Abstract
ABSTRACT
Masticatory performance is a measure of oral physiology that affects food breakdown and eating behavior. Existing methods to quantify masticatory performance rely on labor‐intensive procedures (e.g., sieving expectorated samples), or proprietary software (e.g., ViewGum), limiting adoption and reproducibility. In this cross‐sectional methods validation study, we: (1) examine the convergent validity of chewR, a user‐friendly R package that quantifies masticatory performance in a 2‐color gum mixing task via automated image analysis, and (2) examine how chewR based masticatory performance measures relate to other oral processing behaviors. Adults (
n
= 47, 601% female) completed a laboratory visit with a 2‐colored gum mixing task, stimulated salivary flow, and eating rate objectively measured using Tang's carrot test. Chewed gum samples were flattened with a standardized method, scanned, and resulting images were analyzed using ViewGum and chewR. ViewGum generates a score based on the gum color mixing, with lower scores indicating greater masticatory performance. chewR produces a raw score based on gum color mixing; from this, a transformed chewR Masticatory Performance Score was calculated, with higher values indicating greater masticatory performance. chewR measures showed strong convergent validity with ViewGum Score: the raw chewR score and transformed chewR Masticatory Performance Score were each strongly correlated with ViewGum Score (
r
's = ±0.81,
p
r
= 0.12), self‐reported eating rate (
r
= 0.20), and stimulated salivary flow (
r
= 0.21); the same associations were similarly weak for ViewGum Score. These findings support chewR as an open‐source alternative to ViewGum to quantify masticatory performance in a gum mixing task.