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Online Interventions for Social Marketing Health Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors

机译:社会营销健康行为改变运动的在线干预:心理结构和坚持因素的荟萃分析

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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 < .001, number of interventions k = 30). The largest impact with a moderate level of efficacy was exerted from online interventions when compared with waitlists and placebos (d = 0.28, 95% CI = 0.17 - 0.39, P < .001, k = 18), followed by comparison with lower-tech online interventions (d = 0.16, 95% CI = 0.00 - 0.32, P = .04, k = 8); no significant difference was found when compared with sophisticated print interventions (d = –0.11, 95% CI = –0.34 to 0.12, P = .35, k = 4), though online interventions offer a small effect with the advantage of lower costs and larger reach. Time proved to be a critical factor, with shorter interventions generally achieving larger impacts and greater adherence. For psychological design, most interventions drew from the transtheoretical approach and were goal orientated, deploying numerous influence components aimed at showing users the consequences of their behavior, assisting them in reaching goals, and providing normative pressure. Inconclusive results suggest a relationship between the number of influence components and intervention efficacy. Despite one contradictory correlation, the evidence suggests that study adherence, intervention adherence, and behavioral outcomes are correlated.Conclusions: These findings demonstrate that online interventions have the capacity to influence voluntary behaviors, such as those routinely targeted by social marketing campaigns. Given the high reach and low cost of online technologies, the stage may be set for increased public health campaigns that blend interpersonal online systems with mass-media outreach. Such a combination of approaches could help individuals achieve personal goals that, at an individual level, help citizens improve the quality of their lives and at a
机译:背景:研究人员和从业人员开发了许多在线干预措施,以鼓励人们减少饮酒,增加运动量并更好地控制体重。开发eHealth干预措施的动机可能是由Internet的覆盖范围,交互性,成本效益以及表明在线干预措施有效的研究驱动的。但是,在设计适用于公共活动的在线干预措施时,基于证据的指南很少,分类法难以应用,许多研究缺乏影响数据,并且以前的荟萃分析不适用于针对自愿行为改变的大规模公共活动。目标:这项荟萃分析评估了在线干预设计的功能,以告知在线运动的发展,例如社会营销人员雇用的,旨在鼓励自愿改变健康行为的运动。方法:利用系统的综述方法,在5个书目数据库中使用84个查询词,并附加了灰色文献检索方法,以进一步了解干预依从性,研究依从性和行为结果之间的关系。由此产生了1271篇摘要和论文; 31个符合纳入标准。初步的荟萃分析总共包括29篇描述30种干预措施的论文,另外2篇研究符合纳入分析的条件。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功能的影响,这些功能采用基于证据的行为医学,说服技术和其他行为影响领域的分类法进行编码。这些独立的系统被集成到称为基于通信的影响组件模型的编码框架模型中。最后,第三项分析评估了干预依从性与行为结果之间的关系。结果:所有研究中在线干预的总体影响虽小,但具有统计学意义(标准均数差效应大小d = 0.19,95%置信区间[CI] = 0.11) -0.28,P <.001,干预次数k = 30)。与候补名单和安慰剂相比,在线干预产生的影响最大,具有中等水平的疗效(d = 0.28,95%CI = 0.17-0.39,P <.001,k = 18),然后是技术含量较低的在线干预(d = 0.16,95%CI = 0.00-0.32,P = .04,k = 8);与复杂的印刷干预相比,没有发现显着差异(d = –0.11,95%CI = –0.34至0.12,P = 0.35,k = 4),尽管在线干预的效果较小,且成本较低且更大的覆盖范围。时间被证明是一个关键因素,较短的干预通常会产生更大的影响和更大的依从性。对于心理设计,大多数干预措施都是从跨理论方法出发的,并且是针对目标的,部署了许多影响力组件,旨在向用户展示其行为的后果,帮助他们实现目标并提供规范性压力。没有结论的结果表明,影响因素的数量与干预效果之间存在关系。尽管存在一种相互矛盾的相关性,但证据表明研究依从性,干预依从性和行为结果之间存在相关性。结论:这些发现表明,在线干预具有影响自愿行为的能力,例如社会营销活动经常针对的行为。鉴于在线技术的覆盖面广且成本低,可以为增加公众健康运动奠定基础,该运动将人际在线系统与大众媒体推广相结合。这种方法的组合可以帮助个人实现个人目标,从而在个人层面上帮助公民改善生活质量,并在一定程度上提高人们的生活水平。

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