From Paper to Digitalized Body Map: A Reliability Study of the Pain Area

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Test-retest reliability of pain extent and pain location using a novel pain drawing analysis software application, on patients with shoulder pain

OBJECTIVES

A method of pain assessment is the drawing of pain on a specially designed manikin where the patients color the area representing their pain distribution. In recent years, software applications have been developed for the purpose of digital pain drawing data acquisition and processing. Although such specific software applications have already been released, they have been built with obsolete programming tools. The purpose of the study was to investigate the test - retest reliability of a new pain drawing analysis software, in a sample of patients with shoulder pain.

METHODS

Data collected from 31 subjects with shoulder pain. Participants were asked twice to color their pain distribution in the painting environment of a tablet software application called 'Pain Distribution.'

RESULTS

The reliability of pain extent was found to be good (ICC = 0.80). The Jaccard index for the reliability of pain location was found to be moderate, equal to 42.02 ± 19.13%.

CONCLUSION

The results demonstrated good reliability of pain extent and moderate reliability of pain location using the new pain distribution analysis application 'Pain Distribution.' This pain drawing software application could be a reliable, inexpensive, and clinically usable solution for assessing the distribution of pain in patients with shoulder pain.

Quantification of Patient-Reported Pain Locations: Development of an Automated Measurement Method

Patient-reported pain locations are critical for comprehensive pain assessment. Our study aim was to introduce an automated process for measuring the location and distribution of pain collected during a routine outpatient clinic visit. In a cross-sectional study, 116 adults with sickle cell disease-associated pain completed PAIN Report It Ⓡ . This computer-based instrument includes a two-dimensional, digital body outline on which patients mark their pain location. Using the ImageJ software, we calculated the percentage of the body surface area marked as painful and summarized data with descriptive statistics and a pain frequency map. The painful body areas most frequently marked were the left leg-front (73%), right leg-front (72%), upper back (72%), and lower back (70%). The frequency of pain marks in each of the 48 body segments ranged from 3 to 79 (mean, 33.2 ± 21.9). The mean percentage of painful body surface area per segment was 10.8% ± 7.5% (ranging from 1.3% to 33.1%). Patient-reported pain locations can be easily analyzed from digital drawings using an algorithm created via the free ImageJ software. This method may enhance comprehensive pain assessment, facilitating research and personalized care over time for patients with various pain conditions.

‘Painting my pain’: the use of pain drawings to assess multisite pain in women with primary dysmenorrhea

Background

To verify the use of pain drawing to assess multisite pain in with primary dysmenorrhea (PD) and to assess its divergent validity, test–retest reliability, intra- and inter-rater reliability and measurement errors.

Methods

Cross-sectional study. Adult women with self-reported PD three months prior to the study. Women answered the Numerical Rating Scale (NRS) and the pain drawing during two consecutive menstruations. The pain drawings were digitalized and assessed for the calculation of total pain area (%). Intra- and inter-rater reliability and the test–retest reliability between the first and the second menstruations were assessed with the intraclass correlation coefficient (ICC). Measurement errors were calculated with the standard error of measurement (SEM), smallest detectable change (SDC) and the Bland–Altman plot. Spearman correlation (rho) was used to check the correlation between the total pain area and pain intensity of the two menstruations.

Results

Fifty-six women (24.1 ± 3.1 years old) participated of the study. Their average pain was 6.2 points and they presented pain in the abdomen (100%), low back (78.6%), head (55.4%) and lower limbs (50%). All reliability measures were considered excellent (ICC > 0.75) for the total pain area; test–retest SEM and SDC were 5.7% and 15.7%, respectively. Inter-rater SEM and SDC were 8% and 22.1%, respectively. Correlation between total pain area and pain intensity was moderate in the first (rho = 0.30; p = 0.021) and in the second menstruations (rho = 0.40; p = 0.002).

Conclusion

Women with PD presented multisite pain, which could be assessed with the pain drawing, considered a reliable measurement.

Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps

BACKGROUND

Pain is an unpleasant sensation that signals potential or actual bodily injury. The locations of bodily pain can be communicated and recorded by freehand drawing on 2D or 3D (manikin) surface maps. Freehand pain drawings are often part of validated pain questionnaires (eg, the Brief Pain Inventory) and use 2D templates with undemarcated body outlines. The simultaneous analysis of drawings allows the generation of pain frequency maps that are clinically useful for identifying areas of common pain in a disease. The grid-based approach (dividing a template into cells) allows easy generation of pain frequency maps, but the grid's granularity influences data capture accuracy and end-user usability. The grid-free templates circumvent the problem related to grid creation and selection and provide an unbiased basis for drawings that most resemble paper drawings. However, the precise capture of drawn areas poses considerable challenges in producing pain frequency maps. While web-based applications and mobile-based apps for freehand digital drawings are widely available, tools for generating pain frequency maps from grid-free drawings are lacking.

OBJECTIVE

We sought to provide an algorithm that can process any number of freehand drawings on any grid-free 2D body template to generate a pain frequency map. We envisage the use of the algorithm in clinical or research settings to facilitate fine-grain comparisons of human pain anatomy between disease diagnosis or disorders or as an outcome metric to guide monitoring or discovery of treatments.

METHODS

We designed a web-based tool to capture freehand pain drawings using a grid-free 2D body template. Each drawing consisted of overlapping rectangles (Scalable Vector Graphics <rect> elements) created by scribbling in the same area of the body template. An algorithm was developed and implemented in Python to compute the overlap of rectangles and generate a pain frequency map. The utility of the algorithm was demonstrated on drawings obtained from 2 clinical data sets, one of which was a clinical drug trial (ISRCTN68734605). We also used simulated data sets of overlapping rectangles to evaluate the performance of the algorithm.

RESULTS

The algorithm produced nonoverlapping rectangles representing unique locations on the body template. Each rectangle carries an overlap frequency that denotes the number of participants with pain at that location. When transformed into an HTML file, the output is feasibly rendered as a pain frequency map on web browsers. The layout (vertical-horizontal) of the output rectangles can be specified based on the dimensions of the body regions. The output can also be exported to a CSV file for further analysis.

CONCLUSIONS

Although further validation in much larger clinical data sets is required, the algorithm in its current form allows for the generation of pain frequency maps from any number of freehand drawings on any 2D body template.

Spatiotemporal patterns of pain distribution and recall accuracy: a dose-response study

OBJECTIVES

Clinical decisions rely on a patient's ability to recall and report their pain experience. Monitoring pain in real-time (momentary pain) may reduce recall errors and optimize the clinical decision-making process. Tracking momentary pain can provide insights into detailed changes in pain intensity and distribution (area and location) over time. The primary aims of this study were (i) to measure the temporal changes of pain intensity, area, and location in a dose-response fashion and (ii) to assess recall accuracy of the peak pain intensity and distribution seven days later, using a digital pain mapping application. The secondary aims were to (i) evaluate the influence of repeated momentary pain drawings on pain recall accuracy and (ii) explore the associations among momentary and recall pain with psychological variables (pain catastrophizing and perceived stress).

METHODS

Healthy participants (N=57) received a low (0.5 ml) or a high (1.0 ml) dose of hypertonic saline (5.8%) injection into the right gluteus medius muscle and, subsequently, were randomized into a non-drawing or a drawing group. The non-drawing groups reported momentary pain intensity every 30-s. Whereas the drawing groups reported momentary pain intensity and distribution on a digital body chart every 30-s. The pain intensity, area (pixels), and distribution metrics (compound area, location, radiating extent) were compared at peak pain and over time to explore dose-response differences and spatiotemporal patterns. All participants recalled the peak pain intensity and the peak (most extensive) distribution seven days later. The peak pain intensity and area recall error was calculated. Pain distribution similarity was determined using a Jaccard index which compares pain drawings representing peak distribution at baseline and recall. The relationships were explored among peak intensity and area at baseline and recall, catastrophizing, and perceived stress.

RESULTS

The pain intensity, area, distribution metrics, and the duration of pain were lower for the 0.5 mL than the 1.0 mL dose over time (p<0.05). However, the pain intensity and area were similar between doses at peak pain (p>0.05). The pain area and distribution between momentary and recall pain drawings were similar (p>0.05), as reflected in the Jaccard index. Additionally, peak pain intensity did not correlate with the peak pain area. Further, peak pain intensity, but not area, was correlated with catastrophizing (p<0.01).

CONCLUSIONS

This study showed differences in spatiotemporal patterns of pain intensity and distribution in a dose-response fashion to experimental acute low back pain. Unlike pain intensity, pain distribution and area may be less susceptible in an experimental setting. Higher intensities of momentary pain do not appear to influence the ability to recall the pain intensity or distribution in healthy participants.

IMPLICATIONS

The recall of pain distribution in experimental settings does not appear to be influenced by the intensity despite differences in the pain experience. Pain distribution may add additional value to mechanism-based studies as the distribution reports do not vary with pain catastrophizing. REC# N-20150052.

Current management of cancer pain in Italy: Expert opinion paper

Introduction

Chronic pain and breakthrough cancer pain (BTcP) have a high prevalence in all cancer types and cancer stages, combined with a significant physical, psychological, and economic burden. Despite efforts to improve appropriate management of cancer pain, a poor assessment and guilty undertreatment are still reported in many countries. The purpose of this expert opinion paper is to contribute to reduce and clarify these issues with a multidisciplinary perspective in order to share virtuous paths of care.

Methods

Common questions about cancer pain assessment and treatment were submitted to a multidisciplinary pool of Italian clinicians and the results were subsequently discussed and compared with the findings of the published literature.

Conclusion

Despite a dedicated law in Italy and effective treatments available, a low percentage of specialists assess pain and BTcP, defining the intensity with validated tools. Moreover, in accordance with the findings of the literature in many countries, the undertreatment of cancer pain is still prevalent. A multidisciplinary approach, more training programs for clinicians, personalised therapy drug formulations, and virtuous care pathways will be essential to improve cancer pain management.

Nociplastic Pain Criteria or Recognition of Central Sensitization? Pain Phenotyping in the Past, Present and Future

Recently, the International Association for the Study of Pain (IASP) released clinical criteria and a grading system for nociplastic pain affecting the musculoskeletal system. These criteria replaced the 2014 clinical criteria for predominant central sensitization (CS) pain and accounted for clinicians' need to identify (early) and correctly classify patients having chronic pain according to the pain phenotype. Still, clinicians and researchers can become confused by the multitude of terms and the variety of clinical criteria available. Therefore, this paper aims at (1) providing an overview of what preceded the IASP criteria for nociplastic pain ('the past'); (2) explaining the new IASP criteria for nociplastic pain in comparison with the 2014 clinical criteria for predominant CS pain ('the present'); and (3) highlighting key areas for future implementation and research work in this area ('the future'). It is explained that the 2021 IASP clinical criteria for nociplastic pain are in line with the 2014 clinical criteria for predominant CS pain but are more robust, comprehensive, better developed and hold more potential. Therefore, the 2021 IASP clinical criteria for nociplastic pain are important steps towards precision pain medicine, yet studies examining the clinimetric and psychometric properties of the criteria are urgently needed.

The Reliability and Concurrent Validity of PainMAP Software for Automated Quantification of Pain Drawings on Body Charts of Patients With Low Back Pain

BACKGROUND

The assessment of painful areas through printed body charts is a simple way for clinicians to identify patients with widespread pain in primary care. However, there is a lack in the literature about a simple and automated method designed to analyze pain drawings in body charts in clinical practice.

PURPOSE

To test the inter- and intra-rater reliabilities and concurrent validity of software (PainMAP) for quantification of pain drawings in patients with low back pain.

METHODS

Thirty-eight participants (16 [42.10%] female; mean age 50.24 [11.54] years; mean body mass index 27.90 [5.42] kg/m² ; duration of pain of 94.35 [96.11] months) with a current episode of low back pain were recruited from a pool of physiotherapy outpatients. Participants were instructed to shade all their painful areas on a body chart using a red pen. The body charts were digitized by separate raters using smartphone cameras and twice for one rater to analyze the intra-rater reliability. Both the number of pain sites and the pain area were calculated using ImageJ software (reference method). The PainMAP software used image processing methods to automatically quantify the data from the same digitized body charts.

RESULTS

The reliability analyses revealed that PainMAP has excellent inter- and intra-rater reliabilities to quantify the number of pain sites (intraclass correlation coefficient [ICC]_2,1 : 0.998 [95% confidence interval (CI) 0.996 to 0.999]; ICC_2,1 : 0.995 [95% CI 0.991 to 0.998]) and the pain area [ICC_2,1 : 0.998 (95% CI 0.995 to 0.999); ICC_2,1 : 0.975 (95% CI 0.951 to 0.987)], respectively. The standard error of the measurement was 0.22 (4%) for the number of pain sites and 0.03 cm² (4%) for the pain area. The Bland-Altman analyses revealed no substantive differences between the 2 methods for the pain area (mean difference = 0.007 [95% CI -0.053 to 0.067]).

CONCLUSION

PainMAP software is reliable and valid for quantification of the number of pain sites and the pain area in patients with low back pain.