背景:饮食行为显著影响不同人群的健康结果。不健康的饮食与严重的疾病和巨大的经济负担有关,每年导致约1100万人死亡和重大的残疾调整生命年。数字饮食干预为改善饮食行为提供了可访问的解决方案。然而,自然减员,定义为参与者在干预完成前退出,是一个重大挑战,率高达75%-99%。高减员损害了干预的有效性和可靠性,并加剧了健康差异,强调需要理解和解决其原因。
目的:本研究系统回顾了数字饮食干预中减员的文献,以确定根本原因,提出潜在的解决方案,并将这些发现与行为理论概念相结合,形成一个全面的理论框架。该框架旨在阐明减员背后的行为机制,并指导更有效的数字饮食干预措施的设计和实施。最终降低流失率,减轻健康不平等。
方法:我们进行了系统评价,荟萃分析,和专题综合。跨7个电子数据库的全面搜索(PubMed,MEDLINE,Embase,中部,WebofScience,CINAHLPlus,和学术搜索完成)是针对2013年至2023年之间发表的研究进行的。资格标准包括探索数字饮食干预中的减员的原始研究。数据提取侧重于研究特征,示例人口统计,流失率,减员的原因,和潜在的解决方案。我们遵循了ENTREQ(增强定性研究综合报告的透明度)和PRISMA(系统评论和荟萃分析的首选报告项目)指南,并使用RStudio(Posit)进行荟萃分析和NVivo进行主题综合。
结果:在442项确定的研究中,21符合纳入标准。荟萃分析显示,对照组的平均流失率为35%,38%为干预组,40%用于观察性研究,具有高度异质性(I²=94%-99%),表明影响因素不同。主题综合确定了15个相互关联的主题,这些主题与行为理论概念保持一致。基于这些主题,力-资源模型的开发是为了探索流失的根本原因,并从行为理论的角度指导未来干预措施的设计和实施。
结论:高流失率是数字饮食干预的一个重要问题。开发的框架通过驱动力系统和支持资源系统之间的相互作用概念化了减员,提供对参与者流失的细微差别的理解,概括为动力不足、资源不足或匹配不良。它强调了数字饮食干预的关键必要性,以动态地平衡动机成分与可用资源。主要建议包括用户友好的设计,行为因素激活,识字训练,力量-资源匹配,社会支持,个性化适应,和动态跟进。将这些策略扩展到人口水平可以增强数字健康公平性。有必要对该框架进行进一步的实证验证,同时制定了行为理论指导的数字饮食干预指南。
背景:PROSPEROCRD42024512902;https://tinyurl.com/3rjt2df9。
BACKGROUND: Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes.
OBJECTIVE: This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities.
METHODS: We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis.
RESULTS: Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective.
CONCLUSIONS: High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions.
BACKGROUND: PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.