Validation of sleep-based actigraphy machine learning models for prediction of preterm birth
Abstract Disruptive sleep is a well-established predictor of preterm birth. However, the exact relationship between sleep behavior and preterm birth outcomes remains unknown, in part because prior work has relied on self-reported sleep data. With the advent of smartwatches, it is possible to obtain...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Women's Health |
| Online Access: | https://doi.org/10.1038/s44294-025-00082-y |
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| author | Benjamin C. Warner Peinan Zhao Erik D. Herzog Antonina I. Frolova Sarah K. England Chenyang Lu |
| author_facet | Benjamin C. Warner Peinan Zhao Erik D. Herzog Antonina I. Frolova Sarah K. England Chenyang Lu |
| author_sort | Benjamin C. Warner |
| collection | DOAJ |
| description | Abstract Disruptive sleep is a well-established predictor of preterm birth. However, the exact relationship between sleep behavior and preterm birth outcomes remains unknown, in part because prior work has relied on self-reported sleep data. With the advent of smartwatches, it is possible to obtain more reliable and accurate sleep data, which can be utilized to evaluate the impact of specific sleep behaviors in concert with machine learning. We evaluate motion actigraphy data collected from a cohort of participants undergoing pregnancy, and train several machine learning models based on aggregate features engineered from this data. We then evaluate the relative impact from each of these actigraphy features, as well as features derived from questionnaires collected from participants. Our findings suggest that actigraphy data can predict preterm birth outcomes with a degree of effectiveness, and that variability in sleep patterns is a relatively fair predictor of preterm birth. |
| format | Article |
| id | doaj-art-ffdf7405e46c47c2a37814802be512a0 |
| institution | Kabale University |
| issn | 2948-1716 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Women's Health |
| spelling | doaj-art-ffdf7405e46c47c2a37814802be512a02025-08-20T03:47:17ZengNature Portfolionpj Women's Health2948-17162025-06-013111310.1038/s44294-025-00082-yValidation of sleep-based actigraphy machine learning models for prediction of preterm birthBenjamin C. Warner0Peinan Zhao1Erik D. Herzog2Antonina I. Frolova3Sarah K. England4Chenyang Lu5AI for Health Institute, Washington University in St. LouisCenter for Reproductive Health Sciences, Department of Obstetrics & Gynecology, Washington University School of Medicine in St. LouisDepartment of Biology, Washington University in St. LouisCenter for Reproductive Health Sciences, Department of Obstetrics & Gynecology, Washington University School of Medicine in St. LouisAI for Health Institute, Washington University in St. LouisAI for Health Institute, Washington University in St. LouisAbstract Disruptive sleep is a well-established predictor of preterm birth. However, the exact relationship between sleep behavior and preterm birth outcomes remains unknown, in part because prior work has relied on self-reported sleep data. With the advent of smartwatches, it is possible to obtain more reliable and accurate sleep data, which can be utilized to evaluate the impact of specific sleep behaviors in concert with machine learning. We evaluate motion actigraphy data collected from a cohort of participants undergoing pregnancy, and train several machine learning models based on aggregate features engineered from this data. We then evaluate the relative impact from each of these actigraphy features, as well as features derived from questionnaires collected from participants. Our findings suggest that actigraphy data can predict preterm birth outcomes with a degree of effectiveness, and that variability in sleep patterns is a relatively fair predictor of preterm birth.https://doi.org/10.1038/s44294-025-00082-y |
| spellingShingle | Benjamin C. Warner Peinan Zhao Erik D. Herzog Antonina I. Frolova Sarah K. England Chenyang Lu Validation of sleep-based actigraphy machine learning models for prediction of preterm birth npj Women's Health |
| title | Validation of sleep-based actigraphy machine learning models for prediction of preterm birth |
| title_full | Validation of sleep-based actigraphy machine learning models for prediction of preterm birth |
| title_fullStr | Validation of sleep-based actigraphy machine learning models for prediction of preterm birth |
| title_full_unstemmed | Validation of sleep-based actigraphy machine learning models for prediction of preterm birth |
| title_short | Validation of sleep-based actigraphy machine learning models for prediction of preterm birth |
| title_sort | validation of sleep based actigraphy machine learning models for prediction of preterm birth |
| url | https://doi.org/10.1038/s44294-025-00082-y |
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