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If you'd like more neuroscience information from the author, listen to this VrainWaves episode about another one of his books, Brail Rules.

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We've been waiting for years now for the chance to interview Dr. John Medina: Developmental Molecular Biologist, Professor of Bioengineering at the University of Washington School of Medicine, and author of Brain Rules, Brain Rules for Baby, Brain Rules for Aging Well, and Attack of the Teenage Brain, to name a few. Dr. Medina takes time out of his busy schedule to chat with us about Theory of Mind, stress and learning, and some history behind our fancy brains.

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vrain waves: teaching conversations with minds shaping education

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‎Vrain Waves: Teaching Conversations with Minds Shaping Education: Dr. John Medina, Brain Rules on Apple Podcasts

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We've been waiting for years now for the chance to interview Dr. John Medina: Developmental Molecular Biologist, Professor of Bioengineering at the University of Washington School of Medicine, and author of Brain Rules, Brain Rules for Baby, Brain Rules for Aging Well, and Attack of the Teenage Brai...

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Here's a good, short video that explores more about executive function, particularly discussing how individuals with ADHD struggle with many executive function tasks.

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What is Executive Function and Why Do We Need it?

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If you're interested in learning more about Social and Emotional Learning, here is a great podcast with Denise Pope.

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Step into the classroom of one of Stanford's most engaging professors as we interview Dr. Denise Pope! Denise discusses the role that stress plays in our students lives, how we can work to decrease that cognitive takeover, and some digestible research we can both learn from and share with our parents.

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social and emotional learning with denise pope

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vrain waves: teaching conversations with minds shaping education

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‎Vrain Waves: Teaching Conversations with Minds Shaping Education: Social and Emotional Learning with Denise Pope on Apple Podcasts

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Step into the classroom of one of Stanford's most engaging professors as we interview Dr. Denise Pope! Denise discusses the role that stress plays in our students lives, how we can work to decrease that cognitive takeover, and some digestible research we can both learn from and share with our parent...

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Book: Spark: The Revolutionary New Science of Exercise and the Brain by John J. Ratey, MD

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Spark: The Revolutionary New Science of Exercise and the Brain

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Spark: The Revolutionary New Science of Exercise and the Brain [Ratey MD, John J., Hagerman, Eric] on Amazon.com. *FREE* shipping on qualifying offers. Spark: The Revolutionary New Science of Exercise and the Brain

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The Effects of Acute Exercise on Mood, Cognition, Neurophysiology, and Neurochemical Pathways: A Review

Julia C. Basso and Wendy A. Suzuki*

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Brain Plast. 2017; 2(2): 127-152.

Abstract

A significant body of work has investigated the effects of acute exercise, defined as a single bout of physical activity, on mood and cognitive functions in humans. Several excellent recent reviews have summarized these findings; however, the neurobiological basis of these results has received less attention. In this review, we will first briefly summarize the cognitive and behavioral changes that occur with acute exercise in humans. We will then review the results from both human and animal model studies documenting the wide range of neurophysiological and neurochemical alterations that occur after a single bout of exercise. Finally, we will discuss the strengths, weaknesses, and missing elements in the current literature, as well as offer an acute exercise standardization protocol and provide possible goals for future research.

Keywords: Running, affect, prefrontal cortex, hippocampus, electroencephalography, neuroimaging, neurochemistry, neurogenesis

INTRODUCTION

The positive influence of exercise on mood and cognition across the lifespan has become a topic of much excitement [ 1]. In particular, abundant data suggest that physical activity can reduce the risk of various neurological diseases and protect the brain from the detrimental effects of aging [ 2-4]. Animal models have focused mainly on the effects of long-term exercise (i.e., weeks to months of increased exercise) on hippocampal function, with special emphasis on exercise-induced adult hippocampal neurogenesis and hippocampal-dependent learning and memory [ 5, 6]. Studies in rodents have also documented the neuroanatomical, neurochemical, and cellular/molecular changes associated with long-term exposure to exercise [ 7, 8]. In humans, both behavioral and functional imaging approaches have started to identify the neuroanatomical systems modulated by long-term increases in exercise. The most commonly reported area to undergo improvement is the prefrontal cortex, with exercise-induced enhancements observed in attention and other executive functions [ 9]. As well as for improving cognition in children and healthy adults, these long-term effects of exercise are of particular interest for their possible role in improving cognitive function during aging [ 10-12]. As we age, cognitive decline, though not inevitable, is a common occurrence resulting from the process of neurodegeneration. In some instances, neurodegeneration results in mild cognitive impairment or more severe forms of dementia including Alzheimer's, Parkinson's, or Huntington's disease. Because of the role of exercise in enhancing neurogenesis and brain plasticity, physical activity may serve as a potential therapeutic tool to prevent, delay, or treat cognitive decline. Indeed, studies in both rodents and humans have shown that long-term exercise is helpful in both delaying the onset of cognitive decline and dementia as well as improving symptoms in patients with an already existing diagnosis [ 13-15].

In addition to the robust literature on the effects of long-term exercise on brain function, a growing literature has also examined the influence of a single bout of exercise, termed "acute exercise", on human behavior. A recent set of excellent meta-analyses has detailed a range of behavioral changes, concluding that acute exercise has an overall small positive effect on cognitive functioning, especially in areas of prefrontal cortex-dependent cognition [ 9, 16-19]. Additionally, acute exercise has been shown to enhance affective, mood, and emotional states [ 20]. Because of the lack of a general criteria regarding low-, moderate-, and high-intensity aerobic exercise, human studies have utilized a range of acute exercise protocols. This has led to a diverse set of findings, which are highlighted throughout the manuscript; implications for such findings are considered in the discussion. For this review, we have primarily focused on aerobic exercise, but have included some studies that utilize resistance exercise. Additionally, we have mainly reviewed studies in healthy adults, but have included some discussion of the effects of acute exercise in individuals with various diseases/disorders. Finally, our focus has centered around the effects of acute exercise on behaviors dependent on the prefrontal cortex, mood, and stress responses, but a small collection of literature is starting to reveal that acute exercise may be beneficial on a variety of other brain processes as well, including motor learning and memory dependent on striatal-cortical circuits.

To maximize results, the majority of rodent studies investigating the effects of physical activity on brain structure and function utilize running protocols typically lasting three weeks or more. Therefore, acute exercise, as it is used in humans (e.g., approximately one hour of exercise), is rarely studied in rodents. The majority of studies that have utilized a single bout of exercise typically examine the effects on neurochemical changes. Many fewer studies have examined the behavioral effects of acute exercise in rodents. Because of the limited number of these studies, we have included those that investigated the effects of one week or less of exercise, which is considered an acquisition time period for running in the rodent [ 21], on behavioral and brain outcomes.

The goal of this review is to summarize the wide range of neurophysiological and neurochemical changes that have been described after an acute bout of exercise, from studies in both animal models and human subjects, and to link these changes to a range of behavioral effects that have been described mainly in humans. As chronic exercise is ultimately the result of regular bouts of acute exercise, understanding the changes induced by a singular bout of exercise may provide novel insight into how to interpret and even approach the study of the effects of chronic increases in physical exercise on cognitive function.

THE EFFECTS OF ACUTE EXERCISE ON COGNITION, MOOD, AND STRESS RESPONSES IN HUMANS

A comprehensive set of narrative [ 22-26] and meta-analytic reviews [ 9, 16-19] that focused on the effects of acute exercise on cognition in people concluded that acute exercise has an overall small positive effect on cognitive functioning. However, the individual studies supporting this conclusion are highly variable, demonstrating behavioral effects ranging from strongly positive to detrimental. One of the main reasons this spread of results exists is because of the diverse exercise regimens that have been utilized. Notably, there is currently no standardized way to assess the influence of exercise on cognitive functioning, which has resulted in a wide variety of physical activity protocols that differ in the critical factors of format (i.e., the type of exercise used), intensity, and duration (but see Table 2 ). Despite this wide range of testing parameters, three of the most consistent effects reported are (1) improvements in cognitive tasks that depend primarily on the prefrontal cortex [ 27], (2) enhancements in mood state [ 28, 29], and (3) decreases in stress level [ 30] ( Fig. 1A ). More limited evidence exists to show that acute exercise improves long-term memory [ 31-34] and associative memory [ 35, 36] dependent on the hippocampus, learning [ 37] and retention [ 38, 39] of motor skills dependent on the striatum, emotional memory dependent on the amygdala [ 40, 41], and skills dependent on both the primary visual and motor cortices [ 42].

Table 2

Stroop Color and Word Task

Eriksen Flanker Task

N-Back Task

Trail Making Task A and B

Wisconsin Card Sorting Task

Hopkins Verbal Learning Task

Symbol Digit Modalities Task

Go/No Go Task

Stop Signal Task

Simon Task

Random Number Generation Task

Tower of London Task

Digit Span Task

Reading Span Task

Operation Span Task

The tasks most often used to test the effects of acute exercise on cognition are those that assess the functions of the prefrontal cortex, including attention and perception tasks that focus primarily on reaction time, and both verbal and visual working memory tasks. These include the Eriksen Flanker Task, the Stroop Color and Word Task, the Go/No Go Task, and the N-back Task among others ( Table 1 ). Collectively, this literature shows that executive functions including attention, working memory, problem solving, cognitive flexibility, verbal fluency, decision making, and inhibitory control receive the most benefit from acute exercise [ 9], with effects lasting for up to two hours post-exercise cessation ( Fig. 1A ) [ 27]. Moreover, many of these neuropsychological assessments measure several aspects of behavior including both accuracy of performance and speed of processing. McMorris and Hale performed a meta-analysis examining the effects of acute exercise on both accuracy and speed of processing, revealing that speed significantly improved post-exercise, with minimal or no effect on accuracy [ 17]. These authors concluded that increasing task difficulty or complexity may help to augment the effect of acute exercise on accuracy.

Table 1

Acute Exercise Measurement and Category

Values

1) Duration (measured in minutes)

Short

0 to 15 minutes

Moderate

16 to 45 minutes

Long

46 minutes or longer

2) Intensity (measured in percentage of VO2 max)

Low

≤39

Moderate

40 to 59

High

≥60

3) Percevied exertion

Measured by the Borg Ratings of Perceived Exertion Scale

6 to 20

4) Exercise index

A combined value of duration, intensity, and perceived exertion

Calculation: (% of hour + % of VO2 max + % of scale) / 3

A major factor that has been shown to influence the cognitive effects of acute exercise in people is exercise intensity. Yerkes and Dodson's law suggests that an inverted-U relationship exists between arousal and performance [ 43, 44]. Based on this idea, which predicts that moderate-, but not low- or high-, intensity exercise supports enhanced cognition, the majority of studies have utilized moderate-intensity exercise protocols. However, in a comprehensive meta-analysis, Chang and colleagues found that exercise intensities ranging from very light (<50% MHR) to very hard (>93% MHR) have all been reported to improve cognitive functioning [ 9]. Some studies have examined the effects of varying doses of exercise [ 34, 45-48]. One study, for example, that compared the effects of various maximal resistance regimens (none versus 40%, 70%, or 100% of 10-repetition maximum) on cognitive functioning revealed that different intensities of exercise might differentially improve distinct types of brain functions. Specifically, moderate-intensity exercise may be more beneficial for executive functions (Tower of London and Stroop), whereas high-intensity exercise may be more beneficial for information processing (Paced Auditory Serial Addition) [ 45, 49]. Another study in individuals with Down syndrome investigated the effects of 20 minutes of moderate- (50-74% age-predicted MHR) versus high-intensity (75-85% age-predicted MHR) treadmill walking on information processing speed (assessed by a choice reaction time test) and executive function (attention shifting assessed by the dimensional change card sorting test and inhibitory control assessed by the knock-tap test) [ 50]. In this population, moderate-intensity, but not high-intensity, exercise facilitated information process speed, whereas both moderate- and high-intensity exercise were beneficial for inhibitory control. Comparison of these studies indicate that exercise type as well as health status of the individual may be important factors affecting the impact of acute exercise on cognitive functioning.

Acute exercise is one of the most effective behavioral techniques for self-regulation of mood in healthy populations [ 51]. The effects of acute exercise on mood state have been evaluated using a variety of self-reported questionnaires such as the Profile of Mood States (POMS; a questionnaire that measures tension, depression, anger, fatigue, confusion, vigor and overall mood disturbance) and the Positive and Negative Affects Scale (PANAS) [ 28]. These acute interventions have shown that exercise alleviates negative as well as enhances positive mood states [ 52], with effects occurring immediately after and lasting up to one day post-exercise cessation [ 28, 29, 53] ( Fig. 1A ). As a large number of studies have utilized the POMS, one review sought to determine the areas of mood that are most influenced by acute exercise [ 54]. They found that the largest improvements in mood were decreases in tension, depression, anger, and confusion. One study examined the effects of acute moderate-intensity exercise on affect using the circumplex model of emotional state [ 55], which examines affect based on two scales, valence and arousal, in young (19-39), middle-aged (40-64), and older (65+) individuals [ 56]. Results revealed that compared to a picture-viewing control group, acute exercise significantly increased high-arousal positive affect (HAP) (e.g., feeling energized) in all age groups. In contrast, low-arousal positive affect (LAP) (e.g., feeling calm) decreased for young adults but remained stable for older adults. This study highlights the importance of assessing the influence of acute exercise on both low- and high-arousal affective states across different age groups.

As the acute exercise protocols used in these studies vary dramatically, the best protocol for improving mood is unclear. Some evidence exists, however, to suggest that the most effective type of exercise at producing mood improvements may be a workout experience that participants find enjoyable. For example, in a study that compared varying exercise intensities (low-, moderate-, and high-intensity) to a self-chosen exercise intensity, the greatest mood benefits in the form of high enjoyment scores after exercise came from those who were able to choose their own workout experience (based on the Physical Activity Enjoyment Scale) [ 57]. However, other interpretations of this finding exist, and as this study included only a small sample size ( n  = 10 for this condition), future studies will be needed to assess whether this finding is related to the subject's sense of control over the workout or the workout that best matches the subject's physical fitness or perceived fitness level, for example.

In addition to improving mood, studies also show that acute exercise is effective at reducing stress both on self-reported questionnaires [ 58] and from anxiety-inducing procedures conducted in the laboratory [ 59] ( Fig. 1A ). Acute exercise, for example, decreases stress-related blood pressure responses to laboratory-based stressors including mental arithmetic, public speaking, neuropsychological assessments, or the cold pressor test [ 30, 60]. Brownley and colleagues found that this effect was most likely a result of acute exercise suppressing the sympathetic nervous system response to stress [ 61]. These studies suggest that acute exercise may be an effective strategy for alleviating the psychological symptoms produced by an acute stressor. Additionally, studies have shown that acute exercise helps relieve symptoms associated with mood and psychological disorders such as depression, anxiety, schizophrenia, and post-traumatic stress disorder [ 62-64].

Summary of acute exercise-induced behavioral changes (Fig. 1A )

A large collection of research in humans has shown that a single bout of exercise alters behavior at the level of affective state and cognitive functioning in several key ways. In terms of affective state, acute exercise decreases negative affect, increases positive affect, and decreases the psychological and physiological response to acute stress [ 28]. These effects have been reported to persist for up to 24 hours after exercise cessation [ 28, 29, 53]. In terms of cognitive functioning, acute exercise primarily enhances executive functions dependent on the prefrontal cortex including attention, working memory, problem solving, cognitive flexibility, verbal fluency, decision making, and inhibitory control [ 9]. These positive changes have been demonstrated to occur with very low to very high exercise intensities [ 9], with effects lasting for up to two hours after the end of the exercise bout ( Fig. 1A ) [ 27].

THE EFFECTS OF ACUTE EXERCISE ON THE BRAIN: INSIGHT FROM FUNCTIONAL IMAGING AND ELECTROPHYSIOLOGICAL STUDIES

Brain imaging and electrophysiological techniques including electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and transcranial magnetic stimulation (TMS) in humans have started to provide insight into how an acute bout of exercise affects the physiological and functional activity patterns of the brain ( Fig. 1B ). Of all these approaches, only EEG studies have examined the effects of acute exercise on baseline brain functions in the absence of a cognitive load. We start with a description of these baseline EEG studies in both humans and rodents. We then turn to results from studies using this and other approaches that describe how the brain responds to an acute bout of exercise during the performance of various cognitive demands.

EEG Studies

The majority of studies assessing the effect of acute exercise on the EEG signal have focused on the alpha frequency band, a neural oscillatory pattern in the frequency range of 7.5 to 12 hertz (Hz) in humans. Alpha oscillations indicate a state of decreased cortical activation and are associated with states of fatigue, relaxation, and diminished anxiety [ 65]. Because acute exercise causes these types of behavioral changes, the predominant hypothesis was that acute exercise should also increase alpha activity. However, in their review examining the effects of acute exercise on resting state brain activity, Crabbe and Dishman found significant increases not only in alpha activity but in delta, theta, and beta activity as well [ 66] ( Fig. 1B ). This review included 18 studies of acute exercise that incorporated protocols of walking, running, cycling, aqua, and isometric exercises that ranged from 2 to 60 minutes, with heart rates of 89 to 200 beats per minute, and EEG activity measured anywhere from immediately after and up to 30 minutes post-exercise cessation [ 67]. Despite this range in exercise protocols, the effect size was moderately large, ranging from 0.38 to 0.75 standard deviations, with similar patterns of activation seen across different brain regions including the frontal, temporal, central/parietal, and occipitalcortices.

Though capturing electrophysiological changes in the human brain during exercise is difficult, microelectrodes and local field potential (LFP) recordings enable this kind of data collection in rodents. Using a 30-minute treadmill running paradigm at 13 meters per minute, Li and colleagues found similar increases in alpha and theta power (amplitude) in rodents [ 68, 69], with these effects dissipating within 10 minutes of the end of exercise [ 70] ( Fig. 1B ). An extensive body of work examining LFP patterns in rodents during locomotion has shown that the hippocampal theta oscillation is invariably present during spatial navigation, wheel, and treadmill running [ 71]. During theta, the hippocampus is bathed in an increased concentration of acetylcholine that is believed to facilitate plasticity [ 72, 73]. In addition to promoting synaptic plasticity, theta organizes hippocampal neurons into sequential activity patterns that are later repeated during subsequent behaviors [ 74, 75]. In the situation of acute exercise, this sequencing of neural firing may reinforce the synapses between neurons to support subsequent memory for events that unfold over time [ 76]. As the prefrontal cortex is a recipient of this sequential activity from the hippocampus [ 77, 78], prefrontal cortical neurons exhibit similar predictive or preparatory behavior during running [ 79], which is of particular interest as prefrontal cortex function clearly benefits from acute exercise (see above). These observations suggest that the exercise-induced sustained theta oscillation, and in particular the organized sequential activity and elevated cholinergic tone, may specifically enhance performance on tasks that depend on hippocampal-prefrontal interactions [ 80-82].

Studies using EEG in humans have also examined the effects of acute exercise on the electrophysiological signal during cognitive task performance. Hillman and colleagues provided a comprehensive review that nicely describes the effects of acute exercise on electrophysiological indices of cognitive processes including stimulus discrimination, inhibition, and action monitoring [ 83]. Event related potentials (ERPs) are brain responses measured through EEG that are stereotyped and time-locked to specific internal or external events. The P300 or P3 component, for example, is elicited during decision making and is represented as a positive deflection in amplitude with a latency between 250 and 500 ms, with larger amplitudes and shorter latencies corresponding to superior cognitive performance. One study examined the influence of acute low- (30% VO ₂ max) and moderate-intensity (50% VO ₂ max) aerobic exercise on the P3 component during a modified flanker task in both healthy younger (19-25 years) and older (60-74 years) adults [ 84]. Compared to a resting baseline and low-intensity exercise, moderate-intensity exercise decreased reaction time in both young and old adults. This behavioral improvement was accompanied by decreased P3 latencies in both young and old adults as well as increased P3 amplitudes in young adults. Following acute exercise, other studies have reported similar increases in P3 amplitude and decreases in P3 latency during tasks including the oddball task, the Go/No Go task, and an attention network task similar to the Eriksen flanker task [ 85-90]. Though the precise neural origins of the P3 component are unknown, its generation is thought to reflect the synchronized firing of large groups of excitatory pyramidal neurons of the cortex [ 91, 92] and appears to be mediated by interactions between the frontal and temporal/parietal cortices [ 93, 94], with contributions from dopaminergic and noradrenergic inputs [ 95-97]. This work suggests that one mechanism underlying the acute exercise-induced improvements in cognition may be the enhancement of synchronous firing between cortical neurons.

fNIRS Studies

fNIRS is a non-invasive optical technique that utilizes near-infrared light to measure the hemodynamic response of the cerebral cortex. This technique has been used to examine the influence of acute exercise on human brain function. In particular, fNIRS has been useful in evaluating Dietrich's reticular-activating hypofrontality theory of exercise [ 98]. This theory states that during exercise, the brain's finite supply of energy and resources is shifted from areas involved in cognitive processing like the prefrontal cortex towards areas that are important for motor movement and coordination like the primary motor cortex, striatum, and cerebellum, causing a hypoperfusion or decreased activation of the former area during exercise [ 99]. Following this exercise-induced prefrontal hypoperfusion, this region is thought to experience a rebound surge of oxygenated blood, resulting in enhanced prefrontal function.

Yanagisawa and colleagues conducted the first study to assess the neural substrates of acute exercise-induced enhancements in cognitive functioning using fNIRS [ 100]. In healthy, young adults, fNIRS measurements were captured during the Stroop Task both before and 15 minutes after a 10-minute bout of moderate-intensity aerobic exercise (50% of VO ₂ peak) or rest. Compared to the resting control condition, acute moderate exercise significantly increased inhibitory control as measured by the interference component of the Stroop Task (incongruent minus neutral), and this effect was accompanied by increased activation in the left dorsolateral prefrontal cortex, a brain region integral to executive functioning ( Fig. 1B ). Similar findings were also more recently reported for low-intensity exercise (10 minutes at 30% VO ₂ peak) [ 101]. The same group then repeated this study in older individuals to determine whether the beneficial effects of acute exercise could be induced in an elderly population. As we age, a compensatory phenomenon occurs known as hemispheric asymmetry reduction in older adults (HAROLD) [ 102]. In this process, older adults have less lateralized prefrontal activity than do older adults. For example, in young adults performing the Stroop Task, left regions of the prefrontal cortex are engaged; however, in some older adults, both sides become engaged, with older adults who show task-related recruitment of the bilateral prefrontal cortices outperforming those individuals who show only unilateral recruitment. As predicted, compared to young adults [ 100], the older population demonstrated the HAROLD effect, with increased activation reported bilaterally in the dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, and frontal polar area during Stroop interference trials [ 103]. Acute exercise enhanced performance on the Stroop task and increased task-related activity in the right frontal polar area, indicating that exercise increased the HAROLD effect or the older brain's ability for compensatory brain activation ( Fig. 1B ). An important area of future research will be to determine how long these acute exercise-induced enhancements in inhibitory control mechanisms persist, as the studies described above looked only up to 15 minutes after exercise cessation.

fMRI Studies

fMRI is another non-invasive technique that takes advantage of the brain's hemodynamic response, allowing it to be imaged with high spatial resolution during rest or cognitive engagement. A number of cross-correlational and interventional studies have shown that long-term exercise causes changes in task-related brain activation and functional connectivity [ 104-106]. For example, high-fit older adults outperform their low-fit counterparts on the attention-related cognitive test, the Eriksen Flanker task, and this boost in performance is accompanied by increased activation in brain regions associated with attentional control (i.e., middle frontal gyrus, superior frontal gyrus, and the superior parietal lobe) and decreased activation in the inhibitory control region, the anterior cingulate cortex [ 105]. Compared to a stretching and toning control group, six months of aerobic exercise produced the same results [ 105]. Additionally, cardiopulmonary fitness is associated with and aerobic training can increase functional connectivity between frontal, posterior and temporal cortices in both the Default Mode and Frontal Executive Networks [ 104, 107, 108]. Many fewer studies, however, have examined the effects of acute exercise on cognition using fMRI. Though each of these studies describe a range of acute exercise-induced changes (both increases and decreases) in fMRI signaling [ 109-112], these findings were not accompanied by behavioral improvements, making the fMRI results difficult tointerpret.

One recent study examined the influence of a single session of aerobic exercise after learning on memory consolidation and hippocampal functioning [ 113]. Subjects encoded a series of picture-location associations and then either rested or exercised (35 minutes of interval training on a cycle ergometer at 80% of their maximum heart rate) immediately after or four hours after encoding. Forty-eight hours after this experience, subjects returned for a cued-recall test while their brains were imaged in the magnetic resonance scanner. Compared to the no exercise controls, performing exercise four hours, but not immediately, after encoding enhanced retention of the picture-location associations. Additionally, compared to the no or immediate exercise groups, those individuals who exercised after a delay showed enhanced hippocampal pattern similarity during correct trials ( Fig. 1B ). These exciting results suggest that acute exercise may be used after learning to enhance memory and hippocampal functioning, but that these effects may be regulated by the timing of exercise in relation to the timing of learning.

Another area of research where fMRI has been used is to study the underlying mechanisms of the stress-buffering effects of acute exercise in humans [ 114]. After 30-minutes of either aerobic exercise (walking/running at 60-70% of VO ₂ max) or "placebo" exercise (light stretching and gymnastic exercises), subjects completed mood questionnaires and a modified version of the Montreal Imaging Stress Test (MIST), a test combining mental arithmetic with social evaluation that serves as an acute stressor. Compared to the control condition, acute exercise increased positive affect. Additionally, compared to the control group, acute aerobic exercise significantly increased MIST-induced BOLD signaling bilaterally in the hippocampus and parahippocampal gyrus and decreased BOLD signaling in frontal cortical regions including unilaterally in the right dorsolateral (dl) and dorsomedial (dm) prefrontal cortex (PFC), right inferior frontal gyrus (IFG), right middle cingulate cortex, and bilaterally in the paracentral lobule, and pre- and post-central gyrus ( Fig. 1B ). These findings indicate that acute exercise alters the brain's response to an acutestressor.

TMS Studies

Transcranial magnetic stimulation (TMS) is a procedure whereby a magnetic field generator is placed above the head creating small electric currents in the brain through the process of electromagnetic induction. This technique has been used to assess brain damage resulting from stroke and movement disorders as well as to treat conditions such as chronic pain and major depression. TMS has also been used to determine whether acute exercise promotes neuroplasticity. Different TMS protocols are used to induce different types of brain changes. For example, continuous theta-burst stimulation (cTBS), thought to work through long-term depression-like mechanisms, serves to inhibit the motor cortex, whereas paired associative stimulation (PAS), thought to work through long-term potentiation-like mechanisms, serves to excite the motor cortex. Using these techniques, acute exercise has been shown to facilitate both the inhibition [ 115] and excitation [ 116-118] of the motor cortex, with this enhanced neuroplastic capacity relating to acute exercise-induced improvements in implicit motor learning [ 118] ( Fig. 1B ). In one study, paired associative stimulation (PAS), which is a method of repeatedly pairing peripheral nerve stimulation of the right median nerve with TMS over the primary motor cortex (M1), was administered after seated rest or 25 minutes of stationary cycling at 65-70% of the age-predicted MHR [ 116]. Changes in pyramidal neuron excitability were assessed by examining the stimulus-response curve of motor-evoked potentials (MEPs) from the electromyogram (EMG), while intracortical circuits were assessed through testing short-interval intracortical inhibition (SICI), long-interval intracortical inhibition (LICI), and intracortical facilitation (ICF). Acute exercise enhanced PAS-induced increases in M1 excitability as well as decreased SICI. Considering that a reduction in intracortical inhibition is necessary for the induction of short-term plasticity, this research indicates that acute exercise may prime the brain for neuroplasticity to occur. These results have implications for the use of acute exercise as a neurorehabilitation tool in clinical populations.

Summary of acute exercise-induced neurophysiological changes (Fig. 1B )

Several techniques in both rodents and humans have been used to assess the effects of acute exercise on brain neurophysiology and function including, multi-unit extracellular recordings, EEG, fNIRS, fMRI, and TMS. Together, these studies demonstrate that a single bout of exercise increases hippocampal theta as well as enhances delta, theta, alpha, and beta power throughout the cortex [ 68, 69, 71, 83]. In addition, acute exercise increases P300 amplitude and decreases P300 latency during cognitive task performance, a physiological indicator of enhanced decision making [ 84-90]. This work also shows that acute exercise-induced enhancements in cognition are accompanied by increased blood flow to frontal regions of the brain in both young and old adults [ 100, 101, 103]. Finally, through the use of TMS, studies have shown that a single bout of exercise facilitates both the inhibition and excitation of the motor cortex - a finding linked to improvements in motor learning and potential enhancements in cortical plasticity [ 116-118].

THE EFFECTS OF ACUTE EXERCISE ON THE BRAIN: INSIGHT FROM NEUROCHEMICAL STUDIES

Studies from both animal model systems and human subjects have started to shed light on the possible signaling pathways involved in the acute exercise-induced improvements in mood and cognitive function ( Fig. 1C ). A complex set of different signaling pathways, including those activated by hormones, neurotransmitters, growth factors, and neuromodulators, are stimulated with a single bout of exercise. Below, we summarize the changes that have been reported mainly in animals, but also in humans.

Lactate, glutamate and glutamine

During acute aerobic exercise, in the process of aerobic glycolysis, glucose is broken down to pyruvate, which then further breaks down to lactate or lactic acid. When exercise transitions from aerobic to anaerobic in nature, the "anaerobic threshold" is met. At this point, the body is at its peak of oxygen uptake. Beyond this point, lactic acidosis occurs. The excess H+ ions of lactate must be buffered to maintain a physiological pH, and because bicarbonate buffering of lactate produces carbon dioxide (CO ₂) in the blood, ventilation is significantly increased [ 119, 120].

Lactate is able to cross the blood brain barrier, is independently made by astrocytes in the brain, can be used by the brain as an alternate energy source to glucose [ 121], and has been implicated in memory processes [ 122, 123] and neuronal plasticity [ 124]. In the brain, lactate serves as a precursor to glutamate, which is the main excitatory neurotransmitter in the brain. Glutamate is then taken up by astrocytes and converted to glutamine in the glutamate-glutamine cycle. Acute exercise has been known for some time to increase peripheral lactate levels; however, expensive and invasive procedures have made direct quantification of acute exercise-induced brain lactate levels difficult in humans ( Fig. 1C ; but see [ 125-128]). Two recent studies used proton magnetic resonance spectroscopy (H ¹MRS) to investigate brain-level changes of lactate, glutamate, and glutamine [ 129, 130]. Using an exercise protocol of 15 minutes of discontinuous, graded exercise at 85% of the subject's age-predicted MHR, these studies revealed that lactate, glutamate, and glutamine levels transiently increased by approximately 20% in the cortex ( Fig. 1C ).

Recent animal research provides new insight into the link between lactate and memory [ 131]. Using a hippocampal-dependent inhibitory avoidance task, Suzuki and colleagues revealed that the transfer of lactate from astrocytes to neurons plays a direct role in long-term memory formation [ 122]. They also found that lactate is involved in the maintenance of long-term potentiation (LTP), a physiological marker of learning and memory that is induced in hippocampal area CA1 by inhibitory avoidance learning [ 132]. Lactate has also been shown to be involved in spatial working memory as assessed by a spontaneous alternation task [ 123], and may enhance memory by stimulating plasticity-related genes such as Arc, c-Fos, and Zif268 through NMDA receptor signaling [ 124]. An interesting future direction for research will be to explore the relationship between acute exercise-induced increases in central lactate levels and cognitive performance.

The HPA axis response: Cortisol

Exercise not only affects brain lactate levels, but is also a well-known stressor that stimulates the hypothalamus to secrete corticotropin-releasing hormone (CRH). CRH stimulates the anterior pituitary to secrete adrenocorticotropic hormone, which then stimulates the adrenal cortex to release cortisol, a glucocorticoid steroid hormone that crosses the blood brain barrier to reach the brain [ 133, 134]. These effects have been documented in both rodents [ 135, 136] as well as humans [ 137]. In humans, acute exercise stimulates the HPA axis in an intensity-dependent fashion, with increases in cortisol occurring after a threshold amount of exercise that equates to a duration of 10 minutes or more at approximately 60% of VO ₂ max or greater [ 138-140]. After exercise cessation, peripheral cortisol levels peak around 30 minutes and remain elevated for up to two hours [ 141]. Surprisingly, the overall time course for changes in central levels of cortisol following an acute bout of exercise has not been reported. In this transient exercise-induced stress response, cortisol has an inhibitory feedback response on the hypothalamus and pituitary gland, thus completing this homeostatic circuit.

A collection of work has shown that cortisol strongly influences learning and memory. Cortisol receptors, including mineralocorticoid and glucocorticoid receptors, are located throughout the brain, with heavy concentrations in areas supporting learning and memory, such as the prefrontal cortex, hippocampus, and amygdala [ 142]. The extent to which cortisol influences learning and memory processes depends on several factors including the intensity of cortisol elevation. For example, moderate increases in cortisol enhance working memory as well as LTP [ 143, 144]. Cortisol elevation over a certain threshold, however, serves to impair memory [ 145, 146]. This is, of course, an important concept when thinking about exercise as a stressor that raises cortisol levels and impacts learning andmemory.

As was discussed in the fMRI section above, acute exercise also helps mitigate the debilitating effects of stress [ 114]. Along with the behavioral data, saliva cortisol and α-amylase were captured before and after both the acute exercise intervention and behavioral stress test (MIST). In addition to improving mood, acute exercise prevented the stress-induced increases in cortisol, with this effect being inversely correlated to the exercise-induced increases in cortisol and α-amylase. That is, individuals who had the greatest exercise-induced increases in cortisol and α-amylase experienced the lowest physiological response to the acute stressor. Compared to the stretching control group, acute exercise significantly increased stress-induced BOLD activity in several frontal and temporal regions. Further, the exercise-induced changes in cortisol and α-amylase were positively associated with the BOLD signal in the hippocampus and negatively associated with the BOLD signal in the dlPFC, dmPFC, and IFG. Interestingly, exercise-induced mood improvements were also negatively correlated with dlPFC and IFG activation. Considering that the hippocampus and prefrontal cortex are both integrally involved in negative feedback of the HPA axis [ 147], this evidence suggests that acute exercise-induced activation of the HPA axis helps to reduce the physiological response to acute stress, and that acute exercise-induced improvements in mood may be a good indicator of the effectiveness of the exercise intervention on stress buffering. These findings are in line with the cross-stressor adaptation hypothesis, whereby high-fit individuals, compared to their low-fit counterparts, show decreased physiological and psychological responses to stressors other than exercise (e.g., psychosocial stress) [ 148]. This suggests that acute bouts of exercise in high-fit individuals help prime the brain to deal with exposure to later acute stressors.

Using rodent models, many have shown that chronic exercise produces potent anti-depressant- and anxiolytic-like effects (e.g., [ 149-153]), at similar or greater levels than that of pharmacological interventions [ 154-156]. Greenwood and Fleshner have conducted some elegant work revealing the underlying mechanisms of the effects of chronic exercise on stress resistance [ 157, 158]. For example, they showed that six weeks of voluntary wheel running protects against the depressive- and anxiety-like behaviors that result from uncontrollable stress (i.e., social avoidance, exaggerated freezing, and the escape deficit elicited by the learned helplessness paradigm). The negative effect of uncontrollable stress on behavior is mediated by hyperactivation and sensitization of the serotonergic system, and exercise appears to provide stress resistance by providing plasticity at the level of the serotonergic neurons [ 158]. Based on this work, one recent study examined the effects of a single bout of exercise on contextual fear conditioning [ 159]. Extinction in this paradigm is the decline of the fear response (i.e., freezing) during repeated presentations of the conditioned stimulus (i.e., sound) in the absence of the unconditioned stimulus (i.e., shock). Previously sedentary rats that experienced three hours of voluntary wheel running either before or after extinction, froze significantly less in a test session 24 hours later, with those rats that ran the farthest distances showing the lowest levels of freezing. Even more recently, Mika and colleagues determined that acute exercise during extinction reduced the fear response during relapse of the conditioned fear experience [ 160]. This behavioral response was accompanied by attenuated stress-induced increases in plasma corticosterone and cfos mRNA in the prelimbic medial prefrontal cortex and central nucleus of the amygdala as well as enhanced cfos mRNA in direct pathway, dynorphin-containing neurons of the dorsomedial striatum and nucleus accumbens core. The authors suggest that acute exercise may decrease the fear response through dopaminergic mechanisms, and that this intervention may provide the basis for a novel therapy for the treatment of anxiety and trauma-related disorders.

Neurotrophins

Neurotrophic factors, including brain derived neurotrophic factor (BDNF), insulin-like growth factor 1 (IGF-1), and vascular endothelial growth factor (VEGF) stimulate growth of new neurons and enhance synaptic plasticity and LTP, placing them at the forefront of factors that may promote learning and memory ( Fig. 1C ). In humans, acute exercise stimulates the peripheral increase of these three neurotrophins from sites including the liver, muscles, and blood cells [ 161, 162]. Some reports show that the effect is intensity-dependent, with higher-intensity workouts producing larger increases in peripheral neurotrophin levels [ 137, 163]. A caveat to this work is that peripherally measured BDNF may not accurately reflect central levels [ 164], despite many previous studies using this strategy [ 161] and arguments to the contrary [ 165]. We do know, however, that BDNF, VEGF, and IGF-1 all cross the blood brain barrier and then function to stimulate central neurotrophin increases, especially in the hippocampus [ 166]. New findings are emerging that the well-studied ketone body, β-hydroxybutyrate, secreted by the liver after exercise [ 167] and known to pass the blood brain barrier, activates central BDNF promoters and increases BDNF gene transcription in the hippocampus by inhibiting the histone deacetylases (HDAC), HDAC2 and HDAC3 [ 168]. Additionally, Moon and colleagues recently discovered that the myokine, cathepsin B, which is secreted from muscles with exercise, passes the blood brain barrier and stimulates the secretion of BDNF in the hippocampus [ 169]. Irisin is yet another hormone that is secreted by muscle tissue during exercise and thought to stimulate the release of BDNF in the hippocampus [ 170].

BDNF

BDNF is one of the main regulators of neurogenesis and is also involved in enhancing neuronal differentiation and survival, synaptic transmission, and LTP. Because of its involvement in these important plasticity-related processes, the majority of acute exercise studies have focused on BDNF. Surprisingly few, however, have assessed the correlation between acute exercise-induced increases in BDNF and cognitive function. One study investigated this relationship in 27 males between the ages of 19 and 27 [ 36]. In this cross-over design study, all subjects engaged in 15 minutes of relaxation (control condition), 40 minutes of low-intensity running (aerobic condition), and two three-minute sprints separated by a two-minute break (anaerobic condition). Though the majority of studies utilize aerobic exercise protocols, previous studies that have assessed the effects of anaerobic exercise on cognitive functioning report mixed findings (i.e., impairment, no effect, or enhancement of cognition) [ 23, 171, 172]; protocols that were too exhaustive led to impairment [ 173]. In Winter et al. [ 36], the anaerobic protocol was one of short duration, and therefore not expected to produce an exhaustive experience. Immediately before and after each treatment session, blood samples were taken for analysis of BDNF and fifteen minutes post-session, subjects underwent an associative learning task with pairings of object pictures and novel pseudowords over five trials. Compared to both groups (control and aerobic conditions), individuals in the anaerobic condition learned at a 20% faster rate and showed a significant increase in serum BDNF levels. Further, in this anaerobic group, sustained levels of BDNF (BDNF post-learning minus BDNF post-exercise) positively correlated with learning success (accuracy on final learning block minus accuracy on initial learning block). Other studies have shown a positive association between acute exercise-induced peripheral BDNF levels and short-term memory [ 174] as well as motor skill memory [ 39].

Hopkins and Bucci [ 175] took advantage of a genetic polymorphism in the BDNF gene, Val66Met, which occurs in 33% of the population [ 176] and causes a decrease in activity-induced secretion of BDNF [ 177], to examine the role of BDNF in both the acute and long-term effects of exercise. They found that acute exercise improved hippocampal-dependent recognition memory, but only in those individuals who also participated in a four-week exercise program and who had a fully functional BDNF system (i.e., those who were Val homozygotes) [ 175]. The authors make the important point that the effects of acute exercise on cognition may be modulated by an individual's previous physical activity habits. Consistent with this hypothesis, rodent work has shown that compared to naïve runners, habitual runners (i.e., those with 3 months of running experience) experience significantly faster exercise-inducedhippocampal BDNF protein increases [ 178], suggesting that chronic exercise may alter how the brain responds to acute exercise, especially at the level of neurotrophic factor production.

Though studies in rodents tend to focus on chronic exercise paradigms, some have investigated the effects of acute exercise on brain BDNF levels. These findings parallel the human findings, showing that acute exercise upregulates BDNF expression. One study investigated the effects of low- (treadmill running for 30 minutes at 15 m/min) and moderate-intensity (the same protocol at 25 m/min) acute exercise on hippocampal BDNF expression in rats [ 179]. Low-intensity exercise caused significant elevations of BDNF mRNA in the hippocampal CA1, CA3, and dentate gyrus subregions, with the largest peaks occurring approximately 1.5 hours post-exercise cessation ( Fig. 1C ). Moderate-intensity exercise, however, did not produce this increase, most likely because this exercise regimen significantly increased corticosterone levels in the rats to levels observed to be produced by restraint stress [ 180], suggesting that it was a negative, stressful experience. Other studies have also shown no significant increase in hippocampal BDNF with high-intensity exercise regimens (60 minutes of treadmill running at 20 m/min) [ 181]. These negative results suggest that central BDNF levels may not increase if the workout is too stressful, as stress has been shown to decrease BDNF levels in the hippocampus [ 182], and acute exercise-induced increases in BDNF have been shown to negatively correlate with increases in corticosterone [ 181].

IGF-1

IGF-1 regulates a variety of processes both peripherally and centrally including muscle hypertrophy, neurogenesis, neuronal survival and differentiation, and enhancement of other neurotrophins including BDNF [ 183, 184]. Collectively, the literature suggests that by exercising at high-intensities around the anaerobic or lactate threshold, an increase of approximately 15% above pre-exercise levels can be achieved, lasting for up to 20 minutes post-exercise cessation [ 185-187]. One study examined the relationship between executive functioning and peripheral IGF-1 in a group of healthy, young adult males [ 188]. While having their brain activity recorded through EEG, subjects completed a Go/No-Go task combined with an Eriksen Flanker task both before and after 40 minutes of rest or a moderate- (50% 1 repetition maximum) or high-intensity (80% 1 repetition maximum) resistance exercise that included a 10-minute warmup. Blood samples for analysis of IGF-1 were also taken at these same time periods. Compared to controls, both moderate- and high-intensity exercise improved behavioral performance in terms of both reaction time and accuracy as well as increased the amplitude of the P3 component of the EEG signal. Contrary to their hypothesis, exercise-induced changes in IGF-1 did not correlate with changes in cognitive performance or the electrophysiological signal. In contrast, a study that investigated the effects of long-term aerobic exercise on changes in peripheral levels of IGF-1, hippocampal volume, and verbal long-term memory found significant positive correlations between changes in IGF-1 and both morphological and behavioral improvements; however, these changes were independent of exercise or changes in fitness [ 189].

One study in rodents strongly supports the hypothesis that IGF-1 is involved in acute exercise-induced increases in hippocampal functioning [ 184]. Five days of voluntary wheel running (defined here as acute exercise in the context of the rodent exercise literature), increased both the acquisition and retention of spatial memory as weIl as IGF-1 mRNA levels in the hippocampus. Acute exercise-induced IGF-1 was then blocked through microinjection of the IGF-1 receptor binding antibody, alphaIR3, into the hippocampus. Though blockage of IGF-1during this exercise period did not affect the acquisition of spatial memory, it abolished the enhancement in spatial memory retention, suggesting that IGF-1 may support acute exercise-induced improvements in long-term memory. Collectively, this work shows that several days of exercise may be needed to induce the behavioral/brain changes caused by IGF-1, and perhaps, IGF-1 may be more involved in the chronic rather than the acute effects ofexercise.

VEGF

VEGF serves to support vascular growth both peripherally and centrally as well as provide both neurotrophic and neuroprotective effects [ 184, 190, 191]. Submaximal acute exercise (1 hour of cycling at 50% VO ₂ max) has been shown to increase peripheral levels of VEGF by approximately 20%, with effects lasting for up to two hours [ 192]. One study that investigated the relationship between acute exercise-induced increases in peripheral VEGF and motor memory found no correlation between the two; however, the exercise protocol utilized did not significantly increase VEGF levels [ 39]. Studies in rodents have shown that long-term exercise increases hippocampal VEGF [ 193] and that these increases are critical for exercise-induced increases in neurogenesis [ 194]. Currently, these studies indicate that VEGF may play more of a critical role in the chronic effects of exercise on the brain. Future studies are warranted to determine whether immediate exercise-induced increases in VEGF support cognitive functioning, especially hippocampal-dependent behaviors.

Neurotransmitters

In addition to activating the HPA axis and a variety of neurotrophins, acute exercise also changes levels of a range of neurotransmitters that have been implicated in both the cognitive as well as mood effects seen with acute exercise ( Fig. 1C ). In humans, acute exercise has been shown to increase peripheral levels of monoamines including dopamine, epinephrine, and norepinephrine [ 36, 39, 195, 196]. Because of technical challenges, few studies have examined acute exercise-induced neurotransmitter changes in the brain of humans. Rather, much of this work has been conducted in rodents using techniques such as in vivo microdialysis or high-performance liquid chromatography (HPLC) analysis of post-mortem brain tissue. These studies have shown that acute exercise causes immediate changes in dopamine, serotonin, norepinephrine, acetylcholine, gamma-aminobutyric acid (GABA), and glutamate in a brain-region specific manner [ 151, 197-200]. Collectively, this work supports the idea that neurotransmitter changes may contribute to the acute exercise-induced behavioral changes highlighted above.

Dopamine

In rodents, dopamine and its metabolites increase significantly in several brain regions including the hippocampus, prefrontal cortex, striatum, midbrain, and pons-medulla after a single bout of exercise [ 197, 199, 201]. Levels return to baseline approximately two hours after exercise cessation, with this timeline depending on the exact exercise protocol utilized ( Fig. 1C ). Dopamine has been implicated in the motivation for and the rewarding effects of exercise [ 202, 203], and rodents bred to run excessive amounts (i.e., high running lines) show dysregulated dopaminergic systems; that is, compared to control lines, dopaminergic neurotransmission is disrupted in these high-running lines, including elevated dopamine and dopamine metabolite levels and down-regulated dopamine receptor gene expression [ 204, 205]. Interestingly, in humans, taking dopaminergic (and noradrenergic) reuptake inhibitors before a workout has been shown to enhance endurance exercise performance or prolong the amount of time that individuals can exercise [ 206-208].

Dopamine has also been implicated in the positive effects of exercise on cognitive functioning [ 36]. In a recent study, 297 adolescent students (ages 13 to 17) who were genotyped for a variety of single nucleotide polymorphisms (SNPs) and variable number tandem repeats (VNTRs) targeting the dopamine system engaged in a 10- to 14-minute graded exercise test to exhaustion and a seated cartoon watching session on separate days in a randomized, counterbalanced design [ 209]. After each intervention, cognitive flexibility was assessed with a modified Rogers and Monsell task that tested the ability to shift between different, yet predictable rules. Compared to the control condition, lower switch costs were seen after exercise indicating that acute exercise enhanced shifting efficiency. Additionally, this exercise-induced enhancement was predicted by a polymorphism targeting the dopamine transporter, DAT1/SLC6A3, responsible for transporting dopamine out of the synapse and back into the presynaptic terminal.

Positron emission tomography (PET) is one of the few ways that neurochemical changes can be assessed in the brains of humans. PET is a noninvasive functional imaging technique that requires the injection of biologically active molecules and measures the brain's metabolic processes by detecting gamma rays that are emitted by a positron-emitting radionucleotide. Using this technique, one study utilized PET with [ ¹¹C]raclopride [ 210] to measure the effects of acute exercise on dopamine concentrations in the brains of humans. Twelve, healthy, regular exercisers underwent PET scans after either rest or 30 minutes of treadmill running at 85% of age-predicted MHR. Surprisingly, dopamine was not found to increase after acute exercise in these humans. This finding is consistent with previous rodent studies showing that dopamine concentrations only increase once a threshold intensity is reached [ 211]. Considering that this PET study utilized individuals who on average were exercising 5±3 hours per week, this protocol may not have been strenuous enough to increase central dopamine levels.

Serotonin

Rodent studies indicate that acute exercise increases serotonin and its metabolites in the frontal cortex, hippocampus, striatum, and midbrain [ 212-214] ( Fig. 1C ). One study in rats that utilized a protocol of 60 minutes of treadmill running at 20 m/min showed no increase in hippocampal serotonin, a protocol that increased dopamine levels [ 181]. Discrepancies like this have been found in other studies [ 197], perhaps indicating that a certain level of exercise may be needed to increase central serotonergic levels. Serotonin has been implicated in the anti-depressant and anxiolytic action of exercise, with chronic exercise being just as effective as anti-depressant treatment in humans [ 215]. A recent study in rodents examined the involvement of serotonergic neurons in acute exercise-induced changes in depressive- and anxiety-like behaviors [ 216]. Compared to sedentary controls, rats that engaged in low-intensity exercise (15 m/min for 30 min) showed increased c-Fos expression in the serotonergic neurons of the dorsal raphe nucleus. High-intensity exercise (25 m/min for 30 min), on the other hand, induced c-Fos expression in the corticotropin-releasing factor neurons of the hypothalamic paraventricular nucleus, an area involved in the body's response to stress, an indication that this exercise protocol may have been a stressful experience. Additionally, compared to high-intensity runners, low-intensity runners showed significant decreases in both depressive- and anxiety-like behaviors. The authors suggest that these findings indicate that low-intensity acute exercise may be the best workout to stimulate serotonergic activity and enhance mood states.

Serotonin has also been implicated in the beneficial effects of acute exercise on cognition. Healthy, young adults (ages 18 to 35) were randomly assigned to either a sedentary, control group (myofascial release training), or a low- (45-50% MHR), moderate- (65-70% MHR), or high-intensity (85-90% MHR) exercise group [ 217]. Each exercise group started with a 5-minute warmup and then cycled for 30 minutes at their designated heart rate. Before and after the acute intervention, subjects had their blood drawn for analysis of serum serotonin and performed the Stroop task, a test of attention and response inhibition that is dependent on the prefrontal cortex. Acute exercise significantly increased plasma serotonin, with a positive linear correlation found between serotonin concentration and exercise intensity. Additionally, acute exercise improved performance on the Stroop task; individuals with the largest enhancements in response inhibition showed the greatest increases in serotonin.

Additionally, acute exercise-induced changes in serotonin (and dopamine) are thought to contribute to central fatigue. During prolonged exercise, the physical fatigue that sets in is thought to be a result of both peripheral (including depletion of muscle glycogen, decreased plasma glucose, and increased plasma free fatty acids) and central mechanisms. One study in rodents sought to determine the neurotransmitter changes that occur in response to exercising to the point of fatigue [ 218]. After rest, one hour, or three hours (fatigue condition) of treadmill running, hippocampal, striatal, midbrain, and hypothalamic levels of serotonin, dopamine, and their primary metabolites were assessed. Over the course of running to exhaustion, serotonin and 5-hydroxyindoleacetic acid (5-HIAA) continued to increase; however, though dopamine and 3,4-dihydroxyphenylacetic acid (DOPAC) increased at the one-hour time-point, these levels returned to baseline after three hours. Additionally, studies have shown that decreasing serotonergic activity through intraperitoneal (IP) administration of a serotonergic antagonist increases the amount of time that rodents can run before they reach the point of exhaustion [ 219]. Conversely, IP administration of a serotonergic agonist decreases the run time to exhaustion. As serotonin regulates mood, emotion, sleep, and appetite, and dopamine regulates motivation memory, reward, and attention, the central fatigue hypothesis claims that the interaction between these two neurotransmitters contributes to exercise-induced fatigue, with a high serotonin to dopamine ratio supporting a low activity, exhausted state and a low ratio supporting a high activity, activated state [ 220, 221].

Norepinephrine and epinephrine

A collection of studies show that the effects of acute exercise on norepinephrine are brain-region specific [ 197, 222]. Acute exercise has been shown to increase levels in the cortex, striatum, and preoptic area but decrease levels in the hippocampus, brainstem, pons-medulla, and hypothalamus. These discrepancies may be due to differences in the various exercise protocols utilized, with different levels of training, and in different environmental conditions (see [ 197] for review). Using microdialysis, one study examined the influence of one or two hours of treadmill running at 25 meters per minute at a 3% incline on norepinephrine levels in the frontal cortex. After 40 minutes of running, norepinephrine and its metabolites showed significant increases from baseline and remained elevated for up to 70 minutes after exercise cessation; longer duration exercise resulted in higher levels of norepinephrine that remained elevated for a longer time period after post-exercise cessation ( Fig. 1C ). Further, peripheral levels of epinephrine positively correlated with central levels of norepinephrine, indicating that blood levels of epinephrine may be a good indicator of exercise-induced noradrenergic changes in the brain. Interestingly, a study by the same group revealed that in humans, peripheral (i.e., urine) levels of epinephrine and its metabolite, metanephrine, positively correlated with exercise-induced improvements in a time-limited word discrimination task [ 223].

In humans, acute exercise stimulates increases in plasma norepinephrine and epinephrine [ 224]. At the point of maximal exertion, these two catecholamines display an exponential increase that strongly correlates with the level of plasma lactate [ 225]. This point is known as the cathecholamine threshold [ 226] and occurs at a similar time as both the lactate and ventilatory threshold. Peripheral norepinephrine and epinephrine are involved in a variety of physiological processes that help to support exercise such as glycogenolysis or the breakdown of glycogen to glucose, which the muscles use as a main source of energy supply during exercise. Though these catecholamines do not cross the blood brain barrier, one study found that maximal acute exercise increased noradrenergic levels in cerebral spinal fluid, which the authors suggested supports the idea that exercise increases levels of norepinephrine in the human brain [ 126]. Though norepinephrine is certainly involved in a variety of cognitive processes [ 200], little has been done to directly examine the influence of norepinephrine on acute exercise-induced changes in cognitive functioning.

Acetylcholine

Peripherally, acetylcholine is released from the neuromuscular junction as well as from preganglionic fibers of the autonomic nervous system, and is therefore, not surprisingly, involved in physical movement. Upon initiation of exercise, brain acetylcholine levels increase, specifically in the hippocampus and cortex. This increase in acetylcholine supports the generation of hippocampal theta, which serves to enhance synaptic plasticity and memory formation [ 72, 73]. Only one study in rodents examined post-exercise changes in cortical acetylcholine levels and found no change from baseline, which is most likely due to the minimal exercise protocol utilized (walking at a rate of 2.3 meters per minutes for 5 minutes) [ 227]. In humans, plasma choline, a precursor to acetylcholine, has been shown to decrease by 40% after marathon running [ 228], and along with serotonin and dopamine, studies suggest that acetylcholine may be involved in exercise-induced fatigue [ 229]. Though acetylcholine is involved in cognitive processes such as memory, and decreased central cholinergic levels have been found in Alzheimer's disease [ 230], little has been done to investigate the involvement of acetylcholine in acute exercise-induced improvements in cognitive function.

Glutamate and GABA

Glutamate and GABA, the brain's main excitatory and inhibitory neurotransmitters, respectively, have received surprisingly little attention in the acute exercise literature. Similar to the neurotransmitter-related work mentioned above, the main finding is that acute exercise increases levels of glutamate and GABA in the brain. Rodent work suggests that acute exercise upregulates glutamatergic-related genes [ 231] and increases mitochondrial glutamate oxidation or the conversion of glutamine to glutamate by neuronal mitochondria [ 232]. In humans, proton magnetic resonance spectroscopy (MRS) was used to visualize changes in glutamate and GABA in the primary visual cortex (V1) as well as the anterior cingulate cortex post-exercise [ 233]. Compared to pre-exercise, after a graded exercise test where subjects reached ≥80% of age-predicted MHR, glutamate significantly increased (4.9% ∼18 minutes post-exercise) in both cortical areas, returning to baseline approximately 30 minutes post-exercise ( Fig. 1C ). GABA also showed significant post-exercise increases in the visual cortex. This work corroborates studies showing that exercise shifts cortical circuits into excitatory states through GABAergic interneuron-mediated disinhibition of pyramidal neurons [ 234]. Future studies are needed to investigate the relationship between acute-exercise induced changes in cognition and these two prominent brain neurotransmitters.

Neuromodulators: Endogenous opioids and endocannabinoids

Neuromodulators, including the endogenous opioids and endocannabinoids, also increase after acute bouts of exercise in both animals and humans ( Fig. 1C ). The endogenous opioid system includes three families of opioid peptides, namely the ß-endorphins, enkephalins, and dynorphins, which are involved in a variety of processes including pain modulation, reward, response to stress, and autonomic control. In humans, acute exercise causes significant increases in peripheral levels of endogenous opioids; this effect is intensity-dependent, corresponds to acute exercise-induced changes in HPA axis hormones, and is linked to improvements in mood [ 235-239].

One common hypothesis is that the "runner's high", or the euphoric sensations that can be experienced after intense exercise (often long-duration running), is due to increases in endogenous opioids. As peripheral opioids have limited ability to pass the blood brain barrier, a seminal study utilized PET to examine the effects of acute exercise on changes in endogenous opioids in the brains of humans. Boecker and colleagues conducted PET with [ ¹⁸F]FDPN, a radiotracer with similar affinity for μ, δ, and κ opioid receptors, to examine the effect of intense exercise (2 hours of running) in 10 trained male athletes (ages 33 to 40, with a minimum of 4 hours of weekly training for the past 2 years) [ 240]. Compared to rest, 30 minutes after running, mood (euphoria and happiness) was significantly enhanced and opioid concentrations significantly increased in the prefrontal/orbitofrontal cortices, dorsolateral prefrontal cortex, anterior and posterior cingulate cortex, insula, parahippocampal gyrus, sensorimotor/parietal cortices, cerebellum, and basal ganglia ( Fig. 1C ). Additionally, euphoria ratings were negatively correlated with [ ¹⁸F]FDPN binding in the prefrontal/orbitofrontal cortices, the anterior cingulate cortex, bilateral insula, and parainsular cortex. As the endogenous opioids displace [ ¹⁸F]FDPN, this suggests that increases in endogenous opioids may underlie exercise-induced mood improvements. Other evidence corroborates this finding as compared to placebo, administration of the opioid antagonists, naloxone or naltrexone, blocks the exercise-induced improvements in mood [ 241-243]; however, others have found this not to be the case [ 244, 245].

Though the endogenous opioids have received much attention in terms of their involvement in the "runner's high", scientists are beginning to understand that endocannabinoids may be equally or perhaps more involved [ 246]. Endocannabinoids are a family of lipids including N-arachidonoylglycerol (anandamide, AEA) and 2-arachidonoylglycerol (2-AG), which are involved in a variety of processes including pain, mood, memory, and appetite. Acute exercise increases peripheral levels of endocannabinoids [ 247], with moderate- (70% and 80% age-predicted MHR) rather than low- (<50%) or high-intensity (90%) exercise eliciting the greatest effects [ 248]. In one study, 11 young male cyclists (ages 23.3±5.1 standard deviations) underwent 60 minutes of cycling at 55% of their maximal power output (W max) followed by an intense endurance test where subjects performed an amount of exercise equal to 30 minutes at 75% of W max as quickly as possible [ 249]. Blood samples were collected at rest, immediately after the continuous exercise, immediately after the endurance test, and 15 minutes post-exercise cessation for analysis of AEA, 2-AG, ß-endorphin, BDNF, and cortisol. AEA and its congeners N-oleylethanolamine (OEA) and N-palmitoylethanolamine (PEA), but not 2-AG, increased significantly post-endurance exercise and continued to increase 15 minutes post-exercise cessation. Additionally, AEA increases were significantly correlated to increases in cortisol and BDNF, but not ß-endorphin, suggesting an interconnectivity between exercise-induced increases in neurotrophins and endocannabinoids. One elegant study in mice combined pharmacological, genetic, and behavioral approaches to study the involvement of the endocannabinoid system in the "runner's high" [ 250]. Compared to sedentary controls, mice that were exposed to five hours of voluntary wheel running showed decreased anxiety as evidenced by the dark-light box test, decreased pain sensitivity as evidenced by the hot plate test, and enhanced sedation as evidenced by a decrease in running wheel activity post-exercise, which the authors suggest is indicative of a "runner's high" effect in mice. Additionally, running significantly enhanced plasma levels of AEA, 2-AG, OEA, and PEA. When the cannabinoid receptor 1 (CB1) was blocked with the inverse agonist, AM251, the exercise-induced anxiolytic effect went away. Additionally, blockage of both CB1 and CB2 receptors eliminated the exercise-induced analgesic effect. Surprisingly, these effects were not seen for blockage of opiate receptors. They then utilized a GABAergic neuron-selective CB1 knockout mouse (GABA-CB ₁-/-) to examine the involvement of the endocannabinoid system in the exercise-induced anxiolytic effect. Compared to wild-type mice (GABA-CB ₁+/+), running had no effect on anxiety-like behaviors in GABA-CB ₁-/- mice, suggesting that acute exercise-induced mood effects are mediated by the endocannabinoid system and specifically dependent on CB1 receptors expressed by GABAergicinterneurons.

Summary of acute-exercise induced neurochemical changes (Fig. 1C )

A large body of research has investigated the neurochemical changes that occur after an acute bout of exercise. Together, these studies demonstrate that acute exercise stimulates the increase of a variety of neurochemicals: lactate; cortisol; neurotrophins, including BDNF, IGF-1, and VEGF; neurotransmitters, including dopamine, norepinephrine, serotonin, acetylcholine, GABA, and glutamate; and neuromodulators, including endogenous opioids and endocannabinoids [ 36, 39, 129, 130, 135-141, 151, 161-163, 195-200, 235-240]. Though many of these neurochemicals have been confirmed peripherally following acute bouts of exercise, gaps still exist in the literature as to where these changes occur in the brain ( Fig. 1C ). Future work is needed to link the acute-exercise induced changes in peripheral levels to central levels and to investigate how these neurochemicals are involved in the acute-exercise induced changes in affective state and cognitive functioning.

NEUROGENESIS IS ENHANCED FOLLOWING JUST THREE DAYS OF WHEEL RUNNING

One of the most notable effects of chronic increases in exercise on the brain is the enhancement of adult hippocampal neurogenesis [ 251, 252]. Adult neurogenesis only occurs in two areas of the brain, the subventricular zone and the subgranlar zone of the hippocampal dentate gyrus; running increases neurogenesis in the latter region only [ 253]. Chronic exercise causes double the amount of new neurons to form [ 252], but levels up to five-fold have been found in certain high-running lines of rodents [ 254]. This potent neurogenic effect is thought to result from the exercise-induced increases in neurotrophic factors, specifically, BDNF and IGF-1 [ 183, 184, 194, 255, 256]. In certain, but not all, strains of rodents, the number of proliferating neurons show a significant positive correlation to the total distance run [ 254, 257]. Along with enhancing the production of new neurons, exercise also increases the differentiation and survival of these cells [ 251, 252]. Because newly born hippocampal neurons have lower thresholds of excitability [ 258], they are thought to mediate the exercise-mediated enhancement of perforant-path-based LTP [ 259], which may underlie the wide range of hippocampal-dependent tasks shown to improve with exercise including spatial learning and memory [ 260], pattern separation [ 261], object recognition [ 262], and contextual fear conditioning [ 263]. Recently, these newly generated neurons have been shown to integrate into the hippocampal circuits that subserve improvements in hippocampal-dependent behaviors [ 264, 265].

While most of this work has utilized chronic exercise paradigms, one study determined that as little as three days of voluntary wheel running increases rates of neurogenesis [ 266]. This protocol can be considered acute in the context of the rodent literature that frequently uses running paradigms lasting three weeks or longer. Kronenberg and colleagues followed cell proliferation through 32 days of voluntary wheel running, examining neurogenesis levels at days 3, 10, and 32. Interestingly, rates of neurogenesis were highest at day 3 (50% increase), then declined over time, returning to baseline levels by day 32; rates of neuronal development continued to increase over the 32 days [ 266]. These findings suggest that as little as 3 days of increased exercise can effect rates of neurogenesis in the hippocampus in rodents.

SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS

We have summarized a relatively large and growing body of research examining the changes that occur at the cognitive/behavioral, neurophysiological, and neurochemical levels after a single bout of physical exercise. While comparisons of the cognitive/behavioral effects of acute exercise studies in humans remain handicapped by the lack of clear or common standards in the way acute exercise is defined or measured, general trends can be appreciated. In particular, this review has highlighted the breadth and complexity of changes that have been reported in the minutes to hours after a single bout of aerobic exercise. We show that the three most consistent cognitive/behavioral effects of a single bout of exercise in humans are improved executive functions, enhanced mood states, and decreased stress levels ( Fig. 1A ). Neurophysiological changes have been reported after an acute bout of exercise (mainly in humans) and these results emphasize the widespread brain areas where these physiological changes have been seen ( Fig. 1B ). One of the most dramatic effects seen after acute exercise is the change in neurochemical levels including neurotransmitters, metabolites, growth factors, and neuromodulators, which perhaps provides the best illustration of the complexity of brain responses to acute exercise ( Fig. 1C ).

While clear progress has been made in our understanding of the effects of acute exercise on the brain, a number of fundamental questions/issues remain to be addressed. At the top of this list is the establishment of a standard definition for what constitutes acute exercise in people. This kind of standardization will not only facilitate comparisons across studies, but will enhance the impact of each study that uses an acute exercise standard ( Table 2 ), as each study will be able to leverage the effects of previous studies in a more effective way. To address this issue, we suggest that standardizing the following four key acute exercise measures will go a long way to help comparisons across future acute exercise studies in people ( Table 2 , Supplementary Figure 1). First, based on the studies reviewed here and on previous meta-analyses [ 9], we suggest standardizing the duration of acute exercise studies in the following way: short duration exercise = 1 to 15 minutes; moderate duration exercise = 16 to 45 minutes; and long duration exercise = 46 minutes or longer. Second, based on the American College of Sports Medicine guidelines [ 267, 268], we propose defining the intensity of acute exercise in the following way: low-intensity exercise =≤39% of VO ₂ max; moderate-intensity exercise = 40% to 59% of VO ₂ max; and high-intensity exercise =≥60% VO ₂ max. In addition, minute by minute recordings of heart rate data during exercise will be a valuable confirmation of the actual percentage of VO ₂ max levels reached during the workout. Third, to obtain a psychological measurement of perceived difficulty of the workout, the Borg Ratings of Perceived Exertion Scale [ 269] can be administered immediately after the end of the exercise session. Finally, we propose that all future acute exercise studies that report duration, intensity and perceived difficulty as described above can be compared using a standardized "exercise index". This is calculated by first standardizing each of the scores above (by converting to percentage) and then averaging these scores (i.e., calculate the average of the percentage of time spent exercising in an hour (duration), the percentage of VO ₂ max at which the exercise was conducted (intensity), and the average percentage of the Borg Ratings of Perceived Exertion Scale reached as a result of the exercise session (perceived exertion)). We acknowledge that many other factors may be influencing the effects of acute exercise on brain physiology and function including age, body mass index, gender, health status, and fitness level; this exercise index will capture 3 core features of the acute exercise session. Moreover, to facilitate collection of this data and calculation of the exercise index, we have provided a template to use for data collection in future acute exercise studies ( Supplementary Figure 1). The hope is that this guideline for acute exercise standardization will allow for the consistent collection across studies of both physiological and psychological data and will facilitate easier comparisons across studies in the future.

Another key open question highlighted by Fig. 1 is understanding the relationship between the central neurochemical changes following acute exercise ( Fig. 1C ), mainly described in rodents, and the behavioral changes seen after acute exercise ( Fig. 1A ), mainly described in humans. This is related to another gap in the literature, which is the surprising lack of studies examining the effects of acute exercise on behavior/cognition in rodents. This may be, at least in part, because of the relatively long duration of typical behavioral tasks used in rodents, making it difficult to administer immediately after just a single bout of exercise. However, given the relatively large amount of data already collected in rodents on the central neurochemical changes after an acute bout of exercise, a direct examination of the relationship between the effects of acute exercise on both behavior and neurochemistry in rodents would be a valuable addition to this field. While studying acute neurochemical changes following exercise is more challenging in humans, it has been successfully examined for endorphins [ 240]. The development of sensitive in vivo human measurements for the many other growth factors and neurotransmitters that have been reported to respond to exercise in rodents would be of great interest.

While this review focused on the improvements in executive functioning, mood, and stress resistance seen with acute exercise ( Fig. 1A ), the widespread neurophysiological and neurochemical changes suggest an even larger array of potential behavioral/cognitive changes. In particular, acute exercise-induced changes in the hippocampus, parietal cortex, striatum, and cerebellum suggest a more pervasive effect, including improvements in long-term memory, recollection [ 270], reward-based learning [ 271, 272], and motor functions [ 273]. Certainly, these diffuse brain changes are consistent with findings from the more limited number of reports showing that acute exercise improves learning and memory functions of the hippocampus and motor functions dependent on the striatum and motor cortices [ 31-33, 35-39, 42]. Following up on these preliminary reports to define the full range of behavioral tasks affected by acute exercise will be an important area of future research.

A detailed understanding of the acute effects of exercise is also fundamental to a full understanding of the long-term effects of exercise that has been examined in detail in both animal models and humans [ 1, 5]. As exercise is repeated in a regular fashion over time, the brain changes illustrated in Fig. 1 will not only recur on a regular basis, but will also likely engage a variety of feedback, compensatory, and long-term, plasticity-based brain mechanisms. These changes may modulate these acute effects over time, resulting in modifications in baseline neurochemical levels that could lead to the long-term structural/anatomical and physiological changes that have been described following long-term increases in exercise. A basic and detailed comprehension of the effects of acute exercise on the brain is key to understanding the evolution of exercise-induced changes (i.e., the effects of long-term exercise), and may also help to determine optimal strategies to maximize the effects of both acute and chronic exercise on brain functioning. This information will aid in our ability to utilize exercise not only as a therapeutic tool to help delay, prevent, or treat cognitive decline in aging individuals, but also to use it as a powerful tool to enhance brain functioning in healthy individuals. Indeed, one of the most important implications for the positive behavioral effects of a single bout of exercise is the exciting potential for acute exercise to not only address conditions where attention and mood may be compromised (e.g., aging, attention-deficit hyperactivity disorder, and depression), but also to enhance attention, mood, and stress resistance in normal populations.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

REFERENCES

[57] Zervas Y, et al. The acute effects of increasing levels of aerobic exercise intensity on mood states, in Proceedings of the 8th World Congress of Sport Psychology, Serpa S, et al., Editors. 1993. Lisbon, Portugal. [ Google Scholar]

[83] Hillman CH, Kamijo K, Pontifex MB. The Relation of ERP Indices of Exercise to Brain Health and Cognition, in Functional Neuroimaging in Exercise and Sport Sciences Boecker H., et al., Editors. 2012, Springer: New York, NY: pp. 419-46. [ Google Scholar]

[120] Myers JM. Essentials of Cardiopulmonary Exercise Testing 1996, Champaign, IL: Human Kinetics. [ Google Scholar]

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Articles from Brain Plasticity are provided here courtesy of IOS Press

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The Effects of Acute Exercise on Mood, Cognition, Neurophysiology, and Neurochemical Pathways: A Review

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A significant body of work has investigated the effects of acute exercise, defined as a single bout of physical activity, on mood and cognitive functions in humans. Several excellent recent reviews have summarized these findings; however, the neurobiological ...

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"How Exercise Can Transform Your Brain" by Wendy Suzuki

This Ted Talk explains the science behind how exercise affects the brain and its potential for enhancing cognitive function and emotional health.

Wendy Suzuki is an American neuroscientist. She is a professor at the New York University Center for Neural Science.

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Wendy Suzuki: The brain-changing benefits of exercise | TED

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This article shows the Health Benefits of Physical Activity for Children.

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In this article, we learned that physical activity is good for children in many ways. Benefits include improved academic performance, brain health, muscular fitness, heart and lung health, cardiometabolic health, long-term health, bone strength, and measures of a healthy weight.

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Physical activity is good for children in many ways. Benefits include improved academic performance, brain health, muscular fitness, heart and lung health, cardiometabolic health, long-term health, bone strength, and measures of a healthy weight.

Benefits

Improves attention and memory.

Reduces risk of depression.

Builds strong muscles and endurance.

Improves blood pressure and aerobic fitness.

Helps maintain normal blood sugar levels, sometimes called cardiometabolic health.

Reduces risk of several chronic diseases, including type 2 diabetes and obesity.

Strengthens bones.

Helps regulate body weight and reduce body fat.

See similar content in Spanish.

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Health Benefits of Physical Activity for Children

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Eight benefits of physical activity for children.

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Danielle Feaster  → Executive Functioning

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Danielle Feaster  → Executive Functioning

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Can strengthening executive function help us be our best selves? | Sucheta Kamath

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Can strengthening executive function help us be our best selves? | Sucheta Kamath | TEDxAtlantaWomen

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Danielle Feaster → Executive Functioning

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Danielle Feaster → Executive Functioning

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Guiding Students to Improve Executive Functioning Skills by Anne M. Fein

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Guiding Students to Improve Executive Functioning Skills

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With explicit coaching, high school students can learn to manage their increasingly complex commitments.

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Here is a great article out Outside Movement with Executive Functioning (especially students)

Stacy Fox

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With technology evolving at breath-taking speed, we must prepare children to live in a future world that we can neither predict nor imagine today. Who knows what new advances and technologies will be available when today's young children reach adulthood? To help young children become college and career ready, we need to encourage them to be deep thinkers and to approach problems with flexibility and creativity. Children will need to focus their attention on what is important while ignoring other distractions. It's not just about knowing facts, we live in a world where facts are at our fingertips. It's about being able to use what we know; to apply our knowledge to meet the demands of the current situation.

In short, we need to facilitate the development and use of executive functions. These functions help us analyze, plan, and reflect on our actions in order to achieve a goal. These skills combine our intellectual, emotional, and social abilities. Executive function skills are prerequisites for success in most facets of life. Many current studies indicate that executive function skills lead to greater academic success. Children who have strong working memory, better mental flexibility, and greater self-control make more academic progress than their peers (Kamkar & Morton 2017). Strong executive function skills in early childhood are linked to higher math, vocabulary, and literacy skills (Harvey & Miller 2017). In order to prepare children for the world of tomorrow, we need to focus on helping them develop and refine executive function skills.

While executive functions are important for academic and life success, it is clear that, unlike tireless machines, our brains need regular rejuvenation and care. It is easy to become weary in our fast-paced, digital world. Our hectic, frenzied lives may be the very reason we need to take time to connect with nature. Connecting with nature allows us to feel restored and enhances our mental performance (Bratman et.al 2015). There are also clear indicators that spending time in nature supports the development of executive function.

Increasing evidence shows that positive experiences in nature lead to numerous cognitive, emotional, and physical benefits. Children have better concentration, show improved academic performance, have less aggression, and are at a reduced risk for obesity when they have opportunities to spend time in nature (American Institutes for Research 2005; Faber Taylor & Kuo 2011). All of these factors underscore the importance of providing children opportunities to connect with nature on a regular basis.

In today's world where children are becoming increasingly disconnected from nature, this is even more important. Childhood is often the time when lifelong relationships with nature are built. Taking part in relaxed, pleasant nature experiences builds positive feelings and happy memories for children. But there are other benefits as well. Spending time in nature helps children develop observation skills and enhances creativity (Crain 2001). The more attention we give, the deeper and richer our learning. When children learn to pay close attention to details, they are strengthening their focus and self-control, which are key aspects of executive function. Research shows that children with attention deficit disorder are better able to concentrate and follow directions after playing in an outdoor setting than they were after playing indoors (Martensson et. al 2009).

Playing in nature encourages children's social interaction with one another and helps children improve their social skills by encouraging positive relationships and facilitating their language and communication skills (Pyle 2002). Children who regularly play in natural environments show better physical coordination, fitness, and motor skills than children who spend less time outdoors (Fjortoft & Sageie 2001). These findings are linked to academic success because children require a solid foundation of mental and physical health to build academic skills.

Spending time outdoors supports development of executive functions in several ways. Developing executive function skills is a combination of brain development and life experience, for this the early years are crucial. Nature play focuses on problem solving, creativity, and emotional and intellectual development. Children are natural scientists, experimenting and exploring to see how the world works. Providing time and opportunity to explore the natural world encourages children to focus their attention, utilize their working memory, and practice basic self-inhibitory control. Skills that are directly related to executive functions.

Spending un-structured time in nature reaps the most benefits. When children are allowed to engage in less-structured time outdoors, they are able to develop and carry out their own goal-directed actions. In fact, the more time children spend in less structured activities, the better their self-directed executive function (Barker et al 2014).

Here are some ways to support children's development of skills related to executive functions:

Provide sensory experiences

At the earliest ages, provide sensory experiences for infants to explore natural materials. Let them touch tree bark, grasses, and stones. Provide the language to further understanding of what they are experiencing. Talk about the coolness of the stones or the roughness of the bark. Enjoy the shared attention of gazing at the drifting clouds or the chirping of nearby birds.

Create spaces for discovery

As children grow older they are motivated to more fully explore their environment, so creating a safe space for discovery is important. Take children on "adventures" by exploring nearby natural landscapes. These adventures don't need to be extravagant or time-consuming. A simple walk through a wooded area or a visit to a nearby pond can be all that is needed. Provide children with a "treasure bag" to collect items while on a nature walk. Then examine and discuss what was collected. Create a nature bracelet using wide tape. Create a photo book of your adventures so your children can tell and retell the story many times.

Give them something to talk about

Language is closely related to executive functions. Provide children experiences in nature to give them something to talk about. Sit and watch ducks swimming across a pond. Encourage children to talk about what they observe and ask questions. Help them identify ways to find the answers to their questions. Engage in conversations with children about what they are experiencing and encourage them to express plans about what they want to do next. Let them take the lead during a nature walk by deciding what path to follow, and engage their working memory by encouraging them to retrace their steps on the way back. Provide the language for children to formulate and express their thoughts. Play games that involve cause and effect. Collect a small variety of natural objects and throw them in the water to see what happens. Compare the resulting splash of the different materials. Create mud pies and mud soup using different ingredients and discuss which make the best "recipe".

Encourage gardening

Encourage children to help plan and create a garden. Let them choose what plants to grow and design the plot. You could focus on different colors, scents or textures, or plan with the end in mind and grow a pizza or salsa garden! Encourage children to help you weed the garden. Comparing and contrasting the weeds and other plants can be turned into a sorting and matching game. As children decide which plants are weeds, they are using the core features of executive functions: working memory, cognitive flexibility, and inhibitory self-control. Use the garden produce to try out different recipes-you might be surprised at children's willingness to try new foods that they grew themselves!

Provide physical challenges

Include lots of physical movement and motor challenges like climbing trees or creating a nature obstacle course. These types of activities provide opportunities for planning, problem-solving, and working together with peers. Children also feel an inherent sense of pride and accomplishment upon mastering such challenges. You can balance these active times with quiet activities like taking a listening walk through a natural area.

Inspire constructive play

Provide open-ended, loose parts like small tree branches or bamboo poles for children to use to create forts. In order to accomplish the task of building a fort, children must plan, work together, focus, and problem solve. These are important skills children will use again and again throughout their lives to accomplish their goals.

Connecting children to the environment and providing varied opportunities to spend time in nature can result in lifelong benefits for children. These benefits include better physical health, a sense of social connectedness, increased creativity, and reduced stress. Cognitive skills, including the development and refinement of executive function skills, are also supported by spending time in nature. There may be benefits for the teachers too. Teachers that regularly take their students outside report more confidence in their teaching, use more innovative teaching strategies, and are more likely to retain their enthusiasm for teaching than those who stay indoor (Suzuki 2014). A "natural" remedy for teacher burnout if you will. So do yourself and the children a favor and head outside! You'll support children in developing a love of learning as well as a love for the natural world!

References

American Institutes for Research. 2005. "Effects of Outdoor Education Programs for Children in California." American Institutes for Research: Palo Alto, CA. http://www.sierraclub.org/youth/california/outdoorschool_finalreport.pdf (Volume 1)

Barker, J., A. Semenov, L. Michaelson, L. Provan, H. Snyder, and Y. Munakata. 2014. "Less-structured time in children's daily lives predicts self-directed executive functioning." Frontiers In Psychology 5(593): 1-16. doi: 10.3389/fpsyg.2014.00593

Bratman, G.N., J.P. Hamilton, K.S. Hahn,, G.C. Daily, and J.J. Gross. 2015."Nature experience reduces rumination and subgenual prefrontal cortex activation." PNAS 2015 112 (28): 8567-8572; doi:10.1073/pnas.1510459112

Crain, W. 2001. "Now Nature Helps Children Develop." Summer.

Fjortoft, I. & J. Sageie. 2001. "The Natural Environment as a Playground for Children: Landscape Description and Analysis of a Natural Landscape." Landscape and Urban Planning 48(1/2): 83-97

Faber Taylor & Kuo. 2011. "Could exposure to everyday green spaces held treat ADHD? Evidence from children's paly settings." Applied Psychology: health and Well-Being. 3: 281-3-3. doi: 10.1111/j.1758-0854.2011.01052.x

Harvey, H.A. & G.E. Miller. 2017. "Executive function skills, early mathematics and vocabulary in head Start preschool children." Early Education and Development. 28(3): 290-307.

Kamkar, N.H. & J.B. Morton. 2017. "CanDID: A framework for linking executive function and education." Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2017.01187

Martensson, F., C. Boldemann, M. Soderstrom, M. Blennowe, J.E. Englund, & P. Grahn. 2009. "Outdoor environmental assessment of attention promoting settings for preschool children." Health & Place. 15: 1149-1157. doi. 10.1016/j.healthplace.2009.07.002.

Pyle, R. 2002. Eden in a Vacant Lot: Special Places, Species and Kids in Community of Life. In: Children and Nature: Psychological, Sociocultural and Evolutionary Investigations. Kahn, P.H. and Kellert, S.R. (eds) Cambridge: MIT Press

Suzuki, D. 2014. "Learning in nature is good for teachers and students." http://www.straight.com/news/729771/david-suzuki-learning-nature-good-teachers-and-students

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Math Education on Brain Development (How does it work)???

Stacy Fox

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School of Computer Science, Northwestern Polytechnical University, Xi'an, China

Key Laboratory of Big Data Storage and Management, Ministry of Industry and Information Technology, Xi'an, China

This paper explores whether mathematical education has effects on brain development from the perspective of brain MRIs. While biochemical changes in the left middle front gyrus region of the brain have been investigated, we proposed to classify students by using MRIs from the intraparietal sulcus (IPS) region that was left untouched in the previous study. On the cropped IPS regions, the proposed model developed popular contrastive learning (CL) to solve the problem of multi-instance representation learning. The resulted data representations were then fed into a linear neural network to identify whether students were in the math group or the non-math group. Experiments were conducted on 123 adolescent students, including 72 math students and 51 non-math students. The proposed model achieved an accuracy of 90.24 % for student classification, gaining more than 5% improvements compared to the classical CL frame. Our study provides not only a multi-instance extension to CL and but also an MRI insight into the impact of mathematical studying on brain development.

1. Introduction

Mathematical learning has significant impacts on the brain's plasticity and cognitive functions and has been associated with many quality-of-life and development indices ( Beddington et al., 2008; Zacharopoulos et al., 2021). The understanding of these associations could help in utilizing mathematical learning to benefit the individual's development ( Baglama et al., 2017; Steffe, 2017; Zacharopoulos et al., 2021). Toward a better understanding of education behaviors, many researchers made a great number of efforts and yielded a wide range of education discoveries and educational tools from psychological measurements to artificial intelligence (AI) techniques ( Steffe, 2017; Barzagar Nazari and Ebersbach, 2018; Mammarella et al., 2018; Zhang et al., 2020a, 2021a; Peng et al., 2021a, b).

This paper reviewed related works for Educational Information Science and Engineering (EISE) from the four aspects, i.e., psychological measurement ( Mammarella et al., 2018), biological analysis ( Zacharopoulos et al., 2021), educational computer engineering ( Robertson and Howells, 2008), and educational data science ( Zhang et al., 2020a, 2021a). The psychological measurement aims to quantify education behaviors and understand the learning process from sociality and mentality by using statistical and cognitive models, e.g., item response theory (IRT) ( Zhang et al., 2019, 2020a). Leslie reviewed the studies from 1901 to the present and augmented that the mathematics curricula should be constructed following children's psychology ( Steffe, 2017). Yupei et al. developed the classical psychological IRT model by seeking latent factors in response records to predict student responses to exam questions ( Zhang et al., 2019). Robert et al. explored the nature of the relations among prior information to show the effectiveness of the social cognitive theory ( Lent et al., 1993). While psychology explores learning behaviors from phenotypes, biological analysis is used extract the intrinsic impact of education on individuals from brain structure or genotypes ( Liu et al., 2021; Peng et al., 2021c; Zacharopoulos et al., 2021). By investigating the numerical cognition in the brain, Korbinian et al. determined that numerical cognition is subserved by a frontoparietal network that connects the cortex, basal ganglia, and thalamus ( Moeller et al., 2015). Annie et al. explored the association between neural changes and behaviors, suggesting teachers could help students remedy student misconceptions ( Brookman-Byrne and Dumontheil, 2020). Brain et al. reviewed specific learning disabilities to understand the complex etiology and co-occurrences, and accordingly underpin the optimization of learning contexts for individual learners ( Butterworth and Kovas, 2013). Based on the understanding of learning behaviors, computer engineering is introduced to create automatic tools or intelligent games to aid student learning and instructor teaching ( Ng and Chan, 2019; Alur et al., 2020). Oi-Lam et al. examined students mathematics learning with computer-aided learning software and found that the students used 3D CAD to develop spatial skills and to achieve mathematics learning far beyond using formulate and performing procedures ( Ng and Chan, 2019). Christos et al. showed mobile game-based learning could further assist students in higher education toward advancing their knowledge level ( Troussas et al., 2020). Alberto built a multi-view early warning system with genetic-programming classification rules and the multi-view learning strategy to enhance the prediction ( Cano and Leonard, 2019). In this era of big data, educational data science creates a new path toward educational understanding and increasingly becomes a hopeful prospect for education revolution ( Bienkowski et al., 2012). With a sparsity learning model ( Zhang and Liu, 2020), Yupei et al. proposed a meta-knowledge dictionary learning model that learnt the latent meta-knowledge instead of the traditional manual Q-matrix ( Zhang et al., 2020a). They also used the technique of matrix factorization, integrating the side information of students and courses to predict the learning performance on the next-term course ( Zhang et al., 2020c). Through assessing the relations between controlling and autonomy-supportive teaching behaviors on 672 students, Nuria et al. showed that controlling teaching behaviors are negatively associated with psychological needs satisfaction and positively associated with procrastination ( Codina et al., 2018). More works in educational data science can be referred to in Cristobal's recent review ( Romero and Ventura, 2020). Nevertheless, data science needs to consider a wider range of data types in education research.

In recent years, the impact of mathematical learning on brain development has attracted great attention, where the neuroimage is the usually adopted technique ( Kershner, 2020; Zacharopoulos et al., 2021). Mariano et al. discussed four specific cases in which neuroscience synergizes with other disciplines to serve education, ranging from very general physiological aspects of human learning to brain architectures, showing that the neuroscience method, tools, and theoretical frameworks have broadened our understanding of the mind in a way that is highly relevant to educational practices ( Sigman et al., 2014). Marie et al. used quantitative meta-analyses of fMRI studies to identify brain regions concordant among studies on number and calculation, yielding a topographical brain atlas of arithmetic ( Arsalidou and Taylor, 2011). Ching-Lin et al. reviewed the MRI neuroimaging approach in education studies and kinds of learning themes investigated in MRI research and provided objective and empirical evidence to connect learning processes outcomes and brain mechanisms ( Wu et al., 2021). Karin et al. used fMRIs to observe brain activation in mathematical calculation, revealing similar parietal and prefrontal activation patterns in children with developmental dyscalculia compared to controls for various conditions ( Kucian et al., 2006). To probe the impact of a lack of mathematical education on brain development, Georege et al. took more than 120 fMRIs from adolescent students that were allowed to stop studying math in the United Kingdom ( Zacharopoulos et al., 2021). By examining the neurotransmitter concentrations in the brain, they found that the γ-aminobutyric acid (GABA) concentration in the middle frontal gyrus (MFG) is closely associated with mathematical learning and mathematical reasoning. This is evidence that the lack of math education has effects on brain plasticity and cognitive functions.

However, few studies investigated the effects of education on brain development from the perspective of structural neuroimages. The medical image is a technique of probing the intrinsic structure of the human body that is often utilized in disease diagnosis and therapy ( Zhang et al., 2020b, 2021b). While the GABA in the MFG was investigated ( Zacharopoulos et al., 2021), we in this paper looked into the math-learning impact on brain development from the intraparietal sulcus (IPS) region that is also frequently reported in neuroimaging studies of arithmetic. This study made an attempt to assess the problem of whether math students and non-math students could be separated by using brain MRIs. The used method first cropped the voxel of interest (VOI), i.e., IPS, from the MRI and then fed all VOI image patches to our proposed multi-instance contrastive learning (MiCL) model, followed by a linear classifier for student identification. Our contributions could be summarized in two aspects: (1) We developed the classical CL model into the setting of multi-instance learning to solve our problem formulation. (2) This study aimed to explore the impact of mathematical education from structural brain MRIs.

2. Materials and Methods

This study aims to identify math and non-math students by using MRI data to understand the impact of math learning on brain structure in the IPS region. With this purpose, we designed the following workflow: (1) acquiring MRIs from adolescent students including math students and non-math students and cropping all images into the IPS region ( Zacharopoulos et al., 2021), (2) designing a classification tool by using CL for image representations and a linear classifier ( Chen et al., 2020; Xu et al., 2021), and (3) evaluating the performance and experiment analyses on the student classification.

2.1. The Used MRIs

The used MRI data (XNAT Project ID: PN21) were acquired from 16-year-old adolescents that chose to stop or continue math learning in the United Kingdom. Math education was controlled as a single variable to a set math group with 72 students who engaged in A-level math and a non-math group containing 51 students who were not engaged in A-level math. In total, 123 MRIs were acquired on a 3T Siemens MAGNETOM Prisma MRI System equipped with a 32-channel receive-only head coil at the Oxford Centre for Function MRI of the Brain (FMRIB). With an MPRAGE sequence, the anatomical high-resolution T1-weighted MRI was taken by 192 slices, where echo time TE=3.97 ms, repetition time TR =1,900 ms, and voxel size = 1 × 1 × 1 mm. The IPS regions of 20 × 20 × 20 mm were manually defined on the individual's T1-weighted images while the student was lying down in the MR scanner ( Zacharopoulos et al., 2021). Acquisition time was 10-15 min per voxel, including planning and shimming. Figure 1 shows the used T1-weighted MRIs together with the left MFG region. We in this study cropped the left IPS region from the T1-weighted MRIs, leading to 3D image VOI patches of 20 × 20 × 20 mm slices. To ensure the computation in deep learning, we normalized all voxels of image patches by

where I ij is an arbitrary pixel in all images; I max and I min are the maximal and minimal values among all VOI image voxels, respectively. To train the model in a supervised schema, we shuffled all image slices and took the student's label (i.e., class 1: non-math group, class 0: math group) as slice labels.

Figure 1. Positions of VOI in a representative T1-weighted MRI for IPS. Three cyan boxes show the IPS from sagittal, coronal, and axial views, respectively. (A) Sagittal slice, (B) coronal slice, and (C) axial slice.

2.2. Multi-Instance Contrastive Learning

The proposed multi-instance contrastive learning (MiCL) model aims to deal with the problem of student classification where each student involves 20 2D image slices. MiCL includes an input layer of 20 slices per student, a data transform layer for data augmentations, a hidden layer for slice representation learning, a feature layer for student representation learning, and a loss subspace layer for loss computation. Figure 2 shows the framework of the proposed MiCL.

Figure 2. The proposed MiCL model. T ₁ and T ₂ are two data augmentation operators; F ₁ and F ₂ are the ResNets; and G is a multi-layer perception.

2.2.1. Formulation

where F aims to extract the representations from 20 instances per student; is a classifier that maps X i to its label y i; and is the loss function. In this formulation, the major problem is to learn student representations from all the 20 instances, i.e., the function F. A simple method is used to fuse the 20 instances into one student representation, which has been investigated in Dongkuan's work ( Xu et al., 2021). While their model is focused on the time series data in a supervised setting, we in this study proposed a new unsupervised model to learn student representations in a multi-instance setting.

2.2.2. Contrastive Learning

Recently, contrastive learning (CL) has become a popular scheme for robust image representation learning and has been widely used in many fields, e.g., text classification ( Gao et al., 2021), image classification ( Chen et al., 2020), and medical image segmentation ( Chaitanya et al., 2020). CL learns the latent image feature by training a nonlinear model on two noisy versions of each data point toward minimizing the difference between them. SimCLR is a representative framework for CL by training a ResNet for image representations and a multiple-layer perceptron (MLP) for loss calculations ( Chen et al., 2020). In mathematics, SimCLR is used to seek an optimal solution to the following problem,

2.2.3. Objective Function

However, the objective function in Equation (3) fails to handle our multi-instance problem of student classification. To this end, we extended SimCLR into MiCL as

where ⊕ is the concentration operation; z i and ( i = 1, ⋯ , 20) are latent representations for the two transformed versions of an input image X i, i.e., z i = F ₂( F ₁( X i)). As is shown in Figure 2, we implemented T ₁ and T ₂ by randomly cropping and resizing, Gaussian blur, translation, and distortions, and F ₁ and F ₂ by using the classical ResNet, G by using MLP, and CL loss by using Equation (4). After all mappings were achieved, we used outputs of the feature layer as student representations for the subsequent classification tasks.

2.2.4. Linear Classifier

To implement the final student classification, this study employs the single-layer neural network that has been investigated in the evaluation of SimCLR ( Chen et al., 2020). By denoting h i, the resultant representation for the i-th student, the classifier aims to minimize the cost function.

where h denotes the obtained representation from Equation (5) and (·) = Sigmoid(·) is the activated function mapping student representations to the label space. Equation (6) is the function that measures the binary cross-entropy between the target and the output.

2.3. Model Setting and Evaluation

The proposed model shown in Figure 2 was set up in detail as follows. All instances share the same F ₁ and F ₂, so the two functions are implemented by using the ResNet. The ResNet comprises a convolutional layer with a kernel size of 3 × 3, three residual modules of four bottleneck blocks, and an average pooling layer. The number of channels is 64, 128, 256, 512, 256, 128, and 64, respectively. And the bottleneck block is composed of three convolutional layers with ReLU. Besides, batch normalization (BN) is utilized after each convolutional layer. Our model transfers image instances into a 128-dimensional space, and thus, student features into a 2,560 dimensional space. Then, the MLP for G is composed of two fully connected layers of channels 1,024 and 128. Finally, the linear classifier is from 2,560 to 1 and employs the Sigmoid as the activation function to yield the prediction probability. The model was trained by 2,000 iterations with a learning rate of 0.001, and 1,000 iterations trained the linear classifier with a learning rate of 0.005.

In this study, we finally calculated accuracy (ACC), F1-score (F1), and area under the ROC (AUC) on the used 123 MRIs. From the confusion matrix, we calculated the four metrics, i.e., true positive (TP), false positive (FP), false negative (FN), and true negative (TN). ACC and F1 are calculated by

and AUC is defined as the area under the ROC. Besides, the two-tailed t-test is adopted to compute the p-value for the statistic significance test ( Zhang et al., 2018). Due to the small-size dataset, we could conduct five-fold cross-validation on the 123 students. That is to say, the model could be trained on four folds and tested on the remaining fold to obtain the average evaluations.

3. Result

To have a comparison with SimCLR ( Chen et al., 2020), we implemented the student classification by firstly learning an image representation for each slice per student, secondly connecting the 20 representations, and finally reducing them into a 2,560-dimensional PCA subspace ( Zhang et al., 2017). In short, we called this method SimCLR through the following context.

3.1. Visualization

Figure 3 scatters all 123 student representations from SimCLR and MiCL in the 2D subspace. All obtained representations were reduced into 50-dimensional PCA subspaces and then reduced into 2-dimensional t-SNE subspaces. There were 51 students who stopped math education for class 1 and 72 students who continued math studying for class 0, colored in brown and blue in the figures, respectively. As is shown, the student representations yielded from MiCL could be easily separated between class 1 and class 0, compared to SimCLR, in the 2D t-SNE subspace. This observation potentially suggests that joint learning of the 20 image slices in a multi-instance setting could yield more smart student representations.

Figure 3. Visualization of the learned representation in 2D subspaces. There are in total 123 students, including 51 students in class 1 and 72 students in class 0. (A) SimCLR and (B) MiCL.

3.2. Overall Evaluation

Figure 4 shows the confusion matrixes from SimCLR and the proposed MiCL. Note that this study took the non-math group as the positive class and the math group as the negative class. TPSimCLR > TPMiCL shows that SimCLR prefers non-math students, while MiCL prefers math students from TNMiCL > TNSimCLR. SimCLR has a big FN while MiCL has a big FP, where FPSimCLR = TNMiCL. That means that SimCLR is better at identifying non-math students, while MiCL is better at identifying math students. However, the proposed MiCL is better overall than SimCLR at classification.

Figure 4. Confusion matrix. The two matrixes show TN, FP, FN, and TP from the classification results of SimCLR and MiCL, respectively. We here considered non-math students as the positive class. (A) SimCLR and (B) MiCL.

Table 1 reports the overall evaluations in terms of the various metrics. Since SimCLR prefers non-math students, SimCLR achieves higher precision than MiCL. But MiCL obtains a higher recall than SimCLR and furthermore results in a higher F1 score. On the other hand, the proposed MiCL gains significant improvements on ACC and AUC by 5 and 3% with p < 0.01, respectively. The AUC was obtained by the ROCs, shown in Figure 5. ROCs were plotted by the true positive rate (TPR) against the false positive rate (FPR), showing the classification performance at various thresholds. As is shown, MiCL achieves a higher TPR at a low FPR than SimCLR. Controlling FPR is an important research topic in many fields, e.g., disease diagnosis and drug discovery ( Romano et al., 2020). While SimCLR has higher performance at a high FPR, MiCL gains an improvement at AUC that is calculated by the area under ROC in comparison with SimCLR. Overall, the proposed MiCL achieves a better classification performance than SimCLR, while FPR could meanwhile be controlled.

Table 1. Evaluation results.

Figure 5. ROCs. The ROCs show the classification performance of SimCLR and MiCL.

3.3. Individual Evaluation

Figure 6 shows the classification probability for two classes yielded by SimCLR and MiCL. The probability was calculated by normalizing the two outputs to sum 1. That is to say, the sum of the probability belonging to class 1 and the probability belong to class 0 is 100%. In this study, we identified a student to be a math student if the corresponding probability is less than 0.5; otherwise, we identified the student to be a non-math student. As is shown, SimCLR results in most of the probabilities in [0.2, 0.4) for class 0 and most of the probabilities in [0.5, 0.7). And MiCL yields the classification probability concentrated in [0.0, 0.3) for class 0 and the classification probability concentrated in [0.6, 0.9) for class 1. On the other hand, SimCLR leads to more students having a probability of greater than 0.5 for class 0, while MiCL gives rise to more students having a probability of less than 0.5 for class 1. The observation shows that MiCL could yield a more convincing classification for the corrected predictions than SimCLR. Besides, SimCLR leads to more stable predictions for non-math students, and even the probability is concentrated at near 0.5.

Figure 6. The probability distribution. The distribution of the classification probability for math students and non-math students by SimCLR and MiCL. (A) Math students and (B) Non-math students.

Table 2 summarizes the mean and the standard deviation of the classification probability for SimCLR and MiCL, respectively. As is shown, MiCL has a smaller mean with a smaller standard deviation than SimCLR on the tasks of identifying math students. While MiCL has the same mean for non-math students, SimCLR has a smaller standard deviation. However, MiCL yields more confident predictions having benefited from multi-instance joint learning.

Table 2. Means and standard deviations.

4. Conclusion and Discussion

In this paper, we made an attempt to classify students that have stopped studying mathematics and students that have continued their mathematical education by using the popular deep learning technique. To deal with the 3D images, we formulated this problem into multi-instance learning and developed a classical contrastive learning framework in a multi-instance setting.

The proposed MiCL learns the image representation by sharing the weights between the 20 instances and then concatenates 20 image representations, leading to the final student representation. In the two versions of each student, the contrastive loss is employed to encourage a minimal difference. For 123 students, composed of 51 non-math students and 72 math students, MiCL achieves an accuracy of 90.24% that gains a 5% improvement in comparison with SimCLR. Benefitting from the multi-instance joint learning, the same observation has also been obtained for other metrics.

The MRI data have the potential to be used in identifying whether a student has stopped their mathematical education. Both SimCLR and MiCL convey decent accuracy on the classification task of math students or non-math students. Moreover, SimCLR is capable of identifying non-math students more stably, while MiCL prefers to identifying math students. Since the math or non-math student could be separated with a high accuracy using MRIs, mathematical education has a potential impact on adolescent brain development from white matter and gray matter in the IPS region. This conclusion has also been investigated in the work of Karin ( Kucian et al., 2006; Zacharopoulos et al., 2021).

There are two points that should be noticed. (1) MiCL gains an insubstantial improvement in accuracy in the 2,560-dimensional subspace in comparison with the 2-dimensional subspace. It may mean that feature selection could be utilized to discover the brain atlas for mathematical studying. (2) Multi-instance joint features maybe contribute more to math-student identification. It potentially means the impact of mathematical studying is more varied on multiple image slices.

Hence, we should uncover the brain atlas that is affected by mathematical education and further discuss the impact on future attainment for adolescents in future works. The attention mechanism could provide more explanations to understand the latent representation, which is our other future consideration ( Zhang et al., 2021a). Besides, we will investigate more brain regions that are also related to math learning, e.g., the middle front gyrus ( Zacharopoulos et al., 2021), and conduct more experiments to prob the associations between the MRI images and other problems, e.g., student psychology and math anxiety ( Barroso et al., 2021).

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: http://central.xnat.org.

Ethics Statement

The studies involving human participants were reviewed and approved by the School of Computer Science at Northwestern Polytechnical University, Xi'an, China. The participants provided their written informed consent to participate in this study.

Author Contributions

YZ and XS work with the School of Computer Science (SCS) at Northwestern Polytechnical University (NPU), Xi'an, China and funded this study. SL is a Ph.D. student in SCS at NPU, Xi'an, China and collected the data and plotted the figures. YZ designed the study, conducted experiments, and wrote the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was supported in part by National Natural Science Foundation of China (Nos. 61802313 and U1811262), Key Research and Development Program of China (No. 2020AAA0108500), and Reformation Research on Education and Teaching at Northwestern Polytechnical University (No. 2021JGY31).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

All authors thank the editors and the reviewers for their helpful comments.

References

Zhang, Y., Dai, H., Yun, Y., and Shang, X. (2019). "Student knowledge diagnosis on response data via the model of sparse factor learning," in International Conference on Educational Data Mining (Montreal, CA), 691-694.

# data[].attributes.attachment_link.title
Frontiers | An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning

# data[].attributes.attachment_link.description
This paper explores whether mathematical education has effects on brain development from the perspective of brain MRIs. While biochemical changes in the l...

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Meeting challenging academic standards has led to a lower focus and emphasis on physical activity in the classroom.

# data[].attributes.attachment_link.title
The Cognitive Benefits of Exercise in Youth : Translational Journal of the American College of Sports Medicine

# data[].attributes.attachment_link.description
he health benefits of exercise are wide-ranging and powerful. It appears that cognitive function and academic achievement are additional realms beyond the physical where exercise is beneficial. The school setting presents a unique public health opportunity to enact change on a variety of levels. As sports medicine and exercise specialists, we need to support efforts that increase access to quality physical fitness across the board for all children.…

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Maddie Precht -Techniques for Building Positive Habits - Exercise, Sleep Routines, etc.

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Maddie Precht -Techniques for Building Positive Habits - Exercise, Sleep Routines, etc.

# data[].attributes.body
This book makes building positive habits easier (like exercise for adolescents!) and addresses how to deal with maladaptive routines that are not serving us

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Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones

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Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones [Clear, James] on Amazon.com. *FREE* shipping on qualifying offers. Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones

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Maddie Precht: Exercise Interventions on Executive Functioning on Students with ADHD

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Maddie Precht: Exercise Interventions on Executive Functioning on Students with ADHD

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This meta-analysis of 15 research studies identifies the impact of exercise interventions on aspects of executive functioning for students with ADHD diagnoses and what types of interventions are most effective

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Teacher SEL Training

Self-reflection is an important tenet of social and emotional learning, enabling teachers to practice their own self-care and promote well-being. When there are problems in the classroom or with others, it’s vital that teachers reflect on the part they may be playing. So, taking time to reflect on our role in problems we may have in our relationships with students can bring us greater awareness as to how we might change our strategies in the future.

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Executive Functioning Resources (Kristina Stem)

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Executive Functioning Resources (Kristina Stem)

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This is a resource I found in my Drive that I've been want to explore more. Some of it is from "Smart but Scattered" book, but there are other sources as well. I just like looking at the EF Developmental Matrix.

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Guide to Executive Functioning from Harvard University Center on the Developing Child (Kristina Stem)

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Guide to Executive Functioning from Harvard University Center on the Developing Child (Kristina Stem)

# data[].attributes.body
This resource was recommended to me after a PD training a few years ago... I never got around to digging into it very much but I've saved it and want to explore further - especially the "Activity Guide Enhancing and Practicing Executive Function Skills with Children from Infancy to Adolescence"

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Center on the Developing Child at Harvard University

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A Guide to Executive Function - Center on the Developing Child at Harvard University

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Executive function and self-regulation skills are like an air traffic control system in the brain—they help us manage information, make decisions, and plan ahead. We need these skills at every stage of life, and while no one is born with them, we are all born with the potential to develop them. But, how do we […]

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Learn how to leverage a variety of executive functions to help with stress and managing workloads as an (art) teacher.

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How Executive Functions Help You Beat Stress Better in Your Art Teacher Life

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How Executive Functions Help You Beat Stress Better in Your Art Teacher Life Professional Practice % %

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Learn how integrating the arts enhances the processing of information.

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Three Strategies for Using the Arts to Build Student Executive Functions

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Blogger Judy Willis MD describes the positive uses of art as a tool to capture attention and educate young minds.

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Social & Emotional Learning -Tara Chesler

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Social & Emotional Learning -Tara Chesler

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This is a quick video to show all of the dynamics and influences of how social and emotional learning affects our lives, and the kids we teach.

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Studies show that sustained and well-integrated social and emotional learning (SEL) engages students and improves achievement. Explore classroom practices that make up the most effective SEL programs.

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5 Keys to Successful Social and Emotional Learning

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Studies show that sustained and well-integrated social and emotional learning (SEL) engages students and improves achievement. Explore classroom practices that make up the most effective SEL programs.

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Social & Emotional Learning -Tara Chesler

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Social & Emotional Learning -Tara Chesler

# data[].attributes.body
This article explains the importance of learning these essential skills and how impactful they can be, or the result of children (which turn into adults) that lack these skills

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This was a good article that was scary and motivating at the same time. There are so many benefits of exercise, from the ones mentioned in the book to significant health benefits. The reason I use the word "scary" is that if exercise helps prevent cancer, it also means that you are more likely to get cancer if you don't exercise.

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The Physical Activity Guidelines for Americans, 2nd edition recommend that children and adolescents ages 6 to 17 years do 60 minutes or more of moderate-to-vigorous physical activity daily. ²

Regular physical activity can help children and adolescents improve cardiorespiratory fitness, build strong bones and muscles, control weight, reduce symptoms of anxiety and depression, and reduce the risk of developing health conditions such as: ¹

Physical inactivity can

Lead to energy imbalance (e.g., expend less energy through physical activity than consumed through diet) and can increase the risk of becoming overweight or obese.¹⁴

Increase the risk of factors for cardiovascular disease, including hyperlipidemia (e.g., high cholesterol and triglyceride levels), high blood pressure, obesity, and insulin resistance and glucose intolerance.1,5,6

Increase the risk for developing type 2 diabetes.1,7

Increase the risk for developing breast, colon, endometrial, and lung cancers.¹

Lead to low bone density, which in turn, leads to osteoporosis.¹

Less than one-quarter (24%) of children 6 to 17 years of age participate in 60 minutes of physical activity every day. ⁸

In 2017, only 26.1% of high school students participate in at least 60 minutes per day of physical activity on all 7 days of the previous week.⁹

In 2017, 51.1% of high school students participated in muscle strengthening exercises (e.g., push-ups, sit-ups, weight lifting) on 3 or more days during the previous week.⁹

In 2017, 51.7% of high school students attended physical education classes in an average week, and only 29.9% of high school students attended physical education classes daily.⁹

Aerobic: Most of the 60 minutes or more per day should be either moderate- or vigorous-intensity aerobic physical activity and should include vigorous-intensity physical activity on at least 3 days a week.

Muscle-strengthening: As part of their 60 minutes or more of daily physical activity, children and adolescents should include muscle-strengthening physical activity on at least 3 days a week.

Bone-strengthening: As part of their 60 minutes or more of daily physical activity, children and adolescents should include bone-strengthening physical activity on at least 3 days a week.

These guidelines state that children and adolescents be provided opportunities and encouragement to participate in physical activities that are appropriate for their age, that are enjoyable, and that offer variety. ³

The national recommendation for schools is to have a comprehensive approach for addressing physical education and physical activity in schools. ¹⁰⁻¹² This approach is called Comprehensive School Physical Activity Programs. ¹³

Students who are physically active tend to have better grades, school attendance, cognitive performance (e.g., memory), and classroom behaviors (e.g., on-task behavior).14, 15

Higher physical activity and physical fitness levels are associated with improved cognitive performance (e.g., concentration, memory) among students.14, 15

1. Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: US Dept of Health and Human Services; 2018.

2. US Department of Health and Human Services. Physical Activity Guidelines for Americans, 2nd edition. Washington, DC: US Department of Health and Human Services; 2018.

3. National Physical Activity Plan Alliance. The 2018 United States Report Card on Physical Activity for Children and Youth. Washington, DC: National Physical Activity Plan Alliance, 2018.

4. Loprinzi PD, Lee I, Andersen RE, Crespo CJ, Smit E. Association of concurrent healthy eating and regular physical activity with cardiovascular disease risk factors in US youth. American Journal of Health Promotion. 2015; 30(1):2-8.

5. Cuenca-Garcia M; Ortega FB; Ruiz JR; et al. Combined influence of healthy diet and active lifestyle on cardiovascular disease risk factors in adolescents. Scandinavian Journal of Medicine & Science in Sports. 2014;24(3):553-562.

6. Kriska A; Delahanty L; Edelstein S; et al. Sedentary behavior and physical activity in youth with recent onset of type 2 diabetes. Pediatrics. 2013;131(3): e850-e856.

7. The Child & Adolescent Health Measurement Initiative (CAHMI). 2016 National Survey of Childrens Health. Data Resource Center for Child and Adolescent Health;2016.

8. Merlo CL, Jones SE, Michael SL, et al. Dietary and Physical Activity Behaviors Among High School Students - Youth Risk Behavior Survey, United States, 2019. MMWR Suppl 2020;69(Suppl-1):64-76.

9. Centers for Disease Control and Prevention. School Health Guidelines to Promote Healthy Eating and Physical Activity. MMWR. 2011;60(No. RR-5).

10. US Department of Health and Human Services. Physical Activity Guidelines for Americans Midcourse Report: Strategies to Increase Physical Activity Among Youth. Washington, DC: US Department of Health and Human Services: 2012.

11. Institute of Medicine. Educating the Student Body: Taking Physical Activity and Physical Education to School. Washington, DC: The National Academies Press; 2013.

12. Centers for Disease Control and Prevention. A Guide for Developing Comprehensive School Physical Activity Programs. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services; 2013.

13. Centers for Disease Control and Prevention. The Association Between School-Based Physical Activity, Including Physical Education, and Academic Performance. Atlanta, GA; Centers for Disease Control and Prevention, US Department of Health and Human Services; 2010.

14. Michael SL, Merlo C, Basch C, et al. Critical connections: health and academics. Journal of School Health. 2015;85(11):740-758.

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centers for disease control and prevention

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regular physical activity in adolescents

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comprehensive school physical activity program

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Regular physical activity can help children and adolescents improve cardiorespiratory fitness, build strong bones and muscles, control weight, reduce symptoms of anxiety and depression, and reduce the risk of developing health conditions such as heart disease, cancer, type 2 diabetes, high blood pressure, osteoporosis, obesity.

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Ten Tips for Teachers to Practice Social Emotional Learning in the Classroom

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Ten Tips for Teachers to Practice Social Emotional Learning in the Classroom

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This is an article that gives simple suggestions to help teachers practice social emotional learning in the classroom. I liked it because teachers are generally overwhelmed, and this gives an easy way to start.

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Katie Lee, MHA Deputy Director of Communications

# data[].attributes.attachment_link.title
10 tips for teachers to practice social emotional learning in the classroom

# data[].attributes.attachment_link.description
Teachers, aides, and others working with students have enough on their plates already. However, studies show that incorporating social emotional learning (SEL) into regular classroom activities improves classroom behavior and decreases misbehavior and aggression.

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This article explores the benefits of movement breaks in the classroom. It highlights that some children might not be able to get movement outside of school due to living in possible unsafe areas or other circumstances.

-Post by Ciara L

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Prevention Research Center on Nutrition and Physical Activity

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Get the Facts: Movement Breaks in the Classroom Grades K-5

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The information provided here is intended to be used for educational purposes. Links to other resources and websites are intended to provide additional information aligned with this educational pur…

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This is a great scholarly article that talks about the benefits of movement breaks within the classroom. This even relates back to the book when they were talking about how being active helps with cognitive performance.

-Post by Ciara L

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Paul B. Tchounwou, Academic Editor

Abstract

This mixed-methods study aimed to determine the feasibility of incorporating movement breaks into university classes in terms of acceptability (disruption, engagement, satisfaction), practicality (ease of scheduling and conducting breaks) and efficacy (sedentary time, concentration, alertness, enjoyment). Movement breaks of five to 10 min duration were scheduled after 20 min of sedentary time during 2-h classes. Classes without movement breaks were used as a comparison. Data were collected using surveys, objective physical activity monitoring and focus group interviews of students ( n = 85) and tutors ( n = 6). Descriptive statistics (quantitative data) and independent coding and thematic analysis (qualitative data) were completed. Students (mean age 23 ± 2 years, 69% female) actively engaged in movement breaks with no adverse events. Movement breaks were perceived to be beneficial for concentration, engagement and productivity. Timing of the break was perceived to be important to enhance the benefit and reduce disruption. Students preferred outdoor or competitive movement breaks. Students spent 13 min less time sitting (95%CI 10 to 17), took 834 more steps (95%CI 675 to 994) and had higher levels of concentration, alertness and enjoyment ( p < 0.001) in classes with movement breaks compared to classes without. Classroom movement breaks are feasible and may be considered for incorporation into university classes to reduce sedentary behaviour and increase physical activity, alertness, concentration and enjoyment.

Keywords: sedentary behaviour, exercise, mental fatigue, students, universities

1. Introduction

High volumes of sedentary behaviour are associated with increased risk of poor health outcomes including all-cause mortality, obesity, cardiovascular disease, type 2 diabetes mellitus, cancer [ 1], depression [ 2] and anxiety [ 3]. An increasing proportion of young adults are presenting with signs and symptoms of excessive time spent sedentary including obesity, hypertension, dyslipidemia and metabolic syndrome [ 4, 5]. Any waking behaviour with low energy expenditure (≤1.5 METs), including sitting at a desk, is classified as sedentary behaviour [ 6]. As university students may need to spend large amounts of their weekdays sitting in class and studying, they appear to be particularly sedentary compared to other young adults [ 7]. Based on objective assessment (accelerometry), university students spend an average of 9.8 to 10.7 waking hours each day in sedentary activities [ 7, 8]. The sedentary time accrued while studying may be detrimental to the current and future health of university students. In addition to increasing physical activity, national guidelines now recommend regular breaks to interrupt sedentary time [ 9, 10, 11]. One such innovative opportunity may be in the university setting and specifically during seated classes.

Breaking sedentary time with regular, brief, physical activity has general health benefits such as helping to control obesity and glycaemia in adults [ 12] and may improve cognitive capacity and academic performance in university students [ 8]. There is limited research on classroom movement breaks in the university setting, but initial research indicates university students had a positive attitude to movement breaks of three to five minutes duration conducted every 20 min during classes [ 13]. In another study, results indicated exercise breaks increased vigor and decreased fatigue levels for 20 min compared to no breaks [ 14]. A four-minute movement break conducted once in a 2-h lecture was perceived to be feasible and acceptable to students [ 15]. However, there is still concern that movement breaks may disrupt concentration and harm productivity [ 16].

Preliminary evidence suggests that movement breaks may be beneficial for university students, however, the feasibility of including movement breaks in university classes is unknown. Therefore, the primary aim of this study was to determine the feasibility of incorporating movement breaks into university classes. For this study, elements of feasibility considered were acceptability (levels of disruption, engagement and satisfaction), practicality (safety, ease of scheduling and conducting breaks) and efficacy (differences in sedentary time, concentration, alertness and enjoyment between classes with and without movement breaks).

2. Materials and Methods

2.1. Design

A mixed-methods design using quantitative and qualitative approaches was conducted comprising of surveys, objective physical activity monitoring and focus group interviews. This design was chosen for triangulation [ 17] and mutual enhancement as the survey provided raw data on alertness, concentration and enjoyment, accelerometer data provided information on fidelity and efficacy, and focus group data provided participants' experiences of movement breaks from the perspective of university students and tutors. Focus groups can generate rich, detailed data in the participants' own words to explore their experiences of the movement breaks and were chosen over individual interviews as they assist participants to explore and clarify their views relative to other participants. The less formal nature of a focus group also allows participants to express their views candidly and spontaneously, while participant interactions within a focus group can also generate new ideas [ 18]. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist [ 19] and the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) statement [ 20] were used to ensure the rigor of reporting. University Human Research Ethics Committee approval (HEC19527) was obtained prior to commencement of the study. Participation was voluntary and all participants provided written informed consent.

2.2. Participants

A convenience sample of final-year physiotherapy students from two campuses of one Australian university was invited to participate. Potential participants were informed about the study via email from an academic staff member not directly involved with the students. To be eligible, students needed to be concurrently enrolled in two subjects ('chronic disease management' (CLASS A) and 'evidence-based practice' (CLASS B)) in 2020 and be able to participate in physical activity as assessed by the Physical Activity Readiness Questionnaire (PAR-Q) [ 21]. It was anticipated that 9 groups of 20 students would be enrolled in these classes at different times across one semester ( n = 180). Accelerometer data were collected on a sub-set of participants limited by accelerometer availability.

For focus group interviews, purposive sampling occurred to ensure study participants represented a broad sample of the student body (various class groups, genders, campus of study and type of degree). Recruitment to the qualitative component occurred until data saturation (i.e., no new themes or data being identified with further interviews) was achieved [ 22]. All tutors of the subjects ( n = 6) were also invited to participate in a focus group.

2.3. Intervention

Once per week, students had two, 2-h classes back-to-back on the same day (one CLASS A and one CLASS B, in variable order). Using an alternating schedule, students completed one class with movement breaks and one traditional desk-based class without movement breaks each week over a 3-week period. Classroom movement breaks were 5-10 min long [ 13, 23] and consisted of whole-body movements (see Box 1). Tutors aimed to complete one movement break outside the classroom (walking) and two inside per class. Classroom movement breaks were incorporated after approximately 20 min of sedentary classroom time in one two-hour class per study day so that a total of 15-30 min of movement was incorporated into the two-hour class ( Figure 1 ). Two-hour classes with no scheduled movement breaks were used as a comparison. Attempts were made to tailor the class structure to fit the breaks by modifying learning activities to be completed whilst moving, by utilizing walking discussions or by having changes in topic planned for movement break times. Movement breaks conducted in the classroom were led by the tutor or a student volunteer. Outside walking discussions were unsupervised. Participation in each movement break was voluntary and all students were invited to participate in the movement breaks regardless of study enrolment.

Box 1

Example movement break activities

Movement breaks should last between 5 and 10 min in duration. To achieve 5-10 min, a combination of these activities or games could be completed:

1. High knee running on the spot

2. Hopping or jumping Games on the website [ 24]

2.4. Outcomes

Bowen and colleagues [ 25] describe eight general areas of focus that may be addressed in a feasibility study: acceptability, demand, implementation, practicality, adaptation, integration, expansion and limited-efficacy testing. Bowen's framework suggests that researchers should determine which domains to focus on depending on the purpose of the feasibility study and the study question [ 25]. For this study, three domains of feasibility were selected to determine if movement breaks were feasible in university classes: acceptability (levels of disruption, engagement and satisfaction), practicality (safety, ease of scheduling and conducting breaks) and limited efficacy testing of movement breaks. Engagement, ease of scheduling and conducting movement breaks and the perceived amount of disruption to the class were explored through qualitative focus group interviews with tutors and students. Safety was determined by recording adverse events such as injuries, muscle soreness, trips or falls. Limited efficacy was determined by measuring perceived concentration, alertness and enjoyment as previous research in university students has mentioned these concepts in students' feedback on movement breaks and physically active learning [ 13, 14, 26]. Sedentary behaviour (time spent sedentary) and physical activity levels (time spent walking, time spent upright, sit-to-stand transitions and steps) were assessed to determine intervention fidelity and effectiveness.

2.5. Data Collection

Focus group interviews were conducted within a week of the classes finishing for up to one hour each to determine student and tutor perceptions of movement breaks following a semi-structured interview schedule ( Appendix A). Tutors were interviewed by the same facilitator (CP) separately from students so that participants felt free and comfortable talking to each other [ 18]. All participants were provided with information about the researcher (CP) and the project goals prior to participation.

Perceived concentration (the ability to devote your attention to a single activity), mental alertness (a state of being ready to see and understand and being mentally aware and quick) and enjoyment (being fun and giving a sense of satisfaction) were assessed for all participants in all classes using a self-administered 10 cm visual analogue scale [ 27] at the end of each class ( Supplementary file). The visual analogue scale (VAS) has been used to evaluate a number of social, behavioural and clinical subjective phenomena because it is convenient, easily understood and quick to administer. It was chosen for this study because of the speed of administration which allowed students to move on to their next class in a timely manner. Visual analogue scales are considered valid and reliable, especially when the time between repeat measurements is small [ 27]. In our study with outcomes evaluated weekly, it is therefore likely that changes over time reflect actual change rather than a high degree of measurement error.

ActivPAL3 triaxial accelerometers were used to continuously monitor physical activity and sedentary behaviour during classes. Participants wore the monitors on the anterior aspect of their thigh at the central mid-thigh level to objectively assess physical activity and sedentary time to determine intervention fidelity. Students self-applied these monitors under instruction at the beginning of the first class and removed them after their second class. The activPAL3 is valid and has excellent reliability for the detection of posture and purposeful walking in young adults [ 28].

2.6. Data Analysis

Quantitative data on physical activity and sedentary behaviour were downloaded from the accelerometer at the end of each pair of classes. Intervention fidelity was assessed by comparing activity and sedentary behaviour in the control and experimental classes. Group means and standard deviations (SD) were reported and within-group mean differences (MD) and 95% confidence intervals (CI) in physical activity, sedentary behaviour and concentration, mental alertness and enjoyment between classes with and without movement breaks were assessed via paired t-tests using SPSS version 27 [ 29]. Missing data were excluded from the analysis. Two-way repeated-measures ANOVA was used to assess the interaction effects of class type and movement break condition and class time and movement break condition on the outcomes of concentration, alertness and enjoyment.

Interviews were audio-recorded and transcribed verbatim. Transcriptions were transferred to NVivo12 data management software [ 30]. Qualitative data were analysed thematically using an iterative process [ 31]. Two researchers (C.P., L.R.) independently read the transcripts in their entirety. They then re-read and commenced initial coding (open coding) independently identifying key phrases. Re-coding continued until themes emerged. The researchers then met to discuss emerging themes and looked for links between themes before deciding on the main themes. To ensure accuracy the main themes were summarised by the researchers and checked for accuracy by participants to ensure it was an accurate interpretation of their perceptions (member checking) [ 18]. The final step involved returning to the transcripts to selectively search for data related to the identified themes (selective coding) and to check that no perspectives had been missed. Data were collected and analysed simultaneously to guide further interviews and assess for data saturation [ 22].

Quantitative and qualitative data were analysed separately. The identified themes and quantitative results were then compared and contrasted in a process called triangulation for the overall synthesis [ 17]. As researchers bring their own beliefs and experiences into qualitative research and data interpretation, the researchers' backgrounds are briefly described to enhance reflexivity as follows: all researchers are qualified physiotherapists and five have Doctor of Philosophy qualifications (C.P., L.H., M.D.N., A.H., G.O'D.). At the time of the intervention, five researchers were tutors in the subjects (C.P., L.H., M.D.N., A.H., J.L.) and two were students (L.R., D.M.). Three researchers are experienced in qualitative research (C.P., A.H., G.O'D.).

3. Results

Participants were recruited between February and March 2020. Recruitment ceased after 7 groups of students ( n = 140) commenced the subjects due to COVID-19 restrictions on face-to-face teaching. Eighty-five students participated in the feasibility study (61% recruitment rate) and 44 wore accelerometers (52%). Accelerometer data from 43 participants were included in the analysis as one monitor malfunctioned ( Figure 2 ). Overall, participants had similar characteristics to the total potentially eligible population, were a mean age of 23 (SD 2) years and 69% were female ( Table 1 ).

Table 1

Participant Characteristics

All Student Participants

Focus Group-Students

Focus Group-Tutors

Accelerometer Wearers

n =

85

14

6

44

Following purposive sampling, data saturation was reached after a total of 14 students participated in four focus group interviews. All six tutors participated in one focus group interview. The average duration of focus groups was 27 (SD 11) minutes. No corrections or additions were made after member checking.

3.1. Feasibility

Tutors and students perceived that engagement in movement breaks was high with almost all students participating in movement breaks (irrespective of their enrolment in the study). No adverse events were reported but tutors did remark that they actively considered safety when leading and modifying movement breaks. Tutors expressed that movement breaks did not compromise intended curriculum delivery.

3.1.1. Theme 1. Movement Breaks Had Multiple Benefits

Students and tutors expressed improvements in concentration and alertness as well as enhanced rapport between students and tutors and increased camaraderie between students. These changes were perceived to then impact class participation as students described feeling more comfortable and less worried about making mistakes so that they were more willing to speak up and be interactive. More enthusiastic engagement in group tasks and higher productivity were also expressed as benefits by tutors and students.

Students expressed varied and differing views on when they preferred movement breaks, with some preferring movement breaks in the morning class to help rouse them whilst others felt they needed it to aid concentration during the second class after having been sedentary for the previous two hours. Depending on personal preferences related to class type (CLASS A or B), students also expressed differing views on which class they most valued the movement breaks but overall agreed it would have been best if they were in both classes.

3.1.2. Theme 2. Timing Is Important to Enhance Effect and Reduce Disruption

The timing was perceived to play an important role in the effect of movement breaks. When movement breaks did not align well with a natural break in content or a change in topic, they were perceived to be somewhat disruptive:

When movement breaks were timed well, they allowed students to consolidate the knowledge learned in the previous portion of the class and be reinvigorated for the next topic.

Tutors were conscious of the importance of timing and found it challenging to schedule movement breaks according to the research schedule.

When movement breaks were not timed well, tutors described difficulty resettling students.

3.1.3. Theme 3. Some Movement Breaks Were Perceived to Be Better Than Others

The type of movement completed within the break also appeared to have an impact on perceived efficacy. Walking outdoors and competitive activities were highly regarded by students as they were perceived to give a complete mental and physical break from the classroom as opposed to, for example, balance tasks while listening to class content.

3.1.4. Theme 4. Less Frequent Movement Breaks Would Be Better

Students and tutors perceived that having less frequent movement breaks that were flexible in terms of timing to suit the class content and current energy levels of the class would have been more beneficial.

3.2. Limited Efficacy

3.2.1. Alertness

Students reported significantly higher levels of alertness in classes with movement breaks when compared to classes without movement breaks ( p < 0.001) ( Table 2 ). The intervention effect remained regardless of class type or time and there was no interaction effect with either class type ( p = 0.796) or class time ( p = 0.194). Both students and tutors perceived that students were more alert, attentive, awake and energised after a movement break.

Table 2

Class

Movement Break, Mean (SD)

No Movement Break, Mean (SD)

Difference, Mean, 95% CI

p-Value

Alertness ( n = 81)

Concentration ( n = 81)

Enjoyment ( n = 81)

Sedentary behaviour and physical activity ( n = 43)

Students had higher levels of alertness in CLASS A when compared to CLASS B ( p = 0.009), irrespective of movement breaks. Qualitative data helped explain this as tutors and students described differences in class structure:

3.2.2. Concentration

Students reported significantly higher levels of concentration in classes with movement breaks when compared to classes without movement breaks ( p < 0.001) ( Table 2 ). There was no interaction effect with class type ( p = 0.59) or time ( p = 0.984).

Tutors perceived higher levels of concentration in their students and students expressed that movement breaks made it easier to concentrate both after a break and in the lead up to a break.

3.2.3. Enjoyment

Students reported significantly higher levels of enjoyment in classes with movement breaks when compared to classes without movement breaks ( p < 0.001) ( Table 2 ). The intervention effect remained regardless of class type or time and there was no interaction effect for class ( p = 0.61) or time ( p = 0.232). There was a trend towards enjoyment levels being higher in the second (and final) class for the day ( p = 0.067).

Students and tutors both perceived that classes with movement breaks were more fun and enjoyable and added laughter and humor to classes. They also allowed for social interaction and acted as icebreakers between students and tutors and amongst students.

3.2.4. Sedentary Behaviour and Physical Activity

Some movement and upright time occurred during classes without movement breaks due to in-class presentations, movements for group work and students getting up from their desks to relieve discomfort. In classes with movement breaks, students spent 13 (95%CI 10 to 17) minutes less time in sedentary activities and took 834 (95%CI 675 to 994) more steps when compared to classes without movement breaks ( Table 2 ). Students reported discomfort when sitting for prolonged periods so found that breaking up sitting was beneficial.

Students felt that the movement breaks most likely did not contribute to much increased physical activity overall but did report continuing to do them during their own independent study.

4. Discussion

This study provides preliminary evidence that it is feasible to conduct movement breaks during university classes in terms of acceptability, practicality and efficacy. Students spent less time sitting, took more steps and reported significantly higher levels of alertness, concentration, and enjoyment in classes with movement breaks when compared to classes without movement breaks. To further enhance feasibility, tutors could consider fewer movement breaks that can be more flexibly scheduled around class content and class dynamic.

Previous studies in the university classroom also found that movement breaks may be beneficial [ 13, 14]. Blache and colleagues [ 14] found that compared to no break, structured exercise breaks improved vigour and reduced fatigue in 66 university students. Ferrer and Laughlin [ 13] found that students ( n = 53) subjectively reported increases in attention and that movement breaks were fun. Neither study measured objective changes in physical activity. Our study adds quantitative and qualitative data to the small body of evidence that classroom movement breaks are effective in terms of cognitive and physical outcomes without compromise to curriculum delivery.

The rationale for why movement breaks improved concentration and alertness may partially be explained by the effort-recovery model [ 32]. The model is based on the premise that working on mentally demanding tasks (e.g., university classes) leads to fatigue, and in response to fatigue, students need to increase their effort (in stress response) to maintain their performance [ 33]. If this increased effort cannot be achieved or sustained, performance deteriorates [ 33]. The effort-recovery model [ 32] proposes that stress and fatigue are reversible with rest. Based on the effort-recovery model, it appears that the movement breaks conducted did allow sufficient break from the mentally demanding task to allow recovery and therefore, improvements in concentration and alertness.

The optimal dose and timing of movement breaks to produce cognitive benefits in the university classroom is still unknown. One laboratory-based randomised controlled trial [ 34] designed to replicate a university learning environment used 5-min exercise breaks approximately every 15-20 min. They found students who completed exercise breaks were more attentive and focused toward the end of the lecture and performed better on a quiz than students who had no break or a non-exercise break [ 34]. In addition, a classroom-based study reported subjective benefits to enjoyment and alertness following breaks every 20 min [ 13]. However, results from our feasibility study indicate students and tutors felt movement breaks every 20 min were too frequent. Another classroom-based study conducted a movement break after 45 min of sedentary time and reported benefits remained present upon testing 20 min later [ 14]. These results are promising and suggest that less frequent breaks may still be effective due to a lasting influence while also being more practical. However, Niedermeier and colleagues [ 35] evaluated the effect of a 10-min outdoor running break 45 min into a 2-h study course. They found the running break improved visual attention, perceived attention and alertness immediately following the break compared to the control group but there were no differences in enjoyment, and effects on attention and alertness were not maintained 30 min later. Perhaps the less intense movement breaks conducted in our feasibility study allowed more interaction and increased enjoyment and therefore may have longer-lasting effects. Considering the results of our feasibility study and previous studies with university students [ 13, 14, 34, 35], future research may like to focus on the effects of breaks after 30-45 min sedentary time and monitor the duration of the benefit.

Students in our study and a previous study [ 13] expressed that movement breaks were enjoyable. As student satisfaction is increasingly important to universities because of its impact on student recruitment and retention [ 36], anything that can help with this is a positive. In addition, enjoyment may be related to academic outcomes as Pekrun's three-dimension emotional taxonomy categorises enjoyment of an activity as a positive activating emotion which is associated with better student outcomes [ 37]. An unintended consequence of movement breaks being enjoyable was that they enhanced comradery between students and increased rapport between students and tutors. As a result, students expressed feeling more able to speak up and engage in classes. Through enjoyment and engagement, movement breaks may further enhance learning and university outcomes.

The levels of physical activity completed in movement breaks are only a fraction of what is recommended in daily physical activity guidelines, however, if movement break practices flow into students' own personal study the impact may be magnified. The negative effect of high-volume sedentary behaviour (such as that seen on weekdays in university students) may be independent of physical inactivity levels [ 38], however, those who participate in moderate-vigorous intensity exercise may mitigate some risk [ 39]. Students in this study did report that they changed their study habits following participation in class which may increase the potential for reducing risks related to high volume sedentary time. Future research could involve advice and education on breaking up sedentary time outside the classroom and assess carry-over to determine the overall impact on physical activity and sedentary behaviour.

The results from this study, combined with results from other studies in university students [ 13, 14, 34, 35] suggest that movement breaks may be beneficial to university students in terms of enhancing concentration, alertness and enjoyment, reducing sedentary behaviour and increasing physical activity. University educators should consider incorporating movement breaks after 30-45 min of sedentary time in classes of more than 1-h duration. The benefits of these movement breaks have the potential to translate into improved academic performance, enhanced student satisfaction and may improve the health and wellbeing of university students. Possible barriers to implementation that need to be considered include having adequate space, considering safety, structuring breaks around class content so they do not disrupt the flow and having tutors/lecturers who are comfortable leading movement breaks. To facilitate implementation, university educators can refer to existing movement break options [ 24], consider natural breaks in the classes where there is a change in topic or focus and consider opportunities for walking small group discussions when students are asked to brainstorm or discuss a topic.

This study included both qualitative and quantitative data and included the views of students and tutors to determine if it was feasible to conduct movement breaks during university classes. Although most students participated in the classroom movement breaks, results are only generalisable to physiotherapy students who volunteered to participate and complete the outcomes which may have introduced participation bias. In addition, the study needed to be stopped early due to COVID-19 restrictions which limited recruitment. The reliability and validity of the VAS in relation to alertness, concentration and enjoyment have not been established in university students and, therefore, these results should be interpreted with caution. However, the performance properties of VAS scales, in general, are well established and our results were triangulated with qualitative interviews to increase the trustworthiness and consistency of the findings. Although not a randomised controlled trial, this feasibility study did include control classes for comparison. Another strength of the study was that it was conducted pragmatically during usual university classes so that results are relevant to those wanting to implement movement breaks in their classes.

5. Conclusions

This mixed-methods study provides preliminary evidence that classroom movement breaks are feasible, reduce sedentary behaviour and increase physical activity, alertness, concentration and enjoyment in university students. Classroom movement breaks may further improve academic performance through enhanced engagement and may further impact physical activity and sedentary behaviour through independent carry-over of habits. Results suggest that outdoor or competitive movement breaks were the most well-received by students and that a frequency of less than every 20 min may be more practical.

Appendix A

Table A1

Themes

Questions and Prompts

Introduction

Introduce the study

What we aim to find out during the interviews (e.g., general perceptions, effect of movement breaks on concentration, enjoyment and class flow)

Sedentary behaviour

Question: What impact do you think movement breaks could have on your overall sedentary behaviour?
Prompts:

If you were in a busy theory block do you think your answers would differ?

Relevance

Questions: How were the movement breaks relevant or not relevant to you?
How were the movement breaks relevant or not relevant to the subjects?

Table A2

Themes

Questions and Prompts

Introduction

Introduce the study

What we aim to find out during the interviews (e.g., general perceptions, effect of movement breaks on concentration, student participation, enjoyment and class flow)

Challenges

Question: What were some of the challenges in implementing movement breaks?

Mental alertness and enjoyment

Question: What impact (if any) do you feel movement breaks had on your classes mental alertness and enjoyment?
Prompts:

What kinds of things did you notice were different between classes with and without movement breaks?

How did the type of activity completed in a movement break impact your answer (e.g., student-led vs. tutor-led; walking vs. at desk)

Relevance

Question: How were the movement breaks relevant or not relevant to your classes?
Prompts:

What strategies did you use to make the movement breaks relevant and useful?

Author Contributions

Conceptualization, C.L.P., L.C.H., A.H. and M.D.N.; methodology, C.L.P., G.O., L.C.H., A.H. and M.D.N.; data analysis, C.L.P., L.R. and D.M.; data curation, C.L.P., L.R. and D.M.; writing-original draft preparation, C.L.P. and L.C.H.; writing-review and editing, C.L.P., G.O., L.R., D.M., L.C.H., A.H., J.L. and M.D.N. supervision, C.L.P.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Human Research and Ethics Committee of La Trobe University (HEC 19527, Approved 15 January 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study is available on request from the corresponding author. The data are not publicly available due to ethics limitations.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

15. Keating R., Ahern S., Bisgood L., Mernagh K., Nicolson G.H., Barrett E.M. Stand up, stand out. Feasibility of an active break targeting prolonged sitting in university students. J. Am. Coll. Health. 2020:1-7. doi: 10.1080/07448481.2020.1847119. [[ PubMed] [ CrossRef] Google Scholar]

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