Keywords patient engagement - patient-provider communication - workflows and human interactions
- smart phone - mobile platforms - general information systems and technologies in
clinics - consumer health informatics
Background and Significance
Background and Significance
An increasing number of patients are using mobile health (mHealth) technology, including
smartphones and other connected devices, to generate data that provide a rich account
of their day-to-day health.[1 ]
[2 ]
[3 ] These data, termed patient-generated health data (PGHD), may include physiologic
measures, symptoms, and lifestyle data.[4 ]
[5 ] PGHD has garnered excitement for its ability to uncover fluctuations in health-related
factors that may play an important role in an individual's health and wellness.[6 ]
[7 ]
[8 ]
[9 ] PGHD also is valuable for centering care on the patient and their unique environmental,
lifestyle, and biological circumstances.[6 ]
[10 ] As such, PGHD holds particular promise for precision management of individuals living
with chronic conditions.[11 ]
[12 ]
One condition for which PGHD could be particularly valuable is atrial fibrillation
(AF), the most common cardiac arrhythmia encountered in clinical practice.[13 ] AF is difficult to capture outside the clinical setting because it requires documentation
via electrocardiogram (ECG) and is episodic and poorly correlated with patient-reported
symptoms.[14 ]
[15 ]
[16 ] Moreover, AF is deeply influenced by modifiable lifestyle factors such as alcohol
use and obesity.[17 ]
[18 ] Thus, PGHD can improve patient self-management of the arrhythmia, while also offering
clinical benefits to providers seeking to improve detection and tailor care based
on the unique characteristics of the patient.[19 ]
[20 ]
Sustained patient engagement with self-monitoring using mHealth technology is necessary
to generate adequate health data to enable precision management.[21 ] Yet, evidence shows that patient engagement is low over time, with many abandoning
self-monitoring within 3 to 6 months of initiation.[22 ]
[23 ] There is a gap in understanding factors that contribute to sustained engagement,
as much of the extant literature focuses solely on initial uptake of technology.[24 ]
[25 ]
[26 ] Moreover, engagement research has had minimal success improving sustained engagement
with generic design tactics, such as gamification and incentives (e.g., points, money),
that forgo consideration of unique patient characteristics.[27 ]
[28 ]
Objective
The purpose of this study was to investigate individual differences in sustained engagement
among patients with a history of AF who are self-monitoring using mHealth technology.
Specifically, we aimed to uncover factors associated with sustained engagement through
qualitative focus groups and interviews guided by a theoretical model.
Methods
Theoretical Model
Our investigation was guided by the unified theory of acceptance and use of technology
(UTAUT) model. UTAUT is a comprehensive theory derived from eight models of behavior
change and technology acceptance. Validation of the model found that it explains variation
in technology acceptance and use better than its component models (R
2 = 0.69 compared with 0.17–0.53).[29 ] It has since been used in multiple health care studies.[30 ]
[31 ]
[32 ] We chose UTAUT because it has been adapted specifically for sustained engagement.[33 ] This adapted model guided our analysis ([Fig. 1 ]). In the adapted model, the predictors of sustained engagement with ECG mHealth
technology are perceived ease of use, perceived usefulness, and three facilitating
conditions tailored for our patient population: (1) AF knowledge, (2) AF symptoms
severity, and (3) frequency of AF episodes. Age and gender moderate the relationships
between all predictors and the outcome sustained engagement. Experience with technology
moderates only the relationships of perceived ease of use and perceived usefulness
with the outcome.
Fig. 1 Adapted unified theory of acceptance and use of technology model. AF, atrial fibrillation;
ECG, electrocardiogram.
Study Design and Sample
This qualitative descriptive study[34 ] used focus groups and individual interviews with patients, nurse practitioners and
physicians (providers), and research coordinators involved in the iPhone Helping Evaluate
Atrial Fibrillation Rhythm through Technology trial (iHEART; R01NR014853, PI: Hickey).
This is an ongoing, 5-year randomized, controlled trial of adults with a history of
AF who have undergone a procedure to restore a normal rhythm to the heart.[35 ] They are randomized 1:1 to receive usual cardiac care of periodic ECGs during office
visits (control group) or usual cardiac care plus remote monitoring using the AliveCor
device (intervention group; [Fig. 2 ]). This device works with an accompanying smartphone application (app) to capture
heart rate and rhythm via a single-lead ECG. Patients can use the app to document
symptoms experienced during an ECG recording, or potential triggers of an AF episodes
(e.g., exercise). iHEART intervention arm participants were asked to use the AliveCor
device twice daily for 6 months but had the option of continuing beyond this period.
This protocol was intended to facilitate early identification and treatment of AF,
which can be difficult to capture with current standard of care measurements.[14 ]
[35 ]
Fig. 2 AliveCor mobile electrocardiogram (ECG) monitor and smartphone application.
A qualitative approach was used to allow for a richer, more nuanced understanding
of the factors associated with sustained engagement. We recruited a convenience sample
of iHEART intervention group participants who completed the trial within the past
2 months (to minimize recall bias). Providers and research coordinators were recruited
because of their potential for insights into patient engagement stemming from their
close connection to patients during the trial.
Recruitment and Data Collection
Institutional review board approval was obtained from Columbia University Medical
Center. The primary author (M.R.) and the iHEART principal investigator (K.H.) identified
potential participants and contacted them via telephone. Engaged patients, unengaged
patients, and providers/research coordinators were recruited into separate sessions
to facilitate candidness and comparison of engaged and unengaged patients. The level
of engagement was determined by examining the Health Insurance Portability and Accountability
Act-compliant, Web-based AliveCor portal. We defined the engaged patient as one who used AliveCor at least once per day on average during the trial. We defined
the unengaged patient as one who used the device less than once per day on average.
Informed consent was obtained at the beginning of each session. Semistructured focus
groups and interviews were conducted and analyzed until theoretical saturation was
reached. Discussions were guided by interview/focus group guides developed to elicit
understanding on each factor in the adapted UTAUT model (e.g., perceived ease of use).
Each session lasted 30 to 60 minutes and was conducted in a private space at a large,
urban academic medical center or over the phone when needed due to travel or scheduling
reasons. The primary author moderated all sessions. A second researcher (J.M.) was
present for a subset of the sessions to ensure rigor in data collection. Neither researcher
(M.R. or J.M.) was directly involved in the iHEART trial and did not know any subjects.
Participants received a $20 Visa gift card for participation. All focus groups and
interviews were audio-recorded and transcribed verbatim. The primary author (M.R.)
removed all personally identifiable information from all transcripts and checked them
for accuracy prior to analysis.
Data Analysis
The transcripts were analyzed by directed content analysis.[36 ] This method uses factors from a relevant theory to guide data collection and analysis.[36 ]
[37 ] Following this approach, the primary author (M.R.) created a preliminary codebook
of themes based on the factors described in the adapted UTAUT model, with separate
sections for each participant group (engaged patients, unengaged patients, and providers/research
coordinators). The codebook was reviewed for content validity by J.M. All transcripts
were then coded to this codebook by the primary author. New themes that emerged were
reported separately. Two additional analysts (D.B. and M.B.), with no prior knowledge
of the adapted UTAUT model, independently coded two transcripts using open coding
(e.g., no a priori codes). This offered verification that the emergent themes they
identified were congruent with the preliminary codebook, and that the codebook developed
by the primary author was a valid coding instrument. The primary author then provided
the preliminary codebook and they used directed coding to analyze three additional
transcripts, while identifying and separating new themes that emerged.
At each stage, codes were compared. Discrepancies were discussed and resolved. Inter-rater
reliability calculated to quantify coder agreement was high (0.87–0.98). In addition,
all analysts identified and reported on similarities and differences between participant
groups because both variability and consistency in perspectives were considered valuable
in advancing understanding of the theoretical model. All data were analyzed using
NVivo 11 (QSR International, Inc., Burlington, Massachusetts, United States).
Results
Description of the Sample and Overall Engagement
We interviewed a total of 21 individuals: 13 patients (7 engaged, 6 unengaged); 6
providers; and 2 research coordinators. We conducted 13 individual interviews: 10
via phone with patients; 1 in-person with a patient; and 2 in-person with providers.
We also conducted 2 in-person focus groups: 1 with 2 unengaged patients; and 1 with
4 providers and 2 research coordinators.
Providers in this study included 4 nurse practitioners and 2 physicians. They had,
on average, 22.7 years (range: 20–27) of clinical experience and 18.3 years (range:
13–25) working in the electrophysiology clinic from which iHEART participants were
recruited. The 2 iHEART research coordinators reported 3 and 25 years of clinical
research experience, respectively.
Patients were predominantly male (85%) and middle- to older-age (mean: 65.3 years,
range: 50–76 years), which reflects the demographics in the electrophysiology clinic
from which they were recruited. Engaged and unengaged patients had approximately the
same age and gender composition. Participants were asked a series of questions regarding
their comfort with technology at baseline in the iHEART trial. All patients in this
study reported owning a cell phone, with 78% owning a smartphone. All reported experience
searching the Internet for health-related information, and all had a computer or tablet
in their homes.
Engaged patients used AliveCor 31.2 times per month for an average of 11.9 months,
compared with 24.1 times per month and for an average of 9.3 months among unengaged
patients. [Fig. 3 ] illustrates trajectories of AliveCor use over time, showing a clear difference in
engagement between the two groups despite a high level of engagement overall.
Fig. 3 Trajectories of engagement among iPhone Helping Evaluate Atrial Fibrillation Rhythm
through Technology trial (iHEART) participants interviewed in this study (n = 13).
Factors Associated with Engagement in the UTAUT Model
First, we describe themes associated with sustained engagement found in the adapted
UTAUT model. We then describe emergent themes not specified in the adapted UTAUT model.
Each theme and subtheme is presented in [Supplementary Table S1 ] (available in the online version) with illustrative quotes.
Ease of Use
Similarities in Ease of Use
Both engaged and unengaged patients reported that the AliveCor device was easy to
use with minimal, if any, learning curve. They reported that data capture and sharing
was simple with the device, and the lightweight design made it portable and therefore
easy to capture ECGs virtually anywhere. Despite general ease of use, some technical
challenges arose for most patients. The primary challenge reported was difficulty
transmitting an ECG due to poor connectivity between fingertips and the device, or
the device and the application. This led to poor-quality readings and vague output
from the rhythm-identifying algorithm (e.g., “Unclassified”). Another problem described
was background noise interference when symptoms were recorded through voice-enabled
technology. Providers and research coordinators also reported that patients experienced
these technical issues.
Differences in Responses to Technical Issues
The main difference between engaged and unengaged patients was attitude toward handling
technical issues. All engaged patients reported on strategies they used for dealing
with challenges related to transmission and connectivity, such as moving away from
other electronic devices or cleaning their fingers. Some stated that this helped them
avoid becoming anxious. Conversely, many unengaged patients expressed frustration
and anxiety as a result of technical issues, for example: “I didn't feel safe in my
ability to get accurate readings” – Patient 1 (unengaged).
Differences in Health Care Provider Feedback
Many engaged patients reported a small yet adequate amount of guidance from providers,
which allowed them to handle abnormal readings and vague algorithm output: “I did
have several false readings…[the doctor] said don't pay attention to those…He took
that off the table for me to worry about” – Patient 9 (engaged). Most unengaged patients,
however, reported little to no feedback from providers to help them overcome technical
issues. For some, this was the direct reason for abandoning the device: “I stopped
because it said unclassified and…nothing was happening. And I was going insane. What
was going on? I wanted feedback” – Patient 11 (unengaged). All providers acknowledged
this need but also pointed to time being a limiting factor in their ability to provide
constant feedback to patients.
Usefulness of the Technology
Similarities in Usefulness of Identifying Rhythm
Most participants in both groups understood how difficult AF is to identify without
an ECG. For this reason, they reported that AliveCor was useful in giving definitive
rhythm identification, or “proof,” as one patient called it. As a result, most patients
stated that these data had a comforting effect, which providers corroborated.
Differences in Insights and Perceived Value of the Data
A major difference we found between engaged and unengaged patients was their ability
to independently use the data they were collecting. Many engaged patients reported
seeking further insights beyond basic heart rhythm, and stated that the value of the
data was a reason for sustained use: “Sometimes I'll forget to take the medication
but I never forget [AliveCor]… Because I value the feedback that it gives me tremendously”
– Patient 13 (engaged). Conversely, many unengaged patients described confusion and
difficulty interpreting their data: “When I stopped, I think part of it was getting
the message unclassified kind of made wonder what the utility of this thing was” –
Patient 10 (unengaged). Even if confusion did not arise, some unengaged patients did
not attach value to insights beyond rhythm identification: “I'm blissfully unaware…
I don't know if there's any other data that would be meaningful to me” – Patient 2
(unengaged).
Differences in Health Care Provider Feedback
Many engaged patients reported sharing insights about their data (described as “the
signals and symptoms” by one patient) with their providers to tailor their self-management
and medical care. Most providers recognized this supported the usefulness of the device:
“We can try to sort out why they're having this rhythm problem and identify any triggers”
– Provider 5. Most unengaged patients reported a need for interpretation to make the
data useful, but indicated a lack of immediate feedback. This led to anxiety and even
distrust toward providers and researchers: “It seemed like a one-way street where
you guys were just taking my information and I'm out there on my own” – Patient 1
(unengaged). All providers recognized this, and some reported discouraging anxious
patients from frequent monitoring that they felt may only worsen anxiety: “I, in fact,
encourage them to not check it as often – it just doesn't serve any purpose besides
potentially causing more anxiety about it” – Provider 5.
Facilitating Conditions
AF Severity: Long AF Histories but Varying Proactive Behaviors
Many patients in both groups reported living with AF for a long time but differed
in how they reacted. Most engaged patients proactively changed behaviors, including
healthier diets, abstaining from known AF “triggers” (e.g., drinking alcohol), and
self-monitoring using AliveCor more frequently depending on clinical acuity: “I tried
to use it every morning right after the ablation…As my rhythm returned normal it became
something I checked less” – Patient 9 (engaged). In contrast, many unengaged patients
reported being easily discouraged by their AF recurrence, which they said caused them
to self-monitor less and instead rely on office visits with providers for rhythm monitoring:
“I'm no longer in AF, at least, each time that I've been checked… I go in about every
six weeks, just to be checked” – Patient 1 (unengaged).
Some providers observed that patients may appropriately decrease use over time if
their heart rhythms became stable, indicating less AF severity: “For the clinical
part, treatment is achieved and the patients are doing well. They're not less engaged,
they're appropriately using it” – Provider 1. They also pointed out, however, that
this was only the case for patients who were truly clinically stable. If patients
did not consider their clinical acuity, they could inappropriately discontinue use.
AF Knowledge: Differences in Uncovering Self-Knowledge
Most patients had high levels of knowledge about AF in general. In fact, providers
described the participating patients as “very sophisticated and educated” (Provider
6). However, patients' knowledge of personal physiology and self-management needs
(self-knowledge) varied. Approximately half of engaged patients stated that their
self-knowledge improved through self-monitoring: “I think that what changed was my
sense of how this problem was affecting my day to day life” – Patient 13 (engaged).
Most unengaged patients, however, relied on providers to understand their unique physiology
and needs “[My doctor] had told me that relatively speaking [caffeine is] the least
effective trigger for me. He said alcohol is the worst and it definitely is, there's
no question” – Patient 7 (unengaged).
AF Symptoms: Driving Use for Unengaged Patients
The majority of the patients in both groups understood that poor correlation between
AF symptoms and AF episodes[16 ]
[38 ]
[39 ] was a reason to use AliveCor to identify their true cardiac rhythm. Many engaged
patients appropriately considered their actual ECG data versus their symptoms in determining
whether to continue using AliveCor. Conversely, for many unengaged patients, use was
driven by symptoms. They interpreted lack of symptoms as a sign of wellness and a
reason to stop using AliveCor. Alternatively, some unengaged patients experienced
symptoms that they attributed to AF when they were in a normal rhythm, causing them
to use AliveCor too frequently. One unengaged patient described how perceived symptoms
caused anxiety: “I probably use it too much because every time I have chest pain,
I just pull it out. And after a while, I just stop that…Because I can't be doing it
all the time” – Patient 5 (unengaged). Providers noticed this tendency: “They are
not always in A fib when they do document symptoms… what they perceive to be something
is not always the case” – Research Coordinator 2. Most unengaged patients expressed
more confusion about their symptoms, describing them as unclear, inconsistently related
to AF, and shifting over time.
Moderators: Age, Gender, and Experience with Technology
Some providers and patients stated that they thought that age would influence ease
of use and usefulness. Yet, no patient described their own age as being an impediment
to AliveCor use, and most providers expressed confidence in their patients' ability
to use the device regardless of age: “I've been surprised by how easily patients even
in their 60's, 70's and 80's have adopted using this” – Provider 6. Similarly, both
engaged and unengaged patients described comfort with technology, and many reported
tracking other aspects of their health with wearable devices and mobile applications.
Even patients who did not consider themselves “tech savvy” expressed comfort using
AliveCor commenting on its simple design: “I picked it up very easily. It was simple.
And I'm not very good—I can't even program a remote control” – Patient 5 (unengaged).
Providers and research coordinators agreed that tech savvy was unimportant if “enthusiasm
for their care is there” – Research Coordinator 1. Unlike these other moderating factors,
no participant explicitly discussed gender in the context of engagement with technology.
New Findings
Internal Motivation to Manage Health
Most patients in both groups expressed concern about their health. All considered
themselves a part of the collaborative disease management process: “I'd like to live
a long healthy life and being 50 years old, it's time to make a change. I'm hoping…I
can continue to have a quality of life as I grow older” – Patient 4 (engaged). However,
concern tended to escalate to anxiety for many unengaged patients, which providers
corroborated: “Once they see something unusual from the baseline…they panic…they call
right away” – Provider 1.
Relationship with Health Care Provider
Most engaged patients described positive working relationships with their providers.
Some stated that they had a strong relationship prior to using AliveCor, but most
stated that the device and the data it generated improved the collaborative relationship.
One patient said: “I feel like I am… 99% in tune with them, or they with me, because
it just gives them such important information” – Patient 6 (engaged). Some engaged
patients also stated that the device improved collaboration between members of their
care team. However, for unengaged patients, AliveCor did not facilitate collaboration
with providers. They more frequently described relationships that were more patriarchal,
and needing to advocate for themselves: “I wish they would listen to me… They're not
looking at the whole picture” – Patient 5 (unengaged).
Creating Supportive Environments
Both engaged and unengaged patients described routines and reminders to integrate
self-monitoring into daily habits. Many kept the device in the same place as a physical
cue, to make it part of their “daily ritual,” as one patient called it. Others took
the device with them to spot-check if they experienced symptoms.
However, all engaged participants reported they maintained these environments, even
when busy or travelling: “If I've missed the night I know to do it early in the morning
and then just do twice the next day. It's rare…If I'm traveling I'll take it with
me” – Patient 8 (engaged). Moreover, most engaged patients, as well as providers,
described supportive networks of friends and family as critical: “Remembering was
difficult but my wife was very helpful in the evenings and in the mornings” – Patient
13 (engaged). Alternatively, most unengaged participants described busy schedules
and travelling as interfering with use: “On weekends I didn't do it…from the beginning
I wasn't doing it every day. I guess, I just forgot it. I don't take it to work” –
Patient 11 (unengaged). Few discussed support from family members, friends, or providers.
Discussion
Summary of Findings
In this study, we found similarities and differences between engaged and unengaged
patients who used the AliveCor mHealth ECG technology to self-monitor their AF, which
were corroborated by their providers and research coordinators. Patients were similar
in many respects (e.g., most perceived AliveCor as useful on a basic level), but distinguishing
patterns emerged that were both distinct and nuanced. For example, unengaged patients
were generally frustrated by technical issues, confused by their heart rhythm data,
and lacked support to help mitigate these issues. Conversely, most engaged patients
were uninhibited by technical issues, able to interpret their data on deeper levels,
and described supportive environments that promoted engagement.
Revisiting the Concept of Engagement
We found evidence that engagement is more complex than use or nonuse of mHealth technology.
For instance, those patients who appropriately discontinue self-monitoring when clinically
stable may be less frequent users over time, but their independent interpretation
of their self-monitoring data would deem them engaged nonetheless. A clear definition
of engagement with self-monitoring remains lacking in the informatics community. Part
of the reason is because usage data remains the most common approach for measuring
engagement but fails to capture its complexity.[40 ]
[41 ] Future work should seek to develop a standardized definition and measure of engagement
with self-monitoring that still accounts for nuances such as those described above.
Fit with the Adapted UTAUT Model
We found that the adapted UTAUT model adequately describes predictors of sustained
engagement in this population. We found differences in the hypothesized predictors
of sustained engagement between engaged and unengaged patients. For our population,
the hypothesized moderators appeared less influential than we anticipated. This could
reflect the limited variability within the study sample, as participants were similar
in age and experience with technology, and were predominantly male.
Our findings suggest that four additional factors may contribute to sustained engagement
in this population. Three of the four appear to operate as facilitating conditions.
First, internal motivation to manage health was either a motivating force (as they
were for engaged patients), or a mitigating force when concern escalated into anxiety
(for some unengaged patients). Second, supportive environments, when present, fostered
sustained engagement, and when absent was a reason for nonuse among unengaged patients.
Third, patients' relationships with their providers, which ranged from collaborative
(engaged patients) to deferential (unengaged patients), influenced sustained engagement.
The fourth factor, feedback from providers, was discussed in the context of both perceived
ease of use and perceived usefulness, and may moderate these predictors. In the iHEART
trial, study coordinators and providers did not offer technical support follow-up,
but participants had the option of contacting them for assistance. However, in our
study, many unengaged patients reported feeling disconnected from any technical support
or resources, leading to frustration and subsequent discontinuation of use with the
AliveCor device.
The original UTAUT model contained factors that were condensed or eliminated in the
adapted UTAUT model upon which we based our study.[33 ] Three of the four additional factors that emerged in this study align with those
eliminated from the original UTAUT model: internal values and motivations, supportive
environments, and “social influence” (broadly aligning with the patient–provider relationship).[29 ] Recent work revisiting the model supports inclusion of user attributes (e.g., attitudes),
environmental attributes, and organizational attributes (including social influences).[42 ]
[43 ] However, that work does not specifically address sustained engagement.
Thus, we conclude that sustained engagement is a multifaceted concept. Our study uncovered
that the phenomenon of sustained engagement with self-monitoring involves interaction
among three agents: the patient, the provider, and the technology. This conceptualization
helps to explain why research that addressed these agents in isolation has demonstrated
little success improving sustained engagement.[27 ]
[28 ] Further, our findings suggest that all three must be incorporated into the design
and evaluation of self-monitoring technologies that aim to facilitate sustained engagement.
Relationship to Prior Work
To our knowledge, this is the first study to use qualitative, primary source data
to comprehensively describe factors related to sustained engagement with mHealth in
a specific patient population. Jiang et al first used an adapted UTAUT model to predict
sustained engagement among lung transplant patients.[33 ] We extend their work by demonstrating the utility of the adapted model in a different
patient population and, by doing so, identified additional factors relevant to sustained
engagement.
Recent quantitative research more broadly identified factors (e.g., perceived usefulness)
associated with engagement.[44 ]
[45 ] Our qualitative work uncovered nuances within each factor, such as how previously
unidentified variations in the depth of insights obtained from the data influenced
how engaged and unengaged patients perceived usefulness. Such nuances may be testable
in future quantitative studies in this population, using a larger sample size.
Internal motivation, a central construct in self-determination theory is critical
for sustained engagement.[46 ] Although this corresponds with a new factor we identified, “Internal Motivation
to Manage Health,” some responded to self-monitoring with anxiety that dampened their
engagement over time. Self-determination theory therefore may require similar quantitative
inquiry to understand how variation in internal motivation among specific patient
groups contributes to the phenomenon of sustained engagement.
Implications for Design
Knowledge of the factors related to sustained engagement may be useful in tailoring
self-monitoring applications. [Table 1 ] maps these factors to specific design implications. A first set of approaches focuses
on feedback that unengaged patients reportedly lacked. These include links to online
communities that might facilitate patient-to-patient communication, or application-based
messaging with providers that might improve patient–provider communication and overall
relationship. This is a controversial option, however, given the well-documented time,
liability, reimbursement, and scope of practice issues that providers cite in response
to application-based messaging.[47 ]
Table 1
Design implications from adapted UTAUT model
Feedback
Automation
Factor
Online communities
Messaging with provider
Patient decision-support
Infobuttons
Additional relevant data capture
Interactive data visualizations
Perceived ease of use
✓
✓
Perceived usefulness
✓
✓
✓
✓
AF severity[a ]
✓
✓
AF knowledge[a ]
✓
✓
✓
AF symptoms[a ]
✓
✓
✓
✓
Internal motivation[a ]
✓
✓
Relationship with provider[a ]
✓
Supportive environment[a ]
✓
✓
✓
✓
Feedback and guidance[b ]
✓
✓
Abbreviations: AF, atrial fibrillation; UTAUT, unified theory of acceptance and use
of technology.
a Facilitating condition.
b Moderator.
A second set of approaches focuses on automation to satisfy needs described by patients.
These include tested solutions that have yet to be implemented for self-monitoring.
For instance, clinical decision support, previously developed to support providers,[48 ]
[49 ] could guide patients' interpretation and evaluation of their own clinical presentation
through the data. Infobuttons merit application to mHealth applications.[50 ]
[51 ] Interactive visualizations that help individuals make sense of large amounts of
complex data have potential applications to PGHD.[5 ]
[52 ] In this study, all subjects, including providers and research coordinators, noted
that the feature for recording symptoms and triggers within AliveCor was difficult
to use. If application design eases capture of AF symptoms and triggers, those data
points could be triangulated with ECG data to discover individual manifestations of
AF. Visualizations to enhance understanding of these triangulated data could improve
AF management.[52 ]
Implications for Research
Our findings suggest several new lines of inquiry regarding sustained engagement.
Providers observed there is a time to appropriately stop self-monitoring (if clinically
stable for an extended period of time). For what length of time do patients actually
need to self-monitor to receive a clinical benefit for specific conditions? Previous
work has identified exact durations of remote monitoring necessary to diagnose or
manage arrhythmias with implantable cardiac devices,[53 ]
[54 ]
[55 ] but this issue remains inadequately studied in the self-monitoring space. This question
should be considered in light of patients' perceptions of the need to continue self-monitoring,
often based on symptoms, which may differ from their true clinical acuity. Evidence
shows that AF episodes frequently do not correlate with perceived symptoms.[16 ]
[38 ]
[39 ] Patients may be asymptomatic or experience vague symptoms that mimic those of comorbid
conditions, and the type and severity of systems may change over time, which would
necessitate continued use.
While we have identified several application design features that can target engagement,
there remains the larger philosophical question of whether sustained engagement should
be the goal for each patient. Patients and providers alike noted that anxiety can
overcome utility for some patients. Others have found similar negative emotional responses
to self-monitoring.[56 ]
[57 ] While thoughtful design of applications that improve communication and information
regarding the data may help, it will not mitigate anxiety for all patients. In such
cases, the risk of continued anxiety, which itself is a risk factor for AF recurrence,
may outweigh any clinical benefit of self-monitoring for the patient.
Limitations
This study had some limitations. First, while we attempted to classify patients' engagement
from their behavior recorded in the AliveCor portal, more precise classification of
engagement was not possible because raw usage data was not available. We also measured
engagement over 1 year instead of 6 months (iHEART protocol) due to the high level
of overall sustained engagement in the sample. As such, we may have inadvertently
misclassified some patients' engagement. [Fig. 3 ], which is a visualization of the raw usage data, shows clear differences between
those participants that we initially identified as engaged and unengaged, suggesting
that our classifications were accurate. Differences between engaged and unengaged
and symptomatic and asymptomatic AF patients may warrant future confirmation using
quantitative methods.
Second, this patient population was uniquely well-educated regarding their arrhythmia
and highly engaged in their care overall. They were also predominantly male, middle-
to older-age, and moderately to extremely comfortable with technology. Our sample
therefore had little variability and tended toward high engagement with self-monitoring.
While we made every attempt during our analysis to bracket biases that resulted from
these sample characteristics, our findings are likely not generalizable to other patient
populations, who may experience different barriers to sustained engagement. Therefore,
this study underscores that theoretical models guiding data analysis always need to
consider the unique patient population being studied.
Conclusion
This study provides insights on factors related to sustained engagement in a unique
population of adults living with AF. We found evidence that the UTAUT model can serve
as a valid framework for understanding sustained engagement, though it requires modifications
to account for the patient population in consideration. The theory-driven findings
we elicited can guide design and development of personalized mobile application interfaces
for self-monitoring to engage adults living with AF for a sustained period of time.
The UTAUT model also may guide establishment of parameters for sustained engagement
for different patient populations. Theory-based evidence for application design is
one approach for realizing the potential health benefits of PGHD collected with the
mHealth technology.
Clinical Relevance Statement
Clinical Relevance Statement
PGHD is changing the paradigm of care for individuals living with chronic conditions
for whom self-monitoring has the potential to improve clinical outcomes. This study
validates a framework of sustained engagement that can be used to guide design and
development of application interfaces that engage patients in the self-monitoring
process. Through patient engagement, the promise of PGHD may soon be realized.
Multiple Choice Questions
Multiple Choice Questions
In directed content analysis, qualitative data collection and analysis is guided by
what?
Relevant health care policy.
Evidence-based medical guidelines.
A relevant theory.
Existing informatics ontologies.
Correct Answer: The correct answer is option c (see section “Data Analysis”). Direct content analysis
starts with a theory as guidance for initial codes. Findings that emerge outside of
this theory are also considered valuable and may suggest modifications to the theory.
Previous research has identified exact durations of monitoring necessary to detect
or treat arrhythmias with what devices?
Correct Answer: The correct answer is option a (see section “Implications for Research”). Previous
clinical research has determined the diagnostic yield and treatment-related benefits
of monitoring with various implantable cardiac devices over specific time periods
(e.g., 2 weeks of monitoring yields most diagnostic value). However, such research
has not been conducted with smartphone-based ECG monitors, like AliveCor.
