An Evaluation of the Effectiveness of Digital Health Technology to support Mental Health and Well-being

Context:
Digital health technology has a great potential to support mental health and well-being. It is becoming increasingly accessible and acceptable and has substantial benefits over face-to-face interventions, including higher cost-effectiveness, wider reach, and reduced support-seeking barriers.

Alongside supporting mental health and well-being in the general population, a population group that could particularly benefit from digital mental health and well-being support is autistic people (including adults with high levels of autistic traits, self-identifying as autistic). Autistic people experience significantly poorer levels of mental health and well-being and further challenges in social communication and face-to-face interaction.

A crucial question is how mental health and well-being apps work effectively, and for whom. It is important to determine what constitutes effective engagement to reach the intervention's desired outcomes. Previous research has shown that effective engagement patterns can differ across interventions and populations. Therefore, an in-depth usage analysis to evaluate the effectiveness of digital mental health and well-being support is needed.

A second crucial question is why mental health apps do not work, and for whom. Despite the potential of digital mental health and well-being support, it is impeded by high levels of drop-out and 'non-usage attrition' (people not sustaining engagement with an intervention). It is crucial to understand non-usage attrition (e.g. has the intervention been used sufficiently to reach its desired outcomes or have people stopped using it prematurely due to design/development issues?).

Research aims and objectives:
The current research project aims to evaluate the effectiveness of digital health technology to support mental health and well-being. Firstly, the strengths and weaknesses of digital health technology reported in the literature will be investigated. Secondly, engagement with a digital mental health and well-being support app (AIME-Health) will be evaluated in the general population as well as in a well-defined target autistic population.

An in-depth analysis of a digital intervention will be conducted using the Person-Based Approach (PBA). The PBA is an iterative in-depth qualitative approach to digital intervention development. PBA elicits and addresses the needs and perspectives of an intervention user as a vital part of the intervention development, ensuring the intervention will be usable and engaging to its target audience. This will increase engagement and could reduce the risk of non-usage attrition.

Potential applications and benefit:
The PBA will be applied to a digital intervention 'AIME-Health', which is currently being developed by Cyberlimbic systems (partial funders of the research project). AIME-health is a smartphone application ('app') providing evidence-based mental health and well-being support. Anonymised data will be collected from the app (compliant with GDPR).

The collaboration with AIME-Health will allow for the research project to have a direct societal impact. Providing insights into ways to increase engagement in digital mental health and well-being support has the potential to benefit public mental health and well-being and reduce pressure on mental health services. This fits directly with ESRC outlined mental health research goals 2020/30: to develop new interventions and support for mental health and to improve choice and access to mental health care.

Additionally, we will employ a collaboration between the Centre for Applied Autism Research (CAAR) and the Bath Centre for Mindfulness and Compassion (BCMC), and further new collaboration between BCMC, CAAR and the Human Computer Interaction (HCI) Research Group. As intelligent apps like AIME-health support mental health at scale such collaborations are increasingly important in underpinning effective treatment.