Associations Between Engagement and Outcomes in the SmokefreeTXT Program: A Growth Mixture Modeling Analysis
Associations Between Engagement and Outcomes in the SmokefreeTXT Program: A Growth Mixture Modeling Analysis
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DOI：
10.1093/ntr/nty073
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发表时间：
2019-05-01
影响因子：
4.7
通讯作者：
Augustson, Erik
中科院分区：
文献类型：
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作者：
Coa, Kisha, I;Wiseman, Kara P.;Augustson, Erik
吸烟仍然是可预防死亡的主要原因。移动的健康（mHealth）可以扩大戒烟计划的覆盖范围;然而，用户退出，特别是在这些计划的现实世界中，限制了其潜在的有效性。研究需要了解的mHealth戒烟programmes.Methods的参与模式SmokefreeTXT（SFTXT）是国家癌症研究所的6-8周戒烟短信干预。潜在增长混合模型被用来确定独特的参与类SFTXT用户使用真实世界的程序数据从7090 SFTXT用户。生存分析进行模型程序辍学随着时间的推移，类，和多层次建模被用来探索禁欲随着时间的推移的差异。最大比例的用户（61.6%）属于低参与度下降组;这些用户开始时参与度较低，随着时间的推移，他们的参与度会下降。这一组的使用者比其他组的使用者更有可能退出计划，更不可能禁欲。高吸烟者维持组（即，最小但最参与的组）的用户在基线时不太可能每天吸烟，并且比其他组的用户年龄稍大。他们最有可能完成的程序和报告被abstinent.Conclusions我们的研究结果表明，保持积极参与基于文本的戒烟计划的重要性。未来的研究是必要的，以阐明预测的各个层次的参与，并评估是否旨在增加参与的战略，结果在更高的abstinence rates.Implications-目前的研究使我们能够调查不同的参与模式，在非激励计划的参与者，这可以帮助通知程序修改在现实世界中的设置。缺乏参与和辍学继续阻碍移动健康干预措施的潜在有效性，了解参与的模式和预测因素可以增强这些计划的影响。
Introduction Smoking continues to be a leading cause of preventable death. Mobile health (mHealth) can extend the reach of smoking cessation programs; however, user dropout, especially in real-world implementations of these programs, limit their potential effectiveness. Research is needed to understand patterns of engagement in mHealth cessation programs.Methods SmokefreeTXT (SFTXT) is the National Cancer Institute's 6-8 week smoking cessation text-messaging intervention. Latent growth mixture modeling was used to identify unique classes of engagement among SFTXT users using real-world program data from 7090 SFTXT users. Survival analysis was conducted to model program dropout over time by class, and multilevel modeling was used to explore differences in abstinence over time.Results We identified four unique patterns of engagement groups. The largest percentage of users (61.6%) were in the low-engagers declining group; these users started off with low level of engagement and their engagement decreased over time. Users in this group were more likely to drop out from the program and less likely to be abstinent than users in the other groups. Users in the high engagers-maintaining group (ie, the smallest but most engaged group) were less likely to be daily smokers at baseline and were slightly older than those in the other groups. They were most likely to complete the program and report being abstinent.Conclusions Our findings show the importance of maintaining active engagement in text-based cessation programs. Future research is needed to elucidate predictors of the various levels of engagement, and to assess whether strategies aimed at increasing engagement result in higher abstinence rates.Implications The current study enabled us to investigate differing engagement patterns in non-incentivized program participants, which can help inform program modifications in real-world settings. Lack of engagement and dropout continue to impede the potential effectiveness of mHealth interventions, and understanding patterns and predictors of engagement can enhance the impact of these programs.