Breast Cancer Clinical Trial
Official title:
Evolution and Management of a Psychoneurological Symptom Cluster in Patients Who Had a Breast Cancer or a Digestive Cancer
A psychoneurological symptom cluster is increasingly documented in oncology. It is composed of cancer-related fatigue, sleep difficulties, pain, emotional distress, and cognitive difficulties. These symptoms are linked by strong but complex relationships, and reinforce each other, negatively impacting patients' quality of life and survival. The configuration of this cluster (i.e., the relationships between the symptoms) seems different according to the cancer diagnosis or moment in the cancer journey. It has however been very little studied. Network analysis is an innovative method that allows a deeper understanding of the interactions between these symptoms. It also allows to compare patterns of clustering between distinct populations or measurement times. Finally, it allows to determine one core symptom in a cluster (i.e., the one with the strongest associations with the other symptoms), which could represent a target of choice for interventions aiming to improve the whole symptom cluster. This innovative project has then two main goals. First, the investigators will assess the evolution of the psychoneurological symptom cluster in two populations of patients with cancer: women with breast cancer, and patients with digestive cancer, over two years. Second, the investigators will test the feasibility and preliminary benefits of a new mind-body group intervention specifically designed to address the core symptom of the cluster, determined with network analysis in each population. As suggested by many authors, the proposed intervention will be based on the common-sense model of self-regulation developed by Leventhal and focus on cognitive-behavioral, self-care and mind-body (i.e., hypnosis) empowering strategies. The aim is to assess the satisfaction of the participants regarding the intervention, as well as its impact on the symptoms involved in the cluster.
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