Evidence map›Paper›PMID 41929327›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Early Identification of Hospital Visit Risk in Heart Failure Using Wearable-Derived Data.

Vedrana Ivezić, Jack Dawson, Rose Doherty, Sukanya Mohapatra, Mirna Issa, Shawn Chen, Gregg C Fonarow, Michael K Ong, William Speier, Corey W Arnold

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Vedrana IvezićBiomedical AI Research Lab, University of California, Los Angeles, Los Angeles, USA.
Jack DawsonDepartment of Computer Science, Vanderbilt University, Nashville, USA.
Rose DohertyDepartment of Radiology, University of California, Los Angeles, Los Angeles, USA.
Sukanya MohapatraMidwestern University, Arizona College of Osteopathic Medicine, 19555 N 59th Ave, Glendale, Arizona 85308.
Mirna IssaSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, USA.
Shawn ChenDepartment of Radiology, University of California, Los Angeles, Los Angeles, USA.
Gregg C FonarowDepartment of Medicine, University of California, Los Angeles, Los Angeles, USA.
Michael K OngDepartment of Medicine, University of California, Los Angeles, Los Angeles, USA.
William SpeierBiomedical AI Research Lab, University of California, Los Angeles, Los Angeles, USA.
Corey W ArnoldBiomedical AI Research Lab, University of California, Los Angeles, Los Angeles, USA.ORCID 0000-0002-4119-8143

Funding

mHealth for Heart Failure: Predictive Models of Readmission Risk and Self-care Using Consumer Activity TrackersR01HL141773 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARNOLD, COREY WELLS · 2019 to 2022
$3.0M
NHLBI NIH HHS R01 HL141773
6 · The paper itself

Abstract

Objectives: Heart failure is a leading cause of mortality, necessitating identification of patients at increased risk needing intervention. In this study, we investigated if Fitbit data can reveal physiological trends associated with hospital visit risk. Materials and methods: Individuals with heart failure (n=249) were randomized into three arms for prospective 180-day monitoring. All arms received a Fitbit and wireless weight scale. Arm 1 received devices only; Arm 2 received a mobile app with surveys; Arm 3 received the app plus financial incentives. Results: 51 participants had hospital visits during the study period. These individuals took fewer steps (p=.002) and reported increased symptom severity (p=.044). Resting heart rate increased three days prior to a visit (p=.022). Baseline steps revealed a higher visit probability for less active participants (p=.003). Discussion and conclusion: Passive physiological monitoring can effectively identify individuals at risk of health exacerbation, demonstrating the potential of wearable devices for timely clinical intervention.

Identifiers

PMID41929327
PMCPMC13042084

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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.