Statistical Analysis of Age-related Skin Parameters

Background: Due to the increasing interest in human anti-aging, demand for a higher quality of life, and technological advancement, the development of anti-aging skincare has great market prospects. Most cosmetic companies develop products driven by the market or focus on the mechanism of action of substances and the behavior of skin; however, little research utilizes skin parameters and large data methodology to develop skincare products.

Objective: To instruct consumers to purchase skincare products and to guide skincare research toward the development of customer-targeted products.

Methods: A total of 815 Chinese subjects (83 male; 732 female) from five different cities were included. We measured 14 indices in each subject, including moisture, transepidermal water loss (TEWL), and sebum levels. We performed multiple regression analysis to understand the relationship between skin indices and aging; a novel approach is shown using the R software.

Results: The exact age at which changes in each skin index occurred could be demonstrated by this method of analysis: 39, 38, 48, 46, and 56 years old with respect to the L value, Melanin, Rt, Rm, and Rz, respectively.

Conclusion: With the use of statistical analysis, consumers can be more efficiently targeted and choose suitable products considering particular skin parameter changing points; thus, skincare companies will not only meet customer requirements but also better control budgets.