نبذة مختصرة : The authors express their gratitude to the patients and experts for their participation in the evaluation of ATOPEC. They also express their gratitude to the anonymous reviewers for the instructive criticism of an earlier version of this article. ; The introduction of mobile technologies in therapeutic exercise interventions has permitted the collection of fine-grained objective quantified information about patients' health. However, exercise interventions generally fail to leverage these data when personalizing the exercise needs of patients individually. Interventions that include technology-driven personalization strategies typically rely on the use of expensive laboratory equipment with expert supervision, or in the self-management of patients to meet the prescribed exercise levels by an activity tracker. These methods often do not perform better than non technology-driven methods, therefore more sophisticated strategies are required to improve the personalization process. In this paper we present ATOPE+, an mHealth system to support personalized exercise interventions in patients with cancer based on workload-recovery ratio estimation. ATOPE+ enables the remote assessment of workload-recovery ratio to provide optimal exercise dosage by means of a knowledge-based system and by combining physiological data from heterogeneous data sources in a multilevel architecture. The results show that ATOPE+ is a system ready to be used in the context of a clinical trial after being tested with patients with breast cancer and conducting an usability evaluation by clinical experts. ; Spanish Ministry of Science, Innovation, and Universities (MICINN) PGC2018-098813-B-C31 RTI2018-101674-B-I00 ; Health Research Funds of the Carlos III Health Institute PI18/01840 ; German Research Foundation (DFG) FPU16/04201 FPU17/00939
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