Clinical and Functional Outcomes in Faller and Non-Faller Older Adults Clustered by Self-Organizing Maps: A Machine-Learning Approach

被引:0
作者
Almeida, Milena L. S. [1 ]
Cavalcanti, Aline O. [2 ]
Sarai, Rebeca [3 ]
Silva, Mateus A. [3 ]
Melo, Paulo R. V. [3 ]
Silva, Amanda A. M. [2 ]
Caldas, Rafael R. [3 ]
Buarque, Fernando [3 ]
Trombini-Souza, Francis [1 ,2 ]
机构
[1] Univ Pernambuco, Dept Phys Therapy, BR-56328900 Petrolina, PE, Brazil
[2] Univ Pernambuco, Masters & Doctoral Programs Rehabil & Funct Perfor, BR-56328900 Petrolina, PE, Brazil
[3] Univ Pernambuco, Polytech Sch Engn, Recife, PE, Brazil
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 16期
关键词
falls; artificial intelligence; functional mobility; unsupervised algorithm; older adults; DUAL-TASK; PERFORMANCE; GAIT; KINEMATICS; WALKING;
D O I
10.3390/app14167093
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application Increased cognitive demands during motor tasks emerged as a critical discriminator of functional patterns among older adults, shedding light on the intricate interplay between cognitive function and motor performance in fall prevention strategies in community-dwelling older adults.Abstract A wide range of outcomes makes identifying clinical and functional features distinguishing older persons who fall from non-fallers challenging, especially for professionals with less clinical experience. Thus, this study aimed to map a high-dimensional and complex clinical and functional dataset and determine which outcomes better discriminate older adults with and without self-reported falls. For this, clinical, functional, and cognitive outcomes of 60 community-dwelling older adults classified as fallers and non-fallers were selected based on self-report of a single fall in the last 12 months. An unsupervised intelligent algorithm (Self-Organizing Maps-SOM) was used to cluster and topographically represent the data studied. The SOM model mapped and identified two different groups (topographic error: 0.00; sensitivity: 0.77; precision: 0.42; accuracy: 0.53; F1-score: 0.55) based on self-report of a single fall. We concluded that although two distinct groups were mapped and clustered by the SOM, participants were not necessarily fallers or non-fallers. The increased cost of cognitive demands regarding a motor task (Timed Up and Go Test) and the effect of the Trail Making Test (TMT) Part B regarding TMT Part A could discriminate the functional and cognitive patterns in community-dwelling older adults. Therefore, in clinical practice, identifying patterns involving the interaction between cognition and motor skills, even in once-only faller older adults, can be an efficient approach to assessment and, consequently, to compound intervention programs to prevent falls in this population.
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页数:12
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