Validation of Digital Visual Analog Scale Pain Scoring With a Traditional Paper-based Visual Analog Scale in Adults

Abstract

Background:

The visual analog scale (VAS) is a validated, subjective measure for acute and chronic pain. Scores are recorded by making a handwritten mark on a 10-cm line that represents a continuum between “no pain” and “worst pain.”

Methods:

One hundred consecutive patients aged ≥18 years who presented with a chief complaint of pain were asked to record pain scores via a paper VAS and digitally via both the laptop computer and mobile phone. Ninety-eight subjects, 51 men (age, 44 ± 16 years) and 47 women (age, 46 ± 15 years), were included. A mixed-model analysis of covariance with the Bonferroni post hoc test was used to detect differences between the paper and digital VAS scores. A Bland–Altman analysis was used to test for instrument agreement between the platforms. The minimal clinically important difference was set at 1.4 cm (14% of total scale length) for detecting clinical relevance between the three VAS platforms. A paired one-tailed Student t-test was used to determine whether differences between the digital and paper measurement platforms exceeded 14% ( P < 0.05).

Results:

A significant difference in scores was found between the mobile phone–based (32.9% ± 0.4%) and both the laptop computer– and paper-based platforms (31.0% ± 0.4%, P < 0.01 for both). These differences were not clinically relevant (minimal clinically important difference <1.4 cm). No statistically significant difference was observed between the paper and laptop computer platforms. Measurement agreement was found between the paper- and laptop computer–based platforms (mean difference, 0.0% ± 0.5%; no proportional bias detected) but not between the paper- and mobile phone–based platforms (mean difference, 1.9% ± 0.5%; proportional bias detected).

Conclusion:

No clinically relevant difference exists between the traditional paper-based VAS assessment and VAS scores obtained from laptop computer– and mobile phone–based platforms.