Testing Shoulder Pain Mapping

Bayam, Levent; Arumilli, Rajendra; Horsley, Ian; Bayam, Fatma Ece; Herrington, Lee; Funk, Lennard

doi:10.1093/pm/pnw326

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…Moreover, digital pain drawings enable the acquisition and analysis of large amounts of data which can serve as a basis to track, understand, and predict the trajectory of symptoms. Pain drawings lend well to machine learning approaches 39 and are beginning to show clinical utility on a number of fronts ranging from patients suffering from pain in the knee 17 , 32 , back 9 , 11 , 12 , shoulder 13 or neck 4 . As an example, pain or discomfort drawings can capture what magnetic imaging (MRI) may miss 9 as shown in patients with long-standing nerve root symptoms.…”

Section: Discussionmentioning

confidence: 99%

“…Assessing pain distribution by using pain drawings is a clinically relevant and reliable method 1 – 8 . Pain drawings are known as discomfort drawings, pain charts, and pain maps and used to achieve a better understanding of a disease or condition 7 , 9 – 13 . Pain maps can potentially provide insight into the underlying pathology where pain severity so far has contributed inadequately.…”

Section: Introductionmentioning

confidence: 99%

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Distinct patterns of variation in the distribution of knee pain

Boudreau

Royo

Matthews

et al. 2018

Sci Rep

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The patient’s expression of pain using digital-body maps expands analytic opportunities for exploring the spatial variation of bodily pain. A common knee pain condition in adolescents and adults is patellofemoral pain (PFP) and recently PFP was shown to be characterized by a heterogeneous distribution of pain. Whether there are important patterns in these distributions remains unclear. This pioneering study assesses the spatial variation of pain using principal component analysis and a clustering approach. Detailed digital-body maps of knee pain were drawn by 299 PFP patients of mixed sex, age, and pain severity. Three pain distribution patterns emerged resembling an Anchor, Hook, and an Ovate shape on and around the patella. The variations in pain distribution were independent of sex, age, and pain intensity. Bilateral pain associated with a longer duration of pain and the majority characterized by the Hook and Ovate pain distributions. Bilateral and/or symmetrical pain between the left and right knees may represent symptoms associated with longstanding PFP. The distinct patterns of pain location and area suggest specific underlying structures cannot be ruled out as important drivers, although central neuronal mechanisms possibly exemplified by the symmetrical representation of pain may play a role in individuals with longstanding symptoms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distinct patterns of variation in the distribution of knee pain

Boudreau

Royo

Matthews

et al. 2018

Sci Rep

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“…Digital pain drawings offer the advantages of being easily acquired, quickly quantified and interpreted to assist in clinical diagnosis and comparison over time. Previous studies have investigated paper-based pain mapping to assist in clinical diagnosis of knee and shoulder pain ( Elson et al, 2011 ; Bayam et al, 2011 , 2017 ). Participants were instructed to use small crosses (‘X’) ( Elson et al, 2011 ) or symbols ( Bayam et al, 2011 , 2017 ) to mark out their pain location, type, distribution and severity and several ‘X’s if pain was present in more than one location.…”

Section: Discussionmentioning

confidence: 99%

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

Matthews

Rathleff

Vicenzino

et al. 2018

PeerJ

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BackgroundPatellofemoral pain (PFP) is often reported as a diffuse pain at the front of the knee during knee-loading activities. A patient’s description of pain location and distribution is commonly drawn on paper by clinicians, which is difficult to quantify, report and compare within and between patients. One way of overcoming these potential limitations is to have the patient draw their pain regions using digital platforms, such as personal computer tablets.ObjectiveTo assess the validity of using computer tablets to acquire a patient’s knee pain drawings as compared to paper-based records in patients with PFP.MethodsPatients (N = 35) completed knee pain drawings on identical images (size and colour) of the knee as displayed on paper and a computer tablet. Pain area expressed as pixel density, was calculated as a percentage of the total drawable area for paper and digital records. Bland–Altman plots, intraclass correlation coefficient (ICC), Pearson’s correlation coefficients and one-sample tests were used in data analysis.ResultsNo significant difference in pain area was found between the paper and digital records of mapping pain area (p = 0.98), with the mean difference = 0.002% (95% CI [−0.159–0.157%]). A very high agreement in pain area between paper and digital pain drawings (ICC = 0.966 (95% CI [0.93–0.98], F = 28.834, df = 31, p < 0.001). A strong linear correlation (R2 = 0.870) was found for pain area and the limits of agreement show less than ±1% difference between paper and digital drawings.ConclusionPain drawings as acquired using paper and computer tablet are equivalent in terms of total area of reported knee pain. The advantages of digital recording platforms, such as quantification and reporting of pain area, could be realized in both research and clinical settings.

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“…5 Pain mapping is a tool for patients to indicate the location where they experience most of their pain and could assist in the diagnosis of musculoskeletal conditions. [9][10][11][12] Researchers previously suggested a self-administered pain map to be a useful and effective way to diagnose patients with patellar tendinopathy in a large group of subjects. 13 Knowing the reliability of using a self-administered standardized pain map for diagnosing Achilles tendinopathy could help clinicians with adequate triage.…”

Section: Introductionmentioning

confidence: 99%