This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (885) mHealth for Data Collection and Research (952) mHealth for Wellness, Behavior Change and Prevention (2862) Perinatal Depression; Postpartum Depression; PPD (93) mHealth for Screening (209) Digital Biomarkers and Digital Phenotyping (190) Machine Learning (1453) Diagnostic Tools in Mental Health (239) Cross-Sectional Studies in Public Health (274)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54622, first published .
Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study

Authors of this article:

Eric Hurwitz1 Author Orcid Image ;   Zachary Butzin-Dozier2 Author Orcid Image ;   Hiral Master3 Author Orcid Image ;   Shawn T O'Neil1 Author Orcid Image ;   Anita Walden1 Author Orcid Image ;   Michelle Holko4 Author Orcid Image ;   Rena C Patel5 Author Orcid Image ;   Melissa A Haendel1 Author Orcid Image
Article Authors Cited by (2) Tweetations (5) Metrics

Journals

  1. A. Abdel Hady D, Abd El-Hafeez T. Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain. Scientific Reports 2024;14(1) View
  2. Ling K, Zhang S, Jin L, Wang J. Potential association between mobile phone usage duration and postpartum depression risk: Evidence from a Mendelian randomization study. Medicine 2024;103(41):e39973 View