Introduction: Pruritus is a common symptom across various dermatologic conditions, with a negative impact on quality of life. Devices to quantify itch objectively primarily use scratch as a proxy. This review compares and evaluates the performance of technologies aimed at objectively measuring scratch behavior.
Methods: Articles identified from literature searches performed in October 2020 were reviewed and those that did not report a primary statistical performance measure (eg, sensitivity, specificity) were excluded. The articles were independently reviewed by 2 authors.
Results: The literature search resulted in 6231 articles, of which 24 met eligibility criteria. Studies were categorized by technology, with actigraphy being the most studied (n = 21). Wrist actigraphy's performance is poorer in pruritic patients and inherently limited in finger-dominant scratch detection. It has moderate correlations with objective measures (Eczema and Area Severity Index/Investigator's Global Assessment: rs(ρ) = 0.70-0.76), but correlations with subjective measures are poor (r2 = 0.06, rs(ρ) = 0.18-0.40 for itch measured using a visual analog scale). This may be due to varied subjective perception of itch or actigraphy's underestimation of scratch.
Conclusion: Actigraphy's large variability in performance and limited understanding of its specificity for scratch merits larger studies looking at validation of data analysis algorithms and device performance, particularly within target patient populations.
Keywords: AD, Atopic dermatitis; PPV, Positive predictive value; RMSE, Root mean square error; TST%, total scratching time percentage; VAS, Visual analog scale; algorithm; atopic dermatitis; disease management; drug development; eczema; general dermatology; itch; machine learning; pediatric dermatology; pruritus; technology.
© 2021 by the American Academy of Dermatology, Inc. Published by Elsevier Inc.