Evidence map›Paper›PMID 40342625›Full record

ReviewPain reports2025

The current state of digital manikins to support pain self-reporting: a systematic literature review.

Syed Mustafa Ali, Danielle C Mountain, Rebecca R Lee, Darcy Murphy, Alessandro Chiarotto, David C Wong, William G Dixon, Sabine N van der Veer

Abstract readReview
In one paragraph

Review in Pain reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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

8 authors.

Syed Mustafa AliDivision of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0001-9393-9049
Danielle C MountainCentre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Rebecca R LeeCentre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Darcy MurphyDivision of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Alessandro ChiarottoDepartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.
David C WongThe Leeds Institute of Health Sciences, The University of Leeds, Leeds, United Kingdom.
William G DixonDivision of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Sabine N van der VeerDivision of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic literature review aimed to explore the current state of digital manikins to support pain self-reporting and to explore the extent to which manikins had progressed along the translational pathway. We systematically searched six electronic databases using a combination of key words and MeSH terms for "pain" and "manikin" to identify original studies in English that used a digital pain manikin for collecting information from adults with any condition. We extracted and descriptively synthesised data on the characteristics of studies and digital pain manikins and mapped them to a stage of the translational pathway (ie, design, testing, metric validation, and diffusion). We screened 6,189 articles, of which we included 104. Articles reported on 31 unique digital pain manikins. Most studies were conducted in Europe (n = 60; 58%), recruited people with pain/painful conditions (n = 69; 66%) from clinical settings (n = 67; 64%). Of the 31 manikins, the majority were two-dimensional (n = 21; 68%) with a front and back body view (n = 18; 58%) and allowed users to draw their pain on any area of the manikin (n = 23; 74%). Most manikins were still in the development and testing stages (n = 23; 74%). Only eight (26%) had progressed fully through the translational pathway, taking between 7 and 20 years to go from early development to diffusion. We found a substantial number of mostly two-dimensional digital pain manikins reported on in the last decade. However, most were still at early stages of the translational pathway, with only few having progressed through to diffusion into research and health care settings.

Indexed as

Chronic PainDigital pain manikinsPain measurementPatient-generated health dataSystematic review

Identifiers

PMID40342625
PMCPMC12058649

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.