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Physical Activity and Precision Nutrition Interventions for Health and Wellbeing (SLS1)


   School of Life Sciences

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  Dr Ayazullah Safi  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Occupations involving sedentary and physically inactive behavior are major contributors to an unhealthy lifestyle. This can lead to reduced productivity, increased sickness absence, and a higher risk of metabolic diseases such as diabetes and hypertension. The issue has been further amplified during the Covid pandemic, with 88% of organisations globally implementing long-term home-based working leading to an unprecedented transition into a sedentary working lifestyle. This Ph.D. project will use a multi-disciplinary approaches involving nutritional science and data analytics methods to investigate and seek to characterise the effects of shifts to less active work behaviors on health and wellbeing outcomes. We aim to elucidate the role of physical activity and design data-driven precision nutrition interventions for diverse work settings (onsite, hybrid and remote) in the United Kingdom with the overall goal of aiding health and wellbeing and supporting productivity. A major outcome will be an artificial intelligence (AI) platform for customised nutrition and physical activity plan(s). Our outputs and interventions will help individuals to adopt lifestyle changes under their specific settings, reducing risk of lifestyle/nutrition-related behaviours, diseases and consequently lowering the economic and social burden. The outcomes of this project therefore have the potential to make an impact on health and well-being of a large proportion of the adult population. The project will be based in University’s Centre for Nutraceuticals, a first of a kind global initiative (https://www.westminster.ac.uk/research/groups-and-centres/centre-for-nutraceuticals).

This studentship will therefore provide the PhD candidate an opportunity to start a career in a topical, interdisciplinary project that has the potential to have significant relevance in the area of workplace health, wellbeing, nutrition, and productivity. The candidate should have a strong background in sport and exercise science/nutrition and some experience of data science. For any informal queries please contact Dr Ayaz Safi ([Email Address Removed]). 


Funding Notes

Applications are invited for a Full Research Studentship which is tenable for up to three years for full-time study starting in January 2023. Overseas applicants are welcome though will have to pay the difference between the Home and Overseas fee rates. The students will be offered a stipend of £17,285 (fixed to UKRI Rate) per annum and £3000 per annum for consumables. Students will be funded full time for 3 years. Students will also be encouraged to assist with demonstrating practical classes and will be paid the rate for demonstrators.

References

1. Safi, A., Cole, M., Kelly, A., Deb, S., & Walker, N. (2022). A Comparison of Physical Activity and Sedentary Lifestyle of University Employees through ActiGraph and IPAQ-LF. Physical Activity and Health, 6(1), pp. 5–15. DOI: https://doi.org/10.5334/paah.163
2. Safi, A., Cole, M., Kelly, A. L., & Walker, N. C. (2021). An Evaluation of Physical Activity Levels amongst University Employees. Advances in Physical Education, 11, 158-171. https://doi.org/10.4236/ape.2021.112012
3. Sengupta, D. (2021). Machine Learning in Precision Medicine. In Intelligent Data-Centric Systems: Sensor Collected Intelligence Series, Elsevier. http://dx.doi.org/10.1016/B978-0-12-821777-1.00013-6
4. Sengupta, D., Sharma, V.K., Hota, S.K., Srivastava, R.B., & Naik, P.K. (2021). An ensemble approach for evaluating the cognitive performance of human population at high altitude. In Intelligent Data-Centric Systems: Sensor Collected Intelligence Series, Elsevier. http://dx.doi.org/10.1016/B978-0-12-821777-1.00021-5

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