Explain the pain body diagram or PBD anatomy and orientation of body templates in landscape mode. Describe the pressure to hue linear transformation by showing that increased stylus pressure shifts hues from green to blue to red, representing mild, moderate, and severe pain intensity respectively. Then explain the drawing and erasing tools and pinch to zoom and panning functionalities.
Confirm the participant's understanding of the pain body diagram task using the teach back method and have them explain the process in their own words. Allocate 15 minutes for participants to practice drawing pain body diagrams on a flat surface, ensuring an accurate representation of pain location and intensity. Review the PBD with each participant to verify consistency and correct use of colors.
Request participants to finish PBDs during baseline or at different time points following treatment or intervention, allowing ample time for PBD completion. After completion, save PBDs with a standardized file name, including the patient ID, date and time the PBD was finished. Select the desired images for bulk export using the select icon.
Click on the share button to access a list of available image formats. Choose a portable or Photoshop file format that retains image layers and click on the preferred format. Download the desired PBD files and open them in a raster based image editor.
Now, isolate the colored pixels of interest from the top layer of the PBD file by adding a completely black layer below the colored in layer and a black mask layer above it to exclude pixels outside the template body outline, creating a modified PBD with only the colored pixels within the body outline on a black background. To export the processed PBDs as portable network graphic files, click on file, select export, and choose export as, select PNG format, then click export. Convert each pixel value within the PBD from RGB to HSV color space using the open CV2 Python package.
Extract the hue value for every pixel by executing the Python scripts titled, measure underscore SA noblur and RGBA2HSV. Run the quantify pain Python script to calculate the three PBD metrics. First, calculate the PBD coverage by dividing the number of colored pixels by the total number of pixels within the body diagram.
The range for females is one to 820, 452 pixels, and for males it is one to 724, 608 pixels. Multiply the results by 100 to normalize data from a zero to 100 scale. Next, determine the PBD sum intensity by adding the hue values for all pixels In the body diagram, which is done by running RGBA2HSV.
The female range is zero to 114, 453, 054, and the male range is zero to 101, 082, 816. Divide the result by the maximum PBD sum intensity and multiply by 100. The maximum PBD sum intensity depends on the total number of pixels in the body diagram multiplied by 139.5.
Compute the PBD mean intensity by dividing the sum of all hue values by the total number of colored pixels. Finally, process each PBD file and compile the outputs in a spreadsheet for further analysis. The PBD metrics were correlated with the numerical rating intensity, or NRS, visual analog scale, or VAS, and McGill Pain Questionnaire, or MPQ for most patients.
In four of five patients, PBD metrics significantly shared mutual information, or MI, with the NRS-VAS intensity, VAS unpleasantness and MPQ. In all patients, the NRS shared significant MI with VAS intensity, VAS unpleasantness and MPQ, while the PBD sum shared MI with PBD coverage and PBD mean.
