Murphy, D.A., Ali, S.M., Boudreau, S.A. et al. (3 more authors) (2025) Summary and Analysis of Digital Pain Manikin Data in Adults With Pain Experience: Scoping Review. Journal of Medical Internet Research, 27. e69360. ISSN: 1438-8871
Abstract
Background:
A digital pain manikin is a measurement tool that presents a diagram of the human body where people mark the location of their pain to produce a pain drawing. Digital pain manikins facilitate collection of more detailed spatial pain data compared to questionnaire-based methods and are an increasingly common method for self-reporting and communicating pain. An overview of how digital pain drawings, collected through digital pain manikins, are analyzed and summarized is currently missing.
Objective:
This study aimed to map the ways in which digital pain drawings were summarized and analyzed and which pain constructs these summaries attempted to measure. The objectives were to (1) identify and characterize studies that used digital pain manikins for data collection, (2) identify which individual drawing–level summary measures they reported and the methods by which these summaries were calculated, and (3) identify if and how multidrawing (eg, time series) summary and analysis methods were applied.
Methods:
We conducted a scoping review to systematically identify studies that used digital pain manikins for data collection and reported summary measures or analysis of the resulting digital pain drawings. We searched multiple databases using search terms related to pain and manikin. Two authors independently performed title, abstract, and full-text screening. We extracted and synthesized data on how studies summarized and analyzed digital manikin pain data at the individual pain–drawing level as well as across multiple pain drawings.
Results:
Our search yielded 6189 studies, of which we included 92. The majority were clinical studies (n=51) and cross-sectional (n=64). Eighty-seven studies reported at least 1 individual drawing–level summary measure. We identified individual drawing–level manikin summary measures related to 10 distinct pain constructs, with the most common being pain extent (n=53), physical location (n=28), and widespreadness (n=21), with substantial methodological variation within constructs. Forty-two studies reported at least 1 multidrawing summary method. Heat maps were most common (n=35), followed by the number or proportion of participants reporting pain in a specific location (n=14). Sixteen studies reported multidrawing analysis methods, the most common being an assessment of the similarity between pairs of pain drawings representing the same individual at the same moment in time (n=6).
Conclusions:
We found a substantial number of studies that reported manikin summary and analysis methods, with the majority being cross-sectional clinical studies. Studies commonly reported pain extent at the individual–drawing level and used heat maps to summarize data across multiple drawings. Analysis methods that went beyond summarizing pain drawings were much rarer, and methodological variation within pain constructs meant a lack of comparability between studies and across manikins. This highlights a need for development of standardized methods that are applicable across manikins and more advanced methods that harness the spatial nature of pain drawings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Darcy Alex Murphy, Syed Mustafa Ali, Shellie Ann Boudreau, William Dixon, David Wong, Sabine N van der Veer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.08.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
Keywords: | digital pain manikin; digital pain drawing; digital pain body map; digital pain chart; pain measurement; patient-generated health data; artificial intelligence; AI |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Sep 2025 13:24 |
Last Modified: | 02 Sep 2025 13:24 |
Published Version: | https://www.jmir.org/2025/1/e69360 |
Status: | Published |
Publisher: | JMIR Publications |
Identification Number: | 10.2196/69360 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:230952 |
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