Treffer: Prediction of the fertile window and menstruation with a wearable device via machine-learning algorithms.

Title:
Prediction of the fertile window and menstruation with a wearable device via machine-learning algorithms.
Authors:
Luo C; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China; Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China; Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China., Su YF; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China; Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China; Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China., Ren YY; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China., Zhang Q; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China., Li R; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China., Zhang Q; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China., Li C; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China; Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China; Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China., Hao YH; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China; Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China; Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China., Zhang AQ; Huawei Device Co., Ltd., Shenzhen, China., Zhang H; Huawei Device Co., Ltd., Shenzhen, China., Huang HF; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China; Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China; Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China; Key Laboratory of Reproductive Genetics (Ministry of Education), Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China. Electronic address: huanghefg@hotmail.com., Wu YT; Obstetrics and Gynaecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China; Shanghai Key Laboratory of Reproduction and Development, Fudan University, Shanghai, China; Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai, China. Electronic address: yanting_wu@163.com.
Source:
Reproductive biomedicine online [Reprod Biomed Online] 2025 Jul; Vol. 51 (1), pp. 104795. Date of Electronic Publication: 2025 Jan 07.
Publication Type:
Journal Article; Observational Study; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101122473 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1472-6491 (Electronic) Linking ISSN: 14726483 NLM ISO Abbreviation: Reprod Biomed Online Subsets: MEDLINE
Imprint Name(s):
Publication: <2009->: Amsterdam : Elsevier
Original Publication: Cambridge, UK : Reproductive Healthcare Ltd.
MeSH Terms:
Wearable Electronic Devices* , Machine Learning* , Menstruation*/physiology , Menstrual Cycle*/physiology , Fertility*/physiology, Humans ; Female ; Adult ; Heart Rate/physiology ; Prospective Studies ; Algorithms ; Young Adult ; Skin Temperature ; Respiratory Rate
Contributed Indexing:
Keywords: Fertile window; Machine learning; Menstrual cycle; Natural cycle; Non-invasive wearable device; Wrist skin temperature
Entry Date(s):
Date Created: 20250525 Date Completed: 20250705 Latest Revision: 20250811
Update Code:
20250812
DOI:
10.1016/j.rbmo.2025.104795
PMID:
40413850
Database:
MEDLINE

Weitere Informationen

Research Question: Can algorithms for prediction of the fertile window and menstruation be developed through machine learning based on women's physiological parameters collected using a wearable device (Huawei Band 6 Pro) from both regular and irregular menstruators?
Design: This prospective observational cohort study was conducted at the Obstetrics and Gynaecology Hospital of Fudan University (China). Participants were recruited from November 2021 to September 2022. Each participant used a wearable device to record their wrist skin temperature (WST), heart rate, heart rate variability and respiratory rate for two menstrual cycles. Algorithms were developed to predict the fertile window and menstruation based on WST and heart rate data.
Results: Data from 270 and 84 qualifying cycles with confirmed ovulations from 136 regular menstruators and 47 irregular menstruators were included in this study. For regular menstruators, the prediction algorithm based on WST and heart rate data for the fertile window had accuracy of 85.47%, sensitivity of 70.07%, specificity of 89.77%, and area under the curve (AUC) of 0.869. The algorithms for labelling the first day of the menstrual cycle and menstrual prediction 3 days in advance had accuracy of 83.6% and 75.0%, respectively. For irregular menstruators, accuracy, sensitivity, specificity and AUC were 79.85%, 42.79%, 87.28% and 0.763, respectively, for prediction of the fertile window. The accuracy of algorithms for labelling the first day of the menstrual cycle and menstrual prediction were 61.2% and 50.8%, respectively.
Conclusions: Based on WST and heart rate data from the wearable device, the algorithms demonstrated reliable performance in predicting the fertile window and menstruation among regular menstruators. These algorithms also showed potential for assisting irregular menstruators in managing their cycles and planning for conception.
(Copyright © 2025 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.)