ArticleFrontiers in psychology2026
The association between cardiovascular fitness and sustained attention in the elderly-a diffusion decision model approach.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Sustained attention is essential for cognitive functioning and everyday life. However, previous findings regarding the effects of aging and the benefits of cardiovascular fitness on sustained attention remain inconsistent. Moreover, the cognitive mechanisms underlying these associations remain unclear. Therefore, in addition to conventional behavioral measures, the present study applied the diffusion decision model (DDM) to characterize the cognitive processes underlying sustained attention and examine their associations with age and cardiovascular fitness in the elderly. Methods: Eighty-eight adults (Mage = 69.1, SD = 8.4) completed a modified psychomotor vigilance task and the 2-min Step Test. Sustained attention was evaluated using conventional behavioral measures (mean reaction time, Mean-RT; coefficient of variation of reaction time, CV-RT; and the slowest 10% reaction time, Slow-RT; performance decrement of reaction time, PD-RT), and DDM-derived parameters (drift rate, boundary separation, and non-decision time). Hierarchical multiple regression analyses were performed to examine the associations of age and cardiovascular fitness with behavioral and DDM-derived outcomes. Results: Increasing age was associated with longer Mean-RT and Slow-RT, as well as greater boundary separation. Cardiovascular fitness was positively associated with drift rate after controlling for age. Discussion: These findings suggest that age-related behavioral slowing in this sample was accompanied by greater response caution, whereas higher cardiovascular fitness was selectively associated with more efficient evidence accumulation. Importantly, DDM parameter revealed fitness-related differences that were not detected by conventional behavioral measures, demonstrating the added value of computational modeling for understanding exercise-cognition relationships in aging.
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