Mobile Phone App for Parkinson’s Patients Tests New Model for Data Sharing
With mobile phones now practically the third arm of modern (wo)man, researchers have begun to harness the processing power of these increasingly ubiquitous devices for data collection. Because people carry their phones everywhere, they can capture behavioral data on a daily basis. Researchers led by Andrew Trister and Stephen Friend at Sage Bionetworks, a nonprofit research organization in Seattle, developed a mobile phone app called mPower that lets Parkinson’s patients record their movement and memory over time. Such data chronicles the day-to-day variability in symptoms and may help researchers spot trends more quickly, Trister and Friend report in the March 3 Scientific Data. They intend to analyze mPower data to understand medication effects on Parkinson’s symptoms.
“This seems to me an avenue of research well worth pursuing,” John Harrison at Metis Cognition, Wiltshire, U.K., wrote to Alzforum (see full comment below). “This approach may yield interesting information about patient behavior, as well as their responses to treatment.” For example, with advancing disease, Alzheimer’s patients tend to stay at home more, and any device with a GPS could track this behavioral change, Harrison noted.
Along with the publication of their paper, the authors released the first batch of data from the mPower study to researchers worldwide. This was possible because 78 percent of the 12,000 people who participated in the study agreed that qualified researchers could access their data. Allowing research participants themselves to make this decision, a priori, provides a new model that might streamline the process of data sharing, Friend and co-author John Wilbanks, also at Sage, suggest in a Nature Biotechnology editorial. Normally, data-sharing decisions are made by researchers through formal mechanisms such as data access committees. While scientists tend to shy away from saying so on the record, privately, many complain that these committees and their attendant bureaucracy can slow or limit the flow of information to other scientists. mPower represents a pilot project for a new approach to data sharing.
Several academic and industry groups are developing mobile phone apps to collect data from people with various neurodegenerative diseases (see Jun 2012 news; Dec 2012 news). Being a movement disorder, Parkinson’s disease is particularly suited to this type of data collection. Pilot studies have reported success in monitoring various PD symptoms via remote digital devices such as smartphones (see Goetz et al., 2009; Arora et al., 2015).
These studies inspired the researchers at Sage. Friend co-founded the nonprofit in 2009 along with Eric Schadt of Mount Sinai School of Medicine, New York, to foster collaborative research and develop better models for complex diseases. The organization focuses particularly on neuroscience and cancer research. Trister and Friend wondered if mobile phones could improve disease management by capturing the variability in symptoms and how medications affect symptoms. To test this idea, first author Brian Bot and colleagues developed mPower for the iPhone, using Apple’s ResearchKit software. The authors released the free app in the United States in March 2015 through the Apple App Store. Healthy people as well as Parkinson’s patients were encouraged to download and use it.
Before using the app, participants must read through informed consent materials and decide whether to release their data only for Bot and colleagues’ study, or much more broadly to researchers worldwide. During the first six months after the app’s release, 14,684 people downloaded it and completed the enrollment process. Later, 2,483 people withdrew from the study, and 2,681 opted to share data only with the mPower team. This left data from 9,520 people in the broadly available set. Of those people, 6,805 completed a baseline survey. In that group, 1,087 said they had been diagnosed with Parkinson’s, 5,581 had not, and 137 provided no information on diagnosis.
Once enrolled, participants were asked to fill out the Parkinson Disease Questionnaire 8 and a subset of the Movement Disorder Society Universal Parkinson Disease Rating Scale (MDS-UPDRS) every month. In addition, the app prompted them to complete four activities three times each day: a memory test, walking a short distance, tapping the screen for 20 seconds to test hand coordination and speed, and saying “aaaah” for 10 seconds to track the strength of their voice. The app requested that participants complete one session before taking their medication, one shortly after taking it, and the third at another time of day.
In this initial study, few participants followed through. Only 898 users, of whom 150 were Parkinson’s patients, completed tasks on at least five separate days. About 20 percent of those stuck with the tasks for more than a month. Even so, participants generated hundreds of statistical measures that are allowing the authors to analyze differences in how people respond to medication, Trister wrote to Alzforum. For example, the researchers are investigating how medication affects tremor, as measured by the accuracy of tapping a target spot, versus movement speed and gait, and whether there are distinct patterns of response for different people. The authors hope the findings might eventually improve symptom management. Trister noted that he is working to make the app more useful to patients and to build a community around the study. “We hope these incentives will reduce the attrition rate,” he wrote.
Data for All
The authors released the first six months of data, stripped of any identifying information, through Synapse, a data-sharing service run by Sage Bionetworks. To access the data, outside researchers must establish an account with Synapse, explain what they intend to do with the data, and complete an ethics questionnaire. In addition, they must agree not to try to identify participants, not to sell the data or use it for marketing purposes, and to publish any results in open-access journals.
As Wilbanks and Friend explained in their Nature Biotechnology editorial, they chose this format to facilitate broader use of the data than is typically allowed by a data access committee. Such committees are a common means of controlling data sharing. Usually formed at the universities where the principal investigators work, committees evaluate data requests on a case-by-case basis. However, because these committees represent the interests of the principal investigator, data requests can be met with “high friction,” Friend told Alzforum. “The literature indicates that data access committee mechanisms can encode conflicts of interest, leading to data withholding,” Wilbanks and Friend noted in their editorial (see also Shabani et al., 2015).
Others agree that data sharing, particularly in human genetics, often falls short. A 2015 Nature editorial cited the example of a breast cancer project, BRCA Share, that touted the ideals of open data while withholding its findings from the public database ClinVar, run by the National Institutes of Health. “In truth, [BRCA Share] creates more of a walled garden of genetic data than an open field,” the editorial concluded.
Similar problems exist in the neurodegeneration field. For some diseases, genetic data sharing has worked well, for example in the Parkinson’s database PDGene. On the other hand, an update of AlzGene, hosted on Alzforum, has been delayed by difficulty obtaining data published in 2013 (see Alzgene; Lambert et al., 2013).
Some investigators cite participant consent as a roadblock to sharing; however, many research participants express frustraton that data generated from their time, effort, and tissue donations are not shared more readily among scientists. Alison Goate at Mount Sinai School of Medicine wrote to Alzforum, “All Alzheimer’s Disease Research Center/Alzheimer’s Disease Center recruitment currently uses a consent that enables broad sharing of data.” This is mandated by the NIH. Consent is only an issue for genetic samples collected a long time ago, Goate added. For his part, Friend believes that there is a will to share. “I think the Alzheimer’s world wants to be the most cutting-edge place for the sharing of genetic data and for working together as a community. [That vision] hasn’t been fully realized yet,” he told Alzforum.
Data sharing requires standardization and privacy safeguards, researchers noted. Friend pointed out that for general use, data must include the context in which it was gathered, such as whether Parkinson’s movement tests were recorded before or after medication. Data has to be properly annotated, he stressed. For sensitive genetic data, researchers will have to be particularly careful to scrub identifying information and to ensure data is used properly, Friend added. He believes this can be accomplished through streamlined mechanisms like those in the mPower study. In their editorial, Wilbanks and Friend conclude, “Our experience suggests that participants who give their time and their sensitive personal information to researchers often assume that their data will be distributed widely to the full research community, not ‘owned’ as an asset to extract value from, solely by the researchers who happened to collect it.”—Madolyn Bowman Rogers
- Patient-Reported Outcomes Win Alzheimer's Challenge 2012
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Basic page Citations
- Goetz CG, Stebbins GT, Wolff D, DeLeeuw W, Bronte-Stewart H, Elble R, Hallett M, Nutt J, Ramig L, Sanger T, Wu AD, Kraus PH, Blasucci LM, Shamim EA, Sethi KD, Spielman J, Kubota K, Grove AS, Dishman E, Taylor CB. Testing objective measures of motor impairment in early Parkinson's disease: Feasibility study of an at-home testing device. Mov Disord. 2009 Mar 15;24(4):551-6. PubMed.
- Shabani M, Dyke SO, Joly Y, Borry P. Controlled Access under Review: Improving the Governance of Genomic Data Access. PLoS Biol. 2015 Dec;13(12):e1002339. Epub 2015 Dec 31 PubMed.
- Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Morón FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fiévet N, Huentelman MW, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossù P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F, European Alzheimer's Disease Initiative (EADI), Genetic and Environmental Risk in Alzheimer's Disease, Alzheimer's Disease Genetic Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology, Moebus S, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Lannefelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W, Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O'Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead S, Mosley TH Jr, Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RF, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JS, Boerwinkle E, Riemenschneider M, Boada M, Hiltuenen M, Martin ER, Schmidt R, Rujescu D, Wang LS, Dartigues JF, Mayeux R, Tzourio C, Hofman A, Nöthen MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg GD, Amouyel P, Wang J, Uitterlinden AG, Rivadeneira F, Koudstgaal PJ, Longstreth WT Jr, Becker JT, Kuller LH, Lumley T, Rice K, Garcia M, Aspelund T, Marksteiner JJ, Dal-Bianco P, Töglhofer AM, Freudenberger P, Ransmayr G, Benke T, Toeglhofer AM, Bressler J, Breteler MM, Fornage M, Hernández I, Rosende Roca M, Ana Mauleón M, Alegrat M, Ramírez-Lorca R, González-Perez A, Chapman J, Stretton A, Morgan A, Kehoe PG, Medway C, Lord J, Turton J, Hooper NM, Vardy E, Warren JD, Schott JM, Uphill J, Ryan N, Rossor M, Ben-Shlomo Y, Makrina D, Gkatzima O, Lupton M, Koutroumani M, Avramidou D, Germanou A, Jessen F, Riedel-Heller S, Dichgans M, Heun R, Kölsch H, Schürmann B, Herold C, Lacour A, Drichel D, Hoffman P, Kornhuber J, Gu W, Feulner T, van den Bussche H, Lawlor B, Lynch A, Mann D, Smith AD, Warden D, Wilcock G, Heuser I, Wiltgang J, Frölich L, Hüll M, Mayo K, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Singleton AB, Guerreiro R, Jöckel KH, Klopp N, Wichmann HE, Dickson DW, Graff-Radford NR, Ma L, Bisceglio G, Fisher E, Warner N, Pickering-Brown S. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013 Dec;45(12):1452-8. Epub 2013 Oct 27 PubMed.
- Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, Pratap A, Wilbanks J, Dorsey ER, Friend SH, Trister AD. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data. 2016 Mar 3;3:160011. PubMed.
- Trister AD, Dorsey ER, Friend SH. Smartphones as new tools in the management and understanding of Parkinson’s disease. NPJ Parkinson’s Dis. 2016 Mar 3;2:16006.
- Wilbanks J, Friend SH. First, design for data sharing. Nat Biotechnol. 2016 Mar 3; PubMed.
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Metis Cognition Ltd.
This technology has promise, and I had recently the pleasure of discussing opportunities for this approach in Alzheimer’s disease with Steve Friend. With the onset of Alzheimer’s, patients go out less and their world begins to shrink. This change in behavior could be neatly captured with any GPS device. Another big advantage is that this kind of data collection is passive, which when working with Alzheimer’s patients is an advantage. In the mild stages of the disease, active interaction with a digital device would still be achievable but, as dementia progresses, anything beyond simple or passive data collection will be a challenge.
Data sharing is a major asset in uncovering more about the consequences of the disease. The success of the ADNI and AIBL studies is a good indicator of data-sharing benefits. Clearly, there are issues of privacy. One revelation of meeting with Apple representatives last year was the extraordinary amount of information about one’s activities that just carrying a GPS-enabled device can yield. As always with digital information, there will need to be clear safeguards in place to protect privacy.
This seems to me an avenue of research well worth pursuing. Last year at the European Games for Health event, I organized a symposium on digital initiatives in CNS indications. The consensus from our discussion was that this approach may yield interesting information about patient behavior, as well as their responses to treatment.
This type of app has potential in other neurodegenerative diseases such as Alzheimer’s. The smartphone, with all its sensors, is a very powerful data-collection tool. We are excited to see this approach used and validated broadly, since it is related to some of our research going back to 2010. At Ginger.io, we have collected data on, and studied, various diseases, with a core focus on mental health conditions such as depression and anxiety. We have partnered with more than 40 medical institutions in the United States and in all of these cases, data collection using a smartphone has been a central tenet that has appealed to researchers, data scientists, and the medical community.
In terms of potential drawbacks, for example getting people to stick with the app, there are always challenges to user engagement with this kind of approach. Making it useful for the users is important. We have found that for users to stay engaged for long periods and continue to enter data, they need to find the tool and self-reflection important in managing their condition. The advantage of a large-scale research data collection exercise like this one is that even if a subset of users don't stick around, the ones who do can provide valuable data.
With regard to the data-sharing model developed here, the researchers provided participants the option to choose between narrow sharing with just this research team and broad sharing with quality researchers worldwide. The study protocol was approved by an IRB. In particular, (i) the user's permission was obtained, (ii) user information seems to have been de-identified, and (iii) ethical guidelines were provided for other researchers. At first glance, these measures make the data sharing reasonable and alleviate privacy concerns because they offer users the opportunity to choose how their data is shared.
This could be a useful model for the research community to follow when collecting this kind of data. Many research groups have been using smartphones to gather data over the last few years, whether through surveys or sensor data. At Ginger.io, we have worked with several such researchers. Smartphones allow researchers to gather data at a scale that was not feasible in the past. As a result, the medical and research communities have a chance to leverage the power of these devices to advance science and impact people's lives.
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