Digital Tracking Technologies: a New Way to “Achieve” Health?

Digital tracking technologies have become increasingly prevalent in recent years. Products like Fitbits, or apps like Strava and MyFitnessPal, allow individualized data to monitor and improve one’s health. Factors contributing to overall health, like calories burned or amount of sleep, are quantified. The use of such technologies allow a more personable approach to health. Tracking may allow individuals to become more aware and in control of their health habits. They may even empower and motivate individuals for personal improvement. Individuals who use these products have found to have better sleep, weight loss, and decreased consumption of products like cigarettes or alcohol1. Nonetheless, there are also some downsides to using these technologies.

Individualistic culture has created the association between one’s moral character and health. An example in which this can be seen is weight stigma; individuals who are considered overweight are associated with qualities like laziness, irresponsibility, or lacking discipline2. Digital tracking technologies perpetuate a culture of individualism, presenting a solution to health problems through individual tracking1. The products are purchasable tools that individuals can use to “achieve” good health3 4. Those who use digital tracking technologies are framed as more responsible individuals of society1. The products are proof that an individual has taken personal responsibility for their own health. Unfortunately, uncontrollable factors influencing health may not be taken into consideration when using these technologies. If using these products symbolize a morally responsible and healthy individual, what does that say for those who cannot afford digital tracking technologies? Or for those who do use them but do not achieve “optimal” data? 

Let’s examine the quantification of healthy weight. Body mass index (BMI), is a universal weight measurement system that classifies individuals based on their age and weight5. Apps like the Heath App, used on iPhone, use BMI as a way to indicate healthy weight levels. But what are some problems with the use of BMI? The measurement system, used in many clinical evaluations to determine weight categories, is used because it’s easy and efficient. It does not, however, always appropriately measure body fat and skeletal muscle mass6. Individuals who have a higher muscle mass or a lower body fat percentage may be categorized incorrectly into “obese” or “underweight” categories5. Therefore, apps that use BMI may be misleading for those who are tracking their weight, which can be damaging for mental health and self-perception. Individualistic health culture can, ironically, lead to poorer health outcomes like injury and unhealthy exercise or eating habits if data is misrepresented or misunderstood1.

Rise Science, a sleep tracking technology used by teams in the NBA, was implemented with the purpose to improve athletes’ rest and recovery. The data collected provides feedback to coaches, drawing their attention to athletes who may not have fully recovered. However, those who use sleep tracking technologies may experience them differently. Instances of racism within and outside sport can negatively impact the quality and quantity of sleep of Black NBA athletes7. Black athletes may experience prejudicial patterns when using sleep tracking technologies, and may be labelled as “irresponsible” if not obtaining as much sleep according to the data7. Digital monitoring of athletes’ sleep shifts focus towards individual responsibility and away from social and structural forces that may be contributing to sleep disparities. So while apps like Rise Science allow coaches to receive same day data feedback on athletes’ sleep, and their ability to play in a game, the data may not necessarily reflect how well athletes have actually recovered. Players are also concerned that the technologies will interfere with play time, say they are not working hard enough, or simply reduce them to a set of numbers8. Quantification of recovery may also prevent athletes from listening to their own bodies, in terms of their fatigue or readiness to play8.

So is it possible to quantify and “achieve” health with digital tracking technologies? Both advantages and disadvantages can be seen with their use. On one hand, they may present opportunity for motivation, improvement and increased awareness of one’s health habits. On the other hand, an overemphasis on individual responsibility in health, and through dismissal of social and structural forces contributing to health outcomes, shift tendencies of blame to the individual. As technology becomes an increasingly ever present force in society, it is important to be aware of the impacts that these digital tracking technologies may have on our health. 

Sources
  1. Quantifying the body: monitoring and measuring health in the age of mHealth technologies – https://www.tandfonline.com/doi/abs/10.1080/09581596.2013.794931[][][][]
  2. Measuring Weight Self-stigma: The Weight Self-stigma Questionnaire – https://onlinelibrary.wiley.com/doi/full/10.1038/oby.2009.353[]
  3. Reflections from the ‘Strava-sphere’: Kudos, community and (self-)surveillance on a social network for athletes – https://www.tandfonline.com/doi/abs/10.1080/2159676X.2020.1836514[]
  4. Fit for presumption: interactivity and the second fitness boom – https://journals.sagepub.com/doi/abs/10.1177/0163443716643150[]
  5. Obesity prevalence and accuracy of BMI – defined obesity in Russian firefighters – https://academic.oup.com/occmed/article/67/1/61/2623665[][]
  6. Above and beyond BMI : Alternative methods of measuring body fat and muscle mass in critically ill patients and their clinical significance – https://link-springer-com.ezproxy.library.ubc.ca/article/10.1007/s00101-016-0205-0[]
  7. Discrimination, Sleep and Stress Reactivity: Pathways to African American-White Cardiometabolic Risk Inequities – https://www.researchgate.net/profile/Bridget-Goosby/publication/317004359_Discrimination_Sleep_and_Stress_Reactivity_Pathways_to_African_American-White_Cardiometabolic_Risk_Inequities/links/5a4e64a0a6fdccdce2d31d24/Discrimination-Sleep-and-Stress-Reactivity-Pathways-to-African-American-White-Cardiometabolic-Risk-Inequities.pdf[][]
  8. Tracking U.S. Professional Athletes: The Ethics of Biometric Technologies – https://www-tandfonline-com.ezproxy.library.ubc.ca/doi/full/10.1080/15265161.2016.1251633[][]

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