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采用数字疼痛模型进行研究数据收集:系统评价。

Adoption of Digital Pain Manikins for Research Data Collection: A Systematic Review.

机构信息

Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, UK.

Dept of General Practice, Erasmus UMC, Rotterdam, Netherlands.

出版信息

Stud Health Technol Inform. 2022 Jun 6;290:748-751. doi: 10.3233/SHTI220178.

Abstract

Chronic pain is common and disabling. Researchers need robust methods to collect pain data in large populations to enhance knowledge on pain prevalence, causes and treatment. Digital pain manikins address this by enabling self-reporting of location-specific pain. However, it is unknown to what extent pain studies adopted digital manikins for data collection. Therefore, we systematically searched the literature. We included 17 studies. Most were published after 2017, collected pain data cross-sectionally in ≥50 participants, and reported pain distribution and pain extent as manikin-derived summary metrics. Across the studies, 13 unique manikins were used, of which four had been evaluated. Our review shows that adoption of digital pain manikins in research settings has been slow. Harnessing the digital nature of manikins, enabling use of personal devices, and assessing and improving the reliability, validity and responsiveness of digital manikins will expedite their adoption as digital data collection tools for pain research.

摘要

慢性疼痛很常见且会使人丧失能力。研究人员需要强大的方法来在大量人群中收集疼痛数据,以增强对疼痛发生率、原因和治疗的了解。数字疼痛人偶通过实现位置特异性疼痛的自我报告来解决这个问题。然而,尚不清楚有多少疼痛研究采用数字人偶进行数据收集。因此,我们系统地搜索了文献。我们纳入了 17 项研究。大多数研究是在 2017 年之后发表的,以≥50 名参与者进行横断面疼痛数据收集,并报告了疼痛分布和疼痛程度作为人偶衍生的汇总指标。在这些研究中,使用了 13 个独特的人偶,其中 4 个人偶已经过评估。我们的综述表明,数字疼痛人偶在研究环境中的采用速度较慢。利用人偶的数字化特性,使个人设备能够使用,并评估和提高数字人偶的可靠性、有效性和响应性,将加快其作为疼痛研究的数字数据收集工具的采用。

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