Author(s): LIU, S., LEE, L., YEH, K., TSAI,C., KAO,S., MOREAU,D., WANG,C., Institution: NATIONAL CHENG KUNG UNIVERSITY, Country: TAIWAN, Abstract-ID: 1506

Background: Existing literature indicates that aerobic fitness promotes inhibitory control. However, some previous studies have shown inconsistent results between high-aerobic fitness and low-aerobic fitness groups in young adults. Given the inconsistent findings, it is argued that traditional static tasks fail to impose sufficient physical demands, making them less effective at capturing the unique cognitive advantages of high-aerobic populations.
Objective: To address this question, this study employs a novel tool—the Motor-Cognitive Integrated Task (MCIT) to provide higher ecological validity, because it combines physical and cognitive demands, allowing for a better assessment of how aerobic fitness affects overall performance. The primary aim of this research is to compare MCIT performance between individuals with different levels of aerobic fitness and to determine whether fitness-related differences are expressed differentially at the cognitive and motor levels.
Method: Twenty-eight participants were recruited and completed a cardiorespiratory fitness assessment (i.e., PACER test), a static computerized flanker task, and the MCIT across three separate testing days. Aerobic fitness (VO₂max) was estimated from PACER performance. Following the classification criteria outlined in the American Council on Exercise (ACE) Personal Trainer Manual, participants were stratified into a high-fitness group (VO₂max > 52 ml·kg⁻¹·min⁻¹; n = 14) and a low-fitness group (VO₂max < 47 ml·kg⁻¹·min⁻¹; n = 14). The MCIT integrated a flanker paradigm with a whole-body movement task. Performance was recorded using an AI-based 2D pose estimation system, allowing decomposition of behavior into reaction time (RT), motor time (MT), and overall response time (RMT; RT + MT).
Result: The results showed that in the static computerized task, no significant difference in response time was observed between the high- and low-fitness groups, F (1, 26) = 1.34, p = .257, ηp² = .049. The RT in the MCIT remained unable to differentiate between the fitness groups (p = .347) as well. Additionally, no significant interaction between congruency and group was observed across all of the MCIT components. Interestingly, the high-fitness group exhibited a significant advantage in motor execution, as indicated by a main effect of group on MT, F (1, 26) = 6.65, p = .016, ηp² = .204. A similar group effect was observed for RMT, which integrates both cognitive and motor components, F (1, 26) = 5.40, p = .028, ηp² = .172.
Conclusion: These findings indicate that aerobic fitness is not associated with performance on traditional sedentary cognitive tasks, but is meaningfully related to motor execution and integrated motor–cognitive performance under embodied conditions. The MCIT appears sensitive to fitness-related differences that are not captured by laboratory-based measures, highlighting its utility as an ecologically valid tool for assessing executive functioning during active movement.