JMIR Publications

ABSTRACT

Background:

Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting picture is rated based on the depicted pain intensity. Analyzing pain drawings typically involves measuring the size of the pain region. However, there is currently no standardized method for scanning pain drawings.

Objective:

The objective of this study was to evaluate the accuracy of pain drawing analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire pain drawings without losing important information.

Methods:

Two sets of pain drawings were generated: one with the addition of 216 colored circles, and another composed of various red shapes. These drawings were then scanned using different devices and apps, including three flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer/scanner), three smartphones with varying price ranges, and six virtual scanner apps.

Results:

High saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. Additionally, a significant negative correlation was observed between the percentage of error and spot size (R=-0.237, p<0.05).

Conclusions:

The proposed platform proved to be robust and reliable for acquiring paper pain drawings using a wide range of scanning devices. In conclusion, this study demonstrates that a web platform can accurately analyze pain drawings acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for pain drawing acquisition, without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent pain drawing analysis in clinical and research settings.