<abstract xmlns="http://www.w3.org/1999/xhtml">

<italic><bold>Introduction.</bold></italic> Risk factors associated with falling in the elderly are numerous. Most existing tools use a combination of functional assessment and risk scoring based on known risk factors. The aim of the study was to verify which parameters could be used to predict fall risk (FR) in older women.
<italic><bold>Material and Methods.</bold></italic> The study involved 56 inactive females aged 71.77 ± 7.43(SD). Backward stepwise regression analysis was performed to determine which independent variables predict FR in older women.
<italic><bold>Results.</bold></italic> Significant predictors of FR were: in model 1 – age and body mass (in 32%); in model 2 – knee extensor strength of the right lower limb (KEs R) (in 20%); in model 3 – the Timed up and Go test (TUG) (in 25.5%); and in model 4 – medial-lateral stability index with eyes open (MLSI EO) (in 35%). By means of backward stepwise regression analysis using the above models, the variables that significantly influence FR in seniors were body mass, MLSI EO, KEs, and age. The above analysis shows that these indicators (model 5) may predict FR in older women in 59% of cases.
<italic><bold>Conclusions.</bold></italic> It was determined that variables that significantly influence FR in seniors were body mass, age, KEs, and MLSI EO. Research should be continued to identify more predictors and define norms that indicate FR.
</abstract>

Introduction. Risk factors associated with falling in the elderly are numerous. Most existing tools use a combination of functional assessment and risk scoring based on known risk factors. The aim of the study was to verify which parameters could be used to predict fall risk (FR) in older women.
Material and Methods. The study involved 56 inactive females aged 71.77 ± 7.43(SD). Backward stepwise regression analysis was performed to determine which independent variables predict FR in older women.
Results. Significant predictors of FR were: in model 1 – age and body mass (in 32%); in model 2 – knee extensor strength of the right lower limb (KEs R) (in 20%); in model 3 – the Timed up and Go test (TUG) (in 25.5%); and in model 4 – medial-lateral stability index with eyes open (MLSI EO) (in 35%). By means of backward stepwise regression analysis using the above models, the variables that significantly influence FR in seniors were body mass, MLSI EO, KEs, and age. The above analysis shows that these indicators (model 5) may predict FR in older women in 59% of cases.
Conclusions. It was determined that variables that significantly influence FR in seniors were body mass, age, KEs, and MLSI EO. Research should be continued to identify more predictors and define norms that indicate FR.

Determination of Fall Risk Predictors from Different Groups of Variables

Józef Piłsudski University of Physical Education in Warsaw

Polish Journal of Sport and Tourism

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Determination of Fall Risk Predictors from Different Groups of Variables"}

Introduction. Risk factors associated with falling in the elderly are numerous. Most existing tools use a combination of functional assessment and risk scoring...

Determination of Fall Risk Predictors from Different Groups of Variables

Published Online: Dec 30, 2022

Page range: 3 - 8

Received: Sep 25, 2022

Accepted: Dec 08, 2022

DOI: https://doi.org/10.2478/pjst-2022-0020

Keywords
falls, prevention, elderly

© 2022 Patrycja Bobowik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Determination of Fall Risk Predictors from Different Groups of Variables

Published Online: Dec 30, 2022

Page range: 3 - 8

Received: Sep 25, 2022

Accepted: Dec 08, 2022

DOI: https://doi.org/10.2478/pjst-2022-0020

Keywordsfalls, prevention, elderly

© 2022 Patrycja Bobowik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
falls, prevention, elderly