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Intention vs. Perception: Understanding the Differences in Physicians’ Attitudes Toward Mobile Health Applications


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The current state of mobile technology demonstrated that dissemination of mobile health (mHealth) practices is dramatically increasing. However, the success of mobile health technologies does not depend on only the technology but the actual use as well. Understanding the perception and intention about mobile health use is important in order to utilize and adopt the mobile technologies in practice effectively. This chapter provided an overview on two different physician groups (mHealth application users and nonusers) revealing the differences in their attitudes (intentions of users and perceptions of nonusers) toward actual use of mHealth applications. The study employed a secondary research approach. A survey data collected from 137 mHealth user physicians and 122 nonuser physicians were used. A research model was tested in both groups, and the statistical findings were interpreted to identify the differences between the groups. Considering significant and nonsignificant factors influencing in each group, a number of suggestions were outlined in this chapter for developers, managers, and authorities.
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153© Springer International Publishing AG, part of Springer Nature 2018
E. Sezgin et al. (eds.), Current and Emerging mHealth Technologies,
Chapter 10
Intention vs. Perception: Understanding
theDifferences inPhysicians’ Attitudes
Toward Mobile Health Applications
Emre Sezgin, SevgiÖzkanYildirim, andSonerYildirim
10.1 Introduction
Mobile health (mHealth) has becoming a signicant element for healthcare delivery.
As such, the investments and researches on mHealth have been rapidly increasing. A
number of international associations pointed out the growing market of healthcare
services with the digital era, and most of them anticipated the growth in telemedicine
and remote healthcare services in high numbers for the following decades.
McKinsey’s report in 2015 underlined that mobile device (tablet and smartphone)
market may expand 1.1–1.3 times by 2018. The value created by the expansion may
reach to hundreds of billions of dollars, and this growth will affect health and medi-
cal services the most (Atluri etal. 2015). On the other side, the 2015 OECD Digital
Economy Outlook report presented that “the global mHealth market may reach $23
billion in 2017, with Europe accounting for $6.9 billion and Asia-Pacic for $6.8
billion, ahead of the North American market of $ 6.5 billion” (OECD 2015). The
growth was not only triggered the investments but also the reduction of the costs of
healthcare delivery. By 2017, mHealth use in the European Union was reported to
have potential to save 99 billion in healthcare spending (OECD 2015). Furthermore,
global reports presented that in 2025, the use of the mobile Internet, as well as appli-
cations, was estimated to have an economic impact around 3.7–10.8 trillion dollars
E. Sezgin ()
The Research Institute, Nationwide Children’s Hospital, Columbus, OH, USA
S. Ö. Yildirim
School of Informatics, Middle East Technical University, Ankara, Turkey
S. Yildirim
Department of Computer Education and Instructional Technology,
Middle East Technical University, Cankaya, Ankara, Turkey
per year (Manyika etal. 2013). For instance, potential value gain was estimated to be
10–20% cost reduction only in chronic disease treatment via telemedicine.
Considering the current developments and estimations, the worldwide dissemination
and use of mobile health technologies have constantly been increasing. Similarly, use
of mobile technologies and applications by healthcare providers has also increased
(PwC Health Research Institute 2014; Ventola 2014). In that regard, the mobile
application markets (App Stores) presented over thousands of applications related to
healthcare services that are used for checking tests, keeping records, and taking
assistance in diagnoses. These applications aimed to assist physicians or patients to
manage and maintain healthcare-related data by enabling storing, recording, and
accessing information (Hao etal. 2013; Martínez-Pérez etal. 2013).
On the other side, these reports demonstrated that the mHealth technologies have
penetrated to many different segments, and they have been offered to different user
groups in the market (e.g., patients, physicians, nurses). These groups were expected
to use mHealth applications in checking, controlling, and maintaining personal
healthcare or to deliver the services. However, it should be noted that the success of
these technologies does not solely depend on the technological innovations itself.
The perceptions about mHealth and the intention to use these new technologies are
important elements in order to utilize them in practice effectively. In that regard, not
only the mHealth users’ intentions but also the perception of potential users should
be considered, and the assessment of user behavior is an important input for the
success of mHealth use.
10.1.1 Background Information onAssessment
Individuals’ behaviors and attitudes toward information technologies have been
investigated for a long time (King and He 2006; Rondan-Cataluña etal. 2015). The
concept was employed for assessment of technology acceptance in the early 1990s,
and the studies in technology acceptance gained interests (Davis 1989; Wood and
Bandura 1989; Ajzen 1991; Venkatesh and Davis 2000; Venkatesh etal. 2003). One
of the leading theories was proposed by Davis (1989) as the technology acceptance
model (TAM). TAM is used to determine factors inuencing behaviors of users
toward technolo gies. The model argues that the actual use of technologies is
inuenced by perceived ease of use (PEOU) and perceived usefulness (PU). Thus,
PEOU and PU were main contributors to individuals’ attitude and behavioral
intention (BI). In the latter studies, TAM has been modied involving other
constructs to assess effects of different factors about different technologies (Bagozzi
and Warshaw 1992; Venkatesh and Davis 2000). In the literature, there have been a
number of studies about the healthcare technologies successfully using TAM theory
(Holden and Karsh 2010). Furthermore, the studies employed an integrated or
modied TAM to keep up with changing user needs and healthcare technologies.
However, a major drawback of TAM was pointed out as the difculty in the
generalization of results and inconsistency in relationships between constructs
(Venkatesh etal. 2003; Legris etal. 2003; Sun and Zhang 2006). Following TAM,
E. Sezgin et al.
the unied theory of acceptance and use of technology (UTAUT) was proposed as a
new integrated theory, which aims to assess the likelihood of success of new
technologies and determine drivers of acceptance (Venkatesh etal. 2003). In 2012,
Venkatesh, Thong, and Xu (2012) proposed UTAUT 2, which was an updated
UTAUT including hedonic motivation, price value, and habit as additional exogenous
variables inuencing behavioral intention. Similar to TAM, UTAUT has been
successfully implemented in a number of studies (Schaper and Pervan 2007b;
Chang etal. 2007; Aggelidis and Chatzoglou 2009; Kijsanayotin etal. 2009; Pynoo
etal. 2012; Dünnebeil etal. 2012). In addition to that, the theory of planned behavior
(TPB) and innovation diffusion theory (IDT) have also been used in behavioral
researches in healthcare delivery (Sezgin and Özkan-Yildirim 2014).
10.1.2 Aim oftheStudy
This chapter investigated the intentions and perceptions of physicians toward
mHealth applications considering two different perspectives of physicians. In that
regard, following a secondary research methodology, ndings of previous researches
about mHealth application use and adoption were employed to provide a comparison
between two physician groups. Authors believe this comparison would be a valuable
asset providing a distinct overview, which would be used in planning new health
application development, management, and promotion.
10.2 Methodology
The chapter employed a secondary research method, which focuses on the synthesis
of previous researches (Sezgin etal. 2017; Sezgin etal. 2016). In order to provide a
comparative overview, the ndings of these researches were discussed revealing the
similarities and differences in mHealth application adoption by two user groups.
The detailed methodology and research procedure of these researches were given in
this section.
The researches that were held in this study were reported ndings for intentions
and perceptions toward mHealth application by the user and nonuser physicians. In
these researches, similar research and testing procedures were employed which
helped to present a common ground for the comparison. In both researches, to
understand the inuencing factors to use mHealth apps, a systematic method was
followed. At the rst phase, a literature research was conducted to identify researches
about mHealth. It also helped to understand the behavioral theories in the domain as
well as to gather constructs for assessing adoption and acceptance of mobile health
information systems by the physicians. Following that, the conceptual model was
developed, and hypotheses were formulated. In both researches, the same model
was used, and the data collection was completed by employing a structured survey
(questionnaire). Convenience sampling was used as the data collection method, and
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
an online survey tool was employed. Non-mHealth application user physicians
(n=122) and mHealth application user physicians (n=137) participated in the
survey. Conrmatory factor analysis and structural equation modeling (SEM) were
used in the analysis of quantitative data. Figure10.1 provided an outline of the
research processes.
The following constructs were used in the model, and they were tested in both
researches in order to understand perception (of nonusers) and intention (of users)
toward mHealth applications.
Behavioral intention (BI): The act of deciding to use a particular technology
(Venkatesh etal. 2003).
Performance expectancy (PE): Personal beliefs using technology would increase
the job performance (Venkatesh etal. 2003).
Effort expectancy (EE): Personal beliefs using technology would be free of effort
(Venkatesh etal. 2003).
Compatibility (CO): The perception about the use of technology is consistent
with users’ needs, experiences, and values (Rogers 1995).
Mobile self-efcacy (MS): Perceptions about personal abilities to use the tech-
nology to fulll healthcare task and duties on mobile devices (Schaper and
Pervan 2007b).
Fig. 10.1 Flow of the research processes
E. Sezgin et al.
Technical support and training (TT): The perception and the need for support and
training to gain knowledge about the technology (Venkatesh etal. 2003).
Perceived service availability (PS): The perception about the technology which
is able to support “pervasive and timely usage” (Venkatesh etal. 2003).
Personal innovativeness in IT (PI): The state of a person’s willingness to take a
risk in trying a new technology or innovation (Agarwal and Prasad 1998).
Social inuence (SI): The degree of social perceptions about technology’s desir-
ability (Venkatesh etal. 2003).
Mobile anxiety (MA): The apprehension when using or having the possibility to
use mobile devices and applications (Schaper and Pervan 2007b).
Result demonstrability (RD): Tangibility or the level of observability of the
results in using technology (Venkatesh and Davis 2000).
Habit (HB): Repetitiveness and routine act of behavior in using the technology
(Gagnon etal. 2003).
10.3 Comparison ofUser andNonuser Physicians
In this section, the signicant and nonsignicant factors of mHealth application use
were outlined. Figures10.2 and 10.3 presented the research model used for each
group outlining signicant (continuous line) and nonsignicant (dashed line)
relationships. Research model testing resulted differently for each group regarding
signicant relations as well as the implications. In this section, a comparison of
factors inuencing these different groups was given.
Signicant and nonsignicant relationships for both groups were given in
Table10.1. The researches reported that PE and PI inuenced BI for users and EE
and TT inuenced BI for nonusers. This nding revealed that mHealth application
user physicians would perceive their job performance and their willingness to try
new technologies inuential their intention to use mHealth applications (Chau and
Hu 2002). On the other side, the perception of nonusers depended on the ease of
using mHealth, and the support they were receiving would affect their intention to
use mHealth applications (Chang etal. 2007).
The behavioral intention was inuenced by perceived service availability and
mobile anxiety in both groups. Thus, there was a common perception regarding
reachable and accessible mHealth applications in practice (Becker etal. 2014).
Furthermore, compatibility inuenced performance expectancy, and mobile self-
efcacy inuences effort expectancy for both groups. Here, job performance was
perceived to be related to compatible systems by nonusers similar to users, such as
mHealth with hospital systems. In addition to that, the ease of mHealth use was
perceived to be related with personal competency for both groups. However, their
indirect inuence on behavioral intention can be observed differently in each group
due to the signicant impact of PE and EE.Thus, compatibility was rather inuential
on BI over PE for user physicians, and mobile self-efcacy was on BI over EE for
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
nonusers. That impact would be related to perceived job performance of user physi-
cians since they observe the relation of compatibility and job performance. For non-
users, the expected ease of using mHealth applications could be perceived to be
related to personal competency (Schaper and Pervan 2007a).
On the other side, the direct effect of CO, HB, MS, and SI was not inuential on
BI for both groups. Here, there was a consensus of physicians about direct impact
on BI.Even though CO and MS had an indirect effect, they were not perceived to
have a signicant inuence on BI as well as HB and SI.As explained in the previous
section, these factors might have seen rather less relevant or non-applicable by the
physicians considering the current state of mHealth application use in health
institutions (Gagnon etal. 2015).
Social Influence
Technical Support and
Perceived Service
Result Demonstrability
Performance Expectancy
Behavioral Intention
Effort Expectancy
Personal Innovativeness
Mobile Anxiety
Mobile Self-efficacy
Fig. 10.2 Research model for mHealth user physicians
E. Sezgin et al.
10.4 Suggestions
The previous section outlined the ndings of intention and perception to use
mHealth applications and implications. Considering both groups, in this section, a
number of elements were outlined in order to be considered in application
development and managerial processes in the common ground. Becker etal. (2014)
provided psychological, clinical, technological, and regulatory viewpoints to outline
the state of the mHealth. In this section, these viewpoints were used to categorize
the elements in suggestions.
Social Influence
Technical Support and
Perceived Service
Result Demonstrability
Performance Expectancy
Behavioral Intention
Effort Expectancy
Personal Innovativeness
Mobile Anxiety
Mobile Self-efficacy
Fig. 10.3 Research model for nonuser physician
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
10.4.1 Psychological Perspective
Today, more than 75% of world population are able to access mobile communica-
tion services (Becker etal. 2014). In the largest countries, such as the USA and
China, more than 27 thousand medical applications were available on Android and
iOS market (Xu and Liu 2015). However, literature provided that mHealth applica-
tions were underutilized in practice, and it has created no dramatic change in both
organizational culture of health institutions and health behavior (Becker etal.
2014). In that regard, collaboration has been a need among application developers,
physicians, and researchers who have expertise in behavior and attitudes. In this
study, the signicance of perception in job performance, ease of mHealth use, per-
sonal perspectives in new technologies, and potential of anxiety were revealed for
both groups. Thus, the following elements should be considered for mHealth
Table 10.1 Signicant and nonsignicant relations for mHealth user physicians and nonuser
User physicians
Sig. Non-sig. Sig. Non-sig.
PSBI ✓ ✓
MABI ✓ ✓
COPE ✓ ✓
MSEE ✓ ✓
E. Sezgin et al.
Focusing on the job performance and providing simple applications Since the
workload is high and quick access to the information is a need, physicians rather
prefer less exhausting assistive services in practice. Thus, they expect effort-free
and useful, to-the-point applications in healthcare services. The simplicity of the
application and providing quick and relevant information are valuable features in
use (Gagnon etal. 2015).
Promotional activities for new mHealth applications There is a potential interest of
physicians toward new technologies. Utilizing this feature, mHealth applications
could be promoted among physicians for encouraging active use and creating a
positive perception in healthcare services. Thus, instead of basic training or seminars
at the initial stage, the promotional activities, such as meetings or activities including
social interactions, would attract both users and potential users toward using
mHealth applications in practice. Alternatively, key characters in the organizations,
such as “opinion leaders,” would be assistive to disseminate the use of mHealth
applications, which would also impact the organizational culture and mHealth use
“etiquette” in the long term (Hao etal. 2013).
The next level training. Following the promotional activities, training would help
physicians to use mHealth in completing daily tasks. It could be provided as on-the-
job training and in-action implementations. It is especially benecial for new users
in order to eliminate the risk of resistance and reduce potential anxiety in use by
familiarizing the new users to the mHealth applications. In addition to that, it would
reduce the possible risks as errors in multitasking (Wu etal. 2005; Varshney 2014).
10.4.2 Clinical Perspective
In the current state, literature and the study demonstrated that simple features of
mobile technologies work effectively in clinical practice, especially in developing
countries, such as communication applications and SMS (Free etal. 2013; Källander
etal. 2013; Becker etal. 2014).
Collaboration is the core The study provided that there is a social bond among
healthcare providers (i.e., physicians, nurses, technicians). Thus, collaboration
among healthcare providers has been a must, and the applications should be
developed regarding collaboration of the core of the operations. In that regard, easy
sharing methods and collaborative working tools would be benecial in mHealth
Providing continuous services The service availability was perceived to be an
important factor for the physicians. In that regard, one of the major benets of
communication applications was their service availability and providing access to
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
the service time and location independent. Here, the benets of communication
applications could be embraced in a broader extend to include healthcare-specic
services providing signicant functions available.
10.4.3 Technological Perspective
The study provided that the technological infrastructure of healthcare institutions
include the Internet and local area computer network within the institutions. Each
hospital uses a medical health record system to keep the track and to report the
operations. In that regard, a couple of issues should be considered for mHealth
application use.
Compatibility and interoperability of applications Compatibility of mHealth appli-
cations with the healthcare systems would inuence physicians’ working routines
and the job performance as well. The current state of mHealth showed that the
technology is still evolving and incompatible mHealth applications exist (Becker
etal. 2014). Thus, the development of a mobile-compatible healthcare service
platform for institutions is as important as developing mHealth application itself.
In addition to that, the communication among the systems is also crucial for services.
Interoperable systems would also boost the development and use of mHealth
applications in healthcare services.
Providing demonstrable results The ability to demonstrate the medical results, cal-
culations, problems, or processes was perceived important by the physicians. Hence,
the mHealth technology being provided should grant the ability to display and share
high-quality visual medical contents. In that regard, increasing visual quality as
well processing speed in medical contents would be valuable in healthcare
Focusing on infrastructure Technological infrastructure, especially the communi-
cation network, is important for timely delivery of healthcare services (Sezgin and
Özkan-Yildirim 2016). However, the reliability could be an issue, and uninterrupted
service could not be provided for the developing countries (Varshney 2014). Thus,
developing an interoperable and compatible platform does also rely on a reliable
infrastructure. It is suggested to develop a contingency plan and ad hoc solution
maps for unexpected infrastructural issues (such as electricity cuts, network loss,
hardware and software malfunctions).
E. Sezgin et al.
10.4.4 Regulatory Perspective
Laws and regulations regarding mHealth technologies and applications are at the
initial stage (Barton 2012; Becker etal. 2014). In developing countries, it was
estimated to adapt the regulations in the long term. In that regard, the following
points would be considered in mHealth application development.
Acting with the laws and regulations about mHealth Even though the current state
of regulations is in the development phase, the need for laws and regulations is
increasing considering the number of available mHealth applications in the market.
These applications were commercially available and enabled users to share con-
dential information with the third parties. Thus, for security and privacy of informa-
tion, regulatory acts were required by the authorities. In the study, the physicians
have also stated their expectations on regulations about mHealth applications.
Standards for applications This study reported that some mHealth applications
were following international standards in medical practice while providing content
in healthcare. However, the market was crowded with many other unregulated and
unstandardized applications being available for the end users. Considering the
current trajectory, mHealth applications following the standards were found more
reliable by the physicians. Thus, considering international standards in the
developmental phase would help to build the reliability and credibility of the
mHealth applications. In addition to that, providing the procedures for implementing
international standards at national level application development would also be
recommended to the authorities.
Considering the four perspectives, the current stage of mHealth would be an
opportunity for developers to anticipate the trajectory of the transformation in
healthcare services and to release their applications in the market on time. In that
regard, the potential of change in organizational culture and its evolution around
mHealth applications and technologies should be considered in long-term strategic
10.5 Conclusion
In this chapter, a comparative assessment of mHealth application adoption by the
physicians was reported. Considering the intentions and perceptions of physicians,
several suggestions were outlined. The suggestions in this chapter would be helpful
for better understanding the characteristics of two different groups of physicians.
The ndings would guide developers and authorities to understand user needs.
Thus, it would be a valuable input in the mHealth application and healthcare policy
10 Intention vs. Perception: Understanding theDifferences inPhysicians’ Attitudes…
It should be noted that this study has also extended the literature regarding
researches investigating users and nonusers’ behaviors in healthcare technologies
(Cheung etal. 2013; Bidmon etal. 2014; Sims etal. 2014). However, further studies,
employing qualitative designs, would be resourceful to achieve in-depth understanding
in physician intentions and perceptions toward mHealth application use.
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... Instead, it would be interesting to conduct a long-term study where behavior can be observed. Not only the difference between users and non-users [9] is important when investigating the acceptance of technology, but also different fitness apps can have an influence on the intention to do sports using an app. In addition, our sample is rather young and educated and was acquired via social contacts. ...
The intention-behavior gap is a well-known phenomenon in health behavior research. Individuals often intend to engage in healthy behaviors but fail to act. Fitness apps have emerged as a promising tool to bridge this gap and promote physical activity. This study aimed to understand the acceptance factors relevant to intending to use fitness apps (UTAUT2) and factors that prevent people from using fitness apps. By shedding light on behavioral-related factors such as organizational and motivational challenges, social inclusion, and volitional factors, this study contributes to explaining and bridging the intention-behavior gap. An online survey was conducted with a sample size of n = 100. Participants were asked about their fitness app usage, motivation for using fitness apps, and barriers preventing them from using them. The results showed that while hedonic motivation and habit influence users’ intention to use fitness apps, performance expectancy influences the intention to use a fitness app for non-users. Further, the results showed no influence of behavioral-related factors on the intention to use fitness apps but on sport behavior. The study’s findings offer implications for research and actionable guidelines for promoting physical activity and overcoming the intention-behavior gap.KeywordsFitness AppsAcceptanceIntention Behaviour GapVolition
... Consequently, questions remain about the benefits of mHealth tools as a viable and robust care delivery platform (Free et al., 2013;Marcolino et al., 2018). Growing concerns among clinicians about the interoperability of these tools and their integration across older systems suggest more needs to be done to incorporate clinician workflows (Sezgin et al., 2018;Gruson, 2021). Many seem skeptical of how these tools enhance care and perceive ease of use as a complicating factor (Gagnon et al., 2016). ...
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Mobile Health (mHealth) interventions have received a mix of praise and excitement, as well as caution and even opposition over recent decades. While the rapid adoption of mHealth solutions due to the COVID-19 pandemic has weakened resistance to integrating these digital approaches into practice and generated renewed interest, the increased reliance on mHealth signals a need for optimizing development and implementation. Despite an historically innovation-resistant medical ethos, mHealth is becoming a normalized supplement to clinical practice, highlighting increased demand. Reaching the full potential of mHealth requires new thinking and investment. The current challenge to broaden mHealth adoption and to ensure equity in access may be overcoming a “design purgatory,” where innovation fails to connect to practice. We recommend leveraging the opportunity presented by the COVID-19 pandemic to disrupt routine practice and with a new focus on theory-driven replicability of mHealth tools and strategies aimed at medical education and professional organizations.
... Moreover, as an urgent research desideratum, the role of prior experience needs to be further explored. While the difference between users and nonusers [74] of mHealth apps might impact technology acceptance, so too could the usage of different mHealth apps probably influence future acceptance of mHealth apps in general. Additionally, the rather young and educated sample needs to be considered, which was acquired via social contacts. ...
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Background Mobile health (mHealth) care apps are a promising technology to monitor and control health individually and cost-effectively with a technology that is widely used, affordable, and ubiquitous in many people’s lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened health care systems worldwide may directly profit from the use of therapy apps by those patients already in need of medical treatment and monitoring. Objective We aimed to compare the factors influencing the acceptance of lifestyle and therapy apps to better understand what drives and hinders the use of mHealth apps. Methods We applied the established unified theory of acceptance and use of technology 2 (UTAUT2) technology acceptance model to evaluate mHealth apps via an online questionnaire with 707 German participants. Moreover, trust and privacy concerns were added to the model and, in a between-subject study design, the influence of these predictors on behavioral intention to use apps was compared between lifestyle and therapy apps. Results The results show that the model only weakly predicted the intention to use mHealth apps (R2=0.019). Only hedonic motivation was a significant predictor of behavioral intentions regarding both app types, as determined by path coefficients of the model (lifestyle: 0.196, P=.004; therapy: 0.344, P<.001). Habit influenced the behavioral intention to use lifestyle apps (0.272, P<.001), while social influence (0.185, P<.001) and trust (0.273, P<.001) predicted the intention to use therapy apps. A further exploratory correlation analysis of the relationship between user factors on behavioral intention was calculated. Health app familiarity showed the strongest correlation to the intention to use (r=0.469, P<.001), stressing the importance of experience. Also, age (r=–0.15, P=.004), gender (r=–0.075, P=.048), education level (r=0.088, P=.02), app familiarity (r=0.142, P=.007), digital health literacy (r=0.215, P<.001), privacy disposition (r=–0.194, P>.001), and the propensity to trust apps (r=0.191, P>.001) correlated weakly with behavioral intention to use mHealth apps. Conclusions The results indicate that, rather than by utilitarian factors like usefulness, mHealth app acceptance is influenced by emotional factors like hedonic motivation and partly by habit, social influence, and trust. Overall, the findings give evidence that for the health care context, new and extended acceptance models need to be developed with an integration of user diversity, especially individuals’ prior experience with apps and mHealth.
... Moreover, as an urgent research desideratum, the role of prior experience needs to be further explored. While the difference between users and nonusers [74] of mHealth apps might impact technology acceptance, so too could the usage of different mHealth apps probably influence future acceptance of mHealth apps in general. Additionally, the rather young and educated sample needs to be considered, which was acquired via social contacts. ...
BACKGROUND Mobile healthcare applications (mHealth apps) are a promising technology to monitor and control health individually and cost-effective with a technology that is widely used, affordable and ubiquitous in many people's lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened healthcare systems worldwide may directly profit from the use of therapy apps by those patients already in need for medical treatment and monitoring. OBJECTIVE Therefore, by comparing lifestyle and therapy apps, we aim at better understanding what influences potential users’ decisions to use (or not to use) mHealth apps. METHODS We apply the established UTAUT2 technology acceptance model to evaluate mHealth apps in an online questionnaire with n = 707 German participants. Additionally, trust and privacy concerns are added to the model and in a between-subject study design the influence of these predictors on behavioral intention to use is compared between lifestyle and therapy apps. RESULTS The results show that the model does only weakly predict the intention to use mHealth apps (R2 = .019). Only hedonic motivation is a significant predictor of behavioral intentions regarding both app types (lifestyle: .196, p < .01; therapy: .344, p < .001). Habit influences the behavioral intention to use lifestyle apps (.272, p < .001), while social influence (.185, p < .001) and trust (.273, p < .001) predict the intention to use therapy apps. A further exploratory correlation analysis of the relationship between user factors on behavioral intention was calculated. Health app familiarity shows the strongest correlation to the intention to use (r = .469, p < .001), stressing the importance of experiences. Also age, education level, app familiarity, digital health literacy, privacy disposition, and the propensity to trust apps correlate weakly to behavioral intention to use mHealth apps. CONCLUSIONS The results indicate that for the health care context, new and improved acceptance models need to be developed that also integrate user diversity, especially experiences with apps and mHealth.
... However, these aspects intertwined with other important social aspects namely recommendations from people or institutions they can trust, reliability and neutrality of the content (the content provider must be neutral to be trusted, whereas examples given highlighted concerns of bias where content was provided by Pharma companies), social and cultural factors impacting people's attitudes towards technology, and some organizational factors such as workload and internal politics. These findings complement previous research that shed light on non-technical factors that play a crucial role in mHealth adoption [54][55][56][57]. It's also worth noting, however, how the material properties of different technologies can also afford different use cases depending on the context they are being used in [22]; this was clear with participants' experiences around how they switch between the mobile app and the web app on the desktop when they are using ONCOassist. ...
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Background: Despite the existence of adequate technological infrastructure and clearer policies, there are situations where users, mainly physicians, resist mobile health (mHealth) solutions. This is of particular concern, bearing in mind that several studies, both in developed and developing countries, showed that clinicians’ adoption is the most influential factor in such solutions’ success. Objective: This research focuses on understanding clinicians’ roles in the adoption of an Oncology Decision Support App, the factors impacting this adoption, and its implications for organizational and social practices. Methods: A qualitative case study of a decision support app in Oncology, called ONCOassist. The data were collected through 17 in-depth interviews with clinicians and nurses in the UK, Ireland, France, Italy, Spain and Portugal. Results: This case demonstrates the affordances and constraints of mHealth technology at the workplace, their implications for the organization of work, and clinicians’ role in their constant development and adoption. The research findings confirmed that factors such as app operation and stability, ease of use, usefulness, cost, and portability play a major role in the adoption decision; however, other social factors such as endorsement, neutrality of the content, attitude towards technology, existing workload and internal organizational politics are also reported as key determinants of clinicians’ adoption. Interoperability and cultural views of mobile usage at work are the key workflow disadvantages; while higher efficiency and performance, sharpened practice and location flexibility are the main workflow advantages. Conclusions: Several organizational implications emerged, suggesting the need for some actions such as fostering a work culture that embraces new technologies, and the creation of new digital roles for clinicians both on the hospitals/clinics and on the development sides but also more collaboration between healthcare organizations and Digital Health providers to enable Electronic Medical Record (EMR) integration and solve any interoperability issues. From a theoretical perspective, we also suggest the addition of a fourth step to Leonardi’s (2018) methodological guidance that accounts for user engagement; embedding the users in the continuous design and development processes ensures the understanding of user specific affordances that can then be made more obvious to other users and increase the potential of such tools to go beyond their technological features and have a higher impact on workflow and the organizing process.
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This edited volume is a product of the undergraduate and graduate students from the University of Bayreuth who participated in a study tour to the U.S. to learn about the country’s health reform efforts. Through writing about their experiences the students had a chance to reflect on the enormous amount of information they were exposed to during their time in the U.S. Students were free to choose a topic for their essay and to decide on the focus of their work. They all invested substantial amounts of time and effort to present their thoughts and reflections in a clear and informative manner. Nonetheless, this volume does not aim at presenting a comprehensive overview of the U.S. health care system. Rather, it gives an impression of what the students took away from ten extremely intensive days in the U.S. For the reader this volume offers the chance to get an upto-date overview on a range of topics that shape current U.S. health policy.
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The Health Information Technology for Economic and Clinical Health (HITECH) Act has set substantial financial incentives to foster the imple-mentation of electronic health records (EHRs) in the U.S. healthcare system. These incentives contributed to an incr ease to 86% in the use of EHRs among office-based physicians and 95% among hospitals in 2017. In order to assess whether the broad adoption of EHRs had a valuable impact on medical and economic outcomes, this paper will evaluate the findings from the SDL p er-spective. Results show that widespread use of EHRs does not necessarily lead to improved medical and economic outcomes. Empirical evidence suggests that effects range from negative to positive and significantly differ between medical service providers. R easons for the variety of the findings may result in a gap between the value propositions of EHR manufacturers (higher qual-ity, lower costs) and the actual increase in value after implementation. Strat-egies to improve the value achieved may include strateg ic planning for the implementation of EHRs and sufficient training of staff working with EHRs to digitalize and not purely digitize existing processes.
This study explored the factors influencing the continued intention to use mobile money transfer services among university students in Ghana. The UTAUT was used as the research theoretical framework while the analysis was conducted with SPSS and SmartPLS. The results demonstrate that the continued intention to use mobile money transfer services is influenced by performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived service quality. Also, perceived service quality was found to be a significant predictor of the actual use of mobile money transfer services. The study further revealed that the continued intention to use was a positive determinant of the actual usage of mobile money transfer services. The implications of these and other findings of the study are discussed.
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Objective The study aims to understand physicians’ awareness of mobile health (mHealth) apps and their intentions to use these apps in medical practice. Method Mobile Health Technology Acceptance Model (M-TAM) was tested employing the sequential explanatory mixed method. An online survey and focus group interviews were conducted for data collection. Physicians were invited to participate in the survey. Structural Equation Modeling (SEM) was used in quantitative data analysis. Qualitative data were analyzed using coding, memo, and contextual analyses. Results 151 physicians participated in the survey, representing a 15% response rate. The model was able to explain physicians’ intention to use mHealth apps by explaining 59% of the total variance. Performance Expectancy, Mobile Anxiety, Perceived Service Availability and Personal Innovativeness were major influencing factors of Behavioral Intention. Qualitative codes outlined that information gathering and communication purposes were the major enablers in mHealth app usage. In that regard, Communication and Consulting, Clinical Decision Making, Reference and Information Gathering, and Information Management are the most popular app categories. On the other hand, lack of knowledge and lack of investment were seen as the major barriers to mHealth app usage. Conclusions User perception and intentions are important factors in technology use. Thus, the preferences, expectations, and characteristics of physicians which were outlined in this research could be significant inputs for researchers, app developers, managers and policymakers.
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The objective of this study was to investigate physicians’ perceptions to use mobile health applications in practice, and to identify influencing factors to use the technology. An mHealth technology acceptance model was proposed (M-TAM), and a cross-sectional survey was implemented using structured questionnaire to collect data. Online tools were used for inviting participants (physicians) and data collection from Turkey. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). A total of 128 physicians participated in the survey. The model explained the perception of physicians towards mHealth application use by 51% of total variance. The influential factors were identified as Effort Expectancy, Mobile Anxiety, Perceived Service Availability and Technical Training and Support. The study provided a new model to the literature of health information technology. Findings of the research contributed by unveiling latent constructs and their influence on physicians’ perceptions towards a new healthcare technology: mHealth applications.
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Background: Health information technologies have become vital to healthcare services. In that regard, successful use of information technologies in pharmaceutical services is important to manage, control and maintain pharmaceutical transactions, which increase the quality of healthcare delivery. Objective: This study aimed to identify influencing factors on pharmacists’ acceptance of pharmaceutical service systems. Methods: A cross-sectional study was conducted employing a research model based on technology acceptance theories. A parsimonious model was developed, and a self-reported questionnaire was distributed online. Community pharmacists participated voluntarily via the website of Turkish Pharmacists’ Association. The data was analyzed employing Structural Equation Modeling. Results: From 77 out of 81 cities of Turkey, 2169 community pharmacists participated to the survey with 43% response rate. Perceived usefulness, perceived ease of use, system factors and perceived behavioral control explained 47% of total variance in pharmacists’ intention to use the pharmaceutical technology. Conclusion: The findings of the research provided insight about relations of influencing factors and practical implications regarding perceived behaviors and system use. Future researchers would benefit from the study design and findings. The study is also valuable for being the first nationwide study conducted on pharmacists about user attitudes towards a technology
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The aim of this systematic review was to synthesize current knowledge of the factors influencing healthcare professional adoption of mobile health (m-health) applications. Covering a period from 2000 to 2014, we conducted a systematic literature search on four electronic databases (PubMed, EMBASE, CINAHL, PsychInfo). We also consulted references from included studies. We included studies if they reported the perceptions of healthcare professionals regarding barriers and facilitators to m-health utilization, if they were published in English, Spanish, or French and if they presented an empirical study design (qualitative, quantitative, or mixed methods). Two authors independently assessed study quality and performed content analysis using a validated extraction grid with pre-established categorization of barriers and facilitators. The search strategy led to a total of 4223 potentially relevant papers, of which 33 met the inclusion criteria. Main perceived adoption factors to m-health at the individual, organizational, and contextual levels were the following: perceived usefulness and ease of use, design and technical concerns, cost, time, privacy and security issues, familiarity with the technology, risk-benefit assessment, and interaction with others (colleagues, patients, and management). This systematic review provides a set of key elements making it possible to understand the challenges and opportunities for m-health utilization by healthcare providers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email:
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The market of mobile health (mHealth) apps has rapidly evolved in the past decade. With more than 100,000 mHealth apps currently available, there is no centralized resource that collects information on these health-related apps for researchers in this field to effectively evaluate the strength and weakness of these apps. The objective of this study was to create a centralized mHealth app repository. We expect the analysis of information in this repository to provide insights for future mHealth research developments. We focused on apps from the two most established app stores, the Apple App Store and the Google Play Store. We extracted detailed information of each health-related app from these two app stores via our python crawling program, and then stored the information in both a user-friendly array format and a standard JavaScript Object Notation (JSON) format. We have developed a centralized resource that provides detailed information of more than 60,000 health-related apps from the Apple App Store and the Google Play Store. Using this information resource, we analyzed thousands of apps systematically and provide an overview of the trends for mHealth apps. This unique database allows the meta-analysis of health-related apps and provides guidance for research designs of future apps in the mHealth field.
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Research dealing with various aspects of* the theory of planned behavior (Ajzen, 1985, 1987) is reviewed, and some unresolved issues are discussed. In broad terms, the theory is found to be well supported by empirical evidence. Intentions to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior. Attitudes, subjective norms, and perceived behavioral control are shown to be related to appropriate sets of salient behavioral, normative, and control beliefs about the behavior, but the exact nature of these relations is still uncertain. Expectancy— value formulations are found to be only partly successful in dealing with these relations. Optimal rescaling of expectancy and value measures is offered as a means of dealing with measurement limitations. Finally, inclusion of past behavior in the prediction equation is shown to provide a means of testing the theory*s sufficiency, another issue that remains unresolved. The limited available evidence concerning this question shows that the theory is predicting behavior quite well in comparison to the ceiling imposed by behavioral reliability.
Beliefs, attitudes, and intentions are important factors in the adoption of computer technologies. While contemporary representations have focused on explaining the act of using computers, the role of learning to use the computer needs to be better understood within the overall adoption process. Inadequate learning can curtail the adoption and use of a potentially productive system. We introduce a new theoretical model, the theory of trying, in which computer learning is conceptualized as a goal determined by three attitude components: attitude toward success, attitude toward failure, and attitude toward the process of goal pursuit. Intentions to try and actual trying are the theoretical mechanisms linking these goal-directed attitudes to goal attainment. An empirical study is conducted to ascertain the construct validity and utility of the new theory within the context of the adoption of a word processing package. Specifically, we examine convergent validity, internal consistency reliability, stability, discriminant validity, criterion related validity, predictive validity, and nomological validity in a longitudinal field study of 107 users of the program. The new theory is compared to two models: the theory of reasoned action from the field of social psychology and the technology acceptance model, recently introduced in the management literature. Overall, the findings stress the importance of scrutinizing the goals of decision makers and their psychological reactions to these goals in the prediction of the adoption of computers.