The mPower study, Parkinson disease mobile data collected using ResearchKit
Open accessAbstract
Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. 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.
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The mPower app study had 1,087 self-declared persons with PD and 5,581 self-declared persons without PD complete at least one active test or survey; adherence dropped sharply after the first few days post-download, with 898 individuals contributing ≥5 days' data during the first 6 months.
“In this study, 1,087 self‐declared persons with PD and 5,581 self‐declared persons without PD completed at least one active test or survey.28 Adherence dropped sharply after the first few days post‐download; 898 individuals contributed ≥ 5 days' data during the first 6 months following download.”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [28]
Adherence was greater than what was observed with the general population deployment of mPower.
“greater than what was observed with the general population deployment of mPower.28”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [28]
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Deep Learning Identifies Digital Biomarkers for Self-Reported Parkinson's Disease
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The Mole Mapper Study, mobile phone skin imaging and melanoma risk data collected using ResearchKit
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- Authors
- Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D.
- Affiliations
- Sage Bionetworks (US)
- University of Rochester Medicine (US)
- Corresponding author
- Trister, Andrew D.
- ORCID
- Venue
- Scientific Data
- Published
- 2016-03-03
- Volume
- 3
- Issue
- 1
- Pages
- 160011-160011
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC4776701
- OpenAlex
- W2296350496
- Cited by
- 630
- References
- 17
- Citations by year
- 2026: 55 · 2025: 61 · 2024: 58 · 2023: 57 · 2022: 73 · 2021: 84 · 2020: 80 · 2019: 51 · 2018: 60 · 2017: 34 · 2016: 17
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Issue Lab (Candid) (submittedVersion, cc-by)
- Funders
- Robert Wood Johnson Foundation
- National Institute of Neurological Disorders and Stroke (P20NS092529)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, ResearchKit, mobile health, smartphone sensor data, activity measurement, symptom variability, open-source apps
- MeSH terms
- Data Collection, Humans, Parkinson Disease, Telemedicine, Cell Phone, Datasets as Topic
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