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Published on: August 4, 2021
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Mobile device data for the study of miscarriage and its causes.
Jenna Nobles1,2, Lindsay Cannon2, Sungsik Hwang2
1University of California, Berkeley, CA USA.
Npj Women'S Health
|March 5, 2026
Summary
Mobile app data can effectively detect miscarriage, mirroring findings from traditional health records. This approach offers a new way to study miscarriage risk factors, including social and environmental influences.
Area of Science:
- Reproductive Health
- Digital Health
- Epidemiology
Background:
- Miscarriage is a frequent pregnancy complication.
- Studying miscarriage is challenging due to data limitations.
- Existing research often lacks comprehensive data on risk factors.
Purpose of the Study:
- To evaluate the utility of mobile device data for miscarriage detection.
- To assess if app-based data can replicate known miscarriage risk patterns.
- To explore the potential of digital health data for broader miscarriage research.
Main Methods:
- Utilized data from 580,000 US pregnancies tracked via a menstrual and pregnancy app.
- Compared app-derived miscarriage detection with established Norwegian administrative data.
- Validated findings against US clinical cohort data.
Main Results:
- Mobile device data successfully detected miscarriage events.
- App data patterns for miscarriage risk aligned with high-quality administrative and clinical data.
- Demonstrated the feasibility of using app data for miscarriage research.
Conclusions:
- Mobile device data is a viable tool for identifying miscarriage.
- App data can serve as a valuable proxy for traditional health records in reproductive health research.
- Digital health data holds significant potential for investigating understudied miscarriage risk factors, including social, economic, and environmental influences.
