Eboigbe Ukponaye Desmond, Lawan Aliyu, Rushton Alison, Walton David M
School of Physical Therapy, Faculty of Health Sciences, University of Western Ontario, London, Canada.
PLoS One. 2025 Mar 18;20(3):e0319498. doi: 10.1371/journal.pone.0319498. eCollection 2025.
Pain maps are tools used for assessing the extent, location, or distribution of pain or symptoms for clinical or research purposes. Pain mapping involves a transformational representation of patients' experiences of pain into a graphical, numerical, or descriptive form that typically requires a patient to indicate the affected body regions and may include additional information such as qualitative description or intensity. In preparation for innovative technology-enabled development of quantifiable pain maps, this review will focus on the methodological aspects of recent pain maps in addition to the reported measurement properties of each mapping approach. This will identify current gaps in knowledge, consistencies in implementation, and inform directions for future development of more person-centric and meaningful pain maps. The objective of this scoping review is to explore the commonly used types of pain/symptom maps in musculoskeletal pain by classifying design (types) across five categorical features: scalability, region-specificity, aspect or orientation, segmentation, and sex identification, and investigate their methods and modes of implementation.
Key sources of evidence such as Medline, Embase, PsycINFO, CINAHL, Scopus, Web of Science, will be searched from inception to June 5, 2024, including grey literature from reference screening, library and organizational collections such as WorldCat, ProQuest Global Dissertation, Google Scholar, and Google to find descriptions or evaluations of pain/symptom maps in people with pain of a primarily musculoskeletal origin. Studies reporting standard patient-reported pain or body mapping interventions will be considered but studies that present X-ray or CT or MRI scans or artistic body maps will be excluded. Primary outcomes include 'types' of design: scale, segments, sex, orientation, region; pain mapping methods: marking, shading, checking; and mode of implementation: paper, digital, etc. Secondary outcomes include axis I: pain location, extent or distribution; and axis II: pain severity, intensity, and quality. Eligibility screening and data extraction will be conducted by two independent reviewers. The review is intended to initiate research that promotes the integration of data-friendly solutions and supports the application of machine learning in musculoskeletal pain evaluation.
疼痛地图是用于临床或研究目的，评估疼痛或症状的范围、位置或分布的工具。疼痛映射涉及将患者的疼痛体验转化为图形、数字或描述性形式，这通常需要患者指出受影响的身体区域，并且可能包括其他信息，如定性描述或强度。为了为基于创新技术的可量化疼痛地图的开发做准备，本综述除了关注每种映射方法报告的测量属性外，还将重点关注近期疼痛地图的方法学方面。这将识别当前知识上的差距、实施中的一致性，并为未来开发更以人为本且有意义的疼痛地图提供方向。本范围综述的目的是通过对设计（类型）按照五个分类特征进行分类，即可扩展性、区域特异性、方面或方向、分割和性别识别，来探索肌肉骨骼疼痛中常用的疼痛/症状地图类型，并研究其方法和实施模式。
将检索Medline、Embase、PsycINFO、CINAHL、Scopus、Web of Science等关键证据来源，检索时间从数据库创建到2024年6月5日，包括通过参考文献筛选、图书馆和机构馆藏（如WorldCat、ProQuest全球学位论文、谷歌学术和谷歌）获取的灰色文献，以查找对主要源于肌肉骨骼疼痛患者的疼痛/症状地图的描述或评估。将考虑报告标准患者报告的疼痛或身体映射干预措施的研究，但呈现X射线、CT或MRI扫描或艺术身体地图的研究将被排除。主要结果包括设计的“类型”：尺度、分段、性别、方向、区域；疼痛映射方法：标记、阴影、勾选；以及实施模式：纸质、数字等。次要结果包括第一轴：疼痛位置、范围或分布；以及第二轴：疼痛严重程度、强度和性质。资格筛选和数据提取将由两名独立的评审人员进行。本综述旨在启动促进数据友好型解决方案整合并支持机器学习在肌肉骨骼疼痛评估中应用的研究。
