Online Interventions for Social Marketing Health Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors
Online Interventions for Social Marketing Health Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors
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DOI：
10.2196/jmir.1367
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发表时间：
2011-01-01
影响因子：
7.4
通讯作者：
Dawes, Phil
中科院分区：
文献类型：
--
作者：
Cugelman, Brian;Thelwall, Mike;Dawes, Phil
背景资料：研究人员和从业者已经开发了许多在线干预措施，鼓励人们减少饮酒，增加锻炼，更好地管理体重。开发电子保健干预措施的动机可能是由互联网的覆盖面，互动性，成本效益和研究表明在线干预措施的工作。然而，在设计适用于公共活动的在线干预措施时，几乎没有循证指南，分类法难以应用，许多研究缺乏影响数据，先前的荟萃分析不适用于以自愿行为改变为目标的大规模公共活动。这项荟萃分析评估了在线干预设计的特点，以便为在线活动的发展提供信息，例如那些由社会营销人员雇用的，寻求鼓励自愿的健康行为改变的人。进一步的目标是增加干预依从性，研究依从性和行为outcome.Methods之间的关系的理解：借鉴系统评价方法，在5个书目数据库中使用了84个查询词的组合，额外的灰色文献检索。这导致了1271篇摘要和论文; 31篇符合纳入标准。主要荟萃分析共纳入了29篇描述30种干预措施的论文，另外2项研究符合依从性分析的条件。使用随机效应模型，第一次分析估计了总体效应大小，包括按控制条件和时间因素分组。第二项分析评估了心理设计特征的影响，这些特征是用循证行为医学、说服技术和其他行为影响领域的分类编码的。这些独立的系统被集成到一个编码框架模型中，称为基于通信的影响组件模型。最后，第三项分析评估了干预依从性和行为结局之间的关系。结果：所有研究中在线干预的总体影响较小，但具有统计学显著性（标准化均数差异效应量d = 0.19，95%置信区间[CI] = 0.11-0.28，P
Background: Researchers and practitioners have developed numerous online interventions that encourage people to reduce their drinking, increase their exercise, and better manage their weight. Motivations to develop eHealth interventions may be driven by the Internet's reach, interactivity, cost-effectiveness, and studies that show online interventions work. However, when designing online interventions suitable for public campaigns, there are few evidence-based guidelines, taxonomies are difficult to apply, many studies lack impact data, and prior meta-analyses are not applicable to large-scale public campaigns targeting voluntary behavioral change.Objectives: This meta-analysis assessed online intervention design features in order to inform the development of online campaigns, such as those employed by social marketers, that seek to encourage voluntary health behavior change. A further objective was to increase understanding of the relationships between intervention adherence, study adherence, and behavioral outcomes.Methods: Drawing on systematic review methods, a combination of 84 query terms were used in 5 bibliographic databases with additional gray literature searches. This resulted in 1271 abstracts and papers; 31 met the inclusion criteria. In total, 29 papers describing 30 interventions were included in the primary meta-analysis, with the 2 additional studies qualifying for the adherence analysis. Using a random effects model, the first analysis estimated the overall effect size, including groupings by control conditions and time factors. The second analysis assessed the impacts of psychological design features that were coded with taxonomies from evidence-based behavioral medicine, persuasive technology, and other behavioral influence fields. These separate systems were integrated into a coding framework model called the communication-based influence components model. Finally, the third analysis assessed the relationships between intervention adherence and behavioral outcomes.Results: The overall impact of online interventions across all studies was small but statistically significant (standardized mean difference effect size d = 0.19, 95% confidence interval [CI] = 0.11-0.28, P