Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends.
Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends.
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DOI：
10.1145/3287031
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发表时间：
2018-12-01
影响因子：
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通讯作者：
Pfammatter, Angela
中科院分区：
文献类型：
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作者：
Alshurafa, Nabil;Jain, Jayalakshmi;Pfammatter, Angela
行为医学越来越关注参与者的参与及其在有效的移动的健康（mHealth）行为干预中的作用。已经提出并讨论了术语“参与”的几个定义，特别是在数字健康行为干预的背景下。我们认为，参与是指特定的互动和使用模式与移动健康工具，如智能手机应用程序的干预，而坚持是指遵守健康干预的指令，独立的移动健康工具。通过我们的分析参与者的互动和自我报告的行为数据在大学生健康研究的激励措施，我们展示了一个例子来衡量“有效参与”的参与行为，可以链接到所需的干预目标。我们展示了一年的每周健康行为自我报告的聚类如何产生与参与者对所需健康行为的坚持相关的四个可解释的聚类：健康和稳定，不健康和稳定，下降者和改善者。基于本研究的干预目标（健康促进和行为改变），我们表明，并非所有的应用程序使用指标都表明了创造有效参与的预期结果。因此，移动健康干预设计可能会考虑不仅引发更多的参与或整体使用，而且还可以通过与所需行为结果相关的使用模式来定义有效的参与。
Behavioral medicine is devoting increasing attention to the topic of participant engagement and its role in effective mobile health (mHealth) behavioral interventions. Several definitions of the term "engagement" have been proposed and discussed, especially in the context of digital health behavioral interventions. We consider that engagement refers to specific interaction and use patterns with the mHealth tools such as smartphone applications for intervention, whereas adherence refers to compliance with the directives of the health intervention, independent of the mHealth tools. Through our analysis of participant interaction and self-reported behavioral data in a college student health study with incentives, we demonstrate an example of measuring "effective engagement" as engagement behaviors that can be linked to the goals of the desired intervention. We demonstrate how clustering of one year of weekly health behavior self-reports generate four interpretable clusters related to participants' adherence to the desired health behaviors: healthy and steady, unhealthy and steady, decliners, and improvers. Based on the intervention goals of this study (health promotion and behavioral change), we show that not all app usage metrics are indicative of the desired outcomes that create effective engagement. As such, mHealth intervention design might consider eliciting not just more engagement or use overall, but rather, effective engagement defined by use patterns related to the desired behavioral outcome.