About the Project
Safe Steps is a digital falls risk assessment tool, (a tablet based app) designed to reduce the number of falls in residential care homes. Safe Steps uses a dynamic set of assessment questions to create individual, personalised care plans (http://www.safesteps.tech/).
This PhD project will follow MRC Guidance on developing and evaluating complex interventions to further develop the Safe Steps approach, with the aim of undertaking a feasibility randomised controlled trial (RCT) of the technology in residential care.
Initial work will be with Safe Steps to develop an algorithm that provides an individualised intervention plan for a patient/resident presented in a decision support format for care home staff. This will be based on up-to-date evidence on effectiveness of specific regimens requiring the student to undertake evidence review and synthesis to inform this step. The next step will be co-production/co-design with stakeholder groups to design the next iteration of Safe Steps so as to be intuitive for users. This will involve qualitative research work involving health and social care professionals and other stakeholders, patient and public involvement/engagement using focus groups, interviews and product testing.
The updated Safe Steps can then be tested in a usability study involving relatively small numbers of users testing the product in a real world situation in residential care over a number of weeks/months and feeding back experience. Depending on results (as per the MRC Guidance) the next stage will either be to conduct a further iteration of the co-design process or move on to designing and conducting a feasibility RCT of Safe Steps versus usual care.
Falls data are required to be routinely collected by residential care homes and these reports will be the source of primary outcome data, which along with bed occupancy can be used to calculate fall rates. Case mix data, and process measures will also be required. As this will be a feasibility RCT it will not be powered to test the hypothesis that Safe Steps reduces falls rates compared to usual care. The purpose is to test the feasibility of conducting such a study in the future by testing procedures in a relatively small sample – normally 30-35 participants per arm.
Becker C, Woo J, Todd C. Falls. Chapter 50 in Michel JB et al (eds). Oxford Textbook of Geriatric Medicine 3rd Ed, Oxford University Press 2018 doi:10.1093/med/9780198701590.003.0050
Cameron I, Dyer S, Panagoda C et al. Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database of Systematic Reviews 2018, Issue 9. Art. No.: CD005465. doi:10.1002/14651858.CD005465.pub4.
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Stanmore E, Mavroeidi A, Jong LD de, Skelton DA, Sutton CJ, Valerio Benedetto V, Skelton DA, Munford L, Meekes W, Bell V, Todd C. The effectiveness and cost-effectiveness of strength and balance Exergames to reduce falls risk for older people in UK assisted living facilities: A multi-centre, cluster randomised controlled trial. BMC Medicine 2019 17:49 https://doi.org/10.1186/s12916-019-1278-9
Boulton E, Horne M, Todd C. Multiple influences on participating in physical activity in older age: Developing a social ecological approach. Health Expectations 2017 doi:10.1111/hex.12608
Helbostad JL, Vereijken B, Becker C, Todd C, Taraldsen K, Pijnappels M, Aminian K, Mellone S. Mobile health applications to promote active and healthy ageing. Sensors 2017, 17, 622; doi:10.3390/s17030622
Boulton E, Hawley-Hague H, Vereijken B, Clifford A, Guldemond N, Pfeiffer K, Hall A, Chesani F, Mellone S, Bourke A, Todd C for the FARSEEING Consortium Developing the FARSEEING taxonomy of technologies: classification and description of technology use (including ICT) in falls prevention studies. Journal of Biomedical Informatics, 2016 doi: 10.1016/j.jbi.2016.03.017
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