1. mHealth Approaches to Engaging the Patient in Comparative Effectiveness Research
Zubin J. Eapen, MD, MHS
Faculty Director, Duke Clinical Research Institute Innovation Center

2. Care delivery
Clinical Research

3. Data collected by providers in hospitals

4. Problem: Data collection platforms are siloed according to users and venues of care
Provider
Patient
Hospital
Home

5. Data collected by patients at home

6. Integrating data across users and venues of care

7. Admit for Heart Failure/Afib
This is a depiction of the patient’s weight and BP over 90 days. Here, the patient had a noticeable rise of about 6-8 lbs over the course of January before being admitted for heart failure and afib. I thought this was interesting, as perhaps there could have been a preventive intervention. His BP stayed under good control throughout the study period.

9. Care delivery
Clinical Research

10. ResearchKit capabilities
Open framework for easy collection of data needed for medical research
Participants can transmit valuable data to clinical researchers almost instantly through their personal digital devices

11. Revolutionary tool for pragmatic trials
ResearchKit creates a new channel for study discovery and participant recruitment
Local, national and global recruitment
Disrupt the way large cohort studies are executed
Cheaper to send every study participant an iOS device than the current standard surveys
Integration with wearables to passively collect biometrics
Larger sample size expands the diversity of participants in trials?

13. ResearchKit apps at launch
The University of Rochester and Sage Bionetworks
“Parkinson mPower” allows patients to track real-time changes in balance, gait, voice tremors and other symptoms.
Mount Sinai’s Icahn School of Medicine
“Asthma Health” encourages patient education, self-monitoring and behavioral changes, ultimately leading to better asthma symptom control, improved quality of life and less need for healthcare services.
Massachusetts General Hospital
GlucoSuccess helps to manage diabetes symptoms and study how personal habits affect glucose levels.
Dana-Farber Cancer Institute, Penn Medicine, and UCLA’s Jonsson Comprehensive Cancer Center, Sage Bionetworks
Program to track the daily impact of breast cancer.
Stanford University
MyHeart Counts, monitors daily activities, tracks behavior through surveys, and correlates that data with reported cardiovascular health

14. 1st ResearchKit app to get international release
41,000 downloads domestically
Now available to iOS users in Hong Kong and UK

15. Data collection for clinical trials

16. Study modules
Consent
Surveys
Active Tasks

17. Informed consent Flexible framework
Waivers & institution-specific customization
Comprehension tests
Electronic signature

18. Surveys Pre-built interface Flexible question and answer types
Localization and language support

19. Active Tasks
Active tasks invite users to perform activities under partially controlled conditions while iPhone sensors are used to collect data
e.g. active task for analyzing gait and balance might asks the user to walk a short distance, while collecting accelerometer sample data on iPhone.
Data collection capabilities of the HealthKit and CoreMotion APIs on iOS

20. Active Tasks

21. High frequency data acquisition

22. Pros
Cons
Scale
Open source
PRO collection
Novel measures
Missingness
Selection bias
Reporting bias
Data normalization

23. ResearchKit Best Practices: Consent
Consider including a cryptographic signature to protect the consent process.
Consider the level of identity verification required to obtain a valid consent.
Consider whether you can provide for prospective participants to ask additional questions once they have reviewed your consent material.
If your study may recruit minors as participants, you must ensure that you are obtaining consent from the parent or guardian rather than from the minor.

24. ResearchKit Framework Best Practices: Participant Control
Participants should have granular control over what data they choose to share with the study.
If you intend to share the collected data with other researchers, participants must be able to control whether their data is included in this.
Participants should be able to leave a research study at any time if they so choose.

25. ResearchKit Framework Best Practices: Privacy
Participants should be told up front exactly what enrolling in the study would mean for them, what data they are contributing, and who may have access to the data.
Having an explicit privacy policy is highly recommended for every app that collects personal data. It is also required for ResearchKit apps posted to the iOS App Store.
Use touch ID or PIN access to control access to your app, if your app records or displays personal data.
Do not use iCloud to store health information.

26. ResearchKit Framework Best Practices: Security
Use the highest level of file protection possible for the given use case. Usually this should be NSFileProtectionComplete or NSFileProtectionCompleteUnlessOpen. This way, files stored by your app are encrypted automatically whenever the device is locked.
Do not keep personal data for longer than necessary for your app to function.
When transmitting data via networks that terminate SSL early, or when contemplating a store and forward mechanism of any kind for your research data, consider an extra cryptographic wrapper for the data to protect it end to end. For example, a Cryptographic Message Syntax (CMS) envelope, the same technology used in S/MIME, can be used to encrypt data before transmission.

27. ResearchKit Framework Best Practices: Software Development
Give your ResearchKit tasks context by using instruction steps at the beginning of your task. This also allows for clean presentation of any requests for data access.
If your tasks and questions are maintained in a database, have unique keys for them and propagate these through ResearchKit model objects like ORKStep and ORKOrderedTask and into ResearchKit results.
The ResearchKit framework does not include a data management solution. The framework can be used with a data management solution of your choice.

28. The Backend Code: AppCore
AppCore is a layer built on top of ResearchKit which forms the core of the five initial ResearchKit apps
It includes some of the key features of those initial ResearchKit apps, including:
Dashboard with progress graphs
Data storage back end
JSON serialization and deserialization
Integration with Sage Bionetworks' Bridge service
Over time, some of the features in AppCore may be migrated into ResearchKit.

29. Duke ResearchKit 2015 roadmap
Duke ResearchKit infrastructure deployed by Summer 2015
Duke University first ResearchKit app launched in Summer 2015
Prepare Duke guidelines for ResearchKit apps by Summer 2015
Scale backend infrastructure to support large number of ResearchKit apps (2015)
Android version of ResearchKit (2015)