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Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data

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Objectives: Consumer Health Informatics (CHI) and the use of Patient-Generated Health Data (PGHD) are rapidly growing focus areas in healthcare. The objective of this paper is to briefly review the literature that has been published over the past few years and to provide a sense of where the field is going. Methods: We searched PubMed and the ACM Digital Library for articles published between 2014 and 2016 on the topics of CHI and PGHD. The results of the search were screened for relevance and categorized into a set of common themes. We discuss the major topics covered in these articles. Results: We retrieved 65 articles from our PubMed query and 32 articles from our ACM Digital Library query. After a review of titles, we were left with 47 articles to conduct our full article survey of the activities in CHI and PGHD. We have summarized these articles and placed them into major categories of activity. Within the domain of consumer health informatics, articles focused on mobile health and patient-generated health data comprise the majority of the articles published in recent years. Conclusions: Current evidence indicates that technological advancements and the widespread availability of affordable consumer-grade devices are fueling research into using PGHD for better care. As we observe a growing number of (pilot) developments using various mobile health technologies to collect PGHD, major gaps still exist in how to use the data by both patients and providers. Further research is needed to understand the impact of PGHD on clinical outcomes.
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152
IMIA Yearbook of Medical Informatics 2017
© 2017 IMIA and Schattauer GmbH
Present and Future Trends in
Consumer Health Informatics and
Patient-Generated Health Data
A. M. Lai1, P.-Y. S. Hsueh2, Y. K. Choi3, R. R. Austin4
1 Institute for Informatics, Washington University in St. Louis, USA
2 Computational Health Behavior and Decision Science, Center for Computational Health,
IBM T.J. Watson Research Center, USA
3 Department of Biomedical Informatics and Medical Education, University of Washington, USA
4 School of Nursing, University of Minnesota, USA
Summary
Objectives: Consumer Health Informatics (CHI) and the use of
Patient-Generated Health Data (PGHD) are rapidly growing focus
areas in healthcare. The objective of this paper is to briefly review
the literature that has been published over the past few years
and to provide a sense of where the field is going.
Methods: We searched PubMed and the ACM Digital Library for
articles published between 2014 and 2016 on the topics of CHI
and PGHD. The results of the search were screened for relevance
and categorized into a set of common themes. We discuss the
major topics covered in these articles.
Results: We retrieved 65 articles from our PubMed query and
32 articles from our ACM Digital Library query. After a review of
titles, we were left with 47 articles to conduct our full article sur-
vey of the activities in CHI and PGHD. We have summarized these
articles and placed them into major categories of activity. Within
the domain of consumer health informatics, articles focused on
mobile health and patient-generated health data comprise the
majority of the articles published in recent years.
Conclusions: Current evidence indicates that technological
advancements and the widespread availability of affordable
consumer-grade devices are fueling research into using PGHD for
better care. As we observe a growing number of (pilot) develop-
ments using various mobile health technologies to collect PGHD,
major gaps still exist in how to use the data by both patients and
providers. Further research is needed to understand the impact of
PGHD on clinical outcomes.
Keywords
Consumer health information/methods; patient-generated health
data; mHealth; user-computer interface; consumer participation
in delivery of health care
Yearb Med Inform 2017:152-9
http://dx.doi.org/10.15265/IY-2017-016
Published online August 18, 2017
Introduction
In recent years, the widespread adoption of
personal computing technology, availability
of personal health records (PHRs), and utili-
zation of various forms of social media has
invigorated an interest in consumer health
informatics (CHI) and created an explosion
of interest in the potential of patient-gener-
ated health data (PGHD). Internet adoption
by adults in advanced economies reached a
median of 87% and, similarly, 68% of adults
reported owning a smartphone in 2015 [1].
Another factor pushing forward the po-
tential of PGHD is not only the widespread
availability of accelerometers embedded
in smartphones and wearable devices that
can collect physical activity [2], but also
affordable biometric sensors that can collect
and transmit weight, blood pressure, heart
rate, temperature, and even blood glucose
information from patients to their healthcare
providers [3]. This recent ability for consum-
ers to increase their participation in their own
healthcare by recording and sharing health
data through the use of affordable sensors
and PHRs has inextricably tied together the
topics of CHI and PGHD.
While there have been previous reviews
on the topic of CHI, the objective of this
survey paper is to generate a sense of the
recent, past, and current foci in CHI and
PGHD using a semi-structured scoping
review process.
A recent general review of the overall
trends of CHI is discussed by Demiris, in
which a majority of trends involve the use
of patient-generated health data [4]. In fact,
in the past decade, quite a large amount of
evidence has been accumulated with regards
to the use of active and passive monitoring
data from home telehealth and mobile health
technologies, as well as from PHRs [5]. The
role of PHRs has expanded in recent years,
either as parts of electronic health records
(EHRs) offered by providers, or through the
enablement of patients themselves on some
technology-based platforms for data collec-
tion and sharing, or for integration purposes.
In the United States, with the implementa-
tion of the federal government Meaningful
Use incentive program, the proportion of
consumers accessing their records increased
from 27% in 2014 to 45% in 2016 [6]. This
adoption has further enabled more frequent
patient-provider communication, with 64%
of United States physicians in 2015 having
an EHR with the capability to exchange
secure messages with patients, an over 50%
increase since 2013 [7]. In addition, the col-
lection of patient-reported outcomes (PROs)
and their contexts have been further enabled
by the advance of ecological momentary as-
sessment tools, which focus on the collection
of symptoms and behaviors close in time to
their experience.
Under such a healthcare landscape shift,
recent major research foci are the imple-
mentation and understanding of the value
of providing clinical notes through PHRs
and incentivizing patients to participate in
patient-provider communication [8]. In addi-
tion, with the availability of patient-generat-
ed health data from the Quantified Self (QS)
movement through a plethora of consumer
devices, Demiris called for an infrastructure
IMIA Yearbook of Medical Informatics 2017
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Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
that supports clinicians to become engaged
in the processes that QS facilitates and the
technologies that can help make sense of data
through massive data retrieval and trending,
as well as modeling and prediction [4].
In a review of the definitions of CHI,
Flaherty et al. have identified five quality
assessment criteria to evaluate on the defini-
tions in a set of 23 selected CHI papers [9].
Despite the fact that none of the definitions
used in these papers fulfilled all five quality
criteria identified, this review identified some
success stories, including the use of Com-
prehensive Health Enhancement Support
System (CHESS) for helping individuals
deal with health crises or medical concerns,
as well as the use of CHI devices for improv-
ing clinical and behavioral outcomes. The
varying definitions have demonstrated the
multidisciplinary nature of the field of CHI.
This nature has also reflected on the varying
efforts of PGHD evidence collection.
This article differentiates itself from
previously written reviews on the topic of
CHI and PGHD by providing an update and
summary of the recently published scientific
literature on these topics through the conduct
of a scoping review. We also examine not
only the biomedical literature, but also that
of computer science and engineering.
Methods
A scoping review of the literature was con-
ducted using a search strategy to generate
an overview survey analysis of the last three
years of the biomedical, computer science,
and engineering literature focusing on CHI
and PGHD. The authors selected PubMed
and the ACM Digital Library to understand
recent literature and up to date conference
proceedings. In this paper, the rationale for
the database selection is not intended to be
a full all-inclusive systematic review but the
aim is to propose a survey paper to highlight
and provide an overview of current trends
and the future trajectory of CHI and PGHD.
Our initial search strategy for PubMed
was to use a variety of MeSH headings to
identify the articles of interest, but an appro-
priate set of MeSH headings that yielded the
desired articles could not be identified. The
list of MeSH headings included Consumer
Health Information/methods, User-Com-
puter Interface, Health Services Needs and
Demand, Physician-Patient Relations, Con-
sumer Participation, Medical Informatics,
and Delivery of Health Care. This resulted in
the use of key search terms for any mentions
of the terms “patient-generated health data”
or “consumer health informatics” restricted
to the papers published in 2014, 2015, and
2016, and written in the English language.
As a result, the following query was used:
“patient generated health data”[All Fields]
OR “consumer health informatics”[All
Fields] AND (“2014/01/01”[PDAT] :
“2016/12/31”[PDAT])
The search strategy for the ACM Digital
library used key search terms: consumer,
patient, self-quantifier, citizen, “patient-gen-
erated data,” and “consumer health,” re-
stricted to the years 2014–2016, and limited
to English language. The team iteratively
refined the search terms in order to focus
solely on consumer health informatics and
patient-generated data and to reduce the
number of articles that were not related to
these topics and did not address the search
criteria. The key search terms allowed the
team to narrowly focus on a specified time
frame and topic for the scoping review. This
resulted in the following query:
acmdlTitle:(consumer, patient, self-quan-
tifier, citizen -mlearning, -learning, -edu-
cation, -vision, -speech, -preliminary, -3d,
-flash, -API, -search, -retrieval, -seeking,
-gaming, -simulation, -robot, -image,
-virtual, -invasive, -scratch, -middleware,
-immune, -vision, -children, -eye, -VLSI)
AND (“patient generated data”, “con-
sumer health”)
After retrieving the references from these
two queries, the authors screened the articles
based on titles for relevance to CHI and
PGHD. Articles that were selected by two or
more of the authors were placed on the list
for further review. The authors then catego-
rized the articles on the list into themes. The
articles were divided amongst the authors to
retrieve, summarize, and perform a thematic
analysis. A small subset of the articles (n=5)
could not be retrieved by any of the authors
due to limitations in publication availability
at our combined institutions. Due to time
constraints and because this is not an exhaus-
tive literature review the authors decided to
exclude this small subset of articles from
the survey. Articles that were reviewed by
the authors and determined to be general
review or survey articles were also excluded
from this analysis.
Results
The team executed the queries described
above on November 4th, 2016. From the
searches, a total of 97 articles (65 PubMed
and 32 ACM Digital Library) were includ-
ed for screening (Figure 1.). Three of the
authors (AL, PH, and RA) screened the
articles for relevance to CHI or PGHD. After
the review and screening of titles, 47 articles
were included in the survey of the activities
in CHI and PGHD. The authors (AL, PH,
RA, and YC) categorized the remaining 47
titles into three themes: 1) Patient-Generat-
ed Health Data; 2) Mobile Technology; 3)
Human-Computer Interaction. The articles
have been summarized below.
Patient-Generated Health Data
A large fraction of the papers on the topic
of PGHD and its application to various as-
pects of healthcare that we encountered in
our literature search could be categorized as
reviews or opinion papers [10-15]. In these
articles, the definitions of PGHD that were
used were slightly different, but many were
based on a broad definition of PGHD, which
included any observation, result, device
finding, confirmation, change correction, or
addition of data to a patient’s record that was
created, recorded, gathered, or inferred by or
from patients or their designees [10, 12]. In
order to cast a broad net with regards to the
activity in this emerging focus area, we have
used this broadest definition.
The most prominent difference separating
PGHD from data generated during clinical
settings is that with PGHD, it’s the patient
and not the provider that takes the ownership
of generating, capturing, and sharing the
data. The concept of PGHD is not new in
clinical care as patients have always been
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Lai et al.
recording and sharing information on their
health in a variety of ways [12]. However, it
is becoming increasingly common with the
proliferation of more affordable consumer
devices (e.g., smart phones, wearables, and
sensors) and the advent of technologies
such as patient portals and personal health
records. We discuss the use of PGHD with
mobile devices in the following section.
The recent literature points to the many
potential benefits of integrating PGHD into
health care delivery. Cohen et al. conducted
semi-structured interviews to examine the
experiences of health care professionals who
use PGHD in an outpatient clinical setting
[16]. The identified benefits included a deep-
er understanding of a patient’s conditions, the
availability of more accurate patient infor-
mation, and the ability to monitor patient’s
health between clinic visits. PGHD through
monitoring devices could be used to collect
a more comprehensive view of a patient’s
physiology [14].
A number of articles discussed different
ways PGHD can be incorporated into clin-
ical trials and discussed some of the issues
[10, 3, 17]. Wood et al. also described how
PGHD could be used to monitor medication
adherence and to remind patients to take
study drugs or follow study protocols [10].
The authors acknowledged the concern
that PGHD streams have been primarily
from consumer-grade devices rather than
calibrated research-grade devices, and that
further studies are needed to understand the
data quality from these devices.
Furthermore, ubiquitous monitoring of
a patient allowed through PGHD could not
only advance our knowledge of a patient’s
cancer experience but also enhance the ef-
ficiency and productivity of cancer clinical
trials [3, 17]. In addition, PGHD empowers
patients by engaging them as key players in
their care [3, 18, 19]. Petersen discussed how
PGHD could improve cancer survivorship
through “support of survivor autonomy, im-
provement of survivor health, and promotion
of survivor population health” [11].
Another area of interest for the research
community is how to extract and process
meaningful information from unstructured
Fig. 1 Summary of Search Strategy
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Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
PGHD from online health communities.
Hartzler et al. generated health interest
profiles by extracting health-related terms
from users’ past posts from an online health
community and conducted a user study to ex-
amine the validity of such profiles [20]. Their
findings demonstrated that the PGHD-gen-
erated health profiles were matched with
the users’ health interests. Furthermore,
online health community members showed
favorable views on using the profiles to find
a match and connect with other members
for peer support.
Additional challenges of widespread
adoption of PGHD reported in the literature
include: lack of randomized controlled trials
(RCTs) to examine the associations between
PGHD and longitudinal health outcomes [3,
17], PGHD data standardization and device
interoperability [3, 17, 21], integration of
PGHD with EHRs [3, 17], concerns for se-
curity and privacy [3, 17, 21], and building
data analysis and visualization tools that
facilitate informed health decision making
[3, 21-23]. Future research is needed to
address the barriers and gaps identified in
the literature to harness the true potential of
PGHD, bringing value to both patients and
health care providers.
Mobile Technology
Mobile technology or mHealth is transform-
ing the way patients, providers, and research-
ers interact with data. For the purposes of this
review, mHealth includes mobile technolo-
gies such as smartphones, tablets, wearable
devices, and sensor technologies. M-Health
is fundamental to healthcare transformation
since it shifts the ability to collect data
anywhere, at any time, and it is becoming
seamlessly integrated into our lives. This
allows for a gradual shift of healthcare closer
to the patients’ daily living and away from
the traditional clinical environment [24].
One specific application of mHealth
includes monitoring chronic disease and the
potential for enhancing self-management of
chronic disease. Chronic diseases are in the
center of mHealth developments as they re-
quire the continuous and active involvement
of not only healthcare professionals but also
patients, both of whom can be empowered
through the use of specialized mobile
applications and the analysis of data from
modern miniaturized and wearable sensing
devices [24]. Important considerations such
as system design of these technologies can
increase long-term sustained use of consum-
ers but also reduce the burden for healthcare
providers.
An example of the use of mobile health
technologies to help patients with chronic
conditions to collect and monitor their phys-
iological signs is WearCOPD, a mobile app
developed by Liaqat et al. that uses a smart-
watch and a smartphone to collect chronic
obstructive pulmonary disease (COPD)
related symptom data to detect acute exac-
erbations of COPD [25]. Patients complete
a daily health questionnaire prompted by
the app to monitor their status. The app also
collects patients’ peak expiratory flow by
having them blow into the microphone.
Another example is a mobile app that uses
wearable sensors and biochips to facilitate
self-management of Inflammatory Bowel
Disease (IBD) [26]. Pernencar et al. argue
that automatic data collection with the use
of wearables and biochips could reduce
the burden of self-report and should allow
objective physiological data to be captured
and analyzed for self-management of IBD
[21]. The previous two studies [20, 21] were
ongoing at the time of the publication and
therefore relevant evaluation results could
not be assessed.
Another study presented a privacy-pre-
serving video surveillance system using
a network of multiple IP cameras and
motion sensors to monitor patients in their
real-life settings. The system stores and
sends a “ghost” form of an image, which
only contains the information about the
object’s movement in blue color on a black
background to preserve the privacy of the
monitored individual [27]. The proposed
surveillance system demonstrated effective
patient monitoring detection algorithm while
preserving the privacy of monitored patients.
Kumar et al. described their approach to
the integration of continuous glucose mon-
itor data and the transmission of the data to
a Personal Health Record [28]. The glucose
monitor data was collected onto an Apple
mobile device via Bluetooth, stored locally
on the iOS device using the Apple HealthKit
interface, and subsequently transmitted from
HealthKit to an Epic EHR through the Epic
MyChart app running on the mobile device.
Nundy et al. discussed the value of
PGHD gathered using mobile technology
for diabetes self-management [29]. They
explored provider perceptions of a summary
report based on the data extracted from a text
message-based diabetes self-management
program. The patients texted back responses
to automated text messages consisting of
reminders and educational messages. From
these responses, summary reports were
generated to inform providers of clinically
relevant patient-reported health data. The
authors used Likert-like response surveys
and interviews with primary care physicians
(PCPs) and endocrinologists to assess their
responses to summary reports. Only three
out of the 12 providers felt that the report
changed the care they provided. However,
nine of the 12 providers were willing to use
the summary report.
Another growing area of mHealth is mon-
itoring and tracking mental health conditions
with mobile devices and sensing technology.
Mobile technology makes it possible to
extend mood self-assessment, from lab to
real life, by the collection of mood data, fre-
quently, over a long time, and in different life
situations [30]. Improving data collection for
conditions such as mental health increases
the opportunity for early intervention and
improves outcomes. The utilization of per-
vasive technology, including a mobile phone
and its sensors, could potentially provide a
way to make therapies more personalized
and accessible at any time [31].
Telerehab is another area that is capitaliz-
ing on the expansion of mobile technologies.
Rehabilitation sensing and tracking allow for
pattern recognition through data analysis and
visually represent the data needed to share
the patient’s story. Conventional physical
rehabilitation in stroke and other physical
disorders is provided by sparse and home-
based systems that suffer from issues of com-
pliance, low patient engagement, and lack of
personalization [32]. Another program, Back
on Bike, a cycling rehab program, showed
tremendous promise and is expanding the
possibilities of traditional rehabilitation for
cardiac patients [33]. Dynamic approaches
to rehab used in the patient’s home for closer
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Lai et al.
self-monitoring have the potential to increase
adherence and to enhance secondary preven-
tion of cardiac disease.
Despite the potential benefits of col-
lecting PGHD using various mobile health
technologies, challenges remain with how
to analyze and interpret large amounts of
high-dimensional PGHD to detect trends
and to make meaningful inferences [21,
23]. To address this issue, researchers have
developed a new data analysis methodology
and a new framework to analyze PGHD.
Liang et al. proposed a new method based
on association rule mining techniques and
successfully demonstrated its validity to
discover correlations between lifestyle
factors and sleep patterns [22]. Gollamudi
et al. proposed a hypothesis testing the
framework for analyzing unstructured time
series data, a common data type of PGHD,
to discover unique trends and associations
in the data [23]. Using the framework, they
have analyzed blood pressure data collected
by patients at home and they demonstrated
the efficacy of a comprehensive smart-
phone-based health monitoring intervention.
While some initial mHealth studies are
being conducted, more large-scale studies
are however needed to develop best prac-
tices and create a foundation of evidence
to support the use of mHealth across care
settings. Barriers and opportunities exist
that incorporate new technology for both
providers and consumers. Since the locus
of healthcare is shifting to the home and
community setting, there is an increasing
need to adopt a broader approach across the
traditional boundaries of health and social
care in order to operationalize a more inte-
grated and personalized healthcare service
provision [34].
Emerging Research in Human-
Computer Interaction
Historically, a cross-cutting focus in CHI
has been human computer interaction (HCI)
and this trend has continued. While many
existing efforts are focused on the collection
of evidence on existing online health tools,
more researchers are looking for ways to
improve patient-provider communication,
and patient-led, shared experiences, using
methods ranging from theory-informed
models, to surveys, interviews, and personal
constructs.
Jacobs et al. compared health informa-
tion sharing practices between patients,
providers, and healthcare navigators. They
identified gaps between the groups with
implications on design and adoption of
technology [35]. Another study revealed
limitations of existing approaches to support
patient-provider communication and identi-
fied challenges for the design of systems that
honor patient needs and preferences [36].
Chung et al. surveyed 211 patients,
interviewed 18 patients, and re-analyzed
interviews of 21 providers [37]. They found
that there exist needs of support for collab-
oration in every stage of self-tracking and
that patients and providers create boundary
negotiating artifacts to support the collabora-
tion. These are important findings to further
support the design of PGHD tools based on
stage-based models of personal informatics.
Briggs et al. have further supported that
patients and caregivers frequently get their
health information and advice from web-
sites containing patient-led shared health
experiences [38]. This means that they often
engage in a very idiosyncratic selection
process in order to determine which websites
have personally-resonant material. In this
paper, a Repertory Grid (repgrid) technique
was applied on websites for patients with
chronic asthma and caregivers of people
with multiple sclerosis (MS), presenting
each patient/caregiver with a set of health
websites relevant to the condition for which
they were looking for information. Hyper-
personal representations of those constructs
provided new insights on the ways individual
patients can use the online health tool in their
own contexts.
Finally, Maniam et al. applied a more tra-
ditional approach to assess literature, derive
an HIT adoption model, and validated it with
surveys in the case of diabetic patients [39].
The results are used to inform the design
of Diabetes Self-Management Applications
(DSMA) as impactful patient-centered
tools, enabling diabetic patients to manage
their health conditions and thereby prevent
complications. The findings indicate that
perceived financial risk, perceived privacy
and security risk, technology anxiety, and
facilitating conditions have significantly
positive relationships with the intention to
adopt DSMA. This will help to design the
next-generation DSMA applications.
Discussion
Studies in CHI and PGHD have begun to
show positive results that impact care deliv-
ery, improve patient-provider communica-
tion, and enhance health outcomes. Despite
early positive results, significant barriers and
challenges exist related to the use of PGHD,
mobile technologies, and digital sensors
within clinical settings.
A key finding from this review is the
overall increase in acceptance of PGHD to
be used by consumers, healthcare providers,
and researchers. Polling data shows that
the adoption of mobile apps for health has
doubled in two years, reaching 33% in 2016,
and, that patient data is believed to be ben-
eficial for maintaining health [40]. Another
poll from 13,000 consumers in Europe also
reveals that 32% of the general populations
in Europe are considering they are using
wearable devices [41]. In fact, the survey
has further shown that more than 78% of
customers are willing to wear technology
for health tracking. This can be attributed
to the availability of smartphones and to the
increasing prevalence of patient-reported
outcomes and ecological momentary assess-
ment technologies that allow for data collec-
tion [42]. While this signifies an enormous
opportunity, it can also be stated as a concern
for some segments of the population that
may not have access to these technologies
[29]. This disparity was not addressed in this
current review but is something that should
be addressed in future reviews.
Despite the increased use of PGHD, there
exist gaps for how to use the data by both
patients and providers. The first step was to
collect and store data. Now, we need addi-
tional guidance regarding how to operation-
alize and interpret the data. In the following
discussion, we will discuss the gaps existing
in the two latter aspects.
First, operationalizing data can include
adjusting clinical workflow, improving in-
teroperability, and providing guidelines for
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Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
responsibility of the data. Data interpretation
could include the use of a clear language,
enhanced data visualization techniques, and
built-in summaries of the data. The major
barriers commonly faced by healthcare
professionals in routine care include a lack
of proper incentives and of a supportive
infrastructure to integrate PGHD into the
clinical workflow [42, 43]. These barriers
limit the utilization of PGHD in clinical deci-
sion-making and make it hard to assess how
much extra time it would take the healthcare
professionals in a consultation, a home visit,
or a care management call. As a result so far,
the adoption by health professionals to incor-
porate PGHD into routine care is still slow.
Given that 78% of US consumers would be
willing to wear fitness and lifestyle and/or
vital sign monitoring technology [6], the
disparity between the interest of consumers
to provide PGHD and the ability of providers
to incorporate the data into their workflow
is alarming. On the consumer side, current
self-monitoring tools also lack flexibility,
standardized formats, and mechanisms to
share data with providers.
Second, for interpreting data, the major
barriers are the lack of standard data models
for integration and of a supportive infrastruc-
ture to make sense of the collected data for
patients themselves. From the accumulated
evidence in the past, self-monitoring has
been shown to be good for activation, but
not enough for sustaining behavior [44].
More frequent feedback has been expected
as an effective counter strategy to address
barriers such as stress level, lack of social
support, and discomfort with recording that
can affect adherence to self-monitoring [45,
46]. However, as pointed out in [47], in the
area of mobile health, only a limited number
of mHealth apps integrated health behavior
theory and left room for future work.
In order to start addressing the missing
support for healthcare professionals and
the patients themselves, researchers and
policy makers in the field need to re-exam-
ine evidence to select the most important
opportunity areas and prioritize them. This
review thus serves as a status update for
the emerging evidence for the promising
opportunity areas to pursue. In particular, the
review points out the potential of secondary
use of PGHD for a variety of applications,
particularly with respect to the application
of data analytics and mining techniques for
the generation of new knowledge and to
help create effective learning health systems.
However, many open questions still exist and
we anticipate an increased focus of research
in interpreting the context of collected
PGHD, with better automated techniques
for cleaning PGHD collected from sensors,
understanding the reliability of PGHD sen-
sors, and PGHD interoperability.
One such gap relates to standard vocabu-
laries and data models. There have been quite
a few consortium-based and commercial
efforts that have attempted to address these
issues. For example, the new Fast Health-
care Interoperability Resources (FHIR)
specification from the HL7 organization
is designed to allow health consumers to
share their data with clinical systems [48].
Patient-Centered Outcomes Research In-
stitute (PCORI) is starting to collect cross-
site patient ambulatory assessment data to
enable patient-centered effectiveness study
[49]. The creation of ecologically-valid
tools when using ambulatory assessment
can then lend support to the understanding
of bio-psychosocial processes as they unfold
naturally in time and in context. Emerging
mobile health data and research platforms
such as Apple HealthKit and ResearchKit
have also added support to the interchange-
ability of PGHD for future clinical integra-
tion needs [50, 51].
Another open research question is the
sense-making of PGHD. Many have ob-
served the problems but so far no strong
evidence has emerged for the right solutions
yet. While only 10% of the population are
using health-tracking devices/apps to help
manage chronic conditions [52], the pro-
portion of US health consumers accessing
their health records has increased to 45% in
2016 (an increase of 67% in two years) [53].
The increasing access and connection be-
tween clinics and homes have indicated new
opportunities in bridging the gaps. Better
interpretations of patient data for the patients
are expected to provide better connections to
user’s internal motivators for sense-making
and persuasion [54-56]. This will then allow
the adoption of a positive psychology and
help the target users to focus on their own
reason for goal attainment.
In the future, we expect more research
to be conducted to identify the association
between the top barriers and the possible
intervention designs to address the different
opportunity areas of PGHD in clinical care.
Opportunity areas include diagnosis support
(e.g. clinical decision support systems for
better reliability and resolution), interaction
support (e.g., interactive feedback apps for
collaborative reflection and communication),
focus on education goals (e.g., automatic
synthesis of data logs for content absorbing),
and a better understanding of patient values
(e.g., care management support for better
communication of contextual information).
Only when a better assessment is done, then
we can start addressing the health consumer
demands. Research is also needed to extend
the standardized consumer health vocab-
ulary, in which more up-to-date concepts
are included and similar symptoms can be
grouped together to provide standardization.
Research has started to build open-source
Consumer Health Vocabulary (CHV) [57]
to deal with this issue.
Conclusion
Current evidence indicates that the techno-
logical advancement and widespread avail-
ability of affordable consumer-grade devices
are fueling research into using PGHD for
better care. As we see a growing number of
(pilot) developments using various mobile
health technologies to collect PGHD, major
gaps still exist in how to use the data by both
patients and providers. Further research is
needed to understand the impact of PGHD
on clinical outcomes.
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Correspondence to:
Albert M. Lai, PhD
Institute for Informatics (I2)
Washington University in St. Louis
660 S. Euclid Ave., MS 8102-13-610
St. Louis MO 63110, USA
Tel: +1 (314) 273-1391
Fax: +1 (314) 273-1390
E-mail: amlai@wustl.edu
... While collecting data is becoming increasingly easier with advanced sensing technology, the use of data remains challenging because of the lack of tools to make this data useful and limited empirical understanding of the needs and challenges of different data consumers -patients and clinicians [5,50,100]. ...
... Current tools for PGD use provide minimal decision-support [50], which makes it challenging for patients and clinicians to make decisions from the data [12]. The lack of suitable tools could be attributed to the limited understanding of the role and impact of PGD in data-driven decision-making by patients and clinicians [100]. Hence, recent research has indicated the need to better understand how PGD is translated into actionable information within both individual and collaborative contexts of use [5,166]. ...
... Despite growing evidence showing the availability and utility of patient-generated data, its potential remains to be harnessed because of the lack of adequate informatics tools. Limited accounts of analytical activities and processes through which multidimensional data is translated to decisions has challenged the development of tools [100]. To understand the use of patientgenerated data, prior work has primarily focused on establishing information needs of data consumers (what data or insights users want), but limited work has focused on understanding the process of reflection and insight generation [10]. ...
Thesis
People with chronic health conditions, such as diabetes, are now able to capture large amounts of health data every day owing to improved medical and consumer sensing technology. These data, known as patient-generated data, have immense potential to inform the care of chronic conditions, both individually by patients and collaboratively by patients and clinicians. Despite the increasing ability to capture personal health data, informatics tools provide limited support to enable routine use of data for disease management. Lack of support for making sense of different types of health data challenges informed decision-making and results in missed opportunities for improving care, leading to suboptimal control and poor health consequences. Motivated by these problems, my dissertation examines the data practices and decisional needs of patients and clinicians to design novel tools for the presentation of multidimensional health data and evaluates these tools in the context of Type 1 diabetes. It employs several qualitative methods that include interviews, observations, focus groups, diary study, think aloud sessions, and user-centered design. By examining how patients and clinicians interpret multiple streams of data from continuous glucose monitors and insulin pumps, I synthesized the episode-driven sensemaking framework, a novel framework that describes the different analytical stages through which multidimensional health data is made actionable. My work describes the four analytical stages of the episode-driven sensemaking framework that include episode detection, episode elaboration, episode classification, and episode-specific recommendation generation. I show that the episode-driven framework provides a promising basis to guide the design of tools for data-based sensemaking and decision-making as the different stages of the framework lend themselves to opportunities for combining computational and user agency in different ways. By examining existing data review platforms, I show that the exploratory nature of these tools makes them underutilized by lay users like patients, in addition to resulting in negative experiences, such as cognitive burden, misinterpretation, and misrepresentation of reality. Given the limitations of exploratory tools, the potential of the episode-driven framework in providing a basis for tool design, and the promise of data-driven narratives in communicating data to the lay users, I designed episode-driven data narratives to help patients review data from continuous glucose monitors and insulin pumps. An exploratory comparison of the episode-driven narratives with the commercially available data review platforms shows that the former improved data comprehension and patients’ ability to make decisions from data; and lowered the cognitive load of engaging with data. Additionally, in nuanced ways, episode-driven narratives enabled user agency in making decisions for self-care. Based on multiple studies to examine practices, and design and evaluate tools, I suggest that to support people in effectively leveraging multidimensional data for managing chronic conditions, tools must do the following - support effective problem-solving with data by creating a shared understanding of data between stakeholders, enable different types of assessments from data and help connect those assessments, and guide analytic focus using a scaffold (e.g., an episode-driven workflow) to organize and present evidence. One promising approach to implement these suggestions in the design of a tool is an episode-driven data narrative, an embodiment of the episode-driven sensemaking framework using narrative visualization techniques. By supporting the generation and presentation of episode-driven narratives from multidimensional data, tools can augment patients’ abilities to effectively inform self-care of chronic conditions with their data.
... Most of the previous research regarding PGHD had explored the perception of patients and clinicians using semi-structured interview approach (6). Seto et al. (14) explored the experience of heart failure patients using mobile phone-based telemonitoring system to record their daily weight, blood pressure, weekly echocardiogram and symptoms questionnaire. ...
... Mobile Health (mHealth) is the application of mobile technologies, e.g. smartphones, tablets, wearable devices and sensor technologies, for the purpose of healthcare (6). A US study in 2016 showed that 46% of consumers have used three or more digital health tools and 25% owned wearable health-trackers (2). ...
... A US study in 2016 showed that 46% of consumers have used three or more digital health tools and 25% owned wearable health-trackers (2). Lai et al. (6) found that 78% are willing to wear health-tracking technology, while 10% use it to manage chronic conditions. Comparatively, most mHealth studies in low-middle income countries including Malaysia primarily focus on using text messaging for behavioural change and only a few examined how mHealth could strengthen the health system (7). ...
Article
Background: Patient-generated health data (PGHD) is health-related data captured and recorded by patients which informs healthcare practitioners (HCP) about the patients' health status between clinic visits. PGHD could be attributed as part of digital health and technological advancement. Methods: This is an exploratory qualitative study to explore the current PGHD usage and the views and experience of HCP towards PGHD. Semi-structured in-depth online interviews based on the modified Unified Theory of Acceptance and Use of Technology (UTAUT) were conducted with seven Hospital Kuala Lumpur medical- and surgical-based HCP specialists between October 2019 and February 2020. Purposive sampling method was applied to ensure speciality diversity among study respondents. Thematic analysis was performed on the interview transcripts. Results: Four main themes were identified namely the PGHD usage among the study respondents, the benefits of PGHD, the challenges of PGHD usage and the effort needed to use the PGHD. The main finding of this study includes the exploration of the benefits of PGHD usage such as efficient data management in aiding clinical consultation. Nonetheless, the clinical decision making based on PGHD is limited due to poor adoption of PGHD and unavailability of electronic data. This could be due to the lack of awareness, ICT infrastructure, funding, poor health literacy and language and cultural problems. Conclusion: PGHD has huge potential to be adopted in the clinical setting and subsequently benefiting the patients. However, parallel supportive environment is essential in supporting the usage of PGHD in the clinical setting.
... However, our findings directly contradicted those of previous studies [25]. Previously, PGHD was mainly viewed as a facilitator to enhance the patient-provider relationship with evidence for engaging patients in their care and increasing timely communication [26]. The difference in findings may be because the previous study focused on the effectiveness of PGHD from the perspective of system implementation and evaluation with limited insight into patient perception. ...
... This can help them extract the most relevant and helpful information easily. However, only a handful of mHealth and wearable device systems have integrated decision support that can guide users to effectively turn information into meaningful actions [26,28]. Future studies should investigate the types of decision support that can be effectively delivered via mHealth. ...
Article
Background Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults. Objective This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults. Methods A mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics. Results Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care. Conclusions This embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults.
... The use and sharing of PGHD by clinicians or researchers is expected to not only enhance the remote monitoring of specific behaviors that affect patient health, but also support patients' decision-making on preventive care management, resulting in reduced medical expenses [4]. However, sufficient evidence on the use and sharing of PGHD in clinical settings is lacking [5], and the impact of PGHD recording on health behavior changes remains unclear [6][7][8]. Previous studies have incorporated PGHD techniques into multicomponent interventions. A scoping review [9] reported that multicomponent interventions used the following techniques to motivate users for PGHD recording: the provision of rewards and incentives, goal setting, reminders, feedback, social support, and entertainment elements such as gamification. ...
Article
Background The use and sharing of patient-generated health data (PGHD) by clinicians or researchers is expected to enhance the remote monitoring of specific behaviors that affect patient health. In addition, PGHD use could support patients’ decision-making on preventive care management, resulting in reduced medical expenses. However, sufficient evidence on the use and sharing of PGHD is lacking, and the impact of PGHD recording on patients’ health behavior changes remains unclear. Objective This study aimed to assess patients’ engagement with PGHD recording and to examine the impact of PGHD recording on their health behavior changes. Methods This supplementary analysis used the data of 47 postpartum women who had been assigned to the intervention group of our previous study for managing urinary incontinence. To assess the patients’ engagement with PGHD recording during the intervention period (8 weeks), the fluctuation in the number of patients who record their PGHD (ie, PGHD recorders) was evaluated by an approximate curve. In addition, to assess adherence to the pelvic floor muscle training (PFMT), the weekly mean number of pelvic floor muscle contractions performed per day among 17 PGHD recorders was examined by latent class growth modeling (LCGM). Results The fluctuation in the number of PGHD recorders was evaluated using the sigmoid curve formula (R2=0.91). During the first week of the intervention, the percentage of PGHD recorders was around 64% (30/47) and then decreased rapidly from the second to the third week. After the fourth week, the percentage of PGHD recorders was 36% (17/47), which remained constant until the end of the intervention. When analyzing the data of these 17 PGHD recorders, PFMT adherence was categorized into 3 classes by LCGM: high (7/17, 41%), moderate (3/17, 18%), and low (7/17, 41%). Conclusions The number of PGHD recorders declined over time in a sigmoid curve. A small number of users recorded PGHD continuously; therefore, patients’ engagement with PGHD recording was low. In addition, more than half of the PGHD recorders (moderate- and low-level classes combined: 10/17, 59%) had poor PFMT adherence. These results suggest that PGHD recording does not always promote health behavior changes.
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As an indispensable part of contemporary medical services, Internet-based medical platforms can provide patients with a full range of multi-disciplinary and multi-modal treatment services. Along with the emergence of many healthcare influencers and the increasing connection between online and offline consultations, the operation of individual physicians and their teams on Internet-based medical platforms has started to attract a lot of attention. The purpose of this paper is to, based on an Internet platform, study how the information on physicians’ homepages influences patients’ consultation behavior, so as to provide suggestions for the construction of physicians’ personal websites. We distinguish variables into strong- and weak-ties types, dependent on whether deep social interactions between physicians and patients have happened. If there exist further social interactions, we define the variable as the “strong ties” type, otherwise, “weak ties”. The patients’ consultation behavior will be expressed as the volume of online consultation, i.e., the number of patients. We obtained the strong and weak ties information of each physician based on EWM (entropy weight method), so as to establish a regression model with explained variable, i.e., the number of patients, and three explanatory variables, i.e., the strong and weak ties information, and their interaction term. The estimation results verified our hypotheses and proved to be robust. It showed that both strong and weak ties information can positively influence patients’ consultation behavior, and the influence of weak ties information is greater. Regarding the positive influence of strong and weak ties, we found a trade off effect between them. Based on the results, we finalize with some suggestions on how to improve a physician’s online medical consultation volume.
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We describe implementation and usage of a coronavirus disease 2019 (COVID-19) digital information hub delivered through the widely adopted The Weather Company (TWC) application and explore COVID-19 knowledge, behaviors, and information needs of users. TWC deployed the tool, which displayed local case counts and trends, in March 2020. Unique users, visits, and interactions with tool features were measured. In August 2020, a cross-sectional survey assessed respondent characteristics, COVID-19 knowledge, behaviors, and preferences. TWC COVID-19 hub averaged 1.97 million unique users with over 2.6 million visits daily and an average interaction time of 1.63 min. Respondents reported being knowledgeable about COVID-19 (92.3%) and knowing relevant safety precautions (90.9%). However, an average of 35.3% of respondents reported not increasing preventive practices across behaviors surveyed due to information about COVID-19. In conclusion, we find a free weather application delivered COVID-19 data to millions of Americans. Despite confidence in knowledge and best practices for prevention, over one-third of survey respondents did not increase practice of preventive behaviors due to information about COVID-19.
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Background Consuming educational content, adhering to treatment plans and managing symptoms and side-effects can be overwhelming to new oncology patients. Objective The purpose of this study is to engage patients in conceptualization of enhanced clinic processes and digital health tools to support awareness and use of integrative oncology services. Patient Involvement We engaged patients in participatory design to understand lived experiences surrounding use of integrative oncology services during and after conventional cancer treatment. Methods Ten participatory design sessions were held with individual participants. Sessions began with patient story telling regarding diagnosis and paths to awareness and use of integrative oncology services. We then reviewed prototype mobile app screens to solicit feedback regarding digital health functionality to support patient navigation of symptom-alleviating options. Results Oncology patients are active participants in the management of symptoms and side effects. Patients who utilize yoga, acupuncture, and massage report a need for earlier patient education about these services. Patients express interest in digital health tools to match symptoms to options for relief, provide access to searchable information, and facilitate streamlined access to in-person and remote services. Discussion Patients co-produce wellbeing by seeking solutions to daily challenges and consuming educational content. Clinics can collaborate with patients to identify high priority needs and challenges. Practical Value Active collaboration with patients is needed to identify unmet needs and guide development of clinic processes and digital health tools to enhance awareness and use of IO services in conventional cancer care. Funding The principal investigator was supported by the U.S. Agency for Healthcare Research and Quality (AHRQ K12HS026370). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. The sponsor had no role in the study design, data collection, analysis, report writing, or decision to submit for publication.
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Background Individuals increasingly want to access, contribute to, and share their personal health information to improve outcomes, such as through shared decision-making (SDM) with their care teams. Health systems' growing capacity to use person-generated health data (PGHD) expands the opportunities for SDM. However, SDM not only lacks organizational and information infrastructure support but also is actively undermined, despite public interest in it. Objectives This work sought to identify challenges to individual–clinician SDM and policy changes needed to mitigate barriers to SDM. Methods Two multi-stakeholder group of consumers, patients, caregivers; health services researchers; and experts in health policy, informatics, social media, and user experience used a consensus process based on Bardach's policy analysis framework to identify barriers to SDM and develop recommendations to reduce these barriers. Results Technical, legal, organizational, cultural, and logistical obstacles make data sharing difficult, thereby undermining use of PGHD and realization of SDM. Stronger privacy, security, and ethical protections, including informed consent; promoting better consumer access to their data; and easier donation of personal data for research are the most crucial policy changes needed to facilitate an environment that supports SDM. Conclusion Data protection policy lags far behind the technical capacity for third parties to share and reuse electronic information without appropriate permissions, while individuals' right to access their own health information is often restricted unnecessarily, poorly understood, and poorly communicated. Sharing of personal information in a private, secure environment in which data are shared only with individuals' knowledge and consent can be achieved through policy changes.
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Objectives: An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. Methods: We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies-Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. Results: Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. Conclusions: We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.
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Background Patient-generated health data (PGHD) are health-related data created or recorded by patients to inform their self-care and understanding about their own health. PGHD is different from other patient-reported outcome data because the collection of data is patient-driven, not practice- or research-driven. Technical applications for assisting patients to collect PGHD supports self-management activities such as healthy eating and exercise and can be important for preventing and managing disease. Technological innovations (eg, activity trackers) are making it more common for people to collect PGHD, but little is known about how PGHD might be used in outpatient clinics. Objective The objective of our study was to examine the experiences of health care professionals who use PGHD in outpatient clinics. Methods We conducted an evaluation of Project HealthDesign Round 2 to synthesize findings from 5 studies funded to test tools designed to help patients collect PGHD and share these data with members of their health care team. We conducted semistructured interviews with 13 Project HealthDesign study team members and 12 health care professionals that participated in these studies. We used an immersion-crystallization approach to analyze data. Our findings provide important information related to health care professionals’ attitudes toward and experiences with using PGHD in a clinical setting. Results Health care professionals identified 3 main benefits of PGHD accessibility in clinical settings: (1) deeper insight into a patient’s condition; (2) more accurate patient information, particularly when of clinical relevance; and (3) insight into a patient’s health between clinic visits, enabling revision of care plans for improved health goal achievement, while avoiding unnecessary clinic visits. Study participants also identified 3 areas of consideration when implementing collection and use of PGHD data in clinics: (1) developing practice workflows and protocols related to PGHD collection and use; (2) data storage, accessibility at the point of care, and privacy concerns; and (3) ease of using PGHD data. Conclusions PGHD provides value to both patients and health care professionals. However, more research is needed to understand the benefit of using PGHD in clinical care and to identify the strategies and clinic workflow needs for optimizing these tools.
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Importance: Effective long-term treatments are needed to address the obesity epidemic. Numerous wearable technologies specific to physical activity and diet are available, but it is unclear if these are effective at improving weight loss. Objective: To test the hypothesis that, compared with a standard behavioral weight loss intervention (standard intervention), a technology-enhanced weight loss intervention (enhanced intervention) would result in greater weight loss. Design, setting, participants: Randomized clinical trial conducted at the University of Pittsburgh and enrolling 471 adult participants between October 2010 and October 2012, with data collection completed by December 2014. Interventions: Participants were placed on a low-calorie diet, prescribed increases in physical activity, and had group counseling sessions. At 6 months, the interventions added telephone counseling sessions, text message prompts, and access to study materials on a website. At 6 months, participants randomized to the standard intervention group initiated self-monitoring of diet and physical activity using a website, and those randomized to the enhanced intervention group were provided with a wearable device and accompanying web interface to monitor diet and physical activity. Main outcomes and measures: The primary outcome of weight was measured over 24 months at 6-month intervals, and the primary hypothesis tested the change in weight between 2 groups at 24 months. Secondary outcomes included body composition, fitness, physical activity, and dietary intake. Results: Among the 471 participants randomized (body mass index [BMI], 25 to <40; age range, 18-35 years; 28.9% nonwhite, 77.2% women), 470 (233 in the standard intervention group, 237 in the enhanced intervention group) initiated the interventions as randomized, and 74.5% completed the study. For the enhanced intervention group, mean baseline weight was 96.3 kg (95% CI, 94.2-98.5) and 24-month weight 89.3 kg (95% CI, 87.1-91.5). For the standard intervention group, mean baseline weight was 95.2 kg (95% CI, 93.0-97.3) and 24-month weight was 92.8 kg (95% CI, 90.6-95.0). Weight change at 24 months differed significantly by intervention group (estimated mean weight loss, 3.5 kg [95% CI, 2.6-4.5} in the enhanced intervention group and 5.9 kg [95% CI, 5.0-6.8] in the standard intervention group; difference, 2.4 kg [95% CI, 1.0-3.7]; P?=?.002). Both groups had significant improvements in body composition, fitness, physical activity, and diet, with no significant difference between groups. Conclusions and relevance: Among young adults with a BMI between 25 and less than 40, the addition of a wearable technology device to a standard behavioral intervention resulted in less weight loss over 24 months. Devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches. Trial registration: clinicaltrials.gov Identifier: NCT01131871.
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It has been widely recognized that discovering potential contributing factors to personal sleep is as important as understanding sleep pattern per se. However, in large quantified-self datasets, contributing factors may only show correlations to sleep when their values are within certain ranges. Existing correlation analysis using Pearson Correlation Coefficient cannot identify such hidden dependencies. We propose a new method based on association rules mining. Our method not only can discover hidden correlations that existing methods cannot, but also provides users with actionable knowledge to guide sleep improvement through lifestyle change.
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Smart technology, like wearable sensors or biochips, presents a vast capacity for monitoring vital signs, assess patients' behaviour and context, and simultaneously provide feedback with a significant effect in diagnosis, treatment, and control of diseases. Many chronic disease, in particular Inflammatory Bowel Disease (IBD), patients need to monitor their behaviour and register their disease history (e.g. symptoms, medication intake), as well as collect their physiological data, in order to control the disease, find correlations between their behaviour and the disease progress and help doctors to adjust treatment and promote patients behaviour changes. We have been working in the use of m-health applications by chronic disease patients to facilitate self-management of their diseases and increase their autonomy. We are now studying the use of wearable devices and biochips to automatically collect patients' data and empower them in managing their own health conditions.
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Objective: Recognizing a need for our EHR to be highly interoperable, our team at Duke Health enabled our Epic-based electronic health record to be compatible with the Boston Children's project called Substitutable Medical Apps and Reusable Technologies (SMART), which employed Health Level Seven International's (HL7) Fast Healthcare Interoperability Resources (FHIR), commonly known as SMART on FHIR. Methods: We created a custom SMART on FHIR-compatible server infrastructure written in Node.js that served two primary functions. First, it handled API management activities such rate-limiting, authorization, auditing, logging, and analytics. Second, it retrieved the EHR data and made it available in a FHIR-compatible format. Finally, we made required changes to the EHR user interface to allow us to integrate several compatible apps into the provider- and patient-facing EHR workflows. Results: After integrating SMART on FHIR into our Epic-based EHR, we demonstrated several types of apps running on the infrastructure. This included both provider- and patient-facing apps as well as apps that are closed source, open source and internally-developed. We integrated the apps into the testing environment of our desktop EHR as well as our patient portal. We also demonstrated the integration of a native iOS app. Conclusion: In this paper, we demonstrate the successful implementation of the SMART and FHIR technologies on our Epic-based EHR and subsequent integration of several compatible provider- and patient-facing apps.
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An adaptive intelligent video surveillance and motion detection system employing a network of IP cameras for patient monitoring and alarm generation is presented which ensures patients privacy through the use and processing of motion information instead of the real image of a monitored person. Minimization or even complete avoidance of false alarms is achieved, since the system proceeds to the announcement of alarms only when a series of user-defined conditions is met. The proposed system is highly configurable to adapt both to different areas of video surveillance and to several categories of monitored persons as well as to generate those alarms that concern the health carer. The system demonstrates a noteworthy performance enabling efficient system deployments involving large camera networks, as well as the packaging of a full-featured application in tiny cost and size computing devices.
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Consumer health informatics (CHI) is propelling important changes for medical providers and the lives of patients through information and communications technology. Independently, medical consumers seek, collect, and use health information for decision making. However, when constructing a CHI-based medical platform, high technology must be applied in a fully understandable and usable format for both health care providers and consumers. This study examines the present status of CHI and its effect on medical consumers. For the development of CHI, we discuss the need for tailored health communications and capacity building with chronic patients at the medical center. First, empowerment is a key characteristic needed for medical consumer health care management. However, promoting patient self-care management of illnesses and health is necessary to create conjugation where cooperation with medical service providers is possible. Also, establishing a health care delivery system that will support cooperation is necessary. Second, tailored health communications can uniquely construct the health information of patients, which prevents unnecessary or excessive information from leading patients to confused and inappropriate decisions. Ultimately, through the present environment of health communication, the innovation of a consumer health care information system has become the tide of the times and the positive effect of improved health can be expected.
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The wave of digital health is continuously growing and promises to transform the experience of patients, redefining their role as empowered actors of the healthcare processes rather than passive receivers of medical help. Mobile technologies are a fundamental component of this transformation since they have provided a platform for the development of novel solutions, allowing a gradual shift of healthcare closer to the patients' daily living and away from the traditional clinical environment. Chronic diseases are in the center of these developments as they require the continuous and active involvement of not only healthcare professionals but also patients both of who can be empowered through the use of specialized mobile applications and the analysis of data from modern miniaturized and wearable sensing devices. Furthermore, the communication channels introduced by mobile technologies can significantly increase the efficiency of the healthcare system and facilitate the communication between patients and healthcare professionals. The current workshop invites researchers from the fields of Information Technologies and Medical Sciences as well as healthcare professionals and technology developers to demonstrate and discuss innovative approaches related to the utilization of mobile Human Computer Interaction approaches in the modern healthcare environment.
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Persons that suffered from a cardiac disease are often recommended to integrate a sufficient level of physical exercise in their daily life. Initially, cardiac rehabilitation takes place in a closely monitored setting in a hospital or a rehabilitation center. Sustaining the effort once the patient has left the ambulatory, supervised environment is a challenge, and drop-out rates are high. Emerging approaches such as telemonitoring and telerehabilitation have been proven to show the potential to support the cardiac patient in adhering to the advised physical exercise. However, most telerehabilitation solutions only support a limited range of physical exercise, such as step-counting during walking. We propose BoB (Back on Bike), a mobile application that guides cardiac patients while cycling. Design choices are explained according to three pillars: ease of use, reduce fear, and direct and indirect motivation. In this paper, we report the results from a field study with cardiac patients.