76 PREDICTIVE MODEL FOR MOBILITY DECLINE: INDIVIDUAL TRIAL MISTAKE THRESHOLDS IN SUSTAINED ATTENTION TO RESPONSE TASK (SART). (18th November 2021) - Rizzo, R; Knight, S; Newman, L; Davis, J; Duggan, E; Kenny, R A; Romero-Ortuno, R
Abstract: Background: The Sustained Attention to Response Task (SART) is a standard computer-based cognitive test designed to measure the sustained attention, fundamental for completing tasks that require supervision over time (Robertson et al., 1997). However, commonly used average features may result in loss of information and data misinterpretation, leading to inability to detect clinically expected associations (O'Halloran et al., 2014). Methods: Here, we present a new method to visualise the full information obtained from the SART test, ordering by age, and categorising in groups based on mobility status in a large population-based study of ageing in Ireland. A new threshold, derived from the visualisation and based on the individual trial number of mistakes, was employed to individuate poorer SART performances, and to predict mobility and cognitive decline after 4 years in binary logistic regression models. Results: Raw SART data were available for 4, 864 participants aged 50 years and over at baseline. The new variable bad performances, expressing the number of SART trials with at least 4 mistakes, was the most significant predictor of mobility decline, defined as the transition from Timed Up-and-Go (TUG) < 12 to TUG ≥12 seconds (Odds Ratio (OR) = 1.29; 95% Confidence Interval (CI) 1.14–1.46; p < 0.001), and the only significant predictor of new falls (OR = 1.11; 95% CI 1.03–1.21; p = 0.011) compared to traditional SART variables in models adjusted for multiple covariates. No SART-related variables resulted significant predictors of cognitive decline, defined as a decrease of at least 2 points in the Mini-Mental State Examination (MMSE) score. Conclusion: This multimodal visualisation and the new threshold approach could help clinicians to easily develop relevant hypotheses, and better identify subjects at higher risk of future mobility decline.