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Mayo Normative Studies: a conditional normative model for longitudinal change in performance on remote digital cognitive measures including the Stricker Learning Span, Symbols Test and Mayo Test Drive Screening Battery Composite

Year: 2025

Type: article

Abstract: BACKGROUND: Self-administered and remote digital cognitive assessments provide a scalable method for screening and longitudinal cognitive monitoring. Mayo Test Drive (MTD) is one such assessment with previously demonstrated usability, reliability, validity and multi-device compatibility. MTD includes a computer-adaptive word list memory test (Stricker Learning Span) and a measure of processing speed/executive functioning (Symbols Test) that combine to provide an MTD Composite. This study aimed to develop a conditional normative model predicting longitudinal cognitive change for MTD, and to validate the model in two independent samples of cognitively unimpaired (CU) and mild cognitive impairment (MCI)/dementia (DEM) participants. METHOD: 1355 CU participants at initial MTD assessment with 2-4 consecutive MTD assessments every 7.5 months comprised the development dataset. Linear mixed effects models were used to construct a conditional normative model for MTD assessments 2-4. The baseline model was chosen a priori, consisting of age, sex, education, baseline score, number of assessments, and time. Device type, race, and baseline score by time interactions were also considered. Variables were only added if a >1% increment in r-squared was observed. The models were externally validated in 113 CU and 51 MCI/DEM patients. Following model development, the data were updated and additional follow-up MTD assessments were added for many participants, including the addition of additional CU validation data at test 3 (N = 126) and 4 (N = 279). T-tests compared observed and predicted differences and residual z-scores between development and validation at each follow-up assessment. RESULT: Table 1 presents demographic variables. No additional variables increased the r-squared by >1%. The final models are presented in Table 2. Age, baseline score, sex, and education were significant. Residual z-scores were not significantly different between CU development and CU validation cohorts, indicating observed scores were in line with model predicted scores. Residual z-scores were significantly worse among MCI/DEM compared to CU development in 11/12 comparisons (Figure 1), indicating observed scores were below model predicted expectations and implying sensitivity to longitudinal cognitive decline. CONCLUSION: These longitudinal normative models can be used to identify individuals with follow-up scores outside of expected predicted ranges, signifying abnormal longitudinal cognitive decline.

Source: Alzheimer s & Dementia

DOI: 10.1002/alz70857_099872