Working with health and wellbeing data can give individuals, researchers, and organisations important insights. For example, data might allow governments to identify links between health and poverty, or help individuals to notice what they could change to live more healthily.
For third sector organisations, data are the materials that can fuel strategic planning and advocacy work. They not only highlight problems, but also act as the evidence to underpin clear, engaging stories.
What forms do health and wellbeing data take?
1) Patient data: pieces of information on an individual’s health and wellbeing collected by a health body such as a GP surgery or hospital. This might include medication records or blood pressure readings, for example. Some health insurers also collect this information.
Patient data are data collected about a patient whenever they go to a doctor or receive healthcare. These data can include details about the patient’s health, medication records, health and social care needs, and other information. The data are recorded and stored in what is called a “care record”.
By analysing patient data, researchers and health professionals can provide better care and improve diagnosis, enhance treatment and prevention, and improve the planning of the healthcare sector (in the UK this is NHS services). Meanwhile, digital data tools have changed how many people access information and medication. For example, the NHS offers both an e-prescribing system and an online advice service.
However, there are some big challenges for both individuals and organisations around how patient data are collected, used and shared. For many patients, privacy is a huge concern. A 2019 study found that the majority of studies into data privacy reported that people are generally worried about the security of data and fear ‘data leakage’ (for example, through hacking or by companies selling data).
Organisations, meanwhile, need to make sure that they are fully adhering to legal, technical and ethical standards across the whole process of working with health and wellbeing data, from collection to use to storage.
For more information on patient data, see Understanding Patient Data.
2) Data collected by individuals : for example, through mobile phone apps and wearable devices to track exercise activity, diet, sleep and heart rate.
Individuals often collect data by using mobile phone apps, and wearable devices such as Apple watch, Fitbit, Jawbone etc. The number of connected wearable devices worldwide has more than doubled over the last three years, increasing from 325 million in 2016 to 722 million in 2019, and is forecast to reach more than one billion by 2022. Using wearables to collect health and wellbeing data can help individuals to understand and make changes to their behaviour patterns, from diet and exercise to sleep. However, there are many privacy and security issues associated with self-tracking devices, as well as questions such as: who retains rights of the data, how do companies use them, where do they end up, and who profits from selling these data?
Wearables and apps collect a broad range of data, including biological processes such as heart rate and sleep patterns, lifestyle factors such as diet, distance and speed travelled, behavioural and postural data such as walking gait (to prevent falls), and much more besides. They are collected through a combination of automated data collection (for example, a pedometer built into a phone or a wristband) and self-generated data (such as those data provided to health apps on mood, symptoms and physical condition).
In this way, self-tracking might embed a sense of an ‘ideal’ self – a fit, healthy and busy self whose every move is logged. This ideal does not allow for widespread differences in bodies, health conditions or lifestyles. Self-tracking also shifts responsibility onto the individual for their own care, something that sometimes even takes moral undertones . In this way, self-tracking and data sharing for health and wellbeing can be seen as symptoms of a more general phenomenon: a shift towards healthcare systems where the state plays a limited role, in comparison to the commercial sector (the healthcare industry).
Together, these potential issues with self-tracking technologies further complicate the power relations and ideas of data ownership when it comes to health and wellbeing.
3) Data collected by third parties: for example companies, employers, third sector or community groups.
Many kinds of health and wellbeing data are collected by organisations whose main activity lies outside of traditional healthcare. This includes charities, local government, employers, community groups, commercial bodies and many more.
There is a global market in healthcare data which relies on the commercialisation of individual data. Some third parties gather data from healthcare systems, pharmacies and other sources, and sell them on to buyers interested in analysing large data sets. These are known as ‘data brokers’ or ‘information brokers’; their customers include government bodies, marketing agencies, insurance companies, advertisers, and research organisations.
In the UK, there are strict controls around how and why companies can access and use national patient data. However, models of data ownership and sharing differ hugely across the private companies offering internet solutions, wearables and app technologies. For example, a 2018 study found that the majority of 959,000 apps from the US and UK Google Play stores transferred data to third parties, and that many of these operated on a transnational basis (and therefore not necessarily in adherence to the legal system of the country of use).
In the UK and the EU, the General Data Protection Regulation (GDPR) legislation established in 2018 seeks to answer some of these concerns. However, while consent forms may be technically compliant with GDPR, this may not mean that they adhere to high standards of informed consent.
The research fieldwork of the ART/DATA/HEALTH project involved working with community groups that collected health and wellbeing data of both qualitative and quantitative nature. These related to experiences of domestic abuse; data which tracked someone’s journey through a support service, including rates of re-engagement; details of volunteers supporting a ‘befriending’ service with isolated people, and many more.
How to work with health and wellbeing data responsibly
When working with data, it’s vital to think about what ‘problem’ is being explored, who the stakeholders are, and who holds the power to decide what is collected and how it is used. There are several ways that you can work with health and wellbeing data in a way that improves public trust and understanding. The ART/DATA/HEALTH project seeks to provide a model for some of these issues. At the root of this approach is an emphasis on working responsibly with partners and data, and communicating clearly and openly. In particular, the project:
- engages community organisations in research design and data analysis;
- focuses on complex social issues, particularly the factors which contribute to unequal access to healthcare – some of which are hugely stigmatised;
- explores how individual stories can bring shared experiences and themes to light.
Learning about Data: What do we mean by data?
Data for Advocacy: How can we use data to advocate for social change?
Open Data: Why do we need open data?
Data Art: How can data art help improve health and wellbeing?
Telling Stories with Data: Why tell stories using data?
COVID19 Data: What can data tell us about the pandemic?
 Skovgaard, L. L., Wadmann, S., & Hoeyer, K. (2019). A review of attitudes towards the reuse of health data among people in the European Union: The primacy of purpose and the common good. Health Policy, Vol. 123, pp. 564–571. Available at: https://doi.org/10.1016/j.healthpol.2019.03.012.
 Statista. (2020). Connected wearable devices worldwide 2016-2022. Available at: https://www.statista.com/statistics/487291/global-connected-wearable-devices/.
 Fotopoulou, A., and O’Riordan, K. (2016). Training to self-care: fitness tracking, biopedagogy and the healthy consumer. Health Sociology Review, 26(1), pp. 54–68. Available at: https://doi.org/10.1080/14461242.2016.1184582.
 Fotopoulou, A. (2018). From networked to quantified self: Self-tracking and the moral economy of data. In Z. Acharissi (Ed.), A Networked Self: Platforms, Stories, Connections. New York, USA: Routledge.
 Binns, R., Lyngs, U., Van Kleek, M., Zhao, J., Libert, T., and Shadbolt, N. (2018). Third Party Tracking in the Mobile Ecosystem. WebSci ’18: Proceedings of the 10th ACM Conference on Web Science, pp. 23–31. Available at: https://doi.org/10.1145/3201064.3201089.