Type 1 diabetes is a chronic disease that results from insufficient insulin production by the pancreas. While artificial pancreas systems have emerged as an alternative therapy, current commercial devices do not account for physical activity in their control algorithms. A major issue is the scarcity of high-quality and unbiased datasets comprising physical activity records. This study investigates the application of transfer learning techniques and physical activity data to enhance glucose prediction. To this end, we propose three distinct methods: a graph topology model, a substitution model and a retraining method. A comprehensive analysis was conducted to assess accuracy, delay and clinical utility. The study revealed that the substitution model exhibited superior accuracy and reduced delay compared to the base model. The study compares the graph topology model and the retrained model, with the former proving to be the best for a prediction horizon of 30 minutes, obtaining a RMSE of 21.63 mg/dL compared to the RMSE of 21.85 mg/dL obtained with the retrained model. For a prediction horizon of 60 minutes, the graph topology model achieved an RMSE of 33.87 mg/dL in comparison to the 34.14 mg/dL of the retrained model. In the Error Grid analisis for a prediction horizon of 30 minutes, both the graph (98.56\%) and the retrained model (98.41\%) achieve results close to the acceptable range. However, with a prediction horizon of 60 minutes, the clinical utility drops significantly (96.4\% vs. 96.21\%). These findings underscore the importance of incorporating physical activity data and the need for further exploration of approaches that account for their impact.