Mobile Parkinson Observatory for Worldwide, Evidence-based Research (mPower)
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Parkinson Disease
- Sponsor
- Sage Bionetworks
- Enrollment
- 20000
- Locations
- 1
- Primary Endpoint
- Results of participant self-assessment surveys
- Status
- Active, not recruiting
- Last Updated
- 10 months ago
Overview
Brief Summary
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.
Detailed Description
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.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Age 18 years
- •Have a personal (i.e., not shared) iPhone (4s or newer running iOS 8.0 or later)
- •Be able to read and understand an official language of the country of participation
- •Be able to provide informed consent (i.e., pass assessment quiz)
- •Be willing to follow study procedures
Exclusion Criteria
- •Age 17 years or younger
- •Not a resident of the of a country where the app is approved for use
- •Not have a personal (i.e., not shared) iPhone (4s or newer running iOS 8.0 or later)
- •Not be able to read and understand an official language of the country of participation
- •Not be able to give informed consent
- •Not be willing to follow study procedures
Outcomes
Primary Outcomes
Results of participant self-assessment surveys
Time Frame: Through study completion, an average of 1 year
Results of participant self-assessment surveys will be analyzed using descriptive statistics. These results may also be compared with other intervention results.
App usage data for assessment of participant engagement
Time Frame: Through study completion, an average of 1 year
App usage data is used to gauge participant engagement throughout the study period. These results may also be compared with other intervention results.
Sequence length from memory intervention
Time Frame: Through study completion, an average of 1 year
The investigators assess the sequence length completed in the Memory intervention. These results may also be compared with other intervention results.
iPhone screen touch sensor data on rhythm, speed, and location of taps from dexterity intervention
Time Frame: Through study completion, an average of 1 year
The investigators assess participant dexterity through a combination of steadiness, speed, and tap precision. These results may also be compared with other intervention results.
Qualitative analysis of participant open-response and app usage data to assess participant acceptance of app-based research
Time Frame: Through study completion, an average of 1 year
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.
Digital audio signals of sustained phonation from phonation intervention
Time Frame: Through study completion, an average of 1 year
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.
Gyroscope and accelerometer sensor measurements from gait and balance intervention
Time Frame: Through study completion, an average of 1 year
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.