BackgroundFalls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients.MethodA population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types.ResultsThe results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test.ConclusionsThis study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide the right treatment as soon as possible before the fall and its consequences. This tool could also be used to evaluate the risk several times during the revalidation procedure.

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Background As cognitive functions and, more specifically, executive functions (EF) seem to influence autonomy among the elderly, we investigated the role of each of the five EF sub-components (inhibition, spontaneous flexibility, reactive flexibility, planning, and updating in working memory) for the risk of functional decline. Method A total of 137 community-dwelling participants over 75 years of age were included in a prospective cohort study and assigned to three groups: individuals with neuro-degenerative cognitive disorders, those having cognitive disorders with non-degenerative aetiology, and a control group without any cognitive problems. We measured each EF sub-component and assessed functional decline by evaluating basic (b-ADL) and instrumental activities of daily living (i-ADL) at baseline and 6 months later. We conducted three separate multiple logistic regression models to examine the extent to which the five EF facets predicted overall functional decline at the end of the follow-up period. Results We found that people who exhibited a decline in b-ADLs or/and i-ADLs over 6 months had worse performance on inhibition and two flexibility tasks than those who did not experience a decline. The results suggest that decliners have more difficulties in managing unforeseen events. Inhibition and updating in working memory predicted a decline in b-ADL while spontaneous and reactive flexibilities predicted a decline in i-ADL. Conclusion In our sample, specific executive dysfunctions were associated with a decline in functional status. With respect to the risk of decline in b-ADL, deficits in inhibition may represent a risk factor, as it regulates over-learned activities. Bothtypes of flexibility, which allow the shifting and generating of adaptive responses, predicted decline in i-ADL. In sum, paying more attention to particular EF profiles would help clinicians to anticipate some aspects of functional decline.

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See the Reply by Piers et al.