For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.
Supervisors
- Esther Papies: School of Psychology
- Matthew Chalmers: School of Computing Science
Aims and Objectives.
This project will examine which kinds of data to use to best integrate a digital mobile health intervention into a users’ daily life, to lead to habit formation. Previous research has shown that just-in-time adaptive interventions (JITAIs) are more effective than statically controlled interventions (Wang & Miller, 2020). In other words, health interventions are more likely to lead to behaviour change if they are well situated, i.e., with agency adapted to specific user characteristics, and applied in situations where behaviour change should happen. However, there is limited evidence on how to best design JITAIs for health apps, so as to create artificial agents that lead to lasting behaviour change through novel habit formation. In addition, there is no systematic evidence as to which features of situations a health app should use to support a user to perform a healthy behaviour (e.g., time of day, location, mood, activity pattern, social context). We will address these issues in the under-researched domain of hydration behaviours. The aim is to establish—given the same intervention—which type of contextual data, or which heterogeneous mix of types of data, is most effective at increasing water consumption, and at establishing situated water drinking habits that persist when the initial engagement with the intervention has ceased.
Background and Novelty.
Mobile health interventions are a powerful new tool in the domain of individual health behaviour change. Health apps can reach large numbers of users at relatively low cost, and can be tailored to an individual’s health goals and adapted to support users in specific, critical situations. Identifying the right contextual features to trigger an intervention is critical, because context plays a key role both in triggering unhealthy behaviours, and in developing habits that support the long-term maintenance of healthy behaviours. A particular challenge, which existing theories typically don’t yet address, lies in the dynamic nature of health behaviours and their contextual triggers, and in establishing how these behaviours and contexts can best be monitored (Nahum-Shani et al., 2018). This project will take on these challenges in the domain of hydration, because research suggests that many adults may be chronically dehydrated, with implications for cognitive functioning, mood, and physical health (e.g., risk of diabetes, overweight, kidney damage; see Muñoz et al., 2015; Perrier et al., 2020). Our previous work has shown that healthy hydration is associated with drinking water habitually across many different situations each day (Rodger et al., 2020). This underlines the particular importance of establishing dynamic markers of situations that are cognitively associated with healthy behaviours so that they can support habit formation.
Methods.
(1) We will examine the internal (e.g., motivation, mood, interoception) and external (e.g., time of day, location, activity pattern, social context) markers of situations in which high water drinkers consume water, using objective intake monitors. Then, integrating these findings with theory on habit formation and motivated behaviour (Papies et al., 2020), and using an existing app platform (e.g. AWARE-Light), (2) we will test which types of data or mixes of data types are most effective in an intervention to increase water consumption in a sample of low water drinkers in the short term, and(3) whether those same data types are effective at creating hydration habits that persist in the longer term.
Outputs.
This project will lead to presentations and papers of three quantitative subprojects at both Computer Science and Psychology conferences, as well as a possible qualitative contribution on the dynamic nature of habit formation.
Impact.
Results from this work will have implication for the design of health behaviour interventions across domains. This work will further contribute to the emerging theoretical understanding of the formation and context sensitivity of the cognitive processes that support healthy habits. It will explore how sensing and adaptive user modeling can situate both user and AI system in a common contextual frame and whether this facilitates engagement and behavior change.
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