Empirical Software Engineering (M3S) Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
Division of Human-Computer Interaction, Department Of Applied Information Technology, University of Gothenburg, Gothenburg, Sweden.
Methods Inf Med. 2023 Dec;62(5-06):165-173. doi: 10.1055/s-0043-1775718. Epub 2023 Sep 25.
Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.
The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.
A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.
The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.
HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.
患者生成的健康数据(PGHD)是通过移动设备和健康应用等技术收集的数据。将 PGHD 整合到医疗保健工作流程中,可以支持对多发性硬化症(MS)等慢性病的护理。患者通常愿意与护理团队中的医疗保健专业人员(HCP)共享数据;然而,如果 HCP 认为数据没有用处,PGHD 的好处就会受到限制,导致患者最终停止数据跟踪和共享。因此,了解移动医疗(mHealth)解决方案的有用性,这些解决方案提供 PGHD 并作为 HCP 参与参与式护理的推动者,可以激励他们继续使用这些技术。
本研究的目的是探讨 mHealth 解决方案中不同类型的 PGHD 的感知有用性,这些解决方案可以作为 HCP 支持 MS 参与式护理的工具。
采用混合方法,结合定性研究和参与式设计。本研究包括三个连续阶段:数据收集、PGHD 效用评估和数据可视化设计。在第一阶段,对 16 名 HCP 进行了访谈。第二和第三阶段通过参与式研讨会进行,根据效用概念化 PGHD 类型。
研究发现,HCP 对 MS 护理中的 PGHD 持乐观态度。对 MS 护理中的 HCP 最有用的 PGHD 类型是患者的习惯、生活方式和引起疲劳的活动。尽管这些主观数据对 HCP 似乎更有用,但以有用和可操作的方式对其进行可视化更具挑战性。
HCP 对 mHealth 和 PGHD 持乐观态度,认为它们是进一步了解患者需求和支持 MS 护理的工具。来自不同学科的 HCP 对哪些类型的 PGHD 有用有不同的看法;然而,主观类型的 PGHD 似乎对 MS 护理更有潜力。