在设计治疗心房颤动的移动医疗干预措施时考虑用户体验和行为方法:系统综述。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-10-04 DOI:10.2196/54405
Sagar Suresh Kumar, Patricia Connolly, Anja Maier
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引用次数: 0

摘要

背景:心房颤动(房颤)是一种主要的慢性心脏病,会增加中风、心脏并发症和一般死亡率的风险。移动医疗(mHealth)干预措施,包括可穿戴设备和应用程序,可以帮助检测、筛查和管理心房颤动,从而改善患者的预后。将考虑用户体验和行为的方法纳入医疗干预措施的设计中,可以提高移动医疗干预措施的可用性,从而有望为用户的生活带来更多积极成果:本研究旨在说明在设计用于房颤检测、筛查和管理的移动医疗干预措施时,研究人员是如何考虑用户体验和行为方法的;本研究参考了英国医学研究委员会(MRC)设计复杂干预措施的各个阶段:即识别、开发、可行性、评估和实施:方法:从多个数据库中提取了截至 2022 年 9 月 7 日发表的研究,这些研究考察了与房颤相关的移动医疗干预措施的用户体验和行为方式。研究采用了 PRISMA(系统综述和 Meta 分析首选报告项目)指南:结果:共提取了 2219 条记录,其中仅有 55 条记录对房颤情况下移动医疗干预设计的可用性、用户体验或行为方法进行了更广泛的报告。在将这些研究与英国医学研究中心的复杂干预措施开发与评估指南的各个阶段进行映射时,发现了以下几点:在识别阶段,患者和医护人员的需求存在显著差异。在开发阶段,在 4 项研究中,用户观点指导了应用程序、界面和干预方案的迭代开发。大多数研究(43/55,78%)将干预措施在可行性阶段的可用性作为一项成果进行了评估,尽管数据收集工具并不是与用户和利益相关者共同设计的。对评估和实施阶段进行审查的研究需要报告用户参与、接受度和工作流程方面的挑战,而这些挑战是前几个阶段的研究无法捕捉到的。为了通过治疗实现所设想的人类行为,审查结果突出表明,在社会技术医疗保健系统的多个层面上,移动医疗干预很少采用改变行为的方法。虽然在审查的研究中发现了个人(微观)和社区(中观)层面的干预措施,但没有发现在社会层面(宏观)进行干预的研究。在设计长期行为干预措施时,研究也没有考虑用户目标和反馈的时间变化:在这篇系统性综述中,我们提出了两个贡献:第一,将研究映射到 MRC 框架的不同阶段,以开发和评估复杂的干预措施;第二,将行为方法映射到医疗保健系统的不同层面。最后,我们讨论了我们的研究结果对指导未来移动医疗研究的广泛意义。
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Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review.

Background: Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users.

Objective: This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation.

Methods: Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used.

Results: A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC's guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions.

Conclusions: In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
期刊最新文献
Engine of Innovation in Hospital Pharmacy: Applications and Reflections of ChatGPT. Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study. Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review. Patient Safety Incident Reporting and Learning Guidelines Implemented by Health Care Professionals in Specialized Care Units: Scoping Review. Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention.
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