sustainability

Review

The Intention to Use Fitness and Physical Activity Apps: A Systematic Review

Salvador Angosto 1 , Jerónimo García-Fernández 2,* , Irena Valantine 3 and Moisés Grimaldi-Puyana 2

1 Department of Physical Education and Sports, Faculty of Sports Sciences San Javier, University of Murcia, 30720 Santiago de la Ribera (Murcia), Spain; [email protected]

2 Department of Physical Education and Sports, Faculty of Educational Sciences, Universidad de Sevilla, 41013 Seville, Spain; [email protected]

3 Department of Sport and Tourism Management, Lithuanian Sports University, 44221 Kaunas, Lithuanian; [email protected]

* Correspondence: [email protected]; Tel.: +34-696-584-788

Received: 16 July 2020; Accepted: 15 August 2020; Published: 17 August 2020 !"#!$%&'(! !"#$%&'

Abstract: Recently the development of new technologies has produced an increase in the number of studies that try to evaluate consumer behavior towards the use of sports applications. The aim of this study is to perform a systematic review of the literature on the intention to use mobile applications (Apps) related to fitness and physical activity by consumers. This systematic review is a critical evaluation of the evidence from quantitative studies in the field of assessment of consumer behavior towards sport applications. A total of 13 studies are analyzed that propose models for evaluating the intentions to use fitness applications by sport consumers. The results revealed several key conclusions: (a) Technology Acceptance Model is the most widely used model; (b) the relationship between perceived utility and future intentions is the most analyzed; and (c) the most evaluated applications are diet/fitness. These findings could help technology managers to know the most important key elements to take into account in the development of future applications in sport organizations.

Keywords: physical activity; sport application; marketing consumption; technology acceptance model; smartphone app

1. Introduction

The constant technological evolution and the development of new mobile devices such as Smartphones or tablets o↵er a higher level of comfort and practical use, thus making this type of device the center of life for current consumers [1]. Globally, it is estimated that in 2019, there were 6.8 billion users worldwide and it is expected that in 2023 the number of users will increase to 7.33 billion [2]. In particular, 90% of the time dedicated to the Smartphone is for the use of mobile applications (Apps) [3].

Sustainability takes equal account of economic, environmental and social factors in any e↵ort to improve quality of life [4]. The dissemination and integration of information and communication technologies (ICT) and data management functionalities have been widely leveraged through the adoption of mobile devices, which allow people to participate in a larger way in society [5,6]. European Union (EU) policies emphasize the synergy between smart technologies and sustainable urban development because of the need for accurate, consistent and timely data for new policy formulation and the use of ICTs to facilitate service improvement [7,8].

The role of ICTs in sustainable development is clearly reflected in Goal 11 “make cities and human settlements inclusive, safe, resilient and sustainable” of the Sustainable Development Goals of the

Sustainability 2020, 12, 6641; doi:10.3390/su12166641 www.mdpi.com/journal/sustainability

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United Nations Agenda 2030 [9], which considers ICTs as a means to advance human progress and knowledge in societies, to increase resource e�ciency, to promote economic development and protect the environment or to modernize industries on the basis of sustainable design [9,10]. Online tools and platforms contribute significantly to the repression of energy demands or pollution, promoting cities to a more environmentally sustainable economy [11]. Angleidou et al. [7] show that ICTs and the use of Apps help reduce the need for physical travel and the existence of physical workplaces.

An App is defined as “software applications usually designed to run on a Smartphone or tablet device and provide a convenient means for the user to perform certain tasks” [12] (p. 211). The increase is such that Blair [13] reported that the trade of Apps generates 189 billion dollars a year being used at least 11 times by 49% of users while 21% of the millennials open them at least 50 times a day.

Among them, health, fitness and physical activity Apps represent 5.18% of the total market [14], being used daily by 35% of people and several times a week by 40% [15]. In recent years, McKay, Wright, Shill, Stephens, and Uccellini [16] report a proliferation of Apps to improve health, including Apps to count the steps or promote physical activity in fitness centers, Apps to control diet and caloric intake or reduce poor habits such as smoking or alcohol consumption and improve mental health.

This increase in interest and number of Apps associated with physical activity could also have benefits for society. Therefore, the current situation of confinement caused by Covid-19 and consequently the reduction of physical activity, has encouraged di↵erent organizations such as the World Health Organization [17] to promote the need for physical activity at home. In fact, authors such as Banskota, Healy, and Goldberg [18] proposed di↵erent Apps as tools to maintain and improve physical and mental fitness in the Covid-19 pandemic. These Apps are linked to the fitness sector, revolutionizing the ways of doing physical activity and the relationships between fitness providers and consumers [19].

These new communication and prescription tools in sport could therefore have an impact on how organizations interact with consumers, with the appearance in recent years of studies that evaluate consumers’ motivations for using devices, the usefulness of Apps or consumers’ intentions to adopt them in di↵erent areas [20,21]. Particularly, researchers have begun to identify the factors that lead to the intention to use technologies, Smartphones and Apps in di↵erent sectors [22,23], but it is limited in the sports context.

Among the theories related to the intention to use of technologies, in the context of marketing we find the “Theory Acceptance Model” (TAM). This is the most used model by researchers to evaluate the intention to use of new technologies proposed by Davis [24]. TAM is an adaptation of the psychological theory, the “Theory of Reasoned Action” (TRA), which states that a person’s real behavior is determined by his or her intention to perform that behavior [25]. For instance, the TAM tries to explain how consumers use and accept new technologies based on two key beliefs, namely the usefulness of use and the ease of use that are predictive of consumers’ attitude towards future intention to use the new technologies [24]. Research based on TAM is one of the most widely used in professional settings because it focuses on the utilitarian aspect of the technology [26], with the intention of understanding the consumer’s intention to use it [22]. In particular, TAM has been used in di↵erent contexts such as finance, instant messaging, healthcare, gaming and tourism [27].

Although TAM has great robustness and applicability in terms of intention to use, attitude and perceived utility [27], di↵erent authors have developed new theories based on TAM such as the “Innovation and Di↵usion Theory” (IDT) [28] which considers that the user’s behavioral potential is driven by the user’s beliefs about innovation. Later, there is the “Unified Theory of Acceptance and Use of Technology Model (UTAUT)” [29] that proposes four constructs to develop TAM: performance expectation, social influence, e↵ort expectation and facilitation conditions. A second version of this model (UTAUT2) adds the constructs of hedonic motivation, price and habit, being adapted by Yuan, Ma, Kanthawala, and Peng [30] to measure the intention to use of health and fitness Apps. In addition, in sport, the “Sport Website Acceptance Model” (SWAM) is proposed by Hur, Ko, and Claussen [31] and is based on a framework of understanding how sport fans perceive and accept the websites of

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their sport teams, how their level of participation and commitment to the sport team influences the intention to use the website and the actual consumption behavior they ultimately perform. Based on these models and theories, in recent years researchers have paid attention to the intention to use new technologies in di↵erent contexts such as e-payment, e-government, e-banking, retail or education [32–36]. Similarly, in academic sports literature there is also an increasing attention to the behavior of fans and consumers, with studies with di↵erent approaches such as motivation on sports websites [31], loyalty [37], participation, commitment and attributes [38,39], marketing opportunities [40], intention to use sports wearable [41], consumption of Smartphones and sports Apps [42], sports team Apps [1], fitness Apps [43,44] and sports products [45,46].

However, existing research does not provide clear results on what factors drive sports fans or consumers to use Smartphones or Apps and to benefit from new forms of experiences in sport [42]. In fact, the factors influencing the intention to use Smartphones and Apps di↵er depending on the types of products consumed and the marketing implications [47]. Therefore, while studies have been conducted on the intentions of use of technology, Smartphones and fitness Apps, there is not a review that captures the main findings of these studies. For this reason, the aim of this study is to conduct a systematic review of the literature on consumers’ intention to use Apps related to fitness and physical activity by consumers.

2. Materials and Methods

2.1. Search Strategy

The search terms for Smartphone use, Fitness and Sport Apps represented the concepts of App, Physical Activity and Use, with the search strategy for the di↵erent databases presented in Table 1. Di↵erent databases were selected to include a wide range of areas related to this interdisciplinary study, including sports science, marketing, health and psychology. The databases used were Web of Science, Scopus, SPORTDiscus (EBSCO), PsycINFO (Ovid), ABI/Inform (Ovied) and MEDLINE (Pubmed). The search was conducted between 18 March 2019 and 4 August 2020. The search covered all years and no limitations were placed on document type and language.

Table 1. Database search strategy.

Category Search Terms

App (“Smart phone *” or Smartphone * or smart-phone * or “cell * phone” or “cell-phone *” or “mobile phone *” or “mobile-phone” or “mobile device” or “mobile telephone” or * phone or Android * or iOS or app or apps or “mobile application *” or application)

Physical Activity

(“physical activit *” or exercise * or “active living” or walk * or “active transport” or “leisure activit *” or fitness or sport or “sport *” or “weight maintenance” or “maintaining weight” or “weight management”)

Use (“intention to use” or “app * usage” or “intent * to use” or usage or “behavioral intention *” or “behavior * change” or usability or “attitude toward” or consumption or Technology Acceptance Model)

Combination 1 and 2 and 3

* Truncation operator: word-based search.

2.2. Inclusion and Exclusion Criteria

For the purposes of this review, we included empirical papers in peer-reviewed journals, excluding dissertations and abstracts. Grey literature was not included, ruling out evaluation reports, annual reports, articles in nonpeer reviewed journals and other means of publication. The inclusion criteria for the articles in the search were: (i) journal articles; (ii) publications in English; (iii) use of any type of mobile application in the sports and fitness context; and (iv) measurement of the intention to use the App through a questionnaire. As exclusion criteria have been used: (i) Congress proceedings, book chapters, books or other types of publications; (ii) no mobile Apps were used in the sports context,

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(iii) theoretical studies, qualitative approach or reviews; (iv) articles in a language other than English; and (v) duplicate articles.

2.3. Assessment of Methodological Quality

The risk of bias was assessed using a 20-item tool adapted by the authors to the context of sports marketing study typology in which there are no intervention processes on the subjects of the Consolidated Standards of Reporting Trials (CONSORT) checklist [48]. Each study was independently scored by two reviewers evaluating the di↵erent sections that make up the studies and scoring each item with 1 if the study satisfactorily met the criterion, and with 0 if the study did not satisfactorily meet the criterion or if the item was not applicable to the study. Disagreements between the reviewers were resolved by checking and discussing the original study until consensus was reached. Reviewer A is a researcher with extensive experience specializing in the field of sports management, fitness centers and development of new technologies. Reviewer B is a predoctoral fellow in sports management with focus research on methodological and statistical aspects. The results of assessment of methodological quality were shown in Appendix A.

2.4. Data Extraction and Synthesis

Figure 1 shows the Flow Diagram proposed by Moher, Liberati, Tetzla↵, and Altman [49] following the PRISMA methodology in all points that could be common to a systematic review of these characteristics. The initial database search returned 113,537 results, reduced to 36,105 once duplicates were eliminated. One reviewer conducted a full scan of the title, then an abstract review and finally a full text review using the inclusion and exclusion criteria. Among the articles that remained at the abstract level (n = 4), a second reviewer also examined the abstracts of the articles to confirm their eligibility, and there were no discrepancies with the first reviewer.

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any type of mobile application in the sports and fitness context; and (iv) measurement of the intention to use the App through a questionnaire. As exclusion criteria have been used: (i) Congress proceedings, book chapters, books or other types of publications; (ii) no mobile Apps were used in the sports context, (iii) theoretical studies, qualitative approach or reviews; (iv) articles in a language other than English; and (v) duplicate articles.

2.3. Assessment of Methodological Quality

The risk of bias was assessed using a 20-item tool adapted by the authors to the context of sports marketing study typology in which there are no intervention processes on the subjects of the Consolidated Standards of Reporting Trials (CONSORT) checklist [48]. Each study was independently scored by two reviewers evaluating the different sections that make up the studies and scoring each item with 1 if the study satisfactorily met the criterion, and with 0 if the study did not satisfactorily meet the criterion or if the item was not applicable to the study. Disagreements between the reviewers were resolved by checking and discussing the original study until consensus was reached. Reviewer A is a researcher with extensive experience specializing in the field of sports management, fitness centers and development of new technologies. Reviewer B is a predoctoral fellow in sports management with focus research on methodological and statistical aspects. The results of assessment of methodological quality were shown in Appendix A.

2.4. Data Extraction and Synthesis

Figure 1 shows the Flow Diagram proposed by Moher, Liberati, Tetzlaff, and Altman [49] following the PRISMA methodology in all points that could be common to a systematic review of these characteristics. The initial database search returned 113,537 results, reduced to 36,105 once duplicates were eliminated. One reviewer conducted a full scan of the title, then an abstract review and finally a full text review using the inclusion and exclusion criteria. Among the articles that remained at the abstract level (n = 4), a second reviewer also examined the abstracts of the articles to confirm their eligibility, and there were no discrepancies with the first reviewer.

Figure 1. PRISMA flow diagram. Source: Moyer et al. [49].

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A form was developed for data extraction that included the following aspects: (a) year of publication; (b) country of study; (c) number of participants; (d) gender; (e) age of participants; (f) type of application evaluated; (g) theory used; (h) analyses performed; (i) variables included; and (j) main results. In order to homogenize the results of the di↵erent studies and to make the data more homogeneous, the confidence intervals of each correlation (CI 95%) and the e↵ect size with its confidence intervals (CI 95%) of each relationship were calculated through the Fisher’s Z statistics [50].

3. Results

3.1. Analysis of the Risk of Bias in Studies

To test quality, risk of bias analysis of the 19 studies evaluated in the research showed that only three studies had a high score of 15 points or more out of 20 total [1,45,51], most studies (n = 14) had a mean score between 10 and 15 points and only two studies had a score below 10 points [52,53]. It should be noted that none of the studies analyzed carried out a calculation of the sampling required for the generalization of the results, which could be due to the fact that all the studies carried out a selection of the sample for convenience within a certain population. There are also few studies that established criteria for inclusion in the sample to be selected (n = 5) and no study indicates the author who carried out each part of the research.

3.2. Summary of Reported Intervention Outcomes

Results of the descriptive data from the analysis of the articles can be seen in Table 2. The analysis shows that this topic is very recent within the context of sports marketing, with only 13 quantitative studies addressing the intention to use of sports applications by the sports consumer through the use of self-administered questionnaires and online. Of the articles analyzed, the majority were published in 2018 (n = 5) and 2020 (n = 5), followed by those published in 2017 (n = 4), three articles were published in 2015, while only one article was found in 2016 and 2019. Korea has been the country with the highest production with six articles, followed by the United States and Hong Kong with three publications, China had two studies and other countries such as Germany, India, Iran, South Africa and Taiwan each had one publication.

Analyzing the sample used in the di↵erent studies, there is a total of 16,025 subjects with an average sample of 843.42 subjects per study, with the Ndayizigamiye; Kante, and Shingwenyana study [54] having the smallest sample (n = 139) and Wei, Vinnikova, Lu and Xu study [55] having the largest sample with a total of 8840 subjects. Approximately a half of the studies (n = 8) used university students as a sample, followed by studies that considered users of sports applications (n = 4) and other studies took as their general population [54–56], a population of sports consumers [45], employees of a sports organization [57] and members of a fitness community [44,58]. Most studies had a higher proportion of females than males (n = 9), followed by studies that had parity in the sample (n = 5), four studies had a higher proportion of males while one study did not indicate the gender distribution of the sample [34]. Finally, all studies except Ha et al. [42] and Yoo et al. [53] reported some data on the age of the subjects. About half of the studies (n = 10) expressed age using a range, five studies showed age using mean (M = 24.58 years) and two studies did not specify the age [55,59]. The analysis indicated that mainly the study population are young subjects between 20 and 29 years old and all are over 18 years old except Lee, Kim and Wang [45] which also included 17-year-old subjects. Li, Liu, Ma and Zhang [46] sampled subjects over 25 years of age, while Huang and Ren [60] and Mohammadi and Isanejad [57] were at least 30 years old.

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Table 2.D

escriptive data

ofthe analysis

ofthe selected

studies.

A uthors

C ountry

Sam ple

A pp

Type T

heory D

ata A

nalysis M

ethods M

easure O

utcom es

Beldad &

H egner

[43] G

erm any

G erm

an’s app

user (n =

476) M

ale:50.0% Fem

ale:50.0% A

ge: 26.7±

5.0

Sport inform

ation TA

M C

ontentA nalysis

Trustin the

Fitness A

pp D

eveloper;D escriptive

SocialN orm

;Injunctive SocialN

orm ;Perceived

Ease ofU

se;Perceived U

sefulness;Intention to

C ontinue

U sing

a Fitness

A pp

Byun,C hiu,&

Bae [45]

K orea

K orean

consum ers

(n =

261) M

ale:4 9.1%

;Fem ale:50.9%

A ge:20–29

(29.9% );30–39

(34.9% );40+

(5.2% )

SportBrand TA

M C

ontentA nalysis

Perceived Enjoym

ent;Perceived Ease

ofU se;

Perceived U

sefulness;Intention to

use;A ctual

usage

C hen

& Lin

[44] Taiw

an

Fitness C

om m

unity (n =

994) A

ge:20� (10.06%

);20–29 (56.14%

);30–39 (1.83%

);40–49 (8.65%

);50–59 (3.32%

)

D iet/Fitness

TR A

M C

ontentA nalysis

H ealth

C onsciousness;O

ptim ism

;Innovativeness; D

iscom fort;Insecurity;Perceived

Ease ofU

se; Perceived

U sefulness;A

ttitude tow

ard U

sing A

pp;Intention to

dow nload

app

C hiu

& C

ho [61]

H ong

K ong

K orean

university students

(n =

204) M

ale:51.9% ;Fem

ale:48.1% A

ge:19–25 (71.8%

);26–30 (10.7%

);30+ (17.5%

)

H ealth

/Fitness TR

A M

D escriptive

C ontentA

nalysis

O ptim

ism ;Innovativeness;Insecurity;

D iscom

fort;Perceived U

sefulness;Perceived Ease

ofU se;Perceived

Enjoym ent;Intention

to use

C hiu,C

ho,& C

hi[56] H

ong K

ong

C hinese

population (n =

342) M

ale:45.6% ;Fem

ale:54.4% A

ge:20� (1.2%

);21–25 (14.9%

); 26–30

(35.4% );31–35

(29.8% );

36–40 (11.1%

);40+ (7.6%

)

H ealth

/Fitness EC

M D

escriptive C

orrelational C

ontentA nalysis

Investm entsize;Q

uality ofalternative;

C om

m itm

ent;C onfirm

ation ofexpectations;

Satisfaction;Perceived U

sefulness;C ontinuance

Intention

C ho,Lee,K

im ,&

Park [59]

K orea

U niversity

students (n =

294) M

ale:33.0% Fem

ale:67.0% A

ge:23.2 D

iet/Fitness TA

M C

orrelational C

ontentA nalysis

A ppearance

Evaluation;Fitness Evaluation;

A ppearance

O rientation;Fitness

O rientation;

Perceived U

sefulness;Intention to

U se

A pp

C ho,Lee,&

Q uinlan

[51] K

orea U

niversity students

(n =

508) M

ale:34.6% ;Fem

ale:65.4% A

ge:21.5 D

iet/Fitness TA

M D

escriptive C

ontentA nalysis

Subjective N

orm s;Entertainm

ent;R ecordability;

N etw

orkability;Perceived Ease

ofU se;Perceived

U sefulness;BehavioralIntention

to U

se

C ho

& K

im [52]

K orea

U niversity

students (n =

277) M

ale:34.3% ;Fem

ale:65.7% A

ge:22.5 D

iet/Fitness TA

M C

ontentA nalysis

Sm artphone

U se

E �

cacy;InternetInform ation

U se

E �

cacy;InternetInform ation

C redibility;

Perceived Ease

ofU se;Perceived

U sefulness;

BehavioralIntention

D him

an,A rora,

D ogra,&

G upta

[58] India

Indian fitness

lefts users

(n =

324) M

ale:54.0% ;Fem

ale:46.0% A

ge:20� (16.0%

);20–40 (80.0%

); 40+

(4.0% )

Fitness U

TA U

T2 D

escriptive C

orrelational C

ontentA nalysis

Perform ance

Expectancy;E ↵ortExpectancy;Self

E �

cacy;SocialInfluence;Facilitating C

onditions; H

edonic M

otivation;Price Value;Personal

Innovativeness;H abit;BehavioralIntention

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Table 2.C

ont.

A uthors

C ountry

Sam ple

A pp

Type T

heory D

ata A

nalysis M

ethods M

easure O

utcom es

H a,K

ang,& K

im [42]

K orea

U niversity