Mobile Parkinson Observatory for Worldwide, Evidence-based Research (mPower) (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.

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.

Parkinson Disease, Neurodegenerative Diseases, Movement Disorders, Central Nervous System Diseases, Brain Diseases, Basal Ganglia Diseases, Parkinsonian Disorders

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.

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.

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 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 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 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 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.

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

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

Results of participant self-assessment surveys will be analyzed using descriptive statistics. These results may also be compared with other intervention results.

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.

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.

The investigators assess the sequence length completed in the Memory intervention. These results may also be compared with other intervention results.

The investigators assess participant dexterity through a combination of steadiness, speed, and tap precision. These results may also be compared with other intervention results.

App usage data is used to gauge participant engagement throughout the study period. 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

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.

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

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.

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.

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.