Rasch analysis of the anterior knee pain scale in adolescents with patellofemoral pain. (December 2020)

• Record Type: Journal Article
• Authors: Selhorst, Mitchell; Fernandez-Fernandez, Alicia; Cheng, M Samuel
• Is Part Of: Clinical rehabilitation. Volume 34:Number 12(2020)
• Page Start: 1512
• Page End: 1519
• Publication Date: 2020-12
• Subjects: Anterior Knee Pain Scale -- patellofemoral pain -- Rasch Model -- rehabilitation; Medical rehabilitation -- Periodicals; 617.03
• DOI: 10.1177/0269215520942950
• Languages: English
• ISSNs: 0269-2155

Abstract

Objective: The aim of this study was to evaluate the Anterior Knee Pain Scale in a cohort of adolescents being treated conservatively for patellofemoral pain using Rasch analysis. Design: This is a psychometric study. Setting: Physical therapy clinics of a large pediatric hospital in Columbus, Ohio (United States) Subjects: A total of 646 adolescent patients with patellofemoral pain (76% female, 14.6 ± 1.6 years old). Intervention: Not applicable. Main Measure: The Anterior Knee Pain Scale. Results: The median Anterior Knee Pain Scale score was 73 (interquartile range 64–81), with scores ranging from 7 to 100 on the 100-point scale. The Rasch person reliability for the Anterior Knee Pain Scale was 0.74 and the Cronbach's alpha was 0.75, representing an acceptable person reliability. Principal component analysis revealed a ratio of 5.2:1 demonstrating acceptable unidimensionality of the Anterior Knee Pain Scale. A significant misfit was observed in the item "Abnormal Painful Kneecap Movements" (Outfit Means Square 2.74, Infit Means Square 1.41). Ordering of item responses was unsatisfactory as only five of the 13 items demonstrated appropriate distinction between each of the responses. There was no differential item functioning for sex or age for all items of the Anterior Knee Pain Scale, based upon the criterion of ⩾ 0.5 logit difference. Conclusion: The Anterior Knee Pain Scale does not meet interval-level measurement criteria and should be considered ordinal level data.