Supplementary Material for: A Prediction Model for Falls in Community-Dwelling Older Adults in Podiatry Practices

Introduction: Falls are a worldwide health problem among community-dwelling older adults. Emerging evidence suggests that foot problems increase the risk of falling, so the podiatrist may be crucial in detecting foot-related fall risk. However, there is no screening tool available which can be used in podiatry practice. The predictive value of existing tools is limited, and the implementation is poor. The development of risk models for specific clinical populations might increase the prediction accuracy and implementation. Therefore, the aim of this study was to develop and internally validate an easily applicable clinical prediction model (CPM) that can be used in podiatry practice to predict falls in community-dwelling older adults with foot (-related) problems. Methods: This was a prospective study including community-dwelling older adults (≥65 years) visiting podiatry practices. General fall-risk variables, and foot-related and function-related variables were considered as predictors for the occurrence of falls during the 12-month follow-up. Logistic regression analysis was used for model building, and internal validation was done by bootstrap resampling. Results: 407 participants were analyzed; the event rate was 33.4%. The final model included fall history in the previous year, unsteady while standing and walking, plantarflexor strength of the lesser toes, and gait speed. The area under the receiver operating characteristic curve was 0.71 (95% CI: 0.66–0.76) in the sample and estimated as 0.65 after shrinkage. Conclusion: A CPM based on fall history in the previous year, feeling unsteady while standing and walking, decreased plantarflexor strength of the lesser toes, and reduced gait speed has acceptable accuracy to predict falls in our sample of podiatry community-dwelling older adults and is easily applicable in this setting. The accuracy of the model in clinical practice should be demonstrated through external validation of the model in a next study.