Handgrip strength time profile and frailty: an exploratory study
| Autor: | M. D. Chousal, Maria Teresa Restivo, A. Fernandes, Paulo Abreu, Tiago Coelho, Diana Urbano, M. R. Barbosa |
|---|---|
| Přispěvatelé: | Repositório Científico do Instituto Politécnico do Porto |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Matching (statistics)
medicine.medical_specialty Technology Coronavirus disease 2019 (COVID-19) Computer science QH301-705.5 QC1-999 Exploratory research Sample (statistics) Interval (mathematics) 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Robustness (computer science) Smart systems medicine Handgrip strength time profile General Materials Science 030212 general & internal medicine Biology (General) Instrumentation QD1-999 Fluid Flow and Transfer Processes Artificial neural network Artificial neural networks Frailty Occupational health Process Chemistry and Technology Physics General Engineering 030229 sport sciences Engineering (General). Civil engineering (General) Computer Science Applications Test (assessment) Chemistry TA1-2040 |
| Zdroj: | Applied Sciences Volume 11 Issue 11 Applied Sciences, Vol 11, Iss 5134, p 5134 (2021) Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | This study aims to explore the use of force vs. time data obtained from an isometric handgrip test to match a frailty state based on the TFI score. BodyGrip, a novel prototype system, is used for handgrip strength over 10 s time interval tests. A cross-sectional study with a non-probabilistic sample of community-dwelling elderly women was conducted. The force/time data collected from the dominant handgrip strength test, together with the Tilburg Frailty Indicator (TFI) test results, were used to train artificial neural networks. Different models were tested, and the frailty matching of TFI scores reached a minimum accuracy of 75%. Despite the small sample size, the BodyGrip system appears to be a promising tool for exploring new frailty-related features. The adopted strategy foresees ultimately configuring the system to be used as an expedite mode for identifying individuals at risk, allowing an easy, quick, and frequent person-centered care approach. Additionally, it is suitable for following up of the elderly in particular, and it may assume a relevant role in the mitigation of the increase in frailty evolution during and after the imposed isolation of the COVID-19 pandemic. Further use of the system will improve the robustness of the artificial neural network algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|