Mobile Research Apps: A look at ResearchKit App Study Results from Stanford, Johns Hopkins and Rochester

by Priya Menon

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The introduction of ResearchKit, Apple’s open-source platform in clinical research, piqued medical community’s interest  even amidst apprehensions about skewed data and anticipation of performance in the ‘real world’. It has been two years since, emerging results from researchkit app studies are demonstrating that data for studies can be collected entirely using smartphones. With android libraries of ResearchDroid and ResearchStack being available, mobile health app studies can now reach out to a larger population too!

Since launch, over thirty studies have been using ResearchKit apps for collecting data. The year 2016, saw the launch of almost twelve ResearchKit study apps. Applied developed ResearchKit app Phendo (Endometriosis tracking app) for Columbia Univ’s Citizen Endo project in 2016.

researchkit apps list 2016

 

Let us take a look at preliminary results being reported from some of the ResearchKit app studies : MyHeart Counts from Stanford University, mPower of University of Rochester, Mt Sinai Icahn Medical School’s Ashtma Health app and EpiWatch of Johns Hopkins University. 

 

mobile research apps researchkit

mobile research apps researchkit results

 

MyHeart Counts

App details

  • Between March and October 2015, 48,968 participants consented to participating in the study
  • Group was dominated by healthy young men (82%,median age 36yrs)
  • Only about 6987 reported having any disease
  • 3,185 reported taking a medication
  • Participants were asked to complete an initial 7-day monitoring period where motion was tracked using the M7 co-processor chip in the iPhone
  • Daily surveys and a 6-minute walk test (MWT) at the end of the 7-day period
    • 9% completed the entire 7 days of motion data collection
    • 41% did at least 4 days
    • 10% of participants did the 6-MWT
    • ~3% did the Heart Age Questionnaire, which assesses risk factors for heart disease.

Success

  • Demonstrated the feasibility of consenting and engaging a large population across the United States using only smartphones
  • Gathered large-scale data in real time from mobile devices, stored securely, transferred, deidentified, and shared securely, including with participants.
  • A data set for the 6-minute walk test larger than any previously collected could be generated in weeks.
  • The study reports that state transition patterns of activity relate to the reported presence of disease.

Challenges

  • Poor association between perceived and recorded physical activity
  • Poor association between perceived and formally estimated cardiovascular disease risk
  • Major challenges and limitations of mobile health research,
    • Skewed age and sex of participants
    • Rapid drop-off in engagement over time, with the resulting loss of data collection for several measures.
    • Participant engagement needs to be optimized to maximize full participation of those who have expressed at least enough interest to download the app and consent to join the study.
    • Individuals do not carry their smartphones with them at all times; therefore, physical activity measurements are a lower bound for actual physical activity.
    • Motion trackers cannot distinguish the cause of periods of lack of motion

 

mPower

App details

  • A clinical observational study about Parkinson disease conducted purely through an iPhone app interface.
  • To learn about the variations of Parkinson’s’ Disease
  • To improve the way we describe and manage these variations
  • To learn whether mobile devices and sensors can help measure PD and its progression to ultimately improve the quality of life for people with PD.
  • App collected frequent information about the daily changes in symptom severity and their sensitivity to medication in PD. 
  • First 6 months of the launch of mPower app, about 48K people downloaded this app.
  • 16.5 K provided informed consent.
  • Of the 12,000 mPower study participants, about 9,500 participants chose to share their data with all researchers.
  • 8,320 participants completed at least one survey or task after joining the study.
  • 6,805 participants completed the enrollment survey.
  • 1,087 self-identified as having a professional diagnosis of PD.
  • 5,581 did not
  • 898 participants contributed data at least five separate days over the course of the first six months of the study

Success

  • Large enrollment numbers
  • Enabled frequent quantitative assessments leading to a better understanding of the disease heterogeneity
  • Unique opportunity to engage research participants without requiring physical interactions.
  • Sensors (accelerometers, gyroscopes, and microphones) provided quantitative surrogates of PD symptoms with minimal or no interruption in the participant’s daily life.

Challenges

  • Participant adherence to study tasks
  • Self-reported outcomes could contain errors and inconsistent information
  • Study follow up is nonuniform across individuals

 

researchkit app results

 

Asthma Health App

App details

  • Track asthma symptoms
  • Review trends
  • Provide feedback on progress
  • Provides personalized reminders to take prescribed medications.
  • Allows sharing of information collected on the app with doctor.
  • App downloaded almost 50,000 times in the first six months.
  • 7,593 people completed the electronic informed consent process.
  • 85% of users completed at least one survey offered by the app.
  • 2,317 users filled out multiple surveys throughout the six-month study

Success

  • Detected  increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires.
  • Scientists were able to correlate data from patients with external factors, including air quality, which also appeared to match existing studies.

Challenges

  • Selection bias
  • Low retention rates
  • Reporting bias
  • Data security.

 

EpiWatch

App details

  • To be able to use wearable technology (Apple Watch) to predict an oncoming seizure.
  • Exploring whether a future app could potentially detect seizures, estimate their duration and contact caregivers, all using Apple Watch.
  • ResearchKit-based study involving 598 individuals.
  • 10 month long study duration.
  • Method: Using the Apple Watch’s sensors, EpiWatch recorded participants’ heart rate and movements for 10 minutes from the time participants felt a seizure starting. The app asked participants to perform tasks to test responsiveness, and after the seizure ended — participants were given a brief survey about seizure type, aura, loss of awareness and possible seizure triggers.
  • In all, 40 percent of the group tracked a total of 1,485 seizures, with 177 participants reporting what triggered their seizures.
  • Preliminary results: Stress was the most common trigger, linked to 37% of seizures, followed by lack of sleep (18%), menstruation (12%) and overexertion (11%).

epiwatch results

Success

  • It may be a life changer for people with epilepsy.
  • Preliminary results show that most seizures are linked to stress and lack of sleep.
  • Full results of the study will be presented at the American Academy of Neurology’s 69th Annual Meeting in Boston, April 22 to 28, 2017.

 

researchkit apps challenges

 

These results demonstrate that conducting some studies entirely via smartphones is feasible, fruitful and scalable: it stands up to scientific rigor, allows for large-scale participant enrollment, and captures unique environmental data not available through traditional methods.

TrialX recently conducted a webinar on Mobile Research Apps  and how the apps above are ‘Mobile-izing data collection’. The webinar can be accessed here.

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