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

Wearable Devices and Sensors (1133) mHealth for Data Collection and Research (1196) mHealth for Wellness, Behavior Change and Prevention (3315) Perinatal Depression; Postpartum Depression; PPD (133) mHealth for Screening (296) Digital Biomarkers and Digital Phenotyping (284) Machine Learning (2076) Diagnostic Tools in Mental Health (326) Cross-Sectional Studies in Public Health (431)

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 (8) 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
  3. Stevenson D, Chang A, Wong R, Reiss J, Gaudillière B, Sylvester K, Ling X, Angst M, Shaw G, Katz M, Aghaeepour N, Marić I. Towards a new taxonomy of preterm birth. Journal of Perinatology 2025;45(8):1158 View
  4. Lin Y, Zhou D. A method for predicting postpartum depression via an ensemble neural network model. Frontiers in Public Health 2025;13 View
  5. Gaine M, Jagodnik K, Baweja R, Bobo W, McGlade E, Weiss S, Beal M, Dekel S, Ozerdem A. Targeted Research and Treatment Implications in Women With Depression. Focus 2025;23(2):141 View
  6. Hurwitz E, Meltzer-Brody S, Butzin-Dozier Z, Patel R, Elhadad N, Haendel M. Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study. JMIR Formative Research 2025;9:e67585 View
  7. Cunningham K, Mărginean V, Hylock R. Navigating promise and perils: applying artificial intelligence to the perinatal mental health care cascade. npj Health Systems 2025;2(1) View
  8. Adaimi R, Thigpen N, Clausel A, Gotlieb N, Patel K, de Zambotti M. Temporal Trajectories in Sleep, Temperature Trends, Cardiorespiratory, and Activity Metrics Measured via Oura Ring During Pregnancy: Large-Scale Observational Analysis. JMIR mHealth and uHealth 2025;13:e80213 View