Evidence map›Paper›PMID 40409746›Full record

ArticleJMIR formative research2025

Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study.

Eric Hurwitz, Samantha Meltzer-Brody, Zachary Butzin-Dozier, Rena C Patel, Noémie Elhadad, Melissa A Haendel

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Eric HurwitzDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0002-6581-7754
Samantha Meltzer-BrodyDepartment of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, United States.ORCID 0000-0002-8509-408X
Zachary Butzin-DozierDivision of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, United States.ORCID 0000-0001-6419-0008
Rena C PatelDepartment of Infectious Disease, University of Alabama at Birmingham, Birmingham, AL, United States.ORCID 0000-0001-9893-5856
Noémie ElhadadDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States.ORCID 0000-0001-9721-5240
Melissa A HaendelDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0001-9114-8737

Funding

Technology to Empower Changes in Health (TECH) Network Participant Technologies CenterU24OD023176 · OD · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2016 to 2022
$204.7M
Precision Medicine Initiative Cohort Program BiobankU24OD023121 · OD · MAYO CLINIC ROCHESTER · PI CEKANOVA, MARIA, CICEK, MINE · 2016 to 2024
$185.5M
Enhancing All of Us Data Resources for Nutrition Precision Health: the All of Us Data and Research CenterU2COD023196 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, DAVID, HARRIS, PAUL A. · 2016 to 2022
$143.7M
Adaptive Platform for Personalized EngagementU24OD023163 · OD · VIGNET, INC. · PI JAIN, PRADUMAN · 2017 to 2020
$102.6M
University of Arizona-Banner Health All of Us Research Program OT2OD026549 · OD · UNIVERSITY OF ARIZONA · PI MORENO, FRANCISCO A, REIMAN, ERIC MICHAEL · 2018 to 2023
$78.9M
California Precision Medicine Research Program ConsortiumOT2OD026552 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANTON-CULVER, HODA A, OHNO-MACHADO, LUCILA · 2018 to 2023
$73.4M
All of Us PennsylvaniaOT2OD026554 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E, VISWESWARAN, SHYAM · 2018 to 2023
$72.1M
New York City Consortium for Precision MedicineOT2OD026556 · OD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BIER, LOUISE E, GHARAVI, ALI G · 2018 to 2023
$67.3M
SouthEast Enrollment Center (SEEC) OT2OD026551 · OD · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI CARRASQUILLO, OLVEEN, COLON, VIVIAN · 2018 to 2023
$62.8M
Southern All of Us NetworkOT2OD026548 · OD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N., KORF, BRUCE R · 2018 to 2023
$60.5M
Illinois Precision Medicine Consortium OT2OD026557 · OD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AHSAN, HABIBUL, ARGOS, MARIA · 2018 to 2023
$60.5M
The New England Precision Medicine Consortium of the All of Us Research ProgramOT2OD026553 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI CLARK, CHERYL RENEE, KARLSON, ELIZABETH W · 2018 to 2023
$58.8M
Federally Qualified Health Centers HHSN 263201600085UNIH HHS OT2 OD023205NIH HHS OT2 OD023206NIH HHS OT2 OD025276NIH HHS OT2 OD025277NIH HHS OT2 OD025315NIH HHS OT2 OD025337NIH HHS OT2 OD026548NIH HHS OT2 OD026549NIH HHS OT2 OD026550NIH HHS OT2 OD026551NIH HHS OT2 OD026552NIH HHS OT2 OD026553NIH HHS OT2 OD026554NIH HHS OT2 OD026555NIH HHS OT2 OD026556NIH HHS OT2 OD026557NIH HHS U24 OD023121NIH HHS U24 OD023163NIH HHS U24 OD023176NIH HHS U2C OD023196NIMH NIH HHS R01 MH131542
6 · The paper itself

Abstract

backgroundPostpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth that is often underscreened and underdetected. If not diagnosed and treated, PPD is associated with long-term developmental challenges in the child and maternal morbidity. Wearable technologies, such as smartwatches and fitness trackers (eg, Fitbit), offer continuous and longitudinal digital phenotyping for mood disorder diagnosis and monitoring, with device wear time being an important yet understudied aspect.

objectiveWe aimed to suggest that wear time of a wearable device may provide additional information about perinatal mental health to facilitate screening and early detection of PPD. We proposed that wear time of a wearable device may also be valuable for managing other mental health disorders.

methodsUsing the All of Us Research Program dataset, we identified females who experienced childbirth with and without PPD using computational phenotyping. We compared the percentage of days and number of hours per day females with and without PPD wore Fitbit devices during prepregnancy, pregnancy, postpartum, and PPD periods, determined by electronic health records. Comparisons between females with and without PPD were conducted using linear regression models. We also assessed the correlation between Fitbit wear time consistency (measured as the maximum number of consecutive days the Fitbit was worn) during prepregnancy and PPD periods in females with and without PPD using the Pearson correlation. All analyses were run with Bonferroni correction.

resultsOur findings showed a strong trend, although nonsignificant after multiple testing correction, that females in the PPD cohort wore their Fitbits more than those in non-PPD cohort during the postpartum (PPD cohort: mean 69.9%, 95% CI 42.7%-97%; non-PPD cohort: mean 50%, 95% CI 25.5%-74.4%; P=.02) and PPD periods (PPD cohort: mean 66.6%, 95% CI 37.9%-95.3%; non-PPD cohort: mean 46.4%, 95% CI 20.5%-72.2%; P=.02). We found no difference in the number of hours per day females in the PPD and non-PPD cohorts wore their Fitbit during any period of pregnancy. Finally, there was no relationship between the consistency of Fitbit wear time during prepregnancy and PPD periods (r=-0.05, 95% CI -0.46 to 0.38; P=.84); however, there was a trend, though nonsignificant, in Fitbit wear time consistency among females without PPD (r=0.25, 95% CI -0.02 to 0.49; P=.07).

conclusionsWe hypothesize that increased Fitbit wear time among females with PPD may be attributed to hypervigilance, given the common co-occurrence of anxiety symptoms. Future studies should assess the link between PPD, hypervigilance, and wear time patterns. We envision that wear time patterns of a wearable device combined with digital biomarkers such as sleep and physical activity could enhance early PPD detection using machine learning by alerting clinicians to potential concerns and facilitating timely screenings, which may have implications for other mental health disorders.

Indexed as

Depression, PostpartumMass ScreeningWearable Electronic DevicesAdultCross-Sectional StudiesFemaleHumansPostpartum PeriodPregnancyTime FactorsYoung AdultAll of UsFitbitpostpartum depressionscreeningwearable devicewear time

Identifiers

PMID40409746
PMCPMC12144471

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.