Publications by authors named "M Holko"

Background: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging.

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Data from digital health technologies (DHT), including wearable sensors like Apple Watch, Whoop, Oura Ring, and Fitbit, are increasingly being used in biomedical research. Research and development of DHT-related devices, platforms, and applications is happening rapidly and with significant private-sector involvement with new biotech companies and large tech companies (e.g.

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Article Synopsis
  • Postpartum depression (PPD) affects 1 in 7 women and is often missed due to reliance on in-person visits for detection.
  • A study explored using digital biomarkers from consumer wearables to identify PPD, showing that machine learning models can effectively distinguish between different stages related to pregnancy and postpartum mental health.
  • The key finding was that calories burned from the basal metabolic rate were the most predictive of PPD, with individualized machine learning models outperforming traditional methods, suggesting a new, effective approach for early detection of the condition.
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As biomedical research data grow, researchers need reliable and scalable solutions for storage and compute. There is also a need to build systems that encourage and support collaboration and data sharing, to result in greater reproducibility. This has led many researchers and organizations to use cloud computing [1].

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The National Institutes of Health's (NIH) All of Us Research Program aims to enroll at least one million US participants from diverse backgrounds; collect electronic health record (EHR) data, survey data, physical measurements, biospecimens for genomics and other assays, and digital health data; and create a researcher database and tools to enable precision medicine research [1]. Since inception, digital health technologies (DHT) have been envisioned as essential to achieving the goals of the program [2]. A "bring your own device" (BYOD) study for collecting Fitbit data from participants' devices was developed with integration of additional DHTs planned in the future [3].

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