A Robust Skin Particulate Sebum, Oil, and Pollution for Assessing Cleansing Efficacy

Abstract

With increasing concerns over the rise of atmospheric particulate pollution globally and its impact on systemic health and skin aging, we have developed a pollution model to mimic particulate matter trapped in sebum and oils creating a robust (difficult to remove) surrogate for dirty, polluted skin.

Objective

To evaluate the cleansing efficacy/protective effect of a sonic brush vs. manual cleansing against particulate pollution (trapped in grease/oil typical of human sebum).

Methods

The pollution model (Sebollution; Sebum Pollution Model; SPM) consists of atmospheric particulate matter/pollution combined with grease/oils typical of human sebum. Twenty subjects between the ages of 18 to 65 were enrolled in a single-center, cleansing study comparisons between the sonic cleansing brush (normal speed) compared to manual cleansing.

Equal amount of SPM were applied to the center of each cheek (left and right). Method of cleansing (sonic vs. manual) was randomized to the side of the face (left or right) for each subject. Each side was cleansed for five seconds using the sonic with sensitive brush head or manually, using equal amounts of water and a gel cleanser.

Photographs (VISIA CR, Canfield Imaging, NJ, USA) were taken at baseline, before application of the SPM and following cleansing. Image analysis (Image J, NIH, Bethesda, MD, USA) was used to quantify color intensity (amount of particulate pollutants on the skin) using a scale of 0 to 255 (0=all black pixels; 255=all white pixels). Differences between the baseline and post-cleansing values (pixels) are reported as the amount of SPM remaining following each method of cleansing.

Results

Using a robust cleansing protocol to assess removal of pollutants (SPM; atmospheric particulate matter trapped in grease/oil), the sonic brush removed significantly more SPM than manual cleansing (p<0.001). While extreme in color, this pollution method easily allows assessment of efficacy through image analysis.

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