Getting fit with a smartphone

Abstract

Maintaining physical health using fitness applications on the smartphone has become an increasingly popular trend in today’s society. The technical capabilities of the smartphone allows for the tracking and recording of user movement, which is the hallmark of all location based services. This paper presents an investigation of Endomondo, a Danish fitness application and social fitness network. Based upon an analysis of the application and gathered user-survey data, the paper seeks to uncover underlying motivational facilitators, which are created by the application. The research presented here, finds that users become increasingly engaged and motivated in workouts when using this app. Utilizing the social elements of gamification and the affordances of a location based network, the app succeeds in keeping the user motivated. Furthermore, the users become motivated by the locative tracking data collection, which is automatically gathered and saved during workouts.

Keywords: Fitness apps, Endomondo, location aware services, motivation, gamification, quantification

1. Introduction

Mobile communication technologies are increasingly influencing our lives. To a great extent, they have become ubiquitous artifacts. The modern smartphone is specifically an amazing device. Its uses seem almost endless. With this device we are able to coordinate our everyday lives, keep in touch with our loved ones, maintaining our social networks, watch our favorite TV shows or handle our taxes. Of course, much of this was also possible before the arrival of the smartphone, as seen with the basic texting and voice calling capabilities of lower entry cell phones. However, what makes the smartphone especially interesting, is the capability brought forward by its large screen, fast processor, wireless transceivers/senders and its fast mobile broadband data connections.    

Utilizing some of these capabilities, the smartphone has now entered the arena of personal health, which promises to be a big deal in 2013. A new trend in smartphone applications, and indeed in society itself, seems to be geared towards maintaining and leading healthy lifestyles in one form or another. If you want to shed a few pounds, optimise your sleeping pattern, quit drinking or simply monitor your blood glucose levels, there is an app out there to help you. In this sense the modern smartphone is becoming an extension of our very body, always there to monitor and guide us towards specific health goals. This new trend has even spawned a new term mHealth, which is broadly defined as the emerging mobile communications and network technologies for healthcare systems (Istepanian, 2005).

Within the arena of health applications are those specifically geared towards physical activity, namely the fitness apps. Fitness applications such as Strava, Nike+, Runkeeper, SoFit and Endomondo are examples of smartphone applications, which facilitate physical activity by tracking all sorts of outdoor workouts such as running, cycling, swimming or similar types of sport. What makes these apps especially interesting, is their distinct ability to influence, for good and for worse, the lives and behavior of the people using them. One such example, albeit a tragic one, can be found in the case of an American cyclist who lost his life in a bike crash while trying to reclaim his first place ranking in one of Strava integrated competitions (Forbes, 2012). As this example illustrates, tragic as it is, these applications make for a compelling research area within the field of mobile communication. Therefore the focus of this paper is aimed towards these types of applications but in particular the Danish sports tracker; Endomondo.com           

Endomondo is a personal trainer application and part fitness-focused social network. The app is used to track workouts, which are shared with a community of like-minded fitness enthusiast. Users can cheer each other on, through comments or pep-talks (transmitted to the receiving smartphone during the workout). Furthermore, challenges between the users are an integrated part of the application framework. Endomondo describes itself as:

A social sports and fitness network with community tools that enable people worldwide to engage around their passions for living vital, healthy lives. It centers on activities and sports that unify physically active individuals and promote exercise for recreational and serious athletes alike. The mission is to make fitness fun and engaging.

Endomondo, 2013

The motivation to use such an application may be plentiful, ranging from the basic desire to lose weight, maintain certain fitness levels, burning more calories than your colleague or getting fit to run the New York Marathon. This paper seeks to investigate the impact this application has on its users, no matter what their individual goals might be i.e. losing weight, getting fit etc.

2.1 Research question

Through analysis of social impacts of the app, the paper seeks to identify if Endomondo is in fact changing the fitness habits of its users and why. A working hypothesis is that people become increasingly motivated to exercise when they use such a fitness application. Therefore, the main question for this paper is: Does the smartphone application Endomondo affect the motivation and fitness habits of its users and why?

2. Method

The approach of this paper is based on a triangulation approach, which is broadly defined by Denzin (1978, p. 291) as “the combination of methodologies in the study of the same phenomenon”. In the case of this paper, this is achieved by the combination of theoretical analysis and analysis of quantitative survey data.

2.1 Theoretical Analysis

The theoretical analysis aims at outlining Endomondo within the context of location aware services. Specifically the analysis is concerned with the elements and specific affordances present within the ecosystem of Endomondo, which might support user motivation.

2.2 Quantitative data collection

At the onset of this project an online with the users of the Endomondo survey was conducted. The purpose of the survey was to gain insight into the most valued features and general usage patterns of the application. The survey was advertised within the Endomondo forum threads. The survey received answers from a total of 113 Endomondo users, which forms part of the following analysis.

3. Location based services

Location based services (LBS) are those services where the geographic location of the users is an integral part of the application. However, the key to LBS is the user’s involvement and their distinct ability to interact, but more importantly, generate the content. Hence, an LBS is characterized as a service, which enables users to determine their location, access information related to the location and dynamically interact with the information or content. In order to make the LBS function, the following four components are required: a mobile handset, a content provider, the communication network and the positioning component (Aktihanoglu & Ferraro, 2011). Essential to LBS’s is the positioning component, which makes it possible for the system to determine the actual location of the handset. There a couple of methods of establishing the position of a device, such as cell tower triangulation, Wi-Fi and GPS. In the case of Endomondo, the GPS is utilized since this provides an accurate tracking position of the user whilst conducting workouts outdoors.      

Within the ecosystem of LBS exists the location based social networks (LBSNs). The basic premise of LBSN is that it allows the users of mobile devices to exchange details of their location as a key point of interaction i.e. the user interacts with the application in order to make their current location known on the network. Depending on the service, the shared location information can be viewed by a subset of other users e.g. only friends of the user, or the entire network of users (Curran & Traynor, 2013). Endomondo falls within the above category of one such location based social network and it is indeed the social capabilities of the application, which are highlighted.

The social dimension is what really makes Endomondo engaging and sets it apart from other fitness apps. Your friends can follow your workout live and if they think you need their help to give it a little extra, they can send you peptalks that are read aloud to you in real time – a popular feature used for both encouragement and harassment! The app also allows you to race against a friend’s previous workout with help from the audio coach

Endomondo, 2013

But what does this mean in terms of driving motivation and altering behavior of the people. In order to answer this question we need to probe into the workings of the application and its users.

3.1 Overview of Endomondo

Endomondo can be broken up in two main parts; the web-based desktop application and the actual smartphone application. The smartphone application is an essential part in the system since it is responsible for recording the real-time movement i.e. location, speed and distance of the active user. When the users initiate their workout, the tracked information is automatically uploaded and stored on the Endomondo web application. Previously, management of tracked data and interaction with other users was only possible on the desktop application but the boundary between the two parts are becoming increasingly blurred, since the smartphone application can now do most of what is possible with the desktop application.

Overall, the system function in much the same way as any other online social network site i.e. Google+ or Facebook. Within the network, the user maintains a profile and has the possibility of interacting with a network of known and unknown users. Highlighted features consist of commenting and “liking” functions and public workout competitions. Furthermore, users have the possibility to challenge one or more friends in individual workout competitions, share annotated favorite workout routes, compare personal best results with other users or evaluate individual workout data gathered during the workouts. The general use of these features is analyzed in the following section.

3.2 Dissecting the collected user data

The purpose of the user survey was to identify, which features the users value the most and if the application has indeed altered any behavior towards new fitness habits. Figure 1 shows the age and gender distribution across the network of users. A quite substantial majority of the users are males within the age range between 26-40 yrs. followed by a second group of users within the range of 41-55 yrs.

The fact that the majority of the users belong to a mature segment of users is somewhat surprising and goes against the common assumption that young people are more likely to this type of application. According to Castells (2007), “several of the trends that seem to be most significant in transforming communicative practices have been observed primarily among young users of wireless communication” (p. 246). It should therefore be highly expected to see more than 4% of users under the age of 19. A possible reason can perhaps be explained by the nature of this specific type of application combined with life passages undertaken by users. Ling and Yttri (2002) states “The child’s experience of and attitude towards technology is likely to be different from their parents’ and perhaps even their older siblings. Thus, one must select that information that is relevant rather than accept it whole cloth” (p. 10). Teens are generally more focused on tending to their social relationships, whereas older segments may be in a state of life where the traditional family life takes precedence and focus is increasingly aimed towards personal health and well being.

Figure 2 shows the fitness level of the users before and after joining Endomondo. The numbers are somewhat arbitrary since they are based on the subjective opinion of each respondent. They do nevertheless indicate a clear tendency towards improved fitness levels on users after joining the network. This tendency is supported by the fact that 87% of the respondents agrees or strongly agrees that Endomondo has motivated them to work out more often.  This is rather important since it clearly indicates a behavioral change. The users are not just increasingly motivated to do the workouts; they actually go out and do them. The relatively high percentage of “poor-shaped” beginners, further suggests that the motivation to train has indeed been fueled upon downloading the application.

But which features are most valued by the users and what is the actual driving force of these behavioral changes? With a couple of exceptions, the overall usage patterns of the various features such as peptalks, comments, audio coach and “likes” are quite fragmented. These features seem to be of little importance for a large portion of the users. In fact, when asked whether peptalks, likes or comments increase the motivation of the user, only 46% agrees or strongly agrees. This is rather surprising considering these are the exact social dimensions, which Endomondo emphasize as being specially engaging features of the network. The responses reveal two other areas, which are highly valued by the users; the challenges and the individual statistics.

In the public challenges on Endomondo, users are competing across the network to burn the most calories, or track most miles. The challenges are often sponsored and users get the chance to win different prices. A total of 62% users agree or strongly agree that these challenges makes them workout more. This indicates that there is a great deal of competitive behavior at work here, which will be discussed in the following section.
Figure 3 shows the use of individual statistical data on the network. The statistical data section is the most commonly used feature within the network. 57% answered that they are using this feature a great deal, whilst 30% said they are using it quite a bit.

The collected data from the survey broadly suggests that the users motivation to participate is mainly fueled by achieving personal goals rather than nurturing social interests. This is not entirely unexpected, since the prominent workout forms within the network such as running, cycling or swimming are often individual activities. The next section will discuss what exactly this means in the greater context of the application.

4. Discussion

It has been established that the application does in fact motivate the users towards physical activity. The following section is divided into two parts, each discussing how the application actually facilitates this engagement and motivation. Firstly, the social aspect of the application is discussed and secondly the app is discussed as being an instrument of the individual.

4.1 Gaming and social cohesion

The fact that the users seem to care little about certain socially oriented functions does not mean the application is stripped of social impacts. The application can be viewed as more than just the fitness network, it can also be considered as a location-based game. Location based mobile games (LBMGs) are quite similar to the location based networks described above, but with the added inclusion of some gaming logic. According to Gordon and de Souza (2011), location based mobile games are characterized by incorporating competition and cooperation awards such as points or badges for participation. The addition of these game-like features can potentially create user motivation (Gordon & de Souza, 2011) This is exactly what is seen with challenges on Endomondo. The users are motivated to participate since they get the chance to both beat other users but also win a prize.

In location based mobile treasure hunt games such as Botfighter and Mogi, the users are following specific paths through the city in order to collect virtual objects. Users of these games have been observed to change their day-to-day movement patterns through the city, just to play the game (Gordon & de Souza, 2011, p.70). While the game on Endomondo may seem rather simple compared with these LBMGs, the challenges do in fact foster similar kinds of behavior change. A user might for example gain some extra mileage by taking a detour on the bicycle on the way to work. In this manner, the gamification elements made possible by the network facilitate the motivation to workout more.

In addition to the these gaming elements, the motivational driving forces are also enhanced by a form of social cohesion within the network of users. Ling (2008) argues that that the mobile phone has the potential to connect individuals in new ways in that mobile communication can be used to develop and maintain social groups into a form of bounded solidarity. I believe such a bounded solidarity is in fact formed within the network since all the users are essentially heading towards a common or shared goal, which is characterized by physical activity of some such.  Furthermore, the users may experience what I will define here as positive peer pressure i.e. an increased incentive to try to match or outperform other users. This notion is supported by various research on how mobile communication influence behavior change. Gay (2009) found:

Mobile technologies can encourage social facilitation—the notion that individuals are more likely to perform simple or well-learned behaviors if they believe that their behavior is being observed; social comparison, which posits that individuals seek to perform at a similar level to those around them; and social cognition, which among other things asserts that individuals tend to model their behavior around those around them, particularly those who appear to be successful.

Gay, 2009, p. 56

Since the workout data of users is publicly visible across the entire network, unless the user has made the data private, users are influenced by one another. This is one of the common traits of social facilitation where an individual will perform better if he is surrounded by others performing similar tasks, and if these others are slightly better at performing the task (Gay, 2009).One could argue that the users may not pay much attention to their immediate close ties within the network, but the social aspects of the broader array of users (weaker ties) are prevalent factors for creating and maintaining an important part of the motivation for tracking workouts.

4.2 Endomondo as a bookkeeper (Instrumental usage)

In addition to the social facilitators embedded in the application, the instrumental features in the system also facilitate user motivation. These instrumental features are mainly those bound to the application framework and the technical capabilities provided by the handset itself. An instrumental feature is for example the ability to track and record the pace or workout distance. As seen in the survey data, the most common used feature of the application is keeping track of training results using the statistical data, which is recorded during the actual workout. This kind of personal data recording has given birth to the term “Quantified self”, which is defined as the act of leading a better life by continuous numerical measurements of bodily functions and activities such as sleep, diet or athletic performance throughout a substantial amount of time (Roberts, 2013). In all its simplicity the recorded data on Endomondo provides the users with a clear visual representation of their activities and their improvements or lack thereof. Some of this statistical data is represented as a type of user achievements. Examples of this include the amount of burgers burned during exercising or trips to the moon, based on the tracked distance of workouts. This specific type of data representation serves as positive form of feedback, which increases motivation to do more.  

Finally, “numbers don’t lie”, which may also act as a great motivation to improve results. Consider a distance runner wanting to beat his previous record on a specific route through the countryside. He is able to do exactly so by loading up his previous results into the smartphone app and start competing against his previous result. Of course, there may be instances where the overall data collection could also work in a counterproductive manner, by discouraging the user to track workouts if he is unable to beat a previous result. However, the survey does not initially suggest that this is a common issue amongst the respondents.

The discussion of the social and instrumental affordances of the application as outlined above reveals the main motivators needed to continuously keep tracking workouts. The identified elements align quite well with earlier findings concerning intrinsic motivation on mobile communication units. Gay (2009) found the following:

These motivators are frequently referenced as factors contributing to successful behavior change—in this case, using a device—and they include challenge (the setting of goals that are adequately difficult, but not impossible to reach), control (the individual’s perceived ability to exert control over their environment or an application), competition (comparing oneself to others with a desire to outperform), cooperation (working with others toward a common goal), and recognition (positive feedback in direct response to an accomplishment).

Gay, 2009, p.50

Hence the motivation on Endomondo is shaped by the combination of social facilitators and the individual activities represented as quantified user data, all of which is made possible by the affordances of the location based system.

5. Conclusion

The ability to shape user-motivation is one of the distinct characteristics of the Endomondo application. This is indeed an application, which facilitates behavior change. The users, which primarily consist of demographically mature audiences, are increasingly improving their fitness levels upon downloading the application. The motivation to train is largely propelled by underlying social aspects. These include gaming, competition and elements of social cohesion. Furthermore, the collection of tracking data serves as a valuable feedback, which users utilize by comparing personal and public training results. The key component of the application is thus the gathering and representation of location-based data, which is stored and shared across the network of users.

Further investigation need to be done within this area, in order to fully understand the social impacts of these applications. Implications in regards to privacy, negative peer pressure or destructive behaviour such as over training may act as the foundation for future studies.