Evidence map›Paper›PMID 40844827›Full record

ArticleJournal of medical Internet research2025

Summary and Analysis of Digital Pain Manikin Data in Adults With Pain Experience: Scoping Review.

Darcy Alex Murphy, Syed Mustafa Ali, Shellie Ann Boudreau, William Dixon, David Wong, Sabine N van der Veer

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Darcy Alex MurphyDivision of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0001-9662-3840
Syed Mustafa AliDivision of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0001-9393-9049
Shellie Ann BoudreauContent Avenue, Aalborg, Denmark.ORCID https://orcid.org/0000-0002-8310-2744
William DixonDivision of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0001-5881-4857
David WongInstitute of Health Informatics, University of Leeds, Leeds, United Kingdom.ORCID https://orcid.org/0000-0001-8117-9193
Sabine N van der VeerDivision of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0003-0929-436X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA 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.

objectiveThis 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.

methodsWe 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.

resultsOur 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).

conclusionsWe 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.

Indexed as

ManikinsPainPain MeasurementAdultHumansAIartificial intelligencedigital pain body mapdigital pain chartdigital pain drawingdigital pain manikinpain measurementpatient-generated health data

Identifiers

PMID40844827
PMCPMC12413573

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.