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.
|Design Type(s)||observation design • time series design • repeated measure design|
|Measurement Type(s)||disease severity measurement|
|Technology Type(s)||Patient Self-Report|
|Sample Characteristic(s)||Homo sapiens|
Machine-accessible metadata file describing the reported data (ISA-Tab format)
Background & Summary
Parkinson disease (PD) is a movement disorder related to loss of dopamine producing cells in the midbrain. Manifestations of the disease can include tremor, changes in gait, slowness (bradykinesia) and rigidity. There is significant variability in the presentation and progression of these symptoms, and while there is no known cure for PD, treatments can mitigate the effects of these symptoms. Given that typical PD patients have visits with a physician every 4–6 months, the day-to-day variability of symptoms and the effects of medications on these symptoms could reveal opportunities for interventions that might improve quality of life for those with PD. We hypothesize that more frequent quantitative assessments could lead to a better understanding of the disease heterogeneity, as well as provide individual benefit to those living with the condition. Mobile phones and other networked devices offer a unique opportunity to engage research participants without requiring physical interactions. This approach allows classic implements, such as surveys, to be administered remotely. More interestingly, sensors such as accelerometers, gyroscopes, and microphones can provide quantitative surrogates of PD symptoms with minimal or no interruption in the participant’s daily life1.
In March 2015, we launched mPower (https://github.com/Sage-Bionetworks/mPower), an observational smartphone-based study developed using Apple’s ResearchKit library (http://researchkit.org/) to evaluate the feasibility of remotely collecting frequent information about the daily changes in symptom severity and their sensitivity to medication in PD. These data provide the ability to explore classification of control participants and those who self-report having PD, as well as to begin to measure the severity of PD for those with the disease. There are myriad additional questions from each of the varying streams of data that will require a community of researchers to explore fully.
The study utilized a novel remote approach to enrollment in which participants self-guide through visually engaging yet complete informed consent process prior to deciding to join the study. A critical aspect of this transparent consent process is providing participants with an explicit decision point specifying if the data they donate to the study can also be used for secondary research purposes. Data described and made available here are derived from the first six months of the mPower study exclusively from those participants who chose to make their data broadly available for secondary research. We are hopeful that the data donated by mPower participants will encourage the formation of a broad, diverse, and collaborative community of PD researchers. We invite you to join this community to accelerate the research on how mobile technologies can impact PD, and together improve the quality of life for people living with PD.
The mPower app was made available starting in March 2015 through the Apple App Store (https://itunes.apple.com/us/app/parkinson-mpower-study-app/id972191200?mt=8) only in the United States and for iPhone 4S or newer requiring a minimum of iOS 8. Enrollment was open to individuals diagnosed with PD as well as anyone interested in participating as a control. Following download, prospective participants self-navigated through eligibility criteria (18 years of age or older, live in the United States, comfortable reading and writing on iPhone in English) and then through an interactive e-consent process (http://sagebase.org/pcc/). Ethical oversight of the study was obtained from Western Institutional Review Board. Prior to signing an electronically rendered traditional informed consent form, prospective participants had to pass a five-question quiz evaluating their understanding of the study aims, participant rights, and data sharing options. After completing the e-consent process and electronically signing the informed consent form, participants were asked for an email address to which their signed consent form was sent and allowing for verification of their enrollment in the study.
Participants were given the option to share their data only with the mPower study team and partners (‘share narrowly’) or to share their data more broadly with qualified researchers worldwide, and had to make an active choice to complete the consent process (no default choice was presented). The data presented here consist of all individuals who chose to have their data shared broadly (Figure 1).
Once enrolled, participants were presented with a ‘dashboard’ of study tasks. Table 1 lists these tasks and their periodicity. Participants could skip any task or question within a survey at any time. A one-time baseline survey (Table 2, Data Citation 1) was the first task the participants were asked to complete. Additional standard surveys used for PD assessment, Parkinson Disease Questionnaire 8 (PDQ-8, Table 3, Data Citation 2) (ref. 2) and a subset of questions from the Movement Disorder Society Universal Parkinson Disease Rating Scale (MDS-UPDRS, Table 4, Data Citation 3) (ref. 3), were presented at baseline as well as monthly throughout the course of the study. Due to the length of the MDS-UPDRS instrument, we included only a subset of questions taken from the patient questionnaire focusing largely on self-evaluation of the motor symptoms of PD. The data obtained from these surveys is subject to the copyright holder’s license. All surveys represent self-reported outcomes and thus occasionally contain typographic errors and possibly inconsistent information.
Participants were presented four separate activities (referred to as ‘memory’, ‘tapping’, ‘voice’, and ‘walking’) which they could complete three times a day. Participants who self-identified as having a professional diagnosis of PD were asked to do these four tasks (1) immediately before taking their medication, (2) after taking their medication (when they are feeling at their best), and (3) at some other time. Participants who self-identified as not having a professional diagnosis of PD (the controls) could complete these tasks at any time during the day, with the app suggesting to complete each activity three times per day.
The memory activity (Data Citation 4) was developed to evaluate short-term spatial memory (Personal Communication with Katherine Possin). Participants observed a grid of flowers that illuminated one at and time and were then asked to replicate the illumination pattern by touching the flowers in the same order. The first release of the app (version 1.0, build 7) did not include this memory activity, however subsequent releases (starting April 21) included the activity. Included in these data are samples of where and when participants tapped on the iPhone screen and if that was a correct location as per the random pattern presented to them.
The tapping activity (Data Citation 5) measured dexterity and speed. Participants were instructed to lay their phone on a flat surface and to use two fingers on the same hand to alternatively tap two stationary points on the screen for 20 s. Included in these data are samples of where and when participants tapped on the iPhone screen as well as accelerometer readouts of how the phone was moving during the activity.
The voice activity (Data Citation 6) recorded participants’ sustained phonation by instructing them to say ‘Aaaaah’ into the microphone at a steady volume for up to 10 s. The data from this activity include audio files containing measurements from the iPhone microphone for both the 10 s of phonation as well as for the 5 s countdown leading up to the activity.
The walking activity (Data Citation 7) evaluated participants’ gait and balance. The first release of the app (version 1.0, build 7) instructed participants to walk 20 steps in a straight line, turn around, stand still for 30 s, then walk 20 steps back. Subsequent releases omitted the return walk. For each leg of this activity, data included measurements from the phone’s accelerometer and gyroscope in both raw and processed formats.
Data collection and Distribution
The app recorded all data collected for this study through interactions with the Bridge Server, a set of web services developed and operated by Sage Bionetworks. Bridge exposes a REST style web services API designed to allow collection and management of mobile health data from a variety of apps. This service has been used by Sage and other research organizations to support a variety of health studies, including all five of the initial Research Kit apps launched in March 2015. Bridge provides apps the ability to securely create accounts for participants, and record consent and other personal information separately from study data intended to be shared with research teams.
Coded study data, consisting of survey responses and mobile sensor measurements, is exported to Synapse for distribution to researchers. Synapse4 is a general-purpose data and analysis sharing service where members can work collaboratively, analyze data, share insights and have attributions and provenance of those insights to share with others. Synapse is developed and operated by Sage Bionetworks as a service to the health research community. In addition to mobile health data, we have used this system to develop communities centered around shared clinical, genomic, imaging, and other types of biomedical data5–7. In the context of this study, Synapse also hosts the analysis of a small subset of participants that begins to show the usefulness of collecting data from PD patients in this way as well as the power of building personalized metrics of PD derived from the activities in mPower8.
The mPower mobile app (https://github.com/Sage-Bionetworks/mPower) was built using Apple’s ResearchKit framework (http://researchkit.org/), which is open source and available on GitHub (https://github.com/researchkit/researchkit). AppCore (https://github.com/ResearchKit/AppCore) is a layer built on top of ResearchKit share among the five initial ResearchKit apps. The Bridge iOS SDK (https://github.com/Sage-Bionetworks/Bridge-iOS-SDK) provides integration with Sage Bionetworks’ Bridge Server, a back-end data service designed for collection of participant donated study data (https://sagebionetworks.jira.com/wiki/display/BRIDGE/Bridge+REST+API). Example code for accessing data through Synapse as well as code used for summary statistics and generating figures for this paper are also made available (https://github.com/Sage-Bionetworks/mPower-sdata).
A total of 9,520 participants consented to the study and agreed to share their data broadly with the research community. 8,320 participants completed at least one survey or task after joining the study. Of the 6,805 participants who completed the enrollment survey, 1,087 self-identified as having a professional diagnosis of PD while 5,581 did not (137 opted not to answer the question). Data contributed by participants for each survey and activity are enumerated in Table 1 and cumulative tasks completed for each activity are presented in Figure 2. Due to the nature of the study, follow up is nonuniform across individuals, however 898 participants contributed data at least five separate days over the course of the first six months of the study. The number of days participants contributed data were similarly distributed between those who self-reported as having a diagnosis of PD and those participating as controls (Figure 3).
All coded data sets (Table 1, Data Citation 1, Data Citation 2, Data Citation 3, Data Citation 4, Data Citation 5, Data Citation 6, Data Citation 7) are stored and accessible via the Synapse platform in a public project with associated metadata and documentation (https://www.synapse.org/mPower).
The data provided herein are derived from Apple iPhone devices with proprietary technical validation. We do not provide test-retest nor other technical validation data sets here, however others have reported technical validation of the coreMotion sensor in a different context9.
Due to the novel nature and collection method for these data, governance structures have been put in place in order to respect the balance between the desire of participants to share their data with qualified researchers and the respect for privacy of those participants.
Researchers who are interested in accessing these data need to complete the following steps:
Have a Synapse account (https://synapse.org)
Have their Synapse User Profile validated by the Synapse Access and Compliance Team (ACT)
Become a Synapse Certified user by completing a short quiz (https://www.synapse.org/#!Quiz:Certification)
Submit an Intended Data Use statement
Agree to the Conditions for Use associated with each data source (see DOIs for each data source)
While certain data types may have additional Conditions for Use (e.g. survey copyrights), the overarching Conditions for Use are as follows:
You confirm that you will not attempt to re-identify research participants for any reason, including for re-identification theory research.
You reaffirm your commitment to the Synapse Awareness and Ethics Pledge.
You agree to abide by the guiding principles for responsible research use and data handling as described in the Synapse Governance documents (https://www.synapse.org/#!Help:Governance).
You commit to keeping these data confidential and secure.
You agree to use these data exclusively as described in your submitted Intended Data Use statement.
You understand that these data may not be used for commercial advertisement or to re-contact research participants.
You agree to report any misuse or data release, intentional or inadvertent to the ACT within 5 business days by emailing email@example.com.
You agree to publish findings in open access publications.
You promise to acknowledge the research participants as data contributors and mPower study investigators on all publication or presentation resulting from using these data as follows: ‘These data were contributed by users of the Parkinson mPower mobile application as part of the mPower study developed by Sage Bionetworks and described in Synapse [https://www.synapse.org/mPower].’
Examples of client interactions with these data are provided in GitHub: https://github.com/Sage-Bionetworks/mPower-sdata
How to cite: Bot, B. M. et al. The mPower Study, Parkinson Disease Mobile Data Collected Using ResearchKit. Sci. Data 3:160011 doi: 10.1038/sdata.2016.11 (2016).
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).
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).
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).
Derry, J. M. J. et al. Developing predictive molecular maps of human disease through community-based modeling. Nat. Gen. 44, 127–130 (2012).
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).
Akbarian, S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).
Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).
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).
Yu, Y. et al. Initial Validation of Mobile-Structural Health Monitoring Method Using Smartphones. Int. J. Distrib. Sens. N 2015 274391 (2015).
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511429.1 (2016)
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511433.1 (2016)
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511432.1 (2016)
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511434.1 (2016)
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511439.1 (2016)
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511444.1 (2016)
Bot, B. M. Synapse http://dx.doi.org/10.7303/syn5511449.1 (2016)
The authors wish to acknowledge the donations of time and data from all of the mPower study participants, engineering contributions from Dwayne Jeng, Erin Mounts, Alx Dark, Eric Wu, Shannon Young for support of the mPower app and data-collection system (Bridge). Furthermore we are thankful for the guidance from Caroline Tanner on design of the study and Katherine Possin and Joel Kramer for design and providing the memory activity. Y Media Labs for early app development, Apple for initial app development and open-source ResearchKit framework. Funding was provided by the Robert Wood Johnson Foundation.
SF was an employee at Apple at the time of the ResearchKit launch.
About this article
Cite this article
Bot, B., Suver, C., Neto, E. et al. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data 3, 160011 (2016). https://doi.org/10.1038/sdata.2016.11
Journal of Open Source Software (2020)
Journal of Parkinson's Disease (2020)
Annals of Behavioral Medicine (2020)
Frontiers in Aging Neuroscience (2020)
Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants
npj Digital Medicine (2020)