“Attitude”- mHealth Apps and Users’ Insights: An Empirical Approach

to Understand the Antecedents of Attitudes towards mHealth

Applications

Luisa Sophie Vervier, André Calero Valdez and Martina Zieﬂe

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

Keywords:

mHealth, Privacy Concerns, User Diversity, Attitude, eHealth Literacy.

Abstract:

Mobile health apps are a solution to make people more self-sufﬁcient with their health in relation to their

lifestyle or medical needs. The mobile health landscape is growing, but still its usage rate is rather low. Since the

attitude towards mhealth apps has not been extensively studied so far, we conducted a multi-method approach

study in Germany. To understand people’s needs, preferences and attitudes towards mhealth applications

on the one hand and the antecedents of people’s attitudes towards mhealth applications on the other hand,

we chose a user-centered approach. To get ﬁrst insights into users’ needs and attitudes, we conducted two

focus groups (N=11). Topics such as privacy concerns, data sharing and app functions have emerged as key

issues. Quantitatively, we operationalized the collected key topics as well as users’ attributes and personal

health attitudes. We collected 132 complete responses from originally 180 responses. We used partially-least

squares structural equation modeling to identify the antecedents of attitudes towards mhealth applications.

Results indicate that time saving and daily life facilitating functions are favored app features. Further, we found

advantages (e.g. drink reminder) and disadvantages (e.g. data collection). Gender and age effects could be

detected regarding health literacy, privacy concerns, and data sharing. Older participants are more cautious and

reserved with the usage of health apps. Women in general show a higher health-literacy. eHealth literacy and

intention to share data were identiﬁed as being the strongest inﬂuence on a positive attitude towards mhealth

apps. To ensure that mhealth apps further enjoy a rising popularity, it is important to meet the users’ concerns

about privacy and help them to acquire eHealth literacy.

1 INTRODUCTION

Mobile applications are commonly used and affect

daily lives. Especially in the health care sector, the

development of health apps for lifestyle reasons is

increasing. The “quantiﬁed self” movement is one ac-

tuator for people’s interest in using mhealth apps (Lup-

ton, 2013). Moreover, people in general are becoming

more interested in taking an active role in their own

health care. They have become eager to take health

care decisions on their own. Especially, ever since the

physician-patient relationship has turned into a shared

decision-making relationship (Ernst et al., 2014). A

further aspect which gives a special importance to the

mhealth topic is the societal challenge of demographic

change. People will increasingly depend on techni-

cal support in the future (Lupton, 2013). Therefore,

there can be no better starting point than using and

developing existing and well known techniques fur-

ther, such as smartphone apps (Bhavnani et al., 2016).

Mobile health apps are an advanced form of eHealth.

As smartphones enjoy a rise in popularity especially

with versatile software, apps have great potential for

revolutionizing the health-care services. However, the

quality as well as the users’ acceptance of such systems

is still questionable (Powell et al., 2014). Thus, there is

a growing urgency to understand peoples’ needs, pref-

erences, and attitudes towards mhealth applications.

What kind of functions are important for an mhealth

app from a users’ perspective? And on a meta-level,

what are important topics that appear when looking at

peoples’ attitude towards mhealth apps? As the atti-

tude shapes the willingness to use mhealth apps, it is of

special interest to identify such aspects and to consider

them when deﬁning guidelines for mhealth app devel-

opment. In this study, we investigate this topic. The

aim of the study is to explain the antecedents of the

attitude towards mhealth apps and to derive guidelines

for digital health care actors.

Vervier, L., Valdez, A. and Zieﬂe, M.

“Attitude”- mHealth Apps and Users’ Insights: An Empirical Approach to Understand the Antecedents of Attitudes towards mHealth Applications.

DOI: 10.5220/0007720002130221

In Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2019), pages 213-221

ISBN: 978-989-758-368-1

213

2 STATE OF THE ART

In the following section, we present an overview of the

different aspects that play a role in trying to understand

the attitude towards mhealth applications.

2.1 mHealth

The aim of mHealth is to contribute to the health care

of users through the use of mobile devices (Conway

et al., 2016). Mobile devices such as a smart phone or

tablet provide a platform for maintaining motivation,

deﬁning goals, and receiving feedback (Appelboom

et al., 2014). They have a number of characteristics

that are considered to have “(...)great potential for the

healthcare sector” (Matusiewicz et al., 2017).

The most important characteristic of smartphones

is that they are part of everyday life (Gehring et al.,

2014). Since smartphones are highly valued by users

(Dennison et al., 2013) and have a size that makes

them transportable, users usually carry them along

all day long when switched on. That way users are

reachable regardless of time and place. Moreover, it

enables to address all kind of user groups. It offers an

optimal opportunity to disseminate information and

applications on the subject of health in a target-group-

oriented, effective and independent manner, regardless

of time and place.

This is not least due to two other important features

of smartphones: built-in sensors and networking (Den-

nison et al., 2013). The built-in sensors make it possi-

ble to automatically measure health-related data such

as a user’s physical activity. Thus, individual needs

can be met. The interconnection of smartphones also

allows users to share their health data with friends,

acquaintances or even their physicians and health in-

surers.

These numerous advantages of mHealth result in

a great potential for improving and promoting health

care, which could possibly fundamentally change the

health market and the health system. Although many

advantages are present, the attitude towards health

apps is very different. Individual needs are often not

covered to the satisfaction of users. So, there are many

immature health app versions on the market that are

questionable regarding security and privacy. These

aspects, as well as the individual ability to use health

apps, present great challenges for developers. With

our study we want to shed light on the users’ need and

preferences as well as on the main aspects people deal

with when considering health app usage.

2.2 Online Privacy in the Context of

mHealth Apps

Data protection and user privacy are central aspects in

the health care context. The explanation for this lies in

the way health apps work. They can constantly collect

information about the user’s health in real time (Gao

et al., 2015). Since this information is usually per-

ceived by users as sensitive, data protection is a topic

of great importance. Users’ worries include above all

the concern for the protection of privacy and the pro-

tection against unauthorized use of the data (Bansal

et al., 2010). As soon as users disclose information

about their health online, there is a risk that it may be

misused or illegally used by third parties. Thus, when

using an app, the user runs a risk that is directly related

to the concern for the security of personal data (Milt-

gen et al., 2013). Nevertheless, users are often willing

to take the risk and disclose their personal data in an

app in order to gain a personal advantage. In such

cases the perceived advantage must be greater than the

perceived risk for the user to be willing to pass on his

data (Li et al., 2014).

2.3 eHealth Literacy

The consideration of users’ prior knowledge is often

neglected in the development of mHealth offerings. It

is assumed that the user has the necessary knowledge

to deal with it (Norman and Skinner, 2006). However,

studies indicate that this assumption is not necessarily

true. In order to be able to use mHealth services for

their own beneﬁt, however, users must have certain

skills which are summarized under the term “eHealth

Literacy”. These include: “the ability to seek out, ﬁnd,

evaluate and appraise, integrate, and apply what is

gained in electronic environments toward solving a

health problem” (Norman and Skinner, 2006). Users

must therefore be able to independently search, ﬁnd,

evaluate, and apply health information. The prerequi-

site for the success of this process is that users can deal

with the corresponding technology and the countless

sources of information. They must be able to critically

question the information and the media they provide

in order to form trust (Vervier et al., 2018). Only in

this way users can make so-called informed decisions

for their own health. In order to be able to develop

mHealth services that are tailored to the needs and abil-

ities of users, these abilities must ﬁrst be surveyed. For

this purpose, Norman and Skinner have developed the

so-called eHealth Literacy Scale (eHEALS). The scale

is based on elements of socio-cognitive learning theory

and self-efﬁcacy expectations. It combines six subject

areas or basic skills: “traditional literacy, health lit-

ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health

214

eracy, information literacy, scientiﬁc literacy, media

literacy, [...] computer literacy” (Norman and Skinner,

2006). To understand in what extend health literacy

has an impact on the positive attitude on mhealth apps,

we considered this phenomenon as an important aspect

in our study.

3 RESEARCH METHODOLOGY

This study aims at exploring two perspectives regard-

ing the attitude towards mhealth applications. In a ﬁrst

step insights of people’s experience and ideas about

functional aspects as well as barriers and motives of

mhealth apps are collected qualitatively. In a second

step, based on the results of the focus group, all key

factors were included in an online survey and opera-

tionalized quantitatively. Moreover, dimensions such

as personal health attitude implying eHealth literacy

and health orientation, online privacy perception, in-

cluding privacy concerns, as well as the intention to

share data and last but not least user attributes (i.e.,

age, gender) were taken into account in order to under-

stand the attitude towards mhealth applications. This

two-way multi-method approach seemed to be the best

method to identify, evaluate, and quantify these fac-

tors. Two focus groups were run with two different

age groups. Based on the results an online survey was

designed (see ﬁgure 1). The most important research

questions leading the investigation were:

Does gender or age have an impact on user’s pref-

erences (features, disadvantages, advantages)?

How strong do personal health attitudes (health

orientation and eHealth literacy) and online pri-

vacy perception (privacy concerns) as well as the

intention to share data explain the positive attitude

towards mhealth applications?

Do further user factors such as age and gender

have an additional impact on the positive attitude

on mhealth apps?

3.1 Focus Group Study

The aim of the focus group approach was to identify

and discuss young and old adults’ ideas of mobile

health applications, its usage options, functions, ad-

vantages and disadvantages. For this purpose, two

successive focus groups were carried out with 11 (8

female and 3 male) participants with an age range

from 19-57 (

M = 35.6

years,

SD = 16.6

) years. The

younger group was aged between 19 and 25 years and

the older group between 48 and 57 years. In the be-

ginning participants were encouraged to brainstorm

about mobile health apps they know or even use. In a

free discussion, participants shared their experiences

and previous knowledge of mhealth applications. In

a further step topics such as functions of health apps

as well as motives for use and non-use were discussed

and ranked. As main topics privacy concerns, data

sharing, and app functions have emerged as key issues

in both groups. Older participants stated a stronger

privacy concern about their personal data than younger

participants.

3.2 Online Survey

To understand which factors inﬂuence the attitude to-

ward mhealth applications, we conducted a question-

naire study with 132 participants. The survey data was

collected in Germany in summer 2017 via an online

questionnaire. Participants took part in the study vol-

untarily without any compensation. At the beginning

of the survey, detailed information was given on the

purpose and objective of the study. We also stressed

that participants were welcome to express their views

on the subject and exchange their opinions openly. Ad-

ditionally, in accordance with data protection standards

in empirical studies, we informed the participants that

none of their responses could be traced back to them

personally. The survey consisted of four parts starting

with user attributes in part one.

User Attributes.

We assessed age, gender, and self-

rated app experience (“I consider myself a very experi-

enced user of apps” to be answered on a 6-point-Likert

scale). Moreover, we assessed general usage frequency

of health apps (i.e. nutrition app, lifestyle app, ﬁtness

app, disease control app, app for medical service) on a

6-point-Likert scale from never to several times a day.

Variables regarding the personal health attitude were

assessed in a second part.

Personal Health Attitude.

We used the health con-

sciousness scale by Dutta Bergmann (Dutta-Bergman,

2004). The respective items (ﬁve of a kind; e.g. I do

everything I can to stay healthy. or Living life in best

possible health is very important to me.) were added

after having checked the scale reliability (Cronbach’s

α = .773

). The data amounted to an AVE of .528.

Additionally, we measured eHealth literacy with the

eHeals literacy scale by Norman and Skinner (Nor-

man and Skinner, 2006) consisting of 7 items such

as I know how to ﬁnd helpful health resources on the

Internet among others. Cronbach’s alpha for the scale

was .93 and the average variance extracted (AVE) was

.632.

Privacy Attitude.

We used three items adapted by

Li (Li et al., 2014) as the online privacy concern scale

as e.g., I am concerned about threats to my personal

“Attitude”- mHealth Apps and Users’ Insights: An Empirical Approach to Understand the Antecedents of Attitudes towards mHealth

Applications

215

Figure 1: Overview of research process showing both qualitative and quantitative measures to address our research questions.

privacy today. The scale showed a good reliability of

Cronbach’s α = .817 and an AVE of .719.

In the privacy attitude section we additionally

added a scale called intention to share with 3 items

created by the authors. I am willing to provide my

health data to a doctor of my choice., I personally

see an advantage in sharing my health data with my

physician. and By sharing my health data with my

physician, a better health care is guaranteed for me.

with Cronbach’s α=.843 and AVE=.762.

Attitude Towards Health Apps.

Last but not least, the

attitude towards mhealth (3 items, developed by au-

thors: The usage of health apps is fun., The usage of

health apps is a good idea. and I like the idea of using

a health app.) were measured (

α = .871

;

AV E = .795

All items in the scales had to be answered on a 6-point-

Likert scale from I do not agree at all to I totally agree.

Finally, as generated result from the previous focus

group, advantages and disadvantages were also con-

sidered in the last part. Participants had to rank the

aspects in order of individual importance.

3.3 Statistical Method and Procedures

To understand our data, we used descriptive statistics

and report 95%-conﬁdence intervals on all point esti-

mates. When using null-hypothesis-signiﬁcance test-

ing, we set our level of signiﬁcance to

α = .05

. In this

study we used partial least square structural equation

modeling (PLS-SEM) in Smart-PLS 3.0 (Hair Jr et al.,

2016) to understand the causal relationship of the an-

tecedents mhealth attitude. The beneﬁt of PLS-SEM

lies in the possibility of causal inference in model-

ing. We report the path-coefﬁcients, the

was ad-

justed for the predicted variables in the inner model.

We designated the signiﬁcant relationships from the

bootstrapping procedure. Cronbach’s alpha and the

average explained variance as quality measures of our

model are reported (see section 3.2 Online Survey).

As PLS-SEM is used for exploratory approaches, the

following assumption were made: We assume that

with increasing age, privacy concerns increase. The

opposite we assume with health orientation. It will

decrease with increasing age. Female participants will

have a higher health orientation and a higher eHealth

literacy. We assume the stronger the privacy concerns

the lower will be the intention to share data. Moreover,

we assume that a higher health orientation and eHealth

literacy enhances the positive attitude towards mhealth

apps. However, the online privacy perception might

inﬂuence the positive attitude negatively. Figure 2 por-

trays the research model and the underlying theoretical

framework where the latent constructs are assigned to

two thematic topics: personal health attitude and pri-

vacy attitude. Personal health attitude includes health

consciousness and eHealth literacy. Privacy attitude

covers privacy concern and intention to share data. The

arrows symbolize the assumed inﬂuences among the

constructs on the positive attitude towards mHealth

apps. Age and gender are depicted underneath the dia-

gram as two factors which might affect the particular

constructs in general.

3.4 Sample Description

Our sample consisted of 132 participants who com-

pletely answered the online survey from originally

180. 62% were female and 38% were male. The age

range was between 18 and 63 years with

M = 35.9

(

SD = 23.5

, see also Table 1 and Table 2). The sample

was divided into age groups (youngest group 18 to 25

years (39%); middle-aged group 26 to 40 years (28%)

and older group 41 to 63 years (33%)). The educa-

tion level was rather high. 39% had completed a high

ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health

216

Figure 2: Overview of theoretical framework of our research

model.

school degree and 38% held a university certiﬁcate, in-

dicating the heterogeneity of the sample’s educational

level. Most of the participants (30%) allocated their

current activity in the commercial area, 29% in a tech-

nical area, 14% allocated it to the social ﬁeld, 8% to

a medical ﬁeld, 8% to an artistically ﬁeld and 8% to

other areas.

Table 1: Demographic characteristics of aggregated sample

(N=132).

Demographic characteristics Percentage

Age

mean (SD) 35.9 (23.5)

18-25 years 39%

26-40 years 28%

41-63 years 33%

Gender

women 62%

men 38%

Education level

university 38%

high school degree 39%

other 23%

Participants rated themselves as rather experienced

app users with

M = 4.25

(

SD = 1.3

; scale from 1 to 6).

The experience decreases signiﬁcantly (

F(2, 129) =

17.529, p < .001

) between the youngest and mid-

dle age groups (

younger

= 4.86

;

younger

= 1.05

middle

= 4.38

;

middle

= 1.29

), but increases sig-

niﬁcantly with the older participants (

older

= 3.44

;

older

= 1.16

). When asked about general usage fre-

quency, the participants generally seemed to use few

health apps down to none. Fitness-apps (

M = 2.45

;

SD = 1.58

) are still used the most, followed by nu-

trition (

M = 1.80

;

SD = 1.58

), lifestyle (

M = 1.69

;

SD = 1.18

), medical service (

M = 1.38

;

SD = .69

) and

Table 2: Mean (M) and standard deviation (SD) values of

our measured variables.

Variable M SD

age 35.90 23.50

app experience 4.25 1.30

usage ﬁtness app 2.45 1.58

nutrition app 1.80 1.58

lifestyle app 1.69 1.18

medical service app 1.38 0.69

disease control app 1.28 0.91

health consciousness 4.09 0.90

eHealth literacy 3.94 0.93

privacy concerns 4.07 1.13

attitude towards mhealth app 3.52 1.13

disease control apps (

M = 1.28

;

SD = .91

). In general,

the sample constituted a rather healthy group with a

health consciousness value of

M = 4.09

(

SD = 0.9

; 6

points max.). The eHealth literacy level was averagely

high with

M = 3.94

(

SD = 0.93

). In this context, a

signiﬁcant gender (

F(1, 112) = 6.37; p = .013

) effect

could be detected. Female participants (

M = 4.08

SD = 0.80

) rated their competence to evaluate digi-

tal health information higher than male participants

(M = 3.68, SD = 0.97) participants.

Participants reported a rather high privacy con-

cern measured at

M = 4.07

(

SD = 1.13

; 6 points

max.). Again, a signiﬁcant difference in age was ob-

served (

F(1, 115) = 13.108; p < .001

). The concern

for online privacy in general was signiﬁcantly stronger

among older participants (

M = 4.67

SD = 0.93

; 6

points max.) than among younger participants with

a mean of

M = 3.56

/6 points (

SD = 0.92

). A similar

picture emerged in the personal attitude to share data

(

M = 3.84

SD = 0.12

;

F(2, 107) = 7.439, p < .001

Young participants (

M = 4.17

SD = 0.91

) perceived

a signiﬁcantly higher advantage in this and show

a greater willingness to transmit their health data

digitally to their physician than older participants

(

M = 3.84

SD = 1.27

).The attitude towards health

apps was averagely high with a mean of

M = 3.52

points (SD = 1.13).

4 RESULTS

The presentation of the results is guided by our re-

search questions. We ﬁrst look at the impact of user

diversity towards mHealth before looking for causal

explanations. Lastly we include age and gender into

our models.

“Attitude”- mHealth Apps and Users’ Insights: An Empirical Approach to Understand the Antecedents of Attitudes towards mHealth

Applications

217

4.1 Impact of User Diversity on Users’

Preferences Towards mHealth Apps

In order to identify the importance of speciﬁc func-

tions and features of health apps, participants were

asked to rank six options which were characterized as

central aspects in the focus groups for each preference.

As a most important function user-friendly operation

was mentioned, followed by support for the develop-

ment of health awareness, reminder for e.g., eating,

information about health topics as well as tracking

e.g., steps. On the last place of the ranking, agreement

of consultation hours with physicians were mentioned.

No age or gender effects could be detected.

Table 3 shows the results of the ranked advantages

and disadvantages of health apps. Also in the forma-

tion of these rankings, no statistically signiﬁcant user

diversity inﬂuences could be detected. So far a very a

homogeneous picture emerged when taking user diver-

sity such as age and gender into account when looking

at different aspects of preferences.

4.2 Explanation of Attitude Towards

mHealth Apps with Personal Health

Attitudes and Online Privacy

Perception

To ﬁnd out what determinants inﬂuence the attitude

towards mhealth apps we used the structural equation

modeling using partially-least-squares algorithm. In

ﬁgure 3 the inner model of positive attitude towards

health apps is depicted which we introduced earlier as

the theoretical framework of our study (see ﬁgure 2) .

Due to readability we decided not to present the outer

model. The majority of path coefﬁcients for the items

reached a value of 0.7, and 0.8 in most cases. Only the

health orientation items included two of totally ﬁve

of a kind with over 0.6 (scale reliability Cronbach’s

α = .773

AV E = .528

). Path coefﬁcients are written

on the arrows which connect the latent constructs. Neg-

ative inﬂuence is colored red. We identiﬁed signiﬁcant

correlations by annotating the test values with aster-

isks. The adjusted

indicates the extend the variable

is inﬂuenced by others. At a ﬁrst sight, the bold arrow

shows the main determinant of the latent construct of

the positive attitude towards mhealth apps. In the sam-

ple eHealth literacy had the biggest positive impact

as previously assumed. Participants who are familiar

with their smartphones and in general with digital de-

vices and who are able to independently search for

and and ﬁnd health information, evaluate and apply it

according to the deﬁnition of eHealth literacy. Further,

they seem to be open-minded towards mhealth.

Being health conscious also inﬂuences the attitude

positively, however, by far not as strong. It seems

that being interested in leading a healthy life does not

include being interested in electronic services which

might support the way of life. Looking at the privacy

attitude area, privacy concern itself did not inﬂuence

the positive attitude signiﬁcantly at all. It had a nega-

tive impact on the intention to share data. The higher

the personal privacy concerns were the less people

were willing to disclose data in the context of mhealth

apps. Still, looking at the second latent construct of pri-

vacy attitude, the intention to share data correlates pos-

itively with the positive attitude towards mhealth apps.

To sum up, according to the sample, determinants of

the positive attitude of mhealth apps are eHealth liter-

acy and the willingness to pass the required data via

an mhealth app.

Figure 3: Results of the PLS-SEM Algorithm. Only sig-

niﬁcant paths (after bootstrapping) are shown. Red arrows

indicate negative path coefﬁcients.

4.3 Impact of User Diversity on

Antecedents of Attitude Towards

mHealth Apps

Taking a deeper look at our data, age and gender had

different effects on the investigated model. User diver-

sity played an important role. Starting with gender, the

positive attitude is described best by highly health con-

scious male participants (

β = 0.340, p < .05

). Attitude

is further signiﬁcantly inﬂuenced by intention to share

(

β = 0.334, p < .05

). A further negative signiﬁcant

inﬂuence was detected between privacy concern and

intention to share data (

β = −0.328, p < .05

). Looking

at the model through the “female glasses” only one

signiﬁcant impact could be found. However, this one

was very strong between eHealth literacy and positive

ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health

218

Table 3: Ranking (#) of mHealth app advantages and disadvantages (N=132).

advantages # disadvantages #

constant availability of own health data 1 lack of data protection 1

interesting features 2 monitoring by third party 2

check compatibility of drugs 3 lack of personal contact 3

time saving (medi order) 4 no direct questions possible 4

road-saving 5 constant data collection 5

overview of pharmacy nearby 6 dependence on smartphone 6

attitude with a path coefﬁcient of

β = 0.472(p < .001

Age showed effects in the middle-aged and older age

group. Similar to female participants, a signiﬁcant

inﬂuence was found on eHealth literacy and attitude

(

β = 0.513, p < .001

), a negative one on privacy con-

cern and intention to share (

β = −0.337, p < .005

)

and ﬁnally on health orientation and eHealth literacy

(

β = 0.404, p < .001

). The one and only signiﬁcant

inﬂuence in the older age group was detected on in-

tention to share and attitude with a path coefﬁcient of

β = 0.546(p < .001

). To conclude, user diversity such

as age and gender play a dominant role regarding the

attitude towards mhealth apps.

5 DISCUSSION & GUIDELINES

Discussion.

With our paper “Attitude” we wanted to

investigate on people’s ideas and position relating to

the mobile health services or rather health apps. To

understand people’s needs, preferences and attitudes

towards mhealth applications on the one hand and

on the other hand to understand the antecedents of

people’s attitudes towards mhealth applications on a

meta-level, we chose a user-centered approach. That

way, we could gather more robust results and deal

with the research question in detail. In a ﬁrst step

focus groups were run, in which we analyzed users’

ideas of mobile health applications, its usage options,

functions, advantages and disadvantages.

In a second step, an online questionnaire was sent

out in which the results of the focus groups were quan-

tiﬁed as well as the attitude of health apps and its

antecedents were explored. To do so, we formulated

a model with two thematic topics: personal health

attitude consisting of two latent constructs, namely

health orientation and eHealth literacy. The second

topic dealt with privacy attitude, operationalized by

online privacy concern and willingness to share data.

Last but not least user attributes (age, gender) were

taken into account in order to understand the attitude

towards mhealth applications.

Users’ insights won from the focus group study

showed that users favored app features were aspects

which improve and facilitate daily health life routines

and save time. Thus, arranging appointments via app

were highlighted. Also functions, which allow keep-

ing track on and documenting health topics were men-

tioned. In this respect, our results are consistent with

those of Mendiola (Mendiola et al., 2015). The ideas

about functions were in line with the current trend of

the “quantiﬁed self” movement. Moreover, privacy

concerns have emerged as important topics. Partici-

pants considered good data encryption and the protec-

tion of their personal data as very important as well

as the desire for a serious provider (Wilkowska and

Zieﬂe, 2012). Besides privacy aspects, simple and

user-friendly operations were essential features. In

general, participants could see many advantages in

using a mhealth apps which could offer an overview

of pharmacies and physicians close by. Reminders of

different kinds such as a drink reminder were also con-

sidered as an important advantage, especially by older

participants. Not surprisingly, communication with

physicians were mentioned among others. Participants

seemed to be very reﬂected. Among all great features

an app could offer, they also were aware of possible

negative aspects such as smartphone addiction and the

feeling of uncontrolled and unclear data collection. Fi-

nally, lack of personal contact as the fear of feeling

socially alone were mentioned.

When looking at the outcomes in the questionnaire

study, again, it was corroborated that users indepen-

dent of age or gender attached high importance to

the same features, advantages and disadvantages as

discussed above and also portrayed in table 3. In-

terestingly, that the advantage of having a constant

overview about own health data goes hand in hand

with the disadvantage of perceived lack of data pro-

tection. As assumed the topic of the so called privacy

calculus (Li et al., 2014) appears in the context of

mhealth app usage. It seems that the appreciated fea-

tures of mhealth apps might outweigh the perceived

risk (data protection and privacy) as long as the ad-

vantages of facilitating daily life and saving time are

predominant as well as user can trust the provider.

In our study we also tried to shed light on the an-

tecedents of a positive attitude towards mhealth apps.

“Attitude”- mHealth Apps and Users’ Insights: An Empirical Approach to Understand the Antecedents of Attitudes towards mHealth

Applications

219

In advance, we prepared assumptions based on lit-

erature but also on the results of the focus groups.

We could make note that with increasing age, privacy

concerns increase as well. The results of the focus

group already gave a hint, that older people are more

concerned about their online privacy. The results are

consistent with other studies on the subject as e.g.,

Zeissig et al. (Zeissig et al., 2017). No signiﬁcant

results could be found according to the assumption

that health orientation rises up with decreasing age.

That might be due to the fact that the sample in gen-

eral represented a rather healthy one. Another reason

might be attributable to the lower validity of items as

described in the research methodology. The assump-

tion that female participants will have a higher health

orientation and a higher eHealth literacy could empiri-

cally be proven at least for eHealth literacy. The model

showed that females eHealth literacy had the biggest

inﬂuence on the positive attitude towards mhealth apps.

In other words, in our study, women in general were

more familiar with the usage of digital devices and

the health topic. We also learned from this study, that

the extent of privacy concerns represented the biggest

motive or barrier in the same way for the willingness

to share data.

Guidelines.

Our ﬁndings show needs and prefer-

ences of participants who are rather healthy and could

imagine a mhealth app for lifestyle reasons. Therefore,

our ideas will give guidance to developers of lifestyle

health apps. In general, mhealth apps should offer

a user-friendly operation. As people are becoming

more interested in taking an active role in their own

health care, features which improve and facilitate daily

health life routines, save time are appreciated most.

The challenge however occurs right here, where ap-

preciated functions by users collide with individual

privacy concerns and data protection worries. At this

point, mhealth app providers must assure and be able to

let the user use the app with all security and encryption

possibilities of the users sensitive data which are on

the highest standard. As we learned from the study, pri-

vacy concern inﬂuences the willingness to share data

negatively. In other words, as long as the privacy con-

cern can be taken by offering highly secured mhealth

apps, the intention to share data will rise. Thus, people

will beneﬁt from a better personalized service accord-

ing to their needs. Moreover, being eHealth literate

increased the positive attitude and thus possible usage

of a mhealth app enormously. Implementing learning

opportunities in a mhealth app for improving and keep-

ing one owns health behaviour skills up-to-date would

be an interesting further feature.

6 LIMITATION & FUTURE

RESEARCH

As studies always need to ﬁt time and content frame-

work conditions, there are limitations that occur nat-

urally. Since the study was conducted in the authors

and students surrounding at a university, a rather tech-

nically afﬁne sample was measured. Future studies

have to focus on conducting more representative stud-

ies as well as bigger samples to make representative

statements. Moreover, different cultural aspects of dif-

ferent countries might deliver interesting results. So

far, this study aimed at gaining general attitudes to-

wards mhealth applications. At this point it would

be of interest to develop a possible mhealth app and

evaluate it with e.g., the uniﬁed theory of acceptance

and use of technology model (UTAUT) in the health

care sector (Slade et al., 2013). That way the intention

of using such a mhealth app could be explored more

in detail. As the phenomenon of eHealth literacy oc-

curred to be a very important result in our study it is

important to pay close attention to this topic. In this

study, we entered the validated item scale by Norman

and Skinner (Norman and Skinner, 2006) who can be

seen as the pioneers of the ﬁrst eHealth literacy model.

However, in course of time different theoretical ap-

proaches have been made, regarding current digital

standards (Griebel et al., 2018) who also questioned

Norman and Skinner’s approach according to its lack

of usage context and the missing ﬁt with interactive

Web 2.0 contents. The digital health literacy instru-

ment (DHLI) by Rosalie van der Vaart for instance

represents a new approach with a validated item bat-

tery which operationalizes seven different constructs

such as operational skills, navigational skills, informa-

tion searching, evaluation reliability, determining rele-

vance, adding content, and protecting privacy (van der

Vaart and Drossaert, 2017). Another interesting and

validated model which should be considered in a fur-

ther empirical investigation with the same research

question is the e-HLS Instrument by Secking (Seçkin

et al., 2016) who takes a three factor solution into

account (behavioral, communicational and attitudinal

components of health literacy). With the decision and

usage for one of the current theory approaches, a gold

standard could also be set as a side effect which is

still missing in the research of eHealth literacy. A fur-

ther future research idea would be to subject the same

research question to a sample with sick people. Addi-

tionally, needs and preferences should also be explored

for people who need to use such an app for medical

or more severe healthy reasons in form of monitoring

their health. Questions which occur especially in an

individual usage context of health monitoring could

ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health

220

be the way of personalization of health information

in the conﬂict situation of personalization preferences

and privacy concerns (Calero Valdez and Zieﬂe, 2019).

The research ﬁeld of mhealth apps, eHealth literacy

and privacy concerns in the healthcare context is still

wild and huge and offers many empirical research op-

portunities which need to be seized.

ACKNOWLEDGEMENTS

The authors thank all participants for sharing their

thoughts and opinions. We would also like to thank

the reviewers for their valuable feedback. A special

thanks goes to the research support of Kathrin Kohl.

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