Brückner Stefanie, Sadare Olamide, Fesl Sabrina, Scheibe Madlen, Lang Caroline, Gilbert Stephen
Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Fetscherstraße 74, Dresden, Germany.
Center for Evidence-Based Healthcare, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, Dresden, Germany.
NPJ Digit Med. 2025 Mar 30;8(1):186. doi: 10.1038/s41746-025-01568-4.
Patient-generated health data (PGHD) from apps and wearables hold significant potential for enhancing personalised care and medical research. Healthcare professionals (HCPs) are key to its successful adoption, as their attitudes can either support or hinder its integration into clinical practice. This review systematically analysed studies on HCPs' and researchers' perspectives on PGHD for clinical and research use. Three databases were searched for articles published between January 2013 and April 2023. Of 246 articles screened, 33 met the inclusion criteria. While most participants viewed PGHD positively, concerns around data security, reliability, and workflow integration persist. Addressing these barriers is essential to maximising PGHD's benefits for participatory medicine and improved clinical outcomes. The included studies presented medium methodological quality, particularly among quantitative and mixed methods with risks of sampling and nonresponse bias, and often low sample sizes in qualitative studies. However, recurring themes across studies allow a valuable interpretation of the findings.
来自应用程序和可穿戴设备的患者生成健康数据(PGHD)在加强个性化医疗和医学研究方面具有巨大潜力。医疗保健专业人员(HCP)是其成功应用的关键,因为他们的态度既可以支持也可能阻碍其融入临床实践。本综述系统分析了关于HCP和研究人员对用于临床和研究的PGHD的观点的研究。检索了三个数据库中2013年1月至2023年4月发表的文章。在筛选的246篇文章中,33篇符合纳入标准。虽然大多数参与者对PGHD持积极看法,但对数据安全、可靠性和工作流程整合的担忧依然存在。解决这些障碍对于最大限度地发挥PGHD对参与式医学的益处和改善临床结果至关重要。纳入的研究呈现出中等的方法学质量,特别是在存在抽样和无应答偏差风险的定量和混合方法研究中,定性研究的样本量通常较小。然而,各研究中反复出现的主题使得对研究结果能够进行有价值的解读。
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