Type1 diabetes (T1D) is an immune disease characterized by the destruction of the beta cells of the pancreas responsible for the production of insulin, a hormone that plays a primary role in blood glucose regulation. People with T1D are faced with daily challenges of optimization since they require multiple daily infusions of optimal insulin doses. One of the major disturbances of glycemic control is physical activity. Despite its benefits, exercise is usually associated with higher risks of low glucose levels. The fear of hypoglycemia results in either avoidance of engaging in a physical activity or over- compensatory treatment behaviors that lead to a worse metabolic control.This dissertation project focuses on enabling physical activity for T1DM patients by generating real time feedback of the current risks associated with exercise and advising on insulin dose adjustments and carbohydrate intakes.Using linear statistics techniques, we identified the major factors predictive of the post exercise glycemic response in a relatively large dataset of T1D patients. Based on this analysis, we developed a classification method able to warn T1D patients in advance of a high risk for hypoglycemia associated with physical activity, potentially allowing patients to delay exercise or take preventive actions.The linear statistical models were the foundation in the design and implementation of a decision support system (DSS) for people with T1D to safely engage in a physicalactivity. The DSS has built-in optimized strategies to mitigate the risk of exercise- induced low glucose levels. The system has been validated in the University of Virginia/University of Padova FDA approved T1D simulator and will be deployed in clinical trials in the near future.