Oura Ring as a Tool for Ovulation Detection: Validation Analysis
BackgroundOura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phas...
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JMIR Publications
2025-01-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e60667 |
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| author | Nina Thigpen Shyamal Patel Xi Zhang |
| author_facet | Nina Thigpen Shyamal Patel Xi Zhang |
| author_sort | Nina Thigpen |
| collection | DOAJ |
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BackgroundOura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health.
ObjectiveWe assessed the strengths, weaknesses, and limitations of using physiology from the Oura Ring to estimate the ovulation date. We compared performance across cycle length, cycle variability, and participant age. In each subgroup, we compared the algorithm’s performance with the traditional calendar method, which estimates the ovulation date based on an individual’s last period start date and average menstrual cycle length.
MethodsThe study sample contained 1155 ovulatory menstrual cycles from 964 participants recruited from the Oura Ring commercial database. Ovulation prediction kits served as a benchmark to evaluate the performance. The Fisher test was used to determine an odds ratio to assess if ovulation detection rate significantly differed between methods or subgroups. The Mann-Whitney U test was used to determine if the accuracy of the estimated ovulation date differed between the estimated and reference ovulation dates.
ResultsThe physiology method detected 1113 (96.4%) of 1155 ovulations with an average error of 1.26 days, which was significantly lower (U=904942.0, P<.001) than the calendar method’s average error of 3.44 days. The physiology method had significantly better accuracy across all cycle lengths, cycle variability groups, and age groups compared with the calendar method (P<.001). The physiology method detected fewer ovulations in short cycles (odds ratio 3.56, 95% CI 1.65-8.06; P=.008) but did not differ between typical and long or abnormally long cycles. Abnormally long cycle lengths were associated with decreased accuracy (U=22,383, P=.03), with a mean absolute error of 1.7 (SEM .09) days compared with 1.18 (SEM .02) days. The physiology method was not associated with differences in accuracy across age or typical cycle variability, while the calendar method performed significantly worse in participants with irregular cycles (U=21,643, P<.001).
ConclusionsThe physiology method demonstrated superior accuracy over the calendar method, with approximately 3-fold improvement. Calendar-based fertility tracking could be used as a backup in cases of insufficient physiology data but should be used with caution, particularly for individuals with irregular menstrual cycles. Our analyses suggest the physiology method can reliably estimate ovulation dates for adults aged 18-52 years, across a variety of cycle lengths, and in users with regular or irregular cycles. This method may be used as a tool to improve fertile window estimation, which can aid in conceiving or preventing pregnancies. This method also offers a low-effort solution for follicular and luteal phase length tracking, which are key biomarkers for reproductive health. |
| format | Article |
| id | doaj-art-7ec4eb1c0222462da62f69ce794070bf |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
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| spelling | doaj-art-7ec4eb1c0222462da62f69ce794070bf2025-01-31T20:00:34ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e6066710.2196/60667Oura Ring as a Tool for Ovulation Detection: Validation AnalysisNina Thigpenhttps://orcid.org/0000-0002-2810-0009Shyamal Patelhttps://orcid.org/0000-0002-4369-3033Xi Zhanghttps://orcid.org/0009-0007-7648-0310 BackgroundOura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health. ObjectiveWe assessed the strengths, weaknesses, and limitations of using physiology from the Oura Ring to estimate the ovulation date. We compared performance across cycle length, cycle variability, and participant age. In each subgroup, we compared the algorithm’s performance with the traditional calendar method, which estimates the ovulation date based on an individual’s last period start date and average menstrual cycle length. MethodsThe study sample contained 1155 ovulatory menstrual cycles from 964 participants recruited from the Oura Ring commercial database. Ovulation prediction kits served as a benchmark to evaluate the performance. The Fisher test was used to determine an odds ratio to assess if ovulation detection rate significantly differed between methods or subgroups. The Mann-Whitney U test was used to determine if the accuracy of the estimated ovulation date differed between the estimated and reference ovulation dates. ResultsThe physiology method detected 1113 (96.4%) of 1155 ovulations with an average error of 1.26 days, which was significantly lower (U=904942.0, P<.001) than the calendar method’s average error of 3.44 days. The physiology method had significantly better accuracy across all cycle lengths, cycle variability groups, and age groups compared with the calendar method (P<.001). The physiology method detected fewer ovulations in short cycles (odds ratio 3.56, 95% CI 1.65-8.06; P=.008) but did not differ between typical and long or abnormally long cycles. Abnormally long cycle lengths were associated with decreased accuracy (U=22,383, P=.03), with a mean absolute error of 1.7 (SEM .09) days compared with 1.18 (SEM .02) days. The physiology method was not associated with differences in accuracy across age or typical cycle variability, while the calendar method performed significantly worse in participants with irregular cycles (U=21,643, P<.001). ConclusionsThe physiology method demonstrated superior accuracy over the calendar method, with approximately 3-fold improvement. Calendar-based fertility tracking could be used as a backup in cases of insufficient physiology data but should be used with caution, particularly for individuals with irregular menstrual cycles. Our analyses suggest the physiology method can reliably estimate ovulation dates for adults aged 18-52 years, across a variety of cycle lengths, and in users with regular or irregular cycles. This method may be used as a tool to improve fertile window estimation, which can aid in conceiving or preventing pregnancies. This method also offers a low-effort solution for follicular and luteal phase length tracking, which are key biomarkers for reproductive health.https://www.jmir.org/2025/1/e60667 |
| spellingShingle | Nina Thigpen Shyamal Patel Xi Zhang Oura Ring as a Tool for Ovulation Detection: Validation Analysis Journal of Medical Internet Research |
| title | Oura Ring as a Tool for Ovulation Detection: Validation Analysis |
| title_full | Oura Ring as a Tool for Ovulation Detection: Validation Analysis |
| title_fullStr | Oura Ring as a Tool for Ovulation Detection: Validation Analysis |
| title_full_unstemmed | Oura Ring as a Tool for Ovulation Detection: Validation Analysis |
| title_short | Oura Ring as a Tool for Ovulation Detection: Validation Analysis |
| title_sort | oura ring as a tool for ovulation detection validation analysis |
| url | https://www.jmir.org/2025/1/e60667 |
| work_keys_str_mv | AT ninathigpen ouraringasatoolforovulationdetectionvalidationanalysis AT shyamalpatel ouraringasatoolforovulationdetectionvalidationanalysis AT xizhang ouraringasatoolforovulationdetectionvalidationanalysis |