Online cognitive monitoring technology for people with Parkinson’s disease and REM sleep behavioural disorder

Maria Bălăeţ; Falah Alhajraf; Tanja Zerenner; Jessica Welch; Jamil Razzaque; Christine Lo; Valentina Giunchiglia; William Trender; Annalaura Lerede; Peter J. Hellyer; Sanjay G. Manohar; Paresh Malhotra; Michele Hu; Adam Hampshire

doi:10.1038/s41746-024-01124-6

npj Digital Medicine (May 2024)

Online cognitive monitoring technology for people with Parkinson’s disease and REM sleep behavioural disorder

Maria Bălăeţ,
Falah Alhajraf,
Tanja Zerenner,
Jessica Welch,
Jamil Razzaque,
Christine Lo,
Valentina Giunchiglia,
William Trender,
Annalaura Lerede,
Peter J. Hellyer,
Sanjay G. Manohar,
Paresh Malhotra,
Michele Hu,
Adam Hampshire

Affiliations

Maria Bălăeţ: Department of Brain Sciences, Imperial College London
Falah Alhajraf: Oxford Parkinson’s Disease Centre, Nuffield Department Clinical Neurosciences, University of Oxford
Tanja Zerenner: Population Health Sciences, University of Bristol
Jessica Welch: Oxford Parkinson’s Disease Centre, Nuffield Department Clinical Neurosciences, University of Oxford
Jamil Razzaque: Oxford Parkinson’s Disease Centre, Nuffield Department Clinical Neurosciences, University of Oxford
Christine Lo: Oxford Parkinson’s Disease Centre, Nuffield Department Clinical Neurosciences, University of Oxford
Valentina Giunchiglia: Department of Brain Sciences, Imperial College London
William Trender: Department of Brain Sciences, Imperial College London
Annalaura Lerede: Department of Brain Sciences, Imperial College London
Peter J. Hellyer: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
Sanjay G. Manohar: Oxford Parkinson’s Disease Centre, Nuffield Department Clinical Neurosciences, University of Oxford
Paresh Malhotra: Department of Brain Sciences, Imperial College London
Michele Hu: Oxford Parkinson’s Disease Centre, Nuffield Department Clinical Neurosciences, University of Oxford
Adam Hampshire: Department of Brain Sciences, Imperial College London

DOI: https://doi.org/10.1038/s41746-024-01124-6
Journal volume & issue: Vol. 7, no. 1
pp. 1 – 12

Abstract

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Abstract Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson’s Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.

Published in npj Digital Medicine

ISSN: 2398-6352 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics
Website: https://www.nature.com/npjdigitalmed/

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