Influence of hand grip strength test and short physical performance battery on FRAX in post-menopausal women: a machine learning cross-sectional study

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11110/24:10473014 RIV/00216208:11130/24:10473014

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Influence of hand grip strength test and short physical performance battery on FRAX in post-menopausal women: a machine learning cross-sectional study

Popis výsledku v původním jazyce

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&apos;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 &gt;=20 and/or hip &gt;=3 might be reported for an SPPB&lt;6. Concurrently, HGS&lt;17.5 kg correlated with FRAX major &gt;=20 and/or hip &gt;=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.

Název v anglickém jazyce

Influence of hand grip strength test and short physical performance battery on FRAX in post-menopausal women: a machine learning cross-sectional study

Popis výsledku anglicky

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&apos;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 &gt;=20 and/or hip &gt;=3 might be reported for an SPPB&lt;6. Concurrently, HGS&lt;17.5 kg correlated with FRAX major &gt;=20 and/or hip &gt;=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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30306 - Sport and fitness sciences

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Sports Medicine and Physical Fitness

ISSN

0022-4707

e-ISSN

1827-1928

Svazek periodika

64

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

8

Strana od-do

293-300

Kód UT WoS článku

001146360100001

EID výsledku v databázi Scopus

2-s2.0-85186748108