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| ID | Type | Description | Link |
|---|---|---|---|
| 1R44MD013767-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute on Minority Health and Health Disparities (NIMHD) | NIH |
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The purpose of this research study is to:
The investigators aim to develop a standardized tool for identifying patients with Parkinson's disease (PD) who would benefit from advanced therapies (AT) and identify when AT recipients are in need of a therapy adjustment. This system will integrate ambulatory PD monitoring with context aware activity detection as the daily activities a patient performs are often the best predictors of quality of life (QoL). In this study the Kinesia 360 system will collect motion data to measure tremor, bradykinesia (slow movement), and dyskinesia (involuntary movements) from individuals with PD to track their symptoms throughout the day. A smartphone will collect information on subject location and activity using the GPS, accelerometers, and microphone within the phone to find correlations between activity and patient wellness. This data will be used to improve detection over time and predict whether patients are candidates for advanced therapies.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Parkinson's Disease | Parkinson's Disease patients |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Kinesia 360 and Smartphone sensors | Device | Data will be recorded from the Kinesia 360 system and smartphone sensors using the AWARE Framework. |
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| Measure | Description | Time Frame |
|---|---|---|
| Kinesia Symptom Scores during Daily Wear | Kinesia 360 scoring of Parkinson's Disease motor symptoms (tremor, slowness, dyskinesia, gait): The Kinesia 360 system translates recorded motion into 0-4 scores that correlate to rating scales used by clinicians (lower scores are signifiers of better outcomes and higher scores signify worse outcomes). A separate 0-4 score is generated for tremor, slowness, and dyskinesia, and gait is tracked for step count and percent of day walking. | Continuous during wear over four days |
| Measure | Description | Time Frame |
|---|---|---|
| User environment audio activity | Using the AWARE Framework, the microphone in the smartphone will be used to measure ambient noise and detect conversation during wear. The output will be detection of conversation and ambient noise level for each recorded timepoint. The time involved in conversation and around active ambient noise will be compared to the symptom scores to measure user involvement in active environments as an impact on Parkinson's disease symptoms. |
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Inclusion Criteria:
Exclusion Criteria:
Any subject that does not meet the subject selection criteria will be excluded from this study. Children will be excluded from this study due to the fact that they are unlikely to have PD. Subjects that are not capable of functioning independently or are so symptomatic as to compromise their safety will also be excluded.
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The population is people with Parkinson's Disease in the Northeast Ohio region who have expressed interest in participating in clinical trials
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| Name | Affiliation | Role |
|---|---|---|
| Dustin Heldman, PhD | Director of Research | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Great Lakes NeuroTechnologies | Cleveland | Ohio | 44131 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27392872 | Background | Heldman DA, Giuffrida JP, Cubo E. Wearable Sensors for Advanced Therapy Referral in Parkinson's Disease. J Parkinsons Dis. 2016 Jul 2;6(3):631-8. doi: 10.3233/JPD-160830. |
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| ID | Term |
|---|---|
| D010300 | Parkinson Disease |
| ID | Term |
|---|---|
| D020734 | Parkinsonian Disorders |
| D001480 | Basal Ganglia Diseases |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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| Continuous during wear over four days |
| Patient physical activity | Using the AWARE Framework, accelerometers in the smartphone will be used to determine the activity of the user. The accelerometers will detect if the phone is being carried (using orientation of gravity) and predict most likely physical activity (using pre-existing activity recognition algorithms). The output will be predicted user activity for each timepoint recorded which will be correlated to Kinesia symptom scores. | Continuous during wear over four days |
| Patient locations and travel | Using the AWARE Framework, Global Positioning System (GPS) tracking in the smartphone will be used to determine the locations and activity of the user. The GPS sensor will be used to track changes in location and determine when an user is at home (primary location), in a secondary location, or in active motion (car, bike, or walking). The outcome will be time spent in each location, percent of day at each location, and time and speed travelling between locations. These outcomes will be correlated to the Kinesia symptom scores and detected physical activity. | Continuous during wear over four days |
| D009422 | Nervous System Diseases |
| D009069 | Movement Disorders |
| D000080874 | Synucleinopathies |
| D019636 | Neurodegenerative Diseases |