Factors associated with frailty status compared to pre-frailty in community-dwelling older adults: a cross-sectional study

Our findings identify several factors strongly associated with frailty status in a cross-sectional sample. While these factors represent potential targets for intervention, future longitudinal studies are needed to confirm their predictive value for frailty progression. The logistic regression model provides clinically interpretable factors associated with frailty (e.g., SARC-F sarcopenia, ADL disability, nutritional risk). The neural network analysis corroborated the paramount importance of sarcopenia and highlighted additional non-linear associations. Community decisions should be primarily based on the interpretable outputs of the logistic regression model. Given the cross-sectional design, these findings represent associations at a single time point.