Abstract
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.
Highlights
To reduce noise in the data and extract underlying features that are most sensitive to fear of falling (FOF), a dimensionality reduction was performed through principal component analysis (PCA)
In the current study we performed a new set of statistical procedures with the aim to more precisely identify the underlying features that are most sensitive to FOF
We found through dimensionality reduction that a cognitive domain component can serve as an independent predictor of FOF in Parkinson’s disease (PD)
Summary
A common feature of progressive neurodegenerative diseases, such as Parkinson’s disease (PD), is that clinical manifestations often involve degenerative pathology in multi-systems in which motor and cognitive dysfunction can co-exist and interact to complicate efforts in symptom diagnosis and intervention.[1,2,3] In. PD, which carries one of the highest fall risk among neurological illnesses,[4] both cognitive and motor-function decline can affect an individual’s ability to conduct daily tasks.[1,5,6] For example, fear of falling (FOF), a function of one’s perceived risk of falling and cognitive functioning, is a common concern among the elderly and poses a significant barrier to physical and social activities, which can lead to a downward spiral in general health.[1] FOF is significantly associated with actual falls, which together, may lead to a self-induced restriction of activity, reductions in muscle strength, and general physical de-conditioning that may serve to further increase fall risk.[1,7,8] Importantly, FOF in and of itself is a significant determinant of health-related quality of life even more so than balance impairments or actual falling.[9,10,11] motor impairment and poor gait function are associated with FOF,[9,12,13,14,15] individuals with PD can have obvious motor impairment and experience falls, but lack FOF.[16] It would be highly desirable to develop precision medicine methods that would be able to differentiate domain-specific contributions of cognitive or motor impairments with respect to FOF
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