You wake up in the morning and the first thing you see on your smartphone is how you slept that night. It is not you who determines that, but an app on your phone, which says you moved a lot while you were sleeping and your quality of sleep was not good enough. Then your app asks you how you feel today - are you happy? You get up, get a glass of water and make breakfast. While doing this, you fill in all the ingredients you consumed this morning in the mHealth app and you also see that you almost have made two percent of the number of steps you have to make today.
In this hypothetical situation, you have already provided data to your mobile device about your sleep quality, your state of happiness, your eating habits and your movements. These are four aspects of your health that you allow a smartphone app to determine. This already raises questions in terms of what is happiness for you, and when is your quality of sleep 'good enough'? We let this be determined by an app, which tells us what the average is that we should live up to.
The hypothetical situation described above is reality for more and more people. However, should we not determine ourselves when something is ‘good enough’, or what happiness is? Are we in fact losing the connection to our bodies by using self-monitoring mHealth apps?
The rise of the mHealth apps
Mobile health or mHealth is a phenomenon which has become increasingly popular in the last couple of years. While in 2015 there were already 165.000 health apps available (IMS Healthcare Institute for Healthcare Informatics, 2015), this number had already doubled by 2017 (IQvia Institute, 2017). Looking at this data it is not a surprise that the health and medical industry is in the top three fields that have grown significantly in terms of mobile device use (Statista, 2018).
mHealth apps give consumers the possibility to self-monitor several aspects of their health. Wellness, disease prevention and disease management can be monitored through these apps and it is seen as important to give the people an active and informed role in their own healthcare. Mobile health is thus seen as a tool to improve the healthcare system by improving communication, quality and costs for healthcare services (Statista, 2018).
Especially in a time where we are connected more and more through online environments, this has added a new dimension to the production of knowledge by and about ourselves. mHealth apps give the consumer the opportunity to capture their data in real-time - as they are going about their activities. This gives an immediate insight into their state of being in terms of health.
Many of the apps are free, which makes it even more interesting to download one and start using it. Dataveillance - a portmonteau of 'data' and 'surveillance' referring to the practice of digital monitoring - is used in order to immediately put the data provided by a user into action by connecting it back to healthcare providers (IMS Healthcare Institute for Healthcare Informatics, 2015).
The information is purposed and repurposed as a part of the global digital knowledge economy (Lupton, 2016)
Lupton (2016) mentions how these apps are a form of dataveillance, by providing you with several ways to share your information digitally, without always knowing what your data is used for. The information is used and repurposed as a part of the global digital knowledge economy. It can be used by both individuals and healthcare providers to see whether one's practices of selfhood conform to cultural expectations (Lupton 2016).
The worth of experience as knowledge and interpreting numbers
While we now monitor ourselves through our mobile devices, we used to do this in the past by connecting to our body and feeling when there was something wrong. You would not do this through numbers provided by a mHealth app, but based on your experiences.
Joan W. Scott wrote about experience as a form of knowledge in 1991. We can consider experiences as social constructions, and when using experience as a form of knowledge it is important to keep in mind various factors such as gender, ethnicity, language and age as well as the different ways information can be interpreted by different people in different contexts. The way a phenomenon is perceived can be very different in another culture or at another age, and certain types of experience can be seen as more legitimate than others.
However, knowledge based on experience is difficult to criticize: as Scott mentions in her article, “What could be truer, after all, than a subject’s own account of what he or she has lived through?” (Scott, 1991, p.777). It should also be kept in mind that experience is always political (Scott, 1991), as the ideas attached to this concept vary depending on the social and historical context.
It is difficult to attach numbers to a subject like health.
These factors should not only be considered when using experience as a form of knowledge in general, but also when using an application on your mobile device. It is difficult to attach numbers to a subject like health. Yet this is what we do when we quantify our experience of health through numbers and graphs on our health apps.
One example of us attaching a certain trustworthiness to numbers is the widespread need to walk 10.000 steps a day. This concept was created in Japan in the 1960s as a marketing campaign for a new pedometer. The nickname for the product was the ‘Manpo-kei’ which translates to ‘ten thousand steps meter’.
The 10.000 steps were based on the average number of steps from people with various lifestyles in the 1960s (Tudo-Locke et al., 2008). Thus what is now for many people a daily health goal with the help of their self-monitoring apps was set in the 1960s, and originates from a nickname for a pedometer. Also here we should consider the different target groups. This number of steps is for example too low for children and too high for older people (Tudor-Locke et al., 2008). Needless to say, the lifestyle of people in Japan in the 1960s might not be relevant for people in the 21st century globally either.
When health apps become unhealthy
The example of the origin of the ’10.000 steps a day’ as a part of a healthy lifestyle shows how we cannot always rely on numbers given to us to live up to. Of course it is not wrong to want to live up to this number of steps a day; however, it should not control one's life. The BBC (2018) featured an article about how people let a health app control their life and shows how a health app became unhealthy. The situation sketched in this news article is about Lara, a 29-year-old who started tracking her steps and running. The aim was a healthier lifestyle by increasing her physical activity.
The interviewee Lara describes how at first the app motivated her, but after a few weeks it made her competitive against the data she had produced about herself. She wanted to do better than the day before, day after day. This resulted in bad habits in for example terms of sleep, because she did not go to bed before she had reached her goal for that day (Davies, 2018). We can think of many other similar scenarios in terms of using mHealth. While the morning routine I described in the introduction was fictional, it is not something unthinkable these days.
Promoting safer use
How can we keep benefitting from health apps without them taking control of our lifestyle? Lewis & Wyatt (2014) wrote about the need for safer apps because “It is important that mobile medical apps used in health care settings are accurate and reliable, especially as health care professionals and patients may make critical decisions based on information from an app” (p.1).
Health care professionals should have better knowledge about mHealth apps, and patients should be better informed about the usage of the apps.
Health care professionals should have better knowledge about mHealth apps, and patients should be better informed about the usage of the apps. This is because it is not only the provided data that is important, but also how the algorithms in apps react to it. When algorithms make bad calculations it can lead to serious health problems (Lewis & Wyatt, 2014). Keeping in mind that many of the available health apps are free and easy to install on your mobile device, it is even more important to promote safer use. The question also arises whether it would in some cases be better to let people only use health apps when prescribed, just as with medicines.
Making health apps healthy
We started off the introduction with the question of whether people lose connection to their body because of mHealth and self-monitoring. Health apps can help us when used in a responsible way. More transparency and information about these apps could help people be more responsible when using them and thus not lose the human connection with their body.
What we experience and how we feel can be more important than what an app tells us by using our data and drawing conclusions from algorithms.
More and better research about certain health norms can also help to be more responsible. We should keep in mind that every individual is different, which means a certain health norm can apply for one person, but can be different for another person based on their cultural background, age, gender, etc.
Health apps can be a way to improve the healthcare system by improving communication with patients and maybe even keeping some of the costs for healthcase services lower. However this can only be done when health apps are created together with healthcare providers. With over 160.000 health apps in 2015, and the number already doubling in 2017, we can conclude that many of these apps will probably not be of good quality. However not everyone is aware of this. Promoting safer use and requiring better quality from apps and working together with healthcare providers can be a way to keep health apps healthy.
Davies, A. (2018, May 29). Are health apps actually bad for your health?
IMS Healthcare institute for Healthcare Informatics. (2015). Patient adoptation of mHealth: Use, Evidence and Remaining Barriers. Parsipanny USA: IMS Healthcare institute for Healthcare Informatics.
IQvia Institute. (2017). The Growing Value of Digital Health: Evidence and Impact on Human Health and the Healthcare System. Durham, USA: IQvia Institute.
Lewis, T. L. & Wyatt, J. C. (2014). mHealth and Mobile Medical Apps: A Framework to Assess Risk and Promote Safer Use. Journal of Medical Internet Research, 16(9): e210, 1-8.
Lupton, D. (2016). The diverse domains of quantified selves: selftracking modes and dataveillance. Economy and Society, 45:1, 101-122.
Scott, J. (1991). The evidence of experience. Critical inquiry, Vol. 17, No. 4 (Summer, 1991), 773-797.
Statista. (2018). mHealth - Statistics & Facts.
Tudor-Locke, C., Hatano, Y., Pangrazi R. P., & Kang M. (2008). Revisiting "How many steps are enough?". Medicine & Science in Sports & Exercise, pp. 537-543.