iStoppFalls – Innovative Fall Prevention

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Falls are a significant safety issue in healthcare, particularly for older people and people with cognitive impairment who are at increased risk due to projected longer life expectancy. Statistics show that 35% of people aged 65 and over suffer at least one fall per year, and there are around 600,000 fatal falls worldwide each year. The fall rate in hospitals is between 3.5 and 18 falls per 1000 occupied bed days, with 3 to 37% of patients falling during their hospital stay.

The use of voice assistants to prevent falls in older adults is an area of research with a gap, as there is little literature on this topic. The implementation of a bed exit sensor in combination with a smart speaker aims to address this issue and assist the elderly population and individuals with cognitive impairment in fall prevention. Balaguera et al (2017) found that the frequency of bed falls in patients in healthcare facilities was reduced by using a bed exit sensor in combination with an audible alert.

The fall prevention project uses purposive sampling to select participants from the researcher’s circle of acquaintances based on defined criteria. A quantitative questionnaire with a Likert scale and semi-structured interview questionnaires as well as focus group guidelines are used for data collection. The quantitative data will be analyzed using IBM SPSS Statistics 29, whereby the anonymity of the participants will be preserved. Qualitative data will be paraphrased and coded using MAXQDA, and results will be validated through consensus building within the research team. The analysis of the data is based on descriptive analyses without hypothesis testing, according to current scientific standards.

In summary, positive outcomes could occur in the form of increased safety, better communication and an overall improved care experience for the patients and caregivers involved. This would underline the importance and benefits of this research project.

A project at the St. Pölten University of Applied Sciences

Master Program Digital Healthcare

Project Coaches: FH-Prof. Romana Bichler

Team members:

Alexander Dellert
Magdalena Druml
Flora Hamar
Maximilian Puhr
Ines Seidl