Effects of acute exercise at different intensities on fine motor‐cognitive dual‐task performance while walking: A functional near‐infrared spectroscopy study

The present study aimed to explore the neural correlates underpinning the acute effects of HIIE compared to MCE on the performance of the dTMT, a fine motor-cognitive task, while walking on a treadmill. Healthy young participants performed the dTMT while walking at a fixed speed of 5 km/h before and after acute exercise, while cerebral oxygenation was recorded by fNIRS over the prefrontal and the motor cortex. Notably, this was the first study to examine such effects during exercise execution considering different training intensities compared to previous studies in which the cognitive task was performed while sitting at a table (Byun et al., 2014; Hyodo et al., 2012; Kujach et al., 2018; Yanagisawa et al., 2010). We found an overall increase in task performance at the behavioral level (dTMTM: 25.3%, dTMTA: 15.1%, dTMTB: 17.7%) independent of training intensity. Contrary to our hypothesis, this is initially surprising, as different performance effects are usually found for HIIE than MCE (Kamijo et al., 2004, 2007; Labelle et al., 2013). However, the results of a meta-analysis by Lambourne and Tomporowski (2010) also show that cognitive impairment occurs only during the first 20 min of exercise.

Similarly, the studies included in Chang et al. (2012) showed no effects during the first 10 min of exercise, an adverse effect between 11 and 20 min, and positive effects after 20 min. However, when we considered the hemodynamic changes induced by each type of exercise, we found different effects between the two groups. These results suggest that the distribution of neural resources differed according to training intensity to maintain the same task performance level.

4.1 Physiological effects of an acute bout of HIIE or MCE

The two types of exercise performed in this study induced different HR changes and resulted in different RPEs with higher values in the HIIE compared to the MCE group (Yanagisawa et al., 2010). HIIE protocols are generally more physically demanding than MCE protocols, and our exercise protocols effectively induced such differential exertion. Moreover, both measures, HR and RPE, were positively correlated, suggesting that objective and subjective indices of exertion are comparable. Similar effects have been found in a previous study by Green et al. (2006), in which the RPE scale was sensitively correlated with acute changes in HR during HIIE. However, the MPSTEFS did not show any effects of exercise. Because its measurement was not taken immediately after the exercise protocol, but only after the second dTMT, participants already had some time to recover (approximately 12 min in total). For this reason, it may be challenging to confirm the changes in physical and mental states before and after exercise using this scale.

4.2 Cognitive effects of an acute bout of HIIE or MCE

To date, a large number of studies have examined the beneficial effects of acute exercise on cognitive function at the behavioral level immediately after exercise (Chang et al., 2012; Kujach et al., 2018; Lambourne & Tomporowski, 2010; Mekari et al., 2020; Moreau & Chou, 2019; Tsukamoto et al., 2016). Surprisingly, our study found that both types of exercise (intensities) had a positive effect on dTMT performance (dTMTM: 25.3%, dTMTA: 15.1%, dTMTB: 17.7%).

One of the factors that may explain the gains in cognitive performance is the timing of the cognitive tasks' performance (i.e., before, during, or immediately after exercise). Most studies measure cognitive performance before and after training, whereas fewer studies have examined what happens to cognitive performance during training, which is more relevant to sport. Only two of the previous meta-analyses (Chang et al., 2012; Lambourne & Tomporowski, 2010) described impaired cognitive performance during exercise with a small effect size. However, it was also shown that at least 20 min of training is required to observe an improvement in cognitive performance (Chang et al., 2012).

Another factor lies in the motor task itself: Our results are similar to the findings of Tsukamoto et al. (2016), who also demonstrated an improvement in cognitive performance for both types of exercise. However, unlike in our study, participants performed the protocol on a bicycle ergometer. Exercise on a treadmill was found to be more likely to impair cognitive functions, while cycling tended to enhance cognitive functions (Lambourne & Tomporowski, 2010) due to the varying degrees to which attentional resources are used to control body movements (Soga et al., 2015). A study by Penati et al. (2020) might help to explain the inconsistent effects: They compared performance on a cognitive task during overground self-paced walking to fixed-speed walking on a treadmill and demonstrated better performance when walking during fixed-speed walking. In this case, walking is an almost automatic process without external environmental changes (Wrightson & Smeeton, 2017), prioritizing a cognitive task over walking, leading to higher cognitive performance.

Another possible explanation for this observation could relate to motivational aspects underlying motor and cognitive performance. It has been shown in other research that motivation increases during endurance training due to proximity to the goal and progress relative to goal pursuit (Schiphof-Godart & Hettinga, 2017).

4.3 Exercise-enhanced hemodynamic response

We found an overall increase in HbO concentration in all ROIs after exercise. In all frontal ROIs, this effect was more robust in the HIIE group than in the MCE group. Some authors have argued that increases in CBF during physical exertion lead to this higher neuronal activity (Ogoh & Ainslie, 2009; Steventon et al., 2020; Thomas et al., 1989). Our results contradict Moriarty et al. (2019)'s findings, who suggested that the overall blood flow of the brain remains more or less constant during acute aerobic exercise. Dietrich and Audiffren (2011) proposed a shift in CBF from areas needed for cognitive function to areas needed for motor control and maintenance of vital function. We did not find a shift from frontal to motor areas but rather a general increase in hemodynamic activation. Of course, there could be a shift from other areas not involved in our task. However, because we did not measure the changes in HbO throughout the brain, we can neither confirm nor exclude this hypothesis. Furthermore, the hypofrontality hypothesis of reticular activation by Dietrich and Audiffren (2011) postulated effects during exercise and not after exercise.

Methodologically, performing the dTMT while running on a treadmill is challenging to implement. Instead, we chose to perform the dTMT during walking after the training intervention. Many studies instead examined exercise effects while sitting after exercise (Bediz et al., 2016; Byun et al., 2014; Ji et al., 2019; Kujach et al., 2018; Yanagisawa et al., 2010). Our study was the first, to our knowledge, to investigate the effects on cognition during walking after exercise interventions with different intensities. It is well known that walking is a complex process that relies on cognitive and executive functions (Schott, 2019; Sheridan & Hausdorff, 2007; Yogev-Seligmann et al., 2008). Interferences resulting from the simultaneous performance of other tasks can cause motor-cognitive interference that can impair overall performance. This effect is more substantial with the increasing complexity of motor and/or tasks (Klotzbier & Schott, 2017; Li et al., 2018; Schott, 2019; Schott et al., 2016). However, it has also been shown that DT training may benefit not only motor performance and physiological health but also cognitive functioning (Ghai et al., 2017). Therefore, studying the effects of different types of exercise protocols on motor-cognitive performance in dual- or even multi-task situations may improve performance in various situations in daily life, elderly care, or athlete's development.

Since walking, even at fixed speed on a treadmill, is an automated process leading to low prefrontal activation (Thumm et al., 2018), higher increases in HbO in frontal areas for the HIIE group can be interpreted as a higher need for resources for postural control after intensive training (González-Fernández et al., 2017). Thus, it can be surmised that the cognitive tasks can only be fully attended to if the corresponding postural responses had been initiated (or inhibited). Thus, in the context of the present study, HIIE might pose a greater threat to postural stability, resulting in poorer cognitive performance. Indeed, HIIE participants had a visibly higher effort to maintain balance while walking on the treadmill during dTMT than MCE participants. Nevertheless, participants were not impaired at the behavioral level and even improved their performance in the dTMT in the same way as the MCE group. This could be due to our participants' high cardiorespiratory fitness level, which was excellent in 41.4%, good in 37.9%, and above average in 17.2% of all cases (ACSM, 2013; Heywood, 2006). A meta-analysis by Chang et al. (2012) on the effects of acute exercise on cognitive performance pointed to the fitness level of participants as a significant mediator. Based on the neurotrophic hypothesis (Stillman et al., 2016), an increase of neuronal growth factors (BDNF, insulin-like growth factor 1, vascular endothelial growth factor) through regular exercise leads to an upregulation of neurogenesis, synaptogenesis, gliogenesis, and angiogenesis. This may lead to structural changes in the brain through improved cerebrovascular function and perfusion as a consequence of habitual physical activity (Schott, 2020). Zoladz et al. (2008) demonstrated that high resting BDNF levels occur in well-trained participants with high VO2max compared to untrained participants with low VO2max. Notably, Ainslie et al. (2008) also demonstrated that high cardiorespiratory fitness levels were associated with increases in CBF. Our participants' high fitness levels may have induced positive structural and functional adaptations in the brain that allowed them to perform the task without any difficulty, using improved cerebrovascular functions that respond effectively to different types of exercise. Therefore, the higher frontal activation pattern of the HIIE group when walking during dTMT performance suggests a higher recruitment of neural resources for increased demands on postural control to ensure safe walking on the treadmill.

Another explanation could be the rebound effect of cortical activation during postexercise (Basso & Suzuki, 2017; Moriarty et al., 2019). To confirm this, one would have to record brain activation not only after but also during exercise, which represents a methodological challenge when running on a treadmill, even with fNIRS, which is usually considered less sensitive to motor artifacts (Wolff, 2017). Here, exercise on a bicycle ergometer, where the upper body moves less than on a treadmill, might be more promising.

There is another major difference between our study and most other studies that use a cognitive task to measure motor-cognitive performance in DT situations: While most studies used a cognitive task that requires the participant to respond quickly to a given stimulus (with reaction time as the dependent parameter), we used a continuous cognitive task performed in 30-s blocks during walking. This block design leads to additional motor-cognitive demands that increase over time. We examined the effects of increasing load on motor-cognitive performance by comparing the activation pattern for two time points within these 30-s blocks. We found a main effect of these time windows for all ROIs and showed that T2 exhibited higher activation than T1, indicating an increase in activation over the block duration as expected due to increasing task demands. This effect also varied for different dTMT conditions. For example, there was a main effect of task condition in the frontal cortex that showed an increase of activation from dTMT-M to dTMT-B. This can be attributed to the increasing cognitive demands and was also confirmed in previous studies (Hagen et al., 2014; Müller et al., 2014). The dTMT-B showed higher activation in frontal areas than dTMT-M and dTMT-A. This finding is related to the frontal lobe's involvement in executive functions and cognitive control processes (Shibuya-Tayoshi et al., 2007). In general, the TMT-A consists of primarily visual search and processing speed (Ríos Lago et al., 2004; Sánchez-Cubillo et al., 2009), whereas the TMT-B requires more complex cognitive abilities such as cognitive flexibility and inhibition (Arbuthnott & Frank, 2000; Kortte et al., 2002).

After the exercise interventions, we found an effect of type of exercise in all brain areas measured in the present study, with the most substantial increases irrespective of task condition at T2 for the HIIE group, resulting in significant differences between groups with effect sizes of at least medium or greater values in all task conditions of all ROIs (Cohens d, greater than 0.5). Among them, significant increases were found in the HIIE group compared to MCE in the frontal lobe, especially FPA and DLPFC, which play a crucial role in the cognitive control of motor behavior for the performance of the dTMT after exercise (Cieslik et al., 2013). According to Kahneman (1973), it is assumed that our brain system has limited attention resources that can be used flexibly in a given situation and influence the DT performance. Considering this, the attentional resources for performing the dTMT during a task with high demands on postural control during HIIE might be limited due to the exercise intensity. However, due to our participants' high fitness level, the increase in activation of all brain regions measured in our study was able to meet the demands of performing the fine- and the gross-motor task, which can be attributed to the provision of additional resources. Overall, the increase in cognitive performance after two types of exercise (intensity) can be considered as different resource allocation, which can be explained as a response to exercise by effective resource allocation in young adults due to the high fitness level.

One should interpret our findings in light of the study limitations. First, we did not have a control group that completed only the dTMT under walking conditions. Hence, additional possible explanations for the between-test changes could be learning effects or changes in participant motivation and stress levels following the test. However, in a pilot study in our laboratory, we were able to show that learning effects do not occur before the 6th block of testing. Therefore, we have grounds to attribute our findings to the different exercise protocols. For future studies, a relevant control condition could consist of very low exercise intensity or simply standing on the treadmill. We also acknowledge that our sample size was relatively small, although similar in size to most acute exercise studies in the literature (Chang et al., 2012; Lambourne & Tomporowski, 2010); thus, larger-scale double-blinded experiments are needed to confirm our findings. Another limitation in the present study is that since HIIE and MCE can increase body blood flow, the head surface's skin blood flow is also increased to test it. Physiological noise, such as blood flow in the extracerebral compartment (Scholkmann et al., 2014), may result in false-positive results (Tachtsidis & Scholkmann, 2016). Thus, a systematic review by Herold et al., (2018) recommended monitoring heart rate to support the interpretation of cortical hemodynamic response measured with fNIRS. In the present study, we found no significant correlation between HR and cortical hemodynamic response after exercise in both the HIIT group and the MCE group, so these two factors can be considered independently. Notably, skin blood flow may drop rapidly immediately after exercise (Endo et al., 2013), which may have been achieved in our study during 2-min walking without a task. For this reason, the interpretation of the effects of exercise with two different intensities on neural correlates during a fine motor-cognitive task seems reasonable.

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