Reliability of the Pen-on-Paper Pain Drawing Analysis Using Different Scanning Procedures.

Pain extent body chart pain drawing pain location reliability

Journal

Journal of pain and symptom management
ISSN: 1873-6513
Titre abrégé: J Pain Symptom Manage
Pays: United States
ID NLM: 8605836

Informations de publication

Date de publication:
26 Oct 2023
Historique:
received: 15 07 2023
revised: 05 10 2023
accepted: 16 10 2023
pubmed: 29 10 2023
medline: 29 10 2023
entrez: 28 10 2023
Statut: aheadofprint

Résumé

Pen-on-paper pain drawing are an easily administered self-reported measure that enables patients to report the spatial distribution of their pain. The digitalization of pain drawings has facilitated the extraction of quantitative metrics, such as pain extent and location. This study aimed to assess the reliability of pen-on-paper pain drawing analysis conducted by an automated pain-spot recognition algorithm using various scanning procedures. One hundred pain drawings, completed by patients experiencing somatic pain, were repeatedly scanned using diverse technologies and devices. Seven datasets were created, enabling reliability assessments including inter-device, inter-scanner, inter-mobile, inter-software, intra- and inter-operator. Subsequently, the automated pain-spot recognition algorithm estimated pain extent and location values for each digitized pain drawing. The relative reliability of pain extent analysis was determined using the intraclass correlation coefficient, while absolute reliability was evaluated through the standard error of measurement and minimum detectable change. The reliability of pain location analysis was computed using the Jaccard similarity index. The reliability analysis of pain extent consistently yielded intraclass correlation coefficient values above 0.90 for all scanning procedures, with standard error of measurement ranging from 0.03% to 0.13% and minimum detectable change from 0.08% to 0.38%. The mean Jaccard index scores across all dataset comparisons exceeded 0.90. The analysis of pen-on-paper pain drawings demonstrated excellent reliability, suggesting that the automated pain-spot recognition algorithm is unaffected by scanning procedures. These findings support the algorithm's applicability in both research and clinical practice.

Identifiants

pubmed: 37898312
pii: S0885-3924(23)00761-3
doi: 10.1016/j.jpainsymman.2023.10.019
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosures and Acknowledgments No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication.

Auteurs

Marco Barbero (M)

Rehabilitation Research Laboratory 2rLab, Department of Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland (M.B., C.C., A.S.). Electronic address: marco.barbero@supsi.ch.

Corrado Cescon (C)

Rehabilitation Research Laboratory 2rLab, Department of Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland (M.B., C.C., A.S.).

Alessandro Schneebeli (A)

Rehabilitation Research Laboratory 2rLab, Department of Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland (M.B., C.C., A.S.).

Deborah Falla (D)

Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom (D.F.).

Giuseppe Landolfi (G)

Institute of Systems and Technologies for Sustainable Production, ISTePS, SUPSI, Lugano, Switzerland (G.L.).

Marco Derboni (M)

Dalle Molle Institute for Artificial Intelligence, IDSIA, USI-SUPSI, Lugano, Switzerland (M.D., V.G., A.E.R.).

Vincenzo Giuffrida (V)

Dalle Molle Institute for Artificial Intelligence, IDSIA, USI-SUPSI, Lugano, Switzerland (M.D., V.G., A.E.R.).

Andrea Emilio Rizzoli (AE)

Dalle Molle Institute for Artificial Intelligence, IDSIA, USI-SUPSI, Lugano, Switzerland (M.D., V.G., A.E.R.).

Paolo Maino (P)

Pain Management Center, Neurocenter of Southern Switzerland, EOC, Lugano, Switzerland (P.M., E.K.); Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland (P.M.,E.K.).

Eva Koetsier (E)

Pain Management Center, Neurocenter of Southern Switzerland, EOC, Lugano, Switzerland (P.M., E.K.); Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland (P.M.,E.K.).

Classifications MeSH