A Meta-analytical View on the Acceptance of Mhealth Apps

Luisa Vervier

and Martina Zieﬂe

Human Computer Interaction Center, RWTH Aachen University, Campus Boulevard 57, Aachen, Germany

Keywords:

Mhealth Apps, Acceptance, User Diversity, Online Privacy, Trust.

Abstract:

Never before has there been such a driving transformation of healthcare concerning digitalization as in early

2020 due to the pandemic. The digital transformation in the health sector holds great potential to meet the

challenges of demographic change, the ﬁnancial burden on the health system and the shortage of skilled

workers. One digital solution is the usage of modern information and communication technologies (ICT),

such as mhealth applications (mhealth apps). However, the usage of mhealth apps especially in the German

healthcare system is still evolving and the usage rate of mhealth apps among the population is rather low. To

understand acceptance-relevant factors for the usage, this meta-analytical study presents two samples before

and during pandemic times (survey times 2019 and 2021). To analyse these samples the UTAUT2 acceptance

model in an extended version was applied. The additional factors are online privacy, trust and ehealth literacy.

In total 644 participants took part. The results show, that the factors habit, hedonic motivation, performance

expectancy, social inﬂuence and effort expectancy contribute most strongly to the intention to use mhealth

apps as well as the added factor trust. The decisive factor for the user proﬁles was age.

1 INTRODUCTION

Shortly after the ﬁrst study in this paper was collected,

the infectious disease SARS-CoV-2 (coronavirus or

COVID-19) spread worldwide. As never before, the

virus has driven digitalization in healthcare. The pan-

demic holds the opportunity to bring forth new ap-

proaches to digitalization in healthcare, which are

currently being developed and applied. Video con-

sultations with doctors, platforms used for clinical

data collection (for example, (climedo, 2020)), dig-

ital help for monitoring COVID-19 and health apps

are some examples of digital applications, that have

gained new prominence. All these telemedicine of-

ferings have given digitalization a new key role in

healthcare, primarily to prevent the spread of infec-

tions. These digital solutions should not only help in

the healthcare sector in the acute phase of the pan-

demic, but also sustainably improve healthcare be-

yond that. The potential of the technical prerequi-

sites (infrastructure, end devices) already exists, but

the widespread use of digital ICT in healthcare is still

far from being achieved. The interlinking of digital

and realistic health services is, in addition to technical

conditions and developments on the part of the health

https://orcid.org/0000-0002-9030-6999

https://orcid.org/0000-0002-6105-4729

system, also dependent on the acceptance of potential

users.

2 STATE OF THE ART

In the following mhealth apps, acceptance factors and

user diversity is described.

2.1 Mhealth Apps

A trend that has aroused great interest in society in

very recent years is the use of smartphone apps for

self-determined measurement of health parameters.

Maintaining and optimising one’s own health as well

as increasing health awareness contribute to recording

the technical possibilities for measuring vital param-

eters, physical activities as well as exchanging these

data with other users. Despite the wide range of health

apps on the market, usage is low at 14 per cent in

Germany (Meinungsforschungsinstitut, 2019). Most

popular among these apps are sports, nutrition and

weight management apps. Considering that diseases

can be prevented through a healthy lifestyle, plenty

of exercise, a healthy diet and sufﬁcient sleep, it is

beneﬁcial if health-related mhealth apps trigger lively

interest and use among end users. After all, the po-

322

Vervier, L. and Zieﬂe, M.

A Meta-analytical View on the Acceptance of Mhealth Apps.

DOI: 10.5220/0011088400003188

In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2022), pages 322-329

ISBN: 978-989-758-566-1; ISSN: 2184-4984

tential that mhealth apps offer for good healthcare is

very rich. However privacy concerns and usage bar-

riers arise (Powell et al., 2014). mhealth apps are a

highly relevant topic from various perspectives - ei-

ther from a health policy point of view, from a social

point of view or from an economic point of view. The

current state of research still has many research gaps

with regard to user acceptance and user diversity. Es-

pecially in the german-speaking countries, the empir-

ical research on the acceptance of health apps is very

incomplete. For this reason, this study aims to inves-

tigate the user-speciﬁc and acceptance-relevant moti-

vations and factors for the use of mhealth offerings

for health maintenance.

2.2 Measuring Acceptance

This study is based on the description of attitudinal

acceptance and considers acceptance in this context

as the willingness to use a health app (technological

innovation). Numerous acceptance models have at-

tempted to map the factors inﬂuencing the develop-

ment of acceptance. The majority of current accep-

tance models are based on the Theory of Reasoned Ac-

tion (TRA), postulated by Fishbein and Ajzen (Fish-

bein and Ajzen, 1977). TRA contains basic assump-

tions that were adopted in the development of subse-

quent acceptance models. The theory states that ac-

tual behaviour is the immediate predictor of the use

of a technology in terms of the behavioural or usage

intention. This behavioural intention is in turn de-

termined by the attitude towards the behaviour and

the social norm. In the chronological sequence of

the development stages from the Technology Accep-

tance Model (TAM)1 to TAM3, the Uniﬁed Theory of

Acceptance and Use of Technology model (in short:

UTAUT) was developed, which unites the predeces-

sors of the TAMs (Venkatesh and Bala, 2008). The

models were developed with a focus on predicting

adoption in the work context. With the increased use

of technological innovations outside the work context,

the UTAUT and also the UTAUT2 including three ad-

ditional predictors, such as hedonic motivation, mon-

etary value and habit (Venkatesh et al., 2012) cov-

ered a possible model proposal for investigation in the

commercial context or beyond, e.g. the digital health

sector. Therefore, the conceptual basis of the study is

the acceptance model UTAUT2 with its seven factors.

Due to previous studies (Schomakers et al., 2022)

three further constructs were added to the model: (1)

trust in apps, (2) online privacy and (3) ehealth liter-

acy.

2.3 Extension of UTAUT2 Model

The application of the UTAUT2 in the health sector

requires a renewed adaptation. Empirical studies have

shown that speciﬁcally when investigating the accep-

tance of mhealth apps, various factors can be both mo-

tive and barrier at the same time and play a decisive

role in the willingness to use them. Trust (Deng et al.,

2018), online privacy (Peng et al., 2016)

and ehealth literacy (Griebel et al., 2018); rep-

resent factors to be considered. Trust is a multidi-

mensional construct that is conceptualised in different

ways depending on the discipline. This paper focuses

on the dimension of technical trust. Trust in technol-

ogy arises from the expected predictability, credibility

and usefulness of the technology (Lippert, 2002). In

the context of health, trust is of relevance since one’

s own health is a valuable resource. People who do

not see health apps as serious and safe support, but

rather a risk to their own health, will not use them. In

the context of ICT, many studies have been conducted

investigating trust in online banking (Luarn and Juo,

2010), eCommerce (Lee et al., 2006) or social media

(Lankton and McKnight, 2011). However regarding

trust in mhealth apps the state of the art is very lim-

ited. Since previous studies have shown that trust is

an important indicator of the intention to use a tech-

nology, it is included in this investigation (Deng et al.,

2018). With the many technical and health beneﬁts of

using mhealth apps, there are factors that work against

using such offerings from the user’s point of view as

e.g. the online privacy attitude. It is a multidimen-

sional construct that can be measured across different

dimensions. For instance, the behaviour of a person,

such as the degree of disclosure of one’s own data on

the Internet, is used to analyse online information pri-

vacy (Acquisti et al., 2015).

Data classiﬁed as particularly sensitive may in-

clude, e.g., genetic information, information about

psychological behaviour, illnesses or sexual prefer-

ences (Valentino-Devries, 2010). Especially through

the use of mhealth apps, it is possible to draw conclu-

sions about the person via the phone number (ibid.),

which triggers concerns about social discrimination

among users (Applebaum, 2002). Various studies

conﬁrmed that online privacy is an important factor

for the intention to use digital health offers

(Deng et al., 2018). Therefore it is included in

this study. ehealth literacy - For everyday life, it is

relevant to have a certain ability to deal with one’s

own health and to make conscious decisions about

one’s own health care, disease prevention or even

health promotion. For a long time, the physician,

the pharmacy magazine or even advice from relatives

A Meta-analytical View on the Acceptance of Mhealth Apps

323

and family were the most important source of health

information. With the advent of digital ICT in the

form of ehealth (e.g. platforms) or mhealth apps,

users must have certain digital skills in order to bene-

ﬁt from the offers. The possibility of acquiring health-

relevant knowledge via mhealth apps e.g. can be seen

as an individual and also social opportunity. However,

the great added value of mhealth apps for improving

health care and health care provision, networking and

self-determination of the individual can only be guar-

anteed if the user is able to use them proﬁtably for

himself. ehealth literacy is seen as both a motive and

a barrier for the use of ehealth applications from a

technical and user perspective (Griebel et al., 2018).

The construct of ehealth literacy no longer only de-

scribes the ability to ﬁnd reliable health information

on the internet but also the handling of interactive dig-

ital health platforms, the use of mhealth apps or the

internet – search for and communication with health

care providers. So far, ehealth literacy as a basic pre-

requisite for the use of ICT in the context of accep-

tance research has only been investigated for ehealth

applications. Studies that examine the construct of

ehealth literacy with regard to the complexity of oper-

ating options of health apps and additionally integrate

the relevant factors into the UTAUT2 model are cur-

rently missing in empirical research. That is why it is

considered in this study.

2.4 User Diversity

A main requirement for the use of mhealth apps is that

they must be operable by different types of users. A

general categorisation of the user group is difﬁcult be-

cause of the diversity that users have in terms of their

age, gender, app experience, health status, etc. The

need to capture the diversity of users is an important

part of understanding the readiness to use mhealth

apps (Peng et al., 2016). Only if users are under-

stood in terms of their diversity it is possible to de-

velop individually tailored solutions and thus achieve

the acceptance of many users. For this reason, socio-

demographic factors such as age, gender, health status

as well as app experience are considered in this study.

3 METHODICAL PROCEDURE

To get insights into users’ attitude towards the usage

of mhealth apps, an online survey was administered

twice to different samples, ﬁrst in 2019 and secondly

in 2021. Three research questions were leading the

study: (1) Which factors are relevant for the accep-

tance of mhealth apps? (2) Do the extended factors

Figure 1: Overview of meta-analytical study design.

contribute to the prediction of acceptance? and (3)

Which user types or proﬁles can be formed based on

the socio-demographic factors?

3.1 Study Design

To address the research questions, a meta analysis

was chosen, which statistically analyses data from in-

dependent primary studies that focused on the same

question. The determined effects of the independent

studies are then summarised with the aim of obtain-

ing a more precise estimate of an overall effect (Glass,

1976). Figure 1 gives an overview of all factors which

were included in the meta analysis. Answering the

online survey took about 20 minutes. The question-

naire consisted of three parts. In the ﬁrst part par-

ticipants were familiarized with the topic of mhealth

apps. Furthermore they were informed, that partici-

pation was voluntary, and not rewarded and that they

could withdraw from the study at any time. In a sec-

ond part, demographic data was assessed. The third

and thus also the main part comprised the acceptance-

relevant factors of the extended UTAUT2 Model. In

the following a detailed description of the empirical

measurements will be outlined.

3.2 Evaluation Measures

In the questionnaire all items had to be evaluated on a

six-point Likert-scale from no agreement at all to to-

tal agreement. Demographic Data. In the ﬁrst part

the participants’ age, gender, their highest educational

attainment and experience with the use of mhealth

apps was assessed. UTAUT2 Factors. To measure

attitudes towards health apps, items based on Venak-

tesh’s UTAUT2 model (Venkatesh and Davis, 2000)

were partially adopted and adapted to the context in

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

324

terms of content. For the performance expectancy

factor, the question was raised to what extent the use

of mhealth apps can have a personal advantage. Ef-

fort expectancy evaluates how much effort the user

has to make using the mhealth apps. Social Inﬂuence

includes the social environment of the participant in

the consideration and asks what inﬂuence related per-

sons have on the willingness to use a mhealth app.

Facilitating condition measures whether the user has

the necessary resources and assistance available to use

mhealth apps. Hedonic Motivation or rather fun to

use a health app was asked as well as habit which

describes a regular practice with a health app. Finally

the intention to use a health app as a useful application

in everyday life was assessed. In contrast to the orig-

inal UTAUT2 model, actual usage behavior was not

measured, as no usage data was collected as part of

the studies. The price of a mhealth app was also dis-

regarded, as the studies only refer to ﬁctitious health

apps. Online privacy attitude, trust and ehealth liter-

acy were added to the model.

Added Factors to UTAUT2. Attitudes regarding

individual online privacy in the use of mhealth apps

were assessed with the Mobile User’s Concerns for

Information Privacy Scale (MUCIP) from (Xu et al.,

2012). The scale measures the perceived privacy in

dealing with online data and represents a valid mea-

surement instrument, which in the original explicitly

refers to smartphone use. The three dimensions were

surveyed with three items each: (1) perceived surveil-

lance of the user, (2) perceived invasion of privacy and

(3) concerns about secondary use of the data (second

use). In the original, the scale is measured on a seven-

point Likert scale. In order to relate it statistically to

other scales and to avoid the a central tendency as a

response bias, the response format was changed to a

six-point one. Trust in service and technology was

collected as an extended construct for the UTAUT2

model. The items were adapted from K

orber (K

orber,

2018). To measure ehealth literacy the Digital Health

Literacy Index (short: DHLI) according to van der

Vaart (van der Vaart and Drossaert, 2017) was as-

sessed. In the original version, the DHLI measures

operating, search of information and search for infor-

mation skills. The items were reformulated for the

use of mhealth apps to search for health-related infor-

mation.

3.3 Sample Description

The questionnaire was part of a university seminar

and thus participants were recruited from the stu-

dents’ environment via social networks. As can be

seen in table 1 the two samples were very homoge-

nous except of the grade of school education and app

experience. The sample of study I showed a high ed-

ucational degree and a very high app experience (M =

4.64 / max. = 6). In contrast, the sample of study II

had a low level of education and very little work ex-

perience (M = 1.21 / max. = 6). The average age in

study I was around 30 years and in study II around 40

years rounded off. The proportion of women and men

was almost equally distributed. The health status was

averagely high in both samples.

3.4 Statistical Meta-analysis

In order to be methodologically precise and correct,

due to the different samples, a procedure was chosen

that allows each study to be considered as a single

study and still obtain cumulative results. The R pack-

age ”metacor” (Lalibert

e et al., 2014) was used as the

statistical evaluation program. The estimation proce-

dure used was the DerSimonian-Laird (DSL) proce-

dure (IntHout et al., 2014). The random-effects model

(RE model) incorporates heterogeneity between stud-

ies by considering the effects as random. The method

uses the correlation coefﬁcients of the individual stud-

ies as effect sizes. These are to be interpreted as con-

ﬁdence intervals and stand as an indicator of signiﬁ-

cance with the p-value.

4 RESULTS

The statistical results for the UTAUT2 factors from

the original model are described ﬁrst, followed by a

summary of the extended factors as well as the de-

scription of a user proﬁle based on the samples. Sig-

niﬁcance level of the correlations range from * =

p<.05, ** = p< .01 to *** = p <.001.

UTAUT2 Factors. In the meta analysis, only

one signiﬁcant difference between the individual

UTAUT2 constructs could be found. The factor fa-

cilitating condition differed statistically signiﬁcant (r

= .135 (.230, .037) p = 0.003) which implies that

necessary resources and assistance availability to use

mhealth apps has changed. All other factors do not

show any statistically signiﬁcant variability and can

be concluded as homogeneous. This means that the

constructs of the different studies can be summarised

and be seen as stable and explainable acceptance fac-

tors. The highest inﬂuence on the intention to use

health apps was: (1) habit (r = .787 (.815, .756), p

= n.s.), (2) hedonic motivation (r = .670 (.710, .625),

p = n.s.), (3) performance expectancy (r = ,636 (.695,

.569), p = n.s.),(4) social inﬂuence (r = .390 (.510,

.255), p = n.s.), (5) effort expectancy (r = .361 (.412,

A Meta-analytical View on the Acceptance of Mhealth Apps

325

Table 1: Demographic data of study I and II separated by collection time.

study I 2019 study II 2021

n = 278 n = 366

age [years] M (SD) 29.98 (14.39) 37.90 (18.67)

sex women 57% 60%

men 43% 40%

education no certiﬁcate 1% 31%

certiﬁcate of secondary education 5% 35%

general certiﬁcate of secondary education 7% 8%

general qualiﬁcation for university degree 87% 26%

health status 4.49 (.92) 4.28 (1.07)

app experience 4.64 (.89) 1.21 (.48)

.276), p = n.s.) and ﬁnally (6) faciliating condition (r

= .135 (.230, .037), p = .003).

In Study I, the habit and usage intention had the

highest relationship (r = .804**), followed by the rela-

tionship between performance expectancy and hedo-

nic motivation (r = .715**), hedonic motivation and

usage intention (r = .677**), and the relationship be-

tween hedonic motivation and habit (r = .600**) as

well as between performance expectancy and usage

intention (r = .600**). Study II showed also the high-

est correlation between habit and usage intention (r

= .775**), followed by hedonic motivation and per-

formance expectancy (r = .704**), usage intention

and hedonic motivation (r = .665**), usage intention

and performance expectancy (r = .665**) and faciliat-

ing condition and effort expectancy (r = .662**). All

other correlations from study I and II were moderate

and can be found in table 2.

Impact of Extended Factors on Intention to

Use. Perceived online privacy, ehealth literacy and

trust were included in the meta analysis as compa-

rable extended constructs. The correlations between

the extended factors were not statistically signiﬁcant.

Thus, the results in the individual studies can be sum-

marised. Trust had the strongest positive inﬂuence on

intention to use (r = .577 (.639, .508), p = n.s.).

All other correlations were negative, meaning

the higher the perceived invasion (r = -.215 (-

.110, −.315), p = n.s.), the concern of secondary use (r

= -.189 (-.113, -.263), p = n.s.), the perceived surveil-

lance (r = -.186 (-.111, -260), p = n.s.) and the ehealth

literacy competence (r = -.037 (.204, -.274), p = n.s.),

the lower the intention to use a smartphone with a

mhealth app.

Impact of Socio-deomgraphic Data Among

Each Other and on Intention to Use. Figure 2

shows the signiﬁcant relationships between socio-

demographic data and the intention to use. For a bet-

ter overview, only the signiﬁcant relationships have

been included in the graph and non-signiﬁcant ones

have been omitted. The variables are each connected

usage

intention

-.267**

(n = 684)

sex

-.106*

(n = 693)

training degree

school certificate

.137**

(n = 688)

age health status

-.372**

(n = 679)

.137*

(n = 688)

mhealth app

experience

-.271**

(n = 688)

.129**

(n = 651)

.532**

(n = 688)

-.127*

(n = 481)

Figure 2: Overview of meta-analytical study design.

with arrows on which the positive or negative correla-

tion is shown by indicating the correlation coefﬁcient,

together with the sample size from which the signif-

icant effect results. Regarding age, older participants

were found to have a lower health status (r = -.372**)

as well as a lower experience with apps (r = -.271**)

in contrast to youngers. However, older participants

showed a higher training degree (r = -.132**) in com-

parison with younger participants. Signiﬁcant inﬂu-

ences of the user factor sex was found. Women re-

ported to be willing to use a mhealth app more often

than men (r = -.127*). Moreover, they had a lower

training degree than men (r = -.106*). Another sta-

tistically signiﬁcant correlation was found within the

app experience. The more experienced the partic-

ipants considered themselves to be in dealing with

apps, the better their health status (r = .129**) and

the better their education (r = .532**).

ICT4AWE 2022 - 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health

326

Table 2: Correlation between UTAUT2-factors (PE = performance expectancy, EE = effort expectancy, SI = social inﬂuence,

FC = faciliating condition, HM = hedonic motivation, UI = usage intention). Upper correlations in bold refer to study I, lower

correlations in italics refer to study II.

UTAUT2 factors PE EE SI FC HM H UI

PE 1 .438** .320** .280** .715** .551** .600**

EE .349** 1 -.047 .510** .371** .226** .365**

SI .358** -.084 1 -.150* ,376** .512** .456**

FC .235** .662** -.152** 1 .201** -.058 -.081

HM .704** .428** .332** .293** 1 .600** .677*

H .599** .205** .444** .098 .569** 1 .804**

UI .665** .332** .326** .180** .665** .775** 1

5 DISCUSSION

The meta-analysis’ aim was to aggregate the results

of the two studies conducted and to examine them

with regard to acceptance-relevant factors. It was

found that ﬁve factors were prominent in measuring

mhealth app acceptance: habit, hedonic motivation,

performance expectancy, social inﬂuence and effort

expectancy. The strongest inﬂuence on the inten-

tion to use was found with the factor habit. Due to

the fact that the use of smartphones with integrated

apps is familiar to the sample studied, it has become

a self-evident process (M

oller, 2016). Habit is un-

derstood as an action once learned that runs auto-

matically without conscious control (Stangl, 2018).

Thus, the functions of the app corresponds to the ha-

bitual actions performed when using a health app.

As a recommendation for mhealth app developers it

can be maintained that the operation of the app must

be simple and include the usual interaction gestures

(touch gestures) when using the smartphone. Hedo-

nic motivation was the second most important fac-

tor. This result was already reﬂected in the state of re-

search on the use of health apps in other studies (Peng

et al., 2016). In order to achieve a high usage rate

of health apps, playful elements must be included in

the app that motivate and give pleasure. Playful so-

lutions in the form of gamiﬁcation or augmented re-

ality (computer-supported reality approaches) are ap-

proaches that should be incorporated in the develop-

ment of health apps. Scientiﬁc results on the success

of integrating playful approaches to promote health

already exist (Schlomann et al., 2019), and can serve

as orientation. The third most important factor was

performance expectancy. This result is in line with

other studies on health app acceptance research (Alam

et al., 2020). The performance expectation reﬂects

the utility of the app in terms of ﬁtness apps, monitor-

ing a health situation, managing and controlling cer-

tain health conditions. As a recommendation it can

be stated that the app must have appropriate techni-

cal functions that enable the monitoring, management

and control of ﬁtness or a disease. The app’s perfor-

mance (e.g. measuring vital parameters) must be of

a high standard. Social inﬂuence was ranked fourth

most important. It refers to the degree to which peo-

ple perceive that others they care about believe they

should use a health app. Using a health app strength-

ens the ability to connect with other users they con-

sider important. The ability to share data about per-

sonal ﬁtness levels with other ﬁtness app users is an

important aspect of maintaining motivation that must

be further considered in technical development. Ef-

fort expectancy was the last inﬂuencing factor and

describes how much effort the user has to make us-

ing the app. Therefore, one recommendation could

be that the app should obtain a simple and easy-to-

follow menu structure. Facilitating condition was

the only factor that differed in the meta analysis. It

describes the knowledge of how to use a mhealth app.

Since the sample of study II did not bring a lot of app

experience, this factor was not homogenous with the

other result. The second research question focused on

the added UTAUT2 factors to measure the intention

to use. Trust contributed strongly to the intention to

use. The state of research on the inﬂuence of trust

on intention to use could be proven with this study.

One recommendation that emerges from the results is

that mhealth app providers must transparently explain

quality aspects and trustworthiness. This includes in-

formation about the possibilities of the app as well as

the risks and limitations. The inﬂuence of online pri-

vacy on intention to use had been conﬁrmed in this

study

(Deng et al., 2018). Concerns about data theft

or sharing the information with third parties were

particularly high for health-related data Thus, it can

be recommended that the protection of privacy must

be guaranteed by technical data protection measures.

Users must decide for themselves if and with whom

they want to share data. Studies related to eHealth so-

lutions have already shown that the higher the digital

A Meta-analytical View on the Acceptance of Mhealth Apps

327

skills and knowledge of understanding and processing

health-related information, the higher the willingness

to use it(Griebel et al., 2018).

Based on the results of this study no statistically

effect could be observed for mHealth apps. The third

research question focused on which user groups can

be formed on the basis of socio-deomgraphic data.

Thus, in this work, a user group was determined in

terms of content on the basis of the positive correla-

tions between two variables, and conversely, another

user group from the negative correlations. The fol-

lowing different characteristics emerged: one group

consists of young, low-educated, however healthy and

app-experienced participants who are willing to use

health apps. The other group consists of older par-

ticipants with good health, little experience in using

mhealth apps however with a higher educational at-

tainment. This group has a low intention to use apps.

Both groups differ signiﬁcantly from each other. Age

is to be regarded as the decisive factor that inﬂuences

all other factors. The older the participants, the less

experience they have in using apps (Searcy et al.,

2019) and expertise in using digital ICT in the health

sector

(van der Vaart and Drossaert, 2017), which are rel-

evant for the acceptance of health apps. Due to their

diversity, the different types of users require techni-

cal and content solutions that are specially adapted to

them and contribute to increasing acceptance.

6 LIMITATION AND OUTLOOK

The ﬁndings from the present study have provided in-

sights into acceptance-relevant factors regarding the

intention to use mhealth apps. However, the context

of our study was limited. First of all the study was

mainly conducted in the students surrounding at uni-

versity which resulted in a rather young sample. Even

though results could be detected for exactly this type

of user group, further studies should focus on broader

samples to present a representative whole. Moreover,

this study only evaluated an associated mhealth app.

In order to receive real data in terms of hand-on-

experience, it is necessary to develop a mhealth app

with different features and evaluate the usability with

regard to acceptance-relevant factors. Furthermore,

the app area of mhealth apps is divers and include

all health care areas from information, prevention,

care of complaints or also products, self-management,

therapy, rehabilitation in the form of aftercare and

monitoring. Further studies should consider the dif-

ferent ﬁelds and include them in acceptance studies.

The ability to operate a mhealth app is a basic pre-

requisite for the willingness to use it. The acceptance

of mhealth apps must also be investigated on people

who are ill, as otherwise there is an overestimation

of the capability and an underestimation of the actual

needs. In addition, research activities should be inves-

tigated with regard to the needs and requirements of

mhealth apps on behalf of limited users who require

barrier-free access due to their physical, mental or

even cognitive condition. It is important to understand

how the requirements of healthy people for the de-

sign of mhealth apps differ from those of people with

poorer health, in order to develop mhealth solutions

that can be adequately tailored to the corresponding

user groups. Due to lack of evidence so far on the

effectiveness speciﬁcally with regard to care and ther-

apeutic apps (Payne et al., 2015), further long-term

studies are needed.

ACKNOWLEDGEMENTS

The authors thank all participants for sharing their

thoughts and opinions. We would also like to thank

the reviewers for their valuable feedback.

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