Milne-Ives Madison, Homer Sophie R, Andrade Jackie, Meinert Edward
Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, United Kingdom.
Faculty of Health, Centre for Health Technology, University of Plymouth, United Kingdom.
Mayo Clin Proc Innov Qual Outcomes. 2025 May 27;9(3):100625. doi: 10.1016/j.mayocpiqo.2025.100625. eCollection 2025 Jun.
To map the associations between affective, cognitive, and behavioral components of engagement with digital health interventions to provide a framework to improve intervention design, evaluation, and impact.
An exploratory multiple case study examined 3 studies evaluating a childhood obesity mobile application (NoObesity, data collection: from September 15, 2020 to June 23, 2021), a mental health conversational agent mobile application (Wysa, data collection: from December 13, 2022 to July 31, 2023), and a telephone-delivered conversational agent postsurgical assessment (Dora R1, data collection: from September 17, 2021 to January 31, 2022). Qualitative data from semi-structured interviews (NoObesity: n=15, Wysa: n=4, and Dora R1: n=20) was analyzed using a codebook thematic analysis approach to generate models mapping engagement. A cross-case analysis compared the 3 models with a hypothesized model.
The case studies highlighted close associations between affective, cognitive, and behavioral components throughout the engagement process. Similar patterns of engagement were generated from the case studies, but these patterns differed from the literature-based hypothesized model in the order of influence of cognitive and affective engagement.
Understanding how different components of engagement interact is essential for designing interventions that mitigate barriers to engagement and maximize intervention impact. The framework provides a preliminary guide and recommendations for how to support particular components. Future research on the order of cognitive and affective components (or importance thereof) and testing the influence of particular features on engagement components could improve the framework and clinical impact.
clinicaltrials.gov Identifier: NoObesity: NCT05261555; Wysa: NCT05533190; Dora R1: NCT05213390.
描绘与数字健康干预措施互动时情感、认知和行为成分之间的关联，以提供一个改善干预设计、评估和影响的框架。
一项探索性多案例研究考察了3项研究，评估一款儿童肥胖症移动应用程序（NoObesity，数据收集时间：2020年9月15日至2021年6月23日）、一款心理健康对话代理移动应用程序（Wysa，数据收集时间：2022年12月13日至2023年7月31日）以及一项通过电话进行的对话代理术后评估（Dora R1，数据收集时间：2021年9月17日至2022年1月31日）。使用编码本主题分析方法对来自半结构化访谈的定性数据（NoObesity：n = 15，Wysa：n = 4，Dora R1：n = 20）进行分析，以生成描绘互动情况的模型。跨案例分析将这3个模型与一个假设模型进行了比较。
案例研究突出了在整个互动过程中情感、认知和行为成分之间的紧密关联。案例研究产生了相似的互动模式，但这些模式在认知和情感互动的影响顺序上与基于文献的假设模型不同。
了解互动的不同成分如何相互作用对于设计减少互动障碍并使干预影响最大化的干预措施至关重要。该框架为如何支持特定成分提供了初步指导和建议。未来关于认知和情感成分的顺序（或其重要性）以及测试特定功能对互动成分影响的研究可能会改进该框架和临床影响。
clinicaltrials.gov标识符：NoObesity：NCT05261555；Wysa：NCT05533190；Dora R1：NCT05213390。
