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NPJ Parkinsons Dis. 2018 Aug 20;4:26. doi: 10.1038/s41531-018-0059-z. eCollection 2018.

Differentiating cognitive or motor dimensions associated with the perception of fall-related self-efficacy in Parkinson's disease.

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1Division of Translational Neuroscience, Department of Clinical Neurosciences, Hotchkiss Brain Institute, Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1 Canada.
2Department of Medicine, Division of Neurology, Clinical Sciences Building, University of Alberta, Edmonton, AB T6G 2R7 Canada.
3Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3 Canada.


In Parkinson's disease (PD), concurrent declines in cognitive and motor domain function can severely limit an individual's ability to conduct daily tasks. Current diagnostic methods, however, lack precision in differentiating domain-specific contributions of cognitive or motor impairments based on a patients' clinical manifestation. Fear of falling (FOF) is a common clinical manifestation among the elderly, in which both cognitive and motor impairments can lead to significant barriers to a patients' physical and social activities. The present study evaluated whether a set of analytical and machine-learning approaches could be used to help delineate boundary conditions and separate cognitive and motor contributions to a patient's own perception of self-efficacy and FOF. Cognitive and motor clinical scores, in conjunction with FOF, were collected from 57 Parkinson's patients during a multi-center rehabilitation intervention trial. Statistical methodology was used to extract a subset of uncorrelated cognitive and motor components associated with cognitive and motor predictors, which were then used to independently identify and visualize cognitive and motor dimensions associated with FOF. We found that a central cognitive process, extracted from tests of executive, attentional, and visuoperceptive function, was a unique and significant independent cognitive predictor of FOF in PD. In addition, we provide evidence that the approaches described here may be used to computationally discern specific types of FOF based on separable cognitive or motor models. Our results are consistent with a contemporary model that the deterioration of a central cognitive mechanism that modulates self-efficacy also plays a critical role in FOF in PD.

Conflict of interest statement

B.H. invented the Ambulosono data system and GaitReminder App which is under investigational use. The remaining authors declare no competing interests.

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