calendar method [33,42].

Cycle length significantly influences ovulation estimation performance. Using the calendar method, longer cycles were associated with decreased accuracy, such that the MAE reaches 7.32 days in abnormally long cycles (ie, cycles exceeding 36 days in length). With the physiology method, statistical differences are evident both in the ovulation detection rate and the accuracy of the estimated ovulation date. Specifically, in cycles shorter than 26 days, which constitute 15% of our sample, the detection rate falls to 93%, compared with 98% in cycles of typical length. Furthermore, accuracy diminishes in abnormally long cycles with an MAE of 1.7 days versus 1.18 days in typical cycles. Consequently, users with shorter cycles may experience fewer detected ovulations, while those with abnormally long cycles may experience slightly worse accuracy when using the physiology method. Nevertheless, across all cycle categories, the physiology method significantly outperforms the calendar method.

Compared with wrist-worn devices with reported ovulation detection rates between 54% to 86% [43,44], Oura Ring exhibited superior performance, detecting 96.4% of ovulations. Oura Ring accuracy was similar to that of cervical mucus tracking, with 68% of estimations within 1 day of the reference ovulation date compared with 48% to 76% [19,20]. Oura Ring achieved favorable accuracy to BBT, which has a reported 20% detection rate [19,23], with an accuracy of 22% of ovulations correctly falling within 1 day of the reference ovulation [22,23]. Notably, BBT shows a marked decrease in performance in irregular cycles with a 34% drop in accuracy compared with regular cycles [45], while Oura Ring accuracy remained stable. Direct comparisons between studies should be approached with caution, as they are based on datasets with varying compositions of participant subgroups known to influence accuracy; notably, most studies exclude irregular cycles and cycles of abnormal length for performance analysis. The primary objective of this study is to identify conditions that may hinder the performance of Oura Ring ovulation estimation, rather than directly comparing it with other devices.

The primary limitation of this study is that our dataset consisted solely of ovulatory cycles and thus we cannot address ovulation estimation performance in anovulatory cycles. Without an estimate of the false alarm rate, a lack of detected ovulation by the algorithm should not be interpreted as indicative of an anovulatory cycle. The algorithm is not intended for use in individuals who frequently experience anovulatory cycles or who do not ovulate due to factors such as hormonal contraceptives.

Another limitation of this study is that our reference ovulation date is based on self-reported positive LH surge from participants using ovulation predictor kits (OPKs). While OPKs impose a lower participant burden, the gold standard for ovulation reference is a transvaginal ultrasound, which offers higher accuracy [19]. Given that our approach for collecting LH test results relied on self-report, the ovulation reference labels used here are susceptible to inaccuracies introduced by human error and subjective reporting biases. This approach also limits our sample to individuals who both use an Oura Ring and opt to take OPKs, and thus our cohort may not be representative of the broader population with menstrual cycles. Furthermore, the absence of data on negative OPK results precludes our ability to assess anovulatory cycles. In addition, it is not uncommon for an OPK to return a positive LH surge across multiple days; therefore, if participants logged only 1 positive LH surge and did not test on subsequent days, our reference ovulation day might be prematurely assigned.

A further limitation is that we did not collect data on whether participants had a history of menstrual cycle disorders. This is of particular concern in the 12% of participants with irregular cycles, who may have various underlying causes. A potential factor that could impact the accuracy of Oura Ring ovulation estimation is luteal phase deficiency (LPD), characterized by insufficient progesterone exposure [46]. LPD could influence the magnitude of the postovulatory temperature rise and thus impair the algorithm’s ability to estimate ovulation dates. Until future research is conducted, the estimated ovulation dates provided by the Oura Ring should be interpreted with caution in individuals with menstrual cycle disorders, particularly conditions associated with LPD, such as polycystic ovary syndrome [47,48], thyroid and prolactin disorders [49], and during in vitro fertilization cycles [50].

Overall, the results suggest that Oura Ring offers significantly improved accuracy over the traditional calendar method for estimating ovulation date. Furthermore, the physiology method is associated with relatively robust accuracy across various ages, cycle variability, and cycle lengths, with only small drops in detecting rate for shorter cycles (<26 days) and slight drops in accuracy in abnormally long cycles (>36 days). This is significantly improved compared with the calendar method, which was associated with large accuracy decreases in users with long cycles and irregular cycles. Based on previous reports, Oura Ring ovulation estimation appears to be outperforming other wearables and BBT-based ovulation estimation methods and on par with the cervical-mucus tracking method, although those results should be compared cautiously given that the performance was measured on different datasets. Furthermore, we posit that widespread ovulation tracking could have broad use beyond its use in conception and nonhormonal contraception. Specifically, continuous monitoring of follicular and luteal phase lengths throughout the reproductive lifespan could benefit a wider demographic interested in menstrual cycle–related biomarkers. These biomarkers hold promise for the early detection of disorders related to the menstrual cycle and fertility.