Abstract：
:Falls are a major cause of injury and disability in older people, leading to serious health and social consequences including fractures, poor quality of life, loss of independence, and institutionalization. To design and provide adequate prevention measures, accurate understanding and identification of person's individual fall risk is important. However, to date, the performance of fall risk models is weak compared with models estimating, for example, cardiovascular risk. This deficiency may result from 2 factors. First, current models consider risk factors to be stable for each person and not change over time, an assumption that does not reflect real-life experience. Second, current models do not consider the interplay of individual exposure including type of activity (eg, walking, undertaking transfers) and environmental risks (eg, lighting, floor conditions) in which activity is performed. Therefore, we posit a dynamic fall risk model consisting of intrinsic risk factors that vary over time and exposure (activity in context). eHealth sensor technology (eg, smartphones) begins to enable the continuous measurement of both the above factors. We illustrate our model with examples of real-world falls from the FARSEEING database. This dynamic framework for fall risk adds important aspects that may improve understanding of fall mechanisms, fall risk models, and the development of fall prevention interventions.
journal_name
J Am Med Dir Assocjournal_title
Journal of the American Medical Directors Associationauthors
Klenk J,Becker C,Palumbo P,Schwickert L,Rapp K,Helbostad JL,Todd C,Lord SR,Kerse Ndoi
10.1016/j.jamda.2017.08.001subject
Has Abstractpub_date
2017-11-01 00:00:00pages
921-927issue
11eissn
1525-8610issn
1538-9375pii
S1525-8610(17)30431-0journal_volume
18pub_type
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journal_title：Journal of the American Medical Directors Association
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