Abstract <p>Physical activity (PA) represents a significant challenge in the management of type 1 diabetes (T1D), given its impact on glucose levels, which are influenced by various exercise characteristics, including duration, intensity, and type. The development of strategies that allow for the monitoring of these characteristics is crucial to improve glycemic control during exercise for both conventional therapies and automated insulin delivery systems. This paper presents a state-space model that further exploits the HR signal for the purpose of quantifying and distinguishing between aerobic and anaerobic PA. The model design is based on an analysis of the distinctive features of HR signal, including the mean HR value, the maximum HR, and the presence of pronounced fluctuations in HR. This method does not require any training and offers users interpretability and explainability. Furthermore, it enables intuitive tuning, a feature which is of particular importance in clinical settings. The model is validated using two clinical trials: the T1DEXI study, which is the largest real-world clinical trial including PA in people with T1D conducted to date, and a pilot clinical trial conducted by our research group in Argentina. The findings indicate the model has the capacity to quantify and differentiate between aerobic and resistance PA, which represent the two types of PA exhibiting the most significant and contrasting influence on glucose levels.</p> Graphical abstract <p>Illustrative example of the proposal and result of this paper. The first column shows the changes in glucose levels induced by physical activity in people with type 1 diabetes. The second column illustrates the expected heart rate during aerobic and anaerobic exercise. The third column present the output of the proposed dynamic model, which uses the heart rate signal to quantify whether exercise is predominantly aerobic or anaerobic.</p>