Adoption and Use of Health-related Mobile Applications: A
Qualitative Study with Experienced Users
Elena Smirnova
1
, Niklas Eriksson
1
and Asle Fagerstrøm
2a
1
Arcada University of Applied Sciences, Jan-Magnus Janssons Plats 1, 00420 Helsinki, Finland
2
Kristiania University College, Prinsens Gate 7-9, 0152 Oslo, Norway
Keywords: Adoption and Use of Technology, Mobile Applications, eHealth, Experienced Users, Qualitative Study.
Abstract: Mobile health-related applications (apps) such as physical activity apps and diet apps can help users to
implement a more active and healthier lifestyle. This qualitative study investigates experienced users’ triggers
to initially download mobile health apps, the drivers that keep them using these types of apps, and the barriers
that hinder them from an extended engagement with the apps. Thirteen factors were inductively identified and
matched with constructs in theories of technology adoption and use. Also, results from previous studies on
mobile health apps were used in the discussion. Life situation, Relevant statistics, and Perceived satisfaction
with first health app were identified as initial triggers. Price value, Simplicity, Personalisation, Guidance and
Progress based on data, Flexibility, and Social encounters were identified as drivers for continuous use.
Perceived risk of personal data, Time-consumption, Limited understanding of health data and Adaption to
new routines were identified as barriers for greater engagement with the apps. Managerial implications and
further research are also discussed.
1 INTRODUCTION
According to WHO (2018; 2020), an unhealthy diet
and physical inactivity are becoming a part of
people’s lifestyle. Thus, an unhealthy and less
physically active lifestyle is one of the most important
public concerns. Healthy eating and regular physical
activities can significantly decrease the risk of obesity
and diseases such as diabetes and heart disease
(WHO, 2018; 2020). Today, digital technologies can
provide users with a wide range of options to manage
a healthier lifestyle (Akbar et al., 2020). Mobile
health-related applications (apps) that run on
smartphones and other types of mobile devices have
become popular. There are many mobile health-
related applications on the market that help to track
an individual’s physical activity routines and food
nutrition diets (Wang et al., 2016). These types of
mobile apps empower and engage users in different
ways (Akbar et al., 2020). Murnane et al. (2015)
divided different mobile health apps into six
categories: physical activity, medical, weight-loss,
food, sleep, and well-being. According to Google
Play data, used by Murnane et al. (2015), physical
a
https://orcid.org/0000-0002-8854-1658
activity apps were the most popular. In a large-scale
European survey (N=4000), 27% of the respondents
stated that they had used a health app (Incisive Health
International, 2017). The same study, however,
concluded that there is an unrevealed potential with
mobile health apps to impact the health of citizens
positively. Therefore, it seems important to
understand better how to encourage more people to
use mobile health-related applications and how to
inspire existing users to keep using them. It is
important for developers, researchers, and educators
to understand better why health apps are downloaded
and used on smartphones by individuals (Kanthawala
et al., 2019). There are studies on users’ perception of
mobile health-related apps (Yuan et al., 2015; Peng et
al., 2016), users’ perceived effectiveness of diet and
physical activity apps (Wang et al. 2016), evaluations
of quality and credibility of health apps (Kanthawala
et al., 2019), digital imbalance in the use of self-
tracking diet and fitness apps (Régnier & Chauvel,
2018), users’ desire to continue using a fitness app
(Beldad & Hegner, 2018), health app use among
mobile phone users (Krebs & Duncan, 2015), and
individual differences in mobile health app use (Bol
288
Smirnova, E., Eriksson, N. and Fagerstrøm, A.
Adoption and Use of Health-related Mobile Applications: A Qualitative Study with Experienced Users.
DOI: 10.5220/0010185902880295
In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 288-295
ISBN: 978-989-758-490-9
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
et al., 2018). All these studies contribute from
different perspectives to individuals’ adoption and
use of mobile health apps. Nevertheless, there seems
to be limited research that focuses on experienced
users of mobile health apps. Experienced users are
critical as they can provide detailed insights into the
reasons why they initially adopted mobile health-
related apps, what keeps them continuing to use these
types of apps over time, and what may hinder them
from a greater engagement with these apps. The
adoption of new types of technological services
(innovations) is a long-term process (Rogers, 2003).
Learning from this adoption process can also give
providers of mobile health apps a better
understanding of how to promote and design health
apps from a user perspective. Therefore, this study
aims to expand understanding of the reasons for the
adoption and use of mobile health-related
applications among experienced users. The focus will
primarily be on physical activity apps such as sports
trackers and fitness guides and diet-related apps such
as calorie counters and food trackers.
2 ADOPTION AND USE OF
TECHNOLOGY
There are several models and theories that can be used
to improve the understanding of individuals’ adoption
and use of technology (Taherdoost, 2018). Diffusion
of innovations (DOI) theory by Rogers (2003)
focuses on understanding the adoption of new
innovations such as new technological solutions.
There are five variables that anticipate the adoption
rate of innovations: relative advantage, compatibility,
complexity, trialability, and observability. Innovation
resistance theory (IRT) explains resistance to
adoption based on five variables: value barrier, usage
barrier, risk barrier, tradition barrier, and image
barrier (Ram & Seth, 1989). Another model that has
been extensively used to explain the use of different
types of information technologies is the technology
acceptance model (TAM) by Davis (1989), which is
based on the theory of reason action (TRA) by
Fishbein and Ajzen (1975), with roots in social
psychology theories, and its extension, the theory of
planned behaviour (TPB) by Ajzen (1991). The two
determinants describing intentions to use technology
in TAM are perceived ease of use and perceived
usefulness. Other theories are the unified theory for
the acceptance and use of technology (UTAUT) by
Venkatech et al. (2003) and the unified theory for
users’ acceptance and use of technology (UTAUT2)
by Venkatech et al. (2012). In UTAUT and UTAUT2,
both DOI and TAM have been used as a base along
with some other models and theories such as the
model of PC utilisation (MPCU) (Thompson et al.,
1991) and social cognitive theory (SCT) (Bandura,
1986). UTAUT consists of performance expectancy,
effort expectancy, social influence, and facilitating
conditions as determinants for technology
acceptance, and in UTAUT2, three more
determinants are added: hedonic motivation, price
value, and habit.
The aim of the present study is to expand the
understanding of reasons for adoption and use of
mobile health-related applications, and thus we raised
the following three research questions. Rq1: What
triggered experienced users’ initial download of
mobile health related apps? Rq2: What drive
experienced users to continue using health-related
apps? Rq3: What hinders experienced users from
greater use of health-related apps? Qualitative data
analysis is usually inductively conducted and, thus,
we are not using a model or theoretical framework as
a lens in the data analysis. Instead, we are going to
match and discuss our results with the described
theoretical frameworks and models and with the
results of the previous studies related to individuals’
adoption and use of mobile health-related apps.
3 METHOD
For this study, participants were selected based on
who had been using relevant health-related apps
regularly for at least three years. The selection of
participants having long-term engagement with
health apps is critical since these users are more likely
to have a broad experience of adoption. Thus, five
male and five female participants who have used
health-related mobile apps for at least three years
were selected. Potential participants were asked in
advance how long and how regularly they used these
types of apps. Thus, participants were aware of the
topic of discussion and were also willing to share their
experiences. The participants were younger users,
between 25 to 35 years old. See Table 1.
The data were collected through semi-structured
face-to-face interviews. The questions related to the
three research questions but were flexible, which
allowed for considering individual differences and
taking advantage of the iterative nature of
interviewing. Each interview was conducted by the
same author, lasted between 45 to 70 minutes, and
was audio-recorded. One pilot interview (not
included in the sample) was also made by the
Adoption and Use of Health-related Mobile Applications: A Qualitative Study with Experienced Users
289
Table 1: Participants.
Code A
g
e Descri
p
tion Health a
pp
s Years of usa
g
e
P1 32 Male professional working with Risk analytics Exercising, Yoga 5 years
P2 29 Female universit
y
student Yo
g
a, Exercisin
g
, Diet, Meditation 3
y
ears
P3 29 Male
rofessional business consultant Yo
g
a, Runnin
g
, Exercisin
g
, Diet 3
y
ears
P4 28 Female nutritionist professional Diet, Exercising 5 years
P5 35 Male professional working with management Diet, Exercising 8 years
P6 25 Female university student Diet, Exercising 3 years
P7 31 Male
p
rofessional dentist Diet, Exercisin
g
, Runnin
g
5
y
ears
P8 28 Female
p
rofessional
p
ro
j
ect mana
g
e
r
Diet, Exercisin
g
, Runnin
g
3
y
ears
P9 29 Male professional sales manager Diet, Exercising, Yoga 5 years
P10 29 Female
p
ro
j
ect mana
g
e
r
Diet, Exercisin
g
, Runnin
g
, Yo
g
a 3
y
ears
interviewing author to develop the interview guide
and to gain improved insights on the procedure.
The interviews were manually transcribed in MS
Word by one of the authors. By manually
transcribing the results, an initial understanding of the
results was achieved. Next, the transcriptions were
read thoroughly by another author, and the data was
converted and structured in the software QDA Miner
Lite v. 1.4.1. Two of the authors came up separately
with codes and labels of themes by using inductive
thematic reasoning. In total 36 codes (1
st
order codes)
were generated by identifying underlying ideas and
characteristics in the transcribed texts (Miles et al.,
2014). The codes were then reassessed by the two
authors and, based on similarities, merged into 2
nd
order themes. For example, the 1
st
order codesFree
trial”, “Free app” and Affordable” formed the 2
nd
order theme “Price value”. The final themes are
presented next.
4 RESULTS
The results are presented according to the three
research questions. Direct quotations from the
participants are included in the review of the results.
4.1 Triggers for the Initial Download
(Rq1)
Life Situation: Seven of the participants felt like they
needed a change to the life situation they were in and
that triggered their interest in health-related apps.
Stress, an unhealthy lifestyle, and not enough
exercise, due for instance, to a new job or a crisis in a
personal relationship, were sources that trigged the
initial decision to try health-related apps.
‘I started using an app during a stressful time. I
just changed my job, and I was overloaded with new
tasks, so I started sleeping fewer hours, stopped
exercising, and did not care what I ate… Then I heard
my friends talking about an app, and I decided to try
it.’ (P1)
I downloaded my first yoga app because I wanted
to become more focused on myself. I mean, at that,
time I was very stressed. I just broke up with my
boyfriend, and it was a very difficult time.’ (P10)
Relevant Statistics: Six of the participants also
referred to data and statistics as their initial triggers to
start using a health-related app. Measurements and
calculations of sports data and calories were
perceived as useful data that technology can provide.
So, I downloaded an app because I wanted to
check my running speed and distance. It is actually
very useful because it helps to get statistics.’ (P2)
So, I downloaded a diet app. I wanted to check
nutrition consumption patterns.’ (P4)
Perceived Satisfaction with First Health App:
P4, P5, and P7 stated that their first health app
supported them only to count calories. The
participants wanted to improve the balance of their
diets in general without setting a concrete goal. Being
delighted with the first app and the gained insights,
the participants, after some time, also downloaded
another app for tracking physical exercise. Perceived
satisfaction with one app can lead to the download of
another app.
Basically, I took the first app which sounded
reasonable and downloaded it. And I really liked it…
Well, I also downloaded an app for running.’ (P10)
4.2 Drivers for Continuous Use (Rq2)
Price Value: Free trial periods were positively
referred to by two participants, but once the app has
been paid for, there is a higher barrier to stop using it.
You can have [a] one-month free trial, and if you
like, you can buy it. It is probably another thing that
makes me keep using the app. I paid money for it, and
of course, I want to use it.’ (P1)
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Five other participants also expressed satisfaction
with affordable prices of apps and that there is a
supply of completely free apps.
It is quite cheap to use. Some of the apps are even
for free; it is quite convenient. You can try different
apps, chose the one which works for you the best, and
in case you do not like it, you can easily find another
app.’ (P3)
I like that it is free. There are so many apps that
ask for money, but I see no differences. Yes, maybe
there are more advanced functions.’ (P5)
Simplicity: Eight of the participants mentioned
ease of use or simplicity (not too many features) of
the app as a criterion for continuous use.
I’m happy about my app because it is simple and
easy to use, and I value this quality.’ (P1)
I guess I would not like if the app would be too
complicated. I know that some apps have a lot of
functions and some other things, which I even do not
know how to use. But I do not need it.’ (P10)
Personalisation: Personalisation was also
mentioned by five participants as a key feature for
continued use of the app.
‘I like that it is personalised… I mean that the app
can adjust to your needs.’ (P3)
That there are different ways of exercising, for
example, and you can choose the way which will work
for you.’ (P9)
Guidance and Progress based on Data: All the
participants mentioned statistical data as a way to
receive guidance, follow up on the progress, and
reach goals, which in turn can lead to improved self-
esteem and pushes to a more systematic way of
conducting physical activities.
It is easier to exercise when I am using the app.
I always know what to do and how to do. When I go
to the gym, I do not need to think what I should do
there, the app will tell me… And that you can see the
results later, statistical data.’ (P9)
I like checking the statistics from previous years
and see how everything has changed. It shows that
you are doing good and achieving something. At
least, it increases my self-esteem.’ (P5)
The statistics also improve if the app was used
continuously and on a long-term basis.
The statistics help in a way to control yourself. I
mean, you see how you are eating for a long time.’
(P8)
Flexibility: Flexibility provided by the apps was
also mentioned by five participants. This aspect was
related to independence from teachers or personal
trainers, their style of teaching and when and where
the lessons are taking place.
If you are going to the lessons, you have to follow
a teacher. I do not mean it is a bad thing, but the way
how you are going to be taught will [have] influence
on your willingness to continue to exercise. I think it
is actually a big advantage of apps, that you are very
independent; not only you decide your time table, but
you also decide on a way how you want to exercise.’
(P3)
Social Encounters: Especially three participants
expressed that they are inspired by meeting like-
minded persons and that they are able to exercise
together and share achievements with each other.
This motivates them to use the apps.
Also, an app helps to find like-minded people...
You can see people who also do yoga, and you can
send a notification as [a] thank you for doing it with
me. And when you do it often, you get notifications,
and you enjoy it. It is like a bonus, extra support, that
inspires you.’ (P10)
4.3 Barriers for Increased Use (Rq3)
Perceived Risk of Personal Data: The perceived
risk of entering and storing personal data in the health
apps does not necessarily hinder their use, but it
seems to especially bother six of the participants and,
thus, it may increase intentions to stop using some
types of health-related apps.
I think many people are sceptical about health
apps because they are doubting the security of private
data. I think it is a big issue, which could be
improved.’ (P7)
The perception of risk level can depend on the
type of app and what it is used for.
I know that many people complain about
security… But I think it is probably more relevant to
those kinds of health apps where you have all
information about different health indicators. Like
different illnesses and to which doctors you are
going... That is very personal information and people
do not want to share it.’ (P2)
One participant also perceived that unnecessary
data is registered.
But I understand why some of the services need
that kind of data [personal data] and I cannot avoid
it. But some services should not requite it. I mean, it
is very unnecessary.’ (P8)
Time-consumption: Nine participants perceived
that it is time-consuming to register data such as food
eaten during the day. In the long run, this can become
a burden, which may lead to neglect of the app or its
less frequent use.
Because it [is] required to register something all
the time. You have to insert something and then click
Adoption and Use of Health-related Mobile Applications: A Qualitative Study with Experienced Users
291
again, insert and click, and I like to save my time.
(P6)
I know many people gave up using the apps
because it is time-consuming, and I partly agree. So,
I like that I am stubborn in that way and continue
using the app.’ (P5)
Limited Understanding of Health Data: Some
of the apps require quite extensive knowledge of
health-related data and may thus be perceived as
complex to use. Especially, a need for proper
understanding of nutrition was mentioned by eight
participants.
So, if I want to eat healthier, it means that I
should actually understand what it means in terms of
food. I just find it way too complicated.’ (P3)
Why I think it [a diet app] is complicated,
because it would require some knowledge. I read
literature about healthy food, but to start using the
app, I should read more.’ (P9)
Adaption to New Routines: Despite the
advantages of flexibility with apps (as presented
above), six of the participants mentioned that using
the apps also requires discipline and new routines or
changes in habits over-time. It was noted also that
there is no physical person there to push them.
It [the app] also requires more self-control. I
mean, you have to decide yourself when you want to
exercise and get into a certain routine. It is actually
quite hard because nobody is pushing you.’ (P1)
Because when you follow the same routine for
many years, it becomes a habit and it is difficult to
change the habit. It does not happen in one week or
even one month.’ (P7).
5 DISCUSSION
In this study, we raised three research questions. We
identified 13 possible reasons for the adoption and
use of mobile health-related apps. Next, we will
discuss the results of previous research on the topic
and the theoretical frameworks of adoption and the
use of technology. Similar mapping against
constructs in theoretical frameworks was conducted
by Peng et al. (2016) and Kanthawala et al. (2019).
According to Rogers (2003), in the initial phase of
adoption, individuals lack knowledge or are unaware
of new types of services (innovations) and, therefore,
triggers are needed. Here, we identified three possible
triggers to the initial download of mobile health-
related apps. The first trigger was Life situation:
stress, an unhealthy lifestyle, and not enough
exercise—due, for example, to a new job—seemed to
trigger the participants’ initial decision to try health-
related apps. Other studies have highlighted that the
absence of need (Peng et al., 2016) or interest (Krebs
& Duncan, 2015) are important deterrents to the
adoption of mobile health apps. Consistency with
existing values and individual needs have been
referred to as compatibility by Rogers (2003) and an
attribute for adoption. Here, the life situation clearly
created a need and interest to try mobile health-
related apps.
The second trigger was Relevant statistics. Some
participants were intrigued by measurements of
sports data and calorie calculations that could
enhance their physical activities and diets. This is in
line with TAM that perceived usefulness (Davis,
1989) is a key variable in intentions to use
technology. In diffusion theories, this aspect is
referred to as relative advantage over other options
(Rogers, 2003).
The third trigger, Perceived satisfaction with first
health app, shows the importance of trying
technological solutions and generating a positive
attitude towards health apps. Trialability—the
convenient ability to test new technologies—is an
attribute in DOI (Rogers, 2003). Here, satisfaction
with one app meant that other mobile health-related
apps were also downloaded. Additionally, Wang et al.
(2016) identified that the apps were perceived as
more effective if the user used several of them.
The adoption process is an ongoing process in
which the individual can continue to use or reject an
innovation at any time (Rogers, 2003). We identified
six possible drivers for the continuous use of mobile
health-related apps. The first one was Price value.
The participants referred positively to free-trial
periods, to the supply of free apps, and if they decided
to purchase an app, they mainly perceived the prices
to be affordable. Monetary value is a key determinant
to explain users’ use of technology-based services
(Venkatech et al., 2012). Yuan et al. (2015)
concluded that users perceive a positive price value
regarding paid health-related apps. On the other hand,
the cost of mobile health apps has also been identified
as a barrier (Krebs & Duncan, 2015; Kanthawala et
al., 2019; Peng et al., 2016). Here, the price value was
perceived mainly positively.
The second and third drivers were Simplicity and
Personalisation. Simplicity, such as not too many
features, was mentioned as an important reason for
continuous use by some participants, but also the
possibility to adjust the app to fit personal needs was
pointed out. Perceived ease of use has been
highlighted as a key determinant in technology
acceptance (Davis, 1989), and likewise, the level of
innovation complexity and compatibility with
HEALTHINF 2021 - 14th International Conference on Health Informatics
292
individual needs are key attributes in individuals’
adoption processes (Rogers, 2003). Wang et al.
(2016) stated that more personalisable health apps are
needed, and Beldad and Hegner (2018) concluded
that perceived ease of use is important for continuous
use of fitness apps.
The fourth driver was Guidance and progress
based on data. Peng et al. (2016) identified a similar
driver, tracking for awareness and progress, for
mobile health apps. The same authors argued that this
refers to self-observation in social cognitive theory
(SCT) (Bandura, 1986). Here, data was used as
reference points for self-regulation and goal setting.
This type of self-observation clearly seems to
contribute to the participants’ dedication to use the
apps, which is not surprising as this ought to be a core
feature of mobile health-related apps.
The fifth driver was Flexibility. This aspect
related to the participants’ perceptions of
unattachment from teachers or personal trainers, their
style of teaching and when and where the lessons are
taking place. Users’ perceived usefulness (Davis
1989) and relative advantage over other options
(Rogers, 2003) are relevant explanations for the
adoption and use of technology. It is more probable
that individuals are using physical activity apps if
they perceive that the apps could support their
training efficiently (Beldad & Hegner, 2018).
The sixth driver was Social encounters. Some of
the participants were very inspired by meeting people
with the same interests and ambitions. The social
network using the app influenced them to keep using
the apps, exercise together, and share, for example,
progress data. Social factors (Thompson et al., 1991)
and social influence (Venkatech et al., 2003; 2012)
are key determinants for technology use. Likewise,
the subjective norm has a positive effect on
behavioural intention (Arjzen, 1991; Fishbein &
Ajzen, 1975). Also, previous studies of mobile health
apps highlight social factors as important for using
health apps (Beldad & Hegner, 2018; Peng et al.,
2016). Régnier and Chauvel (2018) concluded that
some diet and fitness app users are highly motivated
by participating in digital communities.
We identified four possible barriers to greater
engagement with mobile health-related apps. The first
one was Perceived risk of personal data. The
participants were concerned with possible
unauthorised use of personal data that is collected by
some of the apps. It seemed that, for some of them,
the concern hindered a greater engagement with some
apps. Perceived risk is a constraint in adoption
processes (Ram & Seth, 1989), and previous studies
have highlighted users’ privacy concerns (Bol et al.,
2018) and collection of personal data (Krebs &
Duncan, 2015) as constraints for the uptake of mobile
health apps. Lack of trust in health apps was as well a
major concern pointed out in a study by Incisive
Health International (2017).
The second barrier was Time-consumption. The
input of food-related data was perceived burdensome
by some participants. They acknowledged that some
of this data is needed for the app to function properly,
but in the long-run, this may hinder them from taking
greater advantage of the app. Users’ effort expectancy
has been found to be a significant determinant for the
use of technology (Venkatech et al., 2012). Likewise,
in IRT, usage barrier refers to obstacles in innovation
functionality that hinder use (Ram & Seth, 1989).
Peng et al. (2016) also found lack of time (and effort)
as a factor that hinders the use of mobile health apps.
The third barrier was Limited understanding of
health data. Some of the apps may require quite
extensive knowledge of health data such as nutrition,
which may increase the users’ perceptions of
complexity and decrease their perceptions of ability
to use the app. Individuals’ perceptions of their ability
to perform a specific behaviour are referred to as
perceived behavioural control (Ajzen, 1991). In
technology acceptance theories, it is referred to as
facilitating conditions, that is, the user’s perception of
possessing the required resources or infrastructure to
use the technology (Venkatech et al., 2003; 2012).
Previous studies have also highlighted individuals’
lack of app literacy (Peng et al., 2016), e-health
literacy skills (Bol et al., 2018), and need for clear
information (West et al., 2012) as obstacles in the use
of mobile health-related apps. Concerns have also
been raised that the apps do not provide sufficient or
correct information (Akbar et al., 2020) and are not
necessarily based on evidence and professional
medical involvement (Higgins, 2016). People are also
unsure about what is a quality and credible health app
(Kanthawala et al., 2019). Therefore, limited e-health
literacy skills can also raise safety concerns for users
of health apps.
The fourth barrier was Adaption to new routines.
Flexibility (as discussed above) can be a driver to use
the apps; however, it may also be a constraint. There
is not necessarily a physical person to push the user
to perform, for example, physical activities. Thus, the
use of the app requires some degree of personal
discipline and routines that last over time. This was
admitted not to be easy, and it may be tempting to fall
back on old habits, which could lead to a rejection of
the app. Habit has been identified as a significant
indicator of technology use (Venkatech et al., 2012)
and, likewise, in resistance theory, the tradition
Adoption and Use of Health-related Mobile Applications: A Qualitative Study with Experienced Users
293
barrier is relevant to explain the rejection of
innovations. Also, in studies on health app use, habit
was determined to be significant (Yuan et al., 2015);
individuals’ lack of discipline is a constraint (Peng et
al., 2016), and users need to engage actively with the
apps to benefit (Higgins, 2016).
5.1 Managerial Implications
To create initial awareness among non-adopters of
health apps, the focus could be on the three triggers
identified in this study. Mass promotion of mobile
health apps could focus on peoples’ life situation
(such as having a ‘stressful job’) and communicating
the usefulness of statistics provided by the apps. As
suggested by Peng et al. (2016), medical
professionals could have an active role in these types
of promotional activities to encourage more use.
Enlisting people to try a quality and credible health
app is important (Kanthawala et al., 2019) because, if
they are happy with it, then the barrier to try other
ones is likely to be lower.
To encourage users to continue to use health apps,
both the identified drivers and barriers should be
considered. The results indicated that free-trial
periods and free apps are important in the adoption
process. Users often prefer free apps, as it gives them
the possibility to try different apps, and if they are not
satisfied, they can easily erase them and try another
one (Kanthawala et al., 2019). Nevertheless,
affordable prices did also keep the participants using
the purchased apps. As pointed out by Peng et al.
(2016), paid apps should have features that are not
included in the free apps. Thus, mixed pricing
principles with free-trial periods, free versions of
apps, and purchasable apps (subscription fees, one-
time fees, etc.) are important to keep different types
of users satisfied.
Despite being experienced users, some
participants perceived simplicity (limited number of
features) as important. Also, better possibilities to
personalise the apps were mentioned. These two
aspects are important to notice when designing health
apps. Simple but customisable apps ought to be
appreciated by many users.
For some participants, social encounters such as
meeting like-minded people and sharing data with
other users are very important app features. However,
at the same time, concerns were raised regarding the
registration of personal health-related information.
This could, to some degree, be solved by better
allowing the user to control options for data
registration and sharing by clearly informing users
about actions taken to secure data and providing
transparent privacy policies in compliance with
directives such as the General Data Protection
Regulation in the EU (Intersoft Consulting, 2020).
The extensive effort to continuously register data,
such as food-related data, was also perceived as a
barrier to the use of some apps. The engagement with
the apps requires discipline and new routines, as
discussed by the participants. These issues are not
easy to overcome as many health apps’ performance
is based on continuous activities and updates of, for
example, nutrition data. Guidance and progress based
on data were perceived by the participants as a major
driver to use the apps. Apps need users to interact
actively with them (Higgins, 2016). Therefore,
reducing the time spent on manual feeding of data
seems crucial to enhance continuous use. One
solution could be to focus more on developing
synchronisations between apps, devices, databases,
and different types of sensors.
Some participants felt that diet-related apps can
be hard to use because they do not have enough
knowledge about nutrition. According to Kanthawala
et al. (2019) health app literacy is a broad area that is
underexplored, but education about health-related
apps should focus on telling users what criteria is
relevant when selecting and using apps.
6 CONCLUSION
By interviewing a sample of experienced users, this
study identified three triggers to the initial
downloading of mobile health apps: Life situation,
Relevant statistics, and Perceived satisfaction with
first health app. Six drivers for continuous use were
identified: Price value, Simplicity, Personalisation,
Guidance and progress based on data, Flexibility, and
Social encounters. Four barriers for greater
engagement with the apps were as well identified:
Perceived risk of personal data, Time-consumption,
Limited understanding of health data, and Adaption
to new routines. We discussed these possible
determinants together with previous studies on the
use of mobile health apps and matched them against
constructs in theories of technology adoption and use.
Hence, we see that we contributed to existing theory
by confirming and specifying previous results on the
adoption and use of mobile health-related apps. The
findings can be used to develop a more fine-tuned set
of adoption factors to conduct a larger survey study.
We should note that the participants in this study
may have other perceptions of health-related apps
than older users and people with chronic diseases
(Yuan et al., 2015). This study primarily focused on
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physical activity and diet-related apps. Thus, the
results may not reflect the perceptions of health
consumers who participate in medical care programs
and use health apps targeted towards specific
diseases.
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