University of Bradford, UK.
Universidade Nova de Lisboa, Portugal.
J Health Psychol. 2024 Jun;29(7):770-781. doi: 10.1177/13591053241229870. Epub 2024 Mar 8.
Automated tools to speed up the process of evidence synthesis are increasingly apparent within health behaviour research. This brief review explores the potential of the Non-adoption, Abandonment, Scale-up, Spread and Sustainability framework for supporting automated evidence synthesis in health behaviour change by applying it to the ongoing Human Behaviour-Change Project, which aims to revolutionize evidence synthesis within behaviour change intervention research. To increase the relevance of NASSS for health behaviour change, we recommend i) terminology changes ('condition' to 'behaviour' and 'patient' to 'end user') and ii) that it is used prospectively address complexities iteratively. We draw conclusions about i) the need to specify the organizations that will use the technology, ii) identifying what to do if interdependencies fail and iii) even though we have focused on automated evidence synthesis, NASSS would arguably be beneficial for technology developments in health behaviour change more generally, particularly for invention development.
自动化工具在健康行为研究中日益凸显,可加速证据综合的过程。本简要回顾探讨了不采纳、放弃、扩展、传播和可持续性框架(Non-adoption, Abandonment, Scale-up, Spread and Sustainability framework,简称 NASSS)通过将其应用于正在进行的人类行为改变项目(Human Behaviour-Change Project),为支持健康行为改变中的自动化证据综合的潜力,该项目旨在彻底改变行为改变干预研究中的证据综合。为了提高 NASSS 对健康行为改变的相关性,我们建议:i)术语变更(将“条件”改为“行为”,将“患者”改为“最终用户”);ii)前瞻性地使用它来迭代解决复杂性。我们得出以下结论:i)需要指定将使用该技术的组织;ii)如果出现相互依存关系失败,确定应采取的措施;iii)尽管我们专注于自动化证据综合,但对于健康行为改变领域的技术发展,特别是发明开发,NASSS 无疑将是有益的。