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  Mr Jared de Bruin  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.


  • Esther Papies: School of Psychology
  • Matthew Chalmers: School of Computing Science

Aims and Objectives:

This project will explore whether data derived from a person’s smartphone can be used to establish that person’s hydration status so that, in a well–guided and responsive way, a system can prompt the person to drink water. Many people are frequently underhydrated, which has negative physical and mental health consequences. Low hydration states can manifest in impaired cognitive and physical performance, experiences of fatigue or lethargy, and negative affect (e.g, Muñoz et al., 2015; Perrier et al., 2020). Here, we will establish whether such social and behavioural markers of dehydration can be inferred from a user’s smartphone, and which of these markers, or their combination, are the best predictors of hydration state (Aim 1). Sophisticated user models of hydration states could also be adapted over time, and help to predict possible instances of dehydration in advance (Aim 2). This would be useful because many individuals find it difficult to identify when they need to drink, and could benefit from clear, personalized indicators of dehydration. In addition, smart phones could then be used to prompt users to drink water, once a state of dehydration has been detected, or when dehydration is likely to occur. Thus, we will also test how hydration information should be communicated to users to prompt attitude and behaviour change and ultimately, improve hydration behaviour (Aim 3). Throughout, we will implement data collection, modelling, and feedback on smartphones in a secure way that respects and protects a user’s privacy.

Background and Novelty.

The data that can be derived from smart phones (and related digital services) ranges from low level data on sensors (e.g. accelerometers) to patterns of app usage and social interaction. As such, ‘digital phenotyping’ is a rich source of information on an individual’s social and physical behaviours, and affective states. Some recent survey papers this burgeoning field include Thieme et al. on machine learning in mental health (2020), Chancellor and de Choudhury on using social media data to predict mental health status (2020), Melcher et al. on digital phenotyping of college students (2020), and Kumar et al. on toolkits and frameworks for data collection (2020). Here, we propose that these types of data may also reflect a person’s hydration state. Part of the project’s novelty is in its exploration of a wider range of phone-derived data as a resource for system agency than prior work in this general area, as well as pioneering work specifically on hydration. We will relate cognitive and physical performance, fatigue, lethargy and affect to patterns in phone-derived data. We will test whether such data can be harnessed to provide people with personalized, external, actionable indicators of their physiological state, i.e. to facilitate useful behaviour change. This would have clear advantages over existing indicators of dehydration, such as thirst cues or urine colour, which are easy to ignore or override, and/or difficult for individuals to interpret (Rodger et al, 2020).


We will build on an existing mobile computing framework (e.g. AWARE-Light) to collect reports of a participant’s fluid intake, and to integrate them with phone-derived data. We will attempt to model users’ hydration states, and validate this against self-reported thirst and urine frequency, and self-reported and photographed urine colour (Paper 1). We will then examine in prospective studies if these models can be used to predict future dehydration states (Paper 2). Finally, we will examine effective ways to provide feedback and prompt water drinking, based on individual user models (Paper 3). Outputs. This project will lead to presentations and papers at both Computer Science and Psychology conferences outlining the principles of using sensing data to understand physiological states, and to facilitate health behaviour change. Impact. Results from this work will have implications for the use of a broad range of data in health behaviour interventions across domains, as well as for our understanding of the processes underlying behaviour change. This project would also outline new research directions for studying the effects of hydration in daily life. 


Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: a critical review. Npj Digital Medicine, 3(1), 1–11. http://doi.org/10.1038/s41746-020-0233-7
Melcher, J., Hays, R., & Torous, J. (2020). Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health, 4, ebmental–2020–300180–6. http://doi.org/10.1136/ebmental-2020-300180
Muñoz, C. X., Johnson, E. C., McKenzie, A. L., Guelinckx, I., Graverholt, G., Casa, D. J., … Armstrong, L. E. (2015). Habitual total water intake and dimensions of mood in healthy young women. Appetite, 92, 81–86. https://doi.org/10.1016/j.appet.2015.05.002
Rodger, A., Wehbe, L., & Papies, E. K. (2020). “I know it’s just pouring it from the tap, but it’s not easy”: Motivational processes that underlie water drinking. Under Review. https://psyarxiv.com/grndz
Perrier, E. T., Armstrong, L. E., Bottin, J. H., Clark, W. F., Dolci, A., Guelinckx, I., Iroz, A., Kavouras, S. A., Lang, F., Lieberman, H. R., Melander, O., Morin, C., Seksek, I., Stookey, J. D., Tack, I., Vanhaecke, T., Vecchio, M., & Péronnet, F. (2020). Hydration for health hypothesis: A narrative review of supporting evidence. European Journal of Nutrition. https://doi.org/10.1007/s00394-020-02296-z
Thieme, A., Belgrave, D., & Doherty, G. (2020). Machine Learning in Mental Health. ACM Transactions on Computer-Human Interaction (TOCHI), 27(5), 1–53. http://doi.org/10.1145/3398069
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