Performance on an Associative Memory Test Decreases 8 hr After Cardiovascular Exercise

Cognitive Benefits of Exercise: Is There a Time-of-Day Effect?

It is well established that physical activity benefits cognition. Further, the time of day one engages in physical activity has been suggested to influence cognition. Here, we aimed to understand if there is a time-of-day effect (morning or afternoon) of physical activity on cognition, i.e., if exercising in the morning or afternoon might bring greater cognitive benefits. A total of 56 participants were allocated to one of two groups with the same baseline cognitive performance as well as fitness level (International Physical Activity Questionnaire-IPAQ): 27 to the morning intervention (M) group; and 29 to the afternoon intervention (A) group. In both groups, the participants engaged in an intermittent recovery test (Yo-yo), 4 times a week for 12 weeks. All participants were assessed with the d2 Test of Attention and the Borg scale of perceived exertion pre- and post- acute and chronic intervention. After the first bout of exercise and after 12 weeks, we observed cognitive improvements both in the M and A groups. Surprisingly, we do not find differences between the time of day regarding cognitive benefits. Our results do not support the existence of a time-of-day effect for the attentional cognitive benefits of exercise.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.