Abstract
The aim of this pilot study was to assess the compliance of breast cancer (BC) patients with fitness tracker (FT) monitoring program during radiotherapy (RT) and to characterize radiation-induced fatigue (RIF) status through objective evaluation using FT-collected parameters. Thirty-six BC patients were invited to wear FT during their RT course for continuous monitoring of heart rate (HR) and step counts (STP). RIF assessment was performed weekly, according to CTCAE v5.0 and dichotomized into G0 vs. any-grade. A novel concept based on patient Repeated Activity Window (RAW) was introduced to evaluate HR and STP variations during RT. Several Machine Learning (ML) methods were trained to characterize RIF on the basis of HR and STP collected data. RIF of any grade was reported by 17 out of 36 patients (47%) included in the study. None of patient clinical variables were significantly correlated with RIF. All patients accepted the FT monitoring program, and for 32 patients FT collection efficiency was greater than 60%. For each patient, a distinct distribution of RAWs was identifiable over RT and across the entire patient cohort, with a total of 7950 RAWs processed. Six features related to RAWs, HR and STP were identified as associated with RIF. The best-performing classifier was the Bagged Trees model, showing a cross-validated ROC-AUC of 89% (95% CI 88-90%). This study confirms the feasibility of continuous biomedical monitoring of BC patients by FT. We successfully identify objective indicators of RIF through HR and STP variation measures within each patient's RAW, thus providing a novel and practical approach to assess and manage RIF. This can significantly aid medical staff in evaluating RIF trajectories, potentially leading to better individualized care strategies and improved patient outcomes.