Clinical trial NCT02696603: Mobile Parkinson Observatory for Worldwide, Evidence-based Research (mPower)
Official title: Mobile Parkinson Observatory for Worldwide, Evidence-based Research (mPower)
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Parkinson Disease
Neurodegenerative Diseases
Movement Disorders
Central Nervous System Diseases
Brain Diseases
Basal Ganglia Diseases
Parkinsonian Disorders
Keywords: Health app research
mHealth research
Mobile health research
Mobile application health research
Lead sponsor: Sage Bionetworks
Sponsor class: OTHER
Collaborators: name: Robert Wood Johnson Foundation
First posted: 2016-03-02
Enrollment: 20000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: BASIC_SCIENCE

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.

Eligibility criteria: 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

Primary outcomes:
measure: Results of participant self-assessment surveys
timeFrame: Through study completion, an average of 1 year
measure: Digital audio signals of sustained phonation from phonation intervention
timeFrame: Through study completion, an average of 1 year
measure: Gyroscope and accelerometer sensor measurements from gait and balance intervention
timeFrame: Through study completion, an average of 1 year
measure: Sequence length from memory intervention
timeFrame: Through study completion, an average of 1 year
measure: iPhone screen touch sensor data on rhythm, speed, and location of taps from dexterity intervention
timeFrame: Through study completion, an average of 1 year
measure: App usage data for assessment of participant engagement
timeFrame: Through study completion, an average of 1 year
measure: Qualitative analysis of participant open-response and app usage data to assess participant acceptance of app-based research
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Seattle
country: United States
facility: Sage Bionetworks
state: Washington
Study officials:
affiliation: Sage Bionetworks
name: Solly Sieberts, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Corsi, P.M. (1972). Human memory and the medial temporal region of the brain (Ph.D.). McGill University.
citation: 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.
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.
citation: 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.