Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| 20141369 | Other Identifier | Western Instiutional Review Board |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Robert Wood Johnson Foundation | OTHER |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
The purpose of this study is to understand variation in the symptoms of Parkinson disease. This study uses an iPhone app to record these symptoms through questionnaires and sensors.
Living with Parkinson disease means coping with symptoms that change every day. Yet these changes are not tracked frequently enough. Most people with Parkinson disease see a clinician only once or twice a year. This study measures changes in Parkinson disease symptoms in real time using an app. The app remotely monitors Parkinson disease symptoms using surveys and the sensors on mobile devices. This study may contribute to increasing our understanding of the variability in Parkinson disease symptoms. This knowledge could be used to improve quality of life for people living with Parkinson disease.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Participants with Parkinson disease | Experimental | People who report a diagnosis of Parkinson disease. Participants are invited via the Parkinson mPower mobile application to complete the following behavioral interventions: Participant self-assessment surveys, Phonation, Gait and balance, Memory, Dexterity, and Participant open-response writing. |
|
| Participants without Parkinson disease | Experimental | People who do not report a diagnosis of Parkinson disease. Participants are invited via the Parkinson mPower mobile application to complete the following behavioral interventions: Participant self-assessment surveys, Phonation, Gait and balance, Memory, Dexterity, and Participant open-response writing. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Participant self-assessment surveys | Behavioral | At enrollment, participants are asked to complete a baseline health history and a participant-reported symptom inventory. Thereafter, participants are asked to respond to commonly used questions that assess Parkinson Disease symptoms and quality of life at regular intervals. |
| Measure | Description | Time Frame |
|---|---|---|
| Results of participant self-assessment surveys | Results of participant self-assessment surveys will be analyzed using descriptive statistics. These results may also be compared with other intervention results. | Through study completion, an average of 1 year |
| Digital audio signals of sustained phonation from phonation intervention | The investigators extract features from the digital audio signals of sustained phonations. The investigators apply feature selection and classifier algorithms and analyze these phonations using methods similar to those employed in the Parkinson Voice Initiative (http://www.parkinsonsvoice.org/science.php). These results may also be compared with other intervention results. | Through study completion, an average of 1 year |
| Gyroscope and accelerometer sensor measurements from gait and balance intervention | The investigators examine step-dependent and sequence-dependent features from gyroscope and accelerometer sensors. The investigators apply feature selection and classifier algorithms to analyze these data. These results may also be compared with other intervention results. | Through study completion, an average of 1 year |
| Sequence length from memory intervention | The investigators assess the sequence length completed in the Memory intervention. These results may also be compared with other intervention results. | Through study completion, an average of 1 year |
| iPhone screen touch sensor data on rhythm, speed, and location of taps from dexterity intervention | The investigators assess participant dexterity through a combination of steadiness, speed, and tap precision. These results may also be compared with other intervention results. |
Not provided
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Solly Sieberts, PhD | Sage Bionetworks | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sage Bionetworks | Seattle | Washington | 98109 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | Corsi, P.M. (1972). Human memory and the medial temporal region of the brain (Ph.D.). McGill University. | ||
| 11296689 | Background | Kessels RP, van Zandvoort MJ, Postma A, Kappelle LJ, de Haan EH. The Corsi Block-Tapping Task: standardization and normative data. Appl Neuropsychol. 2000;7(4):252-8. doi: 10.1207/S15324826AN0704_8. | |
| 23431395 |
| Label | URL |
|---|---|
| mPower study website | View source |
| ID | Type | URL | Comment |
|---|---|---|---|
| Data description | View IPD |
Coded data authorized for sharing by participants will be made available for research purposes via Synapse, Sage Bionetworks' analysis platform. Coded data is study data that does not include participants names or other directly identifying information.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
|
| Phonation | Behavioral | Participants are asked to record themselves saying "Aaah" for 10 seconds using the iPhone microphone. This activity is designed to assess vocal features, including vocal tremor. The investigators extract features from the digital audio signals of these sustained phonations. The investigators apply feature selection and classifier algorithms and analyze these phonations using methods similar to those employed in the Parkinson Voice Initiative. |
|
| Gait and balance | Behavioral | Participants are asked to walk back and forth for 30 seconds and then stand still for 30 seconds. Gait and balance are measured by gyroscope and accelerometer sensors. The investigators examine step-dependent and sequence-dependent features from these sensors. The investigators apply feature selection and classifier algorithms to analyze these data. |
|
| Memory | Behavioral | Participants are asked to complete a visuospatial short-term memory game related to the Corsi block tapping test [Corsi, P.M. (1972)] as adapted by Kate Possin, PhD of the University of California San Francisco Memory and Aging Center (personal communication, 2015). In this activity, participants are presented with a grid of objects that change color in a set pattern. Participants are then asked to tap the objects in that same pattern. The investigators assess the sequence length completed. |
|
| Dexterity | Behavioral | Participants are asked to tap on the phone screen with alternating fingers. This test can be done with either or both hands. The investigators record the rhythm, speed, and location of these taps using the touch sensors of the iPhone screen. The investigators assess participant dexterity through a combination of steadiness, speed, and tap precision. |
|
| Participant open-response writing | Behavioral | Qualitative participant feedback is used to assess participant engagement with, understanding of, and acceptance of app-based research. |
|
| Parkinson mPower mobile application | Other | Participants complete all described behavioral interventions via a dedicated iPhone app, Parkinson mPower. |
|
| Through study completion, an average of 1 year |
| App usage data for assessment of participant engagement | App usage data is used to gauge participant engagement throughout the study period. These results may also be compared with other intervention results. | Through study completion, an average of 1 year |
| Qualitative analysis of participant open-response and app usage data to assess participant acceptance of app-based research | App usage data and qualitative participant feedback are used to assess participant understanding and acceptance of app-based research. These results may also be compared with other intervention results. | Through study completion, an average of 1 year |
| Background |
| Klucken J, Barth J, Kugler P, Schlachetzki J, Henze T, Marxreiter F, Kohl Z, Steidl R, Hornegger J, Eskofier B, Winkler J. Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease. PLoS One. 2013;8(2):e56956. doi: 10.1371/journal.pone.0056956. Epub 2013 Feb 19. |
| 26776193 | Result | Chaibub Neto E, Bot BM, Perumal T, Omberg L, Guinney J, Kellen M, Klein A, Friend SH, Trister AD. PERSONALIZED HYPOTHESIS TESTS FOR DETECTING MEDICATION RESPONSE IN PARKINSON DISEASE PATIENTS USING iPHONE SENSOR DATA. Pac Symp Biocomput. 2016;21:273-84. |
Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health. |
| ID | Term |
|---|---|
| D010300 | Parkinson Disease |
| D019636 | Neurodegenerative Diseases |
| D009069 | Movement Disorders |
| D002493 | Central Nervous System Diseases |
| D001927 | Brain Diseases |
| D001480 | Basal Ganglia Diseases |
| D020734 | Parkinsonian Disorders |
| ID | Term |
|---|---|
| D009422 | Nervous System Diseases |
| D000080874 | Synucleinopathies |
Not provided
Not provided
| ID | Term |
|---|---|
| D010699 | Phonation |
| D005684 | Gait |
| ID | Term |
|---|---|
| D012143 | Respiratory Physiological Phenomena |
| D002943 | Circulatory and Respiratory Physiological Phenomena |
| D010808 | Physical Examination |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D016138 | Walking |
| D008124 | Locomotion |
| D009068 | Movement |
| D009142 | Musculoskeletal Physiological Phenomena |
| D055687 | Musculoskeletal and Neural Physiological Phenomena |
Not provided
Not provided