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CYCORE: A Cyberinfrastructure to Support Comparative Effectiveness Research in Behavioral Medicine
Susan K. Peterson1, Karen Basen-Engquist1, Wendy Demark-Wahnefried2, Alex Prokhorov1, Chaitan Baru3, Emilia Farcas3, Ingolf Krueger3, Doug Palmer3, Fred Raab3,
Phil Rios3, Celal Ziftci3, Stephanie Barrera1, Laura Wolszon3, Kevin Patrick3
1The
University of Texas MD Anderson Cancer Center, 2University of Alabama, Birmingham Comprehensive Cancer Center, 3The University of California at San Diego and the California Institute for Telecommunications and Information Technology (Calit2)
Introduction
Each year, roughly 1.44 million Americans are
diagnosed with cancer and 560,000 die from it
(1). The current annual US health care costs
approximate $228 billion (2). While
breakthroughs in cancer prevention and
treatment have occurred, growing evidence
suggests that lifestyle factors such as diet,
exercise, body weight status, and continuing
tobacco-use significantly impact the course of
disease, and as yet are unaccounted for
between and across trials (3-8). In 2009,
Congress appropriated $1.1 billion via the
American Recovery and Reinvestment Act to
drive a national effort towards comparative
effectiveness research (CER) to “encourage the
development and use of clinical registries,
clinical data networks, and other forms of
electronic data to generate outcomes data” and
to “evaluate the relative effectiveness of
different health care services and treatment
options”(9). The CYCORE (Cyberinfrastructure for Comparative Effectiveness
Research) project is specifically designed to
address each purpose. CYCORE’s primary aim
is to create a prototype for a user-friendly,
open-source cyberinfrastructure (CI) (see
Figure 1) to support the collection, storage,
visualization, analysis and sharing of health
outcome data to enhance cancer-related CER.
Figure 1: Cyber-infrastructure interfaces and services. The CI [1]
provides interfaces to sensors, data and storage resources, and user
applications; [2] provides core capabilities such as data acquisition,
preservation, distribution, visualization, and algorithm execution; and
[3] manages crosscutting concerns such as policy and identity
management within the infrastructure.
A Successful CI
A successful CI has the capability to significantly
improve CE research in cancer prevention and
control by:
 Broadening the scope and quality of data
that can be collected on factors that may
contribute to prevention, treatment and
control outcomes, both for persons with a
cancer diagnosis, as well as those at
increased risk for the disease;
 Exploiting and improving upon software and
database systems utilized in other scientific
domains to efficiently increase capacity for
larger and more complex data sets;
 Enabling the pursuit of new research
questions not addressable using current
systems by improving the integration of
patient data captured through objective
monitoring (i.e., sensor capture) and/or selfreporting, and the subsequent analysis of
those data;
 Providing decision support and patient
adherence-monitoring for care providers;
Funding support provided by National Institutes of Health
RC2 CA148263 (S. K. Peterson, PI)
Task Completion
Vision
 Enabling patients to participate actively in
their prevention and treatment regimens.
The future vision for CYCORE is that it will
serve clinical researchers and physicians by
collecting and analyzing data from patients
(through biometric and environmental sensors,
patient self-reported data, medical records, etc.)
as part of CE studies. In so doing, CYCORE will
enhance CE research efforts by integrating
capabilities for [a] data collection, assimilation,
and quality assurance from existing and
emerging data sources, [b] data distribution and
event notification for use by researchers,
clinicians, physicians, policy makers, and
operators of the CI, [c] data analysis and
visualization capabilities, and [d] governed
service- and resource-integration to address
security and privacy concerns throughout the
infrastructure.
Table 1: Device Usage Protocols and Rationale by Sample
Rationale for Device Usage
Advanced Colon
Cancer Patients
H&N Cancer Patients Currently
Receiving Radiation Therapy
Device
Arm 1: Exercise
Arm 2:
Adherence
Dehydration Risk
(n=15)
(n=15)
Arm 3:
Swallowing
Exercise
Adherence
(n=15)
To collect, encrypt, To collect, encrypt, To collect,
and transmit data and transmit data encrypt, and
from the
from the weight
transmit data from
Home Health Hub accelerometer, BP, scale,
the weigh scale to
HR, and GPS
accelerometer, BP, the CI
devices to the CI
and HR devices to
the CI
Accelerometer
To monitor
To monitor
with HHH
N/A
frequency of activity frequency of activity
interface (#1)
To assess change To assess change
in physiological
in physiological
Blood pressure
parameters related parameters related
N/A
monitor
to physical activity to risk of
dehydration
To monitor weight To monitor weight
fluctuations related fluctuations
to dehydration or related to
Weight scale
N/A
inadequate food/
dehydration or
fluid intake
inadequate food/
fluid intake
To assess change To assess change
in physiological
in physiological
Heart rate monitor parameters related parameters related
N/A
to physical activity to risk of
dehydration
To monitor changes
Global positioning
in movement
N/A
N/A
system
outside of the home
Carbon monoxide
monitor
N/A
N/A
To capture
To capture
behavior-related
behavior-related
data related to
data about
exercise adherence dehydration risks
over multiple time (diet, fluid intake)
points; to initiate
over multiple time
points; to capture
Smart phone with sensor-related
responses about
EMA Capabilities behaviors
urine color; to
initiate sensorrelated behaviors;
to assess pain and
fatigue
symptomology
Smart phone with
video capabilities
N/A
N/A
N/A
To capture
behavior-related
data about
dehydration risks
(diet, fluid intake)
over multiple time
points; to capture
responses about
urine color; to
initiate sensorrelated behaviors;
to assess pain
and fatigue
symptomology
To capture
adherence to
swallowing
exercises
Cancer
Survivors 1Year after
Diagnosis,
Smokers or
Recent Quitters
Arm 4:
Smoking
Cessation
Adherence
(n=15)
N/A
Specific Aims
 To gather requirements data to determine: [a]
parameters to measure and data formats, [b]
storage requirements, [c] needed
manipulations, [d] output formats, and [e] userfriendly interfaces between clinicians,
researchers, and patients;
Requirements for CI development have been
gathered from 77 stakeholders, including
multidisciplinary oncological researchers and
clinicians, who have informed the development
of the initial prototype. The next phase of
CYCORE involves the feasibility testing and
evaluation (see Table 1), in a target patient
population, of behavior-related cancer treatment
and prevention strategies that have been
hampered by well-known problems: poor patient
adherence, inability to accurately measure
environmental influences on health, and limited
objectively-collected data regarding lifestyle
behaviors. The CI will provide the interface
between patients and CYCORE’s intelligent
Home Health Hub (HHH) (Figures 2 and 3), to
which a set of biometric and environmental
sensors will provide objective capture of
behavior-related data that are then relayed to a
data-management system via a web-service
interface.
Home Health Hub Component Diagram
WAN
(Internet)
N/A
N/A
CPU / Core
WIFI
Serial
Sensor
Interfaces
(RS232)
Cellular
N/A
N/A
RF (Bluetooth,
802.11)
Ethernet
 To create CI for data aggregation, integration,
processing, mining, storage and retrieval;
Dialup
LCD /
(POTS)
3 Button Console
USB
User Interface (3 line text/response)
 To build data acquisition hardware and
software, including: interfaces with multi-format
electronic medical records and cancer
Biomedical Informatics Grid (caBIG) data,
physiological sensors for at-home and
unconfined use, and digitization methods for
self-reported data;
Figure 3: Hardware component diagram for the Calit2 Home
Health Hub showing; Internet connection interfaces (blue), core
computer (green), user interface (orange), and data sensor
interfaces (purple).
References
N/A
1.
To capture
carbon monoxide
component of
expired air
To capture
behavior-related
data related to
smokingcessation
adherence over
multiple time
points; to initiate
sensor-related
behaviors
 To develop applications to be used in
conjunction with the CI, including
implementation of novel brain-based device
methods for data analysis;
 To conduct feasibility studies with the new
technology, feeding results back into CI design
for improvement and enhancement.
2.
3.
Figure 2: CYCORE Scenario. Data from self-patient
reports and body-worn sensors (interacting with the Home
Health Hub), complemented by fixed sensors in the
environment, can be collected and analyzed (using, for
example, Brain-Based Devices) to perform comparative
effectiveness studies.
4.
5.
6.
7.
To capture
adherence to CO
monitoring
protocol
8.
9.
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