Parkinson Disease Clinical Trial
— mPowerOfficial title:
Mobile Parkinson Observatory for Worldwide, Evidence-based Research (mPower)
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.
Status | Recruiting |
Enrollment | 20000 |
Est. completion date | February 2018 |
Est. primary completion date | February 2018 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility |
Inclusion Criteria: 1. Age 18 years 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: 1. Age 17 years or younger 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 |
Country | Name | City | State |
---|---|---|---|
United States | Sage Bionetworks | Seattle | Washington |
Lead Sponsor | Collaborator |
---|---|
Sage Bionetworks | Robert Wood Johnson Foundation |
United States,
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. — View Citation
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. — View Citation
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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 | |
Primary | 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 | |
Primary | 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 | |
Primary | 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 | |
Primary | 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. | Through study completion, an average of 1 year | |
Primary | 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 | |
Primary | 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 |
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