Evidence map›Paper›PMID 36713108›Full record

ArticleEuropean heart journal. Digital health2021

An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation.

Georgios Zisis, Melinda J Carrington, Brian Oldenburg, Kristyn Whitmore, Maria Lay, Quan Huynh, Christopher Neil, Jocasta Ball, Thomas H Marwick

Open access · goldAbstract read
In one paragraph

Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 15% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Georgios ZisisBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Melinda J CarringtonBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Brian OldenburgBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Kristyn WhitmoreMenzies Institute for Medical Research, University of Tasmania, Australia.
Maria LayBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Quan HuynhBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Christopher NeilBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Jocasta BallBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.
Thomas H MarwickBaker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC3004, Australia.ORCID https://orcid.org/0000-0001-9065-0899
Baker Heart and Diabetes Institute · AUUniversity of Tasmania · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Effective and efficient education and patient engagement are fundamental to improve health outcomes in heart failure (HF). The use of artificial intelligence (AI) to enable more effective delivery of education is becoming more widespread for a range of chronic conditions. We sought to determine whether an avatar-based HF-app could improve outcomes by enhancing HF knowledge and improving patient quality of life and self-care behaviour. Methods and results: In a randomized controlled trial of patients admitted for acute decompensated HF (ADHF), patients at high risk (≥33%) for 30-day hospital readmission and/or death were randomized to usual care or training with the HF-app. From August 2019 up until December 2020, 200 patients admitted to the hospital for ADHF were enrolled in the Risk-HF study. Of the 72 at high-risk, 36 (25 men; median age 81.5 years; 9.5 years of education; 15 in NYHA Class III at discharge) were randomized into the intervention arm and were offered education involving an HF-app. Whilst 26 (72%) could not use the HF-app, younger patients [odds ratio (OR) 0.89, 95% confidence interval (CI) 0.82-0.97; Conclusions: Although AI-based education is promising in chronic conditions, our study provides a note of caution about the barriers to enrolment in critically ill, post-acute, and elderly patients.

Indexed as

Artificial intelligenceEngagementHeart failure educationm-HealthSelf-care

Identifiers

PMID36713108
PMCPMC9707948
OpenAlexW3217208805

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.