Influence of hand grip strength test and short physical performance battery on FRAX in post-menopausal women: a machine learning cross-sectional study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F24%3A10473014" target="_blank" >RIV/00064165:_____/24:10473014 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/24:10473014 RIV/00216208:11130/24:10473014
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=RaxMuGS0Wn" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=RaxMuGS0Wn</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23736/S0022-4707.23.15417-X" target="_blank" >10.23736/S0022-4707.23.15417-X</a>
Alternative languages
Result language
angličtina
Original language name
Influence of hand grip strength test and short physical performance battery on FRAX in post-menopausal women: a machine learning cross-sectional study
Original language description
BACKGROUND: Impaired physical performance and muscle strength are recognized risk factors for fragility fractures, frequently associated with osteoporosis and sarcopenia. However, the integration of muscle strength and physical performance in the comprehensive assessment of fracture risk is still debated. Therefore, this cross-sectional study aimed to assess the potential role of hand grip strength (HGS) and short physical performance battery (SPPB) for predicting fragility fractures and their correlation with Fracture Risk Assessment Tool (FRAX) with a machine learning approach. METHODS: In this cross-sectional study, a group of postmenopausal women underwent assessment of their strength, with the outcome measured using the HSG, their physical performance evaluated using the SPPB, and the predictive algorithm for fragility fractures known as FRAX. The statistical analysis included correlation analysis using Pearson's r and a decision tree model to compare different variables and their relationship with the FRAX Index. This machine learning approach allowed to create a visual decision boundaries plot, providing a dynamic representation of variables interactions in predicting fracture risk. RESULTS: Thirty-four patients (mean age 63.8+-10.7 years) were included. Both HGS and SPPB negatively correlate with FRAX major (r=-0.381, P=0.034; and r=-0.407, P=0.023 respectively), whereas only SPPB significantly correlated with an inverse proportionality to FRAX hip (r=-0.492, P=0.001). According to a machine learning approach, FRAX major >=20 and/or hip >=3 might be reported for an SPPB<6. Concurrently, HGS<17.5 kg correlated with FRAX major >=20 and/or hip >=3. CONCLUSIONS: In light of the major findings, this cross-sectional study using a machine learning model related SPPB and HGS to FRAX. Therefore, a precise assessment including muscle strength and physical performance might be considered in the multidisciplinary assessment of fracture risk in post-menopausal women.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30306 - Sport and fitness sciences
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Sports Medicine and Physical Fitness
ISSN
0022-4707
e-ISSN
1827-1928
Volume of the periodical
64
Issue of the periodical within the volume
3
Country of publishing house
IT - ITALY
Number of pages
8
Pages from-to
293-300
UT code for WoS article
001146360100001
EID of the result in the Scopus database
2-s2.0-85186748108