Shin Hwayeon Danielle, Hamovitch Emily, Gatov Evgenia, MacKinnon Madison, Samawi Luma, Boateng Rhonda, Thorpe Kevin E, Barwick Melanie
Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
PLOS Digit Health. 2025 Mar 17;4(3):e0000418. doi: 10.1371/journal.pdig.0000418. eCollection 2025 Mar.
The Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework (2017) was established as an evidence-based, theory-informed tool to predict and evaluate the success of implementing health and care technologies. While the NASSS is gaining popularity, its use has not been systematically described. Literature reviews on the applications of popular implementation frameworks, such as the RE-AIM and the CFIR, have enabled their advancement in implementation science. Similarly, we sought to advance the science of implementation and application of theories, models, and frameworks (TMFs) in research by exploring the application of the NASSS in the five years since its inception. We aim to understand the characteristics of studies that used the NASSS, how it was used, and the lessons learned from its application. We conducted a scoping review following the JBI methodology. On December 20, 2022, we searched the following databases: Ovid MEDLINE, EMBASE, PsychINFO, CINAHL, Scopus, Web of Science, and LISTA. We used typologies and frameworks to characterize evidence to address our aim. This review included 57 studies that were qualitative (n=28), mixed/multi-methods (n=13), case studies (n=6), observational (n=3), experimental (n=3), and other designs (e.g., quality improvement) (n=4). The four most common types of digital applications being implemented were telemedicine/virtual care (n=24), personal health devices (n=10), digital interventions such as internet Cognitive Behavioural Therapies (n=10), and knowledge generation applications (n=9). Studies used the NASSS to inform study design (n=9), data collection (n=35), analysis (n=41), data presentation (n=33), and interpretation (n=39). Most studies applied the NASSS retrospectively to implementation (n=33). The remainder applied the NASSS prospectively (n=15) or concurrently (n=8) with implementation. We also collated reported barriers and enablers to implementation. We found the most reported barriers fell within the Organization and Adopter System domains, and the most frequently reported enablers fell within the Value Proposition domain. Eighteen studies highlighted the NASSS as a valuable and practical resource, particularly for unravelling complexities, comprehending implementation context, understanding contextual relevance in implementing health technology, and recognizing its adaptable nature to cater to researchers' requirements. Most studies used the NASSS retrospectively, which may be attributed to the framework's novelty. However, this finding highlights the need for prospective and concurrent application of the NASSS within the implementation process. In addition, almost all included studies reported multiple domains as barriers and enablers to implementation, indicating that implementation is a highly complex process that requires careful preparation to ensure implementation success. Finally, we identified a need for better reporting when using the NASSS in implementation research to contribute to the collective knowledge in the field.
非采用、放弃、扩大规模、传播、可持续性(NASSS)框架(2017年)是作为一种基于证据、理论导向的工具而建立的,用于预测和评估卫生与护理技术实施的成功与否。虽然NASSS越来越受欢迎,但其使用方法尚未得到系统描述。关于流行的实施框架(如RE-AIM和CFIR)应用的文献综述推动了它们在实施科学中的发展。同样,我们试图通过探索NASSS自成立以来五年内的应用情况,推动理论、模型和框架(TMFs)在研究中的实施和应用科学发展。我们旨在了解使用NASSS的研究的特征、其使用方式以及从其应用中吸取的经验教训。我们按照JBI方法进行了一项范围综述。2022年12月20日,我们搜索了以下数据库:Ovid MEDLINE、EMBASE、PsychINFO、CINAHL、Scopus、Web of Science和LISTA。我们使用类型学和框架来描述证据,以实现我们的目标。本综述纳入了57项研究,其中定性研究(n = 28)、混合/多方法研究(n = 13)、案例研究(n = 6)、观察性研究(n = 3)、实验性研究(n = 3)以及其他设计(如质量改进)(n = 4)。正在实施的四种最常见的数字应用类型是远程医疗/虚拟护理(n = 24)、个人健康设备(n = 10)、数字干预措施(如互联网认知行为疗法)(n = 10)以及知识生成应用(n = 9)。研究使用NASSS为研究设计(n = 9)、数据收集(n = 35)、分析(n = 41)、数据呈现(n =