The School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Suzhou Industrial Park Monash Research Institute of Science and Technology, Monash University, Suzhou, China.
J Med Internet Res. 2024 Aug 27;26:e58735. doi: 10.2196/58735.
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.
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.
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.
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.
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.
PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.
饮食行为对不同人群的健康结果有显著影响。不健康的饮食与严重疾病和巨大的经济负担有关，每年导致约 1100 万人死亡和大量的伤残调整生命年。数字饮食干预提供了改善饮食行为的可行解决方案。然而，失访（即在干预完成前参与者退出）是一个主要挑战，其比例高达 75%-99%。高失访率会影响干预的有效性和可靠性，并加剧健康差距，这凸显了理解和解决这一问题的必要性。
本研究系统综述了数字饮食干预中失访的文献，以确定潜在原因，提出潜在解决方案，并将这些发现与行为理论概念相结合，以制定一个全面的理论框架。该框架旨在阐明失访背后的行为机制，并指导更有效的数字饮食干预的设计和实施，最终降低失访率，减轻健康不平等。
我们进行了系统综述、荟萃分析和主题综合。对 2013 年至 2023 年间发表的研究进行了 7 个电子数据库（PubMed、MEDLINE、Embase、CENTRAL、Web of Science、CINAHL Plus 和 Academic Search Complete）的全面检索。纳入标准包括探索数字饮食干预中失访原因的原始研究。数据提取重点关注研究特征、样本人口统计学、失访率、失访原因和潜在解决方案。我们遵循 ENTREQ（提高定性研究综合报告的透明度）和 PRISMA（系统评价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。
在 442 项已确定的研究中，有 21 项符合纳入标准。荟萃分析显示，对照组的平均失访率为 35%，干预组为 38%，观察性研究为 40%，异质性很高（I²=94%-99%），表明存在多种影响因素。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一致。基于这些主题，发展了力-资源模型，从行为理论的角度探讨失访的潜在原因，并指导未来干预的设计和实施。
数字饮食干预中的高失访率是一个重大问题。所制定的框架通过驱动力系统和支持资源系统的相互作用来概念化失访，对参与者失访进行了细致的理解，总结为动机不足和资源不足或不匹配。它强调了数字饮食干预必须在动态平衡中平衡激励因素和可用资源的关键必要性。主要建议包括用户友好的设计、行为因素激活、读写能力培训、力-资源匹配、社会支持、个性化适应和动态随访。将这些策略扩展到人群层面可以增强数字健康公平性。有必要进一步对框架进行实证验证，并制定行为理论指导的数字饮食干预指南。
PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.
