Mobile Parkinson Observatory for Worldwide, Evidence-based Research (mPower)
NCT ID: NCT02696603
Last Updated: 2025-06-11
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
NA
20000 participants
INTERVENTIONAL
2015-03-31
2025-12-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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NON_RANDOMIZED
PARALLEL
BASIC_SCIENCE
NONE
Study Groups
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Participants with Parkinson disease
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.
Participant self-assessment surveys
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.
Phonation
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
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
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
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
Qualitative participant feedback is used to assess participant engagement with, understanding of, and acceptance of app-based research.
Parkinson mPower mobile application
Participants complete all described behavioral interventions via a dedicated iPhone app, Parkinson mPower.
Participants without Parkinson disease
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.
Participant self-assessment surveys
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.
Phonation
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
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
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
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
Qualitative participant feedback is used to assess participant engagement with, understanding of, and acceptance of app-based research.
Parkinson mPower mobile application
Participants complete all described behavioral interventions via a dedicated iPhone app, Parkinson mPower.
Interventions
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Participant self-assessment surveys
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.
Phonation
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
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
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
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
Qualitative participant feedback is used to assess participant engagement with, understanding of, and acceptance of app-based research.
Parkinson mPower mobile application
Participants complete all described behavioral interventions via a dedicated iPhone app, Parkinson mPower.
Eligibility Criteria
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Inclusion Criteria
2. Have a personal (i.e., not shared) iPhone (4s or newer running iOS 8.0 or later)
3. Be able to read and understand an official language of the country of participation
4. Be able to provide informed consent (i.e., pass assessment quiz)
5. Be willing to follow study procedures
Exclusion Criteria
2. Not a resident of the of a country where the app is approved for use
3. Not have a personal (i.e., not shared) iPhone (4s or newer running iOS 8.0 or later)
4. Not be able to read and understand an official language of the country of participation
5. Not be able to give informed consent
6. Not be willing to follow study procedures
18 Years
ALL
Yes
Sponsors
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Robert Wood Johnson Foundation
OTHER
Sage Bionetworks
OTHER
Responsible Party
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Principal Investigators
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Solly Sieberts, PhD
Role: PRINCIPAL_INVESTIGATOR
Sage Bionetworks
Locations
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Sage Bionetworks
Seattle, Washington, United States
Countries
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References
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Corsi, P.M. (1972). Human memory and the medial temporal region of the brain (Ph.D.). McGill University.
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.
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.
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.
Study Documents
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Document Type: Data description
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.
View DocumentRelated Links
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mPower study website
Other Identifiers
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20141369
Identifier Type: OTHER
Identifier Source: secondary_id
201410711
Identifier Type: -
Identifier Source: org_study_id
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