MOBility Disorders Assessment in Patients With Mild COGnitive Disorders

Condition(s) studied

Investigational drug(s) / intervention(s)

Gait disorders and falls risk assessment: Implementation of a standardized assessment of the fall risk and gait disorders in the memory clinic in addition to usual care.

Study summary

Mild cognitive impairment (MCI) is defined by lower performance in one or more cognitive domains with preservation of independence in functional abilities. Sixteen percent of community-dwelling older people (over 65 years) live with MCI. They are both cognitively and physically vulnerable. From a cognitive perspective, they are susceptible to converting to the dementia stage at an annual rate of 10%. From a physical perspective, the proportion of slow gait or neurological gait abnormalities can reach 46% in the population with MCI. Falls in turn increase the risk of accelerated cognitive decline and the risk of institutionalization. In the absence of a curative treatment for dementia, it is essential to have an effective and personalized prevention strategy by identifying the predictive factors for falls in this at-risk population with MCI.

The research goals of this project are 1) to identify specific predictors for falls in clinic attendees with MCI in preparation for a definitive, fully powered study across France, and 2) to demonstrate the feasibility of a pragmatic fall risk assessment in MCs, whatever its setting and location.

We aim to prospectively follow-up people diagnosed with MCI and aged above 65 years old in four MCs in France (three in the North (one community-based MC), and one in the Centre) for one year.

Eligibility

Primary outcome measure(s)

• Existence of at least one fall — 12 months
  The primary objective is to identify factors predictive of having at least one fall at one year First, the relationship between each candidate risk factor (independent variables) and the presence of at least one fall at one year (binary dependent variable) will be studied by bivariate logistic regression models. For quantitative candidate factors, the log-linearity assumption of the logistic regression model will be tested using cubic spline functions, which models the non-linear effect. The 10 pre-selected factors will be included in a multivariate logistic regression model, regardless of the outcome of the bi-variate analyses. The multivariate analysis will be performed following the recommendations of Peduzzi et al on the number of variables to include in a multivariate model (10 events per estimated parameter).

Trial sites (2)

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