Evaluation of Impaired Mobility in Chronic Illness Constitution of a Cohort

Atherosclerosis of the Distal Arteries, Without Gangrene, Chronic Obstructive Pulmonary Disease, Unspecified, Joint Osteoarthritis, Obesity, Unspecified, Parkinson Disease

Prevention, Mobility, Altered mobility, Risk factors, Chronic illness, Chronic pathologies, Physical capacities, Physical test, Activity limitations

Chronic illness is a public health issue and mobility loss is frequent in this population. Among its' multiple physical and psychological consequences, increased mortality and cardiovascular morbidity seem the main concern. Therefore, the exploration of locomotor deficiencies, physical capacities and metabolism of patients with chronic illnesses constitutes a major challenge both for the treatment of causal pathologies, as well as for evaluating the impact of therapeutic interventions, the benefit of which will be an improvement in physical capacities and ultimately mobility. In view of the hypothesis of an increase in the prevalence of mobility disorders in this population, this approach is part of a logic of screening and improving the effectiveness of the care of these patients with a multidisciplinary evaluation of individual risks. The EVALMOB protocol was designed in order to try to determine a standard profile of dysmobility in patients with chronic illness

We propose to constitute a prospective cohort of subjects carrying chronic disease. The objective is to explore all the different components of mobility (balance, muscle force, body composition, walking ability, metabolism, etc.), to assess their impact on the functional capacity of individuals and to identify their potential interactions. Processing this data could ultimately allow the development of a model to determine a composite standard profile of dysmobility in patients with chronic disease. In the present protocol, parameters will be measured on five occasions (at inclusion, at 6 months from the inclusion day, at 1,2 and 5 year(s) from the inclusion day). All tests will be performed on the same day. New assessments will be done on the same principle as the initial evaluation. Statistical analyses will be carried out using Stata software (version 13, StataCorp, College Station, USA). Data will be described by frequencies and percentages for categorical variables and by means and standard deviation (or median and interquartile range if data are not normally distributed) for continuous variables. The normality of continuous data will be assessed graphically and using the Shapiro-Wilk test. The main analysis will consist in determining patient profiles regarding their mobility. Clustering-type approaches (supervised or not) will be proposed: k-means, vector machine support, machine learning, factor analysis. For example, factor analyses on mixed data will allow the main components of mobility to be characterized. They can be followed by an ascending hierarchical classification in order to determine homogeneous groups of patients. These groups will be described and compared on the main criteria evaluated using standard statistical tests: chi2 test (or Fisher's exact test if applicable) for the categorical criteria and using an analysis of variance (or Kruskal-Wallis if data are not normally distributed) for continuous criteria. The main analysis will be broken down more specifically for each of the pathologies considered. In addition, the sensitivity to change will be assessed in each pathology and for each assessment criterion. The search for factors related to the evolution of the different criteria will be carried out using usual tests and mixed multivariate models (logistics for categorical / linear criteria for continuous criteria) considering the subject as a random effect, and adjusting on the time criteria (inclusion / follow-up) and the criteria highlighted in the univariate analyses and in light of the elements reported in the literature. A sensitivity analysis will be proposed in order to study the statistical nature of the missing data and to propose, if necessary, the most suitable method of imputing the data: multiple imputation, maximum bias or LOCF in the case of longitudinal data.

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