Development of Digital Services for Parkinson's Disease
- Conditions
- Healthy ControlsParkinson's Disease
- Registration Number
- NCT06733077
- Lead Sponsor
- University of Exeter
- Brief Summary
In this project, ocular motor, pupil and gait data in people with Parkinson's disease (PD) will be collected in order to develop machine learning models for the diagnosis and monitoring of PD. With this, the investigators aim to advance the state of the art in PD diagnosis and monitoring. By integrating the principles of machine learning with high-quality sensor data, more accurate and earlier diagnosis could potentially be achieved. Ocular motor and pupil data will be collected with the standard clinical examination and with neos, a medical device approved for objective ocular motor and pupil measurement. Gait will be collected using an IMU sensor and GaitQ senti, a consumer device that allows for an objective and continuous remote gait monitoring.
- Detailed Description
Parkinson's disease (PD) is one of the most common neurodegenerative diseases worldwide, affecting 1% of the population older than 65.
Currently, PD diagnosis is based on history, clinical assessments, and neurological examination.
The most widely used criteria for diagnosis are the Movement Disorder Society (MDS) criteria and instrument (i.e. The MDS-UPDRS). Further information may be gained from people's 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, people's symptoms 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 person has cognitive impairment). Therefore, current PD diagnosis criteria are highly dependent on the person and on the diagnosing physician. This subjectivity may lead to a variability in the diagnosis. Furthermore, these clinical assessments are unable to accurately track disease progression over time, making it difficult to provide personalized care. Additionally, manual examinations lack precise measurement instruments, resulting in a low precision of observed measurements and the inability to detect early-stage, subclinical signs. An objective diagnosis based on quantitative data rather than subjective interpretation of clinical findings is important2. Therefore, an early and accurate diagnosis of PD, as well as accurate disease progression monitoring, are still important challenges in PD.
Several oculo-visual abnormalities have been described in PD. Studies report an abnormal ocular motor function in 75-87.5% of people with PD (3,4). These dysfunctions may precede or follow motor symptoms and thus, the evaluation of ocular motor function may provide valuable information regarding early disease detection or disease progression (5). The most commonly reported ocular motor dysfunctions are impairments in saccades, smooth pursuit, and vergence (3,4,6).
Gait impairments are among the most common and disabling symptoms of PD (29). Gait impairments include freezing of gait (FOG), an inability to initiate or maintain normal walking patterns, often resulting in a stochastic stop/start gait, and festinating gait (FSG), which is a shortening of stride length with elevated step frequency, resulting in fast, shuffling steps. Both FOG and FSG contribute to an increased risk of falls (and fall-related injuries) in people with PD relative to the wider elderly population. Objective, and continuous remote gait monitoring would be highly important in people with PD, to objectively track gait impairments in real-time, and potentially contribute to objectively track disease progression, which may lead to personalized care for individuals with PD.
In this project, ocular motor, pupil and gait data in people with Parkinson's disease (PD) will be collected in order to develop machine learning models for the diagnosis and monitoring of PD. With this, the investigators aim to advance the state of the art in PD diagnosis and monitoring. By integrating the principles of machine learning with high-quality sensor data, more accurate and earlier diagnosis could potentially be achieved. Ocular motor and pupil data will be collected with the standard clinical examination and with neos, a medical device approved for objective ocular motor and pupil measurement. Gait will be collected using an IMU sensor and GaitQ senti, a consumer device that allows for an objective and continuous remote gait monitoring.
The primary objective of this project is to collect ocular motor, pupil and gait data from people with PD in order to develop and compare machine learning models for diagnosing and monitoring PD.
Secondary objectives are:
Correlate ocular motor, pupil and gait parameters with several clinical parameters, including the MDS-UPDRS.
Collect real-world evidence (RWE) data regarding health economics parameters to address the individual and combined properties, effects, and/or impacts of the deployed health technologies.
By analysing the data collected, we also aim to contribute to the scientific understanding of PD, potentially uncovering new insights into disease patterns, progression, and response to treatments.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 80
Not provided
Not provided
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Primary Outcome Measures
Name Time Method Step length From lab visit 1 to lab visit 2 (up to 3 weeks). Step length (measured in meters) during the gait tasks (TUG, 5 sit to stand, 15-m walk, 5-min walk).
Step rate From lab visit 1 to lab visit 2 (up to 3 weeks). Step rate (measured in steps per minute) will be measured during the gait tasks (TUG, 5 sit to stand, 15-m walk, 5-min walk).
Step length symmetry index From lab visit 1 to lab visit 2 (up to 3 weeks). Step length symmetry index will be calculated using the following equation:
\[((R - L)/0.5 × (R + L)) × 100\], where R: right leg, L: left legWalking speed From lab visit 1 to lab visit 2 (up to 3 weeks). Walking speed (meters per second) will be measured during the gait activities (TUG, 5 sit to stand, 15-m walk, 5-min walk).
Timed Up and Go test From lab visit 1 to lab visit 2 (up to 3 weeks). The time (in seconds) to complete the Timed Up and Go task will be recorded.
Five times Sit to Stand From lab visit 1 to lab visit 2 (up to 3 weeks). The time (in seconds) a person needs to stand up from a standard height chair and sit back down five times.
15-m Walk From lab visit 1 to lab visit 2 (up to 3 weeks). The time (in seconds) needed to cover 15 meters in straight line walking
5-min Walk From lab visit 1 to lab visit 2 (up to 3 weeks). The distance a participant walks in 5 minutes (measured in meters and can round to the nearest decimal place).
Safety of the gaitQ device From lab visit 1 to the end of the intervention (up to 6 weeks). Safety of the gaitQ device will be assessed by recording the adverse events (expected and unexpected).
Usability of the gaitQ device From lab visit 1 to the end of the intervention (up to 6 weeks). Usability of the gaitQ device will be assessed through successful establishment of a system usability scale target \>68 and through the number of therapy support sessions required.
Ocular motor and pupil function - Standard manual test From lab visit 1 to lab visit 2 (up to 3 weeks). Ocular motor and pupil function scores derived from gaze (left, centre, right), ocular alignment, pupilary function, saccades (horizontal, vertical), smooth pursuit, visual field screening, and convergence.
Ocular motor and pupil function - Neos device From lab visit 1 to lab visit 2 (up to 3 weeks). Ocular motor and pupil function scores derived from gaze (left, centre, right), ocular alignment, pupilary function, saccades (horizontal, vertical), smooth pursuit, visual field screening, and convergence.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
University of Exeter
🇬🇧Exeter, United Kingdom