Can Wrist-Worn Medical Devices Correctly Identify Ovulation?
(1) Background: Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary o...
| الحاوية / القاعدة: | Sensors |
|---|---|
| المؤلفون الرئيسيون: | , , , |
| التنسيق: | مقال |
| اللغة: | الإنجليزية |
| منشور في: |
MDPI AG
2023-12-01
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/1424-8220/23/24/9730 |
| _version_ | 1852664838093799424 |
|---|---|
| author | Angela Niggli Martina Rothenbühler Maike Sachs Brigitte Leeners |
| author_facet | Angela Niggli Martina Rothenbühler Maike Sachs Brigitte Leeners |
| author_sort | Angela Niggli |
| collection | DOAJ |
| container_title | Sensors |
| description | (1) Background: Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary ovulation tests for predicting the fertile window. (2) Methods: We conducted a prospective observational study including women aged 18–45 years without current hormonal therapy who used a wrist-worn medical device and urinary ovulation tests for a minimum of three cycles. We analyzed the accuracy of both the retrospective and prospective algorithms using a generalized linear mixed-effects model. The findings were compared to real-world data from bracelet users who also reported urinary ovulation tests. (3) Results: A total of 61 study participants contributing 205 cycles and 6081 real-life cycles from 3268 bracelet users were included in the analysis. The mean error in identifying ovulation with the wrist-worn medical device retrospective algorithm in the clinical study was 0.31 days (95% CI −0.13 to 0.75). The retrospective algorithm identified 75.4% of fertile days, and the prospective algorithm identified 73.8% of fertile days correctly within the pre-specified equivalence limits (±2 days). The quality of the retrospective algorithm in the clinical study could be confirmed by real-world data. (4) Conclusion: Our data indicate that wearable sensors may be used to accurately detect the periovulatory period. |
| format | Article |
| id | doaj-art-e3bf5e8be741400d9725a1b6d280a22e |
| institution | Directory of Open Access Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-e3bf5e8be741400d9725a1b6d280a22e2025-08-19T21:35:36ZengMDPI AGSensors1424-82202023-12-012324973010.3390/s23249730Can Wrist-Worn Medical Devices Correctly Identify Ovulation?Angela Niggli0Martina Rothenbühler1Maike Sachs2Brigitte Leeners3Department of Reproductive Endocrinology, University Hospital of Zürich, Frauenklinikstrasse 10, 8091 Zürich, SwitzerlandAva AG, Gutstrasse 73, 8055 Zürich, SwitzerlandDepartment of Reproductive Endocrinology, University Hospital of Zürich, Frauenklinikstrasse 10, 8091 Zürich, SwitzerlandDepartment of Reproductive Endocrinology, University Hospital of Zürich, Frauenklinikstrasse 10, 8091 Zürich, Switzerland(1) Background: Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary ovulation tests for predicting the fertile window. (2) Methods: We conducted a prospective observational study including women aged 18–45 years without current hormonal therapy who used a wrist-worn medical device and urinary ovulation tests for a minimum of three cycles. We analyzed the accuracy of both the retrospective and prospective algorithms using a generalized linear mixed-effects model. The findings were compared to real-world data from bracelet users who also reported urinary ovulation tests. (3) Results: A total of 61 study participants contributing 205 cycles and 6081 real-life cycles from 3268 bracelet users were included in the analysis. The mean error in identifying ovulation with the wrist-worn medical device retrospective algorithm in the clinical study was 0.31 days (95% CI −0.13 to 0.75). The retrospective algorithm identified 75.4% of fertile days, and the prospective algorithm identified 73.8% of fertile days correctly within the pre-specified equivalence limits (±2 days). The quality of the retrospective algorithm in the clinical study could be confirmed by real-world data. (4) Conclusion: Our data indicate that wearable sensors may be used to accurately detect the periovulatory period.https://www.mdpi.com/1424-8220/23/24/9730fertilitymenstrual cycleovulationwearablesensor |
| spellingShingle | Angela Niggli Martina Rothenbühler Maike Sachs Brigitte Leeners Can Wrist-Worn Medical Devices Correctly Identify Ovulation? fertility menstrual cycle ovulation wearable sensor |
| title | Can Wrist-Worn Medical Devices Correctly Identify Ovulation? |
| title_full | Can Wrist-Worn Medical Devices Correctly Identify Ovulation? |
| title_fullStr | Can Wrist-Worn Medical Devices Correctly Identify Ovulation? |
| title_full_unstemmed | Can Wrist-Worn Medical Devices Correctly Identify Ovulation? |
| title_short | Can Wrist-Worn Medical Devices Correctly Identify Ovulation? |
| title_sort | can wrist worn medical devices correctly identify ovulation |
| topic | fertility menstrual cycle ovulation wearable sensor |
| url | https://www.mdpi.com/1424-8220/23/24/9730 |
| work_keys_str_mv | AT angelaniggli canwristwornmedicaldevicescorrectlyidentifyovulation AT martinarothenbuhler canwristwornmedicaldevicescorrectlyidentifyovulation AT maikesachs canwristwornmedicaldevicescorrectlyidentifyovulation AT brigitteleeners canwristwornmedicaldevicescorrectlyidentifyovulation |
