Evidence map›Paper›PMID 34633290›Full record

ArticleJournal of medical Internet research2021

Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design.

Dorothy Szinay, Rory Cameron, Felix Naughton, Jennifer A Whitty, Jamie Brown, Andy Jones

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Unraveling acceptance of healthcare innovations in neurorehabilitation: results from a discrete choice experiment.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dorothy SzinayBehavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich, United Kingdom.ORCID 0000-0003-2722-6758
Rory CameronNorwich Medical School, University of East Anglia, Norwich, United Kingdom.ORCID 0000-0002-7442-0935
Felix NaughtonBehavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich, United Kingdom.ORCID 0000-0001-9790-2796
Jennifer A WhittyNorwich Medical School, University of East Anglia, Norwich, United Kingdom.ORCID 0000-0002-5886-1933
Jamie BrownDepartment of Behavioural Science and Health, University College London, London, United Kingdom.ORCID 0000-0002-2797-5428
Andy JonesNorwich Medical School, University of East Anglia, Norwich, United Kingdom.ORCID 0000-0002-3130-9313

Funding

Cancer Research UK 22962Medical Research Council MR/S037519/1
6 · The paper itself

Abstract

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.

Indexed as

Choice BehaviorPatient PreferenceBayes TheoremHealth PolicyHealth ServicesHumansBayesiandesigndigital healthdiscrete choice experimentengagementmethodologymHealthpreferencequalitativequantitative methodologystated preference methodstutorialuptakeuser preference

Identifiers

PMID34633290
PMCPMC8546533

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.