The mPower study, Parkinson disease mobile data collected using ResearchKit

Brian M Bot, Christine Suver, Elias Chaibub Neto, Michael Kellen, Arno Klein, Christopher Bare, Megan Doerr, Abhishek Pratap, John Wilbanks, E Ray Dorsey, Stephen H Friend, Andrew D Trister, Brian M Bot, Christine Suver, Elias Chaibub Neto, Michael Kellen, Arno Klein, Christopher Bare, Megan Doerr, Abhishek Pratap, John Wilbanks, E Ray Dorsey, Stephen H Friend, Andrew D Trister

Abstract

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.

Conflict of interest statement

SF was an employee at Apple at the time of the ResearchKit launch.

Figures

Figure 1. mPower study cohort description.
Figure 1. mPower study cohort description.
Figure 2. Cumulative participation for activities over…
Figure 2. Cumulative participation for activities over time.
Figure 3. Participation shown as number of…
Figure 3. Participation shown as number of days visiting the app for all participants who completed at least one task on five separate days.

References

Data Citations

    1. Bot B. M. 2016. Synapse .
    1. Bot B. M. 2016. Synapse .
    1. Bot B. M. 2016. Synapse .
    1. Bot B. M. 2016. Synapse .
    1. Bot B. M. 2016. Synapse .
    1. Bot B. M. 2016. Synapse .
    1. Bot B. M. 2016. Synapse .
References
    1. Trister A. D. et al. Smartphones as new tools in the management and understanding of Parkinson's disease. npj Parkinson's Disease 2, 16006 doi:10.1038/npjparkd.2016.6 (2016).
    1. Jenkinson C., Fitzpatrick R., Peto V., Greenhall R. & Hyman N. The PDQ-8: development and validation of a short-form parkinson's disease questionnaire. Psychol. Health 12, 805–814 (1997).
    1. Goetz C. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Movement Disord. 23, 2129–2170 (2008).
    1. Derry J. M. J. et al. Developing predictive molecular maps of human disease through community-based modeling. Nat. Gen. 44, 127–130 (2012).
    1. Omberg L. et al. Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas. Nat. Gen. 45, 1121–1126 (2013).
    1. Akbarian S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).
    1. Guinney J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).
    1. Neto E. C. et al. Personalized Hypothesis Tests for Detecting Medication Response in Parkinson Disease Patients Using iPhone Sensor Data. Pac. Symp. Biocomput. 21, 273–284 (2016).
    1. Yu Y. et al. Initial Validation of Mobile-Structural Health Monitoring Method Using Smartphones. Int. J. Distrib. Sens. N 2015 274391 (2015).

Source: PubMed

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