Abstract

In experiments on pain, participants are frequently exposed to nonpainful and painful stimuli; however, the conventional pain-rating scales lack a nonpainful range and a clear point of transition from nonpainful to painful events. The Sensation and Pain Rating Scale (SPARS) assesses the full stimulus intensity range, extending from no sensation (rating: –50) to worst pain imaginable (rating: +50), and it explicitly identifies pain threshold (rating: 0). Here, we tested the SPARS in 2 experiments by using laser heat stimuli to establish its stimulus–response characteristics (Experiment 1, N = 19, 13 stimulus intensities applied 26 times each across a 1–4 J range), and compared it to 0 to 100 scales that assess nonpainful (0: no sensation, 100: pain) and painful (0: no pain, 100: worst pain imaginable) events (Experiment 2, N = 7, 9 stimulus intensities applied 36 times each across a 1.5–4.5 J range). Despite high inter- and intraindividual variations, we found a reasonably consistent curvilinear stimulus–response relationship (the curve flattens around pain threshold), with stable response characteristics across the range of the scale. The SPARS ratings transformed to a 0 to 100 range tended to be lower than the 0 to 100 pain rating scale in the noxious stimulus intensity range and greater than the 0 to 100 nonpainful sensation scale in the non-noxious stimulus range, likely reflecting differences in scale dimensionality. The SPARS overcomes limitations in scale range inherent to conventional pain rating scales. As such, it is well suited to experimental studies that must quantify a wider range of perceptual intensity or distinguish between painful and nonpainful events. PerspectiveThis article presents the stimulus–response characteristics of a new scale designed to allow participants to rate a range of nonpainful and painful stimuli. The scale could be useful for research that involves exposing participants to a range of stimulation intensities or requires a clear distinction between nonpainful and painful events.

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