Mitigating the impact of motor impairment on self-administered digital tests in patients with neurological disorders
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Introduction
Cognitive impairments are prevalent in many neurological disorders and remain underdiagnosed and poorly studied longitudinally. Unsupervised remote cognitive testing is an accessible, scalable, and cost-effective solution, however it often fails to separate cognitive deficits from commonly co-occurring motor impairments. To address this gap, we present a computational framework that isolates cognitive ability from motor impairment in self-administered digital tasks.
Methods
Stroke was chosen as a representative neurological disorder, as patients frequently experience both motor and cognitive impairments. Our validation analyses spanned 18 computerised tasks completed by 171 patients longitudinally, covering a broad spectrum of cognitive and motor domains. The computational model was applied on trial-level data to disentangle the contribution of motor and cognitive processes.
Results
In patients with motor hand impairment, standard accuracy performance metrics were confounded in 6 tasks (p<.05, FDR-corrected). In contrast, the Modelled Cognitive metrics obtained from the computational framework showed no significant effects of impaired hand (p>.05, FDR-corrected). Moreover, the Modelled Cognitive metrics correlated more strongly with clinical pen-and-paper scales (mean R 2 =0.64 vs. 0.43) and functional outcomes (mean R 2 =0.16 vs 0.09). Brain-behaviour associations were stronger when using the Modelled Cognitive metrics, and revealed intuitive multivariate relationships with individual tasks.
Interpretation
We present converging evidence for the improved clinical utility and validity of the Modelled Cognitive metrics within neurological conditions characterised by co-occurring motor and cognitive deficits. Addressing the confounding effect of motor impairments improves the reliability and biological validity of self-administered digital assessments, enhances accessibility, and supports early detection and intervention across neurological disorders.
Funding
This research is funded by the UK Medical Research Council (MR/T001402/1).
