Evidence map›Paper›PMID 42179816›Full record

ArticleFrontiers in digital health2026

Patient engagement with consumer wearable devices in the electronic health record.

Michael Sobolev, Allistair Clark, Mitchell Kamrava, Joshua Pevnick, Brennan Spiegel, Raymond Duncan, Gillian Gresham

Abstract read
In one paragraph

Article in Frontiers in digital health, 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

7 authors.

Michael Sobolev *Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.
Allistair Clark *Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.
Mitchell KamravaDepartment of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.
Joshua PevnickDepartment of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.
Brennan SpiegelDepartment of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.
Raymond DuncanDepartment of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.
Gillian GreshamDepartment of Medicine, Cedars Sinai Medical Center, Los Angeles, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data from wearable devices have the potential to transform personal health, clinical care and biomedical research. The purpose of this study was to quantify early adoption and short-term engagement with consumer-based wearable devices connected to an EHR outside of a study setting. We analyzed device data from 843 patients (mean age 48 years, 41% female) who connected their devices to the electronic health record (EHR) of a large academic medical center in the US between 2023 and 2025. The most popular connected consumer devices were the Apple Watch (56%), followed by the Oura ring (7.7%), and the Fitbit activity tracker (4.6%). Using survival analysis, we found that 692 (82%) patients remained engaged at three months, with actively connected devices. Cox regression models identified statistically significant differences in time to disengagement based on gender and step count. Regular and updated analyses of device adoption and engagement patterns are therefore warranted to increase the value of these devices and their data for the purpose of health monitoring and improvement.

Indexed as

internet of things (IoT)mobile healthpersonal healthremote patient monitoringsurvival analysis

Identifiers

PMID42179816
PMCPMC13189792

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.