research papers on app

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 17 August 2023

A systematic review of intention to use fitness apps (2020–2023)

• Salvador Angosto   ORCID: orcid.org/0000-0001-7281-794X 1 , 2 ,
• Jerónimo García-Fernández   ORCID: orcid.org/0000-0001-6574-9758 2   na1 &
• Moisés Grimaldi-Puyana   ORCID: orcid.org/0000-0003-4722-1532 2   na1  

Humanities and Social Sciences Communications volume  10 , Article number:  512 ( 2023 ) Cite this article

7684 Accesses

4 Citations

22 Altmetric

Metrics details

• Business and management
• Science, technology and society

Technology advances and digital transformation are constantly growing, resulting in an increase in the number of sports-related technologies and apps on the market, particularly during the COVID-19 pandemic. The aim of this study is to update a comprehensive evaluation of the literature published since 2020 on the desire to use and embrace fitness and physical activity-related apps. Using the PERSiST adapted from the PRISMA 2020 statement, a total of 29 articles that provide assessment models of sports consumers’ desires to utilise fitness applications were discovered. Several major conclusions emerge from the findings: (1) the use of alternative models to the Technology Acceptance Model has increased in recent years with new theories not derived from that model now being associated with it; (2) studies in Europe are increasing as well as a specifical interest in fitness apps; (3) the UTAUT and UTAUT2 model are more widely used within the sport sector and new models appear connected with behaviour intentions; and (4) the number of exogenous and endogenous variables that are linked to the main technology acceptance variables and their behavioral intentions is diverse within the academic literature. These findings could help technology managers to increase user communication, physical activity levels and participation in their fitness centres, as well as to modify the policies and services of sports organisations.

Similar content being viewed by others

Determinants of behaviour and their efficacy as targets of behavioural change interventions

Skeletal muscle energy metabolism during exercise

Time of exercise differentially impacts bone growth in mice

Introduction.

In recent years, the number of smartphone users has steadily increased throughout the world, with nearly half of the population now owning a device (Newzoo, 2021 ). As a result, the smartphone is quickly becoming a vital instrument in the lives of the general public (Byun et al., 2018 ). This digital change can also be found in the sports and fitness industry, where the digital explosion in the usage of smartphones and wearables has allowed fitness apps to become one of the market’s most important categories (Jones et al., 2020 ).

Fitness apps are swamping the mobile app market (Beldad and Hegner, 2018 ), with almost one in every five users downloading this type of app on their device (Fox and Duggan, 2021 ). Due to the lockdown placed on people and the requirement to stay at home, the demand for fitness apps has grown significantly since the onset of the COVID-19 pandemic (Clement, 2020 ; Ting et al., 2020 ). A fitness app is a third-party programme for smartphones or wearables that may help consumers in recording physical activity data, guiding sports learning and leading a healthy lifestyle (Eshet and Bouwman, 2015 ). A recent study conducted a social comparison of fitness-related posts on social media platforms by fitness app users. Specifically, Kim ( 2022 ) found that when fitness comparison decreased there was a decrease in user self-efficacy towards physical activity, whereas if fitness comparison increased, self-efficacy towards physical activity increased. Consequently, Kim ( 2022 ) highlighted that self-efficacy is a key element for fitness app users’ motivation and participation in physical activity, and they should be compared to high-performing individuals. In addition, gamification is another important element concerning fitness apps for user satisfaction, and a specific design adapted to the type of user is necessary given the number of existing elements in gamification, highlighting feedback and rewards (Yin et al., 2022 ).

The popularity of fitness apps has grown over the years, coinciding with a greater understanding of the value and advantages of physical activity and a healthy lifestyle (Lim and Noh, 2017 ). Fitness apps have become a trend in the worldwide fitness sector, resulting in new patterns of training behaviour (Hu et al., 2023 ; Kercher et al., 2022 ; Thompson, 2022 ). These new behaviour patterns are connected to physical activity monitoring, a shift in health-care perceptions, and changes in lifestyle habits (Lin et al., 2019 ). Middelweerd et al. ( 2014 ), for their part, emphasise that fitness apps employ many behaviour modification approaches such as goal planning, self-control, feedback, the use of contingent incentives and social support.

In the fitness context, it is also important to address the importance that apps can have in the management of sports centres as a two-way communication tool between the organisation (managers or trainers) and users. In this way, Ferreira-Barbosa et al. ( 2021 ) consider that the use of notifications and communications through the fitness app costs less and produces a greater and better interaction with the client. Thus, the use of applications in fitness centres can enable more direct and dynamic communication with users, providing a better and more personalised service.

Despite this, while studies have begun to find the factors that lead to the desire of using technologies such as apps in numerous fields (Gao et al., 2012 ), a deeper knowledge of the intention to use using certain apps is required (Cho et al., 2020 ). As a result, there are several theoretical frameworks in the scientific literature that explain the acceptance of new technology by sports customers. This ‘acceptance of technology’ refers to an individual’s readiness to adopt technology (Dillon, 2001 ).

The technology adoption model (TAM) developed by Davis ( 1989 ) and Davis et al. ( 1989 ) is the principal model utilised in most research to quantify consumer acceptance of new technologies. The TAM assumes an extension of Ajzen and Fishbein’s ( 1980 ) Theory of Reasoned Action, in which the behavioural intention is decided by the attitude towards this conduct (Davis, 1989 ). According to this author, attitudes are developed around two beliefs: perceived usefulness (PU) and perceived ease of use (PEOU). PU is described as the individual’s belief about the worth of a system, such as its performance or efficiency, in order to gain an advantage, while PEOU is defined as the degree to which the individual believes that the system requires no physical or mental effort and is easily accessible (Davis, 1989 ; Davis et al., 1989 ). PU and PEOU provide for the prediction of user intentions in relation to the adoption of both devices and mobile apps (Kim et al., 2016 ; Koenig-Lewis et al., 2015 ). The TAM has been employed in a variety of areas, including finance, tourism, gaming, health and sports (Rivera et al., 2015 ).

A number of TAM-based theories have been established, including the technology readiness and acceptance model (TRAM), which is derived from the TAM and the "Technology Readiness" (TR) model. Parasuraman ( 2000 ) created the TR with the goal of reflecting consumers’ views and dispositions to implement new technologies, linking their usage with the fulfilment of personal or work objectives. The TRAM has been used in a variety of apps, including social innovation (Rahman et al., 2017 ), branding (Jin, 2020 ) and sports technology (Kim and Chiu, 2019 ). Venkatesh and Davis ( 2000 ) introduced the TAM2 model, which integrates social influence and cognitive belief processes. Other models developed from the TAM are those proposed by Venkatesh et al. ( 2003 ), who suggested the Unified Theory of Acceptance and Use of Technology (UTAUT), its extension called UTAUT2 proposed by Venkatesh et al. ( 2012 ) and UTAUT3 proposed by Farooq et al. ( 2017 ). These theories are concerned with both customers and users (Ferreira et al., 2021 ). According to Venkatesh et al. ( 2003 ), the UTAUT model identifies four elements that influence ‘intention to use’: (i) performance expectancy (PE), or the degree to which individuals believe that using the system will allow them to improve their work performance; (ii) effort expectancy (EE), or the degree to which individuals believe that using the system will allow them to improve their work performance; (iii) social influence (SI), defined as the degree to which individuals believe that their social referents believe that they should use the system; and (iv) facilitating conditions (FC), identified as the degree to which the individual believes in the existence of a technical and organisational benefit.

In addition to the four factors derived from the UTAUT model, the UTAUT2 approach integrates three additional variables (Venkatesh et al., 2012 ): (i) hedonic motivation (HM), which reflects the individual’s intrinsic motivations for accepting new technology; (ii) price value (PV) considered as acceptance of the cost involved in using new technology; and (iii) habit (HA) or the degree to which the individual tends to use the new technology automatically after a learning process. Regarding the UTAUT3 model, Farooq et al. ( 2017 ) introduce a new variable, Personal Innovativeness (PI). Dutta et al. ( 2015 ) indicate that personality traits, such as PI, play an essential role in Information Technology (IT) adoption. As a trait, PI is stable and situation-specific and has a high tendency to influence IT adoption and acceptance (Farooq et al., 2017 ; Thatcher and Perrewé, 2002 ). Thus, PI can be defined as the perceived predisposition or personal attitude of individuals that reflect their tendency to independently experience and adopt new developments in IT (Schillewaert et al., 2005 ). This means that PI can be conceptualised as the willingness to adopt the latest technological gadgets or be linked to trying out new IT features and developments (Farooq et al., 2017 ).

Figure 1 shows the conceptual model of the different theories discussed (TAM, UAUT, UAUT2, UTAUT3). The UTAUT and the UTAUT2 models were performed to investigate consumer acceptance and usage of new technologies (Beh et al., 2021 ), and have been used in a variety of research in the sports, fitness and wearable sectors (Beh et al., 2021 ; Dhiman et al., 2020 ; Yuan et al., 2015 ). However, the UTAUT3 model has not yet been used in the sport context, but it has been employed in other contexts such as tourism (Pinto et al., 2022 ), virtual communication (Gupta et al., 2022 ) and education (Gunasinghe et al., 2020 ).

TAM (Davis, 1989 ), UAUT (Venkatesh et al., 2003 ), UAUT2 (Venkatesh et al., 2012 ), UTAUT3 (Farooq et al., 2017 ). Source: Own elaboration.

In conclusion, despite the recent systematic review conducted by Angosto et al. ( 2020 ) on research that examined the intentions to use and implement apps in the fitness and health sector, or a recent meta-analysis of the Intention to use wearable devices in health and fitness (Gopinath et al., 2022 ), more research is needed. Regarding the need for a new review update, this is necessary for three reasons: (a) the previous review developed by Angosto et al. ( 2020 ) has some shortcomings that will be addressed in the discussion; (b) to analyse the evolution of TAM-derived models such as UTAUT, UTAUT2 or UTAUT3; and (c) the previous review was conducted just before the COVID-19 pandemic, a period in which digitalisation underwent a major evolution to respond to the needs of society. The pandemic has impacted the need to adopt modern technology to monitor, record and control physical activity for both people and sports groups (Núñez Sánchez et al., 2022 ; Ruth et al., 2022 ). As a result, the study’s aim is to perform a comprehensive systematic review that updates the number of studies that have investigated the intention to use or adopt fitness apps from 2020 to May 2023.

Review design and protocol

The Prisma in Exercise, Rehabilitation, Sports Medicine and SporTs science (PERSiST) guidelines (Ardern et al., 2022 ) based on the sports science adaptation of the Prisma 2020 statements (Page et al., 2021 ) were followed for this systematic review. The systematic review was not registered on the PROSPERO platform because, not being in the field of health, it did not meet the requirements for registering the systematic review protocol. Therefore, a prior search protocol was not established and all aspects were marked directly in the methodology of this study.

Inclusion and exclusion criteria

This systematic review includes empirical research published in peer-reviewed journals. However, grey literature was excluded, as were assessment reports, periodic reports, dissertations, abstracts and other forms of publishing. The following criteria were used to include studies in the search: (i) peer-reviewed journal articles; (ii) usage of any form of sports and fitness app; (iii) assessment of the intentions using the app through a survey and (iv) publications in English and Spanish. The following items were excluded: (i) books, book chapters, congress proceedings, or other forms of publications; (ii) qualitative approaches, theoretical research, or reviews; (iii) studies written in a language other than English or Spanish; (iv) no mobile apps were utilised in the sports environment; and (v) duplicate articles.

Search strategy

Table 1 shows the categories of terms that were utilised in the search across multiple databases. Six databases were chosen in an attempt to cover a wide variety of topics linked to this multidisciplinary study, such as sports science, health, psychology and marketing. The databases employed were Pubmed, Web of Science, PsycINFO, Scopus, ABI/Inform and SPORTDiscus. The search lasted from December 27, 2021, through May 26, 2023. The search included all years and there were no restrictions on document type or language from 2020 to the present, considering the previous work by Angosto et al. ( 2020 ).

Figure 2 illustrates the flow chart of all the points proposed by the PRISMA 2020 methodology for conducting systematic reviews (Page et al., 2021 ). The first database search found 8647 results, which were reduced to 3471 once duplicates were removed. A thorough scan of titles and abstracts was carried out by one reviewer, in addition to a full-text review of the selected studies after applying the inclusion and exclusion criteria. A second reviewer evaluated the abstracts of the publications that remained at the abstract level ( n  = 12) to check their eligibility, and there were no disagreements with the first reviewer.

This conceptual diagram shows the protocol of the systematic review process (Page et al., 2021 ).

Assessment of methodological quality

The methodological quality analysis was tested using a rating scale measure of 20 items developed by Angosto et al. ( 2020 ) in the sport consumer research type framework where there were no intervention methods on the themes of the CONSORT checklist (Schulz et al., 2010 ). Two reviewers independently assessed each study by examining the multiple elements that make up an investigation. Each element scored one point if the study met the criterion satisfactorily or zero if the research did not meet the criterion or if the element was not applicable to this study. When disagreement emerged, the reviewers resolved this by re-examining the study until an agreement was reached. Supplementary Table S2 (see the section “Data availability”) indicated the methodological quality evaluation results for each research.

Data extraction

For data extraction, an Excel form was created that includes the following characteristics: (a) publishing year; (b) country of study , country of the institution of the first author of the study; (c) number of participants , total of the sample used in the study; (d) gender , percentage of males and females in the sample; (e) age of participants , average age or age ranges of the study sample; (f) type of Application evaluated , fitness or sport apps and their combination with other types of apps such as health or diet apps.; (g) theory used , evaluation model used in the study; (h) analyses performed , types of analysis used in the results; and (i) variables included , assessed variables included in the model proposed in the study. Supplementary Table S3 (see the “Data availability” section) showed the individual data of each study.

Analysis of the assessment of methodological quality

To assess methodological quality, the analysis of the 29 research papers reviewed in the study (Supplementary Table S2 ) found that 16 studies had the best rating of 15 points or more out of a possible 20. There have been 12 studies with an average score between 10 and 15 points, and one research had a score of <10 points (Jeong and Chung, 2022 ). It should be noted that none of the studies reviewed estimated the sample needed for the generalisability of the results, which could be attributed to the fact that all the studies selected their samples by convenience within a certain group. Furthermore, none of the research defined inclusion criteria for the sample selection. Three studies revealed which author performed each phase of the study (García-Fernández et al., 2020 ; Vinnikova et al., 2020 ; Yu et al., 2021 ), and nine studies indicated whether or not they received funding.

Summary of reported intervention outcomes

Supplementary Table S3 shows the descriptive data taken from each research. According to the findings, this issue of assessing the intention to use applications in the sports marketing industry has garnered considerable attention in recent years. A total of 29 research works were chosen, based on the studies published following the systematic review conducted by Angosto et al. ( 2020 ) that focused on the quantitative evaluation of the intention to use sports applications, using either paper-based or online surveys. The results showed that 2022 was the year with the highest number of publications ( n  = 12), while nine articles were published in 2021, there were five articles published in 2020 and three articles in 2023. The location of the research revealed that 64% of the total articles published were from Asia ( n  = 18), ~32% were from Europe ( n  = 9) and 4% were from America ( n  = 1). Among the countries with the highest number of publications, the following should be highlighted China which had the most papers, with six, followed by Spain with four articles, and Hong Kong, Taiwan, and Germany, each with three articles.

A total of 22,942 respondents were examined in the sample of studies, with a range of total size between 200 and 8840 participants, and an average of 791.1 participants per research work. With respect to the type of the sample, the vast majority considered fitness users or community members, with ten and nine articles respectively. To a limited extent, the authors used students ( n  = 6) or the general population ( n  = 2). The sociodemographic data of the sample revealed that the majority of the studies had a greater proportion of females than males ( n  = 18), with an average of 46.1% males and 53.1% females. Seven articles indicated the average age of the participants, with an average age for all 30 years old. A total of 19 articles indicated age by range, with 10 articles having a higher proportion of young people under 30 years, eight articles having a higher population between 30 and 50 years, and one article with a majority of participants over 50 years. Two articles did not indicate age in any of the above ways. Regarding the type of apps used within the sports context, they were fitness apps used in sports centres ( n  = 18), followed by sports apps ( n  = 6), four used apps that also had a health aspect and one included diet-related aspects.

Analysing the theoretical background on which the authors have based their studies, the use of the TAM model still stands out ( n  = 12), and there was an increase in the number of articles that used the UTAUT or its derivatives (UAUT = 4; UTAUT2 = 6). In addition, three studies were based on another TAM-derived model, TRAM, while one article relied on the expectation-confirmation model (ECM), or the theory of normative social behavior (TNSB), and another study encompassed several models such as the theory of consumption values (TCV) and the theory of perceived risk (TPR). When examining the link between the various constructs studied, 25 studies used structural equation analysis (SEM), while one used regression analysis and another used correlation analysis. The SEM analysis was carried out using the PLS and AMOS statistical tools.

One issue to take into account in the variables used is that intention to use (ITU) is a common variable as it is a criterion for inclusion. Although the intention to use is referred to in many different ways, the concept is the same. The results show that more than 40 variables have been directly or indirectly associated with UTI in the different articles published. The most analysed variables are those that form the basis of the TAM. PU or PE was another of the most important factors analysed together with UTI, appearing in 26 articles, followed by PEOU or EE, which was evaluated in a total of 23 articles. Among the most frequently used variables associated with the different models were Perceived Enjoyment (PEN) in eight articles, Satisfaction (SA) in five articles, Innovativeness (INN) in four studies, and Health Consciousness (HC), Optimism (OP) and Subjective Norms (SN) with three articles each.

The constructs associated with the UTAUT or UTAUT2 models have also been studied in almost all the articles that have considered these models. Among them, the use of SI stands out in eight articles, while other factors such as HA, HM, or FC have been analysed in five studies and PV in four studies. Other variables associated with the UTAUT or UTATU2 models include Self-efficacy (SE) in four articles, and PI, perceived playfulness, goal setting, attractiveness, privacy protection and barriers in one article. Other factors linked with other models that have been studied once were Insecurity, Discomfort, Need for interaction, Personal attachment, Word-of-mouth, Commitment and Quality aspects or Motivations. Appendix B shows all the variables analysed in each individual study.

Finally, considering the main results, it has been shown that, although the TAM factors (PU and PEOU) are widely studied and evidence has been found of the influence of both on UTI and PEOU on PU, there are many factors that also both directly and indirectly influence, using these two constructs as mediators of UTI. For example, PEN is a variable that eight studies have found to influence UTIs. SI and HA were other factors that also significantly influence UTI ( n  = 5 for each one). Other elements from the UTAUT/UTAUT2 models that have also been shown to influence UTI, to a lesser extent across studies, have been PV ( n  = 3), FC ( n  = 2), and HM ( n  = 3). Other aspects external to the TAM-based models that directly and significantly influence ITU were Innovativeness, Subjective Knowledge, Trust, Commitment, Perceived Playfulness, Health Consciousness, Personal Innovativeness, Autonomous Motivation, Self-efficacy, Attractiveness, Perceived Privacy Protection, Subjective Norms, Goal Setting, Risk Perception, Physical Appearance, Affiliation, Condition, Privacy Risk and Security Risk.

As for the indirect effects of the external variables considering PEOU/EE, PU/PE, or PEN as mediating variables, the influence of factors common to these three variables such as Innovativeness, Insecurity, Optimism, Perceived Attractiveness, Information Quality,and System Quality has been evidenced. Other external factors that significantly influenced both PEOU/EE and PU/PE were Subjective Knowledge, Task-Technology Fit, Accuracy, SE, PEN and Subjective Norms. While certain factors only influenced some of the variables considered, especially PU/PE, which was influenced by a greater number of external variables (Discomfort, Confirmation of Expectations, Trustworthiness, Perceived Benefits, Risk Perception, Perceived Threats), PEN only influenced Discomfort and PEOU/EE e-Lifestyles. Therefore, it was observed that there is no consensus in the scientific literature when it comes to addressing common external variables for further research in several contexts.

The aim of this systematic review was to update research that has analysed the intention to use or adopt fitness apps from 2020 to May 2023, following the study conducted by Angosto et al. ( 2020 ). It is relevant to highlight the differences between this review and the previous one by Angosto et al. ( 2020 ). For this purpose, it is important to consider the review of studies that used UTAUT or UTAUT2 developed by Venkatesh et al. ( 2016 ) as a model. In this review, the author argues the need to expand existing reference models with new exogenous, endogenous, moderating, or outcome mechanisms, as well as theorising influences at different levels. As a clear example in this line, the author himself increased the number of endogenous variables of the UAUT model including HM, PV and HA resulting in the UTAUT2 model or, in the case of Farooq et al. ( 2017 ), incorporating PI to obtain the UTAUT3 model. In addition, Davis ( 1989 ) proposed the initial TAM model by inducing external or exogenous variables in order to be able to analyse in different contexts.

Based on these aspects, the review previously carried out by Angosto et al. ( 2020 ) presents a clear limitation as it only focuses on analysing the influence of TAM or TAM2 factors, omitting the possible influences of exogenous, endogenous, or moderating variables. In this way, it should be noted that these authors do not carry out an in-depth analysis of user behaviour and its effects (both direct and indirect) that influence the ITU fitness app. On the other hand, another error is observed because the authors discriminated the variables of the UTAUT or UTAUT2 models, only focusing in the end on the studies based on TAM, TAM2, or TRAM. Therefore, when they conducted their analysis on the influence of variables, they omitted data from these studies as well. It should be noted that the UTAUT and UTAUT2 models are based on TAM, thus PE is the equivalent of PU, while EE is the equivalent of PEOU.

In view of the previous reasons, together with the period experienced by the world population as a result of the COVID-19 pandemic, it is necessary to update the previous review carried out by Angosto et al. ( 2020 ). It should be remembered that during the pandemic the population was forced to be confined to their homes. This has represented a milestone in the digitalisation of society and sports and fitness services. In fact, it can be observed that while in the review by Angosto et al. ( 2020 ), the authors identified 19 articles, from the beginning of the pandemic to the present day this review has found a total of 29 articles that met the inclusion/exclusion criteria. In short, the number of publications has more than doubled in the last three years. It is true that five research works overlapped with the prior review, which might explain why these studies were published in the press, and by assigning them a journal number, they seem published at a later date. This review emphasises the significance of this topic’s rising popularity in the fitness sector from several domains such as sociology, psychology and management (Cai et al., 2022 ).

To summarise, the results of this review and the previous review by Angosto et al. ( 2020 ) will be compared. In general, regarding the location of the studies, an increase in the number of studies conducted in Europe was observed compared to the previous review (Acikgoz et al., 2022 ; Baubonytė et al., 2021 ; Damberg, 2021 ; Ferreira et al., 2021 ; García-Fernández et al., 2020 ; Gómez-Ruiz et al., 2022 ; Pérez-Aranda et al., 2021 ; Schomakers et al., 2022 ; Yang and Koenigstorfer, 2021 ), and a decrease in the number of studies in the Americas (Won et al., 2023 ). Concerning countries, there is an exponential increase in the number of studies conducted by authors in Chinese universities and, when compared to the previous review, there is a majority of studies from South Korea.

In relation to gender, both reviews obtained similar results in which the proportion of female participants was higher than male participants in most of the studies. Although the gender of the customers or users studied was primarily female, Baubonyte et al. ( 2021 ) believe this to be rather immaterial in research that compared the intention to use new technologies based on gender. When the mean age was analysed, this review showed that the mean age of the participants was around 30 years old, while in the review by Angosto et al. ( 2020 ), this was 24 years old. Also, it should be noted that the age groups with the highest representation and the highest proportion of users were either very young (<23 years) or adult (30–50 years), while in this review most studies have a higher proportion of the population under 30 years versus adults. The reason for these results may be due to the fact that females tend to prioritise collective practice over individual practice (Vogler et al., 2008 ), and therefore there is a higher proportion of users of fitness centres or communities, while young people present fewer digital barriers when it comes to using apps than, perhaps, the adult population (Schreurs et al., 2017 ).

Depending on the type of app analysed in the different studies, variations have also been observed with respect to the previous review. The previous review emphasised that most studies considered fitness and diet apps while fitness or sports apps were the least considered. This review reports completely inverse results where the large majority of apps analysed were fitness apps followed by sport, while diet-fitness apps have been the least evaluated, with only one study. This change in trend may be clearly influenced by the context of the COVID-19 pandemic where the population forced to stay at home due to confinement felt the need to do physical exercise to be active and use leisure time in a more entertaining way. A significant proportion of the scientific literature highlights the features and functions and results of using fitness and sports apps (Kim et al., 2017 ), despite the fact that some studies have evaluated other health-related apps alongside this type of app (Aboelmaged et al., 2022 ; Chiu et al., 2021 ; Chiu and Cho, 2021 ; Zhu et al., 2023 ), or that of diet (Chiu et al., 2021 ). It is vital to highlight that the link between physical activity, fitness and health is extremely close, as is eating to live a healthy lifestyle.

Most research that has analysed technology adoption or intention to use has used the TAM model, which offers an understanding of why people embrace these technologies based on their PU and PEOU views (Márquez et al., 2020 ). However, this study found that recent research increasingly employs theories developed from the TAM, such as the TRAM model (Aboelmaged et al., 2022 ; Chiu and Cho, 2021 ), the UTAUT (Guo, 2022 ; Pérez-Aranda et al., 2021 ; Vinnikova et al., 2020 ; Wei et al. 2021 ), or the UTAUT2 model (Damberg, 2021 ; Dhiman et al., 2020 ; Ferreira-Barbosa et al., 2021 ; Kim and Lee, 2022 ; Schomakers et al., 2022 ; Yang and Koenigstorfer, 2021 ). In addition, other theories also appear in different articles such as the ECM (Chiu et al., 2021 ; Zhang and Xu 2020 ), the TNSB (Yeoh et al. 2022 ) or the TCV/TPR (Zhu et al., 2023 ). An interesting aspect to note is that, although no study based on the UTAUT3 model suggested by Farooq et al. ( 2017 ) has been found, Dhiman et al. ( 2020 ) proposed the UAUT2 model, but incorporated the PI variable which is included as a new endogenous variable within the UTAUT3.

In general, previous research on the acceptance of new technologies in the sports industry has found that PEOU (Mohammadi and Isanejad, 2018 ), or PU are the primary influences on the ‘intention to use’ (Kim et al., 2017 ). According to Venkatesh ( 2000 ), when a customer or user sees a technology to be simple to use, he or she would also regard it to be valuable. According to Cho and Kim ( 2015 ), PEOU typically has a benefit for users since it helps them to carry out activities with a more comfortable and simple method while driving the desire to continue using the app. In this regard, Liu et al. ( 2017 ) revealed that PEOU was the most important belief since the majority of fitness users thought apps were easy and simple to use when they met their expectations. Based on one research work, if the user must make an effort to learn how to use the app, this will favourably affect the consumer’s propensity to use the app (Lin et al., 2020 ). When a customer has a strong desire to use the app, the person is more likely to promote it to others (Cheng et al., 2021 ). As a result, the usage of fitness apps will be related to an increase in physical activity levels and, consequently, in health (Kim, 2022 ; Litman et al., 2015 ).

However, in spite of this more than contrasted evidence in the scientific literature, it is important to address the extent to which other variables (exogenous, endogenous, or moderating) can influence the ITU fitness app. To begin with the influence of exogenous variables, the TR model has been shown in different studies to have an external influence on TAM factors (Aboelmaged et al., 2022 ; Chen and Lin, 2018 ; Chiu and Cho, 2021 ). For example, PEOU is moderately influenced by Innovativeness and slightly influenced by Optimism and Insecurity, while PU is moderately influenced by Optimism and slightly influenced by Innovativeness, Discomfort and Insecurity (Aboelmaged et al., 2022 ; Chang et al., 2023 ; Chiu and Cho, 2021 ). Furthermore, Chiu and Cho ( 2021 ) found that both positive (Innovativeness and Optimism) and negative (Discomfort and Insecurity) factors of TR significantly influenced PEN. In another context, Raman and Aashish ( 2022 ), evaluating wearables, revealed that positive aspects of the TR positively influenced PEOU and PU, while negative aspects of TR negatively influenced these variables.

In contrast, Acikgoz et al. ( 2022 ) found a moderate influence of Innovativeness on PU and Subjective Knowledge on both PEOU and PU. Chang et al. ( 2023 ) reported a slight influence of the variable Task-Technology Fit on PEOU and PU. Other influential variables on PEOU have also been shown to be Self-efficacy (Dhiman et al. 2020 ), e-Lifestyles (García-Fernández et al., 2020 ), Perceived Attractiveness (Gómez-Ruiz et al., 2022 ; Jeong and Chung, 2022 ), Accuracy (Jeong and Chung, 2022 ), Information Quality and System Quality (Won et al., 2023 ) and Subjective Norms (Yu et al., 2021 ). As for external influential variables also in PU/PE, there are Confirmation of Expectations (Chiu et al., 2021 ), Perceived Attractiveness (Gómez-Ruiz et al., 2022 ), Accuracy and Trustworthiness (Jeong and Cheung, 2022 ), Self-efficacy, Perceived Barriers, Perceived Benefits, Risk Perception, and Perceived Threats (Wei et al., 2021 ), Information Quality and System Quality (Won et al. 2023 ) and Subjective Norms (Yu et al., 2021 ). Won et al. ( 2023 ) also found the influence of Information Quality and System Quality on PEN.

Some studies have also assessed the effects of exogenous or endogenous variables on attitudes as a moderator with ITU. Some variables that had a significant influence were PU/PE (García-Fernández et al., 2020 , Pérez-Aranda et al., 2021 ; Yu et al., 2021 ), PEOU/EE (Pérez-Aranda et al., 2021 ; Yu et al., 2021 ), PEN, Gamification and Satisfaction (Pérez-Aranda et al., 2021 ). Cai et al. ( 2022 ) found that Satisfaction acted as a moderating variable for PEOU, PU and Trust with ITU. Regarding the influence of endogenous variables that influenced ITU in addition to PEOU, PU, or PEN we found Subjective Knowledge (Acikgoz et al., 2022 ), Commitment (Chiu et al., 2021 ; Cho et al., 2020 ), PV (Damberg, 2021 ; Dhiman et al., 2020 ; Yang and Koenigstorfer, 2021 ), HA (Damberg, 2021 ; Dhiman et al., 2020 ; Ferreira et al. 2021 ; Schomakers et al. 2022 ; Yang and Koenigstorfer, 2021 ), Health Consciousness (Damberg, 2021 ), Perceived Playfulness (Damberg, 2021 ), SI (Dhiman et al., 2020 ; Ferreira et al., 2021 ; Guo, 2022 ; Vinnikova et al., 2020 ), PI (Dhiman et al., 2020 ), HM (Ferreira et al., 2021 ; Schomakers et al., 2022 ); FC (Ferreira et al., 2021 ; Yang and Koenigstorfer, 2021 ), Perceived Trust (Gómez-Ruiz et al., 2022 ), Autonomous Motivation (Guo, 2022 ), SE (Huang and Ren, 2020 ; Vinnikova et al., 2020 ), Privacy Perceived Protection (Kim and Lee, 2022), Subjective Norms (Pérez-Aranda et al., 2021 ) and Goal-setting (Vinnikova et al., 2020 ).

Particularly interesting are the studies that did not rely on TAM models or derivatives that found different variables that significantly influenced ITU. For example, Zhu et al. ( 2023 ) showed that the variables of General Health, Affiliation, Physical appearance, Condition, Perceived Risk and Security Risk influenced UTI. Yeoh et al. ( 2022 ) indicated that Outcome Expectation, Descriptive Norms and Perceived Behavioural Control influence UTI. Pérez-Aranda et al. ( 2023 ) found that attitudinal, cognitive and behavioural antecedents increase the intention to continue using a sports app. Finally, according to the influence on outcome variables, Cheng et al. ( 2021 ) observed that the ITU significantly influenced the Word-of-Mouth outcome variable. On the other hand, Ferreira et al. ( 2021 ) found that ITU influenced current use and Satisfaction, and Guo ( 2022 ) that ITU and Controlled Motivation also influenced current use. At the same time, SI, SE and Goal-setting also influenced current use (Vinnokova et al., 2020 ).

Lastly, we will discuss some evidence reported by other studies focused on the sport context, but which did not take into account fitness apps. For example, Wang et al. ( 2022 ) noted in a fitness software that SI, PE and EE significantly affected the ITU of university students. In an e-Sport game during a pandemic, Ong et al. ( 2023 ) showed that HA was the most significant factor in UTI, followed by usability, FC, SI and HM. In a similar vein, Yang et al. ( 2022 ) found that HA was the only predictor for the use of metaverse technology for basketball learning in college students. Ahn and Park ( 2023 ) showed that hedonic, user burden, pragmatic and social values were key predictors of fitness app user satisfaction. Gu et al. ( 2022 ) observed that attitudes toward exercise and the use of sports apps have a significant impact on physical activity intentions. Finally, Ferreira et al. ( 2023 ) demonstrated that the relationship between UTIs and members’ overall satisfaction with the gym is positively mediated by e-Lifestyles.

Limitations and future research

There are obvious limitations to this systematic review. The first point to mention is maybe the shorter time restriction compared to the prior review by Angosto et al. ( 2020 ). However, this is required since the COVID-19 pandemic is still active and national governments are implementing preventative measures based on the pandemic’s progress (Ferrer, 2021 ; Official State Bulletin, 2021 ). Many nations are enacting new temporary confinements, which may encourage the usage of exercise or health applications. Other potential constraints include publication bias, which occurs when journals publish research with favourable and significant results while rejecting papers with irrelevant outcomes. Another source of bias might have been the language, since there may have been publications in languages other than those specified in the inclusion criteria (English, Spanish and Portuguese). Another constraint might be the choice of search databases, because missing specific databases may result in prospective articles not being detected for inclusion in the review. A third issue is inclusion bias, which occurs when the inclusion or exclusion criteria itself prejudices against a research work. The last limitation is that the great diversity of variables analysed by the authors does not allow the generation of an adequate database that would enable a more in-depth analysis of the results through a meta-analysis beyond the TAM variables such as PEOU and PU.

Future research should try to assess sports consumers or users in other European or American contexts, with the possibility of analysing the results according to socio-demographic characteristics such as gender, age, sport, or digital experience. Age is an interesting aspect to investigate since, depending on the generation to which the person belongs, he or she will identify with new technologies to different degrees. In addition, there are variables such as those in the UTAUT model and derivatives or TR that have been more common than others, but there is still a need to increase the number of studies that use them. Other studies could take a longitudinal approach, assessing the consumer’s desire to use and actual use of the application, as well as whether or not this affects their behaviour towards a more active or healthy life.

Future lines of research relating to the evaluation of the intention to use fitness apps, or any other form of app or wearable, should examine the differences between the models in the same population using the TAM model and some of the other derived models such as the UTAUT or UTATU2. Furthermore, the proposed theoretical models should be assessed by linking them to other factors related to smartphones or other technical devices, such as attachment to the gadget, social influence for its usage, or actual use of the item, among others. Theoretical models such as the TAM, TAM2, UTAUT, UTAUT2 or UTAUT3 should be examined in various sports settings such as the usage of apps for managerial duties, sports training, or marketing/sports products.

Another key issue that has not been studied is the variation in intention to use across the different age groups of the population, since the elderly population may have a different aim than the younger population. Along similar lines, additional elements such as educational level or socioeconomic position may impact the inclination to use the fitness app or any other gadget or technology. Finally, longitudinal research might be utilised to determine how well the intention to use fitness apps matches the actual use of them.

Conclusions

This systematic review update highlights that research on the usage intention and adoption of fitness apps is a topic of interest within the digital sports marketing industry. In recent years there has been a significant increase in the number of publications, with an increasing number of European studies focusing on fitness or sports apps themselves and not associated with health or diet. In addition, the models used beyond the TAM itself are becoming more diversified, as well as the number of exogenous, endogenous and moderating variables in the different studies. Although there is no consensus on analysing the same variables in greater depth in order to generate data for a better joint analysis, there is no consensus on analysing the same variables in greater depth in order to generate data for a better joint analysis.

Finally, a practical aspect of sports organisation management is the desire that this sort of study may assist in learning the opinions of users or customers while adopting or establishing new policies with a digital transformation. This is especially important because it allows for improving the organisation’s communication in a bidirectional way. In short, the implementation of the use of apps in sports centres implies more direct and closer communication with users. In addition, physical activity and management might be monitored without eliminating travel and human interaction. For example, sports organisations make extensive use of sports digital marketing, through the use of social tools, to make the organisation more visible and to offer a more direct image and contact with current or future consumers (Angosto et al., 2022 ). However, not all users have the same social media, therefore the use of push notifications and in-app communication in a venue allows for better notification of relevant news and at a lower cost.

Furthermore, the theoretical models reviewed above identify factors that influence the ITU of technology, such as PU, PEOU, SI and FC. Sport managers can therefore use these models to identify and assess which factors are relevant in their particular context. This will help them to understand the needs and preferences of their users and to adapt their strategies accordingly.

Also, PU is a critical factor in the intention to use technology. Therefore, sports managers should assess how their users perceive the usefulness of technology in their sport context. Among the actions to be taken, they can conduct surveys, interviews or focus groups to collect data on how users feel technology can enhance their sport experience. This will allow sports managers to identify areas for improvement or additional features that can add value to the user experience. Similarly, PEOU is also an important factor in the acceptance and use of technology. In this regard, sports managers must ensure that the technology they use is easy to use and accessible to their users. This involves providing clear instructions, intuitive interfaces and adequate training to ensure that users feel comfortable using the technology.

Another variable that has been shown to influence ITU is SI. In this regard, sports managers could leverage these positive SI to promote the adoption of technology in their sports community. For example, they can collaborate with influential athletes or well-known coaches to support and promote the use of technology. They could also encourage social interaction among technology users by creating online communities or support groups. Finally, FC and perceived barriers have also been shown to influence the intention to use. Sports managers should identify and address any potential barriers that may hinder the adoption and use of technology in their sport environment. This may include a lack of technology resources, resistance to change, or privacy and security concerns. By proactively addressing these barriers, sports managers could encourage greater acceptance and use of technology.

Data availability

The datasets generated during and/or analysed during the current study are available in the Figshare repository, https://figshare.com/s/d0a13d89538847f00b67 .

Aboelmaged M, Ali I, Hashem G (2022) Mobile apps use forwellness and fitness and university students’ subjective wellbeing. Inform Develop 38(4):672–687. https://doi.org/10.1177/02666669211020498

Article   Google Scholar  

Acikgoz F, Filieri R, Yan M (2022) Psychological predictors of intention to use fitness apps: the role of subjective knowledge and innovativeness. Int J Hum Comp Interact. https://doi.org/10.1080/10447318.2022.2074668

Ahn H, Park E (2023) Motivations for user satisfaction of mobile fitness applications: an analysis of user experience based on online review comments. Humanit Soc Sci Commun 10(1). https://doi.org/10.1057/s41599-022-01452-6

Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood Cliffs, NJ

Google Scholar  

Angosto S, García-Fernández J, Valantine I, Grimaldi-Puyana M (2020) The intention to use fitness and physical activity apps: a systematic review. Sustainability 12(16):16. https://doi.org/10.3390/su12166641

Angosto S, Lara-Bocanegra A, Bohorquez MR, García-Fernández J, Zhang JJ (2022) Challenges in sport marketing: a systematic review of digital sport marketing. In: Byon KK, Yim BH, Zhang JJ (eds) Marketing analysis in sport business. Global perspectives, 1st edn. Routledge, London, pp. 70–92

Chapter   Google Scholar  

Ardern CL, Büttner F, Andrade R, Weir A, Ashe MC, Holden S, Impellizzeri FM, Delahunt E, Dijkstra HP, Mathieson S, Rathleff MS, Reurink G, Sherrington C, Stamatakis E, Vicenzino B, Whittaker JL, Wright AA, Clarke M, Moher D, Page MJ, Khan KM, Winters M (2022) Implementing the 27 PRISMA 2020 Statement items for systematic reviews in the sport and exercise medicine, musculoskeletal rehabilitation and sports science fields: the PERSiST (implementing Prisma in Exercise, Rehabilitation, Sport medicine and SporTs science) guidance. Br J Sports Med 56(4):175–195. https://doi.org/10.1136/bjsports-2021-103987

Article   PubMed   Google Scholar  

Baubonytė S, Deliautaitė K, Narkevičienė KM, Valantinė I (2021) The use of sports apps for engaging in physical activity. Balt J Sport Health Sci 121(2):28–33

Beh PK, Ganesan Y, Iranmanesh M, Foroughi B (2021) Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behav Inf Technol 40(3):282–299. https://doi.org/10.1080/0144929X.2019.1685597

Beldad AD, Hegner SM (2018) Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: a structural equation modeling approach. Int J Hum–Comput Interact 34(9):882–893. https://doi.org/10.1080/10447318.2017.1403220

Byun H, Chiu W, Bae J (2018) Exploring the adoption of sports brand apps: an application of the modified technology acceptance model. Int J Asian Bus Inf Manag 9(1):52–65. https://doi.org/10.4018/IJABIM.2018010105

Cai J, Zhao Y, Sun J (2022) Factors influencing fitness app users’ behavior in China. Int J Hum–Comput Interac 38(1):53–63. https://doi.org/10.1080/10447318.2021.1921483

Chang CJ, Yang SC, Wolzok E (2023) Examining the use of fitness apps in sports centers in Taiwan: incorporating task–technology fit into a technology readiness acceptance model. Manag Sport Leis. https://doi.org/10.1080/23750472.2023.2165532

Cheng LK, Huang HL, Lai CC (2021) Continuance intention in running apps: the moderating effect of relationship norms. Int J Sports Mark Spons 23(1):132–154. https://doi.org/10.1108/IJSMS-08-2020-0143

Chen MF, Lin NP (2018) Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Res 28:351–373. https://doi.org/10.1108/IntR-03-2017-0099

Chiu W, Cho H (2021) The role of technology readiness in individuals’ intention to use health and fitness applications: a comparison between users and non-users. Asia Pac J Mark Logist 33(3):807–825. https://doi.org/10.1108/APJML-09-2019-0534

Chiu W, Cho H, Chi CG (2021) Consumers’ continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Inf Technol People 34(3):978–998. https://doi.org/10.1108/ITP-09-2019-0463

Cho H, Chi C, Chiu W (2020) Understanding sustained usage of health and fitness apps: incorporating the technology acceptance model with the investment model. Technol Soc 63:101429. https://doi.org/10.1016/j.techsoc.2020.101429

Cho J, Kim SJ (2015) Factors of leading the adoption of diet/exercise apps on smartphones: application of channel expansion. J Internet Comput Serv 16(1):101–108. https://doi.org/10.7472/jksii.2015.16.1.101

Clement J (2020) Health and fitness App downloads worldwide from 1st quarter 2019 to 2nd quarter 2020. Statista. Available via STATISTA. https://www.statista.com/statistics/1127248/health-fitness-Apps-downloadsworldwide Accessed 19 Dec 2022

Damberg S (2021) Predicting future use intention of fitness apps among fitness app users in the United Kingdom: the role of health consciousness. Int J Sports Mark Spons 23(2):369–384. https://doi.org/10.1108/IJSMS-01-2021-0013

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

Davis FD, Bagozzi RP, Wharshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35(8):982–1003. https://doi.org/10.1287/mnsc.35.8.982

Dhiman N, Arora N, Dogra N, Gupta A (2020) Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective. J Indian Bus Res 12(3):363–388. https://doi.org/10.1108/JIBR-05-2018-0158

Dillon A (2001) User acceptance of information technology. In: Karwowski K (ed) 555 Encyclopedia of human factors and ergonomics. Routledge, London, pp. 556–561

Dutta DK, Gwebu KL, Wang J (2015) Personal innovativeness in technology, related knowledge and experience, and entrepreneurial intentions in emerging technology industries: a process of causation or effectuation? Int Entrep Manag J 11(3):529–555. https://doi.org/10.1007/s11365-013-0287-y

Eshet E, Bouwman H (2015) Addressing the context of use in mobile computing: a survey on the state of the practice. Interact Comput 27(4):392–412. https://doi.org/10.1093/iwc/iwu002

Farooq MS, Salam M, Jaafar N, Fayolle A, Ayupp K, Radovic-Markovic M, Sajid A (2017) Acceptance and use of lecture capture system (LCS) in executive business studies: extending UTAUT2. Interact Technol Smart Educ 14(4):329–348. https://doi.org/10.1108/ITSE-06-2016-0015

Ferreira H, García-Fernández J, Pedragosa V, Cepeda-Carrión G (2021) The use of fitness centre apps and its relation to customer satisfaction: a UTAUT2 perspective. Int J Sports Mark Spons 23(5):966–985. https://doi.org/10.1108/IJSMS-01-2021-0010

Ferreira H, García-Fernández J, Cepeda-Carrión G (2023) The mediating role of e-Lifestyles to use the fitness center app. Int J Human Comp Interact. https://doi.org/10.1080/10447318.2023.2204273

Ferrer I (2021) Países Bajos impone un confinamiento estricto hasta el 14 de enero para frenar los contagios por la variante ómicron. El País. Available via ELPAIS. https://elpais.com/sociedad/2021-12-18/paises-bajos-impone-un-confinamiento-estricto-hasta-el-14-de-enero-para-frenar-los-contagios-por-la-variante-omicron.html Accessed 20 Dec 2022

Fox S, Duggan M (2021) Mobile fact sheet. Pew Research Center. Available via PRC. http://www.pewinternet.org/data-trend/mobile Accessed 20 Dec 2022

Gao T, Rohm AJ, Sultan F, Huang S (2012) Antecedents of consumer attitudes toward mobile marketing: a comparative study of youth markets in the United States and China. Thunderbird Int Bus Rev 54(2):211–224. https://doi.org/10.1002/tie.21452

García-Fernández J, Gálvez-Ruiz P, Grimaldi-Puyana M, Angosto S, Fernández-Gavira J, Bohórquez MR (2020) The promotion of physical activity from digital services: influence of E-lifestyles on intention to use fitness apps. Int J Environ Res Public Health 17(18):6839. https://doi.org/10.3390/ijerph17186839

Article   PubMed   PubMed Central   Google Scholar  

Gómez-Ruiz AA, Gálvez-Ruiz P, Grimaldi-Puyana M, Lara-Bocanegra A, García-Fernández J (2022) Investigating the intention to use fitness app: the role of the perceived attractiveness of fitness center customers. Sport Bus Manag 12(4):537–553. https://doi.org/10.1108/SBM-12-2021-0145

Gopinath K, Selvam G, Narayanamurthy G (2022) Determinants of the adoption of wearable devices for health and fitness: a meta-analytical study. Commun Assoc Inform Syst 50(1):23. https://doi.org/10.17705/1CAIS.05019

Gu P, Liang Z, Zhang H, Zhang D (2022) Effects of attitudes towards exercise behaviour, use of sports apps and COVID-19 on intentions to exercise. J Person Med 12(9):1434. https://doi.org/10.3390/jpm12091434

Gu P, Zhang H, Liang Z, Zhang D (2022) Impact of public risk perception in China on the intention to use sports APPs during COVID-19 pandemic. Int J Environ Res Public Health 19(19):11915. https://doi.org/10.3390/ijerph191911915

Gunasinghe A, Hamid JA, Khatibi A, Azam SF (2020) The adequacy of UTAUT-3 in interpreting academician’s adoption to e-Learning in higher education environments. Interact Technol Smart Educ 17(1):86–106. https://doi.org/10.1108/ITSE-05-2019-0020

Guo J (2022) Influencing factors of college students’ use of sports apps in mandatory situations: based on UTAUT and SDT. BioMed Res Int 2022:9378860. https://doi.org/10.1155/2022/9378860

Gupta S, Mathur N, Narang D (2022) E-leadership and virtual communication adoption by educators: an UTAUT3 model perspective. Glob Knowl Mem Commun. https://doi.org/10.1108/GKMC-01-2022-0001

Hu J, He W, Zhang J, Song J (2023) Examining the impacts of fitness app features on user well-being. Inf Manag 60(5):103796. https://doi.org/10.1016/j.im.2023.103796

Huang G, Ren Y (2020) Linking technological functions of fitness mobile apps with continuance usage among Chinese users: moderating role of exercise self-efficacy. Comput Human Behav 103:151–160. https://doi.org/10.1016/j.chb.2019.09.013

Jeong SH, Chung JY (2022) Integrated technology acceptance model (TAM) of sports team smartphone application (STSA) in the stimulus organism response (SOR) framework. J Phys Educ Sport 22(9):2152–2161

Jin CH (2020) Predicting the use of brand application based on a TRAM. Int J Hum–Comput Interact 36(2):156–171. https://doi.org/10.1080/10447318.2019.1609227

Jones P, Ratten V, Hayduk T (2020) Sport, fitness, and lifestyle entrepreneurship. Int Entrep Manag J 16(3):783–793. https://doi.org/10.1007/s11365-020-00666-x

Kercher VM, Kercher K, Bennion T, Levy P, Alexander C, Amaral PC, Li YM, Han J, Liu Y, Wang R, Huang HY, Gao BH, Batrakoulis A, Gómez Chávez LFJ, Haro JL, Zavalza ARP, Rodríguez LEA, Veiga OL, Valcarce-Torrente M, Romero-Caballero A (2022) Fitness trends from around the globe. ACSM’s Health Fit J 26(1):21–37. https://doi.org/10.1249/FIT.0000000000000737

Kim HM (2022) Social comparison of fitness social media postings by fitness app users. Comput Hum Behav 131:107204. https://doi.org/10.1016/j.chb.2022.107204

Kim B, Lee E (2022) What Factors Affect a User as Intention to Use Fitness Applications? The Moderating Effect of Health Status: A Cross-Sectional Study. INQUIRY J Health Care Organiz Prov Financ 59:1–13. https://doi.org/10.1177/0046958022109582

Kim SC, Yoon D, Han EK (2016) Antecedents of mobile app usage among smartphone users. J Mark Commun 22(6):653–670. https://doi.org/10.1080/13527266.2014.951065

Kim T, Chiu W (2019) Consumer acceptance of sports wearable technology: The role of technology readiness. Int J Sports Mark Spons 20(1):109–126. https://doi.org/10.1108/IJSMS-06-2017-0050

Kim Y, Kim S, Rogol E (2017) The effects of consumer innovativeness on sport team applications acceptance and usage. J Sport Manag 31(3):241–255. https://doi.org/10.1123/jsm.2015-0338

Koenig-Lewis N, Marquet M, Palmer A, Zhao AL (2015) Enjoyment and social influence: predicting mobile payment adoption. Serv Ind J 35(10):537–554. https://doi.org/10.1080/02642069.2015.1043278

Lim JS, Noh GY (2017) Effects of gain-versus loss-framed performance feedback on the use of fitness apps: mediating role of exercise self-efficacy and outcome expectations of exercise. Comput Hum Behav 77:249–257. https://doi.org/10.1016/j.chb.2017.09.006

Lin CT, Shen CC, Mao TY, Yang CC (2019) Empirical investigation of sports management, behavior growth and usage of sports APP: new learning perspective. Pol J Manag Stud 19(1):225–234. https://doi.org/10.17512/pjms.2019.19.1.17

Lin CW, Mao TY, Huang YC, Sia WY, Yang CC (2020) Exploring the adoption of Nike+ Run Club App: an application of the theory of reasoned action. Math Probab Eng 2020:e8568629. https://doi.org/10.1155/2020/8568629

Litman L, Robinson J, Rosenzweig C (2015) The relationship between motivation, monetary compensation, and data quality among US- and India-based workers on Mechanical Turk. Behav Res Methods 47(2):519–528. https://doi.org/10.3758/s13428-014-0483-x

Liu XD, Ly XY, Ren PR (2017) Research on user adoption behavior of fitness App. Enterp Econ 36(11):48–54. https://doi.org/10.13529/j.cnki.enterprise.economy.2017.11.007

Marquez A, Cianfrone BA, Kellison T (2020) Factors affecting spectators’ adoption of digital ticketing: the case of interscholastic sports. Int J Sports Mark Spons 21(3):527–541. https://doi.org/10.1108/IJSMS-07-2019-0080

Middelweerd A, Mollee JS, van der Wal CN, Brug J, Velde SJ (2014) Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nut Phys Act 11(1):97. https://doi.org/10.1186/s12966-014-0097-9

Mohammadi S, Isanejad O (2018) Presentation of the extended technology acceptance model in sports organizations. Ann Appl Sport Sci 6(1):75–86. https://doi.org/10.29252/aassjournal.6.1.75

Newzoo (2021) Global mobile market report. Newzoo. Available via DIALG. https://newzoo.com/key-numbers/ . Accessed 19 Dec 2022

Núñez Sánchez JM, Gómez Chacón R, Jambrino-Maldonado C, García Fernández J (2022) Can a corporate well-being programme maintain the strengths of the healthy employee in times of COVID-19 and extensive remote working? An empirical case study. European J Govern Econ 11(1):51–72. https://doi.org/10.17979/ejge.2022.11.1.8978

Official State Bulletin (2021) Royal Decree-Law 30/2021, of December 23, adopting urgent prevention and containment measures to address the health crisis caused by COVID-19 (No. 30; pp. 159762–159770). Off State Bull. Available via BOE. https://www.boe.es/diario_boe/txt.php?id=BOE-A-2021-21307

Ong AKS, Prasetyo YT, Robas KPE, Persada SF, Nadlifatin R, Matillano JSA, Macababbab DCB, Pabustan JR, Taningco KAC (2023) Determination of factors influencing the behavioral intention to play “Mobile Legends: Bang-Bang” during the COVID-19 pandemic: integrating UTAUT2 and system usability scale for a sustainable E-sport business. Sustainability 15(4):3170. https://doi.org/10.3390/su15043170

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinnes LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10(1):89. https://doi.org/10.1186/s13643-021-01626-4

Parasuraman A (2000) Technology Readiness Index (Tri): a multiple-item scale to measure readiness to embrace new technologies. J Serv Res 2(4):307–320. https://doi.org/10.1177/109467050024001

Perez-Aranda J, González EM, Alarcón P (2023) Understanding antecedents of continuance and revisit intentions: the case of sport apps. J Retail Cons Serv 72:103288. https://doi.org/10.1016/j.jretconser.2023.103288

Perez-Aranda J, González Robles EM, Alarcón P (2021) Sport-related physical activity in tourism: an analysis of antecedents of sport based applications use. Inf Technol Tour 23(1):97–120. https://doi.org/10.1007/s40558-019-00161-2

Pinto AS, Abreu A, Costa E, Paiva J (2022) Using UTAUT-3 to understand the adoption of Mobile Augmented Reality in Tourism (MART). In: Carvalho JVD, Liberato P, Peña A (eds) Advances in tourism, technology and systems. Smart innovation, systems and technologies, vol 284. Springer

Rahman SA, Taghizadeh SK, Ramayah T, Alam MMD (2017) Technology acceptance among micro-entrepreneurs in marginalized social strata: the case of social innovation in Bangladesh. Technol Forecast Soc Change 118:236–245. https://doi.org/10.1016/j.techfore.2017.01.027

Raman P, Aashish K (2022) Gym users: an enabler in creating an acceptance of sports and fitness wearable devices in India. Int J Sport Mark Spons 23(4):707–726. https://doi.org/10.1108/IJSMS-08-2021-0168

Rivera M, Gregory A, Cobos L (2015) Mobile application for the timeshare industry: the influence of technology experience, usefulness, and attitude on behavioral intentions. J Hosp Tour Technol 6(3):242–257. https://doi.org/10.1108/JHTT-01-2015-0002

Ruth J, Willwacher S, Korn O (2022) Acceptance of digital sports: a study showing the rising acceptance of digital health activities due to the SARS-CoV-19 pandemic. Int J Environ Res Public Health 19(1):596. https://doi.org/10.3390/ijerph19010596

Article   CAS   PubMed   PubMed Central   Google Scholar  

Schillewaert N, Ahearne MJ, Frambach RT, Moenaert RK (2005) The adoption of information technology in the sales force. Ind Mark Manag 34(4):323–336. https://doi.org/10.1016/j.indmarman.2004.09.013

Schomakers EM, Lidynia C, Vervier LS, Calero Valdez A, Ziefle M (2022) Applying an extended UTAUT2 model to explain user acceptance of lifestyle and therapy mobile health apps: survey study. JMIR mHealth uHealth 10(1):e27095. https://doi.org/10.2196/27095

Schreurs K, Quan-Haase A, Martin K (2017) Problematizing the digital literacy paradox in the context of older adults’ ICT use: aging, media discourse, and self-determination. Can J Commun 42(2):359–377. https://doi.org/10.22230/cjc.2017v42n2a3130

Schulz KF, Altman DG, Moher D (2010) CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. J Pharmacol Pharmacother 1(2):100–107. https://doi.org/10.4103/0976-500X.72352

Thatcher JB, Perrewé PL (2002) An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Q 26(4):381–396

Thompson WR (2022) Worldwide survey of fitness trends for 2022. ACSM’s Health Fit J 26(1):11–20. https://doi.org/10.1249/FIT.0000000000000732

Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26(4):4. https://doi.org/10.1038/s41591-020-0824-5

Article   CAS   Google Scholar  

Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365. https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36(1):157–178. https://doi.org/10.2307/41410412

Venkatesh V, Thong JY, Xu X (2016) Unified theory of acceptance and use of technology: A synthesis and the road ahead. J Assoc Inform Syst 17(5):328–376. https://ssrn.com/abstract=2800121

Vinnikova A, Lu L, Wei J, Fang G, Yan J (2020) The use of smartphone fitness applications: the role of self-efficacy and self-regulation. Int J Environ Res Public Health 17(20):1–16. https://doi.org/10.3390/ijerph17207639 . Scopus

Vogler C, Lyonette C, Wiggins RD (2008) Money, power and spending decisions in intimate relationships. Sociol Rev 56(1):117–143. https://doi.org/10.1111/j.1467-954X.2008.00779.x

Wang C, Wu G, Zhou X, Lv Y (2022) An empirical study of the factors influencing user behavior of fitness software in college students based on UTAUT. Sustainability 14(15):9720. https://doi.org/10.3390/su14159720

Wei J, Vinnikova A, Lu L, Xu J (2021) Understanding and predicting the adoption of fitness mobile apps: evidence from China. Health Commun 36(8):950–961. https://doi.org/10.1080/10410236.2020.1724637

Won D, Chiu W, Byun H (2023) Factors influencing consumer use of a sport-branded app: the technology acceptance model integrating app quality and perceived enjoyment. Asia Pac J Mark Logist 35(5):1112–1133. https://doi.org/10.1108/APJML-09-2021-0709

Yang F, Ren L, Gu C (2022) A study of college students’ intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon 8(9):e10562. https://doi.org/10.1016/j.heliyon.2022.e10562

Yang Y, Koenigstorfer J (2021) Determinants of fitness app usage and moderating impacts of education-, motivation-, and gamification-related app features on physical activity intentions: cross-sectional survey study. J Med Internet Res 23(7):e26063. https://doi.org/10.2196/26063

Yeoh R, Kim HK, Kang H, Lin YA, Ho AD, Ho KF (2022) What determines intentions to use mobile fitness apps? The independent and joint influence of social norms. Int J Hum Comput Interact. https://doi.org/10.1080/10447318.2022.2111040

Yin S, Cai X, Wang Z, Zhang Y, Luo S, Ma J (2022) Impact of gamification elements on user satisfaction in health and fitness applications: a comprehensive approach based on the Kano model. Comput Hum Behav 128:107106. https://doi.org/10.1016/j.chb.2021.107106

Yu CY, Jeffe DB, Kenna MA, Germiller JA, Lieu JEC (2021) Validation of a parent proxy Quality-of-Life measure for young children with hearing loss. Laryngoscope 131(3):663–670. https://doi.org/10.1002/lary.28891

Yuan S, Ma W, Kanthawala S, Peng W (2015) Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model. Telemed E-Health 21(9):735–741. https://doi.org/10.1089/tmj.2014.0148

Zhang X, Xu X (2020) Continuous use of fitness apps and shaping factors among college students: a mixed-method investigation. Int J Nur Sci 7:S80–S87. https://doi.org/10.1016/j.ijnss.2020.07.009

Zhu Y, Wang R, Zeng R, Pu C (2023) Does gender really matter? Exploring determinants behind consumers’ intention to use contactless fitness services during the COVID-19 pandemic: a focus on health and fitness apps. Internet Res 33(1):280–307. https://doi.org/10.1108/INTR-07-2021-0454

Download references

Acknowledgements

This research was funded by the Junta de Andalucía, Regional Ministry of Economic Transformation, Industry, Knowledge and Universities (grant number AT 21_00031). SA is funded by the European Union—NextGenerationEU through a postdoctoral contract with Margarita Salas.

Author information

These authors jointly supervised this work: Jerónimo García-Fernández, Moisés Grimaldi-Puyana.

Authors and Affiliations

Department of Physical Education and Sports, Faculty of Sports Sciences San Javier, University of Murcia, 30720, Santiago de la Ribera (Murcia), Spain

Salvador Angosto

Department of Physical Education and Sports, Faculty of Educational Sciences, Universidad de Sevilla, 41013, Seville, Spain

Salvador Angosto, Jerónimo García-Fernández & Moisés Grimaldi-Puyana

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, SA, JG-F, and MG-P; methodology, SA and JG-F; formal analysis, SA; investigation, SA, JG-F; resources, SA; data curation, MG-P; writing—original draft preparation, SA, JG-F and MG-P; writing—review and editing, SA and JG-F; project administration, JG-F and MG-P; funding acquisition, JG-F and SA.

Corresponding author

Correspondence to Jerónimo García-Fernández .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Additional information.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Angosto, S., García-Fernández, J. & Grimaldi-Puyana, M. A systematic review of intention to use fitness apps (2020–2023). Humanit Soc Sci Commun 10 , 512 (2023). https://doi.org/10.1057/s41599-023-02011-3

Download citation

Received : 13 February 2023

Accepted : 28 July 2023

Published : 17 August 2023

DOI : https://doi.org/10.1057/s41599-023-02011-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Streamline Your Research Workflow

Seamlessly Search, Save and Organise findings across your papers. Complete your research up to 10x faster.

LOVED BY ACADEMICS

For literature reviews & collecting evidence

"Lateral is an AI-powered app for academics that will completely change the way you read and take notes."

"Lateral presents a visual table which not only allows me to quickly identify themes but also relevant quotes and phrases (with references!) that could have easily been overlooked without."

"Lateral is a necessary tool for academics! It has changed my research...I am good to go for all new opportunities."

"Lateral is an excellent tool for academics and one of the hidden gems among the many productivity tools available out there!"

SMART FEATURES

Your unified workflow

Take the stress out of document review. Find text fast. Stay organised.

Auto-Generated Table

Keep a clear overview of all your findings and never lose a reference.

AI-Powered Concepts

Name a Concept, and Lateral intelligently recommends relevant text across all your papers.

Super Search

Search across all your papers at once & see similar highlights for every search result.

Smart PDF Reader

Read & highlight in the browser. Find connections you may have missed.

Export Your Findings

From the Table View directly into a Word document or Excel file with references.

Find 30m+ research papers

Upload existing PDFs or find new papers through our integrated paper search.

Powerful OCR

Digitize text from documents from scanned PDFs to make the searchable & highlightable .

Collaborate & Share

Invite others to your workspace or share a limited public link of your project.

Zotero integration (coming soon)

Easily sync documents between Zotero and your project.

Literature Reviews

Find patterns and common themes across numerous papers quickly.

Dissertations

Organize thoughts and references to streamline the writing process.

Note-taking

Save text with notes and flags for more organized note-taking.

Collaborative Research

Work on shared project & documents and build on each other's findings.

Gather information across various sources for curriculum development.

Find connections and gather background information for stories.

Get to know Lateral for free.

500 Page Credits one-time

Speed up your research workflow.

21,600 Page Credits per year

Take your research to the next level.

60,000 Page Credits per year

Academic Discount

We offer a 20% Academic Discount on both Premium and Pro Tiers. Simply fill out the form on the pricing page in the app.

Team Pricing

Build a shared, AI supported knowledge repository or purchase multiple licenses.

1,800 Page Credits per month

5,000 Page Credits per month

What are Page Credits?

When you begin using Lateral and add PDF files, it uses up 'Page Credits' based on how many pages are in your PDFs. You start with 1,000 Page Credits when you join. Once you use them all, you’ll have to get a subscription plan, called Lateral Supreme, to get more Credits. If you delete any documents, this will not return Credits. If you have a Supreme subscription, you can buy extra Credits.

What is Lateral Supreme?

Lateral Supreme is a subscription that you can pay for every month or once a year. If you choose to pay each month, you can use up to 2,500 Page Credits within that month. If you decide to pay for the whole year at once, you can use up to 30,000 Page Credits for the entire year. Any unused Page Credits will expire; they don’t roll over to the next month or year.

When you add papers or documents to a project, Lateral does a lot of heavy lifting to make the content searchable and suggestible. Each page uses 1 page credit. Taking an average of 20 pages per paper for example, this means for Premium monthly 2,160 pages are around 108 papers and for Pro monthly 5,000 pages are around 250 papers.   Deleting documents does not re-add page credits, as the processing of the documents has already been completed.

Does Lateral use GPT?

No, Lateral does not use GPT. We have built our own machine learning technology (LIP API) to make content suggestions. Your content is not sent to Open AI servers.  

What happens to the documents I upload to Lateral?

When you upload documents, they stay private and only you can see them in your workspace. We don’t share them with other users. Also, we don’t use your documents to make our AI smarter. Our AI is already trained and it just helps you find related content across your documents.

What languages does Lateral support?

The app works best with English. You can keyword search for text in other languages, but the AI currently can only give suggestions if you're using English.

Does Lateral work on mobile?

Currently Lateral is only available on desktop.

Get into flow.

Your personal research assistant

Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share research.

Available for Mac, Windows, Linux, and iOS

Just need to create a quick bibliography? Try ZoteroBib .

Meet Zotero.

Collect with a click..

Zotero automatically senses research as you browse the web. Need an article from JSTOR or a preprint from arXiv.org? A news story from the New York Times or a book from a library? Zotero has you covered, everywhere.

Organize your way.

Zotero helps you organize your research any way you want. You can sort items into collections and tag them with keywords. Or create saved searches that automatically fill with relevant materials as you work.

Cite in style.

Zotero instantly creates references and bibliographies for any text editor, and directly inside Word, LibreOffice, and Google Docs. With support for over 10,000 citation styles, you can format your work to match any style guide or publication.

Stay in sync.

Zotero can optionally synchronize your data across devices, keeping your files, notes, and bibliographic records seamlessly up to date. If you decide to sync, you can also always access your research from any web browser.

Collaborate freely.

Zotero lets you co-write a paper with a colleague, distribute course materials to students, or build a collaborative bibliography. You can share a Zotero library with as many people you like, at no cost.

Zotero is open source and developed by an independent, nonprofit organization that has no financial interest in your private information. With Zotero, you always stay in control of your own data.

Still not sure which program to use for your research? See why we think you should choose Zotero .

Ready to try Zotero?

A free, AI-powered research tool for scientific literature

• Emmanuelle Charpentier

New & Improved API for Developers

Introducing semantic reader in beta.

Stay Connected With Semantic Scholar Sign Up What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.

AI Search Engine for Research

Find & understand the best science, faster.

Try an example search

See how it works

Used by researchers at the world’s top institutes

Why Consensus?

Consensus responsibly uses AI to help you conduct research faster.

Extensive research coverage

Search through over 200M research papers in every domain of science & academia.

Time-saving AI insights

Gain insight faster with our Copilot and Consensus Meter. We leverage both OpenAI & custom LLMs.

Find the most relevant papers

Our proprietary academic search tools & filters mean you’ll find the very best science for your query.

Results connected to science

We cite every source. You're always only a click away from the underlying research paper.

Who Consensus helps most...

Anyone with curiosity. Whether you’re conducting research or just fact-checking a friend, if you need answers from the literature, Consensus is for you.

Students & researchers

Streamline your literature review process. Quickly see the direction of current findings, and surface the best papers.

Science organizations

Quickly check ingredients, chemicals, or molecules. Understand mechanisms of action, and stay up to date with new research.

Clinicians & doctors

Get answers to patients’ questions that you can trust, share information they can digest, and easily cite your references.

Universities & schools

Students & researchers at over 5,000 universities worldwide search with Consensus. We partner with libraries, higher learning institutes, and universities.

Writers & journalists

Source evidence-based insights on your topic, understand connected fields, and see related suggested searches.

Health & fitness experts

Easily check out the science regarding supplement safety, diet types, and exercise science outcomes.

1,000,000 +

Researchers, students, doctors, professionals, and evidence-based humans choose Consensus.

"It's not every day I find a tool that truly helps with my work. Consensus blew me away when I started using it, I was learning things I had never encountered before. This is an AI product that isn't hype."

"I can make sense of what’s out there a lot faster with Consensus. I jump into different topics with the summary & Copilot before diving deeper. The interface makes it so easy to review individual papers and see what they’re about."

Consensus has been featured in

Consensus vs ChatGPT

ChatGPT predicts the most likely language that should follow. Consensus helps you find & understand the best science, faster.

Results directly connected to scientific papers

Fully machine-generated, prone to hallucinations

Consensus academic features

Consensus Meter

Quickly see the scientific consensus & gain topic context and direction. See exactly which papers were included.

Simply include in your search - ask Copilot to adopt a style, draft content, format, create lists, and more. Read a referenced topic synthesis.

Paper-level Insights

We extract key insights and answers. Locate the most helpful papers and digest their insights faster.

Search Filters

Filter by sample size, study design, methodology, if the paper is open access, a human or animal study (and many more filters).

Quality Indicators

Focus on the best papers - intuitive labels for citations, journal quality, and study type.

Study Snapshot

Our Study Snapshot quickly shows key information like Population, Sample size, Methods, etc. - all within the results page.

How we created the ultimate search engine for science

Search: Find the best papers

Purpose-built academic & vector search functionality. Consensus utilizes important factors like study design, sample size, population details, and more to rank the best research higher.

Dedicated research LLMs

Our proprietary LLMs read research like an expert - we also leverage the best-in-class models from OpenAI. Consensus generates AI insights at both the search and paper level.

• Interesting
• Scholarships
• UGC-CARE Journals

10 Best Apps for PhD Students | 2024

Best Mobile Applications for Researchers

There are millions of mobile applications available in the Android play store, but still, there are very few useful apps only available for researchers. We analyzed more than 50 applications and identified the 10 most appropriate applications for researchers based on daily activities like research collaboration, data collection, document reading, updating real-world issues related to their domain, resource gathering, daily schedule maintenance, and journal reading. This post gives an explanation to use the top 10 Useful Mobile Apps software 2024 for PhD Scholars that are really the best apps for PhD Students.

Research is formalized curiosity. It is poking and prying with a purpose   – Zora Neale Hurston

10 Best Android Apps for PhD Students

Here are 10 of the best Android apps that can be extremely helpful for graduate students in their academic pursuits. I’ll also provide some insights on how these apps can benefit you.

• These apps are essential for creating, editing, and presenting documents, spreadsheets, and presentations.
• Store and sync your documents, making them accessible from any device. Collaboration features are a plus for group projects.
• A reference manager and academic social network. It helps you organize your research, collaborate with peers, and discover the latest research.
• An excellent note-taking app that allows you to capture and organize ideas, research notes, and web clippings.
• Another robust reference manager that simplifies the process of collecting, organizing, and citing research materials.
• Enhance your writing by checking grammar, spelling, and style. It’s an indispensable tool for proofreading.
• Manage your projects and tasks effectively. Great for keeping track of deadlines and staying organized.
• Boost your productivity by using the Pomodoro technique. Stay focused and limit distractions during study sessions.
• Access a wide range of educational content in various subjects, making it a great resource for learning beyond your coursework.
• Turn your smartphone into a portable scanner. Scan and digitize documents, notes, and books for easy reference.

10 Best iOS Apps for PhD Graduate Students

Here are 10 iOS apps that can be incredibly helpful for graduate students:

• An excellent note-taking app that allows you to write, draw, and annotate PDFs.
• A reference management tool that helps you organize and cite your research materials.
• A writing assistant can help you improve the clarity and correctness of your writing.
• Another powerful reference manager with a mobile app for on-the-go access to your research library.
• A versatile note-taking app that syncs across devices and allows you to organize your thoughts and research.
• If you struggle with staying focused, this app helps you stay on track by growing virtual trees when you avoid your phone.
• A comprehensive digital notebook that’s great for organizing your research notes.
• Access a vast database of scholarly articles and research papers.
• A project management app that can help you organize your tasks and deadlines.
• If your research involves multiple languages, this app can help you learn and practice them.

These apps cover a range of functions, from note-taking and reference management to productivity and language learning. They can be valuable tools for graduate students in various disciplines.

Best Apps for PhD Students

1. mendeley.

What is the Mendeley App?

Mendeley is a free reference manager and PDF reader crafted especially for researchers. It helps you to organize your literature in a proper manner so you could effectively use it during your thesis writing or dissertation. Mendeley is available in both mobile and desktop applications, It will help you to read your collected articles wherever you go.  

How to Use it for Research?

• Create a free account at www.mendeley.com and download a desktop version in your system.
• Add all your Journals , Magazines, Book Chapters, Thesis , and other materials in an organized manner.
• Segregate the files based on year, author, publication, and domain using TAG option.
• Download the Mendeley mobile application and log in with the same account.
• Allow the application to synchronize with your account.
• Now you can see all the files that you saved on your desktop that will appear in the mobile application.

Organizing Resources is the first step towards the solution

2. Flipboard

What is Flipboard App?

Flipboard is the most popular mobile application used by millions of people every day, It helps you to keep yourself updated with your domain-related information and also read the daily news, blog posts, and trending topics all together in one place. Flipboard allows the user to collect posts based on their passion and interest. The powerful and smooth user interface gives a pleasant feel to reading more articles.  

• Download the Flipboard mobile application and create your account.
• Flipboard will give a virtual tour to learn the basic features of the application.
• Click  “WHAT’S YOUR PASSION?” Next to the Flipboard icon.
• Search for your domain. Ex: Machine Learning.
• It will show many sub-categories inside the topic. Based on your requirement personalize the magazine. That’s it!
• Keep up with the news related to your domain every day with The Daily Edition.

Don’t be outdated, Because you are working for the future!

3. Researcher

What is a Researcher App?

Researcher is a free journal-finding mobile application that helps you to read new journal papers every day that are relevant to your research. It is the most popular mobile application used by more than 3 million scientists and researchers to keep themselves updated with the latest academic literature.

• Download Researhcer mobile application on your mobile.
• Open and search for your required journal in the search bar.
• You can follow any number of journals and you can read new articles as well.

4. Plagiarism Checker

What is Plagiarism Checker App?

Plagiarism Checker is a free application that works with Plagiarisma.net. It will detect duplicate content in your papers from more than five popular search engines like Google, Yahoo, Babylon, Google Scholar, and Google Books.

• Download Plagiarism Checker mobile application.
• No need for registration so you can directly go into the app.
• Scan your essay, article, term paper, or dissertation with a single tap of your smartphone.
• It also has voice support and direct URL support.
• Save results to HTML, PDF, EPUB. That’s it!

5. Google Keep

What is Google Keep App?

Google Keep is a free remainder application from Google LLC, It helps researchers quickly capture what’s on their mind and get a reminder later at the right place or time. 

• Download Google Keep mobile application.
• Log in with your Google account.
• Record a voice memo and Keep will transcribe it so you can find it later.
• Take quick notes.
• Schedule your daily plan and enable the remainder.
• Take handwritten notes.
• Capture important results using the camera and save them.
• Access it when you working on a desktop or laptop as well.

6. Curiosity

What is Curiosity App?

Curiosity is a very popular mobile application from curiosity.com, it helps researchers to learn new scientific inventions in a more detailed way with videos.

How to Use it for Research? 

• Download the Curiosity mobile application.
• Create an account to access the app.
• Read inspiring articles from your topics.
• Watch smart videos from millions of creators.
• Personalize your favorites, then follow your favorite categories. 

7. Paperity

What is Paperity App?

Paperity is a free mobile application for researchers from www.paperity.org. It helps you to aggregate various open-access journals through your mobile.

• Download Paperity mobile application.
• Browse the most recent articles.
• Browse journals and their Tables of Contents of Open Access papers.
•  Search papers and their full text for specific keywords, phrases, authors, publication dates, languages, and countries of origin. 
• View and read full-text PDFs of more than one million articles. All for FREE!

What is Slack App?

Are you doing Collaborative Research? Then Try this useful mobile application Slack it brings team communication and collaboration into one place so you can get more work done, whether you belong to a small lab or doing collaborative research with researchers from another part of the world. Make effective conversations on different topics using #TAG. 

• Download Slack mobile application.
• Include your lab members using their Email-ID.
• Communicate with your lab mates and organize your conversations by topics, projects, or anything else that matters to your work.
• Message or call any person or group within your team.
• Integrate into your workflow, the tools, and services you already use including Google Drive, Salesforce, Dropbox, Asana, Twitter, and Zendesk.
• Customize your notifications so you stay focused on what matters.

What is Dropbox?

Dropbox is a free another free mobile application for researchers to make creative collaboration space, store all files together in one central place, and safely sync them across all your devices.

• Download Dropbox  mobile application.
• Create a Dropbox account and choose a free version. (Limited space only but more than enough for document files).
•  Work on files with others through shared folders.
• Use the document scanner to turn receipts, whiteboards, and notes into PDFs.
• Comment on files to share feedback with your team
• Sync, share, and edit Word, Excel, and PowerPoint files.

10. Camscanner

What is Camescanner App?

Camescanner is a mobile document scanning and sharing application, It helps you to scan, store, sync and collaborate on various contents across smartphones, iPads, tablets and computers.

• Download the Camscanner application and Create an account.
• Use your mobile camera to scan and digitize all kinds of paper documents.
• Using smart cropping and auto-enhancing ensures the texts and graphics in scanned documents.
• Easily share documents in PDF or JPEG format with friends in various ways.
• Instantly print out any documents in CamScanner with a nearby printer via AirPrint.

Hope, this article would help you to know about the 10 Best Apps for PhD Students.

You Might Also Love

10 Simple Online Drawing Tools for Effective Thesis Diagrams

How to Download Free Mendeley Datasets?

10 Free Plagiarism Checker Tool for Thesis

Video: 10 Types of Plagiarism

Watch this video for more detailed information. Click the Logo to Subscribe

• Apps for PhD Scholars
• apps for research students
• apps for researchers
• apps for thesis writing
• best apps for graduate students
• best apps for researchers
• Free Mobile Applications
• Google Scholar
• graduate students
• Microsoft OneNote
• Mobile Applications
• productivity apps
• Research Tools
• Researchers

List of Open Access SCI Journals in Computer Science

24 best online plagiarism checker free – 2024, 5 free data analysis and graph plotting software for thesis.

[…] 10 Useful Apps for PhD Scholars […]

[…] Earth Institute Postdoctoral Research program provides innovative postdoctoral scholars with the opportunity to build a foundation in one of the […]

[…] tool also available as mobile applications in Android and iOS […]

[…] 10 Useful Apps for PhD Scholars | 2020 […]

Hello, I loved this article! All of your resources listed here are spot on, thank you for taking the time to write this. I sent you an email about a tool we created by (a team of researchers) that is free for PHD and graduate students to use. We started this no-code app to help our own academic research students and faculty, and would love to help more people. Thanks again! – Pathverse Team

Mendeley App was removed in 2021 – the mobile web version is sadly close to unusable. If anybody finds a proper workaround or a usable third party app, please include that in the article.

LEAVE A REPLY Cancel reply

Most popular, scopus indexed journals list 2024, the hrd scheme india 2024-25, 6 best online chemical drawing software 2024, imu-simons research fellowship program (2024-2027), india science and research fellowship (isrf) 2024-25, how to write a research paper in a month, example of abstract for research paper – tips and dos and donts, best for you, what is phd, popular posts, how to check scopus indexed journals 2024, popular category.

• POSTDOC 317
• Interesting 258
• Journals 234
• Fellowship 130
• Research Methodology 102
• All Scopus Indexed Journals 92

Mail Subscription

iLovePhD is a research education website to know updated research-related information. It helps researchers to find top journals for publishing research articles and get an easy manual for research tools. The main aim of this website is to help Ph.D. scholars who are working in various domains to get more valuable ideas to carry out their research. Learn the current groundbreaking research activities around the world, love the process of getting a Ph.D.

Contact us: [email protected]

Google News

Copyright © 2024 iLovePhD. All rights reserved

• Artificial intelligence

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

• Published: 14 September 2020
• Volume 26 , pages 285–303, ( 2021 )

Cite this article

• Iqbal H. Sarker   ORCID: orcid.org/0000-0003-1740-5517 1 , 2 ,
• Mohammed Moshiul Hoque 2 ,
• Md. Kafil Uddin 1 &
• Tawfeeq Alsanoosy 3  

24k Accesses

72 Citations

13 Altmetric

Explore all metrics

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “ mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Similar content being viewed by others

Machine learning and deep learning

A survey on large language model based autonomous agents

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Avoid common mistakes on your manuscript.

1 Introduction

Due to the recent development of science and technology in the world, the smartphone industry has made exponential growth in the mobile phone application market [ 1 ]. These devices are well known as one of the most important Internet-of-Things (IoT) devices as well, according to their diverse capabilities including data storage and processing [ 2 ]. Today’s smartphone is also considered as “a next-generation, multi-functional cell phone that facilitates data processing as well as enhanced wireless connectivity”, i.e., a combination of “a powerful cell phone” and a “wireless-enabled PDA” [ 3 ]. In our earlier paper [ 4 ], we have shown that users’ interest on “Mobile Phones” is more and more than other platforms like “Desktop Computer” , “Laptop Computer” or “Tablet Computer” for the last five years from 2014 to 2019 according to Google Trends data [ 5 ], shown in Fig.  1 .

Users’ interest trends over time where x-axis represents the timestamp information and y-axis represents the popularity score in a range of 0 (min) to 100 (max)

In the real world, people use smartphones not only for voice communication between individuals but also for various activities with different mobile apps like e-mailing, instant messaging, online shopping, Internet browsing, entertainment, social media like Facebook, Linkedin, Twitter, or various IoT services like smart cities, health or transport services, etc. [ 2 , 6 ]. Smartphone applications differ from desktop applications due to their execution environment [ 7 ]. A desktop computer application is typically designed for a static execution environment, either in-office or home, or other static locations. However, this static precondition is generally not applicable to mobile services or systems. The reason is that the world around an application is changing frequently and computing is moving toward pervasive and ubiquitous environments [ 7 ]. Thus, mobile applications should adapt to the changing environment according to the contexts and behave accordingly, which is known as context-awareness [ 8 ].

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. AI can be applied to various types of mobile data such as structured, semi-structured, and unstructured [ 9 ]. Popular AI techniques include machine learning (ML) and deep learning (DL) methods, natural language processing (NLP), as well as knowledge representation and expert systems (ES), can be used according to their data characteristics, in order to make the target mobile applications intelligent. AI-based models and their usage in practice can be seen in many intelligent mobile applications, such as personalized recommendation, virtual assistant, mobile business, healthcare services, and even the corona-virus COVID-19 pandemic management in recent days. A brief discussion of these apps and their relation with AI techniques within the area of mobile data science has been conducted in Section 6. This made a paradigm shift to context-aware intelligent computing , powered by the increasing availability of contextual smartphone data and the rapid progress of data analytics techniques. The intelligent smartphone applications and corresponding services are considered as “context-aware” because smartphones are able to know their users’ current contexts and situations, “adaptive” because of their dynamic changing capability depending on the users’ needs, and “intelligent” because of building the model based on data-driven artificial intelligence, which makes them able to assist the end-users intelligently according to their needs in their different day-to-day situations. Thus AI-based modeling for intelligent decision making, is the key to achieve our goal in this paper.

Based on the importance of AI in mobile apps, mentioned above, in this paper, we study on mobile data science and intelligent apps that covers how the artificial intelligence methods can be used to design and develop data-driven intelligent mobile applications for the betterment of human life in different application scenarios. Thus, the purpose of this paper is to provide a base reference for those academia and industry people who want to study and develop various AI-powered intelligent mobile apps considering these characteristics rather than traditional apps, in which we are interested.

The main contributions of this paper are listed as follows:

To provide a brief overview and concept of the mobile data science paradigm for the purpose of building data-driven intelligent apps. For this, we first briefly review the relevant methods and systems, to motivate our study in this area.

To present AI-based modeling for intelligent mobile apps where various machine learning and deep learning algorithms, the concept of natural language processing, as well as knowledge representation and rule-based expert systems, are used.

To discuss the usefulness of various AI-powered intelligent apps in several application domains, and the role of AI-based modeling in practice for the betterment of human life.

To highlight and summarize the potential research directions relevant to our study and analysis in the area of mobile data science and intelligent apps.

The rest of the paper is organized as follows. Section 2 motivates and defines the scope of our study. In Section 3, we provide a background of our study including traditional data science and context-aware mobile computing, and review the works related to data-driven mobile systems and services. We define and discuss briefly about mobile data science paradigm in Section 4. In Section 5, we present our AI-based modeling within the scope of our study. Various AI-powered intelligent apps are discussed and summarized in Section 6. In section 7, we highlight and summarize a number of research issues and potential future directions. In Section 8, we highlight some key points regarding our studies, and finally, Section 9 concludes this paper.

2 The motivation and scope of the study

In this section, our goal is to motivate the study of exploring mobile data analytics and artificial intelligence methods that work well together in data-driven intelligent modeling and mobile applications in the interconnected world, especially in the environment of today’s smartphones and Internet-of-Things (IoT), where these devices are well known as one of the most important IoT devices. Hence, we also present the scope of our study.

We are currently living in the era of Data Science (DS), Machine Learning (ML), Artificial Intelligence (AI), Internet-of-Things (IoT), and Cybersecurity, which are commonly known as the most popular latest technologies in the fourth industrial revolution (4IR) [ 10 , 11 ]. The computing devices like smartphones and corresponding applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous and context-aware smart computing is accelerating. One key challenge that remains in this emerging research domain is the ability to effectively process mobile data and enhance the behavior of any application by informing it of the surrounding contextual information such as temporal context, spatial context, social context, environmental or device-related context, etc. Typically, by context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment [ 4 , 12 ].

For AI-based modeling, several machine learning and deep learning algorithms, the concept of natural language processing, as well as knowledge representation and rule-based expert systems, can be used according to their data characteristics, in order to make the target mobile applications intelligent. For instance, machine learning (ML) algorithms typically find the insights or natural patterns in mobile phone data to make better predictions and decisions in an intelligent systems [ 13 , 14 ]. Deep learning is a part of machine learning that allows us to solve complex problems even when using a diverse data set. Natural language processing (NLP) is also an important part of AI that derives intelligence from unstructured mobile content expressed in a natural language, such as English or Bengali [ 15 ]. Another important part of AI is knowledge representation and a rule-based expert system that is also considered in our analysis. Expert system (ES) typically emulates the decision-making ability of a human expert in an intelligent system that is designed to solve complex problems by reasoning through knowledge, represented mainly as IF-THEN rules rather than conventional procedural code.

Thus, the overall performance of the AI-based mobile applications depends on the nature of the contextual data, and artificial intelligence tasks that can play a significant role to build an effective model, in which we are interested in this paper. Overall, the reasons for AI-tasks in mobile applications and systems can be summarized as below -

to empower the evolution of the mobile industry by making smartphone apps as intelligent pieces of software that can predict future outcomes and make decisions according to users’ needs.

to learn from data including user-centric, and device-centric contexts, by analyzing the data patterns.

to deliver an enhanced personalized experience while adapting quickly to changing innovations and environments.

to better utilization of available resources with higher effectiveness and efficiency.

to understand the real-world problems and to provide intelligent and automated services accordingly as well as complex problems in this mobile domain.

to enable the smartphones more secured through predictive analytics by taking into account possible threats in real-time.

To achieve our goal, in this study, we mainly explore mobile data science and intelligent apps that aims at providing an overview of how AI-based modeling by taking into account various techniques’ that can be used to design and develop intelligent mobile apps for the betterment of human life in various application domains, briefly discussed in Section 5, and Section 6.

3 Background and related work

In this section, we give an overview of the related technologies of mobile data science that include the traditional data science, as well as the computing device and Internet, and context-aware mobile computing in the scope of our study.

3.1 Data science

We are living in the age of data [ 16 ]. Thus, relevant data-oriented technologies such as data science, machine learning, artificial intelligence, advanced analytics, etc. are related to data-driven intelligent decision making in the applications. Nowadays, many researchers use the term “data science” to describe the interdisciplinary field of data collection, pre-processing, inferring, or making decisions by analyzing the data. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. According to Cao et al. [ 16 ] “data science is a new interdisciplinary field that synthesizes and builds on statistics, informatics, computing, communication, management, and sociology to study data and its environments, to transform data to insights and decisions by following a data-to-knowledge-to-wisdom thinking and methodology”. As a high-level statement, it is the study of data to provide data-driven solutions for the given problems, as known as “the science of data”.

3.2 Computing devices and internet

The advancement of mobile computing and the Internet have played a central role in the development of the current digital age. The use of the Internet with mobile devices makes it the most popular computing device, for the people in the real world.

Mobile devices have become one of the primary ways, in which people around the globe communicate with each other for various purposes. While mobile phones may come in various forms in the real world, in this paper, they refer to smartphones or mobile devices with the capability of computing and Internet access. These devices have incorporated a variety of significant and interesting features to facilitate better information access through smart computing and the proper utilization of the devices for the benefit of the users. In recent times, the smartphones are becoming more and more powerful in both computing and the data storage capacity. As such, in addition to being used as a communication device, these smart mobile phones are capable of doing a variety of things relevant to users’ daily life such as instant messaging, Internet or web browsing, e-mail, social network systems, online shopping, or various IoT services like smart cities, health or transport services [ 2 , 6 ]. The future smartphones will be more powerful than current devices, communicate more quickly, store more data, and integrate new interaction technologies.

3.3 Context-aware Mobile computing

The notion of context has been used in numerous areas, including mobile and pervasive computing, human-centered computing, and ambient intelligence [ 17 ]. In the area of mobile and pervasive computing, several early works on context-aware computing, or context-awareness referred context as the location of people and objects [ 18 ]. Moreover, locational context, or user activities [ 17 , 18 ], temporal information [ 4 , 19 ], environmental information [ 20 ], user’s identity [ 21 ], or social context [ 22 , 23 ] are taken into account as contexts for different purposes. The state of the surrounding information of the applications are also considered as contexts in [ 24 , 25 ]. In [ 26 ], Schilit et al. claim that the important aspects of context are: (i) where you are, (ii) whom you are with, and (iii) what resources are nearby. Dey et al. [ 12 ] define context, which is perhaps now the most widely accepted. According to Dey et al. [ 12 ] “Context is any information that can be used to characterize the situation of an entity. An entity is person, place, or object that is considered relevant to the interaction between a user and an application, including the user and the application themselves”. We can also define context äs a specific type of knowledge to adapt application behavior.”

Based on the contextual information defined above, context-awareness can be the spirit of pervasive computing [ 27 ]. In general, context-awareness has adapting capability in the applications with the movement of mobile phone users, and thecontext-aware computing refers to sense the surrounding physical environment, and able to adapt application behavior. Therefore, context-awareness simply represents the dynamic nature of the applications. The use of contextual information in mobile applications is thus able to reduce the amount of human effort and attention that is needed for an application to provide the services according to user’s needs or preferences, in a pervasive computing environment [ 28 ]. Different types of contexts might have a different impact on the applications that are discussed briefly in our earlier paper, Sarker et al. [ 4 , 29 ].

3.4 Mobile systems and services

Research that relies on mobile data collected from diverse sources is mostly application-specific, which differs from application-to-application. A number of research has been done on mobile systems and services considering diverse sources of data. For instance, phone call logs [ 30 , 31 ] that contain context data related to a user’s phone call activities. In addition to call-related metadata, other types of contextual information such as user location, thesocial relationship between the caller a callee identified by the individual’s unique phone contact number are also recorded by the smart mobile phones [ 31 ]. Mobile SMS Log contains all the message including the spam and non-spam text messages [ 32 ] or good content and bad content [ 33 ] with their related contextual information such as user identifier, date, time, and other SMS related metadata, which can be used in the task of automatic filtering SMS spam for different individuals in different contexts [ 31 , 32 ], or predicting good time or bad time to deliver such messages [ 33 ]. App usages log contains various contextual information such as date, time-of-the-day, battery level, profile type such as general, silent, meeting, outdoor, offline, charging state such as charging, complete, or not connected, location such as home, workplace, on the way, etc. and other apps relatedmetadata with various kinds of mobile apps [ 34 , 35 , 36 , 37 , 38 ]. The notification log contains the contextual information such as notification type, user’s various physical activity (still, walking, running, biking and in-vehicle), user location such as home, work, or other, date, time-of-the-day, user’s response with such notifications (dismiss or accept) and other notification related metadata [ 39 ]. Weblog contains the information about user mobile web navigation, web searching, e-mail, entertainment, chat, misc., news, TV, netting, travel, sport, banking, and related contextual information such as date, time-of-the-day, weekdays, weekends [ 40 , 41 , 42 ]. Game log contains the information about playing various types such games of individual mobile phone users, and related contextual information such as date, time-of-the-day, weekdays, weekends etc. [ 43 ].

The ubiquity of smart mobile phones and their computing capabilities for vairous real life purposes provide an opportunity of using these devices as a life-logging device, i.g., personal e-memories [ 44 ]. In a more technical sense, life-logs sense and store individual’s contextual information from their surrounding environment through a variety of sensors available in their smart mobile phones, which are the core components of life-logs such as user phone calls, SMS headers (no content), App use (e.g., Skype, Whatsapp, Youtube etc.), physical activities form Google play API, and related contextual information such as WiFi and Bluetooth devices in user’s proximity, geographical location, temporal information [ 44 ]. Several applications such as smart context-aware mobile communication, intelligent mobile notification management, context-aware mobile recommendation etc. are popular in the area of mobile analytics and applications. Smart context-aware mobile communication (e.g., intelligent phone call interruption management) is one of the most compelling and widely studied applications [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. For mobile notification management, several research [ 39 , 56 , 57 , 58 , 59 ] has been done. Similarly, a number of research [ 34 , 60 , 61 , 62 , 65 ] has been done on recommendation system.

Various techniques are used in various applications, such as interruption management, activity recognition, recommendation system, mobile commerce, etc. in the area of mobile analytics. For instance, Seo et al. [ 66 ] design a context-aware configuration manager for smartphones PYP. An intelligent interruption management system is proposed in [ 48 ], use decision tree for making decisions. Bozanta et al. [ 67 ], Lee et al. [ 68 ] use classification technique to build a personalized hybrid recommender system. Turner et al. [ 59 , 69 ], Fogarty et al. [ 70 ] use classification technique in their interruptibility predictionand management system. In the area of transportation, Bedogni et al. [ 71 ] use classification techniques in their context-aware mobile applications. To adopt mobile learning, Tan et al. [ 72 ] investigates using a multi-layer perceptron model. In [ 43 ], Paireekreng et al. have proposed a personalization mobile game recommendation system. Moreover, regression techniques such as Linear regression [ 9 ], support vector regression [ 73 ], and ensemble learning techniques, such as Random Forest learning [ 74 ] are popular in the area of supervised learning.

Beside the above mentioned approaches, several researchers [ 34 , 35 , 39 ] use association rules that are used to build various context-aware mobile service according to users needs. A number of research [ 40 , 98 , 99 , 100 , 101 , 102 ] have been done based on clustering approach for different purposes in their study. Moreover, a significant amount of research [ 72 , 94 , 95 , 96 , 97 ] have been done on deep learning for various purposes in the area of mobile analytics. Moreover, context engineering including principal component analysis, or context correlation analysis [ 77 , 78 ] is another important issue to work in this area. In Table 1 , we have summarized this research based on the most popular approaches and data-driven tasks within the scope of our analysis.

Although various types of mobile phone data and techniques discussed above are used in the area of mobile analytics and systems for different purposes, a comprehensive AI-based modeling for building intelligent apps is being interested, according to the needs of the current in the community. Thus, in this paper, we focus on mobile data science and corresponding intelligent apps, where the most popular AI techniques including machine learning and deep learning methods, the concept of natural language processing, as well as knowledge representation and expert systems, can be used to build intelligent mobile apps in various application domains.

4 Mobile data science paradigm

In this section, we provide a brief overview of mobile data science and its related components within the scope of our study.

4.1 Understanding Mobile data

Mobile data science and data-driven intelligent apps are largely driven by the availability of data. Mobile datasets typically represent a collection of information records that consist of several attributes or contextual features and related facts. Thus,it’s important to understand the nature of mobile data containing various types of features and contexts. The reason is that raw data collected from relevant sources for a particular application can be used to analyze the various patterns or insight, to build a data-driven model to achieve our goal. Several datasets exist in the area of mobile analytics, such as phone call logs [ 30 ], apps logs [ 34 , 35 ], weblogs [ 40 ] etc. These context-rich historical mobile phone data are the collection of the past contextual information and users’ diverse activities [ 92 , 103 ]. Moreover, IoT data, smart cities data, business data, health data, mobile security data, or various sensors data associated with the mobile devices and target application can also be used as data sources. Intelligent apps are based on the extracted insight from such kinds of relevant datasets depending on apps characteristics. In the next, we summarize several characteristics of intelligent apps.

4.2 Intelligent apps characteristics

Intelligent apps offer personalized and adaptive user experiences, where artificial intelligence, the Internet-of-Things, and data analytics are the core components. Based on this, we have summarized the characteristics of intelligent apps to assist smartphone users in their daily life activities.

Action-Oriented: The foremost characteristic of intelligent apps is that these applications do not wait for users to make decisions in various situations. Rather, the apps can study user behavior and deliver personalized and actionable results using the power of predictive analytics.

Adaptive in Nature: The apps should be adaptive in nature. Every user is different in their use, the adaptability of the app plays a very crucial role. Meaning, they can easily upgrade their knowledge as per their surroundings to produce a highly-satisfying user experience.

Suggestive and Decision-Oriented: Generating suggestions and making decisions according to users’ needs and interests, could be an interesting characteristic of an intelligent app. Such suggestions may vary from user-to-user according to their interests and helps the users to decide what suits best for them.

Data-driven: Delivering a data-driven output is also one of the key features of intelligent apps. The intelligent apps gather data from a variety of sources, such as online, user interaction, sensors, etc. relevant to the target application and extracting data patterns, thus providing better user experience.

Context-awareness: Context awareness is the ability of an application to gather information about its surrounding environment at any given time and adapt behaviors accordingly. It makes the apps much smarter use by taking into account users contexts as well as the device’s contexts to proactively deliver highly relevant information and suggestions.

Cross-Platform Operation: The app also should have the ability to understand and process the desired output in a way that the users feel the same experience while working on cross platforms.

In this study, we take into account the above-discussed characteristics of mobile apps that could be able to intelligently assist the users in their diverse daily life activities. Based on these characteristics, in the next, we briefly discuss the concept of mobile data science and AI that can help to achieve the goal.

4.3 Mobile data science and AI

Data science is transforming the world’s industries. It is critically important for the future of intelligent mobile apps and services because of “apps intelligence is all about mobile data”. Traditionally, mobile application developers didn’t use data science techniques to make the apps intelligent considering the above characteristics. Although, a number of recent research [ 4 , 29 , 34 , 38 , 48 ] has been done based on machine learning techniques to model and build mobile applications, most of existing mobile applications are static or used custom-written rules like signatures, or manually defined heuristics for their different applications [ 47 , 66 ]. The main drawback of these custom-written rules-based approaches is that the knowledge or rules used by the applications are not automatically discovered; users need to define and maintain the rules manually. In general, users may not have the time, inclination, expertise, or interest to maintain rules manually. Although these rule-based approaches have their own merits in several cases, it needs too much manual work to keep up with the changing of userscontext landscape. On the contrary, data science can make a massive shift in technology and its operations, where AI techniques including machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to learn and making intelligent decisions. Thus, data science is considered as a practical application of machine learning, a major part of AI, with a complete focus on solving real-world problems. Overall, data science is a comprehensive process that involves data collection, pre-processing, data analysis, visualization, and decision making [ 16 ], whereas AI makes use of computer algorithms that can show human intelligence.

The concept of mobile data science incorporates the methods and techniques of machine learning and AI and data science as well as the context-aware computing to build intelligent mobile apps. The combination of these technologies has given birth to the term “mobile data science”, which refers to collect a large amount of mobile data from different sources and analyze it using machine learning techniques through the discovery of useful insights or the data-driven patterns, which is primarily defined in our earlier paper [ 104 ]. It is, however, worth remembering that mobile data science is not just about a collection of AI techniques. Mobile data science is a process that can help mobile application developers or analysts to scale and automate the target apps in a smart way and in a timely manner. Thus in a broader sense, we can say that “Mobile data science is research or working area existing at the intersection of context-aware mobile computing, data science, and artificial intelligence, which is mainly data-focused associated with target mobile apps, applies AI techniques for modeling, and eventually making intelligent decisions in applications. Thus it aims to seek for optimizing solutions to build automated and intelligent mobile applications to intelligently assist the users in their various daily activities.”. Several key modules, such as data collection, data processing, context and usage analysis, and building models, are involved in mobile data science, which are discussed briefly in our earlier paper [ 104 ]. In this paper, we mainly explore on AI-based modeling and its role in mobile apps in various application domains ranging from personalized services to healthcare services, which includes machine learning (ML) and deep learning (DL) methods, the concept of natural language processing (NLP), as well as knowledge representation, and rule-based expert systems (ES).

Overall, the outputs of mobile data science are typically mobile data products, which can be a data-driven AI-based model, potential mobile service and recommendation, or the corresponding intelligent mobile apps. In Section 6, we have discussed about AI-powered intelligent mobile apps in several application domains within the area of mobile data science.

4.4 Mobile security and privacy

Although we focus on intelligent apps from the perspective of artificial intelligence within the scope of our study discussed above, mobile security and privacy could be another part related to mobile data science in terms of data-driven security solutions. In the real world, most of the people including business people use smartphones not only to communicate but also to plan and organize their various kinds of daily works and also in their private life with family and friends. In most cases, both the business or personal information are stored on smartphones and people use such information when needed [ 105 , 106 ]. Thus, in addition to intelligent apps, mobile security and privacy is also important. Smartphones collect and analyze the sensitive information to which access must be controlled to protect the privacy of the user and the intellectual property of the organization or the company. Besides, there are several threats to mobile devices, including mobile malware, botnet, denial-of-service (DoS), eavesdropping, phishing, data breaches, etc. [ 106 , 107 , 108 ]. In terms of security analytics, in our earlier paper, Sarker et al. [ 10 ], we have discussed various types of security data and the effectiveness of the data-driven cybersecurity modeling based on artificial intelligence, particularly using machine learning techniques. Thus data-driven intelligent solutions through finding security insight could be effective to detect and mitigate such kind of mobile security threats.

5 AI-based modeling for Mobile services

As discussed earlier, mobile data science is data-focused, applies various artificial intelligence methods that eventually seek for intelligent decision making in mobile applications or services. In our analysis, we divide the artificial intelligence methods into several categories, such as basic machine learning and deep learning algorithms, natural language processing, knowledge representation and expert systems, within the scope of our study. These AI-based methods potentially can be used to make intelligent decisions in apps, which are discussed briefly in the following.

5.1 Machine learning modeling with Mobile data

Machine Learning (ML) including deep neural network learning is an important part of Artificial Intelligence (AI) which can empower mobile devices to learn, explore, and envisage outcomes automatically without user interference. For instance, machine learning algorithms can do the analysis of targeted user behavior patterns utilizing phone log data to make personalized suggestions as well as recommendations for mobile phone users. Typically, a machine learning model for building intelligent apps is a collection of target app-related data from relevant diverse sources, such as phone logs, sensors, or external sources, etc. and the chosen algorithms that work on that data in order to deduce the output.

To build a model utilizing collected data, supervised learning is performed when specific target classes are defined to reach from a certain set of inputs [ 13 ]. For instance, to classify or predict the future outcome, several popular algorithms such as Navies Bayes [ 109 ], Decision Trees [ 93 , 110 , 111 ], K-nearest neighbors [ 112 ], Support vector machines [ 73 ], Adaptive boosting [ 113 ], Logistic regression [ 114 ] etc. can be used. Such classification techniques are capable to build a prediction model ranging from predicting next usage to smartphone security, e.g., predicting mobile malware attack. Several feature engineering tasks, such as feature selection, extraction, etc., or context pre-modeling [ 78 ] can make the resultant predictive model more effective. On the other hand, in unsupervised learning, data is not labeled or classified, and it investigates similarity among unlabeled data [ 9 ]. Several clustering algorithms such as K-means [ 115 ], K-medoids [ 116 ], Single linkage [ 117 ], Complete linkage [ 118 ], BOTS [ 75 ] can be used for such modeling by taking into account certain similarity measures depending on the data characteristics. For instance, considering certain similarity in users’ preferences or behavioral activities, and to generate suggestions and recommendations accordingly, these algorithms can play a role to achieve the goal. Moreover, association rule learning techniques such as AIS [ 119 ], Apriori [ 120 ], FP-Tree [ 121 ], RARM [ 122 ], Eclat [ 123 ], ABC-RuleMiner [ 29 ] can be used for building rule-based machine learning model for the mobile phone users. In addition to these basic machine learning techniques, several deep neural learning methods such as recurrent neural network, long-short term memory, convolutional neural network, multilayer perceptron, etc. that are originated from an Artificial Neural Network (ANN) can be used in the learning process [ 9 , 13 ]. In these deep learning models, several hidden layers can be included to complete the overall process.

To understand and analyze the actual phenomena with mobile data, the above-discussed machine learning and deep learning techniques are useful to build AI-based modeling, depending on the target application and corresponding data characteristics. Thus the machine learning models and corresponding mobile apps that are close to the reality, are able to make data-driven intelligent decisions in apps and can behave according to users’ needs. Overall, the machine learning models can change the future of mobile applications and industry because of its learning capability from data. Therefore, machine learning methods including deep neural networks, on a global scale, is able to make mobile platforms more user-friendly, improve users’ experiences, and aid in building intelligent applications.

5.2 Natural language processing for Mobile content

Natural Language Processing (NLP) is an important branch of artificial intelligence that typically deals with the interaction between computers and humans using the natural language. One of the ultimate goals of NLP is to derive intelligence from unstructured data or content expressed in a natural language, such as English or Bengali. As each language has a unique set of grammar and syntax, and convention, NLP techniques can make it possible for computers to read text, hear speech, interpret it, measure sentiment or to mine opinions, and eventually determine which parts are important in an intelligent system [ 124 ]. For instance, to extract sentiments associated with positive, neutral, or negative polarities for specific subjects from a text document, an NLP-based methodology can be used. Thus, NLP can play a significant role to build intelligent apps when unstructured mobile content is available, and to be an important part within the scope of our study.

In recent days, a large amount of content read on mobile devices is text-based, such as emails, web pages, comments, blogs, or documents [ 15 ]. NLP techniques particularly, text mining extracts patterns and structured information from textual content that could make the apps smarter and intelligent, in which we are interested. For instance, browsing through large amounts of textual content on a small-screen mobile device may be tedious or time-consuming. In some cases, the important information might be easily overlooked due to the small screen of the devices. Thus, document summarization based on NLP might be the potential solution to provide a summary with high quality and minimal time.

Information extraction from mobile content could be another example of NLP based modeling. It typically identifies instances of a particular class of events, entities, or relationships in a natural language text and creates a structured representation of the discovered information [ 15 ]. For instance, this can be used to automatically find all the occurrences of a specific type of entity, such as ‘business’, and gather complementary information in the form of metadata around them. In addition to information extraction, NLP techniques can also be used when needed to develop the new mobile content. For instance, response generation while replying to an email, question answering, e.g., a company might need a mobile app that can answer questions about various products or services. Similarly, medical information extraction, personalized recommendation system through comments or text mining, context-aware chatbot, etc. are also included within the area. Thus, NLP techniques can play a significant role to build AI-based modeling depending on the target application and corresponding data type and characteristics.

5.3 Domain knowledge representation and Mobile expert system modeling

Due to the diversity of mobile users, contexts, increasing information, and variations in mobile computing platforms, mobile applications today are facing the challenges to provide the expected services. In artificial intelligence (AI), knowledge representation and expert system modeling is considered as another important part to minimize this issue, and to build knowledge-base intelligent systems.

5.3.1 Knowledge representation

In the real world, knowledge is considered as the information about a particular domain. It is typically a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning to solve problems. Thus, the main purpose of knowledge representation is modeling the intelligent behavior of an agent. It allows a machine to learn from that knowledge and behave intelligently like a human being. Instead of trying to understand from the bottom-up learning, its main goal is to understand the problems from the top-down, and to focus on what an associated agent needs to know in order to behave intelligently. Knowledge can be of several types:

Declarative Knowledge: known as descriptive knowledge that represents to know about something, which includes concepts, facts, and objects, and expressed in a declarative sentence.

Structural Knowledge: represents the basic knowledge to solve problems which describes the relationship between concepts and objects.

Procedural Knowledge: known as imperative knowledge that is responsible for knowing how to do something which includes rules, strategies, procedures, etc.

Meta Knowledge: represents knowledge about other types of knowledge.

Heuristic Knowledge: represents knowledge of some experts in a field or subject that could be based on previous experiences.

To represent knowledge in Artificial Intelligence (AI), “Ontology” in general has become popular as a paradigm by providing a methodology for easier development of interoperable and reusable knowledge bases (KB). Ontologies can be used to capture, represent knowledge and describe concepts and the relationship that holds between those concepts. In general, ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. According to [ 125 ], formally, an ontology is represented as “{ O  =  C ,  R ,  I ,  H ,  A }, where { C  =  C 1 ,  C 2 , …,  C n } represents a set of concepts, and { R  =  R 1 ,  R 2 , …,  R m } represents a set of relations defined over the concepts. I represents a set of instances of concepts, and H represents a Directed AcyclicGraph (DAG) defined by the subsumption relation between concepts, and A represents a set of axioms bringing additional constraints on the ontology”. Let’s consider an inference rules in ontologies for deductive reasoning. A rule may exist which states “If a mobile user accepts phone calls from family at work and a phone call is from his mother, then the call has been answered.” Then a program could deduce from a social relationship ontology that the user answers her mother’s incoming call at work. Thus, particular domain ontologies can help for building an effective semantic mobile application. Moreover, ontologies capturing complex dependencies between concepts for a particular problem domain provide a flexible and expressive tool for modeling high-level concepts and relations among the given attributes, which allows both the system and the user to operate the same taxonomy and play an important role to build an expert system [ 125 ].

5.3.2 Mobile expert system modeling

A mobile expert system is an example of a knowledge-based system, which is broadly divided into two subsystems, such as the inference engine and the knowledge base, shown in Fig.  2 . The knowledge base typically represents facts and rules, while the inference engine applies the rules to the known facts to deduce new facts. The knowledge-base module is the core of this expert system as it consists of knowledge of the target mobile application domain as well as operational knowledge of apps’ decision rules. The user interface accepts the original facts and invokes the inference engine to activate the decision rules in the knowledge base. The system uses expert knowledge represented mainly as IF-THEN rules, to offer recommendations or making decisions in relevant application areas. For instance, by using the expert system model, the process of selecting the semantic outcome for mobile users becomes more appropriate according to expert recommendations. A rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).

A structure of a mobile expert system modeling

The basic syntax of a rule is:

IF < antecedent > THEN < consequent  > .

Such an IF-THEN rule-based expert system model can have the decision-making ability of a human expert in an intelligent system that is designed to solve complex problems as well through knowledge reasoning. To develop the knowledge base module, an ontology-based knowledge representation platform discussed earlier can play a major role to generate the conceptual rules. To provide a continuous supply of knowledge to a rule-based expert system, data mining, and machine learning techniques can be used. For instance, in our earlier approach “ABC-RuleMiner”, Sarker et al. [ 29 ], we have discovered a set of useful contextual rules for mobile phone users considering their behavioral patterns in the data. Domain experts having knowledge of business rules can then update and manage the rules according to the needs. Thus, the mobile expert systems can be used to make intelligent decisions in corresponding mobile applications.

6 AI-powered intelligent Mobile apps

An intelligent system typically tells what to do or what to conclude in different situations [ 126 ] and can act as an intelligent software agent. Thus, intelligent mobile apps are those applications that use AI-based modeling discussed above, in order to make intelligent decisions and to provide useful suggestions and recommendations. Based on this, the target mobile applications for various daily life services are outlined in the following subsections ranging from personalized to community services.

6.1 Personalized Mobile user experience

In the real world, people want their experience to be absolutely personalized these days. Thus, most of the mobile apps heavily rely on personalization to keep users engaged and interested. Users also now expect the applications to deliver unique experiences that may vary from user-to-user according to their own preferences. Thus understanding “user persona” is the key to creating personalized mobile applications that are based on users’ past experiences represented by users’ historical data. ML-based models can effectively discover useful insight from individuals’ phone data by taking into account users own behavioral activities, interactions, or preferences, and can be used to perform individual personalized services in various applications. For instance, an intelligent phone call interruption management system can be a real-life application based on the discovered rules, which handles the incoming phone calls automatically according to the behavior of an individual user [ 29 ]. Moreover, mobile notification management [ 58 , 59 ], apps usage prediction and management, etc. can be the real-life examples of personalized services for the end mobile phone users. Thus, the extracted insight from relevant contextual historical and real-time interaction data using ML-based models can be used to deliver rich and personalized experiences to the users in various day-to-day situations in their daily life activities. Similarly, a knowledge-based mobile expert system considering a set of context-aware IF-THEN rules, can also help to provide personalized services for individual users.

6.2 Mobile recommendation

Recommender systems are typically developed to overcome the problem of information overload by aiding users in the search for relevant information and helping them identify which items (e.g., media, product, or service) are worth viewing in detail. This task is also known as information filtering. According to [ 127 ], the most important feature of a recommender system is its ability to “guess” a user’s preferences and interests by analyzing the behavior of the user and/or the behavior of other users to generate personalized recommendations. In general, the traditional recommender systems mainly focus on recommending the most relevant items to users among a huge number of items [ 128 ]. However, mobile recommendation systems based on users’ contextual information such as temporal, spatial, or social etc. could be more interesting for the users [ 62 , 63 , 64 ]. The advanced mobile apps powered by predictive intelligent capabilities using ML-based models make recommend engines smart enough to analyze the user content preferences and cater to the appropriate content that the user is looking for. For instance, a mobile system generating shopping recommendations helps the user to find the most satisfying product by reducing search effort and information overload. Similarly, tourist guides [ 129 ], food or restaurant services [ 130 ], finding cheaper flights, accommodation, attractions, or leisure dissemination, etc. can be other real-life examples for the mobile phone users. Moreover, an NLP-based methodology can be a way to retrieve the best recommendation service based on public comments.

6.3 Mobile virtual assistance

An intelligent virtual assistant is also known as an intelligent personal assistant that is typically a software agent to perform tasks or services for an individual based on queries like commands or questions. The chatbot is sometimes used to refer to virtual assistants, which is a software application used to conduct an online chat conversation via text or text-to-speech. Several key advantages make the chatbots beneficial these days as they are able to provide 24*7 automated support, able to provide instant answers, good in handling customers or users, avoiding repetitive work, as well as save time and service cost. Intelligent mobile apps powered by AI are able to provide such services with higher accuracy. AI-based models including NLP and ML can be used to build such applications. Moreover, people are now typically spending more time on different messaging apps that are the platforms of communication and bots will be how their users access all sorts of services. Thus, chatbots can engage by answering basic questions in various services. For instance, online ordering, product suggestions, customer support, personal finance assistance, searching, and flight tracking, finding a restaurant, etc. A knowledge-based mobile expert system considering a set of IF-THEN rules, can also be applied to provide such service. Thus, different virtual assistant apps like voice assistants or chatbots offer interactive experiences to users, who are able to retrieve the necessary information effectively and efficiently according to their needs.

6.4 Internet of things (IoT) and smart cities

The Internet of Things (IoT) is typically a network of physical devices, and objects which utilize sensors, software, etc. for sending and receiving data. Smart cities use IoT devices as well to collect and analyze data, and become the most extensive application domain these days. In general, the smart city development is considered as a new way of thinking among cities, businesses, citizens, academia, industry people or others, who are the key stakeholders. As today’s smartphones are considered as one of the most important IoT devices [ 2 ], integrating mobile apps with IoT developments can dramatically improve the quality of human life. AI-based modeling in apps can provide relevant intelligent services in this domain, as well as can bring technology, government, and different layers of society together for the betterment of human life. For instance, machine learning-based modeling utilizing sensor data collected from parking places, or traffic signals, can be used for a better city planning for the governments. Similarly, a knowledge-based mobile expert system considering a set of IF-THEN rules, can help to make context-aware and timely decisions. Overall, AI-based modeling can assist the users in our most common daily life issues, such as questions, suggestions, general feedback, and reporting in various smart city services including smart governance, smart home, education, communication, transportation, retail, agriculture, health care, enterprise and many more.

6.5 Mobile business

Smart mobile apps have the potential to increase the operational excellence in the business-to-business as well as business-to-customer sectors. The new availability and advancement of AI and machine learning are causing a revolutionary shift in business and is considered as the new digital frontier for enterprises. Since, almost every organization deal with customer service, the businesses people think about intelligent interactions within mobile applications these days according to consumer demands. Businesses can leverage the data that are collected from various sources such as point-of-sale machines, online traffic, mobile devices, etc. to analyze and strategically improve the user experience. AI techniques can find trends from data and adjust the apps themselves to create more meaningful and context-rich opportunities to engage users. For instance, machine learning algorithms are capable to understand the customer behavior, interests, and provide them with more relevant product recommendations based on purchase history, fraud identification with credit cards, and visual search. By taking into account context-awareness, it can also empower businesses with prominent features, such as delivering precise location-based suggestions. Moreover, an NLP-based methodology of sentiment evaluation such as positive, neutral, or negetive sentiment (also known as opinion mining) on business data, e.g., review comments, can retrieve the best and perfect suggestions and product recommendations in terms of quality and quantity for the customers. AI positively impacts customer behavior by incorporating the chatbots as well in a mobile application, which may reduce the repetitive tasks and optimize manpower utilization. Similarly, knowledge-based mobile expert system considering a set of business IF-THEN rules, can make intelligent decisions. Overall, AI mobile applications in the business domain help in expanding businesses, introducing new products or services, identifying customer interests, and maintaining a prominent position in the global market.

6.6 Mobile healthcare and medicine

Intelligent mobile healthcare applications are bringing better opportunities for both the patients, medical practitioners, or related organizations through simplifying their physical interactions. These apps can provide opportunities to several health-related services such as medical diagnosis, medicine recommendation including e-prescription, suggesting primary precautions, remote health monitoring, or effectively patient management in the hospital. For assessing and strengthening health facilities, or building health management information systems (HMIS), various kinds of health data can be collecting from multiple sources on a wide variety of health topics to analyze [ 131 ]. With the help of AI methods including ML-based models, intelligent health services can be provided. Thus it may reduce the expense and time of the patients and clinics, as they offer customized medicines and drugs as well as give preventive measures through continuous information accumulation. Moreover, AI-powered mobile applications could also be applicable to find the best nearest doctor, to book a consultation, to keep reminders of medication, getting a basic knowledge of each medication, and more. Mobile healthcare app is also able to help doctors with remaining updates with real-time status of consultations, assigning duties to staff, ensuring the availability of equipment, maintaining a proper temperature for medicines, and more. In addition, the healthcare virtual assistant services like chatbots can be used to provide basic healthcare service as well, as these online programs can assist patients in many ways, such as scheduling appointments, answering common questions, aiding in the payment process, and even providing basic virtual diagnostics. Overall, AI-modeling based mobile healthcare services may create a new endeavor for all citizens in a country including the rural people of low-income countries.

6.7 The novel coronavirus COVID-19

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus [ 131 ]. COVID-19 apps typically are known as the mobile software applications that use digital contact tracing in response to the COVID-19 pandemic, i.e. the process of identifying persons (“contacts”) who may have been in contact with an infected person. According to the World Health Organization (WHO) [ 131 ], most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Thus, in order to keep this infectious disease in control, “contact tracing” is an important factor. Smartphone apps are playing a big role in the response to the COVID-19 pandemic. These apps are being used to track infected people, social distancing, detecting COVID-19 symptoms, self-quarantine guidelines, the latest communication to the citizens, and ease the burden on healthcare staff. Thus, mobile apps are considered as an effective control strategy against the spread of COVID-19 or similar future pandemics, considering the patient and social sensing data. An intelligent framework and mobile application design will not only strengthen the fight against ongoing COVID-19 challenges based on the collected data by mobile phones, but also against similar disasters in a post-COVID world.

In addition to these application areas, AI-based models in mobile applications can also be applicable to several other domains, such as financial, manufacturing, smart robotics, security and privacy, and many more. Thus, the impact of AI-models in mobile app development and user experience is significant in these days and can be considered as next-generation mobile learning.

7 Research issues and future directions

With the rapid development of smartphones, Internet-of-Things (IoT), and AI technologies, the most fundamental challenge is to explore the relevant data collected from diverse sources and to extract useful insights for future actions. Thus, in this section, we highlight and analyze the main challenges and research issues in the scope of our study. In the following, the issues that we identified and corresponding future directions are discussed briefly.

According to our study in this paper, source datasets are the primary component to work in the area of mobile data science. Thus, collecting real-world data such as categorical, numerical, or textual relevant to a particular application is the first step for building an intelligent smartphone apps, which may vary from service to service. For instance, to manage mobile interruptions, the relevant contextual information and an individual’s behavioral data is needed to be analyzed [ 4 ]. Similarly, for smart healthcare services, patient data and corresponding contextual information might be useful. Thus, to facilitate the extraction of reliable insight from the data using AI techniques and to use the knowledge in context-aware applications, integrating and effective management of mobile data is important. The reason is that AI methods particularly machine learning techniques highly impact on data [ 9 ]. Therefore, establishing a large number of recent datasets from diverse sources and to integrate and manage such information for effective data analysis is needed, which could be one of the major challenges to work in the area of mobile data science and data-driven intelligent applications.

The next challenge is an effective modeling of mobile users and their activities from the relevant data. The main goal of mobile user modeling is the customization and adaptation of systems to the user’s specific needs. The system needs to output the ‘right’ outcome at the ‘right’ time or contexts in the ‘right’ way [ 4 ]. Thus, several aspects such as context-dependency, individual user behavior, and their preferences in different contexts are needed to take into account for an effective user modeling and to build corresponding intelligent apps. The reason is that usage patterns of mobile phones vary greatly between individuals behaving differently in different contexts. Thus considering various contexts, such as temporal, spatial, social, etc. and their effective modeling based on these contexts are important to build an intelligent app [ 93 ]. For this purpose, data preparation, discretization of contexts, and discovery of useful insights are the key issues [ 4 ]. Moreover, the concept of RecencyMiner [ 76 ] can be more effective because of considering the recent pattern-based insights. Therefore, effectively modeling mobile users considering these aspects, could be another research issue in the area of mobile data science and intelligent applications.

The context-sensitive features in mobile data and their patterns are of high interest to be discovered and analyzed to make context-aware intelligent decisions for a particular application in a pervasive computing environment. The traditional analytical techniques including data science and machine learning may not be applicable to make real-time decisions for analyzing smartphone data, because of a large number of data processing that may reduce the performance of mobile phones. For instance, the association rule mining technique [ 120 ] may discover a large number of redundant rules that become useless and make the decision-making process complex and ineffective [ 29 ]. Such traditional techniques may not be applicable for analyzing smartphone data. Thus, a deeper understanding is necessary on the strengths and weaknesses of state-of-the-art big data processing and analytics systems to realize large-scale context-awareness and to build a smart context-aware model. Therefore effectively building a data-driven context-aware model for intelligent decision-making on smartphones, could be another research issue in the area of mobile data science and intelligent applications.

Real-life mobile phone datasets may contain many features or high-dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the outcome of the resultant AI-based model [ 77 ]. The reason is that the performance of AI methods particularly machine learning algorithms heavily depends on the choice of features or data representation. Having irrelevant features or contextual information in the data makes the model learn based on irrelevant features that consequently decrease the accuracy of the models [ 132 ]. Thus the challenge is to effectively select the relevant and important features or extracting new features that are known as feature optimization. In the area of AI, particularly data science and machine learning, feature optimization problem is considered as an important pre-processing step that helps to build an effective and simplified model and consequently improves the performance of the learning algorithms by removing the redundant and irrelevant features [ 111 ]. Therefore, feature optimization could be a significant research issue in the area of mobile data science and intelligent applications.

The next challenge is the extraction of the relevant and accurate information from the unstructured or semi-structured data on mobile phones. A large amount of content such as emails, web pages, or documents is read on these devices frequently that is text-based [ 15 ]. Thus the problem of information overload arises due to the small screen of the devices rather than the desktop computer. Therefore effectively mining the contents or texts considering these aspects, could be another research issue in the area of mobile data science and intelligent applications. Natural language processing (NLP) techniques can help to make such text-based apps smarter, by automatically analyzing the meaning of content and taking appropriate actions on behalf of their users. Due to the devices’ limited input and processing capabilities rather than desktop computers, it is then needed to develop novel approaches that can bring NLP power to smartphones. Several NLP tasks such as automatic summarization, information extraction, or new content development, etc. could be useful to minimize the issue.

Mobile expert system uses expert knowledge represented mainly as IF-THEN rules, to offer recommendations or making decisions in relevant application areas. However, the development of large-scale rule-based systems may face numerous challenges. For instance, the reasoning process can be very complex, and designing of such systems becomes hard to manage [ 133 ]. There is still a lack of lightweight rule-based inference engines that will allow for reasoning on mobile devices [ 133 ]. Thus a set of concise and effective rules will be beneficial in terms of outcome and simplicity for such a rule-based expert system for mobile devices. Moreover, ontologies [ 125 ] capturing complex dependencies between concepts for a particular problem domain provides a flexible and expressive tool for modeling high-level concepts and relations among the given attributes, which allows both the system and the user to operate the same taxonomy and play an important role to build an expert system. This is where the ontological modeling and reasoning is useful. Thus, an effective design of ontology, or knowledge representation model for the respective problem domain could be another research issue.

The mobility of computing devices, e.g., smartphones, applications, and users leads to highly dynamic computing environments. Unlike desktop applications, which rely on a carefully configured, and largely static set of resources, pervasive computing applications are subjected to changes in available resources such as network connectivity, user contexts, etc. Moreover, they are frequently required to cooperate spontaneously and opportunistically with previous unknown software services to accomplish tasks on behalf of users. Thus, pervasive computing software must be highly adaptive and flexible. As an example, an application may need to modify it’s style of output following a transition from an office environment to a moving vehicle, to be less intrusive [ 4 ]. Thus to effectively adapt to the changing environment according to users’ needs is important, which is important in the area of mobile data science and intelligent applications. Context-awareness represents the ability of mobile devices to sense their physical environment and adapt their behavior accordingly, incorporating this property in the applications could be a potential solution to overcome this issue.

8 Discussion

Although several research efforts have been directed towards intelligent mobile apps, discussed throughout the paper, this paper presents a comprehensive view of mobile data science and intelligent apps in terms of concepts and AI-based modeling. For this, we have conducted a literature review to understand the contexts, mobile data, context-aware computing, data science, intelligent apps characteristics, and different types of mobile systems and services, as well as the used techniques, related to mobile applications. Based on our discussion on existing work, several research issues related to mobile datasets, user modeling, intelligent decision making, feature optimization, mobile text mining based on NLP, mobile expert system, and context-aware adaptation, etc. are identified that require further research attention in the domain of mobile data science and intelligent apps.

The scope of mobile data science is broad. Several data-driven tasks, such as personalized user experience, mobile recommendations, virtual assistant, mobile business, and even mobile healthcare system including the COVID-19 smartphone app, etc. can be considered as the scope of mobile data science. Traditionally mobile app development mostly focused on knowledge that is not automatically discovered [ 47 , 66 ]. Taking the advantage of large amounts of data with rich information, AI is expected to help with studying much more complicated yet much closer to real-life applications, which then leads to better decision making in relevant applications. Considering the volume of collected data and the features, one can decide whether the standalone or cloud-based application is more suitable to provide the target service. Thus, the output of AI-based modeling can be used in many application areas such as mobile analytics, context-aware computing, pervasive computing, health analytics, smart cities, as well as the Internet of things (IoT). Moreover, intelligent data-driven solutions could also be effective in AI-based mobile security and privacy, where AI works with huge volumes of security event data to extract the useful insights using machine learning techniques [ 10 ].

Although the intelligent apps discussed in this paper can play a significant role in the betterment of human life in different directions, several dependencies may pose additional challenges, such as the availability of network and the data transfer speeds or the battery life of mobile devices. Moreover, privacy and security issues may become another challenge while considering the data collection and processing over the cloud or within the device. Taking the advantages of these issues considering the application type and target goal, we believe this analysis and guidelines will be helpful for both the researchers and application developers to work in the area of mobile data science and intelligent apps.

9 Conclusion

In this paper, we have studied on mobile data science and reviewed the motivation of using AI in mobile apps to make it intelligent. We aimed to provide an overview of how artificial intelligence can be used to design and develop data-driven intelligent mobile applications for the betterment of human life. For this, we have presented an AI-based modeling that includes machine learning and deep learning methods, the concept of natural language processing, as well as knowledge representation and expert systems. Such AI-based modeling can be used to build intelligent mobile applications ranging from personalized recommendations to healthcare services including COVID-19 pandemic management, that are discussed briefly in this paper. A successful intelligent mobile system must possess the relevant AI-based modeling depending on the data characteristics. The sophisticated algorithms then need to be trained through collected data and knowledge related to the target application before the system can assist the users with suggestions and decision making. We have concluded with a discussion about various research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps, that can help the researchers to do future research in the identified directions.

Peng M, Zeng G, Sun Z, Huang J, Wang H, Tian G (2018) Personalized app recommendation based on app permissions. World Wide Web 21(1):89–104

Google Scholar  

El Khaddar MA, Boulmalf M (2017) Smartphone: the ultimate iot and ioe device. Smartphones from an applied research perspective , page 137

Zheng P, Ni LM (2006) Spotlight: the rise of the smart phone. IEEE Distributed Systems Online 7(3):3–3

Sarker IH (2019) Context-aware rule learning from smartphone data: survey, challenges and future directions. Journal of Big Data 6(1):1–25

MathSciNet   Google Scholar  

Google trends. In https://trends.google.com/trends/ , 2019

Pejovic V, Musolesi M (2014) Interruptme: designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13–17 September, pp.∼897–908. ACM, New York, USA

Finin T, Joshi A, Kagal L, Ratsimore O, Korolev V, Chen H (2001) Information agents for mobile and embedded devices. Cooperative Information Agents V, pages 264–286

Damiao Ribeiro de Almeida, Cláudio de Souza Baptista, Elvis Rodrigues da Silva, Cláudio EC Campelo, Hugo Feitosa de Figueirêdo, and Yuri Almeida Lacerda (2006) A context-aware system based on service-oriented architecture. In Advanced Information Networking and Applications, 2006 . AINA 2006 . 20th International Conference on , volume 1, pages 6–pp. IEEE

Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam

MATH   Google Scholar  

Sarker IH, Kayes ASM, Badsha S, Alqahtani H, Watters P, Ng A (2020) Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data 7(1):1–29

Ślusarczyk B (2018) Industry 4.0: Are we ready? Polish Journal of Management Studies , 17

Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7

Sarker IH, Kayes ASM, Watters P (2019) Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data 6(1):1–28

Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ (1999) Weka: Practical machine learning tools and techniques with java implementations

Sateli B, Cook G, Witte R (2013) Smarter mobile apps through integrated natural language processing services. In International Conference on Mobile Web and Information Systems , pages 187–202. Springer

Cao L (2017) Data science: a comprehensive overview. ACM Computing Surveys (CSUR) 50(3):43

Dourish P (2004) What we talk about when we talk about context. Pers Ubiquit Comput 8(1):19–30

Schilit BN, Theimer MM (1994) Disseminating active map information to mobile hosts. IEEE Netw 8(5):22–32

Brown PJ, Bovey JD, Chen X (1997) Context-aware applications: from the laboratory to the marketplace. IEEE Pers Commun 4(5):58–64

Brown PJ (1995) The stick-e document: a framework for creating context-aware applications. Electronic Publishing-Chichester 8:259–272

Ryan N, Pascoe J, Morse D (1999) Enhanced reality fieldwork: the context aware archaeological assistant. Bar International Series 750:269–274

Franklin D, Flaschbart J (1998) All gadget and no representation makes jack a dull environment. In Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments , pages 155–160

Hull R, Neaves P, Bedford-Roberts J (1997) Towards situated computing. In Wearable Computers, 1997 . Digest of Papers., First International Symposium on , pages 146–153. IEEE

Ward A, Jones A, Hopper A (1997) A new location technique for the active office. IEEE Pers Commun 4(5):42–47

Rodden T, Cheverst K, Davies K, Dix A (1998) Exploiting context in hci design for mobile systems. In Workshop on human computer interaction with mobile devices , pages 21–22. Glasgow

Schilit B, Adams N, Want R (1994) Context-aware computing applications. In Mobile Computing Systems and Applications, 1994 . WMCSA 1994 . First Workshop on , pages 85–90. IEEE

Shi Y (2006) Context awareness, the spirit of pervasive computing. In Pervasive Computing and Applications, 2006 1st International Symposium on , pages 6–6. IEEE

Christos Anagnostopoulos, Athanasios Tsounis, and Stathes Hadjiefthymiades (2005) Context management in pervasive computing environments. In Pervasive Services, 2005 . ICPS’05 . Proceedings. International Conference on , pages 421–424. IEEE

Sarker IH, Kayes ASM (2020) Abc-ruleminer: User behavioral rule-based machine learning method for context-aware intelligent services. Journal of Network and Computer Applications , page 102762

Phithakkitnukoon S, Dantu R, Claxton R, Eagle N (2011) Behavior-based adaptive call predictor. ACM Transactions on Autonomous and Adaptive Systems 6(3):21:1–21:28

Eagle N, Pentland AS (2006) Reality mining: sensing complex social systems. Personal and ubiquitous computing 10(4):255–268

Almeida TA, Hidalgo JMG, Yamakami A (2011) Contributions to the study of sms spam filtering: new collection and results. In Proceedings of the 11th ACM symposium on Document engineering , pages 259–262. ACM

Fischer JE, Yee N, Bellotti V, Good N, Benford S, Greenhalgh C (2010) Effects of content and time of delivery on receptivity to mobile interruptions. In Proceedings of the 12th international conference on Human computer interaction with mobile devices and services , pages 103–112. ACM

Zhu H, Chen E, Xiong H, Yu K, Cao H, Tian J (2014) Mining mobile user preferences for personalized context-aware recommendation. ACM Transactions on Intelligent Systems and Technology (TIST) 5(4):58

Srinivasan V, Moghaddam S, Mukherji A (2014) Mobileminer: Mining your frequent patterns on your phone. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA, 13–17 September, pp.∼389–400. ACM, New York, USA

Kim J, Mielikäinen T (2014) Conditional log-linear models for mobile application usage prediction. In Machine Learning and Knowledge Discovery in Databases , pages 672–687. Springer

Liao Z-X, Pan Y-C, Peng W-C, Lei P-R (2013) On mining mobile apps usage behavior for predicting apps usage in smartphones. In Proceedings of the 22nd International Conference on Information & Knowledge Management , pages 609–618. ACM

Zhu H, Chen E, Xiong H, Cao H, Tian J (2014) Mobile app classification with enriched contextual information. IEEE Trans Mob Comput 13(7):1550–1563

Mehrotra A, Hendley R, Musolesi M (2016) Prefminer: mining user’s preferences for intelligent mobile notification management. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September, pp. ∼ 1223–1234. ACM, New York, USA.

Halvey M, Keane MT, Smyth B (2005) Time based segmentation of log data for user navigation prediction in personalization. In Proceedings of the International Conference on Web Intelligence, Compiegne, France, 19–22 September, pp. ∼ 636–640. IEEE Computer Society, Washington, DC, USA.

Halvey M, Keane MT, Smyth B (2006) Time based patterns in mobile-internet surfing. In Proceedings of the SIGCHI Conference on Human Factors in computing systems, Montreal, Quebec, Canada, 22–27 April, pp.∼31–34. ACM, New York, USA

Bordino I, Donato D (2012) Extracting interesting association rules from toolbar data. In International Conference on Information and Knowledge Management . ACM

Paireekreng W, Rapeepisarn K, Wong KW (2009) Time-based personalised mobile game downloading. In Transactions on Edutainment II, pp. ∼ 59–69

Rawassizadeh R, Tomitsch M, Wac K, Tjoa AM (2013) Ubiqlog: a generic mobile phone-based life-log framework. Personal and ubiquitous computing 17(4):621–637

Danninger M, Kluge T, Stiefelhagen R (2006) Myconnector: analysis of context cues to predict human availability for communication. In Proceedings of the 8th International Conference on Multimodal Interfaces , pages 12–19. ACM

Khalil A, Connelly K (2005) Improving cell phone awareness by using calendar information. In Human-Computer Interaction , pages 588–600. Springer

Dekel A, Nacht D, Kirkpatrick S (2009) Minimizing mobile phone disruption via smart profile management. In Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services , page 43. ACM

Zulkernain S, Madiraju P, Ahamed SI, Stamm K (2010) A mobile intelligent interruption management system. J. UCS 16(15):2060–2080

Pielot M (2014) Large-scale evaluation of call-availability prediction. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing , pages 933–937. ACM

Knittel J, Shirazi AS, Henze N, Schmidt A (2013) Utilizing contextual information for mobile communication. In Extended Abstracts on Human Factors in Computing Systems , pages 1371–1376. ACM

Smith J, Dulay N (2014) Ringlearn: Long-term mitigation of disruptive smartphone interruptions. In International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) , pages 27–35. IEEE

Stern H, Pammer V, Lindstaedt SN (2011) A preliminary study on interruptibility detection based on location and calendar information. Proceedings of Context-Systems Design, Evaluation and Optimisation(CoSDEO)

Vilwock W, Madiraju P, Ahamed SI (2013) A system implementation of interruption management for mobile devices. In Proceedings of the 16th International Conference on Computational Science and Engineering , pages 181–187. IEEE

Bohmer M, Lander C, Gehring S, Brumby DP, Kruger A (2014) Interrupted by a phone call: exploring designs for lowering the impact of call notifications for smartphone users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages 3045–3054. ACM

Grandhi SA, Jones Q (2015) Knock knock whos there? putting the user in control of managing interruptions. International Journal of Human-Computer Studies 79:35–50

Shirazi AS, Henze N, Dingler T, Pielot M, Weber D, Schmidt A (2014) Large-scale assessment of mobile notifications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages 3055–3064. ACM

Iqbal ST, Horvitz E (2010) Notifications and awareness: a field study of alert usage and preferences. In Proceedings of the 2010 ACM conference on Computer supported cooperative work , pages 27–30. ACM

Kanjo E, Kuss DJ, Ang CS (2017) Notimind: Utilizing responses to smart phone notifications as affective sensors. IEEE Access 5:22023–22035

Turner LD, Allen SM, Whitaker RM (2015) Push or delay? decomposing smartphone notification response behaviour. In Human Behavior Understanding , pages 69–83. Springer

Park M-H, Hong J-H, Cho S-B (2007) Location-based recommendation system using bayesian user’s preference model in mobile devices. In International Conference on Ubiquitous Intelligence and Computing , pages 1130–1139. Springer

Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: A user-centered approach. In AAAI , volume 10, pages 236–241

Kim K-j, Ahn H, Jeong S (2010) Context-aware recommender systems using data mining techniques. In Proceedings of world academy of science, engineering and technology , volume 64, pages 357–362

Liu Q, Ge Y, Li Z, Chen E, Xiong H (2011) Personalized travel package recommendation. In Data Mining (ICDM), 2011 IEEE 11th International Conference on , pages 407–416. IEEE

Shin D, Lee J-w, Yeon J (2009) Context-aware recommendation by aggregating user context. In IEEE Conference on Commerce and Enterprise Computing, Vienna, Austria, Austria, 20–23 July, pp.∼423–430. IEEE Computer Society, Washington, DC, USA

Liu B, Kong D, Cen L, Gong NZ, Jin H, Xiong H (2015) Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining , pages 315–324. ACM

Seo S-s, Kwon A, Kang J-M, Strassner J (2011) Pyp: design and implementation of a context-aware configuration manager for smartphones. In International Workshop on Smart Mobile Applications

Bozanta A, Kutlu B (2018) Developing a contextually personalized hybrid recommender system. Mob Inf Syst 2018:1–13

Lee W-P (2007) Deploying personalized mobile services in an agent-based environment. Expert Syst Appl 32(4):1194–1207

Turner LD, Allen SM, Whitaker RM (2015) Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing , pages 801–812. ACM

Fogarty J, Hudson SE, Atkeson CG, Avrahami D, Forlizzi J, Kiesler S, Lee JC, Yang J (2005) Predicting human interruptibility with sensors. ACM Transactions on Computer-Human Interaction (TOCHI) 12(1):119–146

Bedogni L, Di Felice M, Bononi L (2016) Context-aware android applications through transportation mode detection techniques. Wirel Commun Mob Comput 16(16):2523–2541

Tan GW-H, Ooi K-B, Leong L-Y, Lin B (2014) Predicting the drivers of behavioral intention to use mobile learning: A hybrid sem-neural networks approach. Computers in Human Behavior 36:198–213

Sathiya Keerthi S, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to platt’s smo algorithm for svm classifier design. Neural computation 13(3):637–649

Breiman L (2001) Random forests. Mach Learn 45(1):5–32

Sarker IH, Colman A, Kabir MA, Han J (2018) Individualized time-series segmentation for mining mobile phone user behavior. The Computer Journal, Oxford University, UK 61(3):349–368

Sarker IH, Colman A, Han J (2019) Recencyminer: mining recency-based personalized behavior from contextual smartphone data. Journal of Big Data 6(1):1–21

Sarker IH, Abushark YB, Khan AI (2020) Contextpca: Predicting context-aware smartphone apps usage based on machine learning techniques. Symmetry 12(4):499

Sarker IH, Alqahtani H, Alsolami F, Khan AI, Abushark YB, Siddiqui MK (2020) Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling. Journal of Big Data 7(1):1–23

Pielot M, De Oliveira R, Kwak H, Oliver N (2014) Didn’t you see my message?: predicting attentiveness to mobile instant messages. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages 3319–3328. ACM

Bayat A, Pomplun M, Tran DA (2014) A study on human activity recognition using accelerometer data from smartphones. Procedia Computer Science 34:450–457

Ayu MA, Ismail SA, Matin AFA, Mantoro T (2012) A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition. Procedia Engineering 41:224–229

Fetter M, Seifert J, Gross T (2011) Predicting selective availability for instant messaging. In IFIP Conference on Human-Computer Interaction , pages 503–520. Springer

Fisher R, Simmons R (2011) Smartphone interruptibility using density-weighted uncertainty sampling with reinforcement learning. In 2011 10th International Conference on Machine Learning and Applications and Workshops , volume 1, pages 436–441. IEEE

Swati K, Patankar AJ (2014) Effective personalized mobile search using knn. In 2014 International Conference on Data Science & Engineering (ICDSE) , pages 157–160. IEEE

Middleton SE, Shadbolt NR, De Roure DC (2004) Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS) 22(1):54–88

Anagnostopoulos T, Anagnostopoulos C, Hadjiefthymiades S, Kyriakakos M, Kalousis A (2009) Predicting the location of mobile users: a machine learning approach. In Proceedings of the 2009 international conference on Pervasive services , pages 65–72. ACM

Riboni D, Bettini C (2011) Cosar: hybrid reasoning for context-aware activity recognition. Pers Ubiquit Comput 15(3):271–289

Zhong E, Tan B, Mo K, Yang Q (2013) User demographics prediction based on mobile data. Pervasive and mobile computing 9(6):823–837

Wang Y, Feng D, Li D, Chen X, Zhao Y, Niu X (2016) A mobile recommendation system based on logistic regression and gradient boosting decision trees. In 2016 International Joint Conference on Neural Networks (IJCNN) , pages 1896–1902. IEEE

Ernsting C, Dombrowski SU, Oedekoven M, Julie LO, Kanzler M, Kuhlmey A, Gellert P et al (2017) Using smartphones and health apps to change and manage health behaviors: a population-based survey. Journal of medical Internet research 19(4):e101

Sarker IH (2019) A machine learning based robust prediction model for real-life mobile phone data. Internet of Things 5:180–193

Hong J, Suh E-H, Kim J, Kim SY (2009) Context-aware system for proactive personalized service based on context history. Expert Syst Appl 36(4):7448–7457

Sarker IH, Colman A, Han J, Khan AI, Abushark YB, Salah K (2019) Behavdt: A behavioral decision tree learning to build user-centric context-aware predictive model. Mobile Networks and Applications , pages 1–11

Alawnah S, Sagahyroon A (2017) Modeling of smartphones’ power using neural networks. EURASIP Journal on Embedded Systems 2017(1):22

Leong L-Y, Hew T-S, Tan GW-H, Ooi K-B (2013) Predicting the determinants of the nfc-enabled mobile credit card acceptance: a neural networks approach. Expert Syst Appl 40(14):5604–5620

Chong AY-L (2013) Predicting m-commerce adoption determinants: a neural network approach. Expert Syst Appl 40(2):523–530

Rajashekar D, Nur Zincir-Heywood A, Heywood MI (2016) Smart phone user behaviour characterization based on autoencoders and self organizing maps. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) , pages 319–326. IEEE

Kandasamy K, Kumar CS (2015) Modified pso based optimal time interval identification for predicting mobile user behaviour in location based services. Indian Journal of Science and Technology 8(S7):185–193

Hartono RN, Pears R, Kasabov N, Worner SP (2014) Extracting temporal knowledge from time series: A case study in ecological data. In Proceedings of the International Joint Conference on Neural Networks, Beijing, China, 6–11 July, pp. ∼ 4237–4243. IEEE Computer Society, Washington, DC, USA

Keogh E, Chu S, Hart D, Pazzani M (2004) Segmenting time series: a survey and novel approach. Data mining in time series databases 57:1–22

Shokoohi-Yekta M, Chen Y, Campana B, Hu B, Zakaria J, Keogh E (2015) Discovery of meaningful rules in time series. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10–13 August, pp. ∼ 1085–1094. ACM, New York, USA

Zhang G, Liu X, Yang Y (2015) Time-series pattern based effective noise generation for privacy protection on cloud. IEEE Trans Comput 64(5):1456–1469

Cao H, Bao T, Yang Q, Chen E, Tian J (2010) An effective approach for mining mobile user habits. In Proceedings of the International Conference on Information and knowledge management, Toronto, ON, Canada, 26–30 October, pp. ∼ 1677–1680. ACM, New York, USA

Iqbal H Sarker (2018) Mobile data science: Towards understanding data-driven intelligent mobile applications. EAI Endorsed Transactions on Scalable Information Systems , 5(19)

La Polla M, Martinelli F, Sgandurra D (2012) A survey on security for mobile devices. IEEE communications surveys & tutorials 15(1):446–471

Otrok H, Mizouni R, Bentahar J et al. (2014) Mobile phishing attack for android platform pages 18–23

Dunham K (2008) Mobile malware attacks and defense

Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W (2017) A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J 4(5):1125–1142

John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence , pages 338–345. Morgan Kaufmann Publishers Inc.

Quinlan JR (1993) C4.5: Programs for machine learning. Machine Learning

Sarker IH, Abushark YB, Alsolami F, Khan AI (2020) Intrudtree: A machine learning based cyber security intrusion detection model. Symmetry 12(5):754

Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Machine learning 6(1):37–66

Freund Y, Schapire RE et al. (1996) Experiments with a new boosting algorithm. In Icml , volume 96, pages 148–156. Citeseer

Le Cessie S, Van Houwelingen JC (1992) Ridge estimators in logistic regression. Journal of the Royal Statistical Society: Series C (Applied Statistics) 41(1):191–201

MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In Fifth Berkeley symposium on mathematical statistics and probability , volume 1

Rokach L (2010) A survey of clustering algorithms. In Data Mining and Knowledge Discovery Handbook , pages 269–298. Springer

Sneath PHA (1957) The application of computers to taxonomy. Journal of General Microbiology , 17(1)

Sorensen T (1948) Method of establishing groups of equal amplitude in plant sociology based on similarity of species. Biol. Skr. , 5

Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In ACM SIGMOD Record , volume 22, pages 207–216. ACM

Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In Proceedings of the International Joint Conference on Very Large Data Bases, Santiago Chile, pp. ∼ 487–499. , volume 1215

Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In ACM Sigmod Record , volume 29, pages 1–12. ACM

Das A, Ng W-K, Woon Y-K (2001) Rapid association rule mining. In Proceedings of the tenth international conference on Information and knowledge management , pages 474–481. ACM

Zaki MJ (2000) Scalable algorithms for association mining. IEEE transactions on knowledge and data engineering 12(3):372–390

Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Information fusion 36:10–25

Maedche A, Staab S (2001) Ontology learning for the semantic web. IEEE Intell Syst 16(2):72–79

Grosan C, Abraham A (2011) Rule-based expert systems. Int Underw Syst Des, pages 149–185

Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decision Support Systems 74:12–32

Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

Marine-Roig E, Martin-Fuentes E, Daries-Ramon N (2017) User-generated social media events in tourism. Sustainability 9(12):2250

Belanche D, Flavián M, Pérez-Rueda A (2020) Mobile apps use and wom in the food delivery sector: The role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability 12(10):4275

World health organization: Who. http://www.who.int/

Yi B-J, Lee D-G, Rim H-C (2015) The effects of feature optimization on high-dimensional essay data. Math Probl Eng 2015:1–12

Bobek S, Nalepa GJ, Ślażyński M (2019) Heartdroid†rule engine for mobile and context-aware expert systems. Expert Systems 36(1):e12328

Download references

Author information

Authors and affiliations.

Swinburne University of Technology, Melbourne, VIC, 3122, Australia

Iqbal H. Sarker & Md. Kafil Uddin

Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh

Iqbal H. Sarker & Mohammed Moshiul Hoque

RMIT University, Melbourne, VIC, 3000, Australia

Tawfeeq Alsanoosy

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Iqbal H. Sarker .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Sarker, I.H., Hoque, M.M., Uddin, M.K. et al. Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Netw Appl 26 , 285–303 (2021). https://doi.org/10.1007/s11036-020-01650-z

Download citation

Published : 14 September 2020

Issue Date : February 2021

DOI : https://doi.org/10.1007/s11036-020-01650-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mobile data science
• Artificial intelligence
• Machine learning
• Natural language processing
• Expert system
• Data-driven decision making
• Context-awareness
• Intelligent mobile apps
• Find a journal
• Publish with us
• Track your research

Live Trainings - Join one of our free training sessions to get the most out of Papers

Every Reference Where You Need It, When You Need It

Dramatically improve the way you discover, organize, read, annotate, share, and cite. We have reference management down to a science, so you can focus on what matters most.

Trusted by millions of users and used across thousands of academic institutions since 2010.

Fast, intuitive access to all your reference materials.

Say goodbye to desktop clutter, random folders and clunky shared drives.

Easy Importing

Tools available to bring information in from your desktop, databases, journals, or other reference managers

Metadata Matching

Automatically identifies and matches imported articles with complete metadata and available supplemental data, citations and full-text

Collection Management

Use tags, labels, lists, ratings, smart collections, and watch folders to keep everything sorted and accessible as your libraries grow.

Stop searching and start discovering

Our enhanced reading environment connects to a searchable database of over 130 million articles offering recommended related resources while contextualizing your research with citation, retraction and Altmetric data.

Real-time collaboration & cross-device syncing

Streamline how you and your research colleagues work to keep you in sync and one step ahead.

Shared Libraries Private shared libraries perfect for remote teams, lab collaborations, journal club, or jointly writing that upcoming manuscript.

Device Sync Securely sync your entire online library including notes, lists, annotations, and highlights across all of your desktop (Mac/PC), browser app, and mobile (iOS/Android) devices.

Dynamically annotate and cite from a single source.

Eliminate the strain of sifting through notes disconnected from your references with direct access to supplemental files and ultimately how you’ll cite your sources.

Dynamic Annotations & Notes

Highlight, underline, strike through, make inline annotations, draw and add sticky notes.

Easily access references within your libraries or through our built in search engine, SmartCite. Dynamically generate a bibliography using one of over 9000+ citation styles - including the ability to create your own.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

Status of the research in fitness apps: A bibliometric analysis

a Ph.D. Student in Business Administration, Faculty of Economics, Complutense University of Madrid, Campus de Somosaguas. 28223, Pozuelo de Alarcón, Madrid, Spain

Maria Avello

b Department of Management and Marketing, Faculty of Economics, Complutense University of Madrid, Campus de Somosaguas, 28223, Pozuelo de Alarcón, Madrid, Spain

• • A bibliometric analysis of the fitness apps research field to gain insight into the state of the art.
• • Scopus and Web of Science were used to collect the data (481 records).
• • Statistical analysis and science mapping were used to analyze the data.
• • Provides basic data, research classifications and future research directions in the area.

Fitness applications have undergone considerable development in the last few years and becoming popular and significant in both academic and practical areas. However, contributions to the systematic mapping of this field continue to be lacking. This paper constitutes the first bibliometric study in this field to better understand the current state of research. We examined 481 records from databases Scopus and Web of Science (Core Collection) using several bibliometric analysis methods. All the records on this emerging topic were published between 2011 and 2019. We processed these records using statistical analysis and science mapping. The bibliometric analysis included the year of publication, journal name, citation, author, country, and particularly, research methodology. Additionally, we used the VOSViewer software to perform bibliometric mapping of co-authorship, co-citation of authors, and co-occurrence of keywords. This field of study, it was found, is currently in its precursor stage, contributing primarily to the fields of medicine, computer science, and health sciences. The United States appeared to have made the largest contribution to this field. However, author productivity, number of citations, and number of core journals all indicated a high degree of fragmentation of research in this filed. Remarkably, scientific research in this area is expected to progress tremendously over time. Overall, this study provides basic data and research classifications for the initial phase of research and research direction for future research in this area.

1. Introduction

With the global outbreak of the COVID-19 pandemic in 2020, almost every country is facing problems concerning the shortage of medical and healthcare resources, and people have become more aware of the importance of following a healthy lifestyle and incorporating physical exercise into their daily lives. As the most downloaded type of mobile health applications (mHealth apps), fitness apps can help people manage their nutritional intake, assist their participation in fitness and physical activities, and promote a healthy lifestyle. Therefore, these apps are gradually occupying the commercial mobile app market ( Beldad and Hegner, 2018 ).

Nowadays, fitness apps are rapidly developing in the commercial application market and are attracting the attention of academia ( Beldad and Hegner, 2018 ). Numerous studies have implemented empirical protocols to verify the results of using fitness apps for improving the level of physical activity and/or diet in users ( Schoeppe et al., 2017 ). However, from the academic side, it is still a novel and young area of research.

As a diverse field of research that is related to an emerging phenomenon, and with the integration of new technologies, the research available on fitness apps is still scarce. Both empirical research and theoretical orientation reviews, mostly focus on summarizing the functions and features of fitness apps and user perspectives. As a result, there appears to be a lack of more macro and objective quantitative research in this field. And the various types of literature are not as substantial or abundant compared to other mature areas of research. It is necessary to carry out a bibliometric study to know the main empirical and theoretical orientations in this case. The data obtained from the bibliometric analysis will be essential to assess the intensity and orientation of new lines of research ( Bartoli and Medvet, 2014 ). Moreover, it is essential to classify the existing research in the research field to track the research progress and research trends in the field ( Gaviria-Marin et al., 2019 ). Bibliometrics study can achieve this objective. It helps display past academic research activities and achievements visually.

To our knowledge, there is no bibliometric study in the field of fitness app research, even though this type of literature has been used widely in other fields in recent years ( Zanjirchi et al., 2019 ). Bibliometrics can supplement existing experiments and review studies, help researchers identify hidden research lines, hot issues, and research methods in the field, and reduce the problems of neglecting certain excellent articles due to the deviation of researchers' subjective judgments ( Zanjirchi et al., 2019 , Veloutsou and Mafe, 2020 ).

Therefore, this study offers a bibliometric study of the advancements in research on the mobile-fitness app. It is based on data from a bibliometric analysis. It seeks to assess the intensity and research topics dominant in the scientific community when it comes to this emerging phenomenon, focusing explicitly on the fitness segment of mHealth. This study also aims to provide relevant data and bibliometric indicators for the initial stage of fitness application research and provide primary data for advancing future research in this field. The data used in this study is obtained from two leading databases for scientific research: Scopus and Web of Science.

The research is organized as follows. First, a research background is provided. Second, the research methods and the sources of research data are outlined. Third, the results are presented and discussed. Finally, the main conclusions, limitations, and further opportunities for research are stated.

2. Background

2.1. mhealth apps and fitness apps.

Nowadays, mobile apps pertain to a wide range of topics and areas of users' personal and social lives and fulfill various purposes. The use of advanced medical information systems and telematics applications is one of them, which has resulted in the increased availability of medical services at lower overall costs ( Kao et al., 2018 ). Medical and sanitary institutions have begun to appreciate the potential of mHealth apps for communication with patients as well as for the utilization of mobile devices that are specifically designed to monitor specific biomedical data. mHealth is defined as the provision of medical care and health-related services through mobile communication devices that enable user-interaction capability ( Cummiskey, 2011 , Lupton, 2013 ). “Mobile Health (mHealth) has become an essential field for disease management, assessment of healthy behaviors, and for interventions on healthy behaviors” ( Mas et al., 2016, p. 32 ).

There are two main areas of implementation of mHealth apps: in professional medical practices (both on the side of doctors and patients; e.g., Skyscape, MySugr), and self-monitoring of healthy habits (e.g., MyFitnessPal). The first area has a field of an app exclusively in the healthcare field, involving the relationships between doctors and their patients. The second area represents fitness apps, which is the subject of this study, is concerned with the personal monitoring of the activities of individuals within the framework of adopting healthy lifestyles or disease prevention habits, and this category is often implemented through commercial apps that are developed without the supervision of medical administrations.

The term “fitness” has a wide semantic field: on the one hand, it refers to the practice of physical exercise to obtain or maintain good body shape and composition; on the other hand, more generally, it refers to a good state of vitality and physical well-being ( Corbin et al., 2000 ). Since the 1980s, academic as well as medical attention to Health-Related Physical Fitness (HRPF) has increased considerably. Fitness is understood within the HRPF framework, which is defined as a set of people's abilities to perform certain physical activities, their energy level to perform daily tasks, and their capacity to reduce the risk of diseases related to sedentarism ( Cheng and Chen, 2018 ).

2.2. Importance of fitness apps

The WHO warns of the development of non-communicable diseases, the pathologies of which are associated with unhealthy lifestyles and diets, as these diseases currently constitute a serious cause of death worldwide ( WHO, 2018 ). In particular, the WHO has established a set of minimum criteria for physical activity for different age groups as well as balanced dietary patterns to maintain optimal health conditions such that people can achieve a reduction in risk factors for non-communicable diseases, including cancer, cardiovascular ailments, and diabetes.

The high rate of obesity is one of the most worrying factors for health globally, particularly in developed countries, but also in emerging countries, with a drastic growth among children ( Anderson et al., 2019 ). For this reason, the WHO recommends avoiding a sedentary lifestyle and following balanced diets for all age groups. Interventions for population self-management, based on changes in lifestyle, are effective in reducing risk factors and the incidence of non-communicable diseases ( Burke et al., 2011 ).

The use of applications on mobile devices has become a key factor in helping and advising people on the adoption of healthy lifestyles in the 21st century. Although some clinicians lack confidence in the protocols and recommendations of fitness apps, these fitness apps have a great potential to be effective due to their ability to educate a large portion of the population on healthy habits at a low operating cost ( Blackman et al., 2013 ).

3. Methodology

The methodology used in this research work is depicted in Fig. 1 . It consists of four steps.

The general framework of methodology.

3.1. Step 1: Determining the field of study and database used

We identified “fitness app” as the field for this study with the aim of finding as many articles as possible on fitness-related apps closer to health behaviors than to a professional medical approach. However, in the compilation of the final set of articles, we also included those that, without being strictly articles on fitness apps, contained relevant keywords linked to the subject of study, even though they were papers dealing with other types of mHealth apps.

The data was obtained from two databases: Scopus and Web of Science Core Collection (WoS). These two databases are currently the leading sources for indexing scientific articles and allow for the collection of data from a large number of journals ( Adriaanse and Rensleigh, 2013 ).

Scopus owns high-quality and reliable coverage and complete data for each reference. It is the largest abstract and citation database for peer-review literature ( Zanjirchi et al., 2019 ). The WoS is also recognized by the scientific community as a digital bibliometric platform with high-quality literature, which can also provide metadata for bibliometric analysis and covers a wide range of disciplines ( Gaviria-Marin et al., 2019 , Hew, 2017 ).

The combination of more than one database for mining scientific data can provide more robust results for the bibliometric analysis ( de Oliveira et al., 2019 ) even though it makes it necessary to integrate the information from both databases with different structures and review the articles one by one.

3.2. Step 2: Mining of bibliometric data

Mining the data is the most basic and crucial step to obtain valuable and credible research results. The search for this study was conducted in April 2020 and included all relevant publications until the end of December 2019.

The study focused on scientific research related to personal care applications of fitness, using the keywords “ fitness app” and its plural form in English for searching through titles, abstracts, keywords, or topics. Our search criteria are detailed in Table 1 . These two keywords represent the technological concept (app) associated with the lifestyle (fitness), whose specific relationship makes the object of the present investigation. No more keywords related to the fitness industry were used (e.g., weight loss/running, dieting) since we wanted to examine which other specific categories were reviewed under the category of fitness apps in general. Our search does not have a low-time frame limit, and the aim is to learn about the starting time of research in this field ( Table 1 ).

Search criteria for the study field “fitness apps”.

*No low time frame limit was set, but articles published before 2010, while containing relevant keywords, were seen not to be relevant to the field.

After searching in the two databases separately, we performed a manual review of the titles and abstracts (also full text if necessary), excluding articles whose topics did not meet the criteria of the study, and subsequently removing duplicate literature. When the same article appeared in both databases, we opted to keep the references in Scopus because Scopus provides broader bibliographic information than WoS. The search returned 1095 records. We decided to keep the conference papers and meeting abstracts due to the youth and relative novelty of the field of study. After filtering out the irrelevant and incomplete records, we ended up with a total sample of 481 records ( Table 2 ).

Search results in academic databases.

3.3. Step 3: Analysis of bibliometric data

The records were then analyzed using bibliometric analysis. Bibliometrics is “the quantitative study of physical published units, or bibliographic units, or of the surrogates for either” ( Broadus, 1987, p. 376 ). The bibliometric analysis allows us to understand the intensity of the research available on a topic as well as the different research fields explored by the academic community.

The variables analyzed for the bibliometric study were the year of publication, author, country of institutional origin, language of publication, type of document, journal, number of citations, area of research, topics analyzed, and the research method used.

Additionally, bibliometric mapping was also conducted. The construction of bibliometric maps has always received attention in bibliometric studies ( Van Eck and Waltman, 2010 ). We used Vosviewer software to present the relation of co-citation, co-occurrence of keywords, etc.

3.4. Step 4: Grouping and analysis of trends

Finally, we summarized the current research hotspots and trends in this field, based on the content of these 481 articles and the information presented by the keywords of their authors, to inform and inspire further studies.

4.1. Publication frequency per year

The first article on fitness apps was published in 2011, and until 2014, the intensity of research was very low. 95.2% of the articles are published from 2014 onwards. In 2014, there was a significant increase in the number of publications, doubling the number of 2013 ( Table 3 ).

Frequency of publication of articles related to fitness apps per year.

These results represent a Price's Index of 89.4% until the end of 2019. Price's Index ( Price, 1970 ) refers to the percentage of references less than five-year-old. As the Price Index's value is relatively high, this area is considered to be novel and dynamic.

Price’s Law ( Price, 1963 ) proposes that the development of the scientific field follows an exponential growth, which doubles in size every 10–15 years. The development of the scientific field goes through four stages: the precursor stage, the exponential growth stage, the consolidation of the body knowledge stage, and the decrease in the production stage. As shown in Fig. 2 , publications in related fields underwent a growth process from 2011 to 2019. A linear mathematical adjustment of the measured values provided us with a correlation coefficient r = 0.964, which implies that 7.07% of variance failed to explain this fitting. In contrast, a mathematical adjustment to the exponential curve provides a coefficient r = 0.788, indicating an unexplained variance of 37.86%. This reveals that the data analyzed is more consistent with a linear fitting rather than an exponential one ( Fig. 2 ).

Growth of scientific production in fitness apps.

While the third stage of growth also showed a linear trend, the first contribution in this field was produced in 2011, and the exponential growth trend stage was not detected. So, research in this field is still in its precursor stage. Additionally, the number of publications in 2018–2019 was close to 50% of the total, exhibiting rapid growth. Although there was a small decline in 2019 compared to 2018, we expect the scientific production in this field to enter the exponential growth stage in the coming years.

4.2. Most productive and influential journals/conferences and type of documents

Articles on fitness apps are published in a wide range of journals, from medical and health-related ones to computer science-related ones. Out of the 481 records, 328 were published in academic journals, and 153 were published as conference proceedings. The publication source also indicates a great dispersion: there were 189 journals and 109 different conference proceedings in total Fig. 3 .

Type of documents.

Among all the relevant journals, eight journals have published three or more articles. However, only nine conference proceedings had more than one article. Compared to other fields of study, this number seems very small and indicates a low level of source concentration.

Table 4 presents the field's 18 most productive and influential journals, and Table 5 outlines the nine most productive conference proceedings.

The most productive journals in fitness app research.

The most productive conference proceedings.

JMIR mHealth and uHealth and Journal of Medical Internet Research have achieved a prominent position here, with a relatively high number of articles. Both are sister journals of JMIR Publications. It is worth noting that although only three articles were sourced from the International Journal of Behavioral Nutrition and Physical Activity, it ranked third overall in the number of citations.

Besides, 30% of the publications were from conference proceedings. The first and second positions by the number of publications came from the field of computer science. The high proportion may be explained by the fact that, although the importance of conference proceedings in areas such as the natural sciences is decreasing, they still play an important role in computer science, with nearly 20% of citations also distributed in the proceedings ( Michels and Fu, 2014 , Lisée et al., 2008 ). It also shows the importance of the development of fitness apps in the domain of computer applications.

Bradford’s Law ( Bradford, 1934 ) is a tool used in bibliometric studies to evaluate the concentration/dispersion factor of a set of publications. In essence, it allows the determination of the most productive nucleus in a particular subject. It postulates the existence of a small nucleus of journals that address the topic more broadly as well as a vast peripheral region that is divided into several zones with journals that have a decreasing representation in the subject studied ( Alvarado, 2016 ). The number of journals in the core and the number in the successive zones are in a ratio of 1: n: n 2 .

Therefore, journals included in the core have a comparatively high concentration of publication, while those involved in the surrounding areas are increasingly dispersed. Thus, we can see that there is an unequal distribution of articles in the journals. A large number of articles are found in a small number of journals. As shown in Fig. 4 and Table 6 , within the core of the ring, only 10 journals contained one-third of all published articles (109 records). Zone 1 comprises 70 journals, and zone 2 comprises 109 journals. Zona 2 contains a much smaller number of journals than the theoretical value (570). This result suggests the innovative and youthful nature of the field under study, which has not been considered in depth by many journals.

Dispersion in Bradford rings of scientific production related to fitness apps.

Publication dispersion zones under Bradford's Law.

4.3. Most cited articles

The number of citations is an important indicator of the influence and the attention presented by the scientific community. According to the results shown in Table 7 , a total of 28 articles received more than 60 citations—all from academic journals. This number is relatively low compared to other more mature fields of research.

Most Cited Articles.

The most cited article (598 citations) is a multidisciplinary review by Boulos M.N.K. et al., published in 2011, one of the first published articles in the field, followed by the research by Krebs P., Duncan D.T., published in 2015 with 316 citations.

4.4. Most productive and influential authors

A total of 1,776 authors have contributed to this field. The average number of authors per article was 3.69, which indicates the trend towards multi-author contributions in the field and a wide dispersion of research. Table 8 summarizes the first 30 authors in the list, with more than two contributions ( Table 8 ).

The most productive and influential authors in fitness app research.

The data source was Scopus.

In those cases where the information was not available at Scopus, we used the information provided by WoS.

The most productive authors in terms of the number of articles published are Oyibo K. and Vassileva J., both from the University of Saskatchewan (Canada), with 8 contributions. Third and fourth-ranked Gay V. and Leijdekkers P. are co-authors. In the scope of the subject of our study, they co-authored a total of six articles.

The work of the most productive authors does not attract the highest number of citations. The author, with the highest number of citations in the fitness apps field, is West J.H. His six articles have garnered a total of 655 citations. Three of them are ranked in the top ten most influential papers in Table 8 . They were all published in the journal with the most contributions in the field, Journal of Medical Internet Research .

The author with the highest h-index (78) is Salmon J., from Deakin University, whose research pertains to the fields of medicine, health professions, and nursing. However, the total number of citations for his three articles was only 35. No other author had an h-index above 20.

The high inconsistency in the number of citations, the number of author contributions, and the h-index show that no scholar or team of scholars has yet had a decisive influence on the field, which is also related to the fact that the field is still in the precursor stage of research.

Additionally, the authors in Table 8 are not widely dispersed in terms of institutional affiliation, with several authors (and close rankings) being from the same institution. This suggests that a high proportion of the top 30 productive authors are co-authors, as evidenced in Fig. 5 . It highlights that only four authors did not co-author papers with others. The remaining 26 authors make up the remaining nine clusters. Moreover, members in each group usually come from the same institutions or countries, with less cross-national/interregional cooperation.

Correlation in co-authorship (for top 30 authors with more than two contributions).

The authors' productivity data are much lower than the values suggested by Lotka’s Law ( Lotka, 1926 ). This law states that the number of authors making n contributions in a given period is approximately equal to the number of authors who make 1/n 2 contributions. Generally, the application of Lotka's Law gives the theoretical result that about 60% of authors make only one contribution in their field of study. In the field of research on fitness applications, the value of Lotka's Law is 92.62% ( Table 9 ). This confirms the huge dispersion of the field, which can be explained either by the novelty of the phenomenon or by a multidisciplinary approach.

Productivity of authors.

Additionally, the analysis of co-citation of authors shows the structure and connections of the co-cited authors, i.e., “which authors are cited together more frequently” ( Gaviria-Marin et al., 2019, p. 213 ). Fig. 6 shows the results of the analysis conducted using VOSviewer, and the number of citations for each author is indicated by the size of the colored dot ( Fig. 6 ).

Co-citation of authors.

Authors with more than 35 citations were clustered in five groups. Some of these authors did not contribute directly to our field. However, their articles are frequently cited by other authors in the fitness app research field.

Authors in Cluster 1 mainly tend to focus on research in the areas of social sciences, business, management and accounting, and mathematics. Sub-topics of interest to them include behavior change, physical activities, etc.

Authors in Cluster 2 primarily devote their research to the field of biochemistry, genetics and molecular biology, and health professions. Physical and health education is also one of the sub-topics they are interested in.

In Cluster 3, the main research interests include psychology, and besides, the authors have contributed to the areas of computer science, nursing, and decision making.

The main research interests of the authors of Cluster 4 lie in the arts and humanities, social sciences, computer science, and psychology. They have also undertaken certain interpretative explorations of technological acceptance.

Cluster 5 consisted of only two authors, Richard M Ryan and Edward L. Deci. They are also co-authors of articles with fairly high citations, and both of them have an h-index of no less than 150. Their main areas of research are psychology, in which self-determination theory and motivation are also a point of interest.

4.5. Most productive countries/regions

6 out of the 481 records did not specify the country/region of origin. Of the remaining 475 records, the countries that contributed the most were the United States (29.3%), the United Kingdom (11.2%), and Australia (10%). It should be noted that almost half of the studies were carried out in English-speaking countries. Among the Asian countries, China, India, and South Korea stood out. National/regional contributions are double counted when authors of the same article are affiliated with institutions from different countries ( Table 10 ) ( Fig. 7 ).

Most cited countries/regions.

Map of contributions by countries/regions.

4.6. Most productive fields of research

Our results show that the main research areas of study are medicine (23.95%), computer sciences (17.88%), behavioral sciences (6.7%), computer medicine (6.5%), and psychology (6.2%). Most articles contribute to more than one field ( Table 11 ).

Frequency of published articles by research field.

It seems that research in fitness apps has flourished through its study in the medical area, followed by its computational features. However, the study from the point of view of consumer behavior, integrated into the field of social sciences, seems not to have taken off yet. We predict significant growth in this domain as fitness apps become more popular, and communication through social networking sites goes viral, particularly among young people.

4.7. Most used research methods

The applied research methods allow the collection of empirical data to contribute to scientific knowledge. It is an important variable to understand the empirical orientations of research in this field of knowledge.

As shown in Table 12 , the most frequently used research method was the experiment. The experimental design was used in 24.5% of all research. Most of them were “in the wild” experiments, implemented on a small group of participants (n < 50) who were asked to use a fitness app, developed expressly for the research, for a short period. The second most used research method was the survey (18.5% of the articles), which allowed the evaluation of the user perspective and behavior with self-reported data.

Main research methods used.

*Out of the total 481 articles, 25 articles (5.2%) used multiple methods. Of these, 24 articles used two methods and one article used three methods.

The third-ranked research method was content analysis. The articles that used this method analyzed and evaluated the total or partial functionality of a range of fitness-related apps, their technical characteristics and the attributes that make them more valued by users, more effective in changing consumer behavior, etc. For example, Cowan et al. (2013) calculated a theoretical score for each of the 127 health and fitness applications to determine whether the applications included relevant aspects of the behavioral change theory.

The content analysis articles allow us to understand how fitness-related apps have evolved over the years and how researchers' focus has changed over that same period. By reviewing relevant articles, we found that behavior change techniques, gamification features, and consumer engagement strategies have been attracting attention, as shown in Fig. 8 . Fig. 8 summarizes articles on content analytics from 2012 to 2019 from West et al., 2012 , Cowan et al., 2013 , Direito et al., 2014 , Lister et al., 2014 , Edwards et al., 2016 , Rose et al., 2017 , Moral-Munoz et al., 2018 , Priesterroth et al., 2019 and Cotton and Patel (2019) .

Timeline of hot topics of content analysis articles.

5. Main topics analyzed and lines of research

5.1. keywords.

The analysis of the frequency of appearance of the keywords allows the reader to approach the main topics analyzed in the articles in this field. The analysis of the keywords selected by the authors allows the determination of which relationships are established between a field of research and others close to it ( Duran-Sanchez et al., 2016 ).

As shown in Table 13 , the terms “physical activity” and “mHealth” appear in 28.1% of all the contributions. Both keywords are the conceptual core of fitness app research. Physical activity is also related to the terms “exercise” (6.9%), “obesity” (1.7%), and “weight loss” (2.3%).

Frequency of occurrence of keywords (>6 times).

Portability is a concept associated with new devices for self-monitoring of activity: the terms “wearables” and “fitness tracker(s)” appeared in 3.1% and 4.8% of articles, respectively.

The principle of playful functions is reflected in the term “gamification,” with 3.33% of the articles, which is a factor that can increase user adherence to the programs.

Fig. 9 maps the correlation between the keywords. To make the map clearer, with more focus on the core of the field of study, we removed the keyword “app” and its various related forms from the mapping analysis.

Correlation map between keywords.

The most frequent keywords were located in five differentiated clusters.

Cluster 1, which we named “Digital mHealth” is mainly related to mHealth and eHealth (electronic health). They are platforms for fitness apps. Also included in this group are keywords such as privacy and security, which are all related to the technology and device issues of fitness applications.

Cluster 2, which we named “mHealth and fitness trackers,” is pretty similar to Cluster 1, with only an emphasis on fitness trackers and persuasive technology as well as health apps and wearable electronic devices.

Cluster 3, which we named “Physical activity, motivation, and social support,” comprises keywords such as physical activity, exercise, physical fitness, etc. Social support and motivation are also included in this group, which may be since these two are also important factors that support people to stick to physical activity ( Tang et al., 2015 ).

Cluster 4, which we named “Generalistic keywords,” is more macro in nature and contains a wide range of topics such as fitness, mobile, and public health.

Cluster 5, which we named “Behavior change and gamification,” includes keywords such as behavior change, gamification, wearables, and self-determination theory.

5.2. Main topics of research

Finally, based on all the information obtained as well as our thorough review of the contributions that are part of this bibliometric study, we now describe the main topics of research on the subject of fitness apps:

• 1) Descriptive studies of the possibilities of the applications and the quality of their functions. Most of the research is related exclusively to physical activity, alongside some studies on diet. For example, Li et al. (2019) analyzed the quality of nutritional recommendations of applications available in China for a healthy lifestyle, nutrition, and disease prevention.
• 2) Analysis of the quality and performance of the use of the apps concerning the objectives of the users. The performance is measured through an evaluation of different indicators, such as the level of physical activity or weight loss. In this criterion of research, the use of innovative features is particularly important. For example, Mata et al. (2018) tested the performance of the training planning function of the relevant apps and confirmed the high performance of these app-generated training and nutrition plans through expert validation.
• 3) Analysis of the benefit of the use of fitness apps for the chronically ill. Patients affected by severe chronic diseases can undergo improvement in their general condition through lifestyle improvements. For example, Bonato et al. (2019) analyzed the possibility of using an app for monitoring physical exercise routines for people affected by HIV. The apps are used to encourage patients to exercise to improve their general condition.
• 4) Examination of the use of fitness applications to encourage people with a specific need due to their socio-demographic profile to follow the minimum physical activity requirements established by the WHO. This includes the specific physical exercise needs that can be implemented through apps for the elderly ( Mas et al., 2016 ), children ( Tripicchio et al. 2017 ), or people with disabilities ( Pérez-Cruzado and Cuesta-Vargas, 2013 ).
• 5) Study of factors affecting user motivation to continue using Fitness Apps. Increasing user motivation is an integral part of a significant number of articles. Very high abandonment rates are observed in the use of these applications, and there is a lack of user engagement ( Bardus et al., 2016 ). Among the factors that may influence the use of the apps, some researchers are interested in the aesthetics of the user interface ( Bardus et al., 2016 ), social relations ( Lewis et al., 2019 ) and the personalization ( Zhou et al., 2018 ).
• 6) Exploration of the social problems associated with fitness apps. Some articles focus on the problems related to fitness apps and the adherence to hegemonic beauty canons. In this line of research, Honary et al. (2019) concluded that the use of these apps might increase social pressure to achieve unrealistic beauty ideals and could thus increase the incidence of eating problems, such as anorexia or excessive physical exercise. Another issue of concern relates to the privacy of and the large amount of personal data collected by these apps ( Adhikari et al., 2014 ).
• 7) Examination of fitness apps as complementary products to wearable devices. Wearable devices provide more accurate and convenient data for measuring people's daily activity levels. However, they are usually associated with relevant mobile apps for health data visualization and analysis. For example, Lee et al. (2019) concluded that children who use wearable devices with mobile app interventions increase their physical activity over time. The emergence of the Internet of Things (IoT) has provided more help to improve people's health behaviors. However, this then brings up the issue of information security and privacy. Thus, Bohé et al. (2019) offer complementary approaches for building a better IoT ecosystem.

6. Conclusions and limitations

This study aimed to present in detail the current state of research on fitness applications through an exhaustive bibliometric analysis and bibliometric mapping. The social function and health potential of fitness apps represent a recent and growing phenomenon, which justifies an increase in the intensity of scientific research in recent years. 89.4% of the contributions were published 2014 onwards when the usage of these apps had already been an important trend in the commercial market for several years. Several bibliometric indicators (e.g., distribution of years of publication, Price's index, author productivity, Bradford's Law, h-index, number of citations, source of publication, research areas, research methods, etc.) were analyzed to understand the main features and patterns of research on fitness apps. Moreover, the scientific mapping analysis of the co-occurring keywords, co-authors, and co-citing authors provided an additional analysis from a time-depth perspective.

In general, it is important to note the great dispersion of research, with a very high number of authors who have only made one contribution being a characteristic of a field of research that has not yet reached maturity. Research in this field is still in its precursor stage. Moreover, many of the studies have a relatively high number of co-authors. This situation is reflected in the indicator of author productivity, which is relatively low (Oyibo, K. and Vassileva, J. being the most active author with eight published articles). However, the most productive authors are not the most influential authors. West. J.H. has gained 655 citations for his four articles, ranking first for this field of study.

This dispersion of research is also reflected in the source of the publications. Although there is a specialized journal in mHealth (JMIR mHealth and uHealth), it can be found that submissions on fitness apps are distributed across a large number of academic journals and conference proceedings.

With this data and support from the analysis of scientific mapping, it can be concluded that authors or prestigious journals have not been integrated and the research references in this field are relatively fragmented, partly due to their novelty and multidisciplinary requirements but also due to the technical orientation of the developers to circumvent the basic health, social, and behavioral aspects of health, society, and behavior.

As in many other areas, the United States remains a prominent contributor in this area. China and India are the most productive in developing countries. These two countries are increasing their productivity and expanding their influence in various fields of scientific research at present.

The most common research method used in this field is the experimental procedure that measures behavioral changes or changes in health indicators after a period of use. The second most used method is the survey, followed by the analysis of content.

A considerable amount of literature is related to medicine, computer science, and healthcare. Many authors have also focused on this main area of research.

Additionally, physical activity was the most frequently occurring keyword. “Behavior change” linked to “physical activity” is also an important keyword. Specifically, it refers to concepts such as behavior change theory, behavior change techniques (e.g., goal setting, self-regulation), etc. However, relatively few studies on consumer behavior from a social science perspective have been found. It seems that consumer-related research has mainly focused on analyzing the optimization of the functionalities of mobile applications from a medical or computer science point of view and neglected the aspects intrinsic to consumer behavior such as the motivations for using fitness apps, the attitude towards them, or how social networks influence the choice of the app to be used. The fact that the keyword “motivation” appears only 8 times and all after 2018 is a clear indication of this finding.

Based on the generalization of all the information obtained and the review of the abstract and some of the full text, we found that the performance and function of fitness apps, the benefits for chronic disease treatment, the influence of using fitness app for public health, and factors of motivations of using fitness apps are currently popular research topics in this field. Future research could build on these directions and incorporate relevant issues from a social science perspective (e.g., consumer motivations, consumer engagement, consumer behavior, etc.) to further investigate on fitness applications.

This article is useful in understanding the early state of research in the fitness app field. However, it is necessary to consider several limitations. One of the limitations of this study is the delimitation of the sample search criteria. In essence, the concept of fitness serves as a central reference for the applications that users utilize to perform self-monitoring of health-related factors, particularly the level of physical activity. The control of “diet” is another health factor that overshadows and is superimposed on the concept of fitness, but one that could also be considered as a separate field in future studies, or add it to the keyword search scope for getting more comprehensive results.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Yali Liu: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization. Maria Avello: Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

• Adhikari, R., Richards, D., Scott, K., 2014. Security and privacy issues related to the use of mobile health apps, in: Proceedings of the 25th Australasian Conference on Information Systems. ACIS, Auckland, New Zealand.
• S. Adriaanse L., Rensleigh C. Web of science, scopus and google scholar: a content comprehensiveness comparison. Electron. Library. 2013; 31 (6):727–744. [ Google Scholar ]
• Alvarado R.U. El crecimiento de la literatura sobre la ley de Bradford. Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información. 2016; 30 (68):51–72. [ Google Scholar ]
• Anderson P.M., Butcher K.F., Schanzenbach D.W. Understanding recent trends in childhood obesity in the United States. Econ. Human Biol. 2019; 34 :16–25. [ PubMed ] [ Google Scholar ]
• Bardus M., van Beurden S.B., Smith J.R., Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int. J. Behav. Nutr. Phys. Act. 2016; 13 (1) doi: 10.1186/s12966-016-0359-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bartoli A., Medvet E. Bibliometric evaluation of researchers in the internet age. Inf. Soc. 2014; 30 :349–354. [ Google Scholar ]
• Beldad A.D., Hegner S.M. Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of german users’ willingness to continue using a fitness app: a structural equation modeling approach. Int. J. Human–Computer Interact. 2018; 34 (9):882–893. [ Google Scholar ]
• Blackman K.C.A., Zoellner J., Berrey L.M., Alexander R., Fanning J., Hill J.L., Estabrooks P.A. Assessing the internal and external validity of mobile health physical activity promotion interventions: a systematic literature review using the RE-AIM framework. J. Med. Internet Res. 2013; 15 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bohé I., Willocx M., Naessens V. 2019 IEEE International Congress on Internet of Things (ICIOT). IEEE. 2019. An extensible approach for integrating health and activity wearables in mobile IoT apps; pp. 69–75. [ Google Scholar ]
• Bonato, M., Turrini, F., DE Zan, V., Meloni, A., Plebani, M., Brambilla, E., Giordani, A., Vitobello, C., Caccia, R., Piacentini, M.F., LA Torre, A., Lazzarin, A., Merati, G., Galli, L., Cinque, P., 2019. A Mobile Application for Exercise Intervention in People Living with HIV. Med. Sci. Sports Exerc. https://doi.org/10.1249/MSS.0000000000002125. [ PubMed ]
• Bradford S.C. Sources of information on specific subjects. Engineering. 1934; 137 :85–86. [ Google Scholar ]
• Broadus R. Toward a definition of “bibliometrics.” Scientometrics. 1987; 12 :373–379. [ Google Scholar ]
• Burke L.E., Wang J., Sevick M.A. Self-monitoring in weight loss: a systematic review of the literature. J. Am. Diet. Assoc. 2011; 111 :92–102. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cheng C.H., Chen C.-H. Developing a mobile APP-supported learning system for evaluating health-related physical fitness achievements of students. Mob. Inf. Syst. 2018; 2018 :8960968. doi: 10.1155/2018/8960968. [ CrossRef ] [ Google Scholar ]
• Cotton Victor, Patel M.S. Gamification use and design in popular health and fitness mobile applications. Am. J. Health Promot. 2019; 33 (3):448–451. doi: 10.1177/0890117118790394. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corbin C.B., Pangrazi R.P., Franks B.D. Definitions: health, fitness, and physical activity. Pres. Counc. Phys. Fit. Sports Res. Dig. 2000:1–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cowan L.T., Van Wagenen S.A., Brown B.A., Hedin R.J., Seino-Stephan Y., Hall P.C., West J.H. Apps of steel: are exercise apps providing consumers with realistic expectations? Heal. Educ. Behav. 2013; 40 :133–139. doi: 10.1177/1090198112452126. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cummiskey M. There's an app for that smartphone use in health and physical education. J. Phys. Educ. Recreat. Danc. 2011; 82 :24–30. [ Google Scholar ]
• de Oliveira, O.J., da Silva, F.F., Juliani, F., Barbosa, L.C.F.M., Nunhes, T.V., 2019. Bibliometric Method for Mapping the State-of-the-Art and Identifying Research Gaps and Trends in Literature: An Essential Instrument to Support the Development of Scientific Projects, in: Scientometrics Recent Advances. IntechOpen, pp. 1–20. https://doi.org/http://dx.doi.org/10.5772/intechopen.85856.
• Direito Artur, Dale L.P., Shields E., Dobson R., Whittaker R., Maddison R. Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health. 2014; 14 (1):646. doi: 10.1186/1471-2458-14-646. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duran-Sanchez A., Alvarez-GarcIa J., de la Cruz M., Maldonado-Erazo C.P. Collaborative economy: analysis of scientific production in academic magazines. Revista De Gestao E Secretariado-Gesec. 2016; 7 (3):1–20. [ Google Scholar ]
• Edwards E.A., Lumsden J., Rivas C., Steed L., Edwards L.A., Thiyagarajan A., Sohanpal R., Caton H., Griffiths C.J., Munafò M.R., Taylor S., Walton R.T. Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps. BMJ Open. 2016 doi: 10.1136/bmjopen-2016-012447. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gaviria-Marin M., Merigó J.M., Baier-Fuentes H. Knowledge management: a global examination based on bibliometric analysis. Technol. Forecast. Soc. Change. 2019; 140 :194–220. [ Google Scholar ]
• Hew J.-J. Hall of fame for mobile commerce and its applications: a bibliometric evaluation of a decade and a half (2000–2015) Telemat. Informatics. 2017; 34 :43–66. [ Google Scholar ]
• Honary M., Bell B.T., Clinch S., Wild S.E., McNaney R. Understanding the role of healthy eating and fitness mobile apps in the formation of maladaptive eating and exercise behaviors in young people. JMIR mHealth uHealth. 2019; 7 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kao H.-Y., Wei C.-W., Yu M.-C., Liang T.-Y., Wu W.-H., Wu Y.J. Integrating a mobile health applications for self-management to enhance Telecare system. Telemat. Informatics. 2018; 35 :815–825. [ Google Scholar ]
• Lee A.M., Biang J., Thompson L.A., Gurka M.J., Williamson V.G., Modave F. Efficacy and effectiveness of mobile health technologies for facilitating physical activity in adolescents: scoping review. JMIR mHealth and uHealth. 2019; 7 (2):e11847. doi: 10.2196/11847. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lewis Z.H., Swartz M.C., Martinez E., Lyons E.J. Social support patterns of middle-aged and older adults within a physical activity app: secondary mixed method analysis. JMIR Aging. 2019; 2 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Li Y., Ding J., Wang Y., Tang C., Zhang P. Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality. JMIR mHealth and uHealth. 2019; 7 (7):e13261. doi: 10.2196/13261. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lisée C., Larivière V., Archambault É. Conference proceedings as a source of scientific information: a bibliometric analysis. J. Am. Soc. Inf. Sci. Technol. 2008; 59 :1776–1784. [ Google Scholar ]
• Lister C., West J.H., Cannon B., Sax T., Brodegard D. Just a fad? Gamification in health and fitness apps. J. Med. Internet Res. 2014; 16 doi: 10.2196/games.3413. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lotka A.J. The frequency distribution of scientific productivity. J. Washingt. Acad. Sci. 1926; 16 :317–323. [ Google Scholar ]
• Lupton D. Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit. Public Health. 2013; 23 :393–403. [ Google Scholar ]
• Mas, A.M., Sampol, P.P., Conti, J.V., 2016. Efectos de un programa de entrenamiento presencial vs prescripción a través de una aplicación móvil en personas mayores. RETOS.Nuevas Tendencias en Educ. Física, Deport. y Recreación 32–37.
• Mata F., Torres-Ruiz M., Zagal R., Guzman G., Moreno-Ibarra M., Quintero R. A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning. Telemat. Informatics. 2018; 35 :837–853. [ Google Scholar ]
• Michels C., Fu J.-Y. Systematic analysis of coverage and usage of conference proceedings in web of science. Scientometrics. 2014; 100 :307–327. [ Google Scholar ]
• Moral-Munoz J.A., Esteban-Moreno B., Herrera-Viedma E., Cobo M.J., Pérez I.J. Smartphone applications to perform body balance assessment: a standardized review. J. Med. Syst. 2018 doi: 10.1007/s10916-018-0970-1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pérez-Cruzado D., Cuesta-Vargas A.I. Improving adherence physical activity with a smartphone application based on adults with intellectual disabilities (APPCOID) BMC Public Health. 2013; 13 :1173. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Priesterroth L., Grammes J., Holtz K., Reinwarth A., Kubiak T. Gamification and behavior change techniques in diabetes self-management apps. J. Diabetes Sci. Technol. 2019 doi: 10.1177/1932296818822998. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Price D.J. Columbia University Press; New York: 1963. Little Science, Big Science. [ Google Scholar ]
• Price D.J. Citation measures of hard science, soft science, technology, and nonscience. In: Nelson C.E., Pollak D.K., editors. Communication among Scientists and Engineers. Heath Lexington; MA: 1970. pp. 3–22. [ Google Scholar ]
• Rose J., Jiang Y., Mangematin V. Technological innovation mediated by business model innovation: app developers moving into health. Int. J. Technol. Manage. Inderscience Publishers. 2017:6–27. doi: 10.1504/IJTM.2017.085702. [ CrossRef ] [ Google Scholar ]
• Schoeppe S., Alley S., Rebar A.L., Hayman M., Bray N.A., Lippevelde W. Van, Gnam J.-P., Bachert P., Direito A., Vandelanotte C. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques. Int. J. Behav. Nutr. Phys. Act. 2017; 14 :n/a. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tang J., Abraham C., Stamp E., Greaves C. How can weight–loss app designers' best engage and support users? A qualitative investigation. Br. J. Health Psychol. 2015; 20 :151–171. [ PubMed ] [ Google Scholar ]
• Tripicchio G.L., Ammerman A.S., Neshteruk C., Faith M.S., Dean K., Befort C., Ward D.S., Truesdale K.P., Burger K.S., Davis A. Technology components as adjuncts to family-based pediatric obesity treatment in low-income minority youth. Child. Obes. 2017; 13 :433–442. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Van Eck N., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010; 84 :523–538. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Veloutsou C., Mafe C.R. Brands as relationship builders in the virtual world: a bibliometric analysis. Electron. Commer. Res. Appl. 2020; 39 [ Google Scholar ]
• West J.H., Hall P.C., Hanson C.L., Barnes M.D., Giraud-Carrier C., Barrett J. There’s an app for that: content analysis of paid health and fitness apps. J. Med. Internet Res. 2012; 14 doi: 10.2196/jmir.1977. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• WHO Non-communicable diseases [WWW Document] 2018. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases accessed 7.2.20.
• Zanjirchi S.M., Abrishami M.R., Jalilian N. Four decades of fuzzy sets theory in operations management: application of life-cycle, bibliometrics and content analysis. Scientometrics. 2019; 119 :1289–1309. [ Google Scholar ]
• Zhou M., Fukuoka Y., Mintz Y., Goldberg K., Kaminsky P., Flowers E., Aswani A. Evaluating machine learning–based automated personalized daily step goals delivered through a mobile phone app: randomized controlled trial. JMIR mHealth uHealth. 2018; 6 [ PMC free article ] [ PubMed ] [ Google Scholar ]

Listen to academic papers on the go

Computer science, trusted by 50,000+ students & researchers at ....

The world’s first app for listening to academic papers

“I can’t believe it-she sounds like a real person!

Listen to anything: just upload it from desktop or mobile

Pdfs and academic papers, documents: .doc, .ppt, .txt, .epub, etc.

A world class listening experience on any device

Take notes while on the go , with just one click!

Click the note button

We add the note for you

Export your notes

Automatically skip citations, references, and footnotes.

Save time by choosing which sections to listen to

What people think about us

This transformational tool is more affordable than ...

I don’t want to listen to all the references and citations. Can your app skip those?

Can it pronounce difficult words, like “mesothelioma” or “diphenhydramine”, i only want to listen to the abstract and results. how did you hear about us.

Chat AI - Bot Assistant 12+

Open ai chat writing app, zed italia s.r.l., designed for iphone.

• 4.8 • 572 Ratings
• Offers In-App Purchases

iPhone Screenshots

Description.

ChatAI is revolutionizing the way people live! The app will help you write social posts, find perfect presents for your loved ones or even rewrite essays. Our AI-powered app will provide helpful answers to all of your questions so that you always get the best results quickly and efficiently. Intuitive interface makes it easy to use for everyone from novice writers to professional authors! So if you need help writing something or finding what you're looking for quickly, then ChatAI is the perfect solution! Unlock the potential of AI assistance today! Subscription Terms: $4.99/week to make unlimited requests to chat, ads-free experience. - Payment will be charged to iTunes Account at confirmation of purchase - Subscription automatically renews unless auto-renew is turned off at least 24-hours before the end of the current period - Account will be charged for renewal within 24-hours prior to the end of the current period, and identify the cost of the renewal - Subscriptions may be managed by the user and auto-renewal may be turned off by going to the user's Account Settings after purchase - Any unused portion of a free trial period, if offered, will be forfeited when the user purchases a subscription to that publication, where applicable - You can cancel a free trial or subscription anytime by turning off auto-renewal through your iTunes account settings. This must be done 24 hours before the end of a free trial or subscription period to avoid being charged. The cancellation will take effect the day after the last day of the current subscription period, and you will be downgraded to the free service Privacy Policy: https://www.zedit.info/PrivacyPolicy.html Terms of Use: https://www.zedit.info/Service_Terms.html

Version 2.0.0

Enjoy the new version! -Bugs fixed -UI improvements

Ratings and Reviews

572 Ratings

New Business

I haven’t had the experience with this app to successfully write a review for you. I look forward to writing the best review you’ve ever received Ronald G. Von Fricken

Great App when it was working

This app is amazing but of recent it has stopped working and all you get is the typing screen for ever. And this app is not cheap. Whatever is going on with this app I hope they fix because I am about to ask for a refund.

Developer Response ,

We regret any inconvenience caused to you, and assure you that our team will check the issue you mention as soon as possible. In case you need to contact us, feel free to do it via email to [email protected] Meanwhile, please don't forget to update the app, and consider changing your rating. We would greatly appreciate it!

Cant copy and paste

App gives good answers. Only drawback is that I cant copy and paste using any mobile equipment. Also when trying to share to my social media, instead of sharing the content, the app shares the app link.

App Privacy

The developer, Zed Italia S.r.l. , indicated that the app’s privacy practices may include handling of data as described below. For more information, see the developer’s privacy policy .

Data Used to Track You

The following data may be used to track you across apps and websites owned by other companies:

Data Not Linked to You

The following data may be collected but it is not linked to your identity:

Privacy practices may vary, for example, based on the features you use or your age. Learn More

Information

English, Dutch, French, German, Japanese, Korean, Portuguese, Spanish

• AI Open Chat Assistant Ad-Free $5.99
• Weekly $4.99
• ChatAI - AI Open Chat $69.99
• ChatAI - AI Open Chat $44.99
• ChatAI - AI Open Chat $29.99
• ChatAI - AI Open Chat $7.99
• Weekly $5.99
• ChatAI - AI Open Chat $49.99
• ChatAI - AI Open Chat $19.99
• ChatAI - AI Open Chat $39.99
• Developer Website
• App Support
• Privacy Policy

More By This Developer

Faceover: Photo Face Swap

Smart Switch my Phone

Parallax: 3D Photo Editor

Reelsy Reel Maker IG Templates