Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis.
Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis.
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DOI：
10.1093/ntr/ntab008
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发表时间：
2021-06-08
期刊：
影响因子：
--
通讯作者：
Bricker JB
中科院分区：
文献类型：
--
作者：
Perski O;Watson NL;Mull KE;Bricker JB
使用WebQuit作为一个案例研究，一个基于接受和承诺疗法的戒烟网站，我们的目的是确定内容使用的序列集群，并检查它们与基线特征的关联，改变一个关键的作用机制，以及戒烟。参与者是随机对照试验中分配到WebQuit组的成年吸烟者（n = 1，313）。WebQuit包含理论内容，包括目标设定，自我监控和反馈，以及基于价值观和接受度的练习。序列分析被用来在时间上为每个参与者排序30秒的网站使用片段。用最佳匹配距离算法评估序列之间的相似性，并将其用作凝聚层次聚类分析的输入。序列聚类和基线特征之间的关联，接受渴望在3个月和自我报告的30天点患病率禁欲在12个月进行了检查与线性和逻辑回归。鉴定了三个定性不同的序列簇。“异议者”（576/1，313）几乎只使用目标设定功能。“尝试者”（375/1，313）使用目标设定和两个基于价值观和接受的组成部分（“意识到”，“愿意”）。“承诺者”（362/1,313）主要使用两个基于价值观和接受度的组成部分（“愿意”，“受到启发”），目标设定，自我监控和反馈。与分裂者相比，承诺者表现出更大的接受渴望的增加（p = 0.01）和64%的戒烟成功几率（ORadj = 1.64，95%CI = 1.18，2.29，p = 0.003）。WebQuit用户根据他们不同的内容使用模式被分为Dispensers，Tryers和Commiters。参与者看到了一个关键的行动机制的增加和戒烟成功的可能性更大。本案例研究演示了如何采用使用数据的序列和聚类分析可以帮助研究人员和从业人员更好地了解用户如何随着时间的推移参与给定的电子健康干预，并使用调查结果来测试理论和/或改善未来的迭代干预。未来的WebQuit用户可能会受益于通过程序过程中的提醒被引导到基于价值观和接受度以及自我监控和反馈组件。
Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation. Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression. Three qualitatively different sequence clusters were identified. “Disengagers” (576/1,313) almost exclusively used the goal-setting feature. “Tryers” (375/1,313) used goal setting and two of the values- and acceptance-based components (“Be Aware,” “Be Willing”). “Committers” (362/1,313) primarily used two of the values- and acceptance-based components (“Be Willing,” “Be Inspired”), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003). WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success. This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.
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