Enhancing mHealth Technology in the PCMH Environment to Activate Chronic Care Patients
Phase: NA
Conditions: Diabetes Mellitus, Type 2
Sponsor: Clemson University

 
 
 
 
 
 
 
Enhancing mHealth Technology to Activate Chronic Care Patients 
NCT02949037 
May 1, 2017 
 
Published protocol manuscript 
 

Protocol
Enhancing mHealth Technology in the Patient-Centered Medical
Home Environment to Activate Patients With Type 2 Diabetes: A
Multisite Feasibility Study Protocol
Ronald Gimbel1, PhD; Lu Shi1, PhD; Joel E Williams1, MPH, PhD; Cheryl J Dye1, MA, PhD; Liwei Chen1, MD,
PhD; Paul Crawford2, MD; Eric A Shry3, MD; Sarah F Griffin1, MPH, PhD; Karyn O Jones1, MA, PhD; Windsor W
Sherrill1, MBA, MHA, PhD; Khoa Truong1, MA, MPhil, PhD; Jeanette R Little4, MS; Karen W Edwards1, MEd;
Marie Hing1, MS; Jennie B Moss1, BSN, MS
1Department of Public Health Sciences, Clemson University, Clemson, SC, United States
2Nellis Family Medicine Residency Program, Mike O'Callaghan Federal Hospital, Las Vegas, NV, United States
3Madigan Army Medical Center, Tacoma, WA, United States
4MHIC Laboratory Lead, Telemedicine & Advanced Technology Research Center, U.S. Army Medical Research & Materials Command, Fort Gordon,
GA, United States
Corresponding Author:
Ronald Gimbel, PhD
Department of Public Health Sciences
Clemson University
501 Edwards Hall
Clemson, SC, 29634-0745
United States
Phone: 1 864 656 1969
Fax: 1 864 656 6227
Email: rgimbel@clemson.edu
Abstract
Background: The potential of mHealth technologies in the care of patients with diabetes and other chronic conditions has
captured the attention of clinicians and researchers. Efforts to date have incorporated a variety of tools and techniques, including
Web-based portals, short message service (SMS) text messaging, remote collection of biometric data, electronic coaching,
electronic-based health education, secure email communication between visits, and electronic collection of lifestyle and
quality-of-life surveys. Each of these tools, used alone or in combination, have demonstrated varying degrees of effectiveness.
Some of the more promising results have been demonstrated using regular collection of biometric devices, SMS text messaging,
secure email communication with clinical teams, and regular reporting of quality-of-life variables. In this study, we seek to
incorporate several of the most promising mHealth capabilities in a patient-centered medical home (PCMH) workflow.
Objective: We aim to address underlying technology needs and gaps related to the use of mHealth technology and the activation
of patients living with type 2 diabetes. Stated differently, we enable supporting technologies while seeking to influence patient
activation and self-care activities.
Methods: This is a multisite phased study, conducted within the US Military Health System, that includes a user-centered design
phase and a PCMH-based feasibility trial. In phase 1, we will assess both patient and provider preferences regarding the enhancement
of the enabling technology capabilities for type 2 diabetes chronic care management. Phase 2 research will be a single-blinded
12-month feasibility study that incorporates randomization principles. Phase 2 research will seek to improve patient activation
and self-care activities through the use of the Mobile Health Care Environment with tailored behavioral messaging. The primary
outcome measure is the Patient Activation Measure scores. Secondary outcome measures are Summary of Diabetes Self-care
Activities Measure scores, clinical measures, comorbid conditions, health services resource consumption, and technology system
usage statistics.
Results: We have completed phase 1 data collection. Formal analysis of phase 1 data has not been completed. We have obtained
institutional review board approval and began phase 1 research in late fall 2016.
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Conclusions: The study hypotheses suggest that patients can, and will, improve their activation in chronic care management.
Improved activation should translate into improved diabetes self-care. Expected benefits of this research to the scientific community
and health care services include improved understanding of how to leverage mHealth technology to activate patients living with
type 2 diabetes in self-management behaviors. The research will shed light on implementation strategies in integrating mHealth
into the clinical workflow of the PCMH setting.
Trial Registration: ClinicalTrials.gov NCT02949037. https://clinicaltrials.gov/ct2/show/NCT02949037. (Archived by WebCite
at http://www.webcitation.org/6oRyDzqei)
(JMIR Res Protoc 2017;6(3):e38)   doi:10.2196/resprot.6993
KEYWORDS
mHealth; diabetes mellitus; patient activation; patient-centered medical home; patient centered care; eHealth; health information
Introduction
Diabetes mellitus is a chronic disease with high rates of
disability, impaired quality of life, and premature death [1-4].
The prevalence of type 2 diabetes is increasing at an alarming
rate in the United States; in 2013, the estimated number of
patients was between 20 million and 27 million, or about 7%
to 10% of the adult population [2,3]. Research suggests that, if
current trends continue, diabetes will be diagnosed in 1 in 3
adults in the United States by 2050 [4,5]. Diabetes is the leading
cause of blindness, nontraumatic amputations, and adult renal
failure, and reduces life expectancy by 5-10 years [2]. The
individual symptom burden (eg, chronic pain, neuropathy,
depression, and physical disability) is substantial and
significantly increases in the older adult population [1]. In the
United States, an average individual with diabetes incurs medical
expenditures of about US $13,700 a year, of which about US
$7900 is attributable to diabetes [4]. This represents an
expenditure about 2.3 times greater than that for a diabetes-free
individual [4].
Numerous primary care-based efforts have been aimed at
reducing both the disease burden on individuals and the cost of
diabetes care. A contemporary strategy is the management of
patients with diabetes within the context of the patient-centered
medical home (PCMH) setting. A key PCMH principle is the
appropriate use of information technology to support optimal
patient care, performance measurement, patient education, and
enhanced communication [6]. Several case studies from various
US health systems show the benefit of the PCMH model to
improved diabetes care [7]. There is published evidence on the
positive impact of PCMH-based care in psychosocial outcomes
of patients with diabetes [8].
The potential of mHealth technologies in the care of patients
with diabetes and other chronic conditions has captured the
attention of clinicians and researchers. Efforts to date have
incorporated a variety of tools and techniques, including
Web-based portals [9-11], short message service (SMS) text
messaging [9,12-14], remote collection of biometric data
[12,15], electronic coaching [14], electronic-based health
education [13], secure email communication between visits
[16-18], electronic collection of lifestyle and quality-of-life
surveys, and personal health records (PHRs). Each of these
tools, used alone or in combination, has demonstrated varying
degrees of effectiveness. Some of the more promising results
have been demonstrated using regular collection of biometric
devices (eg, glucometers, activity monitors) [12], SMS text
messaging [12-14], secure email communication with clinicians
and clinical teams [9,16,17], and regular reporting of
quality-of-life variables aligned with decision support. In this
study, we seek to incorporate many of the most promising
mHealth capabilities in a PCMH workflow led by a clinical
advisory team. We aim to address underlying technology needs
and gaps related to the use of mHealth technologies and the
activation of patients with type 2 diabetes.
The Concept of Patient Activation
Self-management for patients with type 2 diabetes and other
chronic conditions includes following complex treatment
regimens, monitoring chronic conditions, and making lifestyle
changes [19-22]. The chronic care model suggests that activated
patients are better able to function in the role of self-manager
[21,23]. An activated patient has the motivation, confidence,
and skills necessary to enact behavioral changes and make
health-related decisions [24-27]. These patients ask questions
and collaborate with their health care provider [19,26-28].
Research shows that activated patients have more positive
clinical outcomes, are more likely to receive preventive care,
and have lower health care-related costs [24,26,29].
A recommended strategy in patient activation is the concept of
“preactivating” patients prior to clinical encounters [20,30]. The
concept incorporates active targeted communication and
follow-up from the health care team [30]. Interventions to
include educational programs [31], care coaching [32], and
motivational interviewing [33] have been attempted to improve
patient activation with varied success [34]. However, these
efforts have infrequently been tailored to potential intrinsic
differences in how the patients approach their disease.
Theoretically, research suggests that patient activation can be
increased [19,35-37]. Conceptualizing activation as a dynamic
variable allows researchers to target this motivating factor that
can potentially influence health behaviors [21,24,38,39].
Previous Research on Patient Portals, Personal Health
Records, Patient Activation, and Improved Outcomes
Federal legislation and movement toward patient centeredness
in the United States has fueled interest in providing patients
with access to their health information, enhanced communication
with clinical environments, and greater emphasis on self-care
[40-43]. Early research on portal and PHR use and patient
activation provided mixed results. Several studies reported a
positive significant relationship between use of portals and
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PHRs and activation of patients [41,43-45], while other studies
did not realize a significant finding [40,46,47]. The design of
these published studies prevented any in-depth inquiry into why
(or not) portal and PHR use influenced patient activation. Their
authors posited a variety of possible factors, including the target
patient population [44], time since severe diagnosis or symptoms
and episodes [46,47], and patient age (activation being higher
in adults than in children) [45]. One study suggested that
tailoring a portal or PHR intervention to the patient activation
level may optimize intervention efficiency [43].
Early research on increased activation and improved clinical
outcomes using patient portal and PHR-based interventions has
also provided mixed results. Several studies demonstrated a
relationship between increased patient activation and improved
intermediate clinical outcomes (eg, hypertension, smoking, body
mass index, and glycated hemoglobin [HbA1c]) [48], while a
major study did not record a significant finding regarding the
same outcomes [42]. It is noteworthy that these early studies
did not provide substantial detail on design issues related to the
portal or PHR, or whether the intervention included behavioral
reinforcement.
User-Centered Design
Design science will inform our development and testing [49,50].
User-centered design will guide development, following
participatory design methods to understand more specifically
how patients experience diabetes on a daily basis, what clinicians
need to know from patients, and how to create a shared
communication system for better decision making [51].
Consistent with the guidelines set forth by the Science Panel
on Interactive Communication and Health [52], our evaluation
design will incorporate the 3 elements of formative, process,
and outcome evaluation. Methods include (1) clinician focus
groups and in-depth patient interviews to define key knowledge
variables that are personally and clinically relevant, (2) iterative
usability testing with patients, and (3) iterative observations of
the system in clinical settings [53].
Military Health System: An Overview
The US Military Health System (MHS) is a large integrated
health system that cares for about 9.39 million beneficiaries
through its TRICARE insurance product and its substantial
direct care system consisting of tertiary facilities, community
hospitals, and clinics globally. Nearly 35% of its beneficiary
pool are active duty members and their dependents, with a larger
population (about 56%) being retirees and their beneficiaries
[54]. The MHS direct care system is robust. Facilities are
accredited by the Joint Commission (formerly the Joint
Commission on Accreditation of Healthcare Organizations),
and the MHS operates a dedicated educational infrastructure to
support medical and nursing education programs [54]. The MHS
has a connecting health information technology infrastructure
to support clinical care and clinical operations.
Hypotheses
Hypothesis 1: User-centered design will allow developers to
create a patient-centered interactive and tailored mobile
technology for use in the PCMH setting.
Hypothesis 2: The use of interactive and tailored mobile
technology, the Mobile Health Care Environment (MHCE),
employed in the PCMH setting will increase the activation of
patients with chronic type 2 diabetes.
Hypothesis 3: The use of interactive and tailored mobile
technology in a PCMH setting will increase diabetes self-care
activities.
Hypothesis 4: Patients who engage at a higher rate with the
interactive and tailored mobile technology in a PCMH setting
will realize greater improvement in clinical measures.
The primary goal of the research is to enhance patient activation
levels and improve self-management of type 2 diabetes through
the use of the MHCE in the PCMH setting. While there are
published studies aimed at improving the activation and care
of patients with diabetes in the United States, to our knowledge,
no study has sought to enhance care of patients with diabetes
using a fully comprehensive and adaptable MHCE-like system.
We seek to demonstrate improvement in patient activation
measured by the Patient Activation Measure (PAM) instrument
[21,55]. We believe that, in improving their activation, patients
will also realize an improvement in diabetes self-care activities
measured by their Summary of Diabetes Self-Care Activities
(SDSCA) [56] scores.
Methods
Trial Design
This is a multisite, phased study conducted within the MHS that
includes a user-centered design phase and a PCMH-based
feasibility trial. In phase 1, we will assess both patient and
provider preferences regarding the enhancement of the MHCE
technology capabilities for type 2 diabetes chronic care
management. The phase 2 research will be a single-blinded
(patients only) 12-month feasibility study that will incorporate
randomization principles. We will employ a 1:1 allocation ratio
between intervention and control.
Inclusion and Exclusion Criteria
Inclusion criteria for patient participation in phase 1 or 2
research are the following: (1) men and women aged 18 years
or older, (2) able to understand and read English, (3) enrolled
for primary care to one of the target PCMH sites, and (4) having
a diagnosis of type 2 diabetes. Additionally, with respect to
phase 2 patients, we will seek to recruit a maximum of 120 (per
site), with a distribution of patients with PAM levels 1-4, a
sample representative of the patients enrolled in the PCMH. We
did not derive the 120 per site recruitment numbers from power
calculations, but deemed them to be sufficient. Finally,
participants for phase 2 must be available for a 12-month study.
Inclusion criteria for clinician participation in phase 1 or 2
research are the following: (1) being a physician, physician
assistant, nurse practitioner, or nurse employed at the target site,
and (2) providing care for patients with type 2 diabetes.
Exclusion criteria for patient participation in phase 1 or 2
research are the following: (1) pregnant women, (2)
non-English-speaking patients, (3) receiving hospice care, (4)
having active cancer and receiving treatment with chemotherapy
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or radiation therapy, (5) taking warfarin, (6) recipient of gastric
bypass or similar procedure, (7) having a diagnosis of
uncontrolled hypothyroidism, (8) having known Cushing
syndrome, (9) being treated with oral steroids, (10) having
known liver disease, (11) having a current diagnosis of cognitive
impairments that would interfere with use of technology, (12)
having congestive heart failure, in New York Heart Association
functional class 3 or 4, and (13) unable to use a mobile device
due to cognitive or physical impairments during initial screening.
We exclude pregnant women because they require careful
monitoring due to potential medical complications for the
woman and unborn child. While some mHealth studies seek to
include additional exclusions based on age, educational level,
or technical literacy, our research team rejected adding any
additional exclusion criteria beyond the 13 listed above. We
purposely seek the “average” patient with type 2 diabetes in the
target population. Feedback from our clinician investigators
and research staff at our clinical sites is encouraging that these
patients will be capable of using the intervention technology.
Exclusion criteria for clinician participation in phase 1 or 2
research are the following: (1) not affiliated with the target site,
and (2) not providing care for patients with type 2 diabetes.
Participant Enrollment
We will recruit patients via review of the PCMH clinic schedule,
referrals from providers, distributed posters and fliers, and
population health databases. Potential participants will be
prescreened through verification of the inclusion and exclusion
criteria based on a medical record review. Interested participants
will be scheduled for a screening visit with study staff to provide
informed consent and be administered the PAM instrument.
Patients’ PAM scores will place them in a stratified group, where
they will be randomly allocated.
Clinicians practicing in the respective PCMH sites will be
invited to participate by word of mouth from the site’s principal
investigator; this is a convenience sample. The clinician
participants who would like to participate in the study will meet
with the senior research associate to review the minimal-risk
information sheet to be included in the study. For phase 2,
clinicians will sign an informed consent form. The clinician
participants will not be blinded in the study, nor allocated to
intervention or control groups.
Setting and Site Selection
We seek to purposefully assess the MHCE implementation for
diabetes care in 2 distinctly different PCMH environments and
geographic locations. The risks of attracting very different
populations are mitigated by rather comprehensive inclusion
and exclusion criteria, which will ensure similarity regarding
patient acuity. The patient base includes those on active duty,
retirees, and dependents who have typically spent years in the
military and have been stationed at various locations. Both of
the selected facilities are federal facilities and operated by the
MHS.
Madigan Army Medical Center, the US Army’s second largest
military treatment facility located in Tacoma, Washington, is a
tertiary facility with a level II trauma center and robust graduate
medical education programs. They serve a patient base of
approximately 118,000 patients; about 7500 (or >6%) are living
with type 2 diabetes. Of the diabetes population, about 15% are
active duty members or their dependents, and about 85% are
retirees and their dependents. Over half of the patients with
diabetes are 57-76 years of age. The study location within the
medical center is a PCMH managed by the Department of
Internal Medicine. There are approximately 14,300 patients
enrolled in this PCMH supported by a staff of 77 (12 staff
physicians; 8 residents) responsible for their care.
Mike O’Callaghan Federal Medical Center is a federal facility
in the greater Las Vegas, Nevada area, that serves approximately
47,000 patients; about 4500 (or >9%) are living with type 2
diabetes. Of the diabetes population, about 4% are active duty
members or their dependents, and about 96% are retirees and
their dependents. Over 72% of the patients with diabetes are in
their 60s or older. The study location within the medical center
is a PCMH managed by the Department of Family Medicine.
There are approximately 7500 patients enrolled in this PCMH
supported by a staff of 62 (9 staff physicians; 26 residents)
responsible for their care.
Description of the Mobile Health Care Environment
The US Department of Defense (DoD) MHCE system is a secure
health information system designed to support health services
delivery and mHealth. The MHCE meets all physical and
information security mandates, as prescribed by federal law and
DoD regulation, for the protection of personal health information
and personally identifiable information. The MHCE was
developed by the DoD Telemedicine and Advanced Technology
Research Center as a platform to support mHealth. Its first major
application was to support patient engagement for wounded
warriors rehabilitating in their communities. In the study,
soldiers on average responded to ≥60% of weekly questionnaires
related to behavioral health challenges, posttraumatic stress, or
traumatic brain injury [57]. Our study is Telemedicine and
Advanced Technology Research Center’s second major
application. The MHCE is designed to remotely support patients
by sending automated reminders, announcements, wellness tips,
alerts, and status questionnaires. Figure 1 is a visual example
of the graphical user interface that patients will see when
accessing the MHCE. In this study, we enhance MHCE
capabilities in several ways.
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Figure 1. Mobile Health Care Environment home screen (patient view). BP: blood pressure.
Intervention Overview
Our intervention is based on an enhanced MHCE in several
ways. First, we add the capability to include collection and
visualization of data from Bluetooth-enabled medical devices.
This includes mapping data from device output into the MHCE,
developing data visualization appropriate for mHealth and
clinical care (eg, graphing outcomes, temporal trend patterns),
migrating data in an analysis cell, and developing
decision-support algorithms that signal safety alerts and need
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for behavioral reinforcement. Devices used in this study include
a scale, glucometer, blood pressure reader, and activity monitor.
Second, we expand the capacity of the MHCE analysis cell to
manage large amounts of data and to conduct both routine
reports and research applications. Third, we add patient
activation and associated measurement instruments for capturing
baseline and ongoing changes to patient activation. Fourth, we
expand the MHCE messaging platform that research associates,
and later clinical support staff, can use to send tailored
behavioral messaging to patients in an effort to influence greater
activation and reinforce positive behavior.
The MHCE can be accessed by mobile phones or tablets that
use either an IOS or Android platform. The MHCE requires
Internet access for patients to sync data from devices (addressed
above) to the MHCE backend portal, to receive tailored
behavioral messages, or to use other functions. During the study,
patients will additionally receive SMS messages with hyperlinks
to a separate secure information system platform used for
administration and analysis of PAM and SDSCA instruments.
MHCE activity, or lack thereof, will be monitored by senior
research associates, who can prompt patients via tailored
behavioral messages or direct contact.
Tailored Behavioral Messaging
A primary component of the MHCE system is tailored
behavioral messaging. Tailored behavioral messages are more
likely than generic messages to facilitate health behavior change
when they are aligned with individuals’ beliefs, lifestyle,
demographics, social norms, or interests [58-60].
In this study, the research team has developed behavioral
messages tailored for each of the 4 PAM score levels; in total
we have developed 360 messages. The messages fall within 9
functional areas common to diabetes care: nutrition, home
monitoring, physical activity, blood pressure, foot care,
medications, smoking, glucose control, and general behavioral
reinforcement. The messages are consistent with general
concepts and goals of self-management behaviors consistent
with the DoD-Veterans Affairs clinical practice guideline for
type 2 diabetes and the SDSCA survey instrument.
Since different PAM levels require different strategies, we
addressed varying needs through a combination of applied
constructs. Specifically, level 1 messages must address the
emotional state of feeling overwhelmed and passive with an
emphasis on the importance of taking action. To address the
needs of PAM level 1 patients, we use constructs from social
networks and social support theory [61], specifically that of
emotional support that emphasizes expressions of empathy and
caring. We encourage a sense of hope by expressing the belief
and expectation that the message recipient can change his or
her situation and overcome difficulties. Constructs from the
transtheoretical model [62] such as visioning, dramatic relief,
self-reevaluation, and environmental reevaluation also guided
level 1 message development.
PAM level 2 messages build knowledge and self-efficacy to
engage in a behavior and focus on ways to take small steps that
don’t require much in-depth knowledge. Self-efficacy and the
confidence a person feels about performing a particular activity
was a primary construct used to develop these messages with
a focus on one of the main strategies to build self-efficacy, that
of taking small steps that are likely to result in performance
accomplishment. Outcome expectations, or the anticipatory
outcomes of a behavior, stated in ways that would likely appeal
to the expectancies or values a person places on the outcome,
was also an important construct [63].
PAM level 3 messages assume some knowledge and focus more
on building self-management skills such as goal setting and
self-monitoring. For messages in this level, we used
transtheoretical model [62] constructs relevant to the preparation
and action stages of behavior change.
PAM level 4 messages about staying the course and avoiding
relapse when stressed were grounded in the transtheoretical
model constructs guiding processes used in the maintenance
stage of change. Also used in level 4 message development
were strategies developed in a relapse prevention model [64]
such as identifying high-risk situations for relapse and the
development of specific coping strategies for those situations.
In phase 2 of our study, tailored behavioral messages will be
sent to each intervention group participant, via the MHCE
accessed through their mobile device, based on both senior
research associate-initiated and algorithm-automated schedules
and thresholds developed according to PAM level, SDSCA
responses, and agreed-upon general rotation. Figure 2 offers
examples. The senior research associates will use the MHCE
backend portal control panel for manual rotational scheduling
of messages to be delivered 3 days per week (typically Monday,
Wednesday, and Friday) within the MHCE system. Participant
responses to the SDSCA may trigger additional messaging if
their clinical readings from biomedical devices exceed
established safety thresholds.
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Figure 2. Example of tailored health messaging.
Phase 1 User-Centered Design Study Flow
In phase 1 we will evaluate and gain feedback from patients
with diabetes regarding MHCE app navigation, use of external
devices, ease of use, and satisfaction. We will collect baseline
research participant data to include basic demographic data and
clinical measures following verification of informed consent.
One researcher-facilitator will lead individual participants
through usability testing and the additional researcher-observer
will document observations. During a facilitator-provided
demonstration of the MHCE, the facilitator will ask each
participant to concurrently navigate to each component of the
MHCE system via a mini tablet device under their control. For
each task, we will ask 3 open-ended questions to evaluate
task-specific user satisfaction regarding the look and layout of
the app, how the app functions, and any specific issues that are
confusing. Next, the facilitator will give a brief demonstration
of the external devices that will be used in the study: a blood
pressure monitor, a glucometer, a digital precision weight scale,
and a Fitbit Charge wireless activity and sleep wristband. For
each device, we will ask participants to (1) manually upload
data, (2) sync each device with the app, and (3) interpret graphs.
While it would be preferable to observe the MHCE in the
context of where the patient would actually use the system,
financial limitations prohibit such expanded usability
observation research.
Research staff will evaluate usability by applying definitions
and usability evaluation metrics guided by the International
Organization for Standardization’s 9241-11 usability framework
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and mHealth usability research [65]. Specific metrics to evaluate
usability are effectiveness, efficiency, and satisfaction. We will
evaluate effectiveness via task completion and error coding. We
will assess timed task completion as a task being completed
with ease, being completed with minor mistakes, or not
completed. Errors will be coded using a codebook developed
by the phase 1 team. The observer will also note when users
commit errors they cannot solve or commit errors that prevent
further progress. We will use the Single Ease Question to
evaluate informant satisfaction immediately after performing
each task [66]. The System Usability Scale (SUS) will evaluate
overall informant satisfaction with the MHCE [67].
We will also assess provider preferences in phase 1 using focus
groups of clinicians and nurses recruited from the 2 study sites.
Two trained qualitative researchers will facilitate the focus
groups. We will take field notes during the focus groups and
audio record each session to ensure accuracy of the field notes.
The facilitators will use a semistructured interview guide to
elicit clinician and nurse feedback about the MHCE. After
briefly demonstrating the app, facilitators will ask 6 broad
questions (with probes), developed by the phase 1 team in
conjunction with study coinvestigators. These questions are
designed to elicit feedback from participants regarding app
design, alerts (general), wording of alerts, perceived usefulness
to patients for promoting self-management, clinical usefulness
and workflow, and backend portal data summaries. We will
probe specific issues related to clinical usefulness of the MHCE
in the context of the clinical workflow of the PCMH
environment.
A 4-member team will complete a thematically organized data
analysis of the clinician and nurse feedback using an inductive
narrative approach [68-70]. We will begin with an analysis of
field notes from 1 randomly selected provider and 1 nurse to
create an initial codebook. We will expand the codebook as we
continue to code field notes. The analysis team will divide the
coding duties so that each transcript is coded by 2 independent
coders [71]. The team will meet during the coding process to
address consensus, update the coding structure, and revisit any
previously coded field notes that need to be reviewed again
based on these updates. Codes will be applied to the transcripts
using Atlas.ti software version 7.5.10. Codes drawn from the
interview guide will serve as the organizing framework for
analysis. As new themes emerge, we will expand the narrative.
Phase 2 Controlled Study: Patient Enrollment and
Study Flow
For phase 2 we will recruit 240 patients (120 per site), with half
assigned as a control group. Eligible patients will be first
assigned to 4 strata according to their PAM score. After all
patients are identified and assigned into the strata, simple
randomizations will be performed within each stratum to assign
patients to either the MHCE or usual care groups. Patients will
be randomly allocated to either the control or the intervention
group based on their PAM scores.
We will modify the MHCE system between phase 1 and phase
2 research, incorporating phase 1 observations and optimizing
system usability at the patient level. We will collect baseline
research participant data, including basic demographic data and
clinical measures, following verification of informed consent.
MHCE Intervention Versus Usual Care
Patients in both the intervention and usual care (control) groups
will receive a device package as outlined in Textbox 1. These
devices will collect and record biometric data. All patients will
be trained in using biomedical devices and peripheral equipment.
Textbox 1. Patient device package (intervention and control groups).
•
Activity monitor (Bluetooth and cloud enabled)
•
Scale (Bluetooth enabled)
•
Blood pressure cuff (Bluetooth enabled)
•
Glucometer (Bluetooth enabled)
For the patient groups allocated to the intervention, their devices
will be mapped to the MHCE system accessible from the
patients’ mobile phone or an iPad mini tablet device. Data from
their biomedical devices will be visually presented in the MHCE
with trend and scalable options (Figure 3).
Safety algorithms will be mapped to these clinical data to alert
the patient and, depending on the measure, the clinical team
when readings exceed established thresholds. The intervention
groups will also have full access to and will receive the tailored
behavioral messaging outlined above. At time of study
enrollment, we will provide a tablet device to patients who are
fully eligible to participate, are allocated to the intervention
group, but do not have a mobile phone (with iOS or Android
operating system).
In both the intervention and control groups, the patients’
clinician and PCMH support team will be notified of the
patients’ enrollment in the study. The intervention patients will
be encouraged to regularly use the MHCE system as a tool to
improve their diabetes self-care.
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Figure 3. Example of visualization of patient data. BP: blood pressure.
Initial Outcome Measures for Patient Component
Primary outcome measures are PAM scores. Secondary outcome
measures in the study are (1) SDSCA responses, (2) clinical
measures (Textbox 2), (3) comorbid conditions (eg, uncontrolled
plasma glucose, hypertension, hyperlipidemia, stoke, eye
disease, coronary heart disease), (4) SUS survey scores, (5)
MHCE usage statistics, and (6) health services utilization
measures.
Textbox 2. Clinical measures in phase 2.
•
Glycated hemoglobin (HbA1c)
•
Low-density lipoprotein
•
High-density lipoprotein
•
Height and weight
•
Abdominal circumference
•
Systolic blood pressure
•
Diastolic blood pressure
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Patient Activation Measure Instrument
The 
self-reported 
PAM 
survey 
is 
associated 
with
self-management behaviors, medication adherence, patient
satisfaction, and 
quality 
of 
life 
[55,72]. 
Within 
a
diabetes-specific population, PAM is not related to knowledge
regarding HbA1c (the standard measure of average blood glucose
level [73]), but is associated with better glycemic control [74].
Interventions, including educational programs [31], care
coaching [32], and motivational interviewing [33], have been
attempted to improve this activation with varied success.
Specifically, patient activation can be increased with targeted,
patient-centered, repeated messaging [19]. The PAM is a valid,
reliable, unidimensional, probabilistic Guttman-like scale that
was validated over a decade ago [21] and is a standard tool to
measure patient activation. We will administer the PAM at
screening visits in phases 1 and 2, and electronically every 3
months during phase 2 for both the intervention and control
groups. Figure 4 outlies the 4 PAM levels.
Figure 4. The 4 levels within the Patient Activation Measure (PAM) survey.
Summary of Diabetes Self-Care Activities Instrument
The SDSCA instrument is a brief self-report instrument for
measuring levels of self-management across different
components of the diabetes regimen [56]. The SDSCA includes
11 core items associated with diabetes self-care. The SDSCA
has been successfully used in numerous diabetes studies both
within and outside the United States [56,75-78]. The SDSCA
has been validated and is considered a standard instrument in
diabetes care for measuring self-care activities, with its
validation and reliability published nearly two decades ago [56].
We will administer the SDSCA at the intake visit for phase 2,
and electronically every 2 weeks during phase 2 for both the
intervention and control groups.
Clinical Measures
We will collect clinical measures (Textbox 2) from patients at
intake during phase 1 research. We will collect and compare
changes in patient clinical measures for both groups in phase 2
at 3 points: intake, midpoint (month 6), and conclusion (month
12). For patients assigned to the MHCE intervention group, the
MHCE system will also record weight, systolic blood pressure,
diastolic blood pressure, and blood glucose values to the MHCE
module on a regular basis via Wi-Fi or Bluetooth-enabled
peripheral equipment.
Clinician Support for Patient Activation Measure
The Clinician Support for Patient Activation Measure (CS-PAM)
instrument 
measures 
clinician 
beliefs 
about 
patient
self-management behavior. The CS-PAM has been a valid and
reliable instrument in use since 2010 [25]. The CS-PAM score
indicates an individual clinician’s overall level of endorsement
or belief about the importance of patient self-management, as
well as beliefs about the importance of specific patient
competency categories [25].
In phase 2, we will measure clinician support for patient
self-management by the CS-PAM. PCMH clinicians (ie,
physicians, nurse practitioners, and physician assistants) in this
study will take the CS-PAM at 3 points in the study: beginning,
midpoint (month 6), and conclusion (month 12).
System Usability Scale Survey
The SUS survey is a 10-item Likert-like scaled survey used to
convey a subjective assessment of system usability. The
instrument was developed over 15 years ago and is used to
measure the usability of websites. The SUS was validated on
several occasions, with perhaps the largest validation study
(including 10 years’ worth of data) conducted in 2008 [79]. In
this study we will substitute the term “MHCE system” for the
term “website” in the instrument. We will conduct the SUS
survey at the conclusion of the encounter for phase 1 patients,
and at midpoint (months 5-6) and study conclusion (months
11-12) for phase 2 patients in the intervention group.
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MHCE Usage Statistics
Our technology enablement partners will embed counters
(invisible to patients) that track usage of MHCE components.
These counters will export usage data to our research analysis
database. Summary statistics and trends will be analyzed with
comparison.
Comorbid Conditions
We will assess and document comorbid conditions (eg,
hypertension, hyperlipidemia) among both the control and
intervention groups during prescreening of eligibility, at intake,
at study midpoint, and at study conclusion. While not primary
outcome measures, any change over time and whether the
number and type of comorbid conditions influence patient use
of MHCE will be assessed.
Data Analysis Strategy
We will conduct the primary analyses for phase 2 using an
intent-to-treat approach. Study participants will be retained in
their original assignment groups after the random allocation in
the analysis. Achievement of randomization will be evaluated
through the comparison of baseline key variables between the
MHCE intervention group and the control group. We will also
compare baseline key characteristics between eligible patients
who participate in the study and those who do not participate
to examine the potential for bias.
To test hypotheses 2 and 3, that patients who participate in
MHCE will have higher PAM, SDSCA, and SUS scores and
improved selected clinical outcomes and comorbid conditions
than their counterparts in usual care, we will use multivariate
regression models (logistic regression if the outcome is a binary
variable and linear regression if the outcome is a continuous
variable) with the intervention assignment as the primary
independent variable. Stratified analyses will be conducted (eg,
sex, race, and initial PAM score).
The primary comparison will be outcomes at 12 months.
Additional analyses will use longitudinal analysis models using
a generalized estimating equation, which will include outcomes
at both 6 and 12 months.
To test hypothesis 4, that patients who engage at a higher rate
with the interactive and tailored mobile technology in MHCE
will realize greater improvement in clinical measures (eg, HbA1c
values; Textbox 2), we will use multivariate linear regression
models. Clinical outcomes will be the dependent variables and
will be tested separately. The main independent variable will
be MHCE usage. We will examine the association between the
dependent variable and MHCE usage by using the generalized
estimate equation with adjustment of potential confounders (eg,
age, sex, race, duration of disease, use or nonuse of insulin).
Trial Status
At the time of publication, we have completed phase 1 data
collection. Formal analysis of phase 1 data has not been
completed. Institutional review board approval (study and site
implementation) has been obtained and phase 1 research
commenced in late fall 2016.
Results
The hypotheses of the study suggest that patients can, and will,
improve their activation in chronic care self-management.
Improved activation should translate into improved diabetes
self-care. While not powered in this study, improved
self-management activities should lead to fewer emergency
situations (and trips to the emergency department), weight loss
(in many cases), improved blood pressure, and improved clinical
measures. Cumulatively, the gains should translate into
improved quality of life if our hypotheses are supported.
This study has been approved by the institutional review boards
of Clemson University (protocol #IRB2015-234) and the
Madigan Army Medical Center (representing both DoD sites;
reference #216073). Study personnel will follow protocol with
all informed consent mandates directed by the institutional
review boards; informed consent in this study includes both
patients and clinicians or key clinical staff. This trial was
registered with ClinicalTrials.gov (NCT02949037) on October
31, 2016.
Discussion
Expected benefits of this research and development effort to
the scientific community and health care services include
improved understanding of how to advance 3 joint PCMH
principles (ie, better coordination of care, improved quality and
safety, and enhanced access to care) through the use of mobile
technology and improved understanding of how to include
mHealth technology in the clinical workflow of the PCMH
health services model, as well as improved understanding of
how to use mHealth technology to activate patients with a
diagnosis of type 2 diabetes in disease self-management
behaviors. We also expect to improve understanding of how
patient complexity and degree of “sickness” may influence
patient use or nonuse of mHealth technologies in
self-management of their disease, and to explore how to map
patient-entered biomedical data onto clinical documentation
and a decision-support platform useful in chronic care
management.
Our study design is not immune from potential threats to
validity. Patients allocated to the control arm will be issued the
same peripheral devices as the intervention group and, while
they may not achieve the same degree of activation, they may
realize improvement if they use the equipment being issued to
them. Though this behavioral mechanism could benefit patients
in the control group, a strong activation change in the control
arm could conceal the behavioral benefit of our intervention
when we compare patient behavior from the 2 arms.
We are aware that we did not conduct a power calculation for
sample size, since this project was funded as a feasibility study,
not a randomized controlled trial. Thus, sample size estimates
are neither required nor appropriate. We additionally recognize
that a formal randomized controlled trial would be preferred to
our current design. A follow-on randomized controlled trial is
our goal once we have collected sufficient data and have a better
understanding of how patients will use this chronic care health
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information technology system. At that point we will
legitimately be able to predict the intervention effect and
properly power the study.
 
Acknowledgments
The authors appreciate and would like to acknowledge the contributions of Terry J Newton, MD (US Army Office of the Surgeon
General), who provided feedback on the systemwide implications of the research efforts and offered general guidance. We also
acknowledge Ms Holly Pavliscsak (US Army Telemedicine and Advanced Technology Research Center), who helped shape early
technology development of our mHealth intervention.
Funding: The study was extramurally funded, via the Broad Agency Announcement, by the US Army Medical Research Acquisition
Activity; contract # W81XWH-15-C-0070. The funder did not influence the design of the study or strategies related to its collection,
analysis, or interpretation of data.
Federal disclaimer: The views expressed are those of the authors and do not reflect the official policy of the Department of the
Army, the Department of the Air Force, the Department of Defense, or the US federal government.
Authors' Contributions
RG initially conceptualized the study, and both RG and LS wrote the initial draft of the manuscript. PC, EAS, RG, WWS, KWE,
MH, and JBM assisted in the setting narrative, enrollment strategies, and institutional review board-related issues. The technical
aspects of the intervention and MHCE description were authored by JRL and RG. The biostatistics and data analysis strategy
were developed by LC and KT. The patient activation and behavioral messaging component, including examples, was developed
by CJD, JEW, SFG, and KOJ. The user-centered design component was researched and authored by JEW and SFG. The outcome
measures component was conceptualized and authored by PC, EAS, KT, LS, CJD, JEW, LC, JBM, MH, WWS, and KWE. The
clinical components, including inclusion and exclusion criteria, were developed by PC, EAS, CD, WWS, JBM, MH, KWE, and
RG. The MHS review and component were authored by JBM, MH, KWE, and RG. All authors read, contributed to, critically
reviewed, and approved the final manuscript.
Conflicts of Interest
None declared.
References
1.
Sudore RL, Karter AJ, Huang ES, Moffet HH, Laiteerapong N, Schenker Y, et al. Symptom burden of adults with type 2
diabetes across the disease course: diabetes & aging study. J Gen Intern Med 2012 Dec;27(12):1674-1681 [FREE Full text]
[doi: 10.1007/s11606-012-2132-3] [Medline: 22854982]
2.
Vigersky RA. An overview of management issues in adult patients with type 2 diabetes mellitus. J Diabetes Sci Technol
2011 Mar 01;5(2):245-250 [FREE Full text] [Medline: 21527089]
3.
Scollan-Koliopoulos M, Bleich D, Rapp KJ, Wong P, Hofmann CJ, Raghuwanshi M. Health-related quality of life, disease
severity, and anticipated trajectory of diabetes. Diabetes Educ 2013;39(1):83-91. [doi: 10.1177/0145721712467697]
[Medline: 23174664]
4.
American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013 Apr;36(4):1033-1046
[FREE Full text] [doi: 10.2337/dc12-2625] [Medline: 23468086]
5.
Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US
adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr 2010 Oct
22;8:29 [FREE Full text] [doi: 10.1186/1478-7954-8-29] [Medline: 20969750]
6.
Patient-Centered Primary Care Collaborative. Joint principles of the patient-centered medical home. 2007 Jan 01. URL:
https://www.pcpcc.org/about/medical-home [accessed 2017-03-02] [WebCite Cache ID 6ofFoxX10]
7.
Nielsen M, Langner B, Zema C, Hacker T, Grundy P. Benefits of Implementing the Primary Care Patient-Centered Medical
Home: A Review of Cost & Quality Results. Washington, DC: Patient-Centered Primary Care Collaborative; 2012:41.
8.
Jortberg BT, Miller BF, Gabbay RA, Sparling K, Dickinson WP. Patient-centered medical home: how it affects psychosocial
outcomes for diabetes. Curr Diab Rep 2012 Dec;12(6):721-728. [doi: 10.1007/s11892-012-0316-1] [Medline: 22961115]
9.
Wade-Vuturo AE, Mayberry LS, Osborn CY. Secure messaging and diabetes management: experiences and perspectives
of patient portal users. J Am Med Inform Assoc 2013 May 1;20(3):519-525 [FREE Full text] [doi:
10.1136/amiajnl-2012-001253] [Medline: 23242764]
10.
Yu CH, Bahniwal R, Laupacis A, Leung E, Orr MS, Straus SE. Systematic review and evaluation of web-accessible tools
for management of diabetes and related cardiovascular risk factors by patients and healthcare providers. J Am Med Inform
Assoc 2012;19(4):514-522 [FREE Full text] [doi: 10.1136/amiajnl-2011-000307] [Medline: 22215057]
11.
Osborn CY, Mayberry LS, Mulvaney SA, Hess R. Patient web portals to improve diabetes outcomes: a systematic review.
Curr Diab Rep 2010 Dec;10(6):422-435 [FREE Full text] [doi: 10.1007/s11892-010-0151-1] [Medline: 20890688]
JMIR Res Protoc 2017 | vol. 6 | iss. 3 | e38 | p.12
http://www.researchprotocols.org/2017/3/e38/
(page number not for citation purposes)
Gimbel et al
JMIR RESEARCH PROTOCOLS
XSL•FO
RenderX

12.
Faridi Z, Liberti L, Shuval K, Northrup V, Ali A, Katz DL. Evaluating the impact of mobile telephone technology on type
2 diabetic patients' self-management: the NICHE pilot study. J Eval Clin Pract 2008 Jun;14(3):465-469. [doi:
10.1111/j.1365-2753.2007.00881.x] [Medline: 18373577]
13.
Arora S, Peters AL, Agy C, Menchine M. A mobile health intervention for inner city patients with poorly controlled diabetes:
proof-of-concept of the TExT-MED program. Diabetes Technol Ther 2012 Jun;14(6):492-496. [doi: 10.1089/dia.2011.0252]
[Medline: 22524591]
14.
Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-randomized trial of a mobile phone
personalized behavioral intervention for blood glucose control. Diabetes Care 2011 Sep;34(9):1934-1942 [FREE Full text]
[doi: 10.2337/dc11-0366] [Medline: 21788632]
15.
Newton KH, Wiltshire EJ, Elley CR. Pedometers and text messaging to increase physical activity: randomized controlled
trial of adolescents with type 1 diabetes. Diabetes Care 2009 May;32(5):813-815 [FREE Full text] [doi: 10.2337/dc08-1974]
[Medline: 19228863]
16.
Harris LT, Haneuse SJ, Martin DP, Ralston JD. Diabetes quality of care and outpatient utilization associated with electronic
patient-provider messaging: a cross-sectional analysis. Diabetes Care 2009 Jul;32(7):1182-1187 [FREE Full text] [doi:
10.2337/dc08-1771] [Medline: 19366959]
17.
Bredfeldt CE, Compton-Phillips AL, Snyder MH. Effects of between visit physician-patient communication on Diabetes
Recognition Program scores. Int J Qual Health Care 2011 Dec;23(6):664-673 [FREE Full text] [doi: 10.1093/intqhc/mzr061]
[Medline: 21937586]
18.
Lyles CR, Grothaus L, Reid RJ, Sarkar U, Ralston JD. Communication about diabetes risk factors during between-visit
encounters. Am J Manag Care 2012 Dec;18(12):807-815 [FREE Full text] [Medline: 23286610]
19.
Ledford CJW, Ledford CC, Childress MA. Extending Physician ReACH: influencing patient activation and behavior through
multichannel physician communication. Patient Educ Couns 2013 Apr;91(1):72-78. [doi: 10.1016/j.pec.2012.11.011]
[Medline: 23219484]
20.
Ledford CJW, Ledford CC, Childress MA. Exploring patient activation in the clinic: measurement from three perspectives.
Health Educ Behav 2013 Jun;40(3):339-345. [doi: 10.1177/1090198112455173] [Medline: 22984210]
21.
Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing
and measuring activation in patients and consumers. Health Serv Res 2004 Aug;39(4 Pt 1):1005-1026 [FREE Full text]
[doi: 10.1111/j.1475-6773.2004.00269.x] [Medline: 15230939]
22.
Ledford CJW. Exploring the interaction of patient activation and message design variables: message frame and presentation
mode influence on the walking behavior of patients with type 2 diabetes. J Health Psychol 2012 Oct;17(7):989-1000. [doi:
10.1177/1359105311429204] [Medline: 22253330]
23.
Wagner EH, Glasgow RE, Davis C, Bonomi AE, Provost L, McCulloch D, et al. Quality improvement in chronic illness
care: a collaborative approach. Jt Comm J Qual Improv 2001 Feb;27(2):63-80. [Medline: 11221012]
24.
Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation
and health-related outcomes. J Gen Intern Med 2012 May;27(5):520-526 [FREE Full text] [doi: 10.1007/s11606-011-1931-2]
[Medline: 22127797]
25.
Hibbard JH, Collins PA, Mahoney E, Baker LH. The development and testing of a measure assessing clinician beliefs about
patient self-management. Health Expect 2010 Mar;13(1):65-72. [doi: 10.1111/j.1369-7625.2009.00571.x] [Medline:
19906211]
26.
Cortes DE, Mulvaney-Day N, Fortuna L, Reinfeld S, Alegría M. Patient--provider communication: understanding the role
of patient activation for Latinos in mental health treatment. Health Educ Behav 2009 Feb;36(1):138-154 [FREE Full text]
[doi: 10.1177/1090198108314618] [Medline: 18413668]
27.
Griffin SJ, Kinmonth A, Veltman MW, Gillard S, Grant J, Stewart M. Effect on health-related outcomes of interventions
to alter the interaction between patients and practitioners: a systematic review of trials. Ann Fam Med 2004;2(6):595-608
[FREE Full text] [doi: 10.1370/afm.142] [Medline: 15576546]
28.
Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management
behaviors? Health Serv Res 2007 Aug;42(4):1443-1463 [FREE Full text] [doi: 10.1111/j.1475-6773.2006.00669.x] [Medline:
17610432]
29.
Greene J, Hibbard JH, Sacks R, Overton V, Parrotta CD. When patient activation levels change, health outcomes and costs
change, too. Health Aff (Millwood) 2015 Mar 1;34(3):431-437. [doi: 10.1377/hlthaff.2014.0452] [Medline: 25732493]
30.
Bodenheimer T. Helping patients improve their health-related behaviors: what system changes do we need? Dis Manag
2005 Oct;8(5):319-330. [doi: 10.1089/dis.2005.8.319] [Medline: 16212517]
31.
Li T, Wu HM, Wang F, Huang CQ, Yang M, Dong BR, et al. Education programmes for people with diabetic kidney
disease. Cochrane Database Syst Rev 2011 Jun 15(6):CD007374. [doi: 10.1002/14651858.CD007374.pub2] [Medline:
21678365]
32.
Lin W, Chien H, Willis G, O'Connell E, Rennie KS, Bottella HM, et al. The effect of a telephone-based health coaching
disease management program on Medicaid members with chronic conditions. Med Care 2012 Jan;50(1):91-98. [doi:
10.1097/MLR.0b013e31822dcedf] [Medline: 21993059]
JMIR Res Protoc 2017 | vol. 6 | iss. 3 | e38 | p.13
http://www.researchprotocols.org/2017/3/e38/
(page number not for citation purposes)
Gimbel et al
JMIR RESEARCH PROTOCOLS
XSL•FO
RenderX

33.
Linden A, Butterworth SW, Prochaska JO. Motivational interviewing-based health coaching as a chronic care intervention.
J Eval Clin Pract 2010 Feb;16(1):166-174. [doi: 10.1111/j.1365-2753.2009.01300.x] [Medline: 20367828]
34.
Ledford CJW, Canzona MR, Cafferty LA, Hodge JA. Mobile application as a prenatal education and engagement tool: a
randomized controlled pilot. Patient Educ Couns 2016 Apr;99(4):578-582. [doi: 10.1016/j.pec.2015.11.006] [Medline:
26610389]
35.
Deen D, Lu W, Rothstein D, Santana L, Gold MR. Asking questions: the effect of a brief intervention in community health
centers on patient activation. Patient Educ Couns 2011 Aug;84(2):257-260. [doi: 10.1016/j.pec.2010.07.026] [Medline:
20800414]
36.
Deen D, Lu WH, Weintraub MR, Maranda MJ, Elshafey S, Gold MR. The impact of different modalities for activating
patients in a community health center setting. Patient Educ Couns 2012 Oct;89(1):178-183. [doi: 10.1016/j.pec.2012.04.012]
[Medline: 22683294]
37.
Lu W, Deen D, Rothstein D, Santana L, Gold MR. Activating community health center patients in developing
question-formulation skills: a qualitative study. Health Educ Behav 2011 Dec;38(6):637-645. [doi:
10.1177/1090198110393337] [Medline: 21558464]
38.
Fowles JB, Terry P, Xi M, Hibbard J, Bloom CT, Harvey L. Measuring self-management of patients' and employees' health:
further validation of the Patient Activation Measure (PAM) based on its relation to employee characteristics. Patient Educ
Couns 2009 Oct;77(1):116-122. [doi: 10.1016/j.pec.2009.02.018] [Medline: 19356881]
39.
Coleman EA, Parry C, Chalmers S, Min S. The care transitions intervention: results of a randomized controlled trial. Arch
Intern Med 2006 Sep 25;166(17):1822-1828. [doi: 10.1001/archinte.166.17.1822] [Medline: 17000937]
40.
Toscos T, Daley C, Heral L, Doshi R, Chen Y, Eckert GJ, et al. Impact of electronic personal health record use on engagement
and intermediate health outcomes among cardiac patients: a quasi-experimental study. J Am Med Inform Assoc 2016
Jan;23(1):119-128. [doi: 10.1093/jamia/ocv164] [Medline: 26912538]
41.
Crouch PB, Rose CD, Johnson M, Janson SL. A pilot study to evaluate the magnitude of association of the use of electronic
personal health records with patient activation and empowerment in HIV-infected veterans. PeerJ 2015;3:e852 [FREE Full
text] [doi: 10.7717/peerj.852] [Medline: 25802815]
42.
Wagner PJ, Dias J, Howard S, Kintziger KW, Hudson MF, Seol Y, et al. Personal health records and hypertension control:
a randomized trial. J Am Med Inform Assoc 2012 Aug;19(4):626-634 [FREE Full text] [doi: 10.1136/amiajnl-2011-000349]
[Medline: 22234404]
43.
Solomon M, Wagner SL, Goes J. Effects of a Web-based intervention for adults with chronic conditions on patient activation:
online randomized controlled trial. J Med Internet Res 2012;14(1):e32 [FREE Full text] [doi: 10.2196/jmir.1924] [Medline:
22353433]
44.
Nagykaldi Z, Aspy CB, Chou A, Mold JW. Impact of a wellness portal on the delivery of patient-centered preventive care.
J Am Board Fam Med 2012 Apr;25(2):158-167 [FREE Full text] [doi: 10.3122/jabfm.2012.02.110130] [Medline: 22403196]
45.
Viau KS, Jones JL, Murtaugh MA, Gren LH, Stanford JB, Bilder DA. Phone-based motivational interviewing to increase
self-efficacy in individuals with phenylketonuria. Mol Genet Metab Rep 2016 Mar;6:27-33 [FREE Full text] [doi:
10.1016/j.ymgmr.2016.01.002] [Medline: 27014576]
46.
Riippa I, Linna M, Rönkkö I, Kröger V. Use of an electronic patient portal among the chronically ill: an observational
study. J Med Internet Res 2014 Dec 08;16(12):e275 [FREE Full text] [doi: 10.2196/jmir.3722] [Medline: 25488754]
47.
Riippa I, Linna M, Rönkkö I. The effect of a patient portal with electronic messaging on patient activation among chronically
ill patients: controlled before-and-after study. J Med Internet Res 2014;16(11):e257 [FREE Full text] [doi: 10.2196/jmir.3462]
[Medline: 25413368]
48.
Kim JY, Wineinger NE, Steinhubl SR. The influence of wireless self-monitoring program on the relationship between
patient activation and health behaviors, medication adherence, and blood pressure levels in hypertensive patients: a substudy
of a randomized controlled trial. J Med Internet Res 2016 Jun 22;18(6):e116 [FREE Full text] [doi: 10.2196/jmir.5429]
[Medline: 27334418]
49.
Hevner A, March S, Park J, Ram S. Design science in information systems research. MIS Q 2004;28(1):75-105.
50.
Peffers K, Tuunanen T, Rothenberger M, Chatterjee S. A design science research methodology for information systems
research. J Manage Inform Systems 2007;24(3):45-77.
51.
Hesse BW, Shneiderman B. eHealth research from the user's perspective. Am J Prev Med 2007 May;32(5 Suppl):S97-103
[FREE Full text] [doi: 10.1016/j.amepre.2007.01.019] [Medline: 17466825]
52.
Robinson TN, Patrick K, Eng TR, Gustafson D. An evidence-based approach to interactive health communication: a
challenge to medicine in the information age. Science Panel on Interactive Communication and Health. JAMA 1998 Oct
14;280(14):1264-1269. [Medline: 9786378]
53.
Neuhauser L, Kreps G. Participatory design and artificial intelligence: strategies to improve health communication for
diverse audiences. 2011 Presented at: AAAI Spring Symposium Series; March 21-23, 2011; Palo Alto, CA, USA.
54.
Gimbel RW, Pangaro L, Barbour G. America's “undiscovered” laboratory for health services research. Med Care 2010
Aug;48(8):751-756. [doi: 10.1097/MLR.0b013e3181e35be8] [Medline: 20613659]
JMIR Res Protoc 2017 | vol. 6 | iss. 3 | e38 | p.14
http://www.researchprotocols.org/2017/3/e38/
(page number not for citation purposes)
Gimbel et al
JMIR RESEARCH PROTOCOLS
XSL•FO
RenderX

55.
Harvey L, Fowles JB, Xi M, Terry P. When activation changes, what else changes? The relationship between change in
patient activation measure (PAM) and employees' health status and health behaviors. Patient Educ Couns 2012
Aug;88(2):338-343. [doi: 10.1016/j.pec.2012.02.005] [Medline: 22459636]
56.
Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and
a revised scale. Diabetes Care 2000 Jul;23(7):943-950 [FREE Full text] [Medline: 10895844]
57.
Pavliscsak H, Little JR, Poropatich RK, McVeigh FL, Tong J, Tillman JS, et al. Assessment of patient engagement with a
mobile application among service members in transition. J Am Med Inform Assoc 2016 Jan;23(1):110-118. [doi:
10.1093/jamia/ocv121] [Medline: 26363048]
58.
Head KJ, Noar SM, Iannarino NT, Grant HN. Efficacy of text messaging-based interventions for health promotion: a
meta-analysis. Soc Sci Med 2013 Nov;97:41-48. [doi: 10.1016/j.socscimed.2013.08.003] [Medline: 24161087]
59.
Nelson LA, Mayberry LS, Wallston K, Kripalani S, Bergner EM, Osborn CY. Development and usability of REACH: a
tailored theory-based text messaging intervention for disadvantaged adults with type-2 diabetes. JMIR Hum Factors 2016
Sep 08;3(2):e23 [FREE Full text] [doi: 10.2196/humanfactors.6029] [Medline: 27609738]
60.
Hibbard JH, Greene J, Tusler M. Improving the outcomes of disease management by tailoring care to the patient's level of
activation. Am J Manag Care 2009 Jun;15(6):353-360 [FREE Full text] [Medline: 19514801]
61.
Israel BA, Rounds KA. Social networks and social support: a synthesis for health educators. Adv Health Educ 1987;12:65-80.
62.
Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus BH, Rakowski W, et al. Stages of change and decisional
balance for 12 problem behaviors. Health Psychol 1994 Jan;13(1):39-46. [Medline: 8168470]
63.
Bandura A. Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice-Hall; 1986:544.
64.
Marlatt G. Relapse prevention: theoretical rationaleoverview of the model. In: Marlatt GA, Gordon JR. editors. Relapse
Prevention. New York, NY: Guilford Press; 1985.
65.
Georgsson M, Staggers N. Quantifying usability: an evaluation of a diabetes mHealth system on effectiveness, efficiency,
and satisfaction metrics with associated user characteristics. J Am Med Inform Assoc 2016 Jan;23(1):5-11 [FREE Full text]
[doi: 10.1093/jamia/ocv099] [Medline: 26377990]
66.
Sauro J, Dumas J. Comparison of three one-question, post-task usability questionnaires. In: Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems. 2009 Presented at: CHI ’2009; April 4-9, 2009; Boston, MA, USA.
[doi: 10.1145/1518701.1518946]
67.
Sauro J, Lewis J. Quantifying the User Experience: Practical Statistics for User Research. Waltham, MA: Morgan Kaufmann;
2012.
68.
Kidd PS, Parshall MB. Getting the focus and the group: enhancing analytical rigor in focus group research. Qual Health
Res 2000 May;10(3):293-308. [Medline: 10947477]
69.
Thorn S. Data analysis in qualitative research. Evid Based Nurs 2000;3:68-70. [doi: 10.1136/ebn.3.3.68]
70.
Thomas DR. A general inductive approach for analyzing qualitative evaluation data. Am J Eval 2006 Jun 01;27(2):237-246.
[doi: 10.1177/1098214005283748]
71.
Miles M, Huberman A. Qualitative Data Analysis. 2nd edition. Thousand Oaks, CA: Sage Publications; 1994.
72.
Mosen DM, Schmittdiel J, Hibbard J, Sobel D, Remmers C, Bellows J. Is patient activation associated with outcomes of
care for adults with chronic conditions? J Ambul Care Manage 2007 Mar;30(1):21-29. [Medline: 17170635]
73.
Rask KJ, Ziemer DC, Kohler SA, Hawley JN, Arinde FJ, Barnes CS. Patient activation is associated with healthy behaviors
and ease in managing diabetes in an indigent population. Diabetes Educ 2009;35(4):622-630. [doi:
10.1177/0145721709335004] [Medline: 19419972]
74.
Rogvi S, Tapager I, Almdal TP, Schiøtz ML, Willaing I. Patient factors and glycaemic control--associations and explanatory
power. Diabet Med 2012 Oct;29(10):e382-e389. [doi: 10.1111/j.1464-5491.2012.03703.x] [Medline: 22540962]
75.
Gonzalez JS, Safren SA, Delahanty LM, Cagliero E, Wexler DJ, Meigs JB, et al. Symptoms of depression prospectively
predict poorer self-care in patients with type 2 diabetes. Diabet Med 2008 Sep;25(9):1102-1107 [FREE Full text] [doi:
10.1111/j.1464-5491.2008.02535.x] [Medline: 19183315]
76.
Watkins YJ, Quinn LT, Ruggiero L, Quinn MT, Choi Y. Spiritual and religious beliefs and practices and social support's
relationship to diabetes self-care activities in African Americans. Diabetes Educ 2013 Feb;39(2):231-239 [FREE Full text]
[doi: 10.1177/0145721713475843] [Medline: 23411653]
77.
Primožič S, Tavčar R, Avbelj M, Dernovšek MZ, Oblak MR. Specific cognitive abilities are associated with diabetes
self-management behavior among patients with type 2 diabetes. Diabetes Res Clin Pract 2012 Jan;95(1):48-54. [doi:
10.1016/j.diabres.2011.09.004] [Medline: 21963107]
78.
Cohen HW, Shmukler C, Ullman R, Rivera CM, Walker EA. Measurements of medication adherence in diabetic patients
with poorly controlled HbA(1c). Diabet Med 2010 Feb;27(2):210-216 [FREE Full text] [doi:
10.1111/j.1464-5491.2009.02898.x] [Medline: 20546266]
79.
Bangor A, Kortum PT, Miller JT. An empirical evaluation of the System Usability Scale. Int J Hum Comput Interact 2008
Jul 30;24(6):574-594. [doi: 10.1080/10447310802205776]
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Abbreviations
CS-PAM: Clinician Support Patient Activation Measure
DoD: Department of Defense
HbA1c: glycated hemoglobin
MHCE: Mobile Health Care Environment
MHS: Military Health System
PAM: Patient Activation Measure
PCMH: patient-centered medical home
PHR: personal health record
SDSCA: Summary of Diabetes Self-Care Activities
SMS: short message service
SUS: System Usability Scale
Edited by A McDougall; submitted 20.11.16; peer-reviewed by K Pal, G Graffigna; comments to author 03.01.17; revised version
received 16.01.17; accepted 12.02.17; published 06.03.17
Please cite as:
Gimbel R, Shi L, Williams JE, Dye CJ, Chen L, Crawford P, Shry EA, Griffin SF, Jones KO, Sherrill WW, Truong K, Little JR, Edwards
KW, Hing M, Moss JB
Enhancing mHealth Technology in the Patient-Centered Medical Home Environment to Activate Patients With Type 2 Diabetes: A
Multisite Feasibility Study Protocol
JMIR Res Protoc 2017;6(3):e38
URL: http://www.researchprotocols.org/2017/3/e38/ 
doi:10.2196/resprot.6993
PMID:
©Ronald Gimbel, Lu Shi, Joel E Williams, Cheryl J Dye, Liwei Chen, Paul Crawford, Eric A Shry, Sarah F Griffin, Karyn O
Jones, Windsor W Sherrill, Khoa Truong, Jeanette R Little, Karen W Edwards, Marie Hing, Jennie B Moss. Originally published
in JMIR Research Protocols (http://www.researchprotocols.org), 06.03.2017. This is an open-access article distributed under the
terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is
properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org,
as well as this copyright and license information must be included.
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