Sensory sensemaking : a study of fitness bands and its quantified body representation of young Indians
Article information
Abstract
There has been an explosion of wearable fitness-tracking consumer devices in India. The body and its functions are now visually analysable using these bands. The notion of being mindful about the body is fused with a fitness culture. This culture is supported by a growing market for fitness commodities that work towards transforming the self into computable data.
This paper attempts to explore the manner in which fitness tracking devices enable positive reinforcement in young Indians. The primary means of data collection for the study was in-depth interviews with young adults (18 to 25 years of age). The participants expressed an affective relationship with the devices, especially the impact of data visuals that was a motivational factor in improving user performance. The study uses the Self-Determination Theory to map user motivation around fitness devices. The qualitative findings revealed the usage of fitness trackers in young Indians to be a private endeavour. While participants felt a positive sense of agency with the devices, they were also averse to the idea of competing within virtual fitness community.
Introduction
In recent times, technology has started to become an indispensable part of our lives. Amidst the COVID-19 pandemic, an unprecedented rupture has brought the world to a halt, changing our ways of living, working, and playing. As digital technology remains the only means of being productive and staying connected in a world of social distancing, it becomes important to critically analyse the current reality of digital datafication of our bodies.
There are numerous applications (apps) on a smartphone that assist us in carrying out our day-to-day activities. From multi-way splitting of bills, to online bookings of tickets, and even to one’s heart rate monitoring, these applications have now come to be indispensable to contemporary lives and using them provides significant insights into individual actions and behaviour. For instance, waking up earlier to give your body more time to meet the daily fitness targets. These insights help us start new activities or change existing ones and to eliminate habits that we no longer find productive (Williamson, 2015). There is an increasing demand for wearable technology, and a significant share of this is contributed by fitness bands. There is also a predicted steady increase in the demand for these devices since 2016 (Jay, 2020; Statista, 2019). These devices provide numerical feedback about various aspects of health and wellness to the user. It measures a user’s step count, running miles, sleep time, pulse or heart rate, body weight, body temperature, calories burnt, moods, and exertion levels. Some of the trackers even measure blood oxygenation, body temperature, and sugar levels, thus, making it convenient for users to monitor their essential body information. In other words, fitness bands help users become more conscious of their bodies (Lupton, 2018).
One of the best ways to make users become aware of their bodies is to sell the idea of weight loss (Liu, Nichols & Zillifro, 2013), although wearing a fitness band in itself does not guarantee a person’s weight loss; it needs to be combined with an exercise plan and a healthy diet. In a data-driven lifestyle, caloric measurement of diet along with an exercise regime makes weight loss goals seem achievable (Eikey & Reddy, 2017). Data in the fitness bands is collected through multiple sensors present in the band like blood pressure, step count, and heart rate, which is displayed in numbers. Numbers are further displayed in presentable formats such as graphs, diagrams, and other kinds of attractive animation depending on the application’s interface. For most users, this numerical feedback reinforces and motivates them to design and change their behaviour based on their goals (Esmonde, 2019). The decision to walk home or to use a vehicle, for some, would depend on their number of steps taken in a day (Lev, 2019).
There is a direct relationship between the visibility of bodily information and an associated responsibility to act (Ruckenstein, 2014). The idea of making this representation fun and engaging gives a new kind of value to one’s personal realities and everyday habits (Hong, Lin, & Hsieh, 2017). The intention of such data visualisation is to change the ways in which people interpret numbers and work with them. Hence, there are frequent developments in the user interface to make the inter-operation of data motivational.
Studies have discussed the challenge that users face in trusting the data of fitness bands as opposed to the sensations and indications their bodies provide them with (Lupton, 2019). Pink et. al. (2016) noted that measuring people’s physical exercise using their fitness devices showed that for most participants their data visualisations on the application were more reliable to them than the body sensations.
The knowledge that is generated by the fitness tracking applications about one’s body has become an extremely valuable feature for users to refer to (Lupton, 2018). The applications constantly attempt to bridge gaps between the tracking and interpretation of data through visuals. Interpretation of the visuals is the first step where data makes entry into cognition (Chen et al., 2018). There is limited study conducted on how people choose to rely on numbers and visuals (McCosker & Wilken, 2014). Although this phenomenon of a third-party enhanced awareness is said to be more impactful and accurate (Lupton, 2019), there is an increasing challenge in how one chooses to interpret the self by looking at data visuals.
This study, therefore, focusses on understanding how users of fitness bands interpret the visuals of their body data. The participants in this study expressed how the digital devices helped them recognise and monitor the impact of exercise on their bodies. They considered the data to be more truthful and accurate. This is primarily due to the ability of the applications and the devices to set, measure, monitor, and automatically track individual fitness goals. The participants mentioned, how until they saw the data provided by their fitness bands, they did not really know if their bodies were responding to the exercise routines. Tracking the data triggered a sense of fitness achievement in them. Using the framework of Self-Determination Theory, this paper attempts to explore the manner in which fitness tracking devices enable positive reinforcement in young Indians.
Literature Review
Quantified Self
Prior to digital revolution, individuals have always collected data to measure various aspects of their lives. It can range from using devices like stopwatches, calculators, and scales to employing more advanced algorithms of machine learning and artificial intelligence. Body tracking devices emerged with pedometers and accelerometers, which helped to quantify the number of steps a user would take in a day. Today, this function of self-tracking is carried out through wrist-worn devices known as fitness bands such as Mi Smart bands or Fitbits. This sector has seen intense competition among major players like Apple, Samsung, Fitbit, Garmin, and Xiaomi for market share of fitness trackers which is believed to grow into a 49.45 billion USD market by 2025 (Impact of COVID-19 Outbreak on Fitness Tracker Market, 2020). The user’s ability to gather, analyse, and interpret their fitness data has increased the utilitarian value of such digital self-tracking devices (Till, 2014).
Quantifying data of one’s body is not new. However, the idea of ‘The Quantified Self’ was initiated recently by Garry Wolf and Kevin Kelly in 2007 (former editors of the Wired magazine). Wolf and Kelly started ‘The Quantified Self’ (QS) movement (Wolf, 2011) with the objective of attaining self-knowledge through numbers (Wired, 2009). The participants consisted of users and tool makers—also known as Quantified-Selfers (QSers)—who practiced tracking and sharing every aspect of their lives on a regular basis. Users shared their data by collaborating on local meetup talks, online conferences, and blogs to make sense of their personalized data (McDonogh, 2015). For instance, one participant from the Quantified Self conference in Amsterdam stated how he lost twenty kilos by monitoring and re-designing his ‘after lunch moods’ with flashcards (The Economist, 2012). By undertaking such practices, the body affordances work with both digital and non-digital affordances to motivate changes in one’s behaviour (Davis & Chouinard, 2016). Since individuals interact with the social world through embodying social codes of behaviour, it is important to reduce uncertainties that could potentially rupture social interactions (Beck, 1992).
The process of building the self in contemporary society relies on reductions of uncertainties surrounding the body (Beck, 1992). Wearable tracking devices create a belief of reduced uncertainties by treating the body similar to a machine-like entity with inputs and outputs presented as performances that can readily be measured and quantified. Quantification of individual actions and behaviours reduces the probability of social ruptures, and subsequently, social risks. The risk involved in every activity an individual performs is embodied with insecurity about their bodies (Beck, 1992). For instance, at-risk bodies, such as people with high cholesterol, blood pressure, asthma, pregnant women, or even those seeking to reach a fitness level benefit from the close monitoring and surveillance (Thomas & Lupton, 2016).
With the use of self-trackers, one attempts to control the risk to some extent. When users gather data of themselves, they are generating knowledge for themselves. This data, when analysed, can be used as a means to avoid probable illnesses associated with the heart, including obesity, high blood pressure, high blood cholesterol, and type 2 diabetes (Hearn et al., 2014). Therefore, accurate numbers are sought out that can contribute towards the feeling of a risk-free individual, by making them believe they have more power over their actions (Lupton, 2012).
Personal Informatics and Visualisations
Increasingly studies are encouraging people to lose fat in order to reduce the risk of heart diseases, kidney problems, high blood pressure, and diabetes amongst others (Zhu et al., 2020; Bennett & Holmes, 2017). In India alone, such diseases are causing 5.8 million deaths per year (Shrivastava et al., 2017) which has spurred the growth of the fitness industry. India has an increasing population of millennials with disposable income who have become conscious of their health, and this has translated into an upward trend in the sale of fitness bands (Financial Express, 2018). Fitness bands in the wearable category are the most popular gadgets in terms of demand (International Data Corporation [IDC], 2019). These devices surpassed smartwatches with an annual growth rate of 140% between 2013 and 2017 (Research and Markets, 2018). The wearable market is growing at an annual rate of 7.9%, with its global sales expected to reach 302 million units by 2023 (IDC Quarterly Wearable Device Tracker, 2019). Millennials globally comprise the largest user group for fitness bands (Escherich & Jump, 2017). This group particularly feels that tracking fitness on the go is the most beneficial aspect available (Escherich & Jump, 2017). Such tracking enables the quantification of all activities one engages in during the day. Such numbers are alluring as they are a seemingly objective representation of an activity. The numerical data helps users make informed decisions (Thomas & Lupton, 2016). There are certain types of software and hardware that assists individuals in collecting information about themselves with the goal of self-understanding, known as personal informatics (Li et al., 2012). A fitness band/activity tracker collects through its sensors a wide range of data like user’s movement, continuous heart rate, duration and quality of time in bed, flight of stairs climbed, water consumption, and calorie consumption, and some even measure breathing quality. Although the band’s light-emitting diode (LED) display shows a limited representation of the body data, the intention is to ensure that users are aware of their physical activity at all times (Consolvo et al., 2008). To get a better understanding of this data, the device is synced with the respective brand’s app on the smartphone or stored on the brand’s website. These services intend to nudge the users to improve their fitness and in turn, their health.
There has been a shift from haptics as a means of knowing one’s body. Visuals have started to play a significant role in communicating about one’s anatomy. The technologies used for screening such as X-rays, tomography, ultrasound, magnetic resonance imaging (MRI) have all altered the way people feel about their bodies. Earlier, sensations felt on the body would be the driving force to go meet a physician, and even the physicians would use touch to diagnose the patient (Rich & Miah, 2009). The visual has taken precedence; medical science opts to show visuals to patients about their bodies. Therefore, visuals or data that are generated are often privileged to be more objective than the signs offered by the body (Rich & Miah, 2009). Digital devices showcase this data displayed through attractive visuals such as colourful bar graphs, pie charts, and frequency polygons. Therefore, users enjoy the visual data as it becomes easier for them to trust numbers over physical sensations.
Many users often find glanceable display, interactive application, and fitness device essential in driving a change in their fitness habits as it allows space for feedback and self-management (Consolvo et al., 2008). The designers of self-tracking wearable devices aim at using interactive tools such as reminders, positive reinforcements, and trackers to ensure longer periods of activity (Munson, 2012). Several studies find that participants agree that visuals are a crucial component of the system of body data representation (Epstein et al., 2014; Karlsson, 2019; Bergroth, 2019). These devices also give the user a choice to opt for various feedback mechanisms. For instance, there are features that provide the individuals a choice to compete with others via ‘Duels’ or social UX (user experience) features (Depper & Howe, 2017; Spillers & Asimakopoulos, 2014). The users have the flexibility to opt out and that allows them to decide what type of encouragement is best suited for them (Gupta et al., 2020); while some enjoy sharing their goals and badges, others benefit from the encouragement they receive (Rapoport, 2017). The feedback mechanism plays a crucial role in deciding how the user will choose to interact with the fitness data generated.
The drive for self-optimisation through monitoring one’s body has reduced humans to mere numbers, in the form of data doubles. Gathering data and presenting them in a visual format gives rise to emotional attachments between the information and the individual (Lupton, 2012). Once data pertaining to the body is generated, affective ties between the user and their actions are observable (Pantzar & Ruckenstein, 2014). For instance, users using a pedometer feel emotionally elated when their step count increases (Giddens et al., 2017). This represents the development of an affective relationship that encourages walking related activities. This affective attachment can also be noticed when users use words such as ‘annoying’ or ‘irritating’ in response to their data. This further reinforces the affective ties between the user and the device, thereby creating new affordances for the users’ bodies.
However, merely knowing the fitness data and seeing it through graphs and charts is not sufficient to understand one’s health. With the popularity of the QS movement, data is seen as an end in itself. This has reduced the understanding of oneself to a quantified uniform body that produces a data driven aesthetic visualisation. This is largely due to the fact that several users are unable to effectively interpret the available data, resulting in them being stuck in a race without a concrete understanding of the fitness features and/or its relationship with one’s health (Marcengo & Rapp, 2012; Fritz et. al., 2014). This paper attempts to explore the manner in which fitness tracking devices enable positive motivation in young Indians. The study also seeks to understand the impact of visual representation of data on its users’ physical activity.
Theoretical Framework
Self-Determination Theory
Human behaviour over time is predictable and controllable by stimuli from the external environment; these are known as antecedents (Edmunds et al., 2007). Antecedents, also known as psychological predictors, are stimuli that the body receives before beginning an activity. An individual is conditioned to respond when his/her body receives the antecedents. This model of Stimulus-Response (S-R) was founded by Skinner in his writings "The Behaviour of Organisms" (1938) and in "Science and Human Behaviour" (1953). In fitness trackers, antecedents could be the reminders—a notification or a vibration from the tracker—which tells the individual to meet the daily goals. On responding to the antecedents, the user is rewarded with virtually animated badges or goal completion messages. The S-R model works towards conditioning behaviours which translates into self-induced initiations of and adherence to fitness related activities. This form of motivation that is triggered by one’s volition is the core idea behind ‘Self-Determination Theory’ (SDT).
SDT examines behavioural change through S-R by focusing on motivation as a key to understanding what drives humans (Deci & Ryan, 2008). SDT postulates that the satisfaction of one’s primary psychological needs of autonomy, competence, and relatedness sustains motivation. Autonomy is experienced when an individual feels a sense of agency over their actions. Users need to feel that they are in control of their own behaviour that is free from all external sources of pressures and control. Competence is the confidence one experiences when executing a task. When an individual develops mastery over certain tasks, they are said to be competent Relatedness is the sense of belongingness that is associated with connecting with others in a community.
This study uses the SDT framework to interpret the experiential and motivational element of the user’s body data. In this theory, there are three basic needs for effective functioning and psychological health, which we will understand through the lens of a fitness tracker:
1. Autonomy: Self-monitoring and reinforcing are two tools employed by trackers. For instance, Fitbit provides its users with their activity summaries via the dashboard (Karapanos et al., 2016). Other fitness bands also have similar applications for their users. Such features assist individuals to monitor their body data, thereby motivating them to perform better.
2. Competence: Goals function as benchmarks allowing users to either customize their own activities or go ahead with the default tasks. Numerical feedback, in terms of achievements and scores, provided by the device on the dashboard fill the users with a sense of pride. Additionally, individual performance is motivated through the possibility of earning digital badges on completion of one’s goals. Collection of points, badges, and other forms of positive reinforcement plays a crucial role in creating a perception of competence amongst the users (Marcengo & Rapp, 2012). This sense of competence is in turn reflected through their goal-directed behaviour (Tak et al., 2017).
3. Relatedness/Connection: The tracking of fitness data allows users to connect with others within their fitness community. This sense of community and belonging promotes healthy competition amongst the members. With features like Fitbit Community and custom leader boards, one can safely assume that users are adding a new value to their day-to-day activities by interacting with each other online. They can challenge each other and feel a greater degree of relatedness while exhibiting higher motivation to exercise. Along with this, the latest self-tracking technology allows users to publish and share their scores and ranks on social networking websites like Facebook and Twitter. This allows people to compare and compete, thereby, motivating them to attain greater fitness levels (Choi & Kim, 2016; Lupton, 2012) and prolonging the usage of such fitness bands. Given that most of these fitness tracking devices make use of data visualisation, the design of the device and its user interface become crucial to the process of self-determination that shapes how users respond to their fitness applications. Empirical research shows how users rely more on data visualization as they find it more real and easier to believe (Lupton, 2016).
Methodology
This is an exploratory study which seeks to understand how young adults interact with data generated through fitness bands in India. The questions that the paper seek to answer include how does the tracking and monitoring of fitness data shape the manner, in which young Indians approach personal fitness and how does it affect their behavioural patterns?
In-depth interviews were conducted to investigate the impact of fitness tracking devices on young Indians. Interviews are a more naturalistic and less structured data collection tool (Alshenqeeti, 2014) as they offer insights into the nature of motivation involved in the use of fitness bands. As part of this study, a survey-based pilot was used to identify the participants. Respondents were asked to fill out a questionnaire based on which the sample size for the study was selected. Interviews were conducted with 20 young Indians between the age group of 18 – 25 years who were identified through non-probability sampling. While purposive sampling was the primary means to filter the participants, a few were also identified through snowball sampling. Research suggests that purposive sampling criteria create homogeneity and ensure data saturation with 12 – 15 interviews (Bertaux, 1981; Creswell, 2007; Guest et al., 2006). To ensure data saturation in this study, 20 interviews were conducted (Creswell, 2007; Guest et al., 2006) between the months of April and May, 2020.
The aim of the study was not to standardize and compare results, but to gain insights into each respondent’s beliefs, opinions, attitudes, and motivations associated with fitness tracking devices. The in-depth interviews were semi-structured. The interview schedule was aligned to the three psychological needs—autonomy, relatedness, and competence—in order to understand how fitness data motivates and shapes perception of individual bodies. The 20 odd participants were enthusiastic about their usage of fitness bands, unlike the general population of users that the pilot study had gathered earlier. Majority of them were graduate students, while others were professionals. The interviews were conducted for a span of 35 – 55 minutes and were audio recorded. Each of the recordings was transcribed. 14 of the 20 respondents used the MI band (manufactured by the Chinese company, Xiaomi), whereas six of them used Fitbit (manufactured by Fitbit, an American electronics company). Although, the Mi bands were all of the same make, the Fitbit devices had different models.
To interpret the data collected, thematic analysis was employed (Pinnegar & Daynes, 2006) which involves “identifying, analysing and reporting patterns (themes) within data” (Braun & Clarke, 2006, p.79). The data collected was coded by identifying the interesting aspects and adding brief descriptions (Denzin & Lincoln, 2005). This assisted in categorising the data by concepts (Green et al., 2007). Both open and axial coding techniques were applied for a comprehensive and flexible analysis of data (Fereday & Muir-Cochrane, 2006). The thematic analysis helped in interpreting, identifying, and coherently presenting the key motifs from the responses provided by the participants.
Findings
Each of the participants had been using the device for about a year or more. They had been wearing them either throughout the day or during their workout sessions. Except for one respondent, who was curious to monitor his heart rate when asleep, all the others would remove the band at night before sleeping. Although none of them claimed to be strongly attached to or dependent on their devices, the study revealed that they were still positively affected by the devices. They were inclined to know more about their bodies and fitness levels based on the data from the fitness devices.
Background Information
Most of the respondent’s focus was to primarily measure two to three parameters, such as step count, calories burnt, and heart rate. Seven respondents hadn’t entered their Body Mass Index (BMI) data and used the device merely to be aware of the number of steps they walked in a day. Another seven respondents wore it to the gym to map their workout sessions in a comprehensive manner. On starting the exercise app, these respondents were able to calculate their workout intensity in the form of duration of their exercise, enhanced heart rate, energy expended, and calories burnt. Interestingly, respondents were excited to use the device during their early stages of adoption, but with time, the device became more of a fashion statement for them. One of respondents had even bought different coloured straps for the band that would match his outfit, thus, adding to his style statement. The same respondent also noted that others in his fitness community were using the device to genuinely track and sustain their fitness goals. Apart from the fitness tracking, several respondents made use of the device’s additional features such as accepting and rejecting incoming calls, reading messages, and setting reminders.
Data Display and Agency as Motivational Regulator
The study showed that both numerical and graphical feedback played a crucial role in determining the degree of interaction respondents had with their fitness tracking devices. Most of them felt that the feedback from the previous day motivated them to pursue higher goals the following day. “It is the fitness band’s ability to push yourself to set higher goals,” expressed respondent 19. According to her, having access to the fitness data made her feel a sense of achievement: “You can see with a momentary glance the progress you are making, it is that extra push, that little motivation you need, which, without the device you wouldn’t have” (Respondent 19, Personal Communication, 12th May, 2020).
The data enabled the respondent to discern the improvement in her body that would otherwise have been hidden. Another respondent felt that the representation of body data through graphs was more effective as he was able to effortlessly understand the indexical relationship between his bodily sensations and its representation. He says:
I like seeing graphs because it is easy to comprehend. Numbers are a very complicated way to understand what’s happening. Graphs help me because I am too tired after the day so the simple graph looking up and down help me understand my overall workout activity of the week in comparison to looking at the preceding days exercise numbers (Respondent 8, Personal Communication, 28th April, 2020).
This indicates that graphs provide a wider, more comprehensive understanding of one’s fitness data by displaying not just the figures of the present day’s data but also accurately compiling the data over a period of time. The collation of data in this manner helps compare and analyse the micro (hourly/daily) and macro (weekly/monthly/yearly) understanding of ones’ progress.
Contrary to respondent 8, a few of the other respondents stated that it did not matter whether they could make sense of the numerical/graphical data as long as they were able to experience a gradual improvement in their overall performance. Respondents were able to exercise their agency by being concerned only with the macro elements and ignoring the micro elements such as in-app notifications and guidance tools. Participants also expressed that they felt more in control of their fitness regimes since the apps provided them with the option to customise and regulate their fitness expectations. The degree of agential control that they could exercise, thus, determined their involvement with the device. Some of the respondents also stated that irrespective of the data analytics they preferred to follow their body cues to set their fitness targets. For instance, one respondent mentioned:
Whenever I’m running, I don’t stop pushing myself until my heart rate goes anaerobic. That’s when you’re supposed to stop. It’s when oxygen stops flowing into your blood. You have a target set and you want to reach it so it acts like a milestone for you and you tend to push yourself to that and when you attain it, it’s done and you can go home (Respondent 3, Personal Communication, 19th May, 2020).
Yet another respondent said that with the passage of time it became easier to understand their fitness requirements and set their goals on the device accordingly (Respondent 4, Personal Communication, 30th April, 2020). Most users also discussed how long-term usage of the bands made them understand and negotiate the features of the fitness band to suite their health requisites. Over a period of time they also realised that the devices were assisting them in making their everyday decisions with ease and clarity. For instance, several of them found it easier to decide whether or not to take the elevator or climb up the stairs based on their fitness data. Even health-based decisions became easier to make through such a data-driven lifestyle. Long term usage of the wearables, thus, established an affective relationship between the body and the device.
As opposed to these respondents, a few others stated that, after having used their devices for a long period of time, they did not pay much attention to the data which was being compiled and generated. Some of them also shared that in the long run there were times when they did not wear the fitness bands either consciously or unconsciously. Sometimes when they were in a rush or did not wish to be distracted by the constant burgeoning of fitness data, they would not wear it. For example, respondent 10 stated that despite it becoming a habit, he wouldn’t wear the device when his university examinations were on (Personal Communication, 18th May, 2020).
However, such temporary lapses (that could arise from illness, increased workload, or vacation) in using the device did not significantly affect their usual physical activities. So, even on days when respondent 10 was not wearing the device to the university he was still aware of the distance covered based on his knowledge of having had his walks to the university quantified.
Two of the respondents stated that they had not uploaded their BMI data on their wearables in order to chart out and manage their personal exercise routines (Respondent 9 & 10, Personal Communication, 18th May, 2020). This shows that users, in managing the data entered, can control the functioning of the device. However, it cannot be claimed that the respondent is completely autonomous in operating the device as these devices are designed with the objective of persuading users to stay on their fitness tracks. Hence, these devices come with features that provide insights, guidance, reminders, notifications, and motivational content to the users. For example, the flashing LED lights on these devices are cues to engage in physical activities. As a result, individuals are driven to complete tasks that they would not normally choose to do or even forget to do.
Affective Engagement for Enhanced Performance
The level of affective engagement users have with their fitness devices plays a crucial role in motivating them to accomplish their goals. For instance, one user stated in response to their goal completion that:
When you start, there’s an emoticon on the smart band that makes a sad face so when you reach fifty or seventy, it shows neutral, and once you show ninety or have almost completed, there’s a smile on the face. This is on the smart band though, not on the app (Respondent 7, Personal Communication, 28th April, 2020).
The apps are designed to encourage users to be positively connected with their devices that in turn encourages them to alter their attitudes towards health and well-being. The role of such fitness devices is important as they help users overcome feelings of doubt about the amount of physical activity they have completed. The quantification of physical exertion offers a degree of clarity to users, thereby, reducing any element of distress linked with their fitness aspirations. One of them shared that:
I feel incomplete when I go for a walk or a run without my band. It happens usually when I forget to charge my band. I find it very frustrating, because when I am running, I usually keep checking my band to measure the distance I have covered or the duration of my workout. It’s like you have a purpose or an intent when you exercise, but when you cannot see it on the band you feel anxious, whether you are doing it properly or not, it is just frustrating (Respondent 17, Personal Communication, 15th April, 2020).
For several respondents, the ability to self-surveil was equivalent to feeling healthy. The respondents found virtual rewards, badges, trophies, etc., to be alluring and motivating, which helped them focus on their fitness goals. External motivators along with notifications acted as stimuli in triggering a behavioural change, and respondents felt that it was far more effective than their own self-motivational system. One respondent stated:
One of the things that MI bands do is that if you don’t move in an hour, it starts vibrating and starts showing some animation where a guy is getting off a chair. Yeah it does. It makes you realize you’ve been sitting idle for too long. So, I just get up and walk around (Respondent 1, Personal Communication, 11th April, 2020).
Most of the respondents shared experiences that showed the affective connection between their physical activities and their devices. However, there were a few who found such recurrent notifications bothersome and intrusive. They noticed that fitness bands tend to overplay the rewards by giving badges for activities as trivial as opening the application for three days in a row. Most respondents said that such accolades do not translate into them opening the application more often. Respondent 13 stated:
Yeah, a lot of badges. All those stupid things that arise out of the app. I have a lot of badges. Every fifteen days, I see a new badge popping up. Sometimes it’ll tell me that I open the app everyday so here’s a badge for that also. And ‘today you completed these steps’ and things like that (Personal Communication, 22nd April, 2020).
Despite these irritants, the majority of the participants felt a relational connect with their devices and they approached their workouts with heightened focus. Together with self-surveillance, the data generated by the fitness devices positively affected the performance of the users.
Privacy and Data Sharing within Fitness Communities
The apps allow users to engage with fitness communities within a competitive setup. This encouraged users to share their individual fitness data with one another and virtually participate in fitness competitions. The larger objective is to prompt users to undertake more physical activities. However, most of the respondents revealed that they did not use such features. Very few users felt motivated when they had to compete against their families on the leader-board. They preferred keeping their data private to avoid experiencing any kind of performance pressure. Respondent 12 shared:
In my class there is another student who has a Fitbit and whenever we meet, she always asks me how many steps have I taken so far. Now, I am a private person and I do not wish to compete with her as I believe she will go around announcing the outcomes to everyone. Due to her nature to constantly compare our data, I don’t want to have any conversations about my data with her (Personal Communication, 16th May, 2020).
Some even cited lack of interest and being embarrassed as reasons for their privacy. Respondent 4 also highlights the perils of unhealthy competition when data is shared within the fitness community. She expressed:
Sometimes I felt guilty about my low step count when I compared it against my friend’s. There were times when instead of going to bed I would just be walking back and forth in the corridor of my house, scaring anyone who came out of their room, just to go to bed with a respectable number (Respondent 4, Personal Communication, 15th May, 2020).
The sharing of data caused the respondent to feel unwarranted peer pressure resulting in guilt and negative feelings about herself. She felt compelled to meet certain standards in order to fit within the fitness community. Respondent 17, on the other hand, was excited to share his data on social media—Instagram and Facebook—as he felt the need to show off his achievement. He said, “[i]t gives me motivation. Once I got a badge for running 100 km in fifteen days. It inspired me and it’s a great thing to put on social media” (Respondent 17, Personal Communication, 12th May, 2020). He also stated that he was inspired by the comments that recognized his effort. Thus, sharing one’s fitness data on occasions when personal achievement was higher than the general trajectory did act as a motivational force. However, the majority of the participants did not belong to any fitness community. They felt that their gym trainer was equipped enough to answer their queries on matters of fitness.
Discussion
The representational data of wearable fitness devices prompts a self-deterministic perspective amongst young Indians with regard to their physical activities. The study shows the strategies respondents engaged with while using their fitness devices in order to satisfy their needs for competence, autonomy, and relatedness. The research shows that overall, the participants felt motivated by the antecedents shared by the fitness devices and their apps.
The study found that the fitness devices allowed individuals to determine their fitness routines based on their perceptions of their fitness levels. The ability to curate a personalised fitness plan by altering the predetermined goals set by the device instilled a sense of control in the users. They could use their agency to override some of the pre-set goals on the device. For instance, they could change the pre-set goal of 10,000 steps to a higher number if they perceived that to be necessary for their body type. This encouraged users to push themselves towards more intense levels of physical activities. The data visuals further reinforced the user’s sense of agency by letting them monitor the indexical relationship between their exercise routine and its graphical representation.
Although, the constant barrage of notifications, reminders, and insights did upset the users—at times making them feel less in control of their fitness plans—for the most part, they were satisfied with their experience of the device as it enabled an in-depth understanding of their physical activities, allowing them to compare data and identify progress. The manner in which the devices allowed the users to perceive themselves became key to satisfying their need for autonomy.
The study also shows that affective attachments were crucial in satisfying the user’s sense of competence. The degree of interaction users had with their apps significantly pointed towards how competent they felt when undertaking their physical activities. Virtual rewards and data graphics did generate strong positive feelings amongst the users about their continued use of fitness trackers.
The devices also created a facilitating environment for the users by providing regular feedback in the form of notifications, rewards, and prompts that boosted their sense of competence. Notifications of having completed the activity goals positively impacted their self-determined motivation. The opposite was also true for some of the users as they felt that their sense of competence and motivation was at times negatively impacted when ‘alerts’ from their devices showed that they had underperformed.
The study also showed a lack of affiliation between users and their fitness communities. Most of the respondents did not seek support through the community features of the apps. They sought out the expertise of their gym trainers instead of their peers from the virtual fitness communities. Moreover, respondents chose to keep their fitness data private either due to a feeling of inadequacy or to avoid being pressured into virtually competing with others from the fitness community.
They found the idea of competing to be emotionally and physically taxing. Most of them also considered their fitness endeavours to be a private exercise.
The study found that apart from relatedness, the fitness trackers did help the young Indians to meet their needs of autonomy and competence. They were motivated to engage in physical activities, although it was largely a private endeavour. The fitness wearables with their alluring data representations and interactive features did manage to establish an affective connect with the users encouraging them to bring about healthy lifestyle changes.
Conclusion
The widespread notion of being mindful about one’s body is supported by the larger rhetoric of fitness culture and in this context, fitness bands have been playing a crucial role. In their ability to generate fitness data, these devices have been spearheading the drive for a healthy lifestyle. Nonetheless, a data driven lifestyle risks reducing one’s understanding of their own bodies to mere numbers. Fitness devices can lead to number fishing, wherein users merely complete activities for the explicit awards designed by the apps (Fritz et al., 2014). The study was designed to investigate how fitness data representations motivate and shape self-determination practices of young Indians. SDT, with particular focus on autonomy, competence, and relatedness, showed how the fitness wearables determined the levels of motivation young Indians felt towards engaging in physical activity.
While existing scholarship primarily approached the areas of fitness and health by focusing on the intrinsic and the extrinsic aspects of motivation (Deci & Ryan, 2010; Koka & Hein, 2005), this study expands on the role of antecedents in determining individual motivation towards the use of fitness devices. Additionally, as opposed to previous literature that connected relatedness to an individual’s increased engagement with physical activity (Zhang et al., 2011; Vallerand & Losier, 1999), this study shows that among young Indians relatedness caused undue performance pressure. The respondents of this study avoided competing with peers because they felt that the comparisons it generated undermined their progress. Competition was perceived to weaken their self-determined motivation. The respondents, therefore, avoided any sense of belongingness by deeming their healthy lifestyle ambitions as private endeavours.
The study highlights the impact of two antecedents—autonomy and competence—that enhanced the user’s sense of self-determination to perform better. Users experienced an increase in their agential capacity as they could tailor their activity goals as well as track the indexical relationship between their work-out session and its data visuals. The study also identified the ways in which users were affectively impacted by the fitness devices and how their sense of competence was interlinked with their fitness routine. Their dependency on the devices for self-surveillance and motivation led to a heightened sense of achievement and competence.
Further Research
The study, however, is not exhaustive. Future works could study these fitness apps and their social impact by creating a sample size based on the Diffusion of Innovation theory (Rogers, 1962) which divides users into innovators, early adopters, early majority, late majority, and laggards. Such categorisation of respondents can elucidate how users negotiate with the three antecedents (autonomy, competence, and relatedness) based on their stages of adopting the fitness devices. Moreover, the notion of neo-liberal sameness in a data driven lifestyle and how it homogenises individual human bodies numerically could also be explored.