Natural Cycles: Where pregnancy meets privacy
People use apps on a daily basis, or as Apple would say: “there is an app for that”. Apps are used for checking weather forecasts, reading the news and even finding – online – love. Nowadays, apps are even used for health purposes. Health apps are one of the latest forms of medical technologies (Lupton 610). In February 2017 the app Natural Cycles has become the first app to be officially approved for use as contraception (Manthorpe). The mobile app is approved by the Department of Health from Germany and can therefore be prescribed by doctors in Europe and the United Kingdom (Dent). The Natural Cycles app is proved ninety-nine per cent effective (Ibid.) and is more effective than the pill (England).
Along with the rise health apps such as Natural Cycles, questions regarding data collecting and privacy increase. The circulation, transformation and repurposing of digital data, personal data security and privacy are topics that requires further attention (Lupton 618). Users are giving their information and donating personal data about their health to health apps without knowing where that information ends up (Kennedy et. al 6). Especially looking at health apps, for users it often remains unclear who benefits from their activity on health apps and the data they are generating (Van Dijck & Poell 3.). The selling of user-generated data is well known, however the potential of combining health information with other types of data is new for this era (van Dijck & Poell 9).
Natural Cycles is an emergent object to investigate, since it is the first officially approved contraception. Furthermore, the lack of transparency about the data of health apps makes the Natural Cycles relevant. It intervenes with the debate of privacy. Health apps are masking their data generating processes with the promises of helping their users – in this case preventing or planning pregnancy – and steering her users in more data generating through the features of the interface.
Who benefits from health apps
To understand how the data is generated it is important to distinguish who benefits from the health apps such as Natural Cycles. The user is the first stakeholder that benefits from the health app by being provided personalized solutions for lifestyle changes or medical issues (Van Dijck & Poell 3). Furthermore, health apps are providing options for users which are not possible or typical in traditional health environments (Cortez n.p.). For example, the Natural Cycles app is based on the rhythm method. This method works by women charting out their periods on a calendar and by doing simple math they can indicate which days they are fertile and which days they are not. Doctors would not recommend this method, since it is not reliable. However, with the development of health apps the user is given an extra option that would otherwise not be available. This means, health apps are opening doors that used to be closed.
The public good is the second stakeholder. Health apps benefit the public good by contributing to medical research (Van Dijck & Poell 3). Researchers began to notice the importance of big data for the field of medicine with the rise of mobile apps (Ostherr et al. 1). Mobile health apps are enabling the user to generate health data for the researchers, outside of controlled settings or lab-based studies (Ostherr et al. 2) Researchers claim that the user-generated health data leads to better health improvements than the data which is being collected from lab-based research (Oshterr et al. 9). This benefits the individual user indirectly and on the long term.
However, the individual use and public good are not the only ones who are benefitting from the health apps. The third stakeholder who benefits from the health apps is the developer of the health app and third parties. The developer makes money of the app through the user in two ways. Firstly, the user purchases their product once or paying on a monthly basis. Secondly, the company has the opportunity to sell the user-generated health data to third parties.
How the data is generated
The health data is generated through the activity from the user. By looking into the use, interface and features of Natural Cycles, the process of generating data will be distinct. The interface of the app shows the different functions and features (figure 1). The calendar shows which days of the week are red or green, the statistics show the different stages of the cycle, the message inbox is for receiving messages from the app and the profile feature shows the personal data as well as the accomplished achievements.
The user starts with Natural Cycles by selection the option prevent pregnancy or plan pregnancy and then fills in personal information such as weight, height, date of birth and address. The app is now ready to use. In short, when the user is fertile and should use protection the app shows a red circle (figure 2).
Figure 1: Screenshot of Natural Cycles interface. Figure 2: Screenshot of Natural Cycles interface.
Natural Cycles is based on the earlier mentioned rhythm method. This rhythm method is combined with an algorithm created by the developers. This algorithm is personalized for every individual user, but is updated based on the data of all the users. The user is asked to add her temperature every morning into the app. Not only does the algorithm takes the temperature of user into account, also other factors such as sperm survival, temperature fluctuations and cycle irregularities (Natural Cycles).
The app is generating more daily data than just the temperature of the user. For instance, the user is also given the questions whether she is on her period or spotting, the outcome of her ovulation test and whether she had – (un)protected – sex or not (figure3). The app also gives the opportunity for the user to synchronise Apple’s HealthKit with the Natural Cycles app (figure 4). This means more data added to Natural Cycles about alcohol intake, energy and heartrate.
Figure 3: Screenshot of temperature feature. Figure 4: Screenshot of synchronizing with HealthKit.
According to Natural Cycles the adding of all the extra information improves the use of the app and therefore benefits the user. What the app fails to mention is how this data is used for different purposes. The feature ‘achievements’ contributes to convincing the user to add more data to the app (figure 5 and figure 6). By fulfilling different tasks – which contains adding more data to the app – the user unlocks new achievements. With these new achievements come new statuses. By adding the achievement feature, the user is willingly to add data since there are rewards involved such as free use of the app.
Figure 5: Screenshot of achievements. Figure 6: Screenshot of accomplished achievement.
Cortez, Nathan G. et al. “FDA regulation of mobile health technologies.” The New England journal of medicine 371.4 (2014): 372.
Dent, Steve. “Mobile app approved as an alternative contraceptive”. Engadget. 09-02-2017. 12-09- 2017. <https://www.engadget.com/2017/02/09/mobile-app-approved-as-an-alternative-contraceptive/>
England, Rachel. “Natural Cycles says contraceptive app is more efficteve than the pill”. Engadget. 13-09-2017.15-09-2017. <https://www.engadget.com/2017/09/13/natural-cycles-contraceptive-app-more-effective-than-pill/>
European Commission. “Transferring your personal data ouside the EU”. 19-09-2017. <http://ec.europa.eu/justice/data-protection/data-collection/datatransfer/index_en.htm>
Kennedy, Helen, Thomas Poell, and José van Dijck. “Data and agency”. Big Data & Society 2.2 (2015): 1-7.
Lupton, Deborah. “Apps as artefacts: Towards a critical perspective on mobile health and medical apps.” Societies 4.4 (2014): 606-622.
Manthorpe, Rowland. “Fertility app Natural Cycles becomes world’s first certified contraception software”. The Wired. 09-02-2017. 12-09-2017. <http://www.wired.co.uk/article/fertility-natural-cycles-app-contraception>
Natural Cycles. “Privacy”. 05-06-2017. 17-09-2017. <https://www.naturalcycles.com/en/other/privacy>
Natural Cycles. “How it works”. 15-09-2017. https://www.naturalcycles.com/en/contraception/howitworks>
Ostherr, Kirsten, et al. “Trust and privacy in the context of user-generated health data.” Big Data & Society 4.1 (2017): 1-11.
Van Dijck, José, and Thomas Poell. “Understanding the promises and premises of online health platforms.” Big Data & Society 3.1 (2016): 1-11.