Predicting the Unpredictable: Advancing Risk Management in Fitness Centers Through Data Science-Driven Judicial Outcome Modeling

This study developed a prediction model of judicial outcomes in fitness exercise-related injury lawsuits to identify key factors influencing fitness centers’ success in litigation. Using 209 U.S. court cases from Westlaw, Phase One employed text mining and content analysis to identify legal variables. Phase Two applied descriptive statistics and multiple logistic regression to evaluate the combined influence of these variables on judicial outcomes. For the multiple logistic regression analysis, the sample size was reduced to 184 cases after excluding 25 remanded cases. Results showed that victim behavior, hazardous facility conditions, and enforceable waivers or exculpatory clause defenses significantly predicted judicial outcomes. Fitness centers implementing staff training, providing warnings of risks, conducting facility and equipment inspections with maintenance, and utilizing enforceable membership agreements demonstrated a higher likelihood of prevailing in litigation. These findings offer a data science-driven framework for refining risk management practices and fostering a safety culture in fitness environments.