Flow diagram of study selection processes.

Introduction

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.

Methods

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.