Mental Health in Mothers and Babies

This project aims to use computational methods to identify behavioural manifestation of problematic parenting in mother-infant interactions. Data modelling techniques will be used to enhance understanding of how mental health problems in a mother are passed on to her child. Such understanding holds the key to breaking an ongoing and reinforcing cycle of mental health disorder risk from generation to generation.

The causes of mental health problems are still poorly understood. However, the association between mother and child mental health disorders is one of the most consistent findings in psychiatry. It is less clear if this is limited to genetic transmission, or if this also translates onto behavioural transmission. This is where novel approaches are critical in order to enhance current understanding. Data modelling in this context will provide critical new evidence regarding the nature of parenting interventions that have potential to improve child mental health and break intergenerational transmission of mental health problems.

The proposed methodology will involve computational modelling of a pre-existing dataset. The dataset comprises a list of mother and child behaviour codes across 15 relevant modalities, taken from video data captured by wearable headcams. Example modalities include alertness, facial expression, vocalisation and touch, and within each mode there are, on average, 6 further detailed options. Additional data is available in terms of the mother's mental health. This data is predominantly continuous, but also involves discrete measures such as mental health test and impulsivity scores.

Initial data analysis will involve computing the frequencies, durations and rates per minute of each individual behaviour. Following this, patterns between modes will be extracted using computational techniques, such as graphical modelling, Bayesian inference and pattern recognition. Additionally, behavioural comparisons will be drawn between mothers with and without mental health difficulties. All programming will most likely take place using Python. This may include the utilisation of pre-existing models/ packages, but will also involve the creation of new ones.

In terms of the PhD structure, this can be roughly broken down by year. Year 1 of the project, I will spend time learning the relevant machine learning (ML) methods that will be applied to the dataset, particularly unsupervised ML concepts such as clustering. This will involve online research and study, but also practicing the newly learned concepts on a smaller subset of the data. Furthermore, I will engage thoroughly will the dataset in question, by spending time learning how the data came to be in it's current format, but also engaging myself in the video coding. Following this, the data will need to be reformatted appropriately for analysis. This will involve exploring which elements of the dataset are important, and which may be removed.

Year 2 will involve initially applying linear analysis to the data, in order to find correlations between modes. From here, graphical models will be implemented in order to further explore the depth of the data. This will involve building sophisticated, non-linear models which are able to combine the many dimensions of the data (i.e. time, behaviour modes, subject). These models will be tested, and the information fed back to the clinicians, parents and psychologists involved. This will be an iterative process, engaging psychologists and data analysts in order to fully scope the depth of the modelling outcomes.

The final project year will involve data visualisation techniques: finding the best and most effective ways of displaying the data, either for the parents or clinicians. This will involve the learning of new concepts, but will follow on well from the previous ML concepts learnt. There will also be considerable write up responsibilities during this year.

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.