Aerobic Capacity and Cognitive Control in Elementary School-Age Children

Abstract

Purpose The current study examined the relationship between children’s performance on the Progressive Aerobic Cardiovascular Endurance Run (PACER) subtest of the FitnessGram® and aspects of cognitive control that are believed to support academic success. Methods Hierarchical linear regression analyses were conducted on a sample of 2nd and 3rd grade children (n = 397) who completed modified versions of a flanker task and spatial n-back task to assess inhibitory control and working memory, respectively. Results Greater aerobic fitness was significantly related to shorter reaction time and superior accuracy during the flanker task, suggesting better inhibitory control and the facilitation of attention in higher fit children. A similar result was observed for the n-back task such that higher fit children exhibited more accurate target detection and discrimination performance when working memory demands were increased. Conclusion These findings support the positive association between aerobic fitness and multiple aspects of cognitive control in a large sample of children, using a widely implemented and reliable field estimate of aerobic capacity. Importantly, the current results suggest that this relationship is consistent across methods used to assess fitness, which may have important implications for extending this research to more representative samples of children in a variety of experimental contexts.

Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children

“…The sample size in these studies has ranged from 24 to 224 children among the ages of 6 and 13 years old (with a mean age of 9 or 10 in 75% of the studies). Fitness has most often been measured using a shuttle-run task (often the PACER) (7, 16, 82, 83, 91, 140, 141) or a graded exercise test (24-27, 29, 50, 60, 96, 129-131, 161, 167). In addition to the large number of studies assessing fitness, there is one study that assessed PA objectively using accelerometry (148) and one study that assessed sport participation in addition to their measure of fitness (7).…”

“…In addition to the large number of studies assessing fitness, there is one study that assessed PA objectively using accelerometry (148) and one study that assessed sport participation in addition to their measure of fitness (7). With regard to the statistical analyses, fitness or activity has either been maintained as a continuous variable (7, 16, 50, 60, 91, 96, 118, 140, 141, 148), has been used to categorize participants as low- or high-fit, with this judgment typically based upon normative PACER data (82, 83) or normative VO 2max data (24-27, 29, 129-131, 161, 167), or participants have been identified as athletes or non-athletes (7). When researchers have categorized participants as low- or high-fit, the average difference in VO 2max among the groups is 14.75 ml/kg/min (SD=4.76, n=13) and the average difference in the number of laps in a shuttle run task is 18.40 laps (SD=0.28, n=2), indicating that there are substantial fitness differences among the groups.…”

“…These studies have included behavioral measures of cognitive performance in isolation (7, 16, 27, 29, 50, 60, 91, 96, 115, 130, 131, 140, 141, 148, 167) or in combination with measures of brain function (82, 83, 118, 129, 161, 168) or brain structure (24-26). In studies that include only behavioral measures of cognitive performance, a wide variety of cognitive outcomes have been assessed including processing speed (7, 115, 167), EF (16, 50, 130, 140, 141, 148, 167), memory (27, 60, 115, 131), learning (131), attention (16, 50, 96, 115, 148), crystallized and fluid intelligence , and a novel street crossing/virtual reality task (29).…”

Cross-Validation of FITNESSGRAM® Health-Related Fitness Standards in Hungarian Youth

“…Additionally, other outcomes besides metabolic syndrome should be evaluated because physical fitness is associated with more than just cardiometabolic risk. For example, recent research has shown the HFZ to be related to risk for depression (Rieck, Jackson, Martin, Petrie, & Greenleaf, 2013), blood markers of nonalcoholic fatty liver disease (Trilk et al, 2013), academic achievement (Wittberg, Northrup, & Cottrell, 2012), and cognitive control (Scudder et al, 2014).…”

Cognitive function of young male tennis players and non-athletes

“…Although the effects of specific sports activities on executive functions are still uncertain (Ishihara, Sugasawa, Matsuda, & Mizuno, 2016), many cross-sectional and longitudinal intervention studies have shown a relationship between a sports activity and cognitive function (Alesi, Bianco, Luppine, Palma, & Pepi, 2016;Davis et al, 2007;Huijgen et al, 2015;Ishihara, Sugasawa, Matsuda, & Mizuno, 2017;Lundgren, Högman, Näslund, & Parling, 2016;Scudder et al, 2014;Vestberg et al, 2012). From the point of view of the level of sports activity, Heppe, Kohler, Fleddermann, and Zentgraf (2016) found that elite athletes (playing different team sports), performed better in sustained attention tests, compared to recreational athletes.…”