Activity Trackers: Comparing Athlete Runners versus Health

Runners through a Dedicated Technology Acceptance Model

Ricardo Sol

and Karolina Baras

Exact Sciences and Engineering, University of Madeira, Madeira, Portugal

Keywords: User Acceptance, Ubiquitous Systems, Personal Informatics, Personal Data Tracking, Wearable Computers,

Sports/Exercise.

Abstract: The conducted study seeks to learn if, why and how two different groups of Activity Trackers users, Athletes

and Health Runners, are utilizing these devices for their self-quantification. The study is based on the content

analysis of 20 semi-structured interviews, 10 of which were with Athletes. To achieve its goals, the authors

use a model based on the Technology Acceptance Model (TAM), a widely adopted technology acceptance

theory. Amongst our findings, the construct Perceived Ease of Use showed that Athletes find it hard to

program the settings for their training and Health Runners expressed that there is too much information

involved. This paper contributes by showing that an all-purpose interface is not suitable and offers new

knowledge for methodological discussions as it is, to the best of our knowledge, the first qualitative study to

employ a TAM like model in order to qualitatively interpret the use of Activity Trackers.

1 INTRODUCTION

Activity Trackers have become mainstream gadgets

for consumers in recent years. However, many people

still do not achieve the recommended levels of

activity for their age groups. For the activity of

walking, it has been widely recommended that

healthy adults should reach the goal of ten thousand

steps per day in order to maintain or improve their

health. The development and the commercialization

of Activity Trackers have showed a positive effect in

helping many users to reach this goal (Laranjo, 2021).

Research has shown that users of Activity Trackers,

when steadily checking their step count walk more

(Carels, 2005), lose more body weight (Akers 2012),

and are more in control of their actions (Burke, 2011).

Nevertheless, a study on the adoption of a specific

Activity Tracker found that half of the users stop

using the device after two weeks (Shih, 2015).

Several models to describe and capture the use of

Activity Trackers have been created since Li et al.’s

seminal work of a model with five iterative stages:

preparation, collection, integration, reflection, and

action (Li, 2010). These authors later refined their

model. Epstein et al. expanded that model by

https://orcid.org/0000-0003-4333-7140

https://orcid.org/0000-0002-2050-6565

including the lapses and interruptions of tracking, and

emphasizing the intricacy of integration, collection

and reflection (Epstein, 2015). This model was also

expanded to count for eudemonia and changing goals

(Niess, 2018). However, these models are not

quantitative, not dedicated exclusively to Activity

Trackers, do not have a Health oriented component,

and fall short in incorporating Data Control and

Privacy issues. To address these shortages, a

quantitative model was created, that has eleven

factors that influence the acceptance and usage of

Activity Trackers (Sol, 2016). Nevertheless,

constructs as Subjective Norm or Attitude failed to be

part of this model.

According to the International Data Corporation,

worldwide shipments of wearables grew 9.9% during

the third quarter of 2021 reaching 138.4 million units

(IDC, 2021). Within these millions of users, one may

be able to predict and identify that diversity can be

found in types of use of trackers. Researchers have

already noticed gender differences (Shih, 2015),

differences between health runners and pleasure

runners (Temir, 2016), others look to naïve users

(Rapp, 2016) and yet others looked at extreme users

(Sol, 2021). In our research we want to find and

Sol, R. and Baras, K.

Activity Trackers: Comparing Athlete Runners versus Health Runners through a Dedicated Technology Acceptance Model.

DOI: 10.5220/0011511400003321

In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2022), pages 78-85

ISBN: 978-989-758-610-1; ISSN: 2184-3201

compare aspects that impact acceptance and use of

Activity Trackers by Athletes whose focus are to

better performance in competitions and Health or

Recreational Runners whose focus is to be healthy.

The used model takes into consideration Data

Control, unlike previous technology acceptance

models in the field (Kim, 2012). This is a construct of

importance, when only 31% of workers disagree to let

their employer make use of wearable devices to

monitor their performance at work, with 44% in favor

(PWC 2021). Specially, when research shows that

third-party vendors are collecting detailed data from

users (Ho 2014).

We applied a quantitative model used to

qualitatively elucidate why people are more or less

likely to adopt and use all kinds of Activity Trackers.

We propose recommendations about how that model

can be supported to enhance analyses of Activity

Trackers use.

2 LITERATURE REVIEW

In this section we review literature related to the use

of Activity Trackers. We also investigate Technology

Acceptance research to understand its associations,

relevance and definitions. The acceptance and use of

Activity Trackers is due to numerous reasons and

motives, some of which appear to conflict. Individual

users may start tracking their activity because they

have a specific goal in mind (Epstein, 2014). These

goals can be a healthier life or a beautiful physical

appearance, or both, the later being an excuse to reach

the former (Kay, 2013). Nevertheless, there are users

who begin to use Activity Trackers having no

objective in mind and use the device to help them set

a specific goal. This goal becomes more defined as

the usage moves from the discovery phase to the

maintenance phase of pondering (Li, 2010). Others

begin tracking simply moved by interest and curiosity

in quantitative data (Lindqvist, 2011). Many users

receive the device as a gift, but when having the

chance to choose specific tracking devices they base

their choices on online reviews, marketing

campaigns, specific features, portability, or follow an

advice given by friends or family members (Kaye,

2011). Goal setting is only one idea to support and

persuade health-related behavior change, others

include feedback, reminder notifications, and social

comparison (Shih, 2015). To become aware of one’s

performance and to regulate performance concerning

the defined objectives, users also tend to check the

data as soon as it is gathered (Fritz, 2014). Users tend

to change their habits, goals, and devices and the

related applications or dashboards are unprepared to

deal with this. When tracking, users tend to change

devices frequently or use several devices at the same

time, which leads to problems in assessing and

consolidating data (Rooksby, 2014).

2.1 Technology Acceptance Model

The conceptual framework applied in this work is

based on the Technology Acceptance Model (TAM),

a widely adopted technology acceptance theory that

can explain why different people have distinct levels

of adoption and use of a specific information

technology. TAM has its roots in Martin Fishbein and

Icek Ajzen’s Theory of Reasoned Action, a theory

that comes from social psychology and illustrates the

human behavior based on his intentions (Fishbein,

1997). TAM introduces two constructs: Perceived

Ease of Use (PEoU) and Perceived Usefulness (PU),

which determine Intention to Use through Attitude

(Davis, 1989). Perceived Usefulness is defined as

“the degree to which a person believes that using a

particular system would enhance his or her job

performance”, while Perceived Ease of Use is “the

degree to which a person believes that using a

particular system would be free of effort” (Davis,

1989). Further constructs used in this study are

detailed as follows: Image: “the degree to which use

of an innovation is perceived to enhance one’s image

or status in one’s social system” (Moore, 1991). Self-

Efficacy: “the judgment of one’s ability to use a

technology (e.g., computer) to accomplish a

particular job or task” (Compeau 1995). Habit: “the

extent to which people tend to perform behaviors

automatically because of learning” (Limayem, 2007).

Hedonic Motivation: “the fun or pleasure derived

from using a technology, and it has been shown to

play a key role in determining technology acceptance

and use” (Brown, 2005). Perceived Privacy Invasion:

“the degree to which a person feels that the

monitoring is invasive of their privacy” (Dryer 1999).

Perceived Data Control: “the degree to which a

person feels they have control over the use of, and

access to, the data collected” (Lindqvist, 2011).

Perceived Severity of Disease: “the beliefs a person

holds concerning the effects a given disease or

condition would have on one's state of affairs”. Health

Threat: “abstract assessing the susceptibility and the

severity, of disease- specificity” (Hochbaum, 1952).

Perceived Susceptibility of Disease: “the perception

of the likelihood of experiencing a condition that

would adversely affect one's health”. Health

Consciousness: “the degree to which health concerns

Activity Trackers: Comparing Athlete Runners versus Health Runners through a Dedicated Technology Acceptance Model

are integrated into a person’s daily activities”

(Jayanti, 1998).

3 METHODOLOGY

The study consisted of semi-structured interviews of

participants that were informed and gave their free

consent, recruited via convenience sample. In total,

the participant group consisted of twenty activity

tracker users. Of the 20 users interviewed for this

study, 10 were female and 10 were male. To help

ensure that there would be a variety of experiences

amongst participants, interviewees were recruited

from amateur running competitors and from the

general public, being 10 self-identified competition-

running athletes (4 females) and 10 self-identified

health runners (6 females) how were not so easy to

reach in our convenience sample. The athletes ranged

in age from 22 to 45 (average 35.5, standard deviation

7.5, median 36), and had the devices from 1 to 150

months (Av=51.1, SD=52.9, M=30). The Health

Runners ranged in age from 26 to 48 (Av=34, SD=8,

M=31.5), and had the devices from 12 to 50 months

(Av=24.5, SD=13.7, M=21). The participants were

from Western Europe. The first author conducted 19

interviews face to face and 1 through Skype video

call. From the 20 interviews 18 were made in the

participants’ native language. The interviews took off

from 22 trigger questions. However, the interview

protocol was used in a flexible manner according to

the flow of the interview. The average time of the

interviews was 18 minutes (12-34). Respondents

were first asked for the activity trackers they knew

and then asked for what purposes they used them. To

make the interview more focused, next we asked

participants to elaborate on their use of the device that

they defined as the most frequently used for

quantifying physical activity. In addition to finding

out about general trends in activity trackers use and

acceptance, we were interested in learning if activity

trackers have changed user’s attitudes. As part of the

interview process, we asked the participants, for

example, whether society recognizes activity trackers

as part of the promotion of one’s image. All

interviews were digitally recorded using Word Audio

Notes and then manually transcribed. The

confidentiality and anonymity of all participants were

safeguarded by making the names of study interview

participants only known to the interviewer, using

aliases for the interviewees in the transcriptions.

To analyze the transcripts, we employed the

activity trackers acceptance model (Sol 2016) that

conducted an extensive review and analysis of

noticeable technology acceptance. Their resulting

model confirms PU and PEoU as strong predictors of

Behavior Intention to use. Although these kinds of

models are usually applied to analyze and explain the

quantitative data collected through a survey

instrument, the current study has taken a different

approach. To the best of our knowledge, this is the

first qualitative study that deploys this quantitative

model to study Activity Trackers use, which is an

evolving line of research.

The results of our study are presented in the

following sections. Firstly, we apply the model to

analyze the interview data and compare with previous

work. Next, we discuss its applicability to explain

Activity Trackers use and make recommendations

supported by the model. The concluding section

summarizes the results.

4 EMPLOYING THE MODEL TO

UNDERSTAND RUNNERS USE

OF ACTIVITY TRACKERS

Here we apply an activity tracker Technology

Acceptance Model to the interview data by examining

each of the eleven constructs shown in Figure 1. In

the following sections, we describe the participants

as, for example: P9H12M18, meaning: Participant

number (P1 to P10), runners’ group (H = Health, A =

Athlete), number of months using activity trackers,

gender (F = Female, M = Male) and age.

Figure 1: Activity Trackers Acceptance Model (Sol 2016).

4.1 Perceived Usefulness

When characterizing the Perceived Usefulness

construct, we used statements such as: How using

Activity Trackers was beneficial for you or not. All

participants mentioned that being aware of the

information collected by these kinds of devices was

particularly useful and stated that they do not use all

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

the information. Nevertheless, Athletes were focused

on the immediacy for trainings, as said by P1A24F34:

“I think it allows you to act on the spot and I think

that's the relevance, knowing where you are and being

able to correct it on the spot, or else correct it in the

coming weeks.” We also noticed that the Athletes

were inclined towards a perspective of near future

actions when using the devices, as emphasized by the

following quote by P1A24F34: “What changed the

most was having more information and with it being

able to plan my activity.” This confirms that the

devices should facilitate more these plans and

inclinations. Athletes also reported a focused

experience, as expressed by P5A150M44: “It gives

the necessary data to experience what I want. That's

why I don't take full advantage of the program.” This

points to the fact that some users are operating the

technology in a limited way. Similarly, young Health

Runners like P5H12M30 corroborate: “I don't think

there's the need to buy dedicated device for activity

tracking.” A few Health Runners were more

concerned with using the devices for an assortment of

situations, like P10H18F31 put it: “It doesn't have the

possibility for me to record a group class. It’s got a

set of activities, and it doesn't have what I need.”

Overall, it was confirmed by this construct that these

devices are useful, however it was also realized that

there are specifics that are distinctive between the two

groups. While both groups reported issues that are

important, these issues are quite different and need

specific design solutions.

4.2 Perceived Ease of Use

When characterizing the Perceived Ease of Use

construct, we used questions such as: Which were the

most difficult aspects when using Activity Trackers?

All participants mentioned that the devices were easy

to use. Nevertheless, Athletes find it hard to program

the settings for their trainings, like P8A1F22 noted:

“What is more complicated to do is, for example, the

programing of the training scheme.” Which was

corroborated by P9A60M41: “Not in the use part, but

in the configuration.” Whereas Health Runners were

more concerned, again, with using the devices for

specific sports, like P1H36F28 put it: “If I want to

ride a bike, I can't, I have to put the watch on my ankle

otherwise it won't register my steps.” There were

concerns about the understanding, meaning and

clarity of the information provided, like P5H12M30

stated: “You don't know in practical terms what that

means.” It is important to notice that users are not

specialists in analyzing data, so the data displayed

would need to be translated for the user. Overall, it

was confirmed that these devices are easy to use,

however some issues were reported, and these are

different between the two groups.

4.3 Perceived Data Control

When characterizing the Perceived Data Control

construct, we used statements such as: What do you

think about your control of the information. In both

groups there were users who did not care and there

were users who were worried. The Athletes who

cared were more concerned about the security

perspective than with the data itself, as described by

P1A24F34: “Hack the data, you can see what time the

person is not at home. To rob houses is good.” This

point was corroborated by P9A60M41: “Any person

that follows me in the social app of the tool, easily

finds out where I live, since my trainings start always

in the same place. That's what worries me the most.”

Most of the Health Runners stated that they knew that

they were not in control of the data and that they did

not introduce much personal information.

Nevertheless, they did not care that this kind of data

was available and exposed, except for participant

P2H12M32 who said: “I know that the data is not

locally stored and have access to the raw files.

Especially in the case of Google fit, I knew that the

data was in Google servers, and since they have

everything, my email, my calendar, and from my

physical performance, I felt a bit scared and stopped

using Google Fit and GPS tracking.” To summarize,

users are aware that their personal information can be

seen, as found before, nevertheless overall most users

in both groups are not overly concerned or worried

about data control. The differences between the two

groups did call our attention but were minor.

4.4 Self-efficacy

When characterizing the Self-efficacy construct, we

used statements such as: How do you feel when

managing the information. All users considered

themselves quite efficient users of the devices, as

stated by P7H15M33: “I'm not using it one hundred

percent in all features. But the ones I use, I use it very

well.” However, P3H24F40 noted: “I think there is

too much information to manage.” Again, it is

highlighted that information must be relevant for the

users or it will be considered superfluous. To

summarize, the major difference between the two

groups is that Health Runners think there is too much

information.

Activity Trackers: Comparing Athlete Runners versus Health Runners through a Dedicated Technology Acceptance Model

4.5 Image

When characterizing the Image construct, we used

statements such as: What do you think about other

people who use Activity Trackers. Both groups in our

sample gave ordinary importance to Image. However,

the idea of the device being an iconic trend was

mentioned by P1A24F34: “Theoretically this turns

out to be a cult.” The importance of the social aspect

is underlined here. On top of this, for Athletes, one

having a device was associated with a more

competitive person, as expressed by P4A24F38:

“They are people that have other goals, they are

committed to progress in their training to reach one

goal after another… improve their performances in

the competitions they enter.” Similarly, Health

Runners mirrored others, as expressed by P1H36F28:

“They are smart like me and want to improve their

health.” Nevertheless, P4H24F26 said: “I think the

devices are ugly, I don't like to wear a black thing.”

This denotes the importance of visual design and the

consideration of gender differences or preferences.

Overall, both groups are of the opinion that other

people who use the devices have the same objectives

as themselves.

4.6 Hedonic Motivation

When characterizing the Hedonic Motivation

construct, we used statements such as: How do you

feel using Activity Trackers. Participants felt good

when using the devices. However, Athletes felt good

when using but also before using the devices, as

described by P5A150M44: “The more goals we

reach, which are the data that the device also gives,

the more desire to go training.” Whereas Health

Runners felt well when running, such as P7H15M33

put it: “I find it more motivating during exercise. Not

necessarily before doing the exercise.” This was

corroborated by P3H24F48: “Not that it's going to

make me get up one day and run-on purpose just

because of the device.” This can indicate that Athletes

are pre-motived to run while Health Runners need

support to encourage behavior change. To

summarize, there is a key difference between the

groups.

4.7 Habit

When characterizing the Habit construct, we used

statements such as: Using an Activity Tracker is or is

not a habit for you. For Athletes using the device, this

can be more than a habit they can be craving for it.

Nevertheless, Health Runners who do not make use

of the device as a habit blamed the recharging of the

device batteries for their inconsistent use of the

device, as said by P1H36F28: “What annoys me

sometimes is putting the watch on charge, I don't want

to get up without it counting those steps.” This point

was corroborated by P4H24F26: “For me it's boring

every three days to remember that the battery is going

to fail.” This shows that the device needs to be able to

encourage and give assistance in the implementation

of habits. To summarize, the differences between the

two groups are noticeable but were minor.

4.8 Perceived Susceptibility to Chronic

Diseases/ Perceived Severity of

Chronic Diseases/ Perceived Threat

- Health Consciousness

In our interviews the three health related constructs

were indistinctive. When characterizing the

Perceived Susceptibility to Chronic Diseases

construct, we used statements such as: What kind of

injury have you had. When characterizing the

Perceived Severity of Chronic Diseases construct, we

used statements such as: How do you react to the

possibility of having an injury. When characterizing

the Perceived Threat-Health Consciousness construct

we used statements such as: What kind of attitudes do

you take concerning your health. All participants

mentioned paying special attention to their diet,

alcohol intake, and sleep. In both groups there were

users with conflicting practices. Health Runners are

more inclined not to think that they could have a

health problem, most Athletes, on the other hand have

this in mind all the time and consider how this can

impact their occupation. As P3A36M45 noted: “I go

to competitions carefully to avoid serious injuries.

With care, with calm, because I see many competitors

especially downhill, and I get scared to see them

going downhill so fast. I'm often overpassed, because

that scares me.” Similar attitude was viewed in a

Health Runners, as described by P1H36F28: “In the

gym there are exercises that I know I cannot do, I only

do if I have someone next to me.” To summarize, the

differences between the two groups grasped our

attention but were minor.

4.9 Behavioral Intention to Use

(Acceptance)

When characterizing the “Behavioral Intention to Use

(Acceptance)” construct we used statements such as:

In the long run do you think you will still use the

device or not. All participants stated that they will

continue to use the device, except P9H12M46: “If I

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

can achieve a weight loss goal without needing to use,

I will not use.”

5 DISCUSSION

Our findings suggest that the model constructs

revealed to be important in studying acceptance and

use of Activity Trackers. The primary performance

booster that participants saw in Activity Trackers was

the ability to show data that one had never been able

to see before. Other common useful practices of using

Activity Trackers included motivational support and

comparison with others and oneself.

We found that for this small sample population,

Perceived Usefulness, Perceived Ease of Use, Image,

Hedonic Motivation, and Habit were strongly

associated with the acceptance and use of Activity

Trackers and show significant differences between

groups. Perceived Usefulness and Perceived Ease of

Use constructs showed that Athletes experienced

difficulties and problems with configuring the

settings for their trainings, corroborating the design

idea of facilitating micro plans (Gouveia, 2018) and

that users are using the technology in a limited way

(Didziokaite, 2017). It also showed differences

among ages especially in young runners (Janssen

2020). Additionally, the study showed that Health

Runners felt that they were faced with too much

information or complex information and wanted the

possibility to track different exercises. A similar issue

with the volume of information has been described in

previous work stating that users are not data scientists

(Rooksby, 2014) and that the information is not in the

user’s language (Lazar, 2015). Perceived Data

Control showed conflicting differences within the

two groups, however generally speaking users are

conscious that their personal information can be

highly sensitive, as previously established (Lupton,

2017). However, most of our interviewees did not

have many concerns in sharing their data. Self-

efficacy construct showed that both groups are

efficient users. Nevertheless, we have seen here the

need of personal relevance of the data (Kim, 2016).

Image construct showed that both groups in our

sample gave ordinary importance to image and it is

underlined here the long-viewed importance of the

social aspect of these devices (Consolvo, 2006)

(Clawson, 2015) (Patel, 2015), also the importance of

aesthetics and form (Harrisson, 2015) and the

significance of gender differences (Shih, 2015).

Hedonic Motivation construct showed that both

groups felt good when using the devices. Athletes

find motivation before and during running, in

accordance with previous findings (Rapp, 2020). The

Health Runners denoted a previously identified need

for behavior change strategies (Klasnja, 2011) and the

two groups showed the need for egocentric design

(Elsden, 2015). Habit construct showed that Athletes

could be addicted to use while Health Runners use the

battery recharge as an excuse. Thus, we have noticed

the importance of the previously identified idea of

implementing routines (Lazar 2015) and adherence

(Tang, 2018). As for Perceived Susceptibility to

Chronic Diseases, Perceived Severity of Chronic

Diseases, Perceived Threat - Health Consciousness,

and Acceptance showed no major differences

between groups or conflicting differences within the

groups.

By the end of conducting this research it was clear

that an all-purpose interface is not suitable. The

novelty of the findings suggests specific design

considerations: designers should look at the ease of

use and usability of the overall settings, the need for

easy-to-use training plans and training settings. In

order to accomplish this, we propose that the software

has different modes that would be selected by

different types of users: pleasure runners, health

runners, athletes, etc. Other possibility would be to

associate the modes to the three classes of tracker

motivations: behavior change, instrumentation, and

curiosity. One limitation of this work is that it does

not make a clear split between intention to use activity

trackers and the actual use. This is because most of

the interviewees were users of at least one activity

tracker. Finally, we anticipate that the establishment

of specific models for each group may be a necessity

to better explain the acceptance and use of Activity

Trackers by these users.

6 CONCLUSIONS

There is an ample amount of studies looking into

users of activity trackers. However, only a small part

has compared different groups of users. This study

was based on 20 semi-structured interviews with

Health Runners and Athlete Runners. They offered us

a diverse variety of information on how users are

integrating Activity Trackers into their lives, their

paybacks, difficulties, and future tendencies. This

study shows that the Activity Trackers Acceptance

Model can be employed in this context and makes

suggestions for its future application. Participants that

use Activity Trackers in their daily lives found them

useful for emotional support, social parallelism and

competition, and, surprisingly interesting for the data

the device provides.

Activity Trackers: Comparing Athlete Runners versus Health Runners through a Dedicated Technology Acceptance Model

Our study revealed that there were significant

differences regarding difficulties of the users

experience, Athletes had issues with configuring the

settings for their trainings whereas Health Runners

found too much or complex information and wanted

the possibility to track different exercises. There were

also significant differences regarding motivation, the

devices motivated Athletes before and during running

while Health Runners were motivated by the devices

only when running. Both groups thought that the

people who use these devices had the same goals as

themselves. Health Runners used the excuse of

having to regularly recharge the device battery as the

reason for not making the use of the device a daily

habit in their lives. Even with a small number of users,

and by utilizing the model constructs we have been

able to gain new insight into the differences of how

these two groups use and accept Activity Trackers in

this initial investigation. This study brings to light the

value of looking more closely at specific types of

users and how their documented experiences and use

of these devices can be analyzed and applied to the

understanding of the use of Activity Trackers. Many

more such studies need to be carried out in order to

gain and maximize data pertaining to the abundant

diversity that exists with regards to user experiences,

which can then also impact future designs of Activity

Trackers.

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