Versluis Anke, Penfornis Kristell M, van der Burg Sven A, Scheltinga Bouke L, van Vliet Milon H M, Albers Nele, Meijer Eline
Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands.
Unit Health, Medical, and Neuropsychology, Institute of Psychology, Leiden University, Leiden, Netherlands.
JMIR Cardio. 2024 Dec 20;8:e47730. doi: 10.2196/47730.
Health care is under pressure due to an aging population with an increasing prevalence of chronic diseases, including cardiovascular disease. Smoking and physical inactivity are 2 key preventable risk factors for cardiovascular disease. Yet, as with most health behaviors, they are difficult to change. In the interdisciplinary Perfect Fit project, scientists from different fields join forces to develop an evidence-based virtual coach (VC) that supports smokers in quitting smoking and increasing their physical activity. In this Viewpoint paper, intervention content, design, and implementation, as well as lessons learned, are presented to support other research groups working on similar projects. A total of 6 different approaches were used and combined to support the development of the Perfect Fit VC. The approaches used are (1) literature reviews, (2) empirical studies, (3) collaboration with end users, (4) content and technical development sprints, (5) interdisciplinary collaboration, and (6) iterative proof-of-concept implementation. The Perfect Fit intervention integrates evidence-based behavior change techniques with new techniques focused on identity change, big data science, sensor technology, and personalized real-time coaching. Intervention content of the virtual coaching matches the individual needs of the end users. Lessons learned include ways to optimally implement and tailor interactions with the VC (eg, clearly explain why the user is asked for input and tailor the timing and frequency of the intervention components). Concerning the development process, lessons learned include strategies for effective interdisciplinary collaboration and technical development (eg, finding a good balance between end users' wishes and legal possibilities). The Perfect Fit development process was collaborative, iterative, and challenging at times. Our experiences and lessons learned can inspire and benefit others. Advanced, evidence-based digital interventions, such as Perfect Fit, can contribute to a healthy society while alleviating health care burden.
由于人口老龄化以及包括心血管疾病在内的慢性病患病率不断上升，医疗保健面临压力。吸烟和缺乏身体活动是心血管疾病两个主要的可预防风险因素。然而，与大多数健康行为一样，它们很难改变。在跨学科的“完美契合”项目中，来自不同领域的科学家联合起来，开发一种基于证据的虚拟教练（VC），以支持吸烟者戒烟并增加身体活动。在这篇观点论文中，介绍了干预内容、设计和实施以及经验教训，以支持从事类似项目的其他研究团队。总共使用并结合了6种不同方法来支持“完美契合”虚拟教练的开发。所采用的方法包括：（1）文献综述，（2）实证研究，（3）与最终用户合作，（4）内容和技术开发冲刺，（5）跨学科合作，以及（6）迭代概念验证实施。“完美契合”干预将基于证据的行为改变技术与专注于身份改变、大数据科学、传感器技术和个性化实时指导的新技术相结合。虚拟教练的干预内容符合最终用户的个人需求。经验教训包括以最佳方式实施和调整与虚拟教练的互动（例如，清楚解释为何要求用户提供输入，并调整干预组件的时间和频率）。关于开发过程，经验教训包括有效跨学科合作和技术开发的策略（例如，在最终用户的愿望和法律可能性之间找到良好平衡）。“完美契合”的开发过程具有协作性、迭代性且有时具有挑战性。我们的经验和教训可以启发他人并使其受益。先进的、基于证据的数字干预措施，如“完美契合”，可以在减轻医疗保健负担的同时为健康社会做出贡献。
