Clinical validation of MyCog Mobile: development of a parsimonious and clinically interpretable prediction model for mild cognitive impairment.

Objectives: To develop and validate a prespecified logistic model for detecting mild cognitive impairment (MCI) using MyCog Mobile, a self-administered smartphone-based screening application, and to evaluate a structured simplification that reduces patient burden while maintaining diagnostic accuracy. Materials and Methods: We analyzed data from 277 older adults (100 electronic health record-confirmed MCI; 177 normal cognitive aging). Guided by the Harrell/Regression Modeling Strategies framework, a prespecified 10-predictor model was compared against reduced models using Wald Results: The final parsimonious model included 6 predictors and achieved an optimism-corrected Conclusion: The final model prioritized parsimony to reduce patient burden while maintaining clinical accuracy to detect MCI. Stability across traditional regression and regularized regression approaches from the statistical learning literature indicated a robust predictive signal. Findings support MyCog Mobile as an accurate and accessible cognitive screener able to detect the earliest signs of cognitive impairment in primary care.