Comparing the Acceptance of Mobile Hypertension Apps for Disease-Management among Patients versus Clinical Use among Physicians: Cross-sectional survey (Preprint)

Abstract

Background:
High blood pressure or hypertension is a vastly prevalent chronic condition among adults that can, if not appropriately treated, contribute to several life-threatening secondary diseases and events, such as stroke. In addition to first-line medication, self-management in daily life is crucial for tertiary prevention and can be supported by mobile health apps, including medication reminders. However, the prescription of medical apps is a relatively novel approach. There is limited information regarding the determinants of acceptance of such mobile health (mHealth) apps among patients as potential users and physicians as impending prescribers in direct comparison.

Objective:
The present study aims to investigate the determinants of the acceptance of health apps (in terms of intention to use) among patients for personal use and physicians for clinical use in German-speaking countries. Moreover, we assessed patients' preferences regarding different delivery modes for self-care service (face-to-face services, apps, etc).

Methods:
Based on an extended model of the unified theory of acceptance and use of technology (UTAUT2), we performed a web-based cross-sectional survey to explore the acceptance of mHealth apps for self-management of hypertension among patients and physicians in Germany. In addition to UTAUT2 variables, we measured self-reported self-efficacy, eHealth literacy, previous experiences with health apps, perceived threat to privacy, and protection motivation as additional determinants of mHealth acceptance. Data from 163 patients and 46 physicians were analyzed using hierarchical regression and mediation analyses.

Results:
As expected, a significant influence of the unified theory of acceptance and use of technology (UTAUT) predictors on intentions to use hypertension apps was confirmed, especially for performance expectancy. Intention to use was moderate in patients (mean 3.5; SD 1.1; range 1-5) and physicians (mean 3.4, SD 0.9), and did not differ between both groups. Among patients, a higher degree of self-reported self-efficacy and protection motivation contributed to an increased explained variance in acceptance with R2=0.09, whereas eHealth literacy was identified as exerting a positive influence on physicians (increased R2=0.10). Furthermore, our findings indicated mediating effects of performance expectancy on the acceptance among patients but not among physicians.

Conclusions:
In summary, this study has identified performance expectancy as the most important determinant of the acceptance of mHealth apps for self-management of hypertension among patients and physicians. Concerning patients, we also identified mediating effects of performance expectancy on the relationships between effort expectancy and social influence and the acceptance of apps. Self-efficacy and protection motivation also contributed to an increase in the explained variance in app acceptance among patients, whereas eHealth literacy was a predictor in physicians. Our findings on additional determinants of the acceptance of health apps may help tailor educational material and self-management interventions to the needs and preferences of prospective users of hypertension apps in future research.

Full text

Original Paper
Comparing the Acceptance of Mobile Hypertension Apps for
Disease Management Among Patients Versus Clinical Use Among
Physicians:Cross-sectional Survey
Bernhard Breil1, BSc, MSc, PhD; Christel Salewski2, PhD; Jennifer Apolinário-Hagen3, PhD
1Faculty of Health Care, Hochschule Niederrhein, University of Applied Sciences, Krefeld, Germany
2Department of Health Psychology, Faculty of Psychology, University of Hagen, Hagen, Germany
3Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Faculty of Medicine, Heinrich Heine University Düsseldorf,
Düsseldorf, Germany
Corresponding Author:
Bernhard Breil, BSc, MSc, PhD
Faculty of Health Care
Hochschule Niederrhein, University of Applied Sciences
Reinarzstraße 49
Krefeld, 47805
Germany
Phone: 49 21518226710
Fax: 49 21518226660
Email: bernhard.breil@hs-niederrhein.de
Abstract
Background: High blood pressure or hypertension is a vastly prevalent chronic condition among adults that can, if not
appropriately treated, contribute to several life-threatening secondary diseases and events, such as stroke. In addition to first-line
medication, self-management in daily life is crucial for tertiary prevention and can be supported by mobile health apps, including
medication reminders. However, the prescription of medical apps is a relatively novel approach. There is limited information
regarding the determinants of acceptance of such mobile health (mHealth) apps among patients as potential users and physicians
as impending prescribers in direct comparison.
Objective: The present study aims to investigate the determinants of the acceptance of health apps (in terms of intention to use)
among patients for personal use and physicians for clinical use in German-speaking countries. Moreover, we assessed patients’
preferences regarding different delivery modes for self-care service (face-to-face services, apps, etc).
Methods: Based on an extended model of the unified theory of acceptance and use of technology (UTAUT2), we performed a
web-based cross-sectional survey to explore the acceptance of mHealth apps for self-management of hypertension among patients
and physicians in Germany. In addition to UTAUT2 variables, we measured self-reported self-efficacy, eHealth literacy, previous
experiences with health apps, perceived threat to privacy, and protection motivation as additional determinants of mHealth
acceptance. Data from 163 patients and 46 physicians were analyzed using hierarchical regression and mediation analyses.
Results: As expected, a significant influence of the unified theory of acceptance and use of technology (UTAUT) predictors
on intentions to use hypertension apps was confirmed, especially for performance expectancy. Intention to use was moderate in
patients (mean 3.5; SD 1.1; range 1-5) and physicians (mean 3.4, SD 0.9), and did not differ between both groups. Among patients,
a higher degree of self-reported self-efficacy and protection motivation contributed to an increased explained variance in acceptance
with R2=0.09, whereas eHealth literacy was identified as exerting a positive influence on physicians (increased R2=0.10).
Furthermore, our findings indicated mediating effects of performance expectancy on the acceptance among patients but not among
physicians.
Conclusions: In summary, this study has identified performance expectancy as the most important determinant of the acceptance
of mHealth apps for self-management of hypertension among patients and physicians. Concerning patients, we also identified
mediating effects of performance expectancy on the relationships between effort expectancy and social influence and the acceptance
of apps. Self-efficacy and protection motivation also contributed to an increase in the explained variance in app acceptance among
patients, whereas eHealth literacy was a predictor in physicians. Our findings on additional determinants of the acceptance of
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health apps may help tailor educational material and self-management interventions to the needs and preferences of prospective
users of hypertension apps in future research.
(JMIR Cardio 2022;6(1):e31617) doi: 10.2196/31617
KEYWORDS
patient acceptance of health care; mobile apps; blood pressure; mobile health; health applications; technology acceptance; patients;
physicians; digital health
Introduction
Background
With 20 to 30 million out of approximately 82 million citizens
affected in Germany alone, chronically increased blood pressure
or hypertension represents a highly prevalent disease in working
people, with a prevalence of 20%-25% in the age cohort of 40
to 49 years [1-3]. International studies also emphasize the role
of hypertension as a leading risk factor for cardiovascular
diseases as the most common cause of morbidity and mortality
[4]. In addition, untreated or poorly treated chronically increased
blood pressure can lead to life-threatening secondary diseases,
such as heart attack or stroke. In Germany, approximately 20%
of the people with high blood pressure are estimated to be
unaware of their condition [5], whereas another study from the
United States revealed that up to 36.2% of the concerned
individuals are not aware that they suffer from hypertension
[6].
Basically, hypertension results from the interaction of several
factors, some of which cannot be changed, such as age or genetic
disposition, whereas others can be influenced by stress, lifestyle,
or health behavior [7] (eg, physical activity). Despite the
availability of effective and relatively safe medication, only
approximately half of the treated patients with high blood
pressure are well adjusted, as indicated by epidemiological data
[7]. Measuring one’s blood pressure values at home regularly
is a further important prerequisite to control the disease because
this promotes the patient's understanding of the disease and
medication adherence [7]. Therefore, regular self-assessment
or monitoring of blood pressure and a healthy lifestyle are
recommended to patients [8]. All the described therapeutic
approaches (self-assessment of blood pressure, taking
medication regularly, and maintaining a healthy lifestyle) require
a high degree of self-management by patients. Consequently,
self-management represents important therapeutic potential for
people suffering from hypertension [9]. However,
self-management can pose high demands on patients with
chronic conditions in daily life. Possible solutions include digital
programs, such as disease management apps [10].
In general, mobile health (mHealth) apps are defined as digital
apps on smartphones or tablets that provide health-related
content and electronically record and evaluate the body data as
well as behaviors of their users [11]. The features of these apps
range from sending reminders via text messages to the
measurement of, for example, blood pressure values via
corresponding sensors. Health apps can be integrated into the
everyday life of patients with hypertension and have the
potential to positively influence the course of the disease in
terms of improved long-term disease management, including
medication reminders and monitoring [12-14]. In addition, the
legal basis for integrating mHealth apps into routine care has
been established in December 2019, making it possible to
prescribe medical apps since October 2020, as statutory health
insurance companies cover the expenses if apps are prescribed
by physicians. However, despite an interest in using health apps
among patients and physicians, their uptake requires
considerable time to reach a population level, especially due to
barriers such as the lack of knowledge about suitable options
[15]. Accordingly, the acceptance of these apps, especially
among patients without experience in using such apps whose
functionalities vary considerably, depends highly on whether
the disease-specific needs and patient preferences are met
[10,12]. In addition, many studies have assessed acceptance of
this technology only among patients as users or in terms of
outcomes in clinical trials (eg, satisfaction), and they have not
focused on early acceptance (eg, use intentions) of potential
users such as patients and providers [16,17].
Determinants of the Acceptance of Health Apps
To improve the adoption of mHealth apps among smartphone
users with hypertension, it is crucial to understand the
determinants of acceptance and use. An approach for predicting
acceptance (ie, intention to use) and usage of innovations such
as health apps is the unified theory of acceptance and use of
technology (UTAUT), including its core predictors namely
performance expectancy, effort expectancy, social influence,
and facilitating conditions [18]. Although the UTAUT model
was developed in the business context, it is being extensively
used to understand acceptance of new health care technologies
[19], for instance, by assessing the usage intentions of a specific
health technology among patients [20,21] and physicians
[17,22]. The traditional UTAUT model was extended in 2012
to include hedonistic motivation, price value, and habit,
subsequently called UTAUT2 [23], and it may be especially
suitable to evaluate the acceptance of apps. Given the contextual
sensitivity of acceptance, several extended UTAUT models and
novel assumptions on mediating effects have been proposed,
such as that of effort expectancy in relation to the other 3
UTAUT determinants (eg, performance expectancy) and
intention to use a technology [20]. However, the remaining
challenges in applying the UTAUT model include the unclarified
mediating role of performance expectancy [20] along with the
scarcity of UTAUT-related studies that clearly conceptualize
and investigate the individual characteristics of technology
acceptance [24].
In addition to the UTAUT2 determinants, self-efficacy has also
been analyzed as a factor in various studies on the acceptance
of innovations in health care, including electronic patient records
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[25], electronic mental health interventions [16,26], and
hypertension apps [21].
Patient empowerment implies that patients are being increasingly
recognized as the experts of their disease. However, making
informed decisions on health apps require a broad range of skills
and abilities, such as eHealth literacy. In the context of health
apps for self-management of chronic diseases, a positive
association with acceptance appears plausible because people
with higher levels of eHealth literacy are expected to be more
likely to find and use effective digital support for
self-management [27] and cultivate preventive health behavior
[28].
In addition to the outlined UTAUT determinants, personal
beliefs or concerns regarding data protection in digital apps also
appear to influence the intended use of health innovations, as
mentioned earlier [29,30]. In contrast to the aforementioned
factors, data protection and privacy concerns represent a barrier
that is not commonly included in UTAUT-based research.
According to Zhang et al [20], the perceived threat to privacy
negatively influences the intention to use digital apps. In
Germany, Heidel and Hagist [15] also confirmed that such
concerns about data privacy and security are very strong or even
stronger than those in many other countries.
Besides the UTAUT, factors such as the duration and therapy
of hypertension, attitudes, and evaluation of the disease also
play an important role in the context of hypertension as the
determinant for the use of health apps. Illness-related predictors
of health app adoption can be covered by the protection
motivation theory (PMT) [31]. According to the PMT, the
motivation to protect arises from the assessment of a threat and
possible coping strategies. Protection motivation with respect
to hypertension is mainly relevant for patients because this
variable reflects beliefs about one’s own health risk and not
those of others. The influence of the PMT factors on the
acceptance of eHealth solutions was confirmed, whereas the
effect on the intention to use was found to be mediated by
attitudes and moderated by age and gender [32].
However, little is known about the relative contribution of the
subjective evaluation of one's disease and technology-related
concerns regarding data protection aspects. Furthermore, most
studies focus on 1 user group (patients or physicians), thus
hampering the direct comparison of the acceptance factors
between patients and health care providers (eg, prescribing
medical apps).
In a prior study conducted by our work group [21], the perceived
threat of the disease was identified as a significant determinant
in an extended UTAUT model for hypertensive patients.
However, with a total R2=0.62 for the whole model, the
explained variance indicated the existence of unconsidered
additional determinants. Besides the UTAUT predictors of
acceptance, other variables, especially self-efficacy and
perceived health threat as well as the motivation to protect
oneself from this threat of the disease, may be worth
investigating. Furthermore, data security concerns or perceived
threats to privacy may also play a major role in the acceptance
of mHealth apps [33]. In addition, our prior work did not involve
the perspectives of physicians who represent the “other side”
of app acceptance, namely the perspective of potential
prescribers of medical apps.
Patients and physicians differ in several aspects regarding their
acceptance of hypertension apps, especially based on their
motivations or reasons to use these apps. For patients, the focus
is on managing their own disease to avoid health deterioration
[12,34]. Thus, acceptance of health apps crucially depends on
whether disease-specific needs are met [12,15]. Telemedicine
is the main category for any eHealth solution that is used by
physicians in health care.
To date, the adoption (or acceptance) of telehealth, including
mHealth apps, by physicians has been studied primarily in terms
of its benefits in supporting their work, such as reduced time
and effort [35], rather than the potential benefits and risks to
their patients with chronic conditions. Regarding specific
differences, physicians have been found to report more open
attitudes with less fear of risk compared to patients in Germany
[36].
Hence, the present study investigates the acceptance of health
apps for managing hypertension among patients and physicians.
Thus, it addresses an area of research that has not yet been
exhaustively investigated across different contexts, beyond the
clinical testing of hypertension apps [16]. To improve the
understanding of the efficient adoption of medical apps, the
perspectives of patients and physicians or providers are
important. Therefore, the present study examines similarities
and differences between the 2 groups. In particular, it examines
beliefs and expectations regarding the use of mHealth apps. To
our knowledge, this is the first study that analyzes determinants
for acceptance by both user groups in Germany.
Objectives
This study aims to complement existing research on mHealth
acceptance by applying the extended UTAUT model and
specifically focusing on other individual predictors related to
hypertension and global user-related characteristics (eg,
self-efficacy, eHealth literacy) [37], differentiated by personal
use (patients) and clinical use (physicians). This is one of the
few studies that investigates a possible disease-specific
influence, examining patient and physician acceptance
simultaneously to determine similarities and differences between
these complementary user groups of mHealth apps [38]. Another
goal is to explore the assumed underlying mechanisms (ie,
mediator effects) in the relationships of the proposed extended
UTAUT2 model [23], particularly regarding the role of
performance expectancy. Although performance expectancy
was only investigated as a predictor with a direct influence on
the intention to use in the original UTAUT model, the extended
UTAUT model for health care developed by Zhang et al [20]
also found that performance expectancy plays a mediating role.
Therefore, we explored the potential mediating effects of this
construct because they are not yet fully understood.
Based on prior research and theoretical considerations, we
propose the following research questions to analyze the
predictors of acceptance.
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1. Which factors determine the acceptance (intention to use)
of medical apps for self-management of hypertension among
patients (personal use) and physicians (clinical use)?
2. Does the acceptance (intention to use) of hypertension apps
differ between patients (personal use) and physicians
(clinical use) based on varying cognitive attitudes, beliefs,
and expectations (eg, UTAUT determinants like
performance expectancy), and affective attitudes or beliefs
(eg, concerns, perceived privacy threat, hedonic
motivation)?
3. Does performance expectancy mediate the relationship
between the other UTAUT determinants and acceptance
among patients?
Methods
Study Design and Participant Recruitment
This study was designed as a cross-sectional web-based
questionnaire study using the Unipark software (Questback
Enterprise Feedback Suite Questionnaire, version 2019).
Inclusion criteria were a minimum age of 18 years, fluency in
written German, and being either a patient with self-reported
hypertension (survey version 1, personal use) or a practicing
physician regardless of specialty (survey version 2, clinical use).
As some of the predictors were operationalized differently or
not applicable to the 2 target groups (eg, duration of disease),
2 versions of the survey with different item sets were created
and displayed after using an initial filter question (see
Multimedia Appendix 1 for the version concerning the user
group). Prior to data collection, a pretest with 7 people (4
patients and 3 physicians) was conducted. After this pretest,
only semantic adjustments were made in the instructional texts,
as some terms were not comprehensible to laypersons (eg,
hypertension). An a priori power analysis was conducted to
calculate the required sample size for multiple linear regression
analyses with a maximum of 14 predictors and an expected
moderate effect of f2=0.15, resulting in an estimated sample
size of 151 persons (with an α error probability of .05 and a
power of 0.9) The data were collected anonymously between
September 14, 2019, and October 31, 2019. Participation was
voluntary. The overall completion time was 10 to 15 minutes
on average. The 2 target groups (adult patients at least 18 years
old with hypertension and physicians) were recruited primarily
via social networks (eg, XING, Facebook, and Twitter), personal
invitations in private and work environments (including medical
conferences), emails, contributions in self-help forums and
interest groups, and a university website. There was no monetary
compensation. Undergraduate psychology students enrolled at
the University of Hagen, a distance-learning university (patients
or physicians in a second degree program), could be
compensated with study credits via a virtual lab. As an incentive
to participate in the study, a summary of the aggregated study
results upon completion of the whole study was offered. The
study was approved by the ethics committee of the University
of Hagen prior to data collection (NR. EA_140_2019).
Measures
Acceptance
The dependent variable for patients and physicians is the
acceptance of health apps, namely the intention to use health
apps for managing chronic diseases (ie, hypertension) either as
patients or physicians. This outcome was operationalized
differently for both user groups. This study addressed the
acceptance of hypertension apps in general, mainly in terms of
the intention to use them (ie, not specific existing apps). For
patients, the intention to use apps was assessed in line with a
study by Breil et al [21] with 3 variables on a 5-level Likert
scale from “do not agree at all” to “fully agree.” Among
physicians, acceptance was operationalized as their intention
to use health apps in their clinical practice and not manage their
health in contrast to patients (ie, “I could imagine incorporating
health apps into my work”) and whether they would recommend
such health apps in general to their patients (“I would
recommend patients use health apps”). For physicians, 4 items
in a 5-point Likert scale were used to determine their intention
to use health apps in their own work. [17].
As illustrated in Figure 1, we have adapted and extended the
UTAUT2 research model. Only the price value in the UTAUT2
model was not considered due to the specific context (statutory
health system in Germany) and the low familiarity with digital
health in Germany [39]. The research model for physicians
omits the PMT factors because this theory is only applicable to
patients and not to physicians in their role as health care
providers.
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Figure 1. Research model depicting the acceptance of hypertension apps by patients. This study analyzes the influence of the determinants in the
adapted UTAUT2 model and the protection motivation theory on the intention of using hypertension apps in addition to self-efficacy and eHealth
literacy. eHealth: electronic health; PMT: protection motivation theory; UTAUT2: unified theory of acceptance and use of technology.
UTAUT2 Determinants of Acceptance
The operationalization of the UTAUT and UTAUT2 variables
was based on the work of Zhang et al [20] with translations
according to Hennemann et al [16], Breil et al [21], and Harboth
and Pape [40]. The constructs hedonistic motivation and habit
(UTAUT2) were additionally included in this study [23]. All
UTAUT items were assessed on a 5-point Likert scale from “do
not agree at all” to “fully agree.” The Cronbach α was in the
acceptable to good range for all scales between .74 and .91,
except for facilitating conditions for physicians (Cronbach
α=.61). The complete questionnaire can be found in Multimedia
Appendix 1.
Further Determinants of Acceptance
Self-efficacy was assessed using the General Self-Efficacy Short
Scale with 3 items on a 5-point Likert scale ranging from “fully
disagree” to “fully agree” [41]. The internal consistency of the
scale was good (Cronbach α=.83).
Technology-Related Determinants
Norman et al [42] define eHealth literacy as the ability to search,
find, understand, and evaluate health information from electronic
sources and to use the knowledge thus gained to address or
solve a health problem. eHealth literacy was operationalized
with 8 items on a 5-point Likert scale ranging from “fully
disagree” to “fully agree” using the electronic health literacy
scale (eHEALS) [42] in German according of Soellner et al [43]
(eg, “I know how to use the health information I find on the
internet to help me“). Internal consistency of the scale was good
with a Cronbach α=.89.
Perceived threat to privacy was operationalized according to
Zhang et al [20]. Instead of the 7-point Likert scale, a 5-level
Likert scale ranging from “strongly disagree” to “strongly agree”
was used [20].
Experience With eHealth
Some studies have shown a significant positive influence of
prior experience with web-based services on the acceptance of
eHealth [44,45]. Even though most of the apps in the cited prior
work were related to mental health services, a positive effect
was also expected for hypertension apps in the present study.
According to Venkatesh et al [23], experience is also a relevant
moderating factor in other contexts; therefore, experience with
health apps was also investigated in this study.
Contrary to the eHealth experience data collected for both user
groups with the same items, the items for physicians were
specifically adapted to the context of smartphone usage in a
professional context with respect to clinical practice. The items
were based on Albrecht et al [35] and included the use of apps
in general (dummy coded, with 1=yes and 0=no), type of usage
(professional, private), and types of the activities and concerns
that prevent them from using smartphone apps.
In addition to the aforementioned constructs, the participants'
age, gender, highest educational attainment, as well as the
current country of residence and region (urban vs rural) were
included as the control variables.
Health-Related Determinants
Information on the patients' own high blood pressure was
obtained with 3 items. The durations of the disease and
medication intake were recorded metrically in years. Comorbid
diseases were chosen based on the list of chronic diseases
provided by the Robert Koch Institute (ie, the German Higher
Federal Authority for Infectious Diseases) [46] (multiple
answers were possible).
Protection Motivation
Protection motivation was measured using the PMT
questionnaire that is based on the following components [32]:
Perceived vulnerability describes the probability of the
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occurrence of an illness-related event. Especially in the context
of hypertension, several risk factors can be identified such as
higher age as well as modifiable lifestyle factors, such as
malnutrition and less physical activity. Perceived severity
describes the extent to which a depicted event is perceived as
harmful. Response effectiveness refers to specific protective
behavior, such as the use of health apps and assessment of their
effectiveness. As a fourth component,self-efficacy is
investigated in terms of the skills needed to perform the
protective behavior.
The PMT variables were operationalized according to Guo et
al [32] with the 4 components, namely perceived vulnerability,
perceived severity, response efficacy, and perceived
self-efficacy. According to the PMT, protection motivation
results from the subjective assessment of a threat and possible
coping strategies. To ensure a concrete reference to high blood
pressure, the sentence “Possible consequences of high blood
pressure are various cardiovascular diseases (including heart
attack, stroke), retinal damage, kidney damage, etc” was placed
at the beginning of the questions.
The influence of the PMT variables was considered relevant
only for the patients participating in this study and was thus not
investigated among physicians.
Statistical Analysis
Only completed surveys were extracted from Unipark and
analyzed (due to option of consent withdrawal by dropping out).
The influence of the different UTAUT and PMT predictors on
the intention to use self-management apps for hypertension (ie,
acceptance) was computed using simple linear and multiple
hierarchical regression analyses separately for the 2 user groups
(ie, patients and physicians). The prerequisites for the parametric
tests were examined and found to be sufficiently applicable.
To investigate the relative influence of variables, the significant
determinants in the simple regression analyses were transferred
to an overall model in 4 blocks, as shown in Table 3. This was
done separately for both user groups due to the different
determinants. First, the patients were considered. In multiple
hierarchical regression, all significant single predictors were
included in blocks. As the UTAUT determinants have already
explained up to 70% of the intention to use in prior research
[23], the UTAUT factors in this study were included as the first
block or step of the hierarchical linear regression model followed
by self-efficacy in the second block. In the third block, eHealth
literacy was included, and the fourth block contained the items
from the perceived threat to privacy. The fifth and last block
comprised the 4 factors from the PMT. For each block, the
increase in the coefficient of determination (ΔR2) was
determined. In line with Zhang et al [20], the effect of the other
factors such as self-efficacy, privacy concerns, and factors from
the PMT were analyzed in the subsequent blocks [20].
Differences in the acceptance scores between the 2 user groups
were calculated through ttests for independent samples.
In addition to analyzing the intention to use health apps, we
assessed preferences in terms of the willingness of patients to
use health apps compared to face-to-face consultations with
physicians, self-help groups, or internet-based information for
managing high blood pressure.
To test the assumed mediation effects, 3 regression analyses
were conducted for each of these assumptions. The first step of
the regression model tested whether the predictor variables
influenced the mediator variable performance expectancy. In
the second step of the regression model, the direct effect of
predictor variables on the dependent variable was determined,
as already confirmed for all the 3 variables in the prior step. In
the third step, the indirect effect was determined.
The analyses were performed using SPSS Statistics (version
25, IBM Corp). Conditional effects, especially related to the
moderation hypotheses, as well as the indirect effects and the
associated mediation hypotheses were calculated with
PROCESS (version 3.4), a macro in SPSS [47]. Bootstrapping
analysis was performed with 5000 bias-corrected samples to
calculate the total direct and indirect effects of the variables.
Hypotheses were tested twofold at α<.05.
Results
Sample Characteristics
In the period mentioned earlier, 337 people accessed the
internet-based survey, with 212 people giving their consent and
completing the survey. This corresponds to a completion rate
of 62.9%. However, 13 respondents did not start the survey at
all, and 112 people did not finish it. Participants dropped out
mainly because of not providing informed consent (26/337,
7.7%), not stating to which of the 2 user groups they belonged
(42/337, 12.5%), and not providing demographic information
(19/337, 5.6%). Moreover, 3 respondents were excluded after
reviewing the raw data, as they had not answered the initial
question regarding the user group (patient or physician) and
thus had not answered the user group–specific questions (eg,
on PMT variables).
The sample for the data analysis consisted of 209 participants
including 163 patients and 46 physicians. The mean age was
35 years (mean 35.3 [SD 13.8] years), with slight differences
between the user groups, as shown in Table 1. More women
(126/209, 60.3%) participated in the survey than men.
Differences between the user groups were apparent in terms of
educational attainment, as shown in Table 1.
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Table 1. Demographic characteristics.
Physicians (n=46)Patients (n=163)Total sample (N=209)Characteristics
Age (years)
34.28 (8.6)35.53 (14.9)35.26 (13.8)Mean (SD)
18-53 (34)18-76 (32)18-79 (33)Range (median)
Sex, n (%)
28 (60.9)98 (60.1)126 (60.3)Female
18 (39.1)64 (39.3)82 (39.2)Male
0 (0)1 (0.6)1 (0.5)Not mentioned
Education, n (%)
10 (21.7)104 (63.8)114 (54.5)High school graduation
36 (78.3)59 (36.2)95 (45.5)University degree
As shown in Table 2, 129 of the 209 participants (61.7%) stated
that they already had experience using mobile health apps. Here,
patients (103/163, 63.2%) differed only slightly from physicians
(26/46, 56.5%) in terms of experience. Both groups had been
using health apps for approximately 2.5 years on average (SD
2.9 and 3.1, respectively). There were clear differences in the
way they used the apps. “Vital value measurements” (51/163,
31% vs 7/46, 15.2%) and “memories” (46/163, 28.2% vs 8/46,
17.4%) were used more frequently by patients, whereas every
physician cited “Search for information” as the reason for use.
Table 2 shows previous experience with health apps
differentiated by user group. Under “Other use,” patients
indicated that they also used “apps for measuring movement or
physical activity (steps),” “menstruation cycle apps,” and
“pregnancy apps.”
Table 2. Experience using electronic health apps.
Physicians (n=46)Patients (n=163)Total sample (N=209)
Experience with health apps, n (%)
26 (56.5)103 (63.2)129 (61.7)Yes
19 (41.3)55 (33.7)74 (35.4)No
1 (2.2)5 (3.1)6 (2.9)Not specified
Purpose of using apps, n (%)
7 (15.2)51 (31.3)58 (27.8)Vital signs measurement
8 (17.4)46 (28.2)54 (25.8)Reminder
10 (21.7)37 (22.7)47 (22.5)Documentation
15 (32.6)35 (21.5)50 (23.9)Electronic communication
23 (50)45 (27.6)68 (32.5)Search for information
13 (28.3)40 (24.5)53 (25.4)Relaxation
7 (15.2)17 (10.4)24 (11.5)Other
App selection basis, n (%)
23 (50)103 (63.2)126 (60.3)Searched or found by self
6 (13)34 (20.9)40 (19.1)Recommendation from friends
18 (39.1)15 (9.2)33 (15.8)Recommendation from physicians
1 (2.2)15 (9.2)16 (7.7)Advertising
N/A
N/Aa
10 (4.8)Other
aN/A: not applicable.
There were differences not only in the purpose, way, and
frequency in which apps were used but also in the search and
selection of apps. In both groups, apps were mainly searched
for or found by the users themselves (98/163 patients and 28/46
physicians, 60.3%). Recommendations from friends were
relevant for 20.9% (34/163) of patients and 13% (6/46) of
physicians. Advertising was a reason for the selection of health
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apps for 9.9% (16/163) patients but only for 2.2% (1/46) of the
physicians.
Patients’Preferences
Mobile health apps were stated as the second most preferred
option to support hypertension management by 30.7% (50/163)
of the patients. Only direct-contact physician care was regarded
as more preferable for 36.8% (60) of the patients. In contrast,
medical care via the internet 9.2% (15, 9.2%) and local
face-to-face groups such as support groups (10, 6.1%) were the
options mentioned much less frequently.
Previous Use of Smartphones by Physicians
Most physicians (35/46, 76.1%) reported using their smartphone
for job-related electronic communication with patients or other
professionals through email, chat, or messenger functions. The
internet was also frequently used for searching literature in
journals or databases by 60.9% (28) of the physicians. Other
frequent responses included information on medication and
treatment options given by 43.5% (20) and access to training
content by 37% (17) of the physicians. Requests for laboratory
tests (4, 8.7%) and access to patient records (5, 10.9%) were
less frequently reported.
Physicians’Concerns When Using Health Apps
Physicians were asked about their concerns when using health
apps. The main concerns were about the security of patient data
(37/46, 80.4%), followed by the trustworthiness of content (23,
50%) and technical reliability of software (20, 43.5%). Concerns
about hygiene were mentioned by only 23.9% (11) of the
physicians. Concerns about reimbursement by German statutory
health insurance companies (3, 6.5%), lack of or limited options
for access by patients (4, 8.7%), and poor acceptance by patients
(8, 17.4%) were relatively low.
Preliminary Analyses
Preliminary analyses were conducted to select significant
determinants for the hierarchical regression model for patients.
In simple linear regression, performance expectancy had a
positive effect on the intention to use hypertension apps, with
the highest explained variance of UTAUT determinants
(R2=0.44; β=.66; P<.001). There were also significant positive
influences of effort expectancy (R2=0.25;β=.50; P<.001), social
influence (R2=0.13; β=.36; P<.001), facilitating conditions
(R2=0.23; β=.48; P<.001), hedonistic motivation (R2=0.15;
β=.39; P<.001), and habit (R2=0.13; β=.36; P<.001) on the
intention to use. Subsequently, all significant UTAUT predictors
were included in a multiple hierarchical model (Table 3). Of
the 6 UTAUT factors, only performance expectancy had a
statistically significant influence on patients (t156=6.27, P<.001).
Except for performance expectancy, the other predictors did
not contribute significantly to the overall model of acceptance.
Multimedia Appendix 2 presents the entire linear model.
Single regressions were conducted for analyzing the influence
of the PMT variables. Perceived vulnerability (R2=0.11; β=.33;
P<.001), perceived severity (R2=0.07; β=.27; P<.001), response
efficacy (R2=0.29; β=.54; P<.001), and perceived self-efficacy
(R2 =0.31; β=.56; P<.001) positively influenced the intention
to use hypertension apps. In the multiple regression model, all
factors except for perceived severity made a significant
contribution (R2=0.42). Multimedia Appendix 3 shows the
multiple linear regression model of the PMT with all 4 factors
and confidence intervals for β.
Next, simple regression analyses were also conducted for the
physician user group. Performance expectancy had a positive
effect on the intention to use (R2=0.21; β=.46; P<.01). In
contrast, effort expectancy, social influence, facilitating
conditions (UTAUT1), hedonistic motivation, and habit
(UTAUT2) did not prove significant in the simple regression
model (P>.05).
Main Results
Research Question 1: Acceptance Determinants for
Patients and Physicians
For the overall model regarding patients, the explained variance
was R2=0.56 (F15= 12.53, P<.01). Of the 15 predictors in the 5
blocks, only 3 predictors were significant in the hierarchical
regression (last step). Performance expectancy significantly
contributed to the prediction of the intention to use hypertension
apps (R2=0.47). Among the remaining variables, only
self-efficacy (ΔR2=0.02) and protection motivation in terms of
the PMT variables (ΔR2=0.07) made significant contributions
to the explained variance of the overall model for the patient
group, as shown in Table 3.
Simple regression analyses showed that previous experience
with health apps contributed to the acceptance of health apps
for managing hypertension among patients (R2=0.20, P<.01).
Determinants of hypertension app acceptance among physicians
were then examined, as demonstrated in Table 4. The UTAUT
variables form the first block. Among physicians, previous
experience with health apps was not significant.
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Table 3. Overall model of the determinants for the intention to use hypertension apps in patients (n=162).
ΔR2
Pvalueβ95% CISEBa
Predictor
.25–4.98 to 1.281.58–1.85Constant
0.47
UTAUTbdeterminants
<.001.420.26 to 0.700.110.48Performance expectancy
.73–.04–0.23 to 0.160.10–0.03Effort expectancy
.82–.02–0.19 to 0.150.09–0.02Social influence
.46.06–0.11 to 0.240.090.07Facilitating conditions
.10.11–0.03 to 0.280.080.13Hedonistic motivation
.24–.09–0.27 to 0.070.08–0.10Habit
0.02Self-efficacy
.05.130.00 to 0.380.100.19Self-efficacy expectation
<0.01.73.06–0.10 to 0.070.04–0.01
eHealthcliteracy
<0.01Threat of privacy
.40.06–0.26 to 0.650.230.19Usage for other purpose
.70–.03–0.59 to 0.400.25–0.10Loss/leakage of personal data
.20–.10–0.75 to 0.160.23–0.30Misuse of personal data by criminals
0.07Protection motivation
<.001.200.09-0.390.080.24Perceived vulnerability
.67.03-0.12-0.180.080.03Perceived severity
.29.09-0.08-0.180.080.09Response efficacy
.09.17-0.03-0.400.110.19Perceived self-efficacy
aB: unstandardized β.
bUTAUT: unified theory of acceptance and use of technology.
ceHealth: electronic health.
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Table 4. Hierarchical regression model of the determinants for the intention to use in physicians (n=46)a.
ΔR2
Pvalueβ95% CISEBb
Predictor
.320–4.41 to 13.034.304.31Constant
0.27
UTAUTcdeterminants
.01.540.20 to 1.320.280.76Performance expectancy
.11–.29–0.74 to 0.070.20–0.33Effort expectancy
.30–.20–0.86 to 0.280.28–0.29Social influence
.82–.04–0.43 to 0.340.19–0.04Facilitating conditions
.82.04–0.40 to 0.510.220.05Hedonistic motivation
.78.06–0.39 to 0.510.220.06Habit
0.10.01.410.07 to 0.530.110.30eHealth literacy
0.05Threat of privacy
.43.12–0.71 to 1.620.570.45Usage for other purposes
.09–.29–2.25 to 0.190.60–1.03Loss or leakage of personal data
.45.11–0.64 to 1.390.500.38Misuse of personal data by criminals
aData concerning self-efficacy and PMT were collected only for patients.
bB: unstandardized β.
cUTAUT: unified theory of acceptance and use of technology.
Research Question 2: Differences Between Patients’and
Physicians’Acceptance of Hypertension Apps
To test for differences between physicians and patients, all
significant influences were transferred to a multiple regression
model for each group and included in blocks. Self-efficacy had
a significant influence only in the patient group and was
therefore omitted in the multiple regression model. In the second
block, eHealth literacy was included. The third and last block
involved the perceived threat to privacy. For each block, the
increase in the coefficient of determination (ΔR2) was identified.
In the case of the patients, only 3 out of the 15 predictors in the
5 blocks were significant in the hierarchical regression.
Performance expectancy contributed significantly to the
prediction with R2=0.47. Beyond that, only self-efficacy
(ΔR2=0.02) and PMT (ΔR2=0.07) made significant contributions.
For physicians, all significant individual predictors were also
included block wise in the multiple hierarchical regression. For
the overall model, the coefficient of determination was R2=0.42.
The UTAUT2 factors explained under one-third of the variance
explained by the overall model, with R2=0.27. A further 10%
increment in R2resulted from the addition of eHealth literacy
and another 5% by accounting for privacy threat.
Direct comparison shows that the R2for patients is slightly
higher and is largely determined by UTAUT2; therefore, the
addition of other determinants resulted in a comparatively small
increase in R2. For physicians, the influence of factors outside
of the UTAUT factors (eHEALS and privacy) is stronger.
Research Question 3: Mediation Effects in the Extended
UTAUT Model for Patients
In line with the UTAUT assumptions, the UTAUT predictors
effort expectancy, social influence, and facilitating conditions
exerted a significant direct influence on performance
expectancy,as illustrated in Figure 2. Performance expectancy
had a significant direct effect on the intention to use
hypertension apps and mediated the relationship between effort
expectancy and intention to use (95% CI 0.04-0.23) as well as
the relationship between social influence and intention to use
(95% CI 0.04- 0.19).
The other 3 factors having a significant influence on the
intention to use hypertension apps in the simple regression
analyses (preliminary analyses) were used as covariates in this
model. Table 5 presents the direct effects on the mediator
performance expectancy as well as the direct and indirect effects
on the intention to use hypertension apps.
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Figure 2. Overall model showing the determinants of the intention to use hypertension apps in patients. Significant influence is shown with solid lines
and corresponding beta values; influences that were investigated but not significant are shown with dashed lines. PMT: protection motivation theory;
UTAUT2: unified theory of acceptance and use of technology.
Table 5. Overall model of determinants of intention to use among patients.
Intention to usePerformance expectancyPredictor
95% CIPvalueβPvalueβ
Effort expectancy
–0.21 to 0.17.82–.02.004.22Direct effect
0.04 to 0.23.12N/A
N/Aa
Indirect effect
–0.11 to 0.30.36.09.004.22Total effect
Social influence
–0.17 to 0.17.98.00.004.19Direct effect
0.04 to 0.19.10N/AN/AIndirect effect
–0.08 to 0.27.27.10.004.19Total effect
Facilitating conditions
–0.10 to 0.25.38.08.93.01Direct effect
–0.10 to 0.08.00N/AN/AIndirect effect
–0.11 to 0.27.40.08.93.01Total effect
aN/A: not applicable.
Discussion
The aim of this study was to determine the subjective factors
that influence the acceptance of hypertension apps among
patients and physicians in Germany. In addition to the UTAUT
determinants that have already been investigated in health care
research, protection motivation, threat to privacy, and
self-efficacy expectations were also considered as further
influencing factors on the intention to use hypertension apps.
Principal Findings and Comparison With Prior Work
As expected, a significant influence of performance expectancy
on the acceptance of hypertension apps was found among
patients and physicians. Among patients, self-efficacy and
protection motivation, including perceived threat, further
contributed to an increase in the explained variance of the
extended UTAUT2 model.
The differences between the 2 user groups indicated that several
factors had a statistically significant influence only in patients,
such as self-efficacy. In the physician group, only performance
expectancy proved significant. In addition to the UTAUT
factors, a significant influence of eHealth literacy was identified
only in physicians. Potentially, physicians had a more
differentiated understanding of the meaning of eHealth literacy
and were thus more critical in assessing their own skills than
patients [48].
Another goal of this study was to gain insights into the role of
performance expectancy as a mediating variable. Although
numerous studies on the UTAUT model [49] have confirmed
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a direct influence of performance expectancy, they have not
clarified whether this variable also mediates the influence of
beliefs on the intention to use mHealth apps. Hence, a
methodologically added value to the UTAUT2 model in this
study can be observed in the demonstrated mediator role of
performance expectancy, as shown earlier by Zhang et al [20].
Specifically, our study demonstrated the mediating effects of
performance expectancy in the relationship between effort
expectancy as well as social influence and the intention to use
hypertension apps in patients. We also identified a direct effect
of perceived vulnerability. Thus, the strong influence of
performance expectancy and its mediating role may explain
why the other UTAUT factors had no statistically relevant
influence in the hierarchical linear model.
In contrast, the expected moderating effects of previous
experience with health apps could not be identified in the group
of patients, which should be interpreted considering the
web-based survey and self-selection bias.
Contrary to our assumptions and previous research indicating
data security concerns as a major barrier to using health apps
[50], perceived threat to privacy had no significant influence
on the acceptance of hypertension apps in our study. Potentially,
the sample was already aware of certified disease management
apps approved by statutory insurance companies and other
trusted sources in Germany.
The explained variance of the UTAUT determinants that we
applied in the extended UTAUT2 model in this study was
R2=0.47. In comparison, for all determinants, the explained
variance regarding app acceptance by patients was only slightly
higher with R2=0.56 (ie, all 5 blocks in the regression model).
This finding corresponds, for instance, to a study by Dou et al
[51], which obtained an R2of 0.412 based on various
determinants regarding the intention to use apps for
self-management of chronic diseases.
More specifically, the predictive value in terms of the explained
variance of the proposed determinants of acceptance is
comparable to our previous study that served as a basis for this
survey [21], which is interesting because of the integration of
more illness-related variables in this study. In our related study
[21], performance expectancy and effort expectancy proved to
be significant predictors, explaining approximately 50% of the
variance in the acceptance scores among patients. For direct
comparison, the Illness Perception Questionnaire [52] that we
used only in our previous study may have been a better choice
for capturing the disease-specific acceptance of apps, as its
contribution to the total variance was higher compared to the
PMT factors. Instead, vulnerability was the only significant
variable of the PMT block in the multiple regression model
used in this study. Remarkably, threat to privacy, which had
not yet been surveyed in our preliminary study [21], was thought
to be another significant determinant in this study. However,
we could not confirm this assumption, which may have been
due to the rather young adults constituting our patient and
physician samples.
Given the unexplained variance, patients’ perspectives,
especially regarding unmet needs and preferences, could be
further explored using mixed methods and qualitative research
methods. Accordingly, a qualitative study by Morrissey et al
[53] also highlighted concerns regarding the risks of health apps
used to improve medication adherence and the need for promote
eHealth literacy among hypertensive patients. Regarding the
real-world assessment of apps, a mixed methods study by
Allessa et al [34] showed that apps for self-management of
hypertension can be functional and acceptable to users, but they
can also be considerably improved through training [34], which
corresponds to UTAUT determinants like performance and
effort expectancy as well as facilitating conditions.
Interestingly, one-third of the patients in our study stated that
they preferred using health apps over physician contact and
face-to-face self-help groups to manage hypertension. This
finding indicates that for a relevant proportion of the patients,
self-management via health apps can be the first choice, which
can be seen as a starting point for the implementation and
additional provision of medical apps. Nonetheless, in line with
prior research [54,55], most patients in our study preferred
personal contact with physicians over digital self-help using
hypertension apps. Hence, further research is needed to
determine how to increase the adoption of mobile solutions in
conjunction with traditional face-to-face health care services
(eg, blended or stepped care approaches). Regular blood pressure
measurement supported by apps may help bridge the gap
between the medical and lay perspectives of optimal and
personalized hypertension treatments in practice and promote
more effective disease management in the long run [56].
Overall, this corresponds to a study by Edwards et al [57]
documenting considerable interest in using telemedicine services
like apps among patients with chronic diseases, regardless of
their health status, access difficulties, as well as age and many
other sociodemographic factors.
Limitations
The present study is subject to several limitations. First, when
considering the demographic distribution of the sample, various
limitations apply that may help explain some of the consistent
findings. For instance, the mean age of the physicians was
considerably less at 34 years (mean 34.3 [SD 8.6] years, median
34 years) compared to all the physicians in Germany. According
to the Federal Statistical Office, the average age of the
physicians was 48 years in 2017 [58]. The respondents were
thus considerably younger and therefore not representative of
physicians in Germany. The patients in our sample were also
relatively young, with the average age being 36 years (mean
35.5 [SD 14.9] years, median 32 years), and may therefore not
necessarily reflect the views of most patients with hypertension,
especially in terms of the acceptance and use of disease
management apps. Further limitations concern the rather high
educational level of the patients, with one-third holding an
academic degree. Nonetheless, this group may represent a
subgroup of patients that have been recently diagnosed and may
thus be easily reached for prevention and health promotion
initiatives.
Second, although the total sample size of 209 individuals was
sufficiently powered for the conducted hierarchical regression
analyses, this only applies to the analyses that concern the entire
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sample. Although the group of patients was sufficiently large
with 163 participants, the group of physicians was relatively
small (46 physicians), thus making it more difficult to identify
effects. Despite including fewer determinants for the regression
model measuring the acceptance of apps among physicians
compared to the patient group, it would have been necessary to
have a considerably higher number of physicians as participants.
However, physicians are usually difficult to recruit via social
media. In addition, the recruitment period of 1 month was
considerably short. Therefore, the small sample size of the
physician group is a major limitation.
Implications
Implications derived from this study, based on several significant
and insignificant findings, especially concern the further
extension and adaptation of the UTAUT2 model in the context
of chronic diseases. Holden and Karsh [59] note that it is
important to continuously adapt the acceptance models for the
use of telemedicine, including health apps to mirror ongoing
technological advances. Thus, future research may also consider
further barriers to using hypertension apps. According to
Schreiweis et al [60], potential barriers and drivers for eHealth
applications can be divided into individual, organizational, and
technical factors. In the current study, organizational and
environmental [33] policy aspects were not investigated. Instead,
we focused on individual acceptance-related factors such as
eHealth literacy and beliefs such as performance expectancy as
well as motivational factors (eg, hedonic motivation, protection
motivation). Other potentially relevant aspects such as training
of physicians or information on the availability of eHealth
services [51] could also be considered in upcoming studies with
a broader scope on the validation of an extended UTAUT2
model for assessing the acceptance of disease management apps
among patients and physicians [48].
Another variable to consider in the investigation of hypertension
apps may be resistance to change among patients [51]. Given
the high prevalence of hypertension, it should be considered
that there is a long-term demand for treatment support and at
least a relevant proportion of patients indicates a preference for
health apps [61]. However, other studies with hypertensive
patients indicated ambivalent views on self-management apps
[53]. Patient preferences for hypertension apps may also vary
depending on the different features of such apps. For instance,
some studies found reminders and personalization to be
important features of hypertension apps [62]. In future studies,
a differentiated assessment of app features, including trade-offs
between preferred features, should be considered.
Given the forecast of the German National Association of
Statutory Health Insurance, physicians state that the demand
for medical care will increase by 2% by 2030, whereas the
supply of medical care, especially in rural areas, will continue
to decline [63]; health apps could be a solution accepted by
relevant target groups, as this study has indicated. Nevertheless,
knowledge on the most important determinants of mHealth
acceptance is required to tailor information as well as
interventions to the needs and preferences of future users. In
this context, it is important to note that our study was conducted
shortly before the global outbreak of the COVID-19 pandemic
and the introduction of the directory for digital health
applications in Germany (German name: Digitale
Gesundheitsanwendung [DiGA]). DiGA are defined as low-risk
medical products based on digital technologies that are intended,
for example, to detect or alleviate illnesses or to support
diagnosis using apps or browser-based applications. The DiGA
directory [64] lists all the DiGA that have successfully
undergone the assessment procedure that is regulated by the
Federal Institute for Drugs and Medical Devices (German name:
Bundesinstitut für Arzneimittel und Medizinprodukte).
Interestingly, there is no app at present (as of October 2021) for
hypertension management in the recently introduced DiGA
directory among the 24 listed medical apps, which may change
soon. In future, the provision of certified hypertension apps may
change the views and uptake of such apps in health care.
In addition, it may be important for future research to consider
the connection of remote and personal treatment assistance in
the management of hypertension in terms of blended or hybrid
treatments [11]. Transparent quality criteria represent another
key strategy for the adoption of health apps. However, the
quality of commercially or publicly available apps for
hypertension management has been classified as overall poor
[55]. Associations between the relevant features and outcomes
of hypertension apps also remain inconclusive [65]. Therefore,
implementation strategies and advances in (digital) health policy,
such as the DiGA registry in Germany, are important steps to
increase the dissemination of quality-approved medical apps
for chronic diseases. With the ongoing diffusion of medical
apps into routine care, research on the acceptance and use of
these apps is required on a longitudinal basis.
Conclusions
In summary, this study identified several relevant determinants
of the acceptance of hypertension apps among patients and
physicians. The ongoing implementation of health apps into
routine care and the COVID-19 pandemic emphasize the
importance of acceptance-related research on disease
management apps. One possible strategy is the targeted
collection and prioritization of patient requirements [66].
Another strategy may be to increase the awareness of
quality-approved self-management apps for hypertension
through targeted information campaigns and training of
physicians, such as general practitioners, which can be grounded
on acceptance-based surveys like the present study.
Thus, the main contribution of this study lies in the identification
of additional disease-related, context-sensitive determinants of
the intention to use hypertension mHealth apps in terms of
acceptance that complement the UTAUT determinants. In
patients, protection motivation and perceived vulnerability made
a significant explanatory contribution and should be thus further
considered in efforts aimed at promoting mHealth acceptance.
Furthermore, a deeper understanding of the underlying
mechanisms in the theoretical model has been achieved by
confirming performance expectancy as the mediator of the
beliefs related to and intention to use mHealth apps.
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Acknowledgments
The authors would like to thank all participants for contributing to this research.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Questionnaire of the German online survey (including translation)–patient and physician versions.
[DOCX File , 42 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Linear model and influence of the unified theory of acceptance and use of technology predictors on utilization in patients (n=163).
[XLSX File (Microsoft Excel File), 12 KB-Multimedia Appendix 2]
Multimedia Appendix 3
Linear model and influence of protection motivation theory predictors on patients’intention to use health apps (n=163).
[XLSX File (Microsoft Excel File), 12 KB-Multimedia Appendix 3]
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Abbreviations
DiGA: Digitale Gesundheitsanwendungen (German term for digital health applications)
eHEALS: electronic health literacy scale
mHealth: mobile health
PMT: protection motivation theory
UTAUT: unified theory of acceptance and use of technology
Edited by G Eysenbach; submitted 01.07.21; peer-reviewed by K Matthias, B Sedlmayr, MDG Pimentel; comments to author 23.07.21;
revised version received 11.09.21; accepted 27.11.21; published 06.01.22
Please cite as:
Breil B, Salewski C, Apolinário-Hagen J
Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among
Physicians: Cross-sectional Survey
JMIR Cardio 2022;6(1):e31617
URL: https://cardio.jmir.org/2022/1/e31617
doi: 10.2196/31617
PMID:
©Bernhard Breil, Christel Salewski, Jennifer Apolinário-Hagen. Originally published in JMIR Cardio (https://cardio.jmir.org),
06.01.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License
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