Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.
Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.
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DOI：
10.2196/23369
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发表时间：
2020-12-11
影响因子：
7.4
通讯作者：
Potts HW
中科院分区：
文献类型：
--
作者：
Bell L;Garnett C;Qian T;Perski O;Williamson E;Potts HW
行为改变应用程序可以迭代开发，其中应用程序通过研究，开发和实施的周期演变为复杂，动态或个性化的干预。了解现有用户如何使用应用程序（例如使用频率，数量，深度和持续时间）可以帮助指导进一步的增量改进。我们的目标是探索如何简单的可视化可以提供一个很好的理解的时间模式的参与，因为使用数据往往是纵向和丰富的。这项研究的目的是可视化行为参与少喝，一个行为改变应用程序，以帮助减少危险和有害的酒精消费在英国的一般成年人。我们在19,233名现有的“少喝”用户中探索了行为参与度。如果用户来自英国，则被纳入样本; 18岁或以上;有兴趣减少饮酒量;基线酒精使用障碍识别测试得分为8分或以上，表明过度饮酒;并在2017年5月17日至2019年1月22日（615天）期间下载了该应用程序。使用热图、时间轴图、k-模式聚类分析和Kaplan-Meier图对疗程开始时间、疗程长度、脱离时间和使用模式进行可视化测量。每天上午11点的通知与接下来一个小时的参与度变化密切相关;随着时间的推移，行为参与度降低，50.00%（9617/19，233）（定义为连续7天或以上未使用）下载后22天;确定3种不同的使用轨迹，即使用者（4651/19，233，占用户的24.18%），慢节奏者（3679/19,233，19.13%的用户），以及快速分散者（10，903/19，233，56.68%的用户）;参与深度有限，85.076%（7，095，348/8，340，005）的屏幕浏览量发生在自我监控和反馈模块中。此外，在晚上观察到每次会议的频率和花费的时间的峰值。可视化在理解行为改变应用程序的参与方面发挥着重要作用。在这里，我们讨论了简单的可视化如何帮助识别与少喝酒的参与的重要模式。我们的行为参与的可视化表明，每天的通知大大影响参与。此外，可视化表明，固定的通知策略可以有效地维持某些用户的参与度，但对其他用户无效。我们的结论是，优化的通知政策，以目标的有效性和参与是一个值得的投资。我们未来的目标是了解通知对参与度的因果影响，并通过随着时间的推移根据个人的背景情况进行定制，进一步优化Drink Less中的通知政策。这种定制将从我们的微随机试验（MRT）的结果中得到信息，这些可视化在更好地理解参与和设计MRT方面都很有用。
Behavior change apps can develop iteratively, where the app evolves into a complex, dynamic, or personalized intervention through cycles of research, development, and implementation. Understanding how existing users engage with an app (eg, frequency, amount, depth, and duration of use) can help guide further incremental improvements. We aim to explore how simple visualizations can provide a good understanding of temporal patterns of engagement, as usage data are often longitudinal and rich. This study aims to visualize behavioral engagement with Drink Less, a behavior change app to help reduce hazardous and harmful alcohol consumption in the general adult population of the United Kingdom. We explored behavioral engagement among 19,233 existing users of Drink Less. Users were included in the sample if they were from the United Kingdom; were 18 years or older; were interested in reducing their alcohol consumption; had a baseline Alcohol Use Disorders Identification Test score of 8 or above, indicative of excessive drinking; and had downloaded the app between May 17, 2017, and January 22, 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualized with heat maps, timeline plots, k-modes clustering analyses, and Kaplan-Meier plots. The daily 11 AM notification is strongly associated with a change in engagement in the following hour; reduction in behavioral engagement over time, with 50.00% (9617/19,233) of users disengaging (defined as no use for 7 or more consecutive days) 22 days after download; identification of 3 distinct trajectories of use, namely engagers (4651/19,233, 24.18% of users), slow disengagers (3679/19,233, 19.13% of users), and fast disengagers (10,903/19,233, 56.68% of users); and limited depth of engagement with 85.076% (7,095,348/8,340,005) of screen views occurring within the Self-monitoring and Feedback module. In addition, a peak of both frequency and amount of time spent per session was observed in the evenings. Visualizations play an important role in understanding engagement with behavior change apps. Here, we discuss how simple visualizations helped identify important patterns of engagement with Drink Less. Our visualizations of behavioral engagement suggest that the daily notification substantially impacts engagement. Furthermore, the visualizations suggest that a fixed notification policy can be effective for maintaining engagement for some users but ineffective for others. We conclude that optimizing the notification policy to target both effectiveness and engagement is a worthwhile investment. Our future goal is to both understand the causal effect of the notification on engagement and further optimize the notification policy within Drink Less by tailoring to contextual circumstances of individuals over time. Such tailoring will be informed from the findings of our micro-randomized trial (MRT), and these visualizations were useful in both gaining a better understanding of engagement and designing the MRT.
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影响因子：
2.7
作者：
Arden-Close EJ;Smith E;Bradbury K;Morrison L;Dennison L;Michaelides D;Yardley L
通讯作者：
Yardley L
影响因子：
7
作者：
Collins, Linda M.;Dziak, John J.;Li, Runze
通讯作者：
Li, Runze
影响因子：
4.5
作者：
Chen, Annie T.;Wu, Shuyang;Mohr, David C.
通讯作者：
Mohr, David C.
影响因子：
5
作者：
Du H;Venkatakrishnan A;Youngblood GM;Ram A;Pirolli P
通讯作者：
Pirolli P
DOI：
10.1111/j.1530-0277.2011.01571.x
发表时间：
2011-12-01
影响因子：
3.2
作者：
Cohn, Amy M.;Hunter-Reel, Dorian;Mitchell, Jessica
通讯作者：
Mitchell, Jessica