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Interpreting fitness: self-tracking with fitness apps through a postphenomenology lens

DOI:10.1007/s00146-021-01146-8
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Elise Li Zheng at Georgia Institute of Technology
Elise Li Zheng
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Abstract and Figures

Fitness apps on mobile devices are gaining popularity, as more people are engaging in self-tracking activities to record their status of fitness and exercise routines. These technologies also evolved from simply recording steps and offering exercise suggestions to an integrated lifestyle guide for physical wellbeing, thus exemplify a new era of "quantified self" in the context of health as individual responsibility. There is a considerable amount of literature in science, technology and society (STS) studies looking at this phenomenon from different perspectives, linking it with the sociology of self-surveillance and neoliberal regimes of health. However, the human-technology interface, through which the micro- (behavioral) and macro- (social) aspects converge, still calls for extensive examination. This paper approaches this topic from the postphenomenological perspective, in combination with empirical studies of design analysis and interviews of fitness apps, to reveal the human-technology link between the design elements and people's perception through the direct experiences and interpretations of technology. It argues that the intentionality of self-tracking fitness app designs mediates the human-technology relations by "guiding" people into a quantified knowledge regime. It shapes the perceptions of fitness and health with representations of meanings about a "good life" of individual success and management. This paper also gives a critique of current individual, performance-oriented fitness app designs and offers the possibility of seeking alternatives through the multistable nature of human-technology relations—how altering interpretation and meaning of the design with a cultural or social context could change the form of technological embodiment.
Diagrams of fitness achievement. The left shows the daily/weekly/monthly summary of exercising time. It shows that the user (the author) has done 36 min of exercise in two routines on this particular day, burning 110 cal. In the middle, it shows "My Data," with a myriad of different types of exercise (Starting from the left blue box counterclockwise): total time, fitness routines, Yoga, walking, running, cycling. On the right, it continues with a weekly chart—if you have exercised, a circle will be filled, followed by the walk steps (the green box, where the number is 0), fitness score (the purple box, linked to Fig. 1 if the user clicks on it) and bodily metrics (here shows body weight, BMI and height)
Diagrams of fitness achievement. The left shows the daily/weekly/monthly summary of exercising time. It shows that the user (the author) has done 36 min of exercise in two routines on this particular day, burning 110 cal. In the middle, it shows "My Data," with a myriad of different types of exercise (Starting from the left blue box counterclockwise): total time, fitness routines, Yoga, walking, running, cycling. On the right, it continues with a weekly chart—if you have exercised, a circle will be filled, followed by the walk steps (the green box, where the number is 0), fitness score (the purple box, linked to Fig. 1 if the user clicks on it) and bodily metrics (here shows body weight, BMI and height)
… 
Exercise plan. The left shows the "personalized plan" in a week's span and one routine for the day. For any routine, it shows an introduction, equipment, and how popular it is among users. The time and calories are the core elements, while there's a description of the "purpose" of this workout routine. However, terminologies are there; users do not have to understand them, and some are confusing due to bad translation. The right is how the training plan looks like in the Chinese version. The day-to-day routines are at the bottom, while the overall length (5 weeks, four days per week, 27 min per day) and goals, running and intensity are briefly displayed. The diagram shows how this plan enhances specific parts/capacity of the body (upper limb, lower limb, whole body, core, and cardiovascular)
Exercise plan. The left shows the "personalized plan" in a week's span and one routine for the day. For any routine, it shows an introduction, equipment, and how popular it is among users. The time and calories are the core elements, while there's a description of the "purpose" of this workout routine. However, terminologies are there; users do not have to understand them, and some are confusing due to bad translation. The right is how the training plan looks like in the Chinese version. The day-to-day routines are at the bottom, while the overall length (5 weeks, four days per week, 27 min per day) and goals, running and intensity are briefly displayed. The diagram shows how this plan enhances specific parts/capacity of the body (upper limb, lower limb, whole body, core, and cardiovascular)
… 
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AI & SOCIETY
https://doi.org/10.1007/s00146-021-01146-8
ORIGINAL ARTICLE
Interpreting fitness: self‑tracking withfitness apps
throughapostphenomenology lens
EliseLiZheng1
Received: 6 August 2020 / Accepted: 12 January 2021
© The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
Abstract
Fitness apps on mobile devices are gaining popularity, as more people are engaging in self-tracking activities to record their
status of fitness and exercise routines. These technologies also evolved from simply recording steps and offering exercise
suggestions to an integrated lifestyle guide for physical wellbeing, thus exemplify a new era of "quantified self" in the context
of health as individual responsibility. There is a considerable amount of literature in science, technology and society (STS)
studies looking at this phenomenon from different perspectives, linking it with the sociology of self-surveillance and neolib-
eral regimes of health. However, the human-technology interface, through which the micro- (behavioral) and macro- (social)
aspects converge, still calls for extensive examination. This paper approaches this topic from the postphenomenological
perspective, in combination with empirical studies of design analysis and interviews of fitness apps, to reveal the human-
technology link between the design elements and people’s perception through the direct experiences and interpretations of
technology. It argues that the intentionality of self-tracking fitness app designs mediates the human-technology relations by
"guiding" people into a quantified knowledge regime. It shapes the perceptions of fitness and health with representations of
meanings about a "good life" of individual success and management. This paper also gives a critique of current individual,
performance-oriented fitness app designs and offers the possibility of seeking alternatives through the multistable nature of
human-technology relations—how altering interpretation and meaning of the design with a cultural or social context could
change the form of technological embodiment.
Keywords Postphenomenology· Self-tracking· Fitness apps
1 Introduction
The spring of 2020 is certainly not the best time to take
exercises—the global pandemic COVID-19 has trapped mil-
lions of people across the world at home (especially those
told to self-quarantine). Gyms are closed; running or walk-
ing outside is not encouraged; there is certainly no team
sports due to social distancing; Those who would like to
stay active have to turn to in-door alternatives like fitness
apps. "Making your home your own gym," claimed a popu-
lar Chinese fitness app "Keep" on one of their campaigns
during the coronavirus outbreak.1 However, for those who
desperately want to keep their shape tuned during a difficult
time, another well-known slogan from "Keep" is much
more needed to progress during isolation when everyone is
trapped indoors—"Self-discipline brings freedom."2
Fitness apps have gained popularity with widely avail-
able mobile personal technology like smartphones and wrist-
bands, years before the COVID-19 outbreak. These "gig-
economy" innovations focus on the consumer’s market and
correspond with self-tracking and self-management trends,
promoting extensive engagement through interactive design
elements. With these innovations, fitness apps have revealed
various intriguing façades of health and fitness in our daily
life. For those who want to exercise everywhere and keep
track of their fitness status, fitness apps can easily help
users be physically active anywhere and everywhere. The
apps help manage their daily physical exercise, movement
* Elise Li Zheng
elise901@gatech.edu
1 Department ofHistory andSociology ofScience
andTechnology, Georgia Institute ofTechnology, 221 Bobby
Dodd Way, Atlanta, GA30309, USA
1 Promotional message retrieved in the “Discovery” and “Commu-
nity” section (where users can find online campaigns or exercise chal-
lenges to complete) of “Keep” Fitness App, February 2020.
2 This message shows up every time a user opens the Keep App.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... For those who want to exercise everywhere and keep track of their fitness status, fitness apps can easily help users be physically active anywhere and everywhere. The apps help manage their daily physical exercise, movement, or even dieting regiment, summarizing one's fitness status with quantified data charts such as the MyFitnessPal in the US or "Keep" in China [23]. ...
... For commercial applications, the goal is to expand the user base and increase engagement. Monthly active users (MAU) and daily active users (DAU) are the two most important indicators for evaluating the success of mobile applications and their profit potential [23]. Keep is available in 15 languages, including Chinese, Korean, Japanese, French, and German. ...
... Map learning enables a model to automatically learn the relationship of attributes from past data [21]. Unsupervised learning exploits unanswered data, but in the case of supervised learning, the answer comes from the data, which creates a general decision rule [23]. ...
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