Dr Nduka Okwose

Supervisor Details

Contact Details

Dr Nduka Okwose

Centre for Discoveries in Life Sciences, Coventry University

Scientific Background

Research Interests

My research focuses on the prevention, diagnosis, and management of cardiometabolic disease through the integration of clinical science, lifestyle medicine, digital health, and artificial intelligence. I am particularly interested in translating novel diagnostic approaches and data-driven technologies into real-world healthcare settings to improve early disease detection, risk stratification, and patient outcomes. My work spans cardiometabolic medicine, physical activity and lifestyle medicine, health inequalities, and the development of AI-enabled clinical decision-support tools, with a strong emphasis on interdisciplinary and translational research.

Scientific Inspiration

Prof Kamlesh Khunti (Leicester Diabetes Centre) is a significant source of scientific inspiration to me because of his outstanding contribution to translational research, particularly his ability to bridge rigorous clinical science with meaningful improvements in patient care and population health. His work has transformed the understanding and management of diabetes and cardiometabolic diseases nationally and internationally, while demonstrating how research can influence clinical guidelines, healthcare policy, and real-world practice.

I am particularly inspired by his commitment to multidisciplinary collaboration, research leadership, and the translation of evidence into scalable interventions that reduce health inequalities and improve outcomes for diverse populations. His career exemplifies the type of impactful, patient-centred research that I aspire to deliver through my own work in cardiovascular health, lifestyle medicine, and digital health innovation.

Supervision Style

In three words or phrases: Supportive and developmental; Collaborative and inclusive; Methodologically rigorous.

Provision of Training

I take a structured and supportive approach to training, providing close guidance in the early stages of the PhD to help develop strong methodological, analytical, and research skills. As your confidence and expertise grow, I will encourage increasing independence, while continuing to provide mentorship and access to wider support from experienced researchers, collaborators, and the broader research team.

Progression Monitoring and Management

I take a supportive but accountable approach to progression monitoring. Together, we will establish clear short-, medium-, and long-term milestones aligned with the PhD timeline, and I will expect you to take increasing ownership of achieving these goals as you develop as an independent researcher. Regular supervisory meetings (two-weekly) will be used to review progress, discuss challenges, troubleshoot obstacles, and agree on actions for the next stage of work. I have high expectations and will encourage you to achieve your full potential while ensuring you receive the guidance, feedback, and support needed to succeed.

Communication

I value open, honest, and regular communication, and I encourage students to raise questions or challenges as they arise rather than waiting for scheduled meetings. While I may occasionally send emails outside normal working hours, I do not expect an immediate response and strongly support maintaining a healthy work-life balance. I am approachable and happy to discuss any academic, professional, or personal factors that may be affecting your progress, so that we can work together to identify appropriate support and solutions. I am open to explore different modes of professional communication.

Meetings

PhD Students can expect scheduled meetings with me at least once per week. These meetings will be a mixture of face-to-face and via video chat or telephone. I have an open door policy, but my pattern of being contactable for an instant response is not predictable.

Work Patterns

Work in my lab requires being present on site (be it in a laboratory or at a field site) during core hours [specify below] (or a subset if working part time).

Notice Period for Feedback

I need at least 1 week's notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Understanding Heterogeneity in Diabetes Prevention and Self-Management: Behavioural and Metabolic Phenotyping for Personalised Intervention

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Understanding Heterogeneity in Diabetes Prevention and Self-Management: Behavioural and Metabolic Phenotyping for Personalised Intervention

Secondary Supervisor(s): Professor Djordje Jakovljevic

University of Registration: Coventry University

BBSRC Research Themes:

Integrated Understanding of Health (Diet and Health)

Project Outline

Type 2 diabetes (T2D) disproportionately affects ethnically diverse populations, yet participation in lifestyle and diabetes education programmes and sustained behavioural change remain suboptimal. In England, the NHS Diabetes Prevention Programme has demonstrated modest improvements in weight and HbA1c, but substantial attrition, with only 19% of participants completing the programme in early national implementation. Digital delivery can improve accessibility, but outcomes remain heterogeneous.

In Coventry and Warwickshire, the Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (DESMOND) programme provide structured diabetes education, alongside the NHS Diabetes Prevention Programme for individuals at high risk of T2D. This project will investigate why individuals from ethnically diverse communities engage differently with these interventions and why metabolic outcomes vary between participants. The PhD will combine routinely collected NHS/service data, behavioural phenotyping, wearable technology and community co-design.

First, the student will quantify ethnic and socioeconomic differences in referral, uptake, retention and one-year metabolic outcomes. Second, machine-learning and statistical approaches will identify behavioural and clinical phenotypes associated with engagement and treatment response. Third, qualitative and community-based co-design will identify modifiable barriers and facilitators and inform a personalised, culturally responsive strategy to support engagement.

The project will generate new knowledge on the biological and behavioural determinants of response to lifestyle interventions and establish a scalable precision-prevention approach for diabetes.