NCT02696603

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.

Trial Health

55
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
20,000

participants targeted

Target at P75+ for not_applicable parkinson-disease

Timeline
Completed

Started Mar 2015

Longer than P75 for not_applicable parkinson-disease

Geographic Reach
1 country

1 active site

Status
active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

March 1, 2015

Completed
12 months until next milestone

First Submitted

Initial submission to the registry

February 23, 2016

Completed
8 days until next milestone

First Posted

Study publicly available on registry

March 2, 2016

Completed
2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

February 28, 2018

Completed
7.8 years until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2025

Completed
Last Updated

June 11, 2025

Status Verified

June 1, 2025

Enrollment Period

3 years

First QC Date

February 23, 2016

Last Update Submit

June 6, 2025

Conditions

Parkinson DiseaseNeurodegenerative DiseasesMovement DisordersCentral Nervous System DiseasesBrain DiseasesBasal Ganglia DiseasesParkinsonian Disorders

Keywords

Health app researchmHealth researchMobile health researchMobile application health research

Outcome Measures

Primary Outcomes (7)

  • 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.

    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

Study Arms (2)

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.

Behavioral: Participant self-assessment surveysBehavioral: PhonationBehavioral: Gait and balanceBehavioral: MemoryBehavioral: DexterityBehavioral: Participant open-response writingOther: Parkinson mPower mobile application

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.

Behavioral: Participant self-assessment surveysBehavioral: PhonationBehavioral: Gait and balanceBehavioral: MemoryBehavioral: DexterityBehavioral: Participant open-response writingOther: Parkinson mPower mobile application

Interventions

Participant self-assessment surveysBEHAVIORAL

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.

Participants with Parkinson diseaseParticipants without Parkinson disease
PhonationBEHAVIORAL

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.

Participants with Parkinson diseaseParticipants without Parkinson disease
Gait and balanceBEHAVIORAL

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.

Participants with Parkinson diseaseParticipants without Parkinson disease
MemoryBEHAVIORAL

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.

Participants with Parkinson diseaseParticipants without Parkinson disease
DexterityBEHAVIORAL

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.

Participants with Parkinson diseaseParticipants without Parkinson disease
Participant open-response writingBEHAVIORAL

Qualitative participant feedback is used to assess participant engagement with, understanding of, and acceptance of app-based research.

Participants with Parkinson diseaseParticipants without Parkinson disease
Parkinson mPower mobile applicationOTHER

Participants complete all described behavioral interventions via a dedicated iPhone app, Parkinson mPower.

Participants with Parkinson diseaseParticipants without Parkinson disease

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • 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

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Sage Bionetworks

Seattle, Washington, 98109, United States

Location

Related Publications (4)

  • Corsi, P.M. (1972). Human memory and the medial temporal region of the brain (Ph.D.). McGill University.

    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.

    PMID: 11296689BACKGROUND

  • 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.

    PMID: 23431395BACKGROUND

  • 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.

    PMID: 26776193BACKGROUND

Related Links

MeSH Terms

Conditions

Parkinson DiseaseNeurodegenerative DiseasesMovement DisordersCentral Nervous System DiseasesBrain DiseasesBasal Ganglia DiseasesParkinsonian Disorders

Interventions

PhonationGait

Condition Hierarchy (Ancestors)

Nervous System DiseasesSynucleinopathies

Intervention Hierarchy (Ancestors)

Respiratory Physiological PhenomenaCirculatory and Respiratory Physiological PhenomenaPhysical ExaminationDiagnostic Techniques and ProceduresDiagnosisWalkingLocomotionMovementMusculoskeletal Physiological PhenomenaMusculoskeletal and Neural Physiological Phenomena

Study Officials

  • Solly Sieberts, PhD

    Sage Bionetworks

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NON RANDOMIZED
Masking
NONE
Purpose
BASIC SCIENCE
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

February 23, 2016

First Posted

March 2, 2016

Study Start

March 1, 2015

Primary Completion

February 28, 2018

Study Completion

December 31, 2025

Last Updated

June 11, 2025

Record last verified: 2025-06

Data Sharing

IPD Sharing
Will share

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.

Available IPD Datasets

Data description Access

Locations

US(1)