Adoption and Use of Health-related Mobile Applications: A

Qualitative Study with Experienced Users

Elena Smirnova

, Niklas Eriksson

and Asle Fagerstrøm

Arcada University of Applied Sciences, Jan-Magnus Janssons Plats 1, 00420 Helsinki, Finland

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

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

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

e Descri

tion Health a

s Years of usa

P1 32 Male professional working with Risk analytics Exercising, Yoga 5 years

P2 29 Female universit

student Yo

a, Exercisin

, Diet, Meditation 3

ears

P3 29 Male

rofessional business consultant Yo

a, Runnin

, Exercisin

, Diet 3

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

rofessional dentist Diet, Exercisin

, Runnin

ears

P8 28 Female

rofessional

ect mana

Diet, Exercisin

, Runnin

ears

P9 29 Male professional sales manager Diet, Exercising, Yoga 5 years

P10 29 Female

ect mana

Diet, Exercisin

, Runnin

, Yo

a 3

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

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

order themes. For example, the 1

order codes “Free

trial”, “Free app” and “Affordable” formed the 2

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)

HEALTHINF 2021 - 14th International Conference on Health Informatics

290

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

HEALTHINF 2021 - 14th International Conference on Health Informatics

294

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.

REFERENCES

Ajzen, I. (1991). The theory of planned

behavior. Organizational Behavior and Human

Decision Processes, 50(2), 179–211.

Akbar, S., Coiera, E., & Magrabi, F. (2020). Safety

concerns with consumer-facing mobile health

applications and their consequences: a scoping

review. Journal of the American Medical Informatics

Association, 27(2), 330–340.

Bandura, A. (1986). Social foundations of thought and

action: A social cognitive theory, Prentice-Hall.

Beldad, A. D. & Hegner, S. M. (2018). Expanding the

technology acceptance model with the inclusion of

trust, social influence, and health valuation to determine

the predictors of German users’ willingness to continue

using a fitness app: A structural equation modeling

approach. International Journal of Human–Computer

Interaction, 34(9), 882–893.

Bol, N., Helberger, N. & Weert, J. C. M. (2018).

Differences in mobile health app use: A source of new

digital inequalities? The Information Society, 34(3),

183-193. doi: 10.1080/01972243.2018.1438550

Davis, F. D. (1989). Perceived usefulness, perceived ease

of use, and user acceptance of information technology.

MIS Quarterly, 13(3), 319–340.

Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention

and behavior: An introduction to theory and research,

Addison-Wesley, Reading, MA.

Higgins, J. (2016). Smartphone applications for patients'

health and fitness. The American journal of medicine,

129(1), 11–19.

Incisive Health International (2017). Taking the pulse of

eHealth in the EU. Retrieved 16 May 2020, from

https://www.incisivehealth.com/wpcontent/uploads/20

18/08/Report-Taking-the-pulse-of-eHealth-in-the-EU-

.pdf.

Intersoft Consulting (2020). General Data Protection

Regulation GDPR. Retrieved 23 June 2020 from

https://gdpr-info.eu/

Kanthawala, S, Joo, E, Kononova, A, Peng, W. & Cotten,

S. (2019). Folk theorizing the quality and credibility of

health apps. Mobile Media & Communication, 7(2)

175–194.

Krebs, P., & Duncan, D. (2015). Health app use among US

mobile phone owners: a national survey. JMIR mHealth

and uHealth, 3(4), doi: 10.2196/mhealth.4924.

Miles, M. B., Huberman, A. M., & Saldana, J. (2014).

Qualitative Data Analysis: A Methods Sourcebook (3rd

ed.). Sage Publications.

Murnane, E. L., Huffaker, D., & Kossinets, G. (2015,

September). Mobile health apps: adoption, adherence,

and abandonment. In Adjunct Proceedings of the 2015

ACM International Joint Conference on Pervasive and

Ubiquitous Computing and Proceedings of the 2015

ACM International Symposium on Wearable

Computers (pp. 261-264). doi:

10.1145/2800835.2800943.

Peng, W., Kanthawala, S., Yuan, S., & Hussain, S. (2016).

A qualitative study of user perceptions of mobile health

apps. BMC Public Health, 16(1). doi: 10.1186/s12889-

016-3808-0.

Régnier, F., & Chauvel, L. (2018). Digital inequalities in

the use of self-tracking diet and fitness apps: Interview

Study on the Influence of Social, Economic, and

Cultural Factors. JMIR Mhealth And Uhealth, 6(4),

e101. doi: 10.2196/mhealth.9189.

Ram, S. & Seth, J. N. (1989). Consumer resistance to

innovations: The marketing problem and its solutions.

Journal of Consumer Marketing, 6(2), 5–14.

Rogers, E. (2003). Diffusion of innovation (5th ed.). New

York: Free Press.

Taherdoost, H. (2018). A review of technology acceptance

and adoption models and theories. Procedia

Manufacturing, 22, 960–967.

Thompson, R. L., Higgins, C. A., & Howell, J, M (1991).

Personal computing: Toward a conceptual model of

utilization. MIS Quarterly, 15(1), 125–143.

Yuan, S., Ma, W., Kanthawala, S., & Peng, W. (2015).

Keep using my health apps: Discover users' perception

of health and fitness apps with the UTAUT2

Model. Telemedicine and e-Health, 21(9), 735–741.

doi: 10.1089/tmj.2014.0148.

Venkatesh, V., Morris, M.G., Hall, M., Davis, G.B. &

Davis, F.D. (2003). User acceptance of information

technology: Toward a unified view. MIS Quarterly,

27(3), 425–478.

Venkatesh, V., Thong J.Y.L., & Xu X. 2012. Consumer

acceptance and use of information technology:

Extending the unified theory of acceptance and use of

technology. MIS Quarterly, 36(1), 157–178.

Wang, Q., Egelandsdal,, B., Amdam, G. V., Almli, V. L.,

& Oostindjer, M. (2016). Diet and physical activity

apps: Perceived effectiveness by app users. JMIR

mHealth uHealth, 4(2), e33. doi:

10.2196/mhealth.5114.

West, J., Hall, P., Hanson, C., Barnes, M., Giraud-Carrier,

C., & Barrett, J. (2012). There’s an app for that: Content

analysis of paid health and fitness apps. Journal Of

Medical Internet Research, 14(3), e72. doi:

10.2196/jmir.1977.

WHO (2018). More active people for a healthier world.

Retrieved 8 June 2020, from

https://www.who.int/publications/i/item/global-action-

plan-on-physical-activity-2018–2030.

WHO (2020). Healthy diet. Retrieved 16 June 2020 from

https://www.who.int/news-room/fact-

sheets/detail/healthy-diet.

Adoption and Use of Health-related Mobile Applications: A Qualitative Study with Experienced Users

295