development of digital services for parkinson's disease

Conditions: Healthy Controls; Parkinson's Disease

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.

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 important. 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. 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. the most commonly reported ocular motor dysfunctions are impairments in saccades, smooth pursuit, and vergence. gait impairments are among the most common and disabling symptoms of pd. 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.

Eligibility criteria:
inclusion criteria participants with parkinson's \[phase 1,2,3,4\] * diagnosis of idiopathic parkinson's disease (uk brain bank criteria) or other appropriate condition specific scale \[stroke, multiple sclerosis, arthritis or osteoporosis\] * able to self-report history of daily gait freezing and/or festination for people with pd or gait and/or transfers affected by condition * able to walk unsupported or using an aid for at least 5 minutes and satisfactory completion of the canadian parq and if over 69 used to carrying out this level of exercise * adult (+18 years old) * normal or corrected-to-normal vision (snellen visual acuity \> 12/18) or safe to mobilise with support * montreal cognitive assessment score \>21 or ability to follow 2 stage commands healthy participants \[phase 1,2,3\] * with no long-term conditions affecting movement * able to walk unsupported or using an aid for at least 3 minutes and satisfactory completion of the canadian parq and if over 69 used to carrying out this level of exercise * adult (+18 years old) * normal or corrected-to-normal vision (snellen visual acuity \> 12/18) or safe to mobilise with support * montreal cognitive assessment score \>21 or ability to follow 2 stage commands exclusion criteria participants with parkinson's * any physical or mental condition affecting ability to safely participate in this level of activity and capacity to understand testing as demonstrated by ability to safely follow commands and pass the parq by the research team. * cognitive impairment affecting ability to safely participate and follow instructions * any injury or disorder that may affect balance (other than parkinson's or referring primary condition) * any skin conditions or broken skin in the calf and behind knee area * deep brain stimulation or pacemaker implants or other implant that may interfere with the measurement system * medications likely to affect eye sight or use of virtual reality sytstem healthy participants * any physical or mental condition affecting ability to safely participate in this level of activity and capacity to understand testing as demonstrated by ability to safely follow commands and pass the parq by the research team. * cognitive impairment affecting ability to safely participate and follow instructions * any injury or disorder that may affect balance (other than parkinson's or referring primary condition) * any skin conditions or broken skin in the calf and behind knee area * deep brain stimulation or pacemaker implants or other implants that may interfere with the measurement system

Keywords: gaitq long-term movement condition digital healthcare machinemd