%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 10
%P e32365
%T Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design
%A Szinay,Dorothy
%A Cameron,Rory
%A Naughton,Felix
%A Whitty,Jennifer A
%A Brown,Jamie
%A Jones,Andy
%+ Behavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich Research Park Earlham Road, Norwich, NR4 7TJ, United Kingdom, 44 1603593064, d.szinay@uea.ac.uk
%K discrete choice experiment
%K stated preference methods
%K mHealth
%K digital health
%K quantitative methodology
%K uptake
%K engagement
%K methodology
%K preference
%K Bayesian
%K design
%K tutorial
%K qualitative
%K user preference
%D 2021
%7 11.10.2021
%9 Tutorial
%J J Med Internet Res
%G English
%X 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.
%M 34633290
%R 10.2196/32365
%U https://www.jmir.org/2021/10/e32365
%U https://doi.org/10.2196/32365
%U http://www.ncbi.nlm.nih.gov/pubmed/34633290