Sadasivam Rajani S, Volz Erik M, Kinney Rebecca L, Rao Sowmya R, Houston Thomas K
Division of Health Informatics & Implementation Science, Quantitative Health Sciences, The University of Massachusetts Medical School, Worcester, MA, United States.
JMIR Res Protoc. 2013 Sep 24;2(2):e37. doi: 10.2196/resprot.2786.
Smoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions.
The objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system.
We will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population.
This protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation.
Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions.
吸烟是美国头号可预防的死因。有效的网络辅助烟草干预措施常常未得到充分利用,需要新的创新参与方式。基于网络的同伴驱动链式推荐在医疗保健之外成功应用,有潜力扩大互联网干预措施的覆盖范围。
我们研究的目的是描述用于开发和测试基于网络的主动链式推荐工具的方案,以增加对Decide2Quit.org(一个网络辅助烟草干预系统)的访问。
我们将构建并完善主动链式推荐工具,包括电子邮件和脸书推荐。此外,我们将实施应答驱动抽样(RDS),这是一种受控的链式推荐抽样技术,旨在消除链式推荐中固有的偏差并获得具有代表性的样本。我们将以招募曾经和现在的吸烟者作为种子(初始参与者)开始链式推荐,他们将接受培训,使用开发的工具从其社交网络中推荐现在的吸烟者。反过来,这些新被推荐的吸烟者也将获得工具,以便从他们的社交网络中推荐其他吸烟者。我们将使用来自RDS的样本权重对推荐成功的预测因素进行建模,以估计该系统在目标人群中的成功率。
本方案描述了对基于网络的主动链式推荐工具的评估,该工具可用于烟草干预,以增加对难以接触到的人群的访问,促进戒烟。
Share2Quit通过利用自然出现的技术趋势招募吸烟者参与网络辅助烟草干预,代表了一种创新进展。