Why Are We Hooked on Self-Tracking Fitness Devices?
New research explains women's fascination with wearable fitness technology.
Posted Jun 08, 2019
When I meet a friend for coffee, she first takes out her phone, as she explains, to check that her fitness app has calculated her steps from her car to our café. Another friend joins us just in time to report how much she loves her Strava: She has been the Queen for her usual bike route since this morning. I feel a bit left out as I have no idea of my step counts and do not even know what Strava is. My friends, seeing my phone, immediately urge me to check the health app that comes with it and if it is automatically turned on. Indeed, I find the app that asks me, the first thing, if I want to share my health data. While I am not quite prepared to do that, we are able to detect the steps I have taken during the day and I can now join the conversation to compare our individual fitness results. While I can connect with my friends’ discussion, I am still wondering why, exactly, have so many women been hooked to follow their fitness apps?
Personal health-tracking technologies are gaining popularity: Consumer studies show that 15% of Americans currently use wearable technologies daily and a further 56% wants to monitor their health behavior through such devices (Sanders, 2017). The expanding technology with internet connection, such as my mobile phone, now links even uninformed citizens unintentionally into their net. Having been caught, I am also wondering what are the exact benefits of knowing my step counts. Can there also be unintended consequences when detecting and sharing one’s personal fitness data? The popularity of fitness tracking, or “sensor mania” as Swan (2012) has described it, has also awakened researchers to comment on the use and meaning of these devices in the current health and fitness conscious era.
Wearable technologies, including mobile phones and such wearable devices as the Fitbit, enable individuals to monitor their behavior by quantifying it into numerical form. With technological advancement, these devices, in addition to steps, can now track and analyze, for example, “physical movement, food and drink intake, energy expended, sleep levels, blood glucose levels, cholesterol levels, calories burned, mood and emotion, inactivity” (Williamson, 2015, p. 137). Obtaining these numbers, then, should help us make informed decisions about health behavior choices. Because we are able to measure our outcomes through mobile devices, this phenomenon is called mHealth. The WHO (2011) has even devised an official definition for mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices” (cited in Rich & Mia, 2017, p. 85). mHealth activity, thus, assumes a digitally engaged patient who takes the responsibility for her own health through preventative measures such as regular physical activity. Rich and Miah (2017) note that lifestyle apps that track health and fitness are, indeed, the largest mHealth category with around 30% of the total share of wearable technologies.
The numbers from our fitness apps (e.g., steps taken, calories burned) turn into ‘personal analytics,’ the numerical data that represent us as individuals. This is what is labelled as self-quantification: in mHealth the individual only exists in a numerical form, as data, or as a ‘data double’ for the actual person wearing and using the devices.
This data can be effectively used to optimize one’s health behavior. We learn how to tune and perfect our bodies to align with recommended health measurements. Gilmore (2016) summarizes that “wearable fitness technology is ostensibly designed to help users live longer through strategies promoting physical wellbeing” (p. 2527). In this sense, mHealth provides a route to self-betterment with a way to effectively monitor one’s behavior against clearly set goals.
We have long been encouraged to set goals to stay physically active, but the data tracking devices seem to have several advantages when compared to simply trying to follow one’s exercise prescription or a healthy diet. They encourage long-term engagement by establishing clear numerical goals that are easy to follow and also compare to others. We quickly assume a habit to check our fitness apps as we check our phones.
Gilmore (2016) has described these technologies as ‘everywear’ technologies that follow us where ever we go during the day. These ever present technologies also enable constant attention to one’s fitness goals. But how do people get hooked into such relentless surveillance of their exercise habits?
Several researchers point to the enjoyment users draw from accumulating information in their devices. Collecting clear numerical information that is easy to understand and comparing them to previous results is motivating. For example, Aristea Fotopoulou (Fotopoulou & O’Riordan, 2017) describes the game features of her Fitbit that make its use playful and fun. Also, Fitbit makes the quantifiable data more accessible with visual images through the user’s online account where one can visually detect how many calories are left to eat, how many more steps to take, or how much water to drink each day. These goals are set through Fitbit’s own health formula (e.g., 10,000 steps/day) or the user can set her own goals. In addition, Fitbit rewards its user with badges and levels through which one can advance one’s performance. These game-like features keep the user motivated and as Fotopoulou concludes, we learn that self-tracking is fun. Having fun, thus, is “a new way of dealing with the ‘serious’ world of health” (p. 60). Because a user can now accumulate data, it is possible to constantly compare one’s performance. This is another feature that makes tracking a habit.
Personal fitness information can be shared with friends through apps and websites. This, Gilmore (2016) notes, distinguishes tracking devices from such tools as pedometers or accelerometers that were previous used to quantify fitness performance. This feature also emphasizes the competitive aspect of personal fitness tracking because we can now easily compare our results to our friends’ or, on a larger scale, many other users’ performance.
This is all positive as these strategies can help individuals lead healthier lifestyles. However, many researchers also point out several potential problems with using tracking devices. The first problem derives from sharing personal data over the internet.
Fotopoulou and O’Riordan (2017), for example, pessimistically note that while comparisons and competition between users can be motivating, “the primary aim, as with other similar health-related businesses and cloud-based tracking devices, is the collection of personal data from the user” (p. 59). For example, Fitbit, similar to my phone health app, prompts users immediately to consent to share their data with the Microsoft Health Vault, a central node for sharing health information on Windows 8. While this is not compulsory, sharing data through apps always provides some access to user’s personal details. This way, the researchers caution, every exerciser becomes a part of an extended, digital information system distributed worldwide. In this system, individuals tend to lose control of the use of their personal data (e.g., Sanders, 2017).
The second problem is related to specifically gendered meanings of health that are reinforced through the use of tracking devices. Fotopoulou and O’Riordan (2017), for example, observe that women, more than men, use fitness tracking to fulfill the expectations of obtaining an ideal body shape. In her research, Sanders (2017) focuses particularly on how women’s health is sold through fitness tracking.
Sanders (2017) counts herself among the many women who calculate their caloric and nutritional content, try to compensate for regrettable food choices with exercise to then measure miles ran or walked, average pace, and minutes exercised. With these measurements, self-tracking devices have also been taken up in the fashion and beauty industry as essential tools to improve one’s body shape: magazines like Elle now promote them as a means to ‘build a better body.’ Using this type of advertising that normalizes the unreachable perfect feminine body ideal, Sanders argues, the fashion and beauty industries continue to create women who are dissatisfied with their bodies and in need of new, more effective programs to perfect their bodies.
Because the tracking technology facilitates a more detailed and nuanced analysis of caloric consumption, diet, and exercise levels, it can work more effectively as a tool for women’s continual body work. Indeed, Sanders (2017) points out, that the devices can be geared towards weight loss as a primary goal. Her own UP app, that does not allow for users to specify their own goals, serves as an example:
“In the past few weeks I have been amiably encouraged to get more sleep if I am ‘looking to drop a few pounds’; to ‘choose protein and whole grains, like eggs and oatmeal, over pastries’; to ‘avoid eating within two or three hours of bedtime’; to ‘fight fat’ by eating apple peels; and to ‘reduce [my] caloric intake by swapping high-cal foods for their healthier brethren’” (pp. 51-52)
Such advice, Sanders (2017) continues, conflate women’s health with norms of feminine beauty. While we might voluntarily track our behavior and choose to set weight loss goals, our choices and enjoyment of following our progress through our devices, are, nevertheless, dictated by larger social ideals of what feminine bodies should ideally look like. Sanders argues that “digital self-tracking devices, and the fashion/beauty, public health, and marketing discourses that endorse them, will amplify women’s sense of obligation to engage in digitally assisted regimes of self-improvement in the name of both health and beauty” (p. 52).
As the digital data is stored in our personal smartphones or computers, we might not realize that collecting confidential and personal information can be steered by larger social norms. The constant surveillance of one’s appearance now empowered by new technology, Sanders points out (2017), can encase women within greater control by patriarchal power relations. Furthermore, she continues, as digital self-tracking devices and apps are typically designed by men, they tend to represent what men define as important aspects of women’s health and well-being.
After reading Sanders’s (2017) account, I am determined not to ride the wave of self-tracking. If its technology only tightens the surveillance, I don’t want to add it to the numerous ways through which I already try to improve my appearance.
Sanders, however, does not endorse such a tactic if I am to resist the ‘normalizing beauty regimes.’ She does not find total digital ‘abstinence’ practical. Instead, she advises first, to turn purposefully ‘goal-unoriented’ to resist, for example, weight loss targets embedded in our devices and instead, surrender “to an open-ended and temporally unbound transformative experience” (p. 56). Second, we should use these devices as tools for inventing ourselves “as something new and not yet imagined” (p. 56). Third, she suggests that we focus, not on the quantifiable, numerical data made available by our devices, but “the quality of one’s interior experience during exercise” (p. 56).
While these can be solid advice, I am wondering if I do not need goals supported by numerical data—the special motivating feature of self-tracking devices—why should I use them at all? Neither do I need a self-tracking device, but mindfulness, to feel and experience my exercise. Can an old-fashioned way of simply attending a mindful exercise class or going for a walk without tracking devices serve, after all, as a tool of reinventing oneself? Or better yet, should I rather think of ways to create more diverse ideals for feminine beauty?
Fotopoulou, A., & O’Riordan, K. (2017). Training to self-care: Fitness tracking, biopedagogy and the healthy consumer. Health Sociology Review, 26(1), 54-68.
Gilmore, J. N. (2016). Everywear: The quantified self and wearable fitness technologies. New Media & Society, 18(11), 2524–2539.
Rich, E., & Miah, A. (2017). Mobile, wearable and ingestible health technologies: towards a critical research agenda. Health Sociology Review, 26(1), 84-97.
Sanders, R. (2017). Self-tracking in the digital era: Biopower, patriarchy, and the new biometric body projects. Body & Society, 23(1) 36–63.
Swan, M. (2012). Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1, 217–253.
Williamson, B. (2015). Algorithmic skin: Health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education, Sport, Education and Society, 20(1,) 133-151.