Predictability of Fall Risk Assessments in Community-Dwelling Older Adults

A Scoping Review

Review (2023)
Authors

N.F.J. Waterval (Amsterdam Movement Sciences, Rehabilitation & Development, VU University Medical Centre)

C.M. Claassen (Student TU Delft)

Frans C T Van Der Helm (TU Delft - Biomechatronics & Human-Machine Control)

E. van der Kruk (TU Delft - Biomechatronics & Human-Machine Control)

Research Group
Biomechatronics & Human-Machine Control
Copyright
© 2023 N.F.J. Waterval, C.M. Claassen, F.C.T. van der Helm, E. van der Kruk
To reference this document use:
https://doi.org/10.3390/s23187686
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 N.F.J. Waterval, C.M. Claassen, F.C.T. van der Helm, E. van der Kruk
Research Group
Biomechatronics & Human-Machine Control
Issue number
18
Volume number
23
DOI:
https://doi.org/10.3390/s23187686
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Abstract

Fall risk increases with age, and one-third of adults over 65 years old experience a fall annually. Due to the aging population, the number of falls and related medical costs will progressively increase. Correct prediction of who will fall in the future is necessary to timely intervene in order to prevent falls. Therefore, the aim of this scoping review is to determine the predictive value of fall risk assessments in community-dwelling older adults using prospective studies. A total of 37 studies were included that evaluated clinical assessments (questionnaires, physical assessments, or a combination), sensor-based clinical assessments, or sensor- based daily life assessments using prospective study designs. The posttest probability of falling or not falling was calculated. In general, fallers were better classified than non-fallers. Questionnaires had a lower predictive capability compared to the other assessment types. Contrary to conclusions drawn in reviews that include retrospective studies, the predictive value of physical tests evaluated in prospective studies varies largely, with only smaller-sampled studies showing good predictive capabilities. Sensor-based fall risk assessments are promising and improve with task complexity, although they have only been evaluated in relatively small samples. In conclusion, fall risk prediction using sensor data seems to outperform conventional tests, but the method’s validity needs to be confirmed by large prospective studies.