Short Camille E, DeSmet Ann, Woods Catherine, Williams Susan L, Maher Carol, Middelweerd Anouk, Müller Andre Matthias, Wark Petra A, Vandelanotte Corneel, Poppe Louise, Hingle Melanie D, Crutzen Rik
Freemasons Foundation Centre for Men's Health, School of Medicine, University of Adelaide, Adelaide, Australia.
Department of Movement and Sports Sciences, Ghent University, Brussels, Belgium.
J Med Internet Res. 2018 Nov 16;20(11):e292. doi: 10.2196/jmir.9397.
Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.
参与电子健康（eHealth）和移动健康（mHealth）行为改变干预措施被认为对干预效果很重要，尽管在文献中，参与的构成要素以及它如何提高疗效尚有些不明确。最近发表的关于参与的详细定义和概念模型有助于围绕参与的定义形成共识，并增进我们对参与如何影响效果的理解。这项工作有助于确立更清晰的研究议程。然而，为了检验概念模型产生的假设，我们需要知道如何以有效且可靠的方式衡量参与度。本观点文章的目的是概述可用于电子健康和移动健康行为改变干预评估的参与度测量选项，讨论方法学考量，并为未来研究提供方向。为了确定测量方法，我们采用了滚雪球抽样法，从参与度研究的系统综述以及作者所知的研究中使用的方法入手。我们确定了广泛的参与度测量方法，包括定性测量、自我报告问卷、生态瞬时评估、系统使用数据、传感器数据、社交媒体数据和心理生理测量。对每种测量方法进行了评估，并提供了示例以说明其在电子健康和移动健康行为改变研究中的可能用途。基于当前方法的局限性以及对系统使用数据作为参与度唯一评估的严重依赖，为未来研究提供了建议。鼓励对更广泛的参与度测量方法进行验证和采用，并将其审慎应用于参与度研究。
