Targeting Key Risk Factors for Cardiovascular Disease in At-Risk Individuals: Developing a Digital, Personalized, and Real-Time Intervention to Facilitate Smoking Cessation and Physical Activity.

Q2 Medicine
JMIR Cardio Pub Date : 2024-12-20 DOI:10.2196/47730
Anke Versluis, Kristell M Penfornis, Sven A van der Burg, Bouke L Scheltinga, Milon H M van Vliet, Nele Albers, Eline Meijer
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引用次数: 0

Abstract

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.

针对高危人群心血管疾病的关键危险因素:开发数字化、个性化和实时干预以促进戒烟和体育活动。
由于人口老龄化,包括心血管疾病在内的慢性病发病率不断上升,医疗保健面临着巨大压力。吸烟和缺乏运动是心血管疾病的两大主要可预防风险因素。然而,与大多数健康行为一样,它们很难改变。在跨学科的 "完美健身"(Perfect Fit)项目中,来自不同领域的科学家联手开发了一种以证据为基础的虚拟教练(VC),帮助吸烟者戒烟并增加体育锻炼。在这篇 "视点 "论文中,介绍了干预的内容、设计和实施,以及吸取的经验教训,以支持其他研究小组开展类似项目。为支持 "完美契合 "自愿咨询项目的开发,共使用并结合了 6 种不同的方法。这些方法包括:(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;(6)迭代概念验证实施。完美契合 "干预将循证行为改变技术与注重身份改变的新技术、大数据科学、传感器技术和个性化实时辅导相结合。虚拟辅导的干预内容与最终用户的个人需求相匹配。经验教训包括如何以最佳方式实施和定制与虚拟中心的互动(例如,明确解释为何要求用户提供意见,以及定制干预内容的时间和频率)。关于开发过程,经验教训包括有效的跨学科合作和技术开发战略(例如,在最终用户的愿望和法律可能性之间找到良好的平衡)。完美契合 "的开发过程是一个合作、反复和充满挑战的过程。我们的经验和教训可以启发和惠及他人。先进的、以证据为基础的数字干预措施,如 Perfect Fit,可以在减轻医疗负担的同时,为健康社会做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Cardio
JMIR Cardio Computer Science-Computer Science Applications
CiteScore
3.50
自引率
0.00%
发文量
25
审稿时长
12 weeks
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