In her article “Big Mother Is Watching You,” Anne Helen Petersen discusses the recent rise in popularity of wearable data tracking devices. Petersen highlights devices that run the gamut in terms of functionality, ranging form the mundane fitness tracker to devices made for children to track their elderly parents’ household routines. Within the article, Petersen also hints at how her own fitness tracker use has affected, and potentially influenced her behavior. She notes that she has become obsessed with her sleeping data, and that she even purposefully excludes certain nights from her data collection, so as not to ruin her nightly averages.
Later in the article, Petersen discusses the potential effects that fitness trackers and other wearable devices may have on the insurance and medical fields. Specifically, she speculates that if wearable devices become more sophisticated, they may be able to help identify certain medical conditions, thereby reducing the number of trips to a physician’s office. Wearable health trackers, pending some technological innovations, have the potential to eliminate the knowledge divide between the general public and highly-educated doctors. While it may be years before these innovations are in place, they would certainly impact the insurance industry as an individual’s riskiness could be more accurately calculated using their accumulated data.
While purposefully altering one’s own sleep data and the potential health industry changes associated with fitness trackers may not seem related at first, there do exist some important interactions between the two. The first effect that comes to mind is the placebo effect that could exist simply from wearing a fitness tracker. It is likely that someone who is constantly reminded of their physical activity will workout more and generally be more health-conscious. While this encourages healthy behavior, it may occur even when the their fitness tracker is not working properly. For example, even when the R65 application was down, I found myself feeling guilty for everyday that I didn’t work out, despite the fact that any exercise I got wouldn’t be recording anyway. Additionally, health trackers pose a potentially adverse effect on the insurance industry. If customer’s data is being mined for insurance purposes, then someone who artificially alters their data has the ability make themselves appear as a less risky investment than they truly are. With the rise of fitness trackers and the overall quantified self movement gaining traction, the consequences of our daily data become increasingly more interesting and controversial. While “forgetting” to wear our bands when we go to sleep may seem harmless now, this could become a real problem if fitness trackers continue their rise in prevalence in our daily lives.
Spencer Gill
The way you describe the guilt one might feel for not exercising while wearing a fitness tracker reminds me of the way a panopticon is supposed to work. The prisoners don’t know for sure that they aren’t being observed, so they tend to act as if they are being observed. Fitness trackers seem to personalize the panopticon. We now carry our own little guard towers on our wrists. I guess the difference is that we face no punishment if we “misbehave” by not exercising; it’s just our own internalized guilt.
I completely agree. The other week, I wrote a little bit about the dangers of fitness trackers and the insurance industry. It seems kind of backwards that our lack of knowledge of how our habits may affect our heath may save us money. One way in which fitness trackers may affect insurance premiums may spring from our desire to advance knowledge by contributing to research.We are on the precipice of a time where our personal fitness data may become of great use to researchers. Researchers looking for correlations between specific health actions and outcomes may find answers in data collected by self-trackers.
On one hand this may have public health benefits, on the other, it may enable insurance companies to apply high premiums just because an individual does not get enough sleep. For example, if scientists were able to find a correlation between sleep amount and specific effects of deficits on one’s health, they may view someone with low sleep values as a high risk client, even in the absence of preexisting conditions.