The influence of the context on mobility in neurological disorders : A wearable technology approach

The evaluation of mobility of patients with neurodegenerative diseases is crucial for healthcare professionals to tailor individual treatments and track disease progression. More individualized treatment has high potential to improve quality of life and mobility, and decrease fall risk. Nowadays mobility is mainly assessed during clinical examinations. However, with the rise of digital wearable technology, it has become possible to quantify mobility objectively in different settings. It is however unclear how mobility data collected in different settings, or more general different contexts, are associated with each other. Therefore, the aim of this dissertation was to understand the influence of context on mobility in older adults and patients with neurodegenerative disorders.

The first study revealed that supervised capacity and unsupervised performance measures can substantially differ from each other. Consequently, both measures provide complementary information that can be used to gain a better understanding of daily function.

In the second study an algorithm to quantify arm swing was developed and validated. This algorithm was used in the third study, which showed that the effect of dopaminergic medication on arm swing in patients with PD is influenced by medication state and task complexity. We therefore highly recommend to assess patients in different contexts to get a better understanding of the effect of treatment or the disease progression. To be able to assess mobility of patients in different context more wearable sensor-based algorithms are required. With the dataset introduced in the last paper, an indefinite number of additional movement and mobility algorithms can be developed and validated. The development and validation of these algorithms can further move our understanding of the influence of context on mobility forward.


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