Editorial
Journal of the Royal Society of Medicine; 2015, Vol. 108(8) 294–296
DOI: 10.1177/0141076815600673
Apple’s ResearchKit: smart data collection for the
smartphone era?
Jennifer Jardine1, Jonathan Fisher2 and Benjamin Carrick3
1
Women’s Health Research Unit, Queen Mary University of London, Barts and the London School of Medicine and Dentistry,
London, UK
2
Institute of Child Health, University College, London, UK
3
Royal Victoria Infirmary, Newcastle, UK
Corresponding author: Jonathan Fisher. Email: jph.fisher@gmail.com
Introduction
Alongside the Apple Watch, in March 2015 Apple
released ResearchKit, an open source software
framework for medical research. Available at
www.researchkit.org, ResearchKit enables investigators to create mobile applications which use the
iPhone’s capacity to collect data, track movement
and take measurements. Intended to facilitate largescale, opt-in surveys and observational studies as well
as providing a new way for researchers to collect
adjuvant data on subjects recruited elsewhere,
ResearchKit is now demonstrating its power. A
recently opened study of cardiovascular health at
Stanford University recruited over 10,000 participants within 24 h of their ResearchKit platform
being launched.1
Engagement with novel electronic research methodologies varies; barriers related to healthcare systems, culture and investigator technological fluency
are challenging. Encouragingly, surveys of junior
doctors indicate high levels of enthusiasm for
developing new smartphone applications (apps) for
use in healthcare and research.2 ResearchKit offers
a development platform with relatively little assumed
knowledge. Here, we outline and evaluate the framework, identifying issues for ethics approval and indicating how this system fits into current practice.
investigator and the patient. Informed consent is
assessed by requiring patients to pass a test showing they understand the consent form prior to
enrolment.
2. Surveys: Using a pre-created user interface, surveys can be built by inserting questions and types
of answers – rather like the functionality currently
available in other online survey tools such as
SurveyMonkey (www.surveymonkey.com).
3. Active tasks: These modules collect data using the
in-built capacity of the mobile device. Participants
perform structured tasks under partially controlled
conditions; inputs to the iPhone’s accelerometer,
gyroscope, screen and microphone are collected.
Five of the available Active Task modules are outlined in Table 1.
Cost
Mobile apps can cost between £1000 and £30,000
depending on their complexity, integration and data
storage,3 with additional costs for distribution in the
iTunes/Google Play store. The cost of developing an
app using ResearchKit is very low; its modular design
minimises the need for specialist coding expertise.
Furthermore, conventional trial recruitment costs
often run in multiples of £100,000 for larger studies,
making the new approach an attractive alternative.
Framework capabilities
ResearchKit is composed of pre-constructed modules
which can be used alone or in combination. There are
three basic modules for developers to customise,
allowing handling of informed consent, surveys, and
‘active tasks’. Creation of new modules is supported.
1. Informed consent: Study information is displayed
and a consent form signed on the mobile device,
then converted to a .pdf and emailed to the
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Population coverage and bias
The potential for bias is significant; demographic
information on study participants should be collected
in order to acknowledge this. These limitations may
decrease with the passage of time as smartphone
prices fall and uptake increases. Since Apple launched
the iPhone in 2007, smartphone use has increased
rapidly; 22% of the global population own a
smartphone – more than the 20% who own a
Editorial
295
Table 1. Five active task modules developed for the
ResearchKit released demonstrate the capacity of the system
to gather medically relevant information through the in-built
sensors in a participant’s smartphone.
Task
Sensors used
Data collected
Gait
Accelerometer
Gyroscope
Accelerometer
Device motion
Pedometer
Tapping
Touch screen
Touch activity
Accelerometer
Six-minute walk
Accelerometer
Accelerometer
Device motion
Pedometer
Location
Heart rate
Spatial memory
Touch screen
Touch activity
Correct and actual
sequences
Phonation
Microphone
Uncompressed audio
personal computer. Smartphone ownership in the
United States increased from 35% in May 2011 to
58% in January 2014, with a predominance of
young adults (83% of 18–29-year-olds and 74% of
30–49-year-olds),4 though only 23% of individuals
with an annual income less than $20,000 owned a
smartphone.5 According to a recent UK OFCOM
report, ownership rates are higher: 69% of respondents from social groups ABC1 and 51% of respondents from social groups C2DE use a smartphone.6
An advantage of smartphone data collection is the
ability to reach younger, more active subjects as
well as groups traditionally isolated from clinical
research such as those with mental illness and in
rural locations.
Ethical considerations
Remote data collection raises several ethical issues,
such as the disconnect between the participant and
the researcher. Participants do not meet the research
team, abrogating discussion before consent to a
study. While ResearchKit’s informed consent structure is impressive, the value of face-to-face explanation of a study with an opportunity for questions
remains high. There is evidence that users spend
little time reading Terms of Service Agreements for
Software,7 which may explain why some smartphonebased health studies have not used online consent
methods. Additionally, there is potential for openendedness in the consent process, raising the
possibility of ongoing background data collection,
with the associated questions about duration of capacity and the potential for subjects to forget and thus
become unwitting participants in research. It will be
difficult to restrict participants a priori, so data may
be collected on ineligible study participants and subsequently not used. Attention has been given in the
UK press to the long-term consequences of agreements for online services, which impose apparently
unanticipated charges following a ‘free-trial’ period,
despite detailed service agreements. Similar negative
coverage for consent to medical trials would be very
damaging.
Confidentiality and the governance of sensitive
medical information is a wider issue that applies to
many platforms onto which patients often upload
personal information.8 While much of this publication of personal information is driven by patients, a
clear distinction must be drawn between the proactive
publication of these details and the passive transmission or retention of personal information by a third
party.
How does this fit into current practice?
Online methods of data collection and data processing are widespread. In 2009, 750 American ethics
committees revealed that online surveys were their
commonest type of application;9 these studies are
often undertaken through third party providers.
The Internet is an increasingly popular way of conducting randomised controlled trials,10 with high participant satisfaction and preference for future
involvement in Internet trials above traditional
designs. The benefits of flexibility and convenience
were felt to outweigh the disadvantages of lack of
connectedness and understanding. However, when
reviewed, these trials had methodological deficiencies
with a high rate of loss to follow-up.10 ResearchKit
could ameliorate this concern by collecting data passively and having the facility to frequently ‘nudge’
patients to participate.
The development of applications has allowed
patients to drive their own data collection. In chronic
diseases such as diabetes, patients already use applications to collect data about their behaviour, wellbeing and priorities, a natural springboard to using
these tools for research.
Conclusion
Attitudes of patients towards third parties sharing
their personal information, and their own tendency
to share personal data through social media are
dichotomous.11 There is an increasing willingness to
296
share personal information with companies or
through social media if correctly incentivised12;
ResearchKit could harness this, an opportunity that
is both tantalising and risky. It has never been possible to capture data about day-to-day activity without either allowing the subject to control that data
flow (e.g. by questionnaire) or placing a high burden
on the subject (constant observation, or the wearing
of a device). Providing that the data collection is
transparent, between strictly defined time points
and with clear informed consent, this tool could be
very useful.
However, we would offer a caution. It is likely that
this platform will be flooded by repetitive small-scale
studies; at some point the information from these will
inadvertently be used in a way that the participant
did not intend. When this happens, the negative publicity could hamper research engagement generally.
We therefore urge rigorous attention to the consent
process and the early involvement of review boards
and regulatory authorities, with urgent training of
these bodies and also of medical researchers in the
potential of this technology.
Declarations
Competing interests: None declared
Funding: None declared
Guarantor: JF
Ethical approval: Not applicable
Contributorship: JF and JJ contributed equally to the writing of
the paper. BC provided advice and guidance on the relevance of
ResearchKit to current practice, as well as editorial advice.
Acknowledgements: None
Provenance: Not commissioned; editorial review
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