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

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Falls often lead to minor and major 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). This can cause long-term physical and mental health issues. 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 recent 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, to improve physical and mental health and prevent falls. A recent scientific review found that artificial intelligence (AI) 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/), provided by a commercial software partner, to measure falls risk and help prevent falls among older adults in the community (Stanmore, 2021). 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. Older people will be at the centre of ACTIVATE, via a patient and public involvement panel, to enhance the conduct, reporting, and impact of the research. Overall, it will improve the prediction of falls risk and the provision of preventative fall strategies by leveraging the KOKU app, AI analytics, and participatory design to help reduce the occurrence of falls among older adults in the community.