State-of-the-Art: Especially in the frail older and neurodegenerative affected population, gait and balance control are, additional to brain changes, affected by a large number of factors, such as medication use and the presence of co-morbidities. These factors are often not sufficiently considered in respective assessment batteries. Also more general factors such as fear of falling are often absent in such assessments, although they are obviously highly relevant to the topic. Physical, emotional, cognitive, behavioural, interpersonal and social factors and the interaction between them can play a major part in causing and/or maintaining impaired gait and balance in older adults and patient groups. Consideration of these aspects may substantially improve study outcomes. Moreover, there is an urgent need to harmonise the assessment approaches of studies in networks (ie, with similar foci) as presented in the proposed ETN. Such a harmonisation will increase the feasibility of including large numbers of variables in statistical approaches, and of generating innovative concepts by meta-analyses.

Approach: This project is dedicated to harmonise data collection across the entire Keep Control network. It will be specifically responsible for the implementation of the domains described by the ICF model. Consideration of the ICF model guarantees that all five domains involved in health and disability, i.e. body functions and structures, activities (of daily living participation (in societal roles), personal features and environmental factors are sufficiently considered in the respective projects. Moreover, the project will be responsible for the validation of the ICF-based protocols over the network and the implementation of participant’s own input on physical function, activity and participation. Another objective will be the aggregation and analysis of the interactions between multiple data streams and information about a participant’s objective and subjective measures across the different ICF domains. Then, this project will perform a meta-analysis using data-mining / pattern recognition methods on data obtained at different research sites. The already available dataset introduced in WP2.3 will serve as a training / hypothesis-generating dataset for this approach. Eventually, the project will develop data reduction algorithms to generate informative user-feedback based on the entire dataset, and targeted feedback on individual capacities based on the different ICF domains.