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Examining machine learning in a digital physical activity intervention to predict and prevent falls in older adults: a mixed methods feasibility study


   Faculty of Biology, Medicine and Health


About the Project

Falls often lead to injuries and are a risk to morbidity and mortality across all age groups. Factors associated with ageing increase the risk of falls, with at least one third of community-dwelling older people falling and injuring themselves each year (Campbell et al., 1990) which can cause long-term physical and mental health problems. In addition, NHS England has over 250,000 falls related hospital admissions per year, costing an estimated £2 billion annually (Public Health England, 2017).

A Cochrane review found exercise as a single intervention prevents falls, reducing rates between 23% and 34% (Sherrington et al., 2020). Accessibility and adherence to in-person exercise programmes among older adults can be low and was severely impacted by the COVID-19 pandemic. Hence, digital physical activity interventions are being designed and tested to improve how often and how well older people exercise at home. A review found that machine learning techniques may improve the prediction of falls among older adults in hospital or simulated settings, but community-based datasets were lacking (O’Connor et al., 2022).

These could offer more accurate and up-to-date predictions of older adults at risk of falling and sustaining injuries at home or in a care home. The ACTIVATE project will utilise a novel digital physical activity application called KOKU (https://kokuhealth.com/) (Stanmore, 2021) to measure falls risk and help prevent falls among older adults in the community. A mixed methods feasibility study will recruit older people to use the KOKU app to collect exercise and falls related data which will be analysed via machine learning techniques. These algorithms will be utilised to create a prediction model of falls risk in older adults in the community. This will inform the co-design of an AI-based digital dashboard with older people to educate them about their falls risk and provide them with evidence-based strategies via the KOKU app to prevent falls. 

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in a related area/subject. Candidates with experience in data analytics are encouraged to apply.

Applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title.

For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences.

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/”


Funding Notes

Applications are invited from self-funded students. This project has a Band 2 fee. Details of our different fee bands can be found on our website View Website

References

• Campbell, A.J., Borrie, M.J., Spears, G.F.x, et al. (1990). Circumstances and consequences of falls
experienced by a community population 70 years and over during a prospective study. Age Ageing, 19(2), 136–41. https://doi.org/10.1093/ageing/19.2.136
• Public Health England (2017). Falls and fracture consensus statement: Supporting commissioning for prevention. Available at: https://www.england.nhs.uk/south/wp-content/uploads/sites/6/2017/03/falls-fracture.pdf
• Sherrington, C., Fairhall, N., Wallbank, G., Tiedemann, A., Michaleff, Z. A., Howard, K., ... & Lamb, S. (2020). Exercise for preventing falls in older people living in the community: an abridged Cochrane systematic review. British Journal of Sports Medicine, 54(15), 885-891. http://dx.doi.org/10.1136/bjsports-2019-101512
• Stanmore, E. (2021). Developing, Testing, and Implementing a Falls Prevention and Healthy Aging App (Keep-On-Keep-Up) for Older Adults. Innovation in Aging, 5 (Suppl 1), 516-516. https://doi.org/10.1093/geroni/igab046.1990
• O’Connor, S., Gasteiger, N., Wong, D., & Stanmore, E. (2022). Artificial Intelligence for Falls Management in Older Adult Care: A Scoping Review of Nurses Role. Journal of Nursing Management – submitted for publication.

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