External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test.

purposeSimple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific properties such as age, body height, and body mass index. A previously developed machine learning-based model for personalized testing using the five-repetition sit-to-stand (5R-STS) test, that estimates personalized upper limits of normal (ULN) to identify objective functional impairment (OFI) was externally validated to evaluate its performance and generalizability across cohorts.

methodsOnly healthy individuals were included in the study. After the machine learning-based model was applied to this external dataset, expected and observed 5R-STS test times were compared using standardized performance assessment metrics including root mean square error (RMSE), mean absolute error (MAE), and R

resultsApplication of the model to 171 healthy individuals resulted in an RMSE of 2.33 (95% CI: 1.93 to 2.73) seconds, MAE of 1.70 (95% CI: 1.47 to 1.94) seconds, and R

conclusionsIn the context of personalized testing, moving toward individually focused precision assessment of patients requires rigorous external validation to ensure the robustness of such applied computational methods. In this external validation, the model demonstrated limited generalization including a systematic underestimation of slower test times and insufficient personalized ULN calibration. These findings indicate that external validity of models derived from single-center data can be limited, underscoring the importance of comprehensive external validation and, potentially, multicenter retraining before clinical implementation.