User Trajectories on Digital Mental Health Platforms

This PhD aims to explore how natural language processing techniques can be applied to data generated by users on digital counselling and support platforms to understand and predict the trajectory of the platform users. A trajectory can be defined as the steps taken by a participant in a service or experience to get to a specific outcome (Benford et al., 2009). Within the context of a counselling platform, these would be the steps taken to achieve a valuable outcome for the user.

Sefi and Hanley (2012) have previously explored the role of standardised measures in understanding the needs and goals of service users on digital support platforms (Kooth). Kooth is a web-based counselling and support platform for children and young people (CYP) aged between 11-25 years old (Hanley et al., 2021). The needs of each user on the Kooth platform is unique, and therefore each trajectory will be different. To ensure the service user gains the maximum value from the service, it is vital that the user is presented with a personalised (idiographic) experience based on their needs and goals while using the platform. However, CYP using the Kooth service were shown to have a broader range of needs than face-to-face services, suggesting that a more personalised approach to defining the needs and goals of a service user would be appropriate.

Natural language processing (NLP) is a collection of computational techniques for learning, understanding, and producing human language content (Hirschberg & Manning, 2015). NLP techniques, including sentiment analysis, topic modelling and text summarisation, can be used to detect suicide ideation from counselling transcripts and depressive symptoms from social media data (Coppersmith et al., 2018). This research aims to apply NLP techniques to user-generated data to define personalised trajectories for service users accessing digital support platforms. The output from this research will enable a more accurate understanding of the needs and goals of the service users and allow for a more tailored experience based on the expected trajectory of the user.

Research Questions and Objectives:
Determine to what extent there are different types of users on digital mental health platforms and how each user type interacts with the platform.
Investigate and apply existing machine learning and statistical modelling techniques to data generated on digital mental health platforms to predict the trajectory of users (In terms of both the use of the platform and user wellbeing).
Explore how natural language processing (NLP) techniques can be applied to text data generated on the digital mental health platforms to understand service user needs, trajectory, and engagement.
Explore how mental health and digital wellbeing theory can validate outputs from data-driven techniques for understanding users of the platform.
How can idiographic (personalised) mental health and wellbeing measures be used to support the understanding of user trajectories on digital mental health platforms?

We will collaborate with over 40 partners drawn from across FMCG and Food; Creative Industries; Health and Wellbeing; Smart Mobility; Finance; Enabling technologies; and Policy, Law and Society. These will benefit from engagement with our CDT through the following established mechanisms:

- Training multi-disciplinary leaders. Our partners will benefit from being able to recruit highly skilled individuals who are able to work across technologies, methods and sectors and in multi-disciplinary teams. We will deliver at least 65 skilled PhD graduates into the Digital Economy.

- Internships. Each Horizon student undertakes at least one industry internship or exchange at an external partner. These internships have a benefit to the student in developing their appreciation of the relevance of their PhD to the external societal and industrial context, and have a benefit to the external partner through engagement with our students and their multidisciplinary skill sets combined with an ability to help innovate new ideas and approaches with minimal long-term risk. Internships are a compulsory part of our programme, taking place in the summer of the first year. We will deliver at least 65 internships with partners.

- Industry-led challenge projects. Each student participates in an industry-led group project in their second year. Our partners benefit from being able to commission focused research projects to help them answer a challenge that they could not normally fund from their core resources. We will deliver at least 15 such projects (3 a year) throughout the lifetime of the CDT.

- Industry-relevant PhD projects. Each student delivers a PhD thesis project in collaboration with at least one external partner who benefits from being able to engage in longer-term and deeper research that they would not normally be able to undertake, especially for those who do not have their own dedicated R&D labs. We will deliver at least 65 such PhDs over the lifetime of this CDT renewal.

- Public engagement. All students receive training in public engagement and learn to communicate their findings through press releases, media coverage.

This proposal introduces two new impact channels in order to further the impact of our students' work and help widen our network of partners.

- The Horizon Impact Fund. Final year students can apply for support to undertake short impact projects. This benefits industry partners, public and third sector partners, academic partners and the wider public benefit from targeted activities that deepen the impact of individual students' PhD work. This will support activities such as developing plans for spin-outs and commercialization; establishing an IP position; preparing and documenting open-source software or datasets; and developing tourable public experiences.

- ORBIT as an impact partner for RRI. Students will embed findings and methods for Responsible Research Innovation into the national training programme that is delivered by ORBIT, the Observatory for Responsible Research and Innovation in ICT (www.orbit-rri.org). Through our direct partnership with ORBIT all Horizon CDT students will be encouraged to write up their experience of RRI as contributions to ORBIT so as to ensure that their PhD research will not only gain visibility but also inform future RRI training and education. PhD projects that are predominantly in the area of RRI are expected to contribute to new training modules, online tools or other ORBIT services.