Evidence map›Paper›PMID 27886024›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2017

Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure.

Lorraine S Evangelista, Hassan Ghasemzadeh, Jung-Ah Lee, Ramin Fallahzadeh, Majid Sarrafzadeh, Debra K Moser

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 14% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
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

6 authors at 4 institutions in 1 country.

Lorraine S EvangelistaProgram in Nursing Science, University of California Irvine, Irvine, CA, USA.
Hassan GhasemzadehSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.
Jung-Ah LeeProgram in Nursing Science, University of California Irvine, Irvine, CA, USA.
Ramin FallahzadehSchool of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.
Majid SarrafzadehSchool of Computer Science & Director, Wireless Health Institute, University of California, Los Angeles, CA, USA.
Debra K MoserCollege of Nursing, University of Kentucky, Lexington, KY, USA.
University of California, Irvine · USWashington State University · USUniversity of California, Los Angeles · USUniversity of Kentucky · US

Funding

UCLA Clinical and Translational Science InstituteUL1TR000124 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$57.0M
The Center for Health Improvement of Minority ElderlyP30AG021684 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CATHERINE A SARKISIAN · 2002 to 2026
$19.7M
Examining the Effects of High Protein Diet on Outcomes in Heart Failure PatientsR01HL093466 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI EVANGELISTA, LORRAINE S · 2009 to 2013
$1.9M
NCATS NIH HHS UL1 TR000124NHLBI NIH HHS R01 HL093466NIA NIH HHS P30 AG021684
6 · The paper itself

Abstract

backgroundIt is unclear whether subgroups of patients may benefit from remote monitoring systems (RMS) and what user characteristics and contextual factors determine effective use of RMS in patients with heart failure (HF).

objectiveThe study was conducted to determine whether certain user characteristics (i.e. personal and clinical variables) predict use of RMS using advanced machine learning software algorithms in patients with HF.

methodsThis pilot study was a single-arm experimental study with a pre- (baseline) and post- (3 months) design; data from the baseline measures were used for the current data analyses. Sixteen patients provided consent; only 7 patients (mean age 65.8 ± 6.1, range 58-83) accessed the RMS and transmitted daily data (e.g. weight, blood pressure) as instructed during the 12 week study duration.

resultsBaseline demographic and clinical characteristics of users and non-users were comparable for a majority of factors. However, users were more likely to have no HF specialty based care or an automatic internal cardioverter defibrillator. The precision accuracy of decision tree, multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively.

conclusionOur preliminary data show that a small set of baseline attributes is sufficient to predict subgroups of patients who had a higher likelihood of using RMS. While our findings shed light on potential end-users more likely to benefit from RMS-based interventions, additional research in a larger sample is warranted to explicate the impact of user characteristics on actual use of these technologies.

Indexed as

Patient ComplianceRemote Sensing TechnologyAgedAged, 80 and overChronic DiseaseDefibrillators, ImplantableFemaleHeart FailureHumansMaleMiddle AgedE-healthtelecardiologytelehealth

Identifiers

PMID27886024
PMCPMC5695547
OpenAlexW2549399970

What OpenQuestion holds

Textmetadata
LicenceTDM
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.