Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol.
Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol.
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DOI：
10.1186/s12889-018-5612-5
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发表时间：
2018-06-05
影响因子：
4.5
通讯作者：
de Vries H
中科院分区：
文献类型：
--
作者：
Hors-Fraile S;Schneider F;Fernandez-Luque L;Luna-Perejon F;Civit A;Spachos D;Bamidis P;de Vries H
吸烟是最可避免的健康风险因素之一，但戒烟成功率很低。使用定制的健康信息来支持戒烟已被证明可以提高戒烟成功率。技术可以提供方便的手段来提供量身定制的健康信息。健康推荐系统是一种信息过滤算法，它可以根据每个用户的个人资料为他或她选择最相关的健康相关项目，例如，旨在戒烟的激励信息。本研究的目标是分析感知质量的移动健康推荐系统，旨在戒烟，并评估通过这种媒介传递给用户的消息的参与程度。将为参与戒烟计划的患者提供一个移动的应用程序，以接收健康推荐系统根据从电子健康记录中检索到的作为初始知识来源的个人资料选择的定制激励性健康信息。患者对消息的反馈及其与应用程序的交互将按照观察性前瞻性方法进行分析和评估，以a）使用精确度和阅读时间指标以及向完成该计划的所有患者提供的18项问卷，评估基于移动的健康推荐系统和消息的感知质量，以及B）使用聚集的数据分析度量来测量患者与基于移动的健康推荐系统的参与度，所述聚集的数据分析度量如会话频率，并且为了确定个体级别的参与度，使用每个用户的阅读消息的速率。本文详细介绍了实施和评估协议，将遵循。这项研究将探讨与电子健康记录集成的健康推荐系统算法是否可以预测患者更喜欢哪些量身定制的激励性健康信息，并认为这些信息具有良好的质量，从而鼓励他们参与该系统。这项研究的结果将有助于未来的研究人员设计更好的戒烟激励信息发送推荐系统，以增加患者的参与度，减少流失，从而提高戒烟率。该试验于2017年7月2日在clinicaltrials.orgClinicalTrials.gov注册，www.example.com标识符为NCT 03206619。已登记的逆行。
Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. The usage of tailored health messages to support quitting has been proved to increase quitting success rates. Technology can provide convenient means to deliver tailored health messages. Health recommender systems are information-filtering algorithms that can choose the most relevant health-related items—for instance, motivational messages aimed at smoking cessation—for each user based on his or her profile. The goals of this study are to analyze the perceived quality of an mHealth recommender system aimed at smoking cessation, and to assess the level of engagement with the messages delivered to users via this medium. Patients participating in a smoking cessation program will be provided with a mobile app to receive tailored motivational health messages selected by a health recommender system, based on their profile retrieved from an electronic health record as the initial knowledge source. Patients’ feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. This paper details the implementation and evaluation protocol that will be followed. This study will explore whether a health recommender system algorithm integrated with an electronic health record can predict which tailored motivational health messages patients would prefer and consider to be of a good quality, encouraging them to engage with the system. The outcomes of this study will help future researchers design better tailored motivational message-sending recommender systems for smoking cessation to increase patient engagement, reduce attrition, and, as a result, increase the rates of smoking cessation. The trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.
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影响因子：
2.4
作者：
deVries, H;Mudde, A;Kremers, S
通讯作者：
Kremers, S
影响因子：
3.5
作者：
de Vries, H;Mesters, I;Honing, C
通讯作者：
Honing, C
影响因子：
2.4
作者：
Dijkstra, A
通讯作者：
Dijkstra, A
影响因子：
1.9
作者：
Bamparopoulos G;Konstantinidis E;Bratsas C;Bamidis PD
通讯作者：
Bamidis PD
影响因子：
1.7
作者：
Alkhaldi, Ghadah;Hamilton, Fiona L.;Murray, Elizabeth
通讯作者：
Murray, Elizabeth