Background People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based soci...
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Background People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been *** In this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving *** The experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving *** The proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.
Background: Exergames often used for training purpose can also be applied to create assessments based on quantitative data derived from the game. A number of studies relate to these use functionalities developing spec...
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Background: Exergames often used for training purpose can also be applied to create assessments based on quantitative data derived from the game. A number of studies relate to these use functionalities developing specific assessment tasks by using the game software and provided good data on psychometric properties. However, (1) assessments often include tasks other than the original game task used for training and therefore relate to similar but not to identical or integrated performances trained, (2) people with diagnosed dementia have insufficiently been addressed in validation studies, and (3) studies did commonly not present validation data such as sensitivity to change, although this is a paramount objective for validation to evaluate responsiveness in intervention studies. Objective: Specific assessment parameters have been developed using quantitative data directly derived from the data stream during the game task of a training device (Physiomat). The aim of this study was to present data on construct validity, test-retest reliability, sensitivity to change, and feasibility of this internal assessment approach, which allows the quantification of Physiomat training effects on motor-cognitive functions in 105 multimorbid patients with mild-to-moderate dementia (mean age 82.7 +/- 5.9). Methods: Physiomat assessment includes various tasks at different complexity levels demanding balance and cognitive abilities. For construct validity, motor-cognitive Physiomat assessment tasks were compared with established motor and cognitive tests using Spearman's rank correlations (r(s)). For test-retest reliability, we used intra-class correlations (ICC3,1) and focused on all Physiomat tasks. Sensitivity to change of trained Physiomat tasks was tested using Wilcoxon statistic and standardized response means (SRMs). Completion rate and time were calculated for feasibility. Results: Analyses have mostly shown moderate-to-high correlations between established motor as well as co
Objectives: To examine the effects of a computerized, game-based training on motor-cognitive performances, the transfer of training effects on untrained tasks, and the sustainability of training gains in people with *...
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Objectives: To examine the effects of a computerized, game-based training on motor-cognitive performances, the transfer of training effects on untrained tasks, and the sustainability of training gains in people with ***: Ninety-nine individuals with a mean age of 82.9 (5.8) and dementia participated in a 10-week randomized controlled trial with three-month follow-up. The intervention group (IG) received a motor-cognitive training on (Physiomat (R)) including concurrent dual-tasks of balance control with cognitive demands (Physiomat (R)-Trail Making Tasks (PTMTs)). The control group (CG) performed non-specific, low-intensity exercises. Duration and accuracy at different complexity levels of trained and untrained PTMTs and the number of successfully performed tasks (PTMT score) were ***: Physiomat (R) training significantly improved the duration and accuracy at almost all complexity levels of trained (P 0.001-0.047, (2)(p) = 0.065-0.589) and untrained PTMTs (P < 0.001-0.005, (2)(p) = 0.073-0.459). Significant effects were also found for the PTMT score of trained (P < 0.001, (2)(p) = 0.211) and untrained PTMTs (P < 0.001, (2)(p) = 0.184). Training gains were partly sustained at ***: Physiomat (R) is feasible and has the potential to sustainably improve motor-cognitive performances in people with dementia.
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