A data driven approach towards understanding deficits in olfactory and motor function and identifying applications of smartphone motor testing in prodromal and manifest Parkinson’s

Thesis

Background:

Significant disease heterogeneity in Parkinson’s means that identifying ways of applying observations made at a group level, to specific individuals, remains challenging. Standardised clinical assessments, critical for capturing the scientific evidence that informs clinical practice, are rarely used outside of research due to the time required for their administration.

Methods:

Since 2010, the Oxford Discovery longitudinal cohort study has recruited over 1600 individuals with Parkinson’s, Rapid Eye Movement Sleep Behaviour disorder (RBD) (a condition associated with a high risk of developing Parkinson’s or another neurodegenerative disorder) and controls. Detailed clinical assessments, including clinical tests of olfactory and motor function and smartphone motor testing, were performed at 18-month intervals. Machine learning algorithms (chiefly random forests) were used to predict clinical scores and outcomes using clinical data or smartphone motor testing data alone.

Results:

The use of three Sniffin’ sticks (Anise, Liquorice, Banana) allowed the identification of individuals with a poor sense of smell (ordinarily requiring assessment with all 16 sticks) with excellent accuracy (area under the curve (AUCs) values of over 0.90 in development and validation cohorts). Summation of scores from 6 tasks of the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor examination: repetitive hand movements, finger tapping, pronation/supination, leg movements, constancy of rest tremor and bradykinesia, yielded an abbreviated score whose correlation coefficient with the total 18 task score was high at 0.91. Three approaches to using smartphone motor testing to quantify motor impairment were explored. Smartphone motor testing was also used to predict dopaminergic deficit in RBD with AUCs>0.73 and the new future onset of motor, cognitive and functional disability in Parkinson’s with AUCs>0.75.

Conclusion:

A data-driven approach can be used to promote understanding of existing clinical tests in prodromal and manifest Parkinson’s, and to drive their refinement. Smartphone motor testing can be used to quantify motor impairment and provide individual estimates of risk; however, both are likely to benefit from further evaluation within the context of clinical trials and routine clinical practice.

Authors: Lo, C

• DOI: 10.5287/ora-azpwb6rkg
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Parkinson's disease; machine learning; smartphone; outcome measures; REM sleep behaviour disorder
• Subjects: Parkinson's disease; Medicine; Rapid eye movement sleep