Persuasive system design does matter: a systematic review of adherence to web-based interventions.
Persuasive system design does matter: a systematic review of adherence to web-based interventions.
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DOI：
10.2196/jmir.2104
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发表时间：
2012-11-14
影响因子：
7.4
通讯作者：
Van Gemert-Pijnen JE
中科院分区：
文献类型：
--
作者：
Kelders SM;Kok RN;Ossebaard HC;Van Gemert-Pijnen JE
尽管基于网络的干预措施对于促进健康和健康相关行为可能是有效的，但依从性差是一个需要解决的常见问题。技术作为基于网络的干预措施中传达内容的一种手段在研究中被忽视了。事实上，技术通常被视为黑匣子，只是一种没有效果或价值的工具，仅作为提供干预内容的工具。在本文中，我们从整体角度审视技术。我们将其视为基于网络的干预措施的一个重要且不可分割的方面，以帮助解释和理解依从性。本研究旨在回顾有关基于网络的健康干预措施的文献，以调查干预特征和说服性设计是否影响对基于网络的干预措施的依从性。我们对基于网络的健康干预措施的研究进行了系统回顾。对每次干预、干预特征、说服性技术元素和依从性进行编码。我们进行了多元回归分析，以研究这些变量是否可以预测依从性。我们收录了关于 83 项干预措施的 101 篇文章。典型的基于网络的干预措施每周使用一次，设置模块化，每周更新一次，持续 10 周，包括与系统、辅导员和同伴在网络上的互动，包括一些有说服力的技术元素，大约 50% 的参与者坚持干预。关于说服性技术，我们发现主要任务支持元素是最常用的（平均为 2.9，满分为 7.0）。对话支持和社会支持不太常用（分别为 1.5 和 1.2，满分为 7.0）。在比较不同卫生保健领域的干预措施时，我们发现预期用途 (p = .004)、设置 (p < .001)、更新 (p < .001)、与顾问互动的频率 (p < .001)、系统 (p = .003) 和同伴 (p = .017)、持续时间 (F = 6.068，p = .004)、依从性 (F = 4.833，p = .010）和主要任务支持要素的数量（F = 5.631，p = .005）。我们最终的回归模型解释了 55% 的依从性方差。在此模型中，与观察性研究相反，随机对照试验研究、与咨询师的互动增加、预期使用更频繁、更新更频繁以及对话支持的更广泛使用显着预测了更好的依从性。使用干预特征和有说服力的技术元素，可以解释依从性的大量差异。尽管不同医疗保健领域的干预特征存在差异，但医疗保健领域本身并不能预测依从性。相反，技术和交互的差异预示着依从性。这项研究的结果可用于就如何设计患者更有可能坚持的基于网络的干预措施做出明智的决定。
Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.
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影响因子：
7.4
作者：
Brendryen H;Drozd F;Kraft P
通讯作者：
Kraft P
影响因子：
4.2
作者：
Abraham, Charles;Michie, Susan
通讯作者：
Michie, Susan
DOI：
10.1080/j.1440-1614.2006.01743.x
发表时间：
2006-01-01
影响因子：
4.6
作者：
Christensen, H;Griffiths, K;Korten, A
通讯作者：
Korten, A
影响因子：
2.4
作者：
Bingham, C. Raymond;Barretto, Andrea Ippel;Raghunathan, Trivellore E.
通讯作者：
Raghunathan, Trivellore E.
DOI：
10.1016/0160-7979(78)90095-4
发表时间：
1978-01-01
期刊：
SOCIAL SCIENCE & MEDICINE PART A-MEDICAL SOCIOLOGY
影响因子：
--
作者：
BAROFSKY, I
通讯作者：
BAROFSKY, I