Suppr超能文献

数字健康参与度的概念化与测量

The conceptualisation and measurement of engagement in digital health.

作者信息

Milne-Ives Madison, Homer Sophie, Andrade Jackie, Meinert Edward

机构信息

Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.

Centre for Health Technology, University of Plymouth, Plymouth, UK.

出版信息

Internet Interv. 2024 Mar 11;36:100735. doi: 10.1016/j.invent.2024.100735. eCollection 2024 Jun.

Abstract

Digital tools are an increasingly important component of healthcare, but their potential impact is commonly limited by a lack of user engagement. Digital health evaluations of engagement are often restricted to system usage metrics, which cannot capture a full understanding of how and why users engage with an intervention. This study aimed to examine how theory-based, multifaceted measures of engagement with digital health interventions capture different components of engagement (affective, cognitive, behavioural, micro, and macro) and to consider areas that are unclear or missing in their measurement. We identified and compared two recently developed measures that met these criteria (the Digital Behaviour Change Intervention Engagement Scale and the TWente Engagement with Ehealth Technologies Scale). Despite having similar theoretical bases and being relatively strongly correlated, there are key differences in how these scales aim to capture engagement. We discuss the implications of our analysis for how affective, cognitive, and behavioural components of engagement can be conceptualised and whether there is value in distinguishing between them. We conclude with recommendations for the circumstances in which each scale may be most useful and for how future measure development could supplement existing scales.

摘要

数字工具在医疗保健中日益成为重要组成部分,但其潜在影响通常因缺乏用户参与度而受到限制。对参与度的数字健康评估往往局限于系统使用指标,而这些指标无法全面了解用户如何以及为何参与某项干预措施。本研究旨在探讨基于理论的、多维度的数字健康干预参与度测量方法如何捕捉参与度的不同组成部分(情感、认知、行为、微观和宏观),并思考其测量中不明确或缺失的领域。我们识别并比较了两项最近开发且符合这些标准的测量方法(数字行为改变干预参与度量表和特温特电子健康技术参与度量表)。尽管它们具有相似的理论基础且相关性相对较强,但这些量表在捕捉参与度的方式上存在关键差异。我们讨论了分析结果对于如何将参与度的情感、认知和行为组成部分概念化以及区分它们是否有价值的影响。我们最后针对每种量表可能最有用的情况以及未来测量方法的开发如何补充现有量表提出了建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/758f/10979253/2c3ede88cdde/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验