This post is the essay from the final exam in my Information Landscape class at Kent State University. I really liked the concepts and problems introduced by the question, and was proud of my reflection on them.
Question: Many of us are using fitness trackers and smartphone apps to track our fitness or health. Discuss the pros and cons of such massive sets of data traces (discuss at least two positive and two negative consequences).
Millions of people are using wearables and smartphone apps to track their health and fitness. By doing so, they generate massive amounts of data. This can be a boon to health professionals all over the world, but it also can expose individuals to privacy risks. Data can be used for great things like disease tracking and personal health improvement, but with so much data out there, we run the risk of it being misused and misrepresented. Data itself is neither good nor bad, it’s all in how people use it (Boyd & Crawford, 2012).
Some of the best things that large health data sets give us include healthcare innovations like disease tracking, and personal health improvement and involvement. In 2014, the Centers for Disease Control were able to figure out where outbreaks of ebola were, or might crop up by mining data from sites like Twitter, and mapping calls to helplines in West Africa. In addition to traditional reporting sources, large data sets allowed local governments to send medicine and medical personnel to the appropriate places at the appropriate times (Wall, 2014).
While health data can be used to benefit humanity on a continental, or even global scale, it can also be individually beneficial. Mapping the human genome is easier and more accessible than ever before, and it gives a person incredible insight into their own health (Forbes, 2017). It can answer questions like what kinds of things are they sensitive to, or do they have any predispositions for diseases or disorders?
Some healthcare companies are creating devices that help patients with a preexisting condition track their health and monitor their disease. Companies like Senseonics and Telcare have created devices to help diabetics track blood glucose levels. Other companies like iRhythm and AliveCor have created stick on chest monitors and phone cases respectively, that monitor heart rates (McCandless, 2017).
People with and without illness can benefit from wearable tech that tracks fitness. Insurance companies offer incentives to people who reach a set number of steps per day (Betzner, 2015). People trying to lose weight are able to track their diet and exercise with health apps and wearables like the FitBit. Athletes training for competitions can track performance and tailor workouts to the needs and abilities of their bodies. The personal and global gains from health data can truly be world changing.
Not everything about tracking health and fitness data is good though. Devices can be hacked, privacy can be compromised, and data can be misused. Recently, Netflix held a competition within the company, asking employees to innovate new features for the site. One group benevolently hacked their FitBits to pause video when they fell asleep. While this is a neat idea, it shows the vulnerability of our wearable devices to being hacked, whether benevolently or not. During exchanges of information with laptops and smartphones, a window is opened to hackers. Intimate health and location data can be altered or stolen. By knowing what places you frequent, hackers could phish you, pretending to be a trusted store offering coupons, but really embedding spyware and viruses in fake links (Betzner, 2015).
Data privacy isn’t just a personal matter, it’s also an ethical one. Just because the CDC can access mobile phone records doesn’t necessarily mean they should. Certainly, having that information in the case of the Ebola outbreak was for the greater good, but what precedent does it set years down the line? How does being marked as a location with a potential for an ebola outbreak affect the industry and locals there? The advance of technology and the creation of data is outpacing our ability to ethically and responsibly regulate how it is used and accessed (Boyd & Crawford, 2012).
Responsible use and access also includes how data sets are represented and who has access to them. For example, when the app Pokemon Go was released it was touted as increasing the amount of exercise people got by walking around playing the game. However, when researchers at Harvard really dug into the data generated by the health and fitness game they found something different in the data from what was being represented more widely. The game did increase physical activity, but only for a little while and only by a little bit. On average players added about 11 minutes more exercise to their days and after about a month of playing, that number started to drop off (Howe, Suharlim, Ueda, Howe, Kawachi, & Rimm, 2016). This goes to prove Boyd and Crawford’s assertion in their “Critical Questions for Big Data” that interpretation always has a subjective aspect to it (2012).
That subjective aspect is important when considering who has access to all this health and fitness data. Certainly users have access to their own data, but data is a commodity and is frequently bought, sold, and shared. Who are the researchers that get special access to the data created, what are their biases, and what are the questions they are asking? In the previous example, Harvard researchers were the ones with the access and they were asking whether or not this fitness game really increased physical activity. Harvard is a rich institution with the ability to pay for access to large data sets. But what about a smaller company with different questions to ask? Without equitable access to data it’s hard to say if we’re truly seeing the data set in context, or if we’re just seeing data sets that reflect well on whoever is creating them (Boyd & Crawford, 2012).
While there are great benefits from digital traces of health data, we must also be careful and responsible while using it. Just as much as we can benefit from this data, we could be mislead by misrepresentation, or bias in the data sets. If used well, health data can help improve health, and control disease around the world and individually. In the end the data we generate is neutral; it is what we do with it that really matters.
Betzner, J. (2015, August 2). Wearable fitness devices carry security risks. Pittsburgh Post-Gazette (PA). Retrieved from https://proxy.library.kent.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip&db=pwh&AN=2W63193875281&site=eds-live&scope=site
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679. doi:10.1080/1369118X.2012.678878
Forbes, J. (2017, February 1). The Human Face of Big Data [Video file]. Retrieved from http://ksutube.kent.edu/secureplayback.php?playthis=b2h5a498z
Howe, K. B., Suharlim, C., Ueda, P., Howe, D., Kawachi, I., & Rimm, E. B. (2016, December 13). Gotta catch’em all! Pokémon GO and physical activity among young adults: difference in differences study. British Medical Journal 355 :i6270
McCandless, D. (2017). The Internet of Things – An Interactive Primer — Information is Beautiful. Retrieved March 24, 2017, from http://www.informationisbeautiful.net/visualizations/the-internet-of-things-a-primer/
Wall, M. (2014, October 15). Ebola: Can big data analytics help contain its spread? Retrieved March 24, 2017, from http://www.bbc.com/news/business-29617831