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Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design
Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences in the absence of observational data. A discrete choice experiment (DCE) is a commonly used stated pr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546533/ https://www.ncbi.nlm.nih.gov/pubmed/34633290 http://dx.doi.org/10.2196/32365 |
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author | Szinay, Dorothy Cameron, Rory Naughton, Felix Whitty, Jennifer A Brown, Jamie Jones, Andy |
author_facet | Szinay, Dorothy Cameron, Rory Naughton, Felix Whitty, Jennifer A Brown, Jamie Jones, Andy |
author_sort | Szinay, Dorothy |
collection | PubMed |
description | Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences in the absence of observational data. A discrete choice experiment (DCE) is a commonly used stated preference method—a quantitative methodology that argues that individuals make trade-offs when engaging in a decision by choosing an alternative of a product or a service that offers the greatest utility, or benefit. This methodology is widely used in health economics in situations in which revealed preferences are difficult to collect but is much less used in the field of digital health. This paper outlines the stages involved in developing a DCE. As a case study, it uses the application of a DCE to reveal preferences in targeting the uptake of smoking cessation apps. It describes the establishment of attributes, the construction of choice tasks of 2 or more alternatives, and the development of the experimental design. This tutorial offers a guide for researchers with no prior knowledge of this research technique. |
format | Online Article Text |
id | pubmed-8546533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-85465332021-11-10 Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design Szinay, Dorothy Cameron, Rory Naughton, Felix Whitty, Jennifer A Brown, Jamie Jones, Andy J Med Internet Res Tutorial Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences in the absence of observational data. A discrete choice experiment (DCE) is a commonly used stated preference method—a quantitative methodology that argues that individuals make trade-offs when engaging in a decision by choosing an alternative of a product or a service that offers the greatest utility, or benefit. This methodology is widely used in health economics in situations in which revealed preferences are difficult to collect but is much less used in the field of digital health. This paper outlines the stages involved in developing a DCE. As a case study, it uses the application of a DCE to reveal preferences in targeting the uptake of smoking cessation apps. It describes the establishment of attributes, the construction of choice tasks of 2 or more alternatives, and the development of the experimental design. This tutorial offers a guide for researchers with no prior knowledge of this research technique. JMIR Publications 2021-10-11 /pmc/articles/PMC8546533/ /pubmed/34633290 http://dx.doi.org/10.2196/32365 Text en ©Dorothy Szinay, Rory Cameron, Felix Naughton, Jennifer A Whitty, Jamie Brown, Andy Jones. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.10.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Tutorial Szinay, Dorothy Cameron, Rory Naughton, Felix Whitty, Jennifer A Brown, Jamie Jones, Andy Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design |
title | Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design |
title_full | Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design |
title_fullStr | Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design |
title_full_unstemmed | Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design |
title_short | Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design |
title_sort | understanding uptake of digital health products: methodology tutorial for a discrete choice experiment using the bayesian efficient design |
topic | Tutorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546533/ https://www.ncbi.nlm.nih.gov/pubmed/34633290 http://dx.doi.org/10.2196/32365 |
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