Online decision aids for primary cardiovascular disease prevention: systematic search, evaluation of quality and suitability for low health literacy patients.


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
13 03 2019
Historique:
entrez: 16 3 2019
pubmed: 16 3 2019
medline: 24 3 2020
Statut: epublish

Résumé

Recent guideline changes for cardiovascular disease (CVD) prevention medication have resulted in calls to implement shared decision-making rather than arbitrary treatment thresholds. Less attention has been paid to existing tools that could facilitate this. Decision aids are well-established tools that enable shared decision-making and have been shown to improve CVD prevention adherence. However, it is unknown how many CVD decision aids are publicly available for patients online, what their quality is like and whether they are suitable for patients with lower health literacy, for whom the burden of CVD is greatest. This study aimed to identify and evaluate all English language, publicly available online CVD prevention decision aids. Systematic review of public websites in August to November 2016 using an environmental scan methodology, with updated evaluation in April 2018. The decision aids were evaluated based on: (1) suitability for low health literacy populations (understandability, actionability and readability); and (2) International Patient Decision Aids Standards (IPDAS). Understandability and actionability using the validated Patient Education Materials Assessment Tool for Printed Materials (PEMAT-P scale), readability using Gunning-Fog and Flesch-Kincaid indices and quality using IPDAS V.3 and V.4. A total of 25 unique decision aids were identified. On the PEMAT-P scale, the decision aids scored well on understandability (mean 87%) but not on actionability (mean 61%). Readability was also higher than recommended levels (mean Gunning-Fog index=10.1; suitable for grade 10 students). Four decision aids met criteria to be considered a decision aid (ie, met IPDAS qualifying criteria) and one sufficiently minimised major bias (ie, met IPDAS certification criteria). Publicly available CVD prevention decision aids are not suitable for low literacy populations and only one met international standards for certification. Given that patients with lower health literacy are at increased risk of CVD, this urgently needs to be addressed.

Identifiants

pubmed: 30872547
pii: bmjopen-2018-025173
doi: 10.1136/bmjopen-2018-025173
pmc: PMC6429890
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

e025173

Informations de copyright

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Carissa Bonner (C)

School of Public Health, The University of Sydney, Camperdown, New South Wales, Australia.
Ask, Share, Know: Rapid Evidence for General Practice Decisions Centre for Research Excellence, The University of Sydney, Camperdown, New South Wales, Australia.

Pinika Patel (P)

School of Public Health, The University of Sydney, Camperdown, New South Wales, Australia.
Ask, Share, Know: Rapid Evidence for General Practice Decisions Centre for Research Excellence, The University of Sydney, Camperdown, New South Wales, Australia.

Michael Anthony Fajardo (MA)

School of Public Health, The University of Sydney, Camperdown, New South Wales, Australia.
Ask, Share, Know: Rapid Evidence for General Practice Decisions Centre for Research Excellence, The University of Sydney, Camperdown, New South Wales, Australia.

Ruixuan Zhuang (R)

School of Public Health, The University of Sydney, Camperdown, New South Wales, Australia.

Lyndal Trevena (L)

School of Public Health, The University of Sydney, Camperdown, New South Wales, Australia.
Ask, Share, Know: Rapid Evidence for General Practice Decisions Centre for Research Excellence, The University of Sydney, Camperdown, New South Wales, Australia.

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Classifications MeSH