Slip and falls continue to be one of the leading causes of work-related injuries. The reduction of these injuries is dependent upon improved identification of slippery conditions and the design of proper shoes and floors for various environments. The long-term goal of this research project, funded by a NIOSH R01 research grant, was to reduce injuries due to slips and falls in the workplace through the development of a computational model that predicted the coefficient of friction of the shoe-floor-contaminant interface. The model was based upon: micro-level properties of the shoe-floor-contaminant interface (material and surface characteristics along with lubrication properties) and macro-level designs of the shoe (i.e., tread and shape) and floor. The micro-level model included measurements of properties of shoes, floors and contaminants that were combined in a computational model based upon fundamental tribological relationships. The macro-level model incorporated the micro-level model into a finite element representation of the shoe-floor interface. Micro-level model predictions were compared to the current tribological coefficient of friction testing and macro-level model predictions were compared to currently used shoe-floor interface slip resistance testing devices. The model predictions were also compared to actual human slips and falls during gait to determine the efficacy in predicting slips and/or falls.