ATOPE+: Supporting Personalized Exercise Interventions in Breast Cancer Care using Mobile Technologies and Machine Learning

Abstract

Alleviating the burden of breast cancer has become in one of the biggest challenges of our times. The advances in surgery, radiotherapy, and systemic therapy have improved the survival rates of patients with breast cancer, but have also produced a higher number of patients suffering short- and long-term side effects, with high the risk of recurrence, developing comorbidities, and death. Therapeutic exercise poses a means to address this issues; however, exercise interventions in patients with cancer are often adhered to the same therapeutic exercise guidelines. This results in one-size-fits-all exercise prescriptions for all adults, regardless their individual exercise capabilities and needs, which may lead to inadequate training adaptation. The mobile health (mHealth) paradigm has enabled the remote and individual monitoring of health through wearable sensors and smartphones. Personalizing training adaptation with an mHealth approach has already been successfully conducted in sports settings, and the literature suggests that similar strategies may translated to patients with chronic conditions such as breast cancer. However, recent works do not target the adjustment of training doses to the individual needs of the patients. This thesis presents three contributions to support the personalization of therapeutic exercise intervention in patients with breast cancer. First, ATOPE+, an mHealth system to support the remote monitoring of patients’ training load through heart rate variability (HRV), self-reported wellness, and Fitbit physical activity and sleep data. ATOPE+ also integrates a decision-support system with expert rules that automatically trigger daily exercise recommendations for patients. Second, the ATOPE+Breast dataset, an open dataset describing the continuous evolution of training load during therapeutic exercise intervention for 23 patients with breast cancer. Third, a clustering approach to assess training needs in patients with breast cancer. Data science and artificial intelligence (AI) are leveraged in this approach to better understand the different states of the patient throughout an exercise intervention, and eventually serve as a tool to make more informed decisions when prescribing an exercise dose. The potential of these contributions may lead to new research directions in the personalization of therapeutic exercise interventions in real-life scenarios, specially regarding the application of mHealth and AI to improve chronic conditions.