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

sustainability Review The Intention to Use Fitness and Physical Activity Apps: A Systematic Review Salvador Angosto 1, Jerónimo García-Fernández 2,* , Irena Valantine 3and Moisés Grimaldi-Puyana 2 1Department of Physical Education and Sports, Faculty of Sports Sciences San Javier, University of Murcia, 30720 Santiago de la Ribera (Murcia), Spain; salvador[email protected] 2Department of Physical Education and Sports, Faculty of Educational Sciences, Universidad de Sevilla, 41013 Seville, Spain; [email protected] 3Department 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 offer 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 effort 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 Sustainability 2020,12, 6641 2 of 24 United Nations Agenda 2030 [ 9 ], which considers ICTs as a means to advance human progress and knowledge in societies, to increase resource efficiency, 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 different 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 different 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 different areas [ 20 , 21 ]. Particularly, researchers have begun to identify the factors that lead to the intention to use technologies, Smartphones and Apps in different 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 different 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 ], different authors have developed new theories based on TAM such as the “Innovation and Diffusion 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, effort 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 Sustainability 2020,12, 6641 3 of 24 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 different 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 different 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 differ 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 different databases presented in Table 1. Different 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, Sustainability 2020,12, 6641 4 of 24 (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 1shows 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. Sustainability 2020, 12, x FOR PEER REVIEW 4 of 25 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]. Sustainability 2020,12, 6641 5 of 24 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 different studies and to make the data more homogeneous, the confidence intervals of each correlation (CI 95%) and the effect 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 different 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. Sustainability 2020,12, 6641 6 of 24 Table 2. Descriptive data of the analysis of the selected studies. Authors Country Sample App Type Theory Data Analysis Methods Measure Outcomes Beldad & Hegner [43] Germany German’s app user (n=476) Male: 50.0% Female: 50.0% Age: 26.7 ±5.0 Sport information TAM Content Analysis Trust in the Fitness App Developer; Descriptive Social Norm; Injunctive Social Norm; Perceived Ease of Use; Perceived Usefulness; Intention to Continue Using a Fitness App Byun, Chiu, & Bae [45]Korea Korean consumers (n=261) Male:4 9.1%; Female: 50.9% Age: 20–29 (29.9%); 30–39 (34.9%); 40+(5.2%) Sport Brand TAM Content Analysis Perceived Enjoyment; Perceived Ease of Use; Perceived Usefulness; Intention to use; Actual usage Chen & Lin [44] Taiwan Fitness Community (n=994) Age: 20−(10.06%); 20–29 (56.14%); 30–39 (1.83%); 40–49 (8.65%); 50–59 (3.32%) Diet/Fitness TRAM Content Analysis Health Consciousness; Optimism; Innovativeness; Discomfort; Insecurity; Perceived Ease of Use; Perceived Usefulness; Attitude toward Using App; Intention to download app Chiu & Cho [61] Hong Kong Korean university students (n=204) Male: 51.9%; Female: 48.1% Age: 19–25 (71.8%); 26–30 (10.7%); 30+(17.5%) Health/Fitness TRAM Descriptive Content Analysis Optimism; Innovativeness; Insecurity; Discomfort; Perceived Usefulness; Perceived Ease of Use; Perceived Enjoyment; Intention to use Chiu, Cho, & Chi [56] Hong Kong Chinese population (n=342) Male: 45.6%; Female: 54.4% Age: 20−(1.2%); 21–25 (14.9%); 26–30 (35.4%); 31–35 (29.8%); 36–40 (11.1%); 40+(7.6%) Health/Fitness ECM Descriptive Correlational Content Analysis Investment size; Quality of alternative; Commitment; Confirmation of expectations; Satisfaction; Perceived Usefulness; Continuance Intention Cho, Lee, Kim, & Park [59]Korea University students (n=294) Male: 33.0% Female: 67.0% Age: 23.2 Diet/Fitness TAM Correlational Content Analysis Appearance Evaluation; Fitness Evaluation; Appearance Orientation; Fitness Orientation; Perceived Usefulness; Intention to Use App Cho, Lee, & Quinlan [51]Korea University students (n=508) Male: 34.6%; Female: 65.4% Age: 21.5 Diet/Fitness TAM Descriptive Content Analysis Subjective Norms; Entertainment; Recordability; Networkability; Perceived Ease of Use; Perceived Usefulness; Behavioral Intention to Use Cho & Kim [52] Korea University students (n=277) Male: 34.3%; Female: 65.7% Age: 22.5 Diet/Fitness TAM Content Analysis Smartphone Use Efficacy; Internet Information Use Efficacy; Internet Information Credibility; Perceived Ease of Use; Perceived Usefulness; Behavioral Intention Dhiman, Arora, Dogra, & Gupta [58]India Indian fitness lefts users (n=324) Male: 54.0%; Female: 46.0% Age: 20−(16.0%); 20–40 (80.0%); 40+(4.0%) Fitness UTAUT2 Descriptive Correlational Content Analysis Performance Expectancy; Effort Expectancy; Self Efficacy; Social Influence; Facilitating Conditions; Hedonic Motivation; Price Value; Personal Innovativeness; Habit; Behavioral Intention Sustainability 2020,12, 6641 7 of 24 Table 2. Cont. Authors Country Sample App Type Theory Data Analysis Methods Measure Outcomes Ha, Kang, & Kim [42] Korea University students (n=226) Male: 50.8%; Female: 49.2% Age: 25.3 Sport Information TAM Descriptive Content Analysis Sport Involvement; Sport Commitment; Social Influence; Personal Attachment; Media Multitasking; Perceived Enjoyment; Perceived Ease of Use; Perceived Usefulness; Usage Intention Huang & Ren [60] Hong Kong Chinese app users (n=449) Male: 43.0%; Female: 57.0% Age: 31.85 ±6.9 Fitness TAM Regression Instruction Provision; Self-Monitoring; Self-Regulation; Goal Attainment; Exercise Self Efficacy; Perceived Usefulness; Perceived Ease of Use; Perceived Enjoyment; Continuance Intention Kim, Kim, & Rogol [ 1 ] United States App users (n=233) Male: 68.2% Female: 31.8% Age: 18–24 (46.8%); 25–34 (31.8%); 35–44 (13.7%); 45–54 (7.3%); 55+(0.4%) Sport Team TAM Descriptive Content Analysis Innovativeness; Perceived Ease of Use; Perceived Enjoyment; Perceived Trust; Perceived Usefulness; Intention; Sport Apps Use Lee, Kim, & Wang [62] United States College students (n=267) Male: 32.2% Female: 67.8% Age: 17–20 (48.3%); 21–25 (40.8%); 26–29 (8.2%); 29+(2.6%) Sport App UTAUT Correlational Content Analysis Entertainment Motivation; Social Utility Motivation; Performance Expectancy; Effort Expectancy; Social Influence; Intention to Mobile Sports Apps Use Li, Liu, Ma, & Zhang [63]China Sport App users (n=211) Male: 45.02% Female: 54.98% Age: 25–30 (41.71%); 30–35 (47.87%) 35+(10.43%) Social Fitness-tracking UTAUT2 Content Analysis Activity Amount Ranking; Activity Frequency Ranking; Confirmation; Upward Comparison Tendency; Continuous Intention Mohammadi & Isanejad [57]Irán Employers Sport Organization (n=332) Male: 37.3% Female: 62.7% Age: 30−(10.0%); 31–40 (44%); 41–50 (38%); 50+(8%) IT information TAM Descriptive Correlational Content Analysis Technology Anxiety; Technology Self-efficacy; Perceived Enjoyment; Perceived Ease of Use; Perceived Usefulness; User Satisfaction; Attitude; Intention to use Ndayizigamiye; Kante, & Shingwenyana [54] South Africa South African population (n=139) Male: 41.5%; Female: 58.5% Age: 18–23 (57.15%); 24–29 (29.9%); 30–35 (7.5%) mHealth UTAUT Correlational Content Analysis Awareness, Effort Expectancy; Facilitating Conditions; Performance Expectancy; Social Influence; Behavioral Intention Sustainability 2020,12, 6641 8 of 24 Table 2. Cont. Authors Country Sample App Type Theory Data Analysis Methods Measure Outcomes Wei, Vinnikova, Lu, & Xu [55]China Chinese population (n=8840) Male: 4.78%; Female: 74.55% Diet/Fitness UTAUT Descriptive Correlational Content Analysis Perceived Benefits; Perceived Barriers; Perceived Threats; Self-Efficacy; Risk Perception; Performance Expectancy; Weight Loss Intention; Behavioral Intention; Use Behavior Yoo, Ko, & Yeo [53] Korea University students (n=1331) Male: 65.9%; Female: 34.1% Sport Content TAM Content Analysis Perceived Trust; Perceived Usefulness; Attitude; Using intention Yuan, Ma, Khantawala & Peng [30] United States University students (n=317) Male: 21.1% Female: 78.9% Age: 21 Diet/Fitness UTAUT2 Content Analysis Performance expectancy; Effort expectancy; Social influence; Facilitating conditions; Price value; Hedonic motivation; Habit; Intention to use ECM: Expectation-Confirmation Model. Sustainability 2020,12, 6641 9 of 24 Regarding the type of App evaluated, six studies evaluated the intention to use diet and fitness applications [ 30 , 44 , 51 , 52 , 55 , 59 ], another five studies evaluated sports information Apps [ 42 , 43 , 53 , 57 , 62 ], two studies measured the intention of fans to use the sports team app [ 1 , 45 ], health and fitness app [ 56 , 61 ], or fitness [ 58 , 60 ] and one study evaluated a social fitness-tracking app [ 63 ] and mHealth related to promote physical activity [ 54 ]. The most widely used theory for the design and use of the mobile sports app intent of use assessment instrument was TAM (n=10). Chen and Lin [ 44 ] and Chiu and Cho [ 61 ] used a variant of the TAM, Theory of Readiness and Acceptance Model (TRAM), three studies used UTAUT [ 54 , 55 , 62 ] and UTAUT2 [ 30 , 58 , 63 ], and an article with the Expectation-Confirmation Model (ECM) [ 56 ]. The most common method of analysis used was a content analysis by structural equations using the AMOS statistical package (n=9) and the rest of the Partial Least Square studies. Seven studies also performed a correlation analysis of the data [ 54 – 59 , 62 ], and eight studies performed descriptive analysis in addition to content analysis [1,42,51,55–58,61]. The variables used by the different studies have been very varied, where the intention to use App has been found in all studies as a common factor. Considering that this systematic review study focused on studies based on TAM as the most commonly used theory in sports marketing studies, it implies that there are other common variables among most studies such as perception of usefulness (n=13) and perception of ease of use (n=10). Some studies have included other different perceptions by relating them to the previous ones and the intention to use, such as the perception of enjoyment [ 1 , 42 , 45 , 60 , 61 ] or the perception of trust [ 1 , 53 ]. The remaining variables used have been very diverse, with each study using different variables that can be seen in Table 2. However, the variable of social influence has received greater interest from researchers and has been considered in five studies [30,42,54,58,62]. Analyzing the quantitative data on the relationships between the most common variables associated with TAM (Table 3), the six studies that had a different theory such as UTAUT [ 54 , 55 , 62 ] or UTATUT2 [ 30 , 58 , 63 ] were excluded; however, the study that used ECM was included because there was a relationship between variables “perception usefulness” and “intention to use” [ 56 ]. The sample was very heterogeneous in terms of the results of the existing relationships and sample size in each study. In order to homogenize these results, the effect size of each correlation was calculated. A total of seven relationships were identified between the different variables associated with TAM such as perception of ease of use (PEOU), perception of utility (PU), perception of enjoyment (PE), perception of trust (PT), intention to use (ITU) and actual usage (AU). Sustainability 2020,12, 6641 16 of 24 to be able to check what the user expresses in the questionnaire with the actual opinion about that App [75,80]. Future lines of research on the intention to use sports Apps or any other device should consider the inclusion of more variables of TAM in their model, as well as other variables specific to the Smartphone (social influence, attachment to the device, etc.), the sports context (motivation, commitment, participation) or variables traditionally linked to sports marketing such as satisfaction [ 44 ]. Therefore, common measuring instruments should be standardized to allow their application in different contexts. Similarly, the different age generations should be taken into account when evaluating and sampling in a population that includes consumers of different age ranges. It would also be interesting to sample populations that do not refer to a single sport or discipline but rather have a variety of Apps from different sports or sports teams. Future work should also be carried out in a longitudinal way, being able to check whether the intention to use predisposed by the subject ends up being the real use of the application or not. Finally, it is interesting to contemplate studies that cover different social groups with cultural diversity analyzing not only the intention to use in the context itself but also in the individual characteristics of the consumer with different status and educational levels to evaluate the digital gap. 5. Conclusions This systematic review responded to the need for a critical evaluation of existing research on the intentions of using sports Apps as this is an emerging field of research. The limited number of academic studies together with the deficiencies in some methodologies as can be seen in the risk analysis of research bias and the evidence found, has not allowed a more critical evaluation. These findings highlight the need for more rigorous and systematic research by researchers in the field, putting factors in common that allow a better evaluation of the context of the use of new technologies in the sports environment. At the same time, these findings have allowed the research team to identify a range of recommendations for sports organizations and researchers, which will help them to address future studies, and thus allow for a better growth and development of the evaluation of the intention to use Apps in sport. Practical Implications Sports organizations, sports marketing experts and new technology developers should make use of the considerations made in this study when developing or upgrading a sports App either for general sports information, a sports brand or exercise or health monitoring. Sports Apps have a great potential for the promotion and sponsorship of different products due to the potential use they have by consumers through advertising in these technologies. Suppliers of sports brands (retail, teams, etc.) should carry out an in-depth analysis of the fans by means of one of the tools evaluated that allow them to design the Apps according to the user’s expectations and to know what type of information or applicability they expect from it. Another practical implication that is obtained is the possibility for sports organizations to develop Apps that have a gamification part that links the fan to consume the sports brand while he can enjoy certain benefits for using the organization’s App. They should also look for ways to engage the consumer with the App during the competition, i.e., generate exclusive content that can only be enjoyed by fans who have attended the live game, causing greater interest in the use of the App. Finally, to improve the quality of the Apps on Smartphones or other devices, industry and marketing professionals should examine all communication channels to ensure convenient access to the different content and services available. Sustainability 2020,12, 6641 17 of 24 In addition, the period of confinement caused by the COVID-19 pandemic, which has forced millions of people to stay at home, highlights the importance of this study to know the current situation on the topic of the intention to use fitness Apps by the population, especially for sports specialists and managers to know how to fit into the periods of new normality in which social distancing is forced. Although they have been in development for a long time, during the period of confinement there has been an increase in the offer of digital channels that help the guided practice of physical activity through the use of safe, simple and easy to implement programs and applications that cover activities related to cross-fit, yoga or dance activities for the general population or for the improvement of physical and mental well-being in older adults [ 18 , 81 , 82 ]. For experts in sports management or trainers, platforms such as Youtube or Zoom allow for individualized methods of physical exercise and real-time contact with the monitor, which allows for feedback to users regardless of where they are located [ 83 ]. Finally, Ammar et al. [ 84 ] suggest that future physical activity intervention to stay active during times of pandemic may be based on information and communication technologies, such as fitness Apps. Therefore, it is necessary to focus on a more in-depth study on the intention of the population to use these types of applications to promote active and healthy lifestyles. Author Contributions: Conceptualization, S.A., J.G.-F., and M.G.-P.; methodology, S.A. and J.G.-F.; formal analysis, S.A.; investigation, S.A., J.G.-F. and I.V.; resources, I.V.; data curation, M.G.-P.; writing—original draft preparation, S.A., J.G.-F. and I.V.; writing—review and editing, S.A. and J.G.-F.; project administration, J.G.-F. and M.G.-P.; funding acquisition, J.G.-F. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by University of Seville grant number 3840/0443/Study and design of technological consumer behavior in Spanish fitness centers, by Valgo Investment, S.L.U. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2020,12, 6641 18 of 24 Appendix A Table A1. Assessment methodological quality (part 1). Item NSection/Topic and Checklist Item Beldad & Hegner [43] Byun et al. [45] Chen & Lin [44] Chiu & Cho [62] Chiu et al. [56] Cho et al. [59] Cho et al. [51] Cho & Kim [52] Dhiman et al. [58] Ha et al. [42] Title and Abstract 1a Identification of the type of study in the title 1 1 1 1 1 1 1 1 1 1 1b Structured summary of objective, methods, results and conclusions 0 1 1 1 1 1 1 1 1 0 Introduction Background and Objectives 2a Scientific background and explanation of rationale 1 1 1 1 1 1 1 1 1 1 2b Specific objectives or hypotheses 1 1 1 1 1 1 1 1 1 1 Methods Participants 3a Eligibility criteria for participants 1 1 0 0 0 0 1 0 0 1 3b Settings and locations where the data were collected 0 0 0 1 1 1 1 0 1 1 3c A table showing baseline demographic characteristics 0 1 1 1 1 0 1 0 1 0 Sample Size 4a The sample size has been determined 0 0 0 0 0 0 0 0 0 0 4b When applicable, explanation of how sample size was determined 0 0 0 0 0 0 0 0 0 0 Procedure 5The procedure has sufficient details to allow replication, including how and when they were actually administered 0 1 0 0 1 1 1 1 1 0 Instrument or Tools 6a Completely defined prespecified primary and secondary outcome measures, including how and when they were assessed 1 1 1 1 1 1 1 1 1 1 6b Use of validity and reliability tools. 1 1 1 1 1 1 1 1 1 1 Implementation 7 Who made each part of study 0 0 0 0 0 0 0 0 0 0 Statistical Methods 8a Statistical methods used to analyze the results 1 1 1 1 1 1 1 1 1 1 8b Use of Methods for additional analyses to objective of study 0 0 1 1 0 1 1 0 0 0 Results Outcomes and Estimation 9 A table or figure showing outputs of analysis more relevant of study 1 1 1 1 1 1 1 1 1 1 Sustainability 2020,12, 6641 19 of 24 Table A1. Cont. Item NSection/Topic and Checklist Item Beldad & Hegner [43] Byun et al. [45] Chen & Lin [44] Chiu & Cho [62] Chiu et al. [56] Cho et al. [59] Cho et al. [51] Cho & Kim [52] Dhiman et al. [58] Ha et al. [42] Discussion Interpretation 10 Interpretation consistent with results, balancing benefits and harms and considering other relevant evidence 1 1 1 1 1 0 1 0 1 1 Limitations 11 Study limitations, addressing sources of potential bias, imprecisions, etc. 1 1 1 1 1 1 1 0 1 1 Practical Implication 12 Main applicability to results of study 1 1 1 1 1 1 1 0 1 1 Other Information Funding 13 Sources of funding and other support, role of funders 1 1 0 0 0 1 1 0 0 0 TOTAL 12 15 13 14 14 14 17 9 14 12 Table A2. Assessment methodological quality (part 2). Item NSection/Topic and Checklist Item Huang & Ren [60] Kim et al. [1] Lee et al. [61] Li et al. [63] Mohammadi & Isanejad [57] Ndayizigamiye et al. [54] Wei et al. [55] Yoo et al. [53] Yuan et al. [30] Title and Abstract 1a Identification of the type of study in the title 1 1 1 1 1 1 1 1 1 1b Structured summary of objective, methods, results and conclusions 1 1 1 1 1 1 1 1 1 Introduction Background and Objectives 2a Scientific background and explanation of rationale 1 1 1 1 1 0 1 1 1 2b Specific objectives or hypotheses 1 1 1 1 0 1 1 1 1 Methods Participants 3a Eligibility criteria for participants 0 1 0 0 0 0 0 0 0 3b Settings and locations where the data were collected 1 1 0 1 0 1 0 0 1 3c A table showing baseline demographic characteristics 1 1 1 1 1 0 1 1 0 Sample Size 4a The sample size has been determined 0 0 0 0 0 0 0 0 0 4b When applicable, explanation of how sample size was determined 0 0 0 0 0 0 0 0 0 Sustainability 2020,12, 6641 20 of 24 Table A2. Cont. Item NSection/Topic and Checklist Item Huang & Ren [60] Kim et al. [1] Lee et al. [61] Li et al. [63] Mohammadi & Isanejad [57] Ndayizigamiye et al. [54] Wei et al. [55] Yoo et al. [53] Yuan et al. [30] Procedure 5The procedure has sufficient details to allow replication, including how and when they were actually administered 0 1 0 0 0 0 0 0 0 Instrument or Tools 6a Completely defined prespecified primary and secondary outcome measures, including how and when they were assessed 1 1 1 1 1 1 1 1 0 6b Use of validity and reliability tools. 1 1 1 1 1 1 1 1 1 Implementation 7 Who made each part of study 0 0 0 0 0 0 0 0 0 Statistical Methods 8a Statistical methods used to analyze the results 1 1 1 1 1 1 1 1 1 8b Use of Methods for additional analyses to objective of study 0 0 1 0 0 0 0 0 1 Results Outcomes and Estimation 9 A table or figure showing outputs of analysis more relevant of study 1 1 1 1 1 1 1 1 1 Discussion Interpretation 10 Interpretation consistent with results, balancing benefits and harms and considering other relevant evidence 1 1 1 1 1 1 1 0 0 Limitations 11 Study limitations, addressing sources of potential bias, imprecisions, etc. 1 1 0 1 0 0 1 0 1 Practical Implication 12 Main applicability to results of study 1 1 1 1 1 0 1 0 0 Other Information Funding 13 Sources of funding and other support, role of funders 0 0 0 1 0 1 1 0 0 TOTAL 13 15 12 14 10 10 13 9 10 Sustainability 2020,12, 6641 21 of 24 References 1. 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