Evidence map›Paper›PMID 35265917›Full record

ArticleCardiovascular digital health journal2021

NExUS-Heart: Novel examinations using smart technologies for heart health-Data sharing from commercial wearable devices and telehealth engagement in participants with or at risk of atrial fibrillation.

Sri Nuvvula, Eric Y Ding, Connor Saleeba, Qiming Shi, Ziyue Wang, Alok Kapoor, Jane S Saczynski, Steven A Lubitz, Lara C Kovell, M Diane McKee and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular digital health journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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  5. Review
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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

11 authors at 3 institutions in 1 country.

Sri NuvvulaDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Eric Y DingDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Connor SaleebaDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Qiming ShiDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Ziyue WangDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Alok KapoorDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Jane S SaczynskiDepartment of Pharmacy and Health Systems Sciences, Northeastern University, Boston, Massachusetts.
Steven A LubitzCardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Lara C KovellDepartment of Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
M Diane McKeeDepartment of Family Medicine and Community Health, University of Massachusetts Medical School, Worcester, Massachusetts.
David D McManusDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
University of Massachusetts Chan Medical School · USMassachusetts General Hospital · USNortheastern University · US

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Nathaniel Scott Hafer · 2018 to 2026
$141.0M
RURAL: Risk Underlying Rural Areas Longitudinal Cohort StudyU01HL146382 · NHLBI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI JUDD, SUZANNE E, RAMACHANDRAN, VASAN · 2019 to 2025
$42.5M
Genomics of Cardiac ArrhythmiasR01HL139731 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS · 2018 to 2022
$3.3M
Detecting bleeding events using EHRs for prediction in AfibR01HL137794 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI MCMANUS, DAVID D., YU, HONG · 2018 to 2021
$3.3M
Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF)R01HL126911 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI MCMANUS, DAVID D., SACZYNSKI, JANE STEELE · 2016 to 2019
$3.2M
Community Surveillance of Coronary Heart DiseaseR01HL135219 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GOLDBERG, ROBERT JOEL, YU, HONG · 2017 to 2020
$3.2M
Smartwatch Monitoring for Atrial Fibrillation After StrokeR01HL137734 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI CHON, KI H, MCMANUS, DAVID D. · 2018 to 2021
$3.1M
Automated detection and prediction of atrial fibrillation during sepsisR01HL136660 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI WALKEY, ALLAN J. · 2017 to 2020
$2.2M
Feasibility of Smartwatches for Atrial Fibrillation Detection in Older AdultsF30HL149335 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DING, ERIC YUXIAO · 2019 to 2021
$92k
NHLBI NIH HHS F30 HL149335NHLBI NIH HHS R01 HL126911NHLBI NIH HHS R01 HL135219NHLBI NIH HHS R01 HL136660NHLBI NIH HHS R01 HL137734NHLBI NIH HHS R01 HL137794NHLBI NIH HHS R01 HL139731NHLBI NIH HHS U01 HL146382NHLBI NIH HHS U54 HL143541
6 · The paper itself

Abstract

Background: Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors. Objective: The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health. Methods: We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA Results: The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02-3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status. Conclusion: Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.

Indexed as

Atrial fibrillationCardiacdataCommercial wearable deviceData sharing behaviorTelehealthTelemedicine

Identifiers

PMID35265917
PMCPMC8890085
OpenAlexW3194393978

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.