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Sun R & Sosnoff JJ. Novel sensing technology in fall risk assessment in older adults: A systematic review Biomed Central Geriatrics. 2018 Jan 16;18(14).
What is the effectiveness of technology-based tests, sensors, and algorithms in identifying older adult at risk of falling?
Falls are the leading cause of accidental death and injury in older adults. Falls and fall-related injuries are predictable and preventable with interventions targeting muscle strength, balance, and mobility.
Effective fall risk screening is not routinely done in clinical practice due to current unreliable measures, lack of cost-effective technology and time constraints.
New technologies can be used to analyze movement. These technologies can provide low-cost and easy-to-implement methods to assess fall risk.
A detailed search of a number of electronic databases was conducted to identify studies published between 2011 and May 2017. Studies that focused on fall risk, walking, older adults, and technology, were included in this review.
A total of 918 studies were identified in searches and 22 were included in the review after assessments for eligibility.
The authors did not acknowledge any funding sources for this review.
Technological devices can provide an accurate, inexpensive, and easy-to-administer fall risk assessment. Fall tracking is best used in future fall risk assessment with a follow-up period of at least 6 months after an initial assessment.
Sensor-based technologies are most commonly used. However, some require personnel for assessment, to operate the system and to interpret the data. Technology that automatically interacts with the user does not require additional supervision, however, there are safety and compliance concerns.
Older adults are willing to use technologies to improve their independence and safety. However, older adults do not want equipment to identify them as “fallers” or in need of help.
Overall, sensor-based technologies can provide an accurate, inexpensive and easy-to-administer objective fall risk assessment. Variations in sensor site, assessment tools, parameters, movement tasks and modelling techniques can affect the accuracy of these technologies. There is still a gap between clinical functionality, user experience and the widespread implementation of these technologies.