Towards the Use of Instrumented Tests for Screening the Risk of Falling in Patients With Chronic Illness.

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

Inertial sensors, Timed Up and Go test, 6-Minute Walk test, Fall, Risk of fall, Altered mobility, Chronic illness, Chronic pathologies, Physical capacities, Physical test, Activity limitations

In patients with chronic illness, screening for falls and their health consequences are major public health issues. Muscle weakness, gait and balance disorders are among the most common risk factors for falling. Assessing these parameters would thus be a crucial step in the evaluation of the risk of falling, allowing to more precisely orient the management strategy. Combining inertial unit sensors with clinically validated tests can provide additional information to improve the assessment of fall risks. We therefore propose to constitute a monocentric exploratory study, testing a prognostic screening tool, in patients suffering from a chronic pathology, in order to assess the risk of falling in this population. Considering the relationship between muscle weakness and the risk of falling, we can assume that a deficit in muscle strength will result in less vertical acceleration which could point to a muscle cause of the balance disorder and thus allow a finer detection of the risk of falling. On the other hand, we hypothesize that spinal static disorders in chronic pathologies and in particular osteoarthritis, as well as balance disorders linked to impaired deep sensitivity lead to an increase in oscillations of the trunk when walking which can cause postural balance disorders thus increasing the risk of falling.

Investigators propose to constitute a prospective cohort of subjects carrying chronic disease. The main objective is to study the correlation between the vertical acceleration during the sit to stand phase of the instrumented Timed Up and Go test (TUG) and the isokinetic muscle strength of the quadriceps. In addition, as secondary objectives, we will study the potential correlations between the other parameters collected with the inertial sensors during instrumented tests (Timed-Up and Go test, and 6-Minute Walk Test) and the isokinetic muscle strength of the lower limb muscles, posturography parameters, gait parameters, apprehension of fall and others. In the present protocol, parameters will be measured once at inclusion day (to). Only the falling incidences will be collected prospectively at 6 months and at 1 year from the inclusion day. Statistical analyses will be carried out using Stata software (version 13, StataCorp, College Station, USA). Qualitative variables will be described in terms of numbers and associated percentages. The quantitative variables will be described in terms of numbers, associated mean and standard deviation, median and interquartile range. Wherever is possible, graphic representations will be associated with these analyses. All tests will be performed for a bilateral hypothesis and a p-value \<5% will be considered statistically significant. For the main outcome, the Pearson correlation coefficient (or Spearman if data are not normally distributed) will be calculated with its 95% confidence interval. The analysis of the relationships between the continuous criteria will be carried out using Pearson correlation coefficients (or Spearman if data are not normally distributed). The search for fall risk factors (at 6 months / 1 year) will be carried out using standard tests: * Chi-square test (or exact Fisher test when appropriate) for the categorical criteria. * Student test (or Mann and Whitney test if data not normally distributed) for continuous criteria. These analyses will be completed using a logistic regression model by adjusting to the clinically relevant criteria or highlighted in the univariate analysis. Results will be presented as an odd ratio with their 95% confidence interval Little or no missing data is expected on the main criteria, however if a rate of missing data\> 5% is observed, a sensitivity analysis for missing data will be carried out in order to characterize their nature (MCAR, MAR, MNAR) in order to propose the most suitable imputation method.

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