Establishing computational behavioural models for the detection of early dementia from speech

The prevalence of dementia in the UK is projected to double by 2040, trebling the current annual cost to the UK economy of £34.7 billion. Dementia is a progressive decline in cognitive function, including problems with memory, executive, and visuospatial functions. Alzheimer's Disease (AD) is the most common cause of dementia and all AD patients transition through an early phase of mild cognitive impairment (MCI), but this can be difficult to distinguish from normal cognitive ageing and is not always recognised or diagnosed. However, early diagnosis is crucial because considerable cortical damage can occur by the time major cognitive symptoms are exhibited, limiting treatment options.
Some signs of future dementia, such as gait and speech disturbances, can present years before the onset of serious cognitive decline. This presents opportunities for digital phenotyping technologies such as smartphones, wearables and in-home monitoring to help in early detection and management of AD and MCI. The SPHERE system at Bristol University is a research test bed for the health-enabled smart home of the future. It uses multiple continuous sensing platforms including video and audio, as well as accelerometer and physiological data from wearable sensors, to monitor health behaviours such as gait, navigation, activities of daily living (ADL) and social contact.
The CUBOiD project aims to combine standard clinical neuropsychological testing with mulitmodal data from SPHERE to derive ground truths on digital signals of early cognitive decline. CUBOiD is funded by the Medical Research Council and has ethical approval from the Wales REC7 committee. Despite the disruption caused by COVID-19, CUBOiD is well-suited to adaptation for online data acquisition. Indeed, an in-home mode of presentation precludes the requirement to attend clinic and could reduce patient anxiety, which can introduce variability into measured data.
The goals of this PhD research project are 1) to develop novel machine learning models that are informed by same-patient data on ADL and can detect changes in speech as diagnostic signatures of early AD and MCI; and 2) to develop novel digital interventions that may alleviate symptoms and slow disease progression. CUBOiD has already been collecting speech data using a tablet-deployed, Gogglebox-inspired talking task in which participants discuss a TV show they enjoy. Combined with standard linguistic clinical tests, this naturalistic speaking task has potential to identify how variability in speech features (e.g., prosody or linguistic compensations) may correlate with changes in ADL such as navigation or household activities in early MCI and AD. It could potentially also show whether subtle changes in speech precede changes in ADL, how this may differ between MCI and early AD- and could form a predictive diagnostic tool for MCI or AD.
Over the summer of 2020 I will conduct a literature review of linguistic anomalies seen in MCI and AD, and of digital therapies and how they relate to linguistic stimulation. Alongside analysis of existing data in year 1, a feasibility study will examine how to push the talking task assessments into an online format using the current tablet-based platform with appropriate approvals from the Sponsor (UoB) and the HRA. Normative data will then be collected from age-sex matched controls (UoB REC approval required) and compared with performance on premorbid intelligence tests (such as NART-IQ) to establish how they relate to educational level in people without dementia. In year 2, I will collect speech task data from people with a diagnosis of MCI and AD to establish correlations with ADL and ground truths on speech patterns. Alongside writing up my thesis, in year 3, I will conduct a study to establish the feasibility of interventions that could alleviate symptoms or facilitate care based on passive sampling of speech.

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.