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Identifying risk of falls in older adults using objective quantification methods, allowing the development of patient-specific treatment plans S&T56

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2022
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded

Overview

NTU's Fully-funded PhD Studentship Scheme 2022

Project ID: S&T56

Falling is a threat to the growing population of older adults. The NHS reports that one in three older adults (3.4m) over the age of 65 years living at home will experience at least one fall a year. Age UK estimated that falls cost the NHS £4.6 million a day in 2010. Loss of stability or balance during walking and the lack of response to sudden disturbance during walking can often lead to falls in older adults. A fall often results in the fear of falling which consequently may adversely affect the independence as well as physical and mental wellbeing of the affected individual.

A major challenge in fall prevention is the identification of older adults who are at an increased risk of falling in a timely manner. Machine learning algorithms have previously been utilised to detect falls Tailored computer training can also identify factors related to specific groups or interventions (Bisele et al., 2017). We take this further and challenge ourselves in stating that implementing such techniques at earlier stages during diagnostic procedures will help identify older adults who may be at risk of falling, resulting in the implementation of patient-specific interventions to reduce such a risk. Therefore, this proposed project will investigate the factors associated with increased risk of falling in older adults and the subsequent implementation of therapeutic intervention to reduce the risk of falling.

In year 1, the student will look to identify various factors which have been associated with falls in older adults in the literature, and he/she will conduct patient and public questionnaires with older adults to identify specific needs and preferences of therapeutic methods. In year 2, the student will develop and test an objective machine learning algorithm to identify factors associated with musculoskeletal and movement parameters associated with reduced dynamic stability and stability. This study will result in a paper titled “Identifying key determinants of dynamic stability and balance in older adults”. In years 3 and 4, the student will develop an algorithm to help identify individuals who are at an increased risk of falling i.e. individuals who display a deterioration of previously defined key determinates. Furthermore, the student will investigate if therapeutic methods such as footwear can be implemented to target these factors at an individual level and thus reduce the risk of falls. This research project will result in 3-5 publications which will be entered in the next REF.

School strategic research priority

The proposed project aligns with the previous and ongoing research activity within the Sport, Health and Performance Enhancement (SHAPE) Research Centre. Furthermore, the project aligns with the Innovation plan of the School of Science and Technology.

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

For guidance and to make an application, please visit our studentship application page. The application deadline is Friday 14 January 2022.

Fees and funding

This is part of NTU's 2022 fully-funded PhD Studentship Scheme.

Guidance and support

Download our full applicant guidance notes for more information.

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