No Plagarism, APA format

Cajita, M.K., Hodgson, N.A., Budhathoki, C., & Han, H. (2017). Intention to use mHealth in older adults with heart failure. Journal of Cardiovascular Nursing, 00(00), 00-00. Doi: 10.1097/JCN.0000000000000401.This article speaks to the protection of human participants. Discuss your thoughts of what is presented within the article that supports the idea that the participants were ethically managed.
intention_to_use_mhealth.pdf

_grading_rubric_module_a_forum_initial_postings.pdf

Don't use plagiarized sources. Get Your Custom Essay on
No Plagarism, APA format
Just from $13/Page
Order Essay

Unformatted Attachment Preview

Journal of Cardiovascular Nursing
Vol. 32, No. 6, pp E1YE7 x Copyright B 2017 Wolters Kluwer Health, Inc. All rights reserved.
Intention to Use mHealth in Older Adults
With Heart Failure
Maan Isabella Cajita, BSN, RN-BC; Nancy A. Hodgson, PhD, RN, FAAN;
Chakra Budhathoki, PhD; Hae-Ra Han, PhD, RN, FAAN
Background: mHealth, or the use of mobile technology in healthcare, is becoming increasingly common. In
heart failure (HF), mHealth has been associated with improved self-management and quality of life. However, it
is known that older adults continue to lag behind their younger counterparts when it comes to mobile technology
adoption. Objective: The primary aim of this study was to examine factors that influence intention to use
mHealth among older adults with HF. Methods: An adapted Technology Acceptance Model was used to
guide this cross-sectional, correlational study. Convenience sampling was used to identify participants from a
large university hospital and online. Results: A total of 129 older adults with HF participated in the study. Social
influence (” = 0.17, P = .010), perceived ease of use (” = 0.16, P G .001), and perceived usefulness (” = 0.33,
P G .001) were significantly associated with intention to use mHealth even after controlling for potential confounders
(age, gender, race, education, income, and smartphone use). Perceived financial cost and eHealth literacy were
not significantly associated with intention to use mHealth. Conclusions: Researchers should consider using the
participatory approach in developing their interventions to ensure that their mHealth-based interventions will not only
address the patient?s HF self-management needs but also be easy enough to use even for those who are less
technology savvy.
KEY WORDS:
eHealth literacy, mHealth, mobile technology, older adults, self-management
H
eart failure (HF) is especially prevalent in the
older population.1 It is estimated that 11.8%
of older adults have HF.2 Older adults also account
for most HF-related hospitalizations,3 which in turn
account for 68% of the total cost of treating HF.
Effective HF self-management is the key to reducing
Maan Isabella Cajita, BSN, RN-BC
PhD Candidate, School of Nursing, Johns Hopkins University,
Baltimore, Maryland.
Nancy A. Hodgson, PhD, RN, FAAN
Associate Professor, School of Nursing, University of Pennsylvania,
Philadelphia.
Chakra Budhathoki, PhD
Assistant Professor, School of Nursing, Johns Hopkins University,
Baltimore, Maryland.
Hae-Ra Han, PhD, RN, FAAN
Professor and Co-Director, Center for Cardiovascular and
Chronic Care, School of Nursing, Johns Hopkins University,
Baltimore, Maryland.
This work is supported by the National Institute of Nursing Research
(5F31NR015943), Council for the Advancement of Nursing
ScienceYSouthern Nursing Research Society Dissertation Grant Award,
and Heart Failure Society of America Nursing Research Mini Grant.
The authors have no conflicts of interest to disclose.
Supplemental digital content is available for this article. Direct URL
citations appear in the printed text and are provided in the HTML and PDF
versions of this article on the journal?s Web site (www.jcnjournal.com).
Correspondence
Maan Isabella Cajita, BSN, RN-BC, School of Nursing, Johns Hopkins
University, 525 N Wolfe St, Baltimore, MD 21205 (mcajita1@jhu.edu).
DOI: 10.1097/JCN.0000000000000401
the enormous healthcare costs associated with treating
HF. However, HF self-management can be complex,
especially for older adults who usually have comorbid
conditions.1 Hence, it is not surprising that nonadherence to recommended treatment plans is common among this population.4
mHealth, or the use of mobile technology in healthcare, has the potential to revolutionize HF selfmanagement. The ubiquity of mobile technology, such
as mobile phones and tablet computers, has made it
an ideal medium to deliver health interventions. In HF
studies, mHealth-based interventions have used mobile
devices as part of a larger monitoring system, usually
in conjunction with a blood pressure measuring device
and a weighing scale.5Y7 Mobile devices have also been
used to deliver HF-related educational messages.8,9
mHealth-based interventions have been associated with
improved HF self-management,8,9 improved quality of
life,8,10 and lower mortality.11 However, despite the
promising impact of mHealth on HF outcomes, very
little is known regarding individual characteristics and
perceptions that influence its adoption, especially
among older adults, who continue to lag behind their
younger counterparts when it comes to technology
adoption.12 Therefore, the primary aim of this study
was to examine factors that influence the intention
to adopt mHealth among older people with HF. The
secondary aims of this study were to explore current
E1
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
E2 Journal of Cardiovascular Nursing x November/December 2017
smartphone use in this population and to assess their
intention to use mHealth if recommended by their primary healthcare provider.
Perceived financial cost, defined as the extent to which
the person believes that using mHealth will cost money,
has been found to be significantly negatively correlated
to behavioral intention.20
Theoretical Framework
An adapted Technology Acceptance Model (TAM) was
used to guide this study. The Technology Acceptance
Model is derived from the Theory of Reasoned Action
and was first proposed by Dr Fred Davis in 1985.13 The
model posits that the strongest predictor of technology
use is behavioral intention, which is in turn influenced
by the individual?s perceived ease of use and perceived
usefulness.13 Even in its most parsimonious version,
the Technology Acceptance Model has been shown to
account for 30% to 40% of technology acceptance.14
In healthcare, Technology Acceptance Model has been
shown to explain from 30% to 70% of the variance in
the acceptance of health technologies.14
Perceived usefulness is conceptually defined as the
degree to which the person with HF believes that using
mHealth will enhance the management of his/her HF.
In previous studies, perceived usefulness has been
consistently shown to be significantly associated with
intention to use technology and is thought to be the most
important predictor of technology acceptance.14 Perceived ease of use is conceptually defined as ??the degree to which a person [with HF] believes that using a
[mHealth] would be free of effort.??15 Although not as
consistent as perceived usefulness, perceived ease of use
has also been shown to be associated with intention to
use behavioral intention in several studies.14 Behavioral
intention is conceptually defined as the intention to use
mHealth in the context of HF self-management. Being
more proximal than the actual use of technology, it is
often the outcome of choice for most of the TAMguided studies. In longitudinal studies that actually measured actual use, behavioral intention has been shown
to significantly predict the actual use of technology.14
To improve the predictive ability of the Technology
Acceptance Model, additional constructs were added
to the model, namely, social influence, eHealth literacy,
and perceived financial cost. Social influence is defined
as a person?s perception that most people who are important to him/her think that he/she should perform
the behavior in question,16 which in this case is technology adoption. Previous studies have shown that older
adults are susceptible to the effects of social influence
when it comes to technology acceptance.16 eHealth literacy is defined as ??the ability to seek, find, understand, and appraise health information from electronic
sources and apply the knowledge gained to addressing
or solving a health problem.??17 Higher eHealth literacy
has been found to be associated with higher perceived
self-efficacy in using health-related mobile apps18 and
with the adoption of a physician-rating mobile app.19
Methods
Study Design and Sample
A cross-sectional, correlational design was used for this
study. A convenience sample was recruited via 2 means:
an ??in-person?? group from a large urban teaching
hospital and an ??online?? group through Qualtrics. We
opted to include an online sample to obtain a more
geographically diverse sample. Potential ??in-person??
participants were identified through an electronic list
of patients admitted with a history of HF, which was
obtained daily from the hospital?s HF care coordinator.
The patients on the list were then screened for eligibility
through an electronic chart review. Online participants
were identified with the help of the Qualtrics project
coordinator, who was given the inclusion and exclusion
criteria for the study. Online sampling was limited to
persons living in the United States.
Qualtrics is partnered with over 20 online panel providers.
Panelists are often recruited to participate in research
through online advertisements, or for groups that are
hard-to-reach on the Internet, Qualtrics utilizes niche
panels brought about through specialized recruitment
campaigns (eg, newspaper ads, inserts in product packaging, at trade events, or through direct mail). Hundreds of
profiling attributes are collected to guarantee detailed
knowledge of every potential respondent. Qualtrics panel
partners randomly select respondents for surveys where
respondents are highly likely to qualify. Each sample from
the panel base is proportioned to the general population
and then randomized before the survey is deployed. All
sample partners redirect members by matching qualifying
demographic information from their profiles to a specific
survey. To ensure the quality of the data, Qualtrics will
replace ??quality check fails,?? or respondents who straightline through surveys, finish in less than 1/3 of the average
survey completion length, or wrongly respond to attention checks (eg, ??This is an attention filter. Please select
?Sometimes? for this statement??). In order to prevent
fraudulent respondents, panel providers utilize confirmation procedures such as TrueSample, Verity, SmartSample.
USPS verification, and digital fingerprinting to verify
respondent address, demographic information, and email
address. (Lincoln Bradshaw, Qualtrics Project Coordinator,
e-mail communication, January 22, 2016)
Participants were recruited if they had a history of
HF and were 65 years or older. Current use of mHealth
or smartphone technology was not an inclusion criterion because we wanted a range of experiences and
perceptions. Potential ??in-person?? participants were excluded if they were unable to read/understand English,
had a history of dementia or had cognitive impairment
(Mini-Cog21,22 score e 2), resided in a nursing home
(before hospital admission), were hospitalized for acute
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
mHealth Use in Older Adults With HF E3
myocardial infarction or advanced stage of HF
(New York Heart Association functional class IVVpatient
exhibits HF symptoms/shortness of breath even at rest
per assigned nurse?s report), and/or need emergent
cardiac surgery. Intact cognitive functioning was assumed for the ??online?? group. Of the 168 who were
eligible, 39 declined to participate in the study (23
were not interested, 12 did not feel well, and 4 had
other reasons). There was no significant difference
between those who participated in the study and those
who declined to participate in terms of gender, race,
educational attainment, income, and marital status.
However, those who declined to participate were significantly older than the study participants (77 vs
71.3 years, P = 0.001). The Figure shows the participant
recruitment flowchart.
permission was obtained, they were approached by a
trained research staff who briefly described the study.
Written informed consent was then obtained from inperson participants, who screened negative for cognitive impairment, before the self- or staff-administered
paper-based survey. The in-person group required approximately 45 minutes to complete the survey. The
online participants were presented with an implied consent form at the beginning of the online survey. The
online group required approximately 30 minutes to
complete the survey. The in-person data collection was
conducted between February and June 2016. The online surveys were collected between March and August
2016. Each participant was given $10 as an incentive
for completing the survey.
Survey
Procedures
The university?s institutional review board approved
this study. Before approaching the potential in-person
participant, we obtained their permission to recruit
them for the study through their assigned nurse. Once
An adapted TAM scale was used to measure the participants? perceived social influence, ease of use, usefulness, and financial cost and their intention to use
mHealth.20,23 The adapted TAM scale had a total of
12 items (5 subscales) and used a 7-point Likert scale
FIGURE. Participant recruitment flowchart. Abbreviation: HF, heart failure.
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
E4 Journal of Cardiovascular Nursing x November/December 2017
(see Appendix, Supplemental Digital Content 1, http://
links.lww.com/JCN/A33). Higher scores indicated
higher perceived social influence, ease of use, usefulness, and financial cost and higher intention to use
mHealth. The internal consistency (Cronbach !) of the
adapted TAM subscales for this sample is as follows:
social influence (! = .91), perceived ease of use (! = .78),
perceived usefulness (! = .92), and behavioral intention (! = .82). To give the participants a general sense
of the types of mobile technology that could be used
in HF self-management, pictures showing examples of
mHealth (ie, physical activity tracker wristband, heart
rate tracker wristband heart rate monitoring app,
electronic blood pressure cuff that connects to an app)
were included in the survey. eHealth literacy was measured using eHEALS, which had 8 items and used a
5-point Likert scale.17 Higher scores indicated higher
eHealth literacy. The internal consistency (Cronbach !)
of eHEALS for this sample was .93. In addition, the
participants were asked whose advice mattered the most
to them when it comes to their health and whether they
would use mHealth if their doctor or primary healthcare provider recommended it. Finally, demographic
information (age, gender, race, educational attainment,
income, and marital status) and information on the
participant?s smartphone use were also collected using
a questionnaire developed for the purpose of the study.
Data Analysis
Descriptive statistics were calculated for all study
variables. Simple linear regression was used to test the
TABLE 1
relationship between the main study variables (eHealth
literacy, social influence, perceived financial cost, perceived ease of use, and perceived usefulness) and intention
to use mHealth. Hierarchical regression analysis was
used to identify correlates of intention to use mHealth
in the study sample and to determine the specific contributions of the main study variables in explaining
intention to use mHealth above and beyond those of
the covariates (age, gender, race, educational attainment, income, and current smartphone use). STATA 14
was used for all analyses. The level of significance was
set at .05.
Results
Sample Characteristics
A total of 129 older adults with HF participated in the
study. The mean (SD) age of the participants was 71.3
(4.6) years, and most were men (73.6%). More than half
(56.6%) identified themselves as white, followed by
22.5% who identified themselves as black and 20.9%
who identified themselves as of another race. Most of
the participants had at least some college education
(79.1%), had an annual income of at least $50000
(55.2%), and were married (64.3%) (Table 1)
Use of Smartphone and Intention to Use
mHealth
Seventy-four of the participants (57.4%) used a smartphone, of which 55 (74.3%) reported using their
smartphones daily. Among the nonsmartphone users,
Participant Characteristics
Variable
Age, mean (SD) and (range), y
Gendera
Male
Female
Race/ethnicityb
White
Black
Other
Educationb
High school graduate or less
College graduate or less
Professional/graduate school
Incomeb
G$15 000
$15 000Y$50 000
$50 001Y$100 000
9$100 000
Marital statusa
Married
Not married
Smartphone usersa
eHealth literacy,b mean (SD) and (range)
Overall (N = 129), n (%)
In Person (N = 29), n (%)
Online (N = 100), n (%)
71.3 (4.6) and 65Y86
71.5 (5.1) and 66Y86
71.2 (4.4) and 65Y83
95 (73.6)
34 (26.4)
17 (58.6)
12 (41.4)
78 (78.0)
22 (22.0)
73 (56.6)
29 (22.5)
27 (20.9)
11 (37.9)
16 (55.2)
2 (6.9)
62 (62.0)
13 (13.0)
25 (25.0)
27 (20.9)
69 (53.5)
33 (25.6)
15 (51.7)
9 (31.0)
5 (17.3)
12 (12.0)
60 (60.0)
28 (28.0)
10 (8.0)
46 (36.8)
46 (36.8)
23 (18.4)
8 (32.0)
5 (20.0)
7 (28.0)
5 (20.0)
83 (64.3)
46 (35.7)
74 (57.4)
27.3 (6.4) and 8Y40
13 (44.8)
16 (55.2)
12 (41.4)
22.1 (7.6) and 8Y37
Difference between the in-person and online groups were significant at aP G .05 or bP G .001.
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
2
41
39
18
(2.0)
(41.0)
(39.0)
(18.0)
70 (70.0)
20 (30.0)
62 (62.0)
28.7 (5.1) and 13Y40
mHealth Use in Older Adults With HF E5
36 (27.9%) reported that they only need their phones
to make calls, 20 (15.5%) indicated that smartphones were too complicated/difficult for them to
use, and 13 (10.1%) reported that smartphones were
too expensive.
Most of the participants (n = 111, 86.1%) indicated
that, when it comes to their health, their doctor/nurse
practitioner?s advice mattered the most. Moreover,
when asked whether they would use mHealth if their
doctor (or primary healthcare provider) recommended
it, 35 (27.1%) strongly agreed, 48 (37.2%) agreed,
27 (20.9%) somewhat agreed, 15 (11.6%) neither
agreed nor disagreed, and the remaining participants
either somewhat disagreed (1.6%) or disagreed (1.6%).
Even among those who did not have a ??high intention??
to use mHealth (behavioral intention score G 12),
55 (54.5%) agreed or strongly agreed that they would
use mHealth if their doctor (or primary care provider)
recommended it.
Correlates of Intention to Use mHealth
Higher perceived ease of use (” = 0.16, P G .001) and
higher perceived usefulness (” = 0.33, P G .001) were
both associated with higher intention to use mHealth,
even after controlling for the covariates. Perceived
TABLE 2
ease of use and perceived usefulness explained 9.5%
and 13%, respectively, of the variability in intention
to use mHealth Higher perceived financial cost was
associated with lower intention to use mHealth at the
bivariate level, but the association was no longer significant after adjusting for the covariates (” = j0.04,
P = .345). We also observed that social influence was
associated with intention to use mHealth (” = 0.17,
P = .010), even after controlling for the covariates;
however, eHealth literacy was not (” = j0.01, P = .799)
(Table 2).
Discussion
Consistent with findings reported in the literature,14
we found that perceived usefulness was significantly
associated with intention to adopt mHealth. In a recent systematic review, Chen and Chan15 reported that
older adults will adopt new technology if it addressed
an existing need or at least improved their daily living.
Rather than focus on the technology?s high-tech features, older adults tend to value technology?s usefulness
more and how it supports their activities and make tasks
convenient.24 Perceived ease of use was also found to
be associated with intention to adopt mHealth. This is
also consistent with previous research.14 The functional
Correlates of Intention to Use mHealth
Variable
Block 1: R2 = 0.223, P e .001
Age, y
Gender
Male
Female
Race
White
Black
Other
Education
Hig …
Purchase answer to see full
attachment

Order a unique copy of this paper
(550 words)

Approximate price: $22

Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency

Order your essay today and save 15% with the discount code ESSAYHELP