Association of a Digital Signature of Physical Activity and Sedentary Lifestyle With the Development of Multimorbidities in Chronic Diseases (eMOB)

Physical activity (PA) and sedentary lifestyle are determinants in the development of chronic diseases and associated quality of life alterations. PA levels are correlated with quality of life and morbidity in chronic lung disease (COPD), cardiovascular disease, diabetes, cancer, chronic inflammatory rheumatism, fibromyalgia and anxiety disorders. In these diseases, low-intensity PA often represents the main PA and the quantity of PA is correlated with health parameters. This study aims to identify a typical profile (signature) in relation to the appearance of other chronic diseases, complications of your disease, from recording your physical activity and sedentary lifestyle by sensors evaluating very precisely the movements and taking into account the characteristics and disability related to your disease. The results of this study would therefore make it possible to identify this signature even before the appearance of complications or other diseases.

Study design : eMOB study is a monocentric prospective cohort study. We aim to explore in a longitudinal study the association of a digital signature of PA and sedentary lifestyle with the development of at least one additionnal comorbidity overs 4 years of follow-up in chronic diseases. Population : Patients with inflammatory rheumatic disease(rheumatoid arthritis and spondyloarthritis), pre-surgical knee and hip osteoarthritis, major depressive disorder and bipolar disorder, type 2 diabetes, obesity, COPD and chronic pain followed up in the observational cohorts specific to each pathology at the CHU of Clermont Ferrand, will be proposed to participate in the study during their follow-up visit. Method : The occurrence of comorbidity is monitored every year during follow-up visits of the patients. Physical activity and sedentary lifestyle is assessed using accelerometer and wearable sensors. The combination of quantitative analysis of movement with a more detailed analysis of movement and sequences of movements, and a personalization of the algorithm according to the disease and the context, could make it possible to establish predictive signatures either of the disease itself or of associated complications. 700 patients will be included in the study.They will be followed for 4 years with : * An annual visit during which : * clinical data in the routine follow-up will be collected (BMI, waist circumference, blood pressure, heart rate, comorbidities, activity and severity scores specific to each disease and used for the routine follow-up, treatments, lifestyle, family and socio-professional situation, education level) * comorbidities will be noted by the list used for the study * physical capacity will be assessed with the 6-minute walk test and the hand grip * questionnaires about Pain (QCD), Fatigue (FACIT-F), Anxiety and Depression (HADs), Quality of life (WhoQol-BREF), Poverty (EPICES), Physical activity questionnaire (RPAQ), Barriers and Facilitators to physical activity questionnaire (B\&F-AP), Sleep and daytime sleepiness (PSQI and Epworth), treatment compliance (MARS) will be completed * specific data for each pathology in each cohort will be collected * Accelerometer data 3 times a year for 5 days (3 weekdays and 2 weekend days) will be collected * Electronic patient report outcomes (e-PRO) every 4 months concerning the socio-professional situation of the patients, alcohol and tobacco consumption, skipping meals and snacking between meals, health problems they may encounter during the study. Numerical scales between 0 and 10 will be completed to assess disease status, health status, pain, sleep, fatigue, psychological well-being, and anxiety. Among the included patients, 10 patients per cohort will participate in the preliminary feasibility phase of the study before starting the follow-up in the study. It is a validation of the acquisition modalities of the pre-selected sensors and a calibration on representative samples of patients with the different chronic pathologies studied (locomotor, respiratory, metabolic, psychiatric, pain).