Open AccessUnderstanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design
Author(s) -
Dorothy Szinay,
Rory Cameron,
Felix Naughton,
Jennifer A. Whitty,
Jamie Brown,
Andy Jones
Publication year - 2021
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/32365
Subject(s) - preference , computer science , product (mathematics) , bayesian probability , discrete choice , field (mathematics) , observational study , management science , service (business) , data science , risk analysis (engineering) , knowledge management , operations research , artificial intelligence , marketing , machine learning , engineering , business , economics , medicine , mathematics , geometry , pathology , pure mathematics , microeconomics
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.