Risk factors for falls and technologies for fall risk assessment in older adults
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Auditorium of Kastelli research centre. Aapistie 1, Oulu
Topic of the dissertation
Risk factors for falls and technologies for fall risk assessment in older adults
Doctoral candidate
Master of Science Immonen Milla
Faculty and unit
University of Oulu Graduate School, Faculty of Medicine, Center for Life Course Health Research and Research Unit of Medical Imaging, Physics and Technology
Subject of study
Medical physics and technology
Opponent
Professor Sari Stenholm, University of Turku
Custos
Professor Raija Korpelainen, University of Oulu
Fall risk of older adults can be predicted by techological solutions
Doctoral dissertation shows that the fall risk of older people can be assessed automatically by utilizing technologies. The study found that a mobile application with a separate accelerometer attached to the lumbar spine was able to reliably detect features from walking style that predict falling.
In addition, a sensor attached to the front of the hip was able to reliably detect some of the features predicting high fall risk. Feedback of tested home training technology was mainly positive from older and professional test users.
In particular, the dissertation examined the accelerometer-based methods and the suitability of the mobile application developed by VTT for risk assessment. In addition, the study tested the suitability of the technology for home training to reduce the fall risk. Injurious falls cause high healthcare costs and can lead to long-term institutional care for the elderly. As the population ages, it is important to find effective ways to prevent falls. Personalized screening and prevention of the risk of falls is expensive and time consuming and it is important to find new and reliable methods for screening people at high fall risk. Mobile and sensor technologies provide new possibilities for screening individuals at high risk of falling.
In addition, a sensor attached to the front of the hip was able to reliably detect some of the features predicting high fall risk. Feedback of tested home training technology was mainly positive from older and professional test users.
In particular, the dissertation examined the accelerometer-based methods and the suitability of the mobile application developed by VTT for risk assessment. In addition, the study tested the suitability of the technology for home training to reduce the fall risk. Injurious falls cause high healthcare costs and can lead to long-term institutional care for the elderly. As the population ages, it is important to find effective ways to prevent falls. Personalized screening and prevention of the risk of falls is expensive and time consuming and it is important to find new and reliable methods for screening people at high fall risk. Mobile and sensor technologies provide new possibilities for screening individuals at high risk of falling.
Last updated: 1.3.2023