Digital Footprint and Health

My major current interest is to understand how we can use novel digital footprint data to study human behaviour and real-life outcomes, such as health. Currently I am focusing on transaction data, specifically loyalty and banking cards, and working on realising the value of using these data to improve population health. In February 2021 I am moving to Population Health Sciences, Bristol Medical School, where I will lead Transaction Data for Population Health programme funded through UKRI Future Leaders Fellowship (2021-2028). This research programme is grouped around five main themes:

Data Donation. Data donation is a concept encapsulating an active decision by an individual to donate their personal data for public good (e.g., population health research).  While data ownership is still a grey area, General Data Protection Regulation (GDPR, introduced in the UK in 2018) allows individuals to exercise “right to portability”. This right allows an individual to request a copy of personal data which is collected on them by an organisation. Individuals can also authorise/consent to a third party (e.g., an academic health researcher) to access their personal data. I work on unpicking motivations and barriers to donate personal data, and study how those can fit with the broader industry and regulatory landscape of personal data sharing.

Example of published work: Skatova, A., & Goulding, J. (2019). Psychology of personal data donation. PloS one, 14(11), e0224240.

The Conversation (media): Donate Your Data.

Analytic techniques for large transaction datasets. Using very large transaction datasets opens up exciting opportunities to study human behaviour and health. However, there are many pitfalls and obstacles to overcome. By drawing on behavioural science, statistics and machine learning I develop new data analytic techniques that can be employed to understand the mechanisms of choices and behaviour using transaction data.

Ground truth for patterns in transaction data. A major difficulty with realising full value of transaction data is the lack of “ground truth” in standalone datasets such as loyalty cards data. The patterns in the data (e.g., escalating alcohol purchasing) need to be verified against other data to be sure that such patterns reflect individual behaviour (e.g., escalating alcohol consumption).

My work on linking transaction data into Longitudinal Population Studies (LPS) allows to use depth and breadth of LPS health and social variables as ground truth for patterns that we can detect from transaction data. By establishing patterns in linked datasets through LPS, we can set out to study those patterns in large standalone transaction datasets contributing to the knowledge about physical and mental health, wellbeing, everyday behaviours and life events.

Data Linkage. I work with Avon Longitudinal Study of Parents and Children (ALSPAC) and other Longitudinal Population Studies (LPS) to create a methodology for linking transaction data into LPS. Using qualitative and quantitative methods, I study a range of issues including participants’ attitudes to sharing transaction data with academic researchers, privacy-preserving, ethical approaches for data linkage, assessing the quality of linked datasets in terms of sampling and various biases, creating appropriate infrastructure for the linkages, creating routes for the linked data to be available for academic research via secure protocols.

Example of published work: Skatova, A., Shiells, K., & Boyd, A. (2019). Attitudes towards transactional data donation and linkage in a longitudinal population study: evidence from the Avon Longitudinal Study of Parents and Children. Wellcome Open Research, 4(192), 192.

CLOSER (media): Accessing Transaction Records for Linkage in Longitudinal Cohort Studies. 

Application areas. There are a lot of interesting research questions which we can address with transaction data. Loyalty cards data can help to understand diet and alcohol consumption, while banking records can help to unpack factors influencing wellbeing and mental health. I am interested in a wide range of applications areas that can benefit from information derived from very large population level datasets including reproductive health, pain management, nutrition, gambling and others.

Example of published work: Skatova, A., Stewart, N., Flavahan, E., Goulding, J. Those Whose Calorie Consumption Varies Most Eat Most.

Other research

Value of Personal Data. Information about individual behaviour is collected regularly by organisations. Much of the digital economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. This programme explores public attitudes for sharing personal data, or keeping it private, as well as what factors affect the decision to share personal data or not.

Example of published work: Skatova, A., McDonald, R. L., Ma, S., & Maple, C. Unpacking Privacy: Willingness to pay to protect personal data. 

Individual differences in decision-making. I employ a range of approaches developed in social, personality and cognitive psychology, behavioural economics and broader decision-making literature to understand real life choices and aspects of individual differences that can predict different decisions. The key research questions I am pursuing is what makes the same people make different choices and what makes different people make the same choices. 

Example of published work: Otto, A. R., Skatova, A., Madlon-Kay, S., & Daw, N. D. (2014). Cognitive control predicts use of model-based reinforcement learning. Journal of cognitive neuroscience, 27(2), 319-333.

Skatova, A., & Ferguson, E. (2014). Why do different people choose different university degrees? Motivation and the choice of degree. Frontiers in psychology, 5, 1244. 

Cooperation and prosocial behaviour. This line of research concerns the psychological underpinnings of cooperation and prosocial behaviour using experimental economics games. For example, I am interested in how different people react to unfair situations, and what factors (e.g., emotions, personality, appraisals) might colour their decisions in social dilemmas. 

Example of published work: Skatova, A., Spence, A., Leygue, C., & Ferguson, E. (2017). Guilty repair sustains cooperation, angry retaliation destroys it. Scientific Reports, 7, 46709.