Development of Digital Diagnostics and Intervention Services for Parkinson's Disease

Diagnosis: The current state of the art in diagnosing PD primarily revolves around clinical assessments based on symptomatology and neurological examination. The most widely used criteria for diagnosis are Movement Disorder Society (MDS) criteria and instrument (i.e. The MDSUPDRS). These criteria are highly dependent on the expertise of the diagnosing physician, and thus, are subject to variability. Furthermore, these clinical assessments are unable to accurately track disease progression over time, making it difficult to provide personalized care.

Assessment: plays an important role in these consultations to help clinicians understand patients' health status and disease progression necessary to adjust treatment plans. Current clinical methods rely on the UPDRS a standard clinical outcome measure that requires clinical expertise and training and is thus measured at clinical appointments.

Further information may be gained from patients' subjective description of their symptoms and/or via some short walking tests, such as 3 meter Timed Up and Go (TUG) performed as a snapshot in the clinic. However, patients' vary through and between days and subjective descriptions rely on their memory and observations at home. These recollections can be unreliable or lack enough detail (particularly when the patient has cognitive impairment). Walking tests, although quick to perform, show high variability due to natural fluctuations of the Parkinson's symptoms throughout the day, on/off medication status as well as the 'performing' effects when doing activities in a clinical setting. Consequently, Parkinson's care regimes are often provided on a trial-and-error basis, with resulting general delay in offering care, with early signs of disease progression often missed until conditions become more severe, and interventions such as physiotherapy are often provided too late. Hence, there is a strong need for more frequent and accurate Parkinson's assessments in a real-world environment to enable more targeted care and preventative measures and interventions via early detection of disease inflection points. Here the investigators propose to evaluate three digital approaches to see if they provide further information to support clinical decision making using gait and ophthalmic features. The current state-of-the-art in neurological diagnostics includes a range of traditional clinical examination techniques, such as manual neuro-ocular and neuro-motor function assessments, as well as more technologically advanced tools, such as imaging technologies (e.g., MRI) and electrophysiological tests (e.g., ECG). While these methods have undoubtedly contributed to significant advances in the detection and management of neurological disorders, they also present several limitations that digital approaches overcome. Traditional examinations of neurological function, such as manual assessments [UPDRS], are heavily reliant on the expertise and subjective judgment of specialized neurologists. This leads to known inconsistencies in interpreting and scoring patient performance over time, particularly in the case of neurodegenerative diseases like Parkinson's disease (PD), where patients typically consult different resident neurologists over several years.

Additionally, manual examinations lack precise measurement instruments, resulting in a low precision of observed measurements (typically ~ 2 cm) and the inability to detect early-stage, subclinical signs. PD is a complex and progressive neurodegenerative disorder with significant variability in clinical manifestations and disease progression.

Its diagnosis and monitoring remain a challenge, primarily relying on clinical examinations that are subjective and often do not capture the full extent of symptom fluctuations.