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 stat...

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Bibliographic Details
Published in:Journal of medical Internet research 2021-10, Vol.23 (10), p.e32365-e32365
Main Authors: Szinay, Dorothy, Cameron, Rory, Naughton, Felix, Whitty, Jennifer A, Brown, Jamie, Jones, Andy
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Choice Behavior
Cognitive load
Discrete choice
Experiments
Health economics
Health Policy
Health Services
Humans
Patient Preference
Preferences
Prior knowledge
Qualitative research
Research methodology
Revealed preferences
Smoking
Smoking cessation
Tutorial
Uptake
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Summary: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.
ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/32365