基于传感器的数字健康技术的人为因素、以人为本的设计和可用性:范围审查。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-11-15 DOI:10.2196/57628
Animesh Tandon, Bryan Cobb, Jacob Centra, Elena Izmailova, Nikolay V Manyakov, Samantha McClenahan, Smit Patel, Emre Sezgin, Srinivasan Vairavan, Bernard Vrijens, Jessie P Bakker
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引用次数: 0

摘要

背景:近年来,基于传感器的数字医疗技术(sDHTs)被越来越多地采用,这让人们看到了在不同患者群体中将这些工具大规模应用于临床试验和患者护理时所面临的诸多挑战;然而,针对sDHT可用性评估所采用的方法却存在明显差异:本综述旨在探索当前与 sDHT 人为因素、以人为本的设计和可用性相关的数据报告研究的现状,为我们同时开展的制定 sDHT 可用性评估框架的工作提供信息:我们对 2013 年至 2023 年间发表的、被 PubMed 索引的研究进行了范围界定,这些研究报告了与 sDHT 人为因素、以人为本的设计和可用性相关的数据。经过系统筛选,我们提取了研究设计、参与者样本、使用的sDHT、数据采集方法以及采集的可用性相关数据类型:通过文献检索,我们找到了 442 篇论文,其中 85 篇符合条件,83 篇可用于提取数据且未被禁止。共评估了 164 种可穿戴式数据采集工具,其中 141 种(86%)为可穿戴式工具,其余 23 种(14%)为环境工具。大多数研究(55/83,66%)报告了对最终设计的 SDHT 的总结性评估。几乎所有的研究(82/83,99%)都采集了目标最终用户的数据,但 83 项研究中只有 18 项(22%)采集了护理合作伙伴或临床医生等其他用户的数据。83%(136/164)和 91%(150/164)的 sDHT 分别对用户满意度和易用性进行了评估;但在 164 项 sDHT 中,分别只有 11(7%)、4(2%)和 2(1%)项报告了可学性、效率和可记性。在 164 个 sDHT 中,共有 14 个(9%)是根据用户能够理解向其展示的临床数据或其他信息的程度(可理解性)或用户应完成的响应操作或任务的程度(可操作性)进行评估的。报告中值得注意的不足之处包括缺乏样本大小说明(21/83,占所有研究的 25%;17/55,占总结性研究的 31%)以及社会人口学描述数据不完整(14/83,占研究的 17%,报告了完整的年龄、性别和种族/民族):根据我们的研究结果,我们为今后的研究提出了四项可行的建议,这些建议将有助于推动 sDHTs 的实施:(1)考虑对技术可用性进行深入评估,而不仅仅局限于用户满意度和易用性;(2)扩大招募范围,将临床医生和护理合作伙伴等重要用户群体纳入其中;(3)报告关键研究设计考虑因素的基本原理,包括样本大小;(4)提供有关研究样本的丰富描述性统计数据,以便全面了解对其他患者群体和使用环境的可推广性。
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Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review.

Background: Increasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken toward sDHT usability evaluation have varied markedly.

Objective: This review aims to explore the current landscape of studies reporting data related to sDHT human factors, human-centered design, and usability, to inform our concurrent work on developing an evaluation framework for sDHT usability.

Methods: We conducted a scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and usability were reported. Following a systematic screening process, we extracted the study design, participant sample, the sDHT or sDHTs used, the methods of data capture, and the types of usability-related data captured.

Results: Our literature search returned 442 papers, of which 85 papers were found to be eligible and 83 papers were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 (86%) sDHTs were wearable tools while the remaining 23 (14%) sDHTs were ambient tools. The majority of studies (55/83, 66%) reported summative evaluations of final-design sDHTs. Almost all studies (82/83, 99%) captured data from targeted end users, but only 18 (22%) out of 83 studies captured data from additional users such as care partners or clinicians. User satisfaction and ease of use were evaluated for 83% (136/164) and 91% (150/164) of sDHTs, respectively; however, learnability, efficiency, and memorability were reported for only 11 (7%), 4 (2%), and 2 (1%) out of 164 sDHTs, respectively. A total of 14 (9%) out of 164 sDHTs were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 21/83, 25% of all studies and 17/55, 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 14/83, 17% of studies).

Conclusions: Based on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: (1) consider an in-depth assessment of technology usability beyond user satisfaction and ease of use, (2) expand recruitment to include important user groups such as clinicians and care partners, (3) report the rationale for key study design considerations including the sample size, and (4) provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.

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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.
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