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.

Intervention:

Enabling independent living for an ageing society is a key challenge for the UK and other developed countries. The number of patients with LTCs are increasing and already have the greatest healthcare needs of the population, requiring 50% of all GP appointments and 70% of all bed days and their treatment and care absorbs 70% of acute and primary care budgets in England. Maintaining safe mobility is a critical factor affecing life quality and life roles and responsibilities. Considering Parkinson's alone, an analysis has shown that 1 in every 37 people will be diagnosed with PD in their lifetime (2). FOG and FSG are severely debilitating aspects of the disease which greatly reduce the quality of life of PwP and contribute to the two-fold increased fall risk and related injuries (2). It is vital that The investigators provide a solution to support PwP with a more fluid and safe gait, greater independence and better disease management and care. Cueing with visual, auditory or somatosensory stimuli is a well-documented and clinically validated method to overcome FOG IRAS Form Reference: IRAS Version 6.3.6

Date:

8 340549/1665763/37/423 and FSG. Research studies have also shown that cueing modalities improve gait in PwP during both free and treadmill walking, whilst improving balance and reducing the need for stabilising support. Gait parameters such as step frequency, stride length and gait symmetry, have been shown to be measurable with inertial motion sensors for gait quality assessment. Studies have shown that objective measurement of the disease can improve treatment outcomes in PD. There are basic visual and rhythmic cueing products available (such as laser shoes, a metronome app and a vibrational button), however they are still based on simple continuous cues which have profound limitations on usability and effectiveness in the everyday environment and do not include gait analytics systems to facilitate better patient outcome and experience. The gaitQ system will be the first unique solution that uses artificial intelligence and smart adaptive cueing to help patients effectively overcome FOG and FSG in their daily environment while improving gait quality. A prior proof-of-concept academic study run at the University of Oxford in 2019 prior to gaitQ company formation, evaluated responsive vibration cueing using 17 patients over 31 hours in a clinical setting. This study demonstrated that responsive cueing can reduce FOG and improve gait quality. More recently, NIHR i4i Connect funding (completed in November 2021) enabled gaitQ to advanced its research device to a commercial prototype and conduct in-house product evaluation with 22 PwP. 91% of participants reported a positive impact of the gaitQ device: it helped prompt leg movement, increase focus during walking, maintain pace, and boost confidence in walking. The quantitative analysis of gait metrics has shown around 60% reduction of FOG and FSG events in both duration and occurrence. Moreover, participants' stride length increased by 11% while step frequency decreased by 16%; both are key outcomes towards more natural and stable walking. Our machine learning algorithms have been tested against two Parkinson's studies, achieving an average of 87% sensitivity and 85% specificity. Ongoing validation in the lab is further determining the validity and reliabiity of the gait metric and estimating the effect of the cuing on transfers and mobility and enduriance.