Mayo Normative Studies: Regression-based normative data for remote self-administration of the Stricker Learning Span, Symbols Test, and Mayo Test Drive Screening Battery Composite and validation in individuals with mild cognitive impairment and demen

<b>Objective:</b> Few normative data for unsupervised, remotely-administered computerized cognitive measures are available. We examined variables to include in normative models for Mayo Test Drive (MTD, a multi-device remote cognitive assessment platform) measures, developed normative data, and validated the norms. <b>Method:</b> 1240 Cognitively Unimpaired (CU) adults ages 32-100 years (96% White) from the Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center with Clinical Dementia Rating^® of 0 were included. We converted raw scores to normalized scaled scores and derived regression-based normative data adjusting for age, age², sex, and education (base model); alternative norms are also provided (age + age² + sex; age + age²). We assessed additional terms using an <i>a priori</i> cut-off of 1% variance improvement above the base model. We examined low test performance rates (< -1 <i>SD</i>) in independent validation samples (<i>n</i> = 167 CU, <i>n</i> = 64 mild cognitive impairment (MCI), <i>n</i> = 14 dementia). Rates were significantly different when 95% confidence intervals (CI) did not include the expected 14.7% base rate. <b>Results:</b> No model terms met the <i>a priori</i> cut-off beyond the base model, including device type, response input source (e.g. mouse, etc.), or session interference. Norms showed expected low performance rates in CU and greater rates of low performance in MCI and dementia in independent validation samples. <b>Conclusion:</b> Typical normative models appear appropriate for remote self-administered MTD measures and are sensitive to cognitive impairment. Device type and response input source did not explain enough variance for inclusion in normative models but are important for individual-level interpretation. Future work will increase the inclusion of individuals from under-represented groups.