Abstract 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 χ2 partitioning. Internal validation used 2000 bootstrap (BS) resamples to calculate optimism-corrected C-statistics/area under the receiver operating characteristic, calibration, and clinical utility via decision curve analysis. Sensitivity analyses compared the primary model to bootstrapped and cross-validated regularized regression approaches (LASSO, Ridge, Elastic Net) to confirm model stability. Results The final parsimonious model included 6 predictors and achieved an optimism-corrected C-statistic of 0.812 with excellent calibration (slope = 0.92). Detection accuracy was 75% (BS 95% CI, 69%-80%), consistent with penalized regression models in sensitivity analyses (accuracy 72%-73%), with overlapping CIs confirming predictive stability. Decision curve analysis showed the model provides net benefit over both “refer-all” and “refer-none” strategies across all examined thresholds, capturing ∼55% of the net benefit achievable by a theoretically perfect screener. 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.
Young et al. (Mon,) studied this question.