Literature DB >> 20850976

The photographic knee pain map: locating knee pain with an instrument developed for diagnostic, communication and research purposes.

D W Elson1, S Jones, N Caplan, S Stewart, A St Clair Gibson, D F Kader.   

Abstract

Pain maps are used to determine the location of pain. Knee pain maps have previously been described, but only one study has reported on reliability and none report validity. The present study describes the generation of a photographic knee pain map (PKPM) together with its validity and reliability. A photographic representation of a pair of knees was chosen by 26 patients, (66.7%) from a group of 39. The selected photograph was modified and a template of anatomical zones was generated. The opinions of 25 independent subject matter experts were canvassed and validity ratios calculated for these zones, ranged from 0.28 to 0.84. Hypothetical comparisons were made between the PKPM and an alternative knee pain map, in a cross-sectional group of 26 patients (35 knees). Convergent patterns of validity were found where hypothesised. Reliability was determined using a different cohort of 44 patients (58 knees) who completed the PKPM before and after a sampling delay. Four of these patients were excluded with a short sampling delay. Calculated agreement of test-retest reproducibility was fair to good. All of the completed PKPM (151 knees) were then subject to further analysis where inter-rater reproducibility was good to very good and intra-rater reproducibility was very good. The PKPM is readily accessible to patients with low completion burden. It is both valid and reliable and we suggest it can be used in both clinical and research settings. Further studies are planned to explore its predictive ability as a diagnostic tool. The PKPM can be found at www.photographickneepainmap.com.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 20850976     DOI: 10.1016/j.knee.2010.08.012

Source DB:  PubMed          Journal:  Knee        ISSN: 0968-0160            Impact factor:   2.199


  6 in total

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Authors:  Paola Dey; Michael Callaghan; Neil Cook; Ruth Sephton; Chris Sutton; Elaine Hough; Jonathan James; Rukhtam Saqib; James Selfe
Journal:  BMC Musculoskelet Disord       Date:  2016-05-31       Impact factor: 2.362

2.  Capturing patient-reported area of knee pain: a concurrent validity study using digital technology in patients with patellofemoral pain.

Authors:  Mark Matthews; Michael S Rathleff; Bill Vicenzino; Shellie A Boudreau
Journal:  PeerJ       Date:  2018-03-08       Impact factor: 2.984

3.  Digital Pain Drawings Can Improve Doctors' Understanding of Acute Pain Patients: Survey and Pain Drawing Analysis.

Authors:  Martin Dusch; Florian Beissner; Nour Shaballout; Anas Aloumar; Till-Ansgar Neubert
Journal:  JMIR Mhealth Uhealth       Date:  2019-01-10       Impact factor: 4.773

4.  From Paper to Digital Applications of the Pain Drawing: Systematic Review of Methodological Milestones.

Authors:  Nour Shaballout; Till-Ansgar Neubert; Shellie Boudreau; Florian Beissner
Journal:  JMIR Mhealth Uhealth       Date:  2019-09-05       Impact factor: 4.773

5.  Do Gender-Specific and High-Resolution Three Dimensional Body Charts Facilitate the Communication of Pain for Women? A Quantitative and Qualitative Study.

Authors:  Line Lindhardt Egsgaard; Trine Søby Christensen; Ida Munk Petersen; Dorthe Scavenius Brønnum; Shellie Ann Boudreau
Journal:  JMIR Hum Factors       Date:  2016-07-20

6.  Novel Software for Pain Drawing Analysis.

Authors:  Asimakis K Kanellopoulos; Emmanouil K Kanellopoulos; Zacharias Dimitriadis; Nikolaos S Strimpakos; Andriana Koufogianni; Anthi A Kellari; Ioannis A Poulis
Journal:  Cureus       Date:  2021-12-14
  6 in total

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