Oxford Mathematician Siddharth Arora talks about his and his colleagues' research in to using smartphone technology to anticipate the symptoms of Parkinson’s disease.
"Parkinson’s disease (PD) is the second most common neurodegenerative disease, the hallmarks of which include tremor, stiffness, and slowness of movement. Existing tests for the assessment of Parkinson’s require in-clinic examination of symptoms by a clinician. This can sometimes cause a delay in diagnosis. It is believed that there are changes in the brain 5 to 10 years before the symptoms of PD become evident.
To try and facilitate early diagnosis of Parkinson’s, together with the Oxford Parkinson's Disease Centre (OPDC) we investigated if smartphones can be used to detect any potential differences in motor symptoms associated with PD and REM Sleep Behaviour Disorder (RBD). It is now increasingly recognised that having RBD may be a risk factor for developing future Parkinson’s. In this study, we used a smartphone app featuring 7 motor tests to measure: (1) Voice, (2) Balance, (3) Gait, (4) Finger tapping, (5) Reaction time, (6) Rest tremor, and (7) Postural tremor. Recordings were collected both in-clinic and at home. Using the smartphone recordings, we extracted key features of interest and used machine learning to quantify patterns of motor impairment that are specific to RBD, PD, and Controls. In one of the largest cohorts of deeply phenotyped participants, we report that smartphones can be used to discriminate between participant groups with a high level of accuracy (84.6% to 91.9% mean sensitivity and specificity). Our research paper focussing on ‘detecting the early motor symptoms’ of Parkinson’s is published here. A pilot study on ‘monitoring the severity of PD symptoms’ can also be accessed here, while a large-scale study using the mPower data to understand the ‘longitudinal characteristics’ of Parkinson's through the analysis of finger tapping and memory tests is published here.
Moreover, we also investigated potential vocal deficits in people who are at an increased risk of Parkinson’s (carriers of LRRK2 mutation). Our preliminary findings suggest that vocal deficits in LRRK2-associated PD may be different than those in idiopathic Parkinson’s. This research could help develop an inexpensive remote screening test for assessing the risk LRRK2-associated Parkinson’s based on voice - click here for the article.
These findings provide an exciting and growing consensus for the utility of digital biomarkers in early and pre-symptomatic Parkinson’s."