The relationship between sustained attention and aerobic fitness in a group of young adults

Introduction

Sustained attention is a fundamental high-order function that enables to direct and focus cognitive activity on specific stimuli over prolonged periods of time, and is required in order to complete any planned activity, sequenced action, or thought. Sustained attention is therefore the basis for many types of information processing and it is crucial for healthy cognitive development that allows both, proper adaptation to environmental demands, and the capacity to modify behavior (Sarter, Givens & Bruno, 2001). Importantly, sustaining attention for long periods of time is necessary in fundamental everyday activities, such as attending to academic lessons at school or driving, and in highly responsible professional tasks like performing surgery or piloting (Di Stasi et al., 2015; Suess, Porges & Plude, 1994).

It is well known that the capacity to stay focused on a particular task is limited, which results in performance decrements with increasing time-on-task. This effect is known as the vigilance decrement and is thought to reflect the depletion of attentional resources over time (Helton & Warm, 2008; Davies & Parasuraman, 1982). Therefore, a profound understanding of the factors influencing sustained attention capacities is highly relevant to both theoretical and applied research.

In the present study, we focus on aerobic fitness as a key feature in young adults related to their ability to sustain attention. Although a substantial amount of studies have investigated the effects of aerobic fitness on cognitive performance in general (Colcombe & Kramer, 2003; Hillman, Erickson & Kramer, 2008), the relation between fitness and sustained attention has not received proper attention by exercise and cognition researchers. Here, we addressed this issue by comparing behavioral performance of high-fit and low-fit young adults in two sustained attention tasks.

In general, the limited previous evidence has indicated that aerobic fitness and sustained attention are positively related across the life span (Bunce, 2001; Prakash et al., 2011). For instance, in a recent study, Getzmann and collaborators (2013) found a higher susceptibility to distraction over time for inactive seniors than their active counterparts. Similar outcomes have been seen in children (Chaddock et al., 2012; Pontifex et al., 2012). With respect to young adults, to the best of our knowledge, there are only three published studies (Luque-Casado et al., 2013; Luque-Casado et al., 2016a; Luque-Casado et al., 2016b) that have reported significant differences in sustained attention capacities as a function of aerobic fitness (see Bunce, Barrowclough & Morris, 1996, for a null result). Luque-Casado and collaborators (2013) reported the first direct evidence of a selective association between aerobic fitness and sustained attention in young adults. They found overall faster responses in a short (i.e., 10 min) Psychomotor vigilance task (PVT) in high fit as compared to lower-fit participants while no differences were observed in a temporal orienting task and a duration perception task. Recently, the same research group (Luque-Casado et al., 2016a; Luque-Casado et al., 2016b) has extended their findings identifying potential neural and autonomic physiological mechanisms underlying the fitness-related improvements in vigilance performance in a 60′ version of the PVT. Their findings showed that a higher level of fitness was related to a better behavioural performance (i.e., in the first half of the task), that was accompanied by larger amplitude in the contingent negative variation (CNV) potential and enhanced cardiac orienting reflex. Crucially, high-fit individuals also maintained larger P3 amplitude throughout the task compared to low-fit, who showed a reduction in the P3 magnitude over time. The authors finally concluded that a higher level of fitness level was related to electrophysiological activity suggestive of better ability to allocate attentional resources over time and a greater attentive preparatory state.

The PVT used by Luque-Casado and collaborators (2013; 2016a; 2016b) is a sensitive and reliable sustained attention reaction time (RT) task that requires participants to react as fast as possible to the onset of a visual (or auditory) stimulus presented on every trial and that occurs at random inter-stimulus intervals. The task is characterized by the high temporal uncertainty of stimuli and the low learning effects (Dinges et al., 1997; Jewett & Kronauer, 1999). The PVT has been commonly used to study the vigilance decrement by the repeated administration of the task in different moments of the experimental session (Arnal et al., 2015; Van Dongen et al., 2003).

Crucially, together with RT tasks like the PVT, sustained attention has been measured by means of accuracy (ACC) tasks, typically involving the presentation of infrequent and unexpected targets presented among frequent non-target stimuli for relatively long periods of time (Bunce, 2001; Getzmann, Falkenstein & Gajewski, 2013; Yagi et al., 1999; Pontifex et al., 2015). In fact, the first approximation to sustained attention study using this type of task was the Mackworth Clock test (Mackworth, 1948). In this task, participants watched a second by second clock that occasionally click forward two seconds instead of one. Participants were asked to press a button whenever they notice the rare “two-seconds” event. Since this first study, ACC oddball tasks have been widely used in cognitive science to study sustained attention (Parasuraman & Mouloua, 1987; García-Larrea, Lukaszewicz & Mauguiére, 1992). These unspeeded tasks reduce motor demands and increase the uncertainty of the target onset. In fact, together with temporal uncertainty, the low probability of target appearance has been shown to be one of the major factors to tax sustained attention (Parasuraman & Mouloua, 1987). It is important to note that this is in line with the traditional view of sustained attention as a high-order cognitive function that enables the accurate detection of infrequent and unexpected targets presented in a sequence of frequent non-targets (Norman & Shallice, 1986; Stuss et al., 1995).

Luque-Casado and collaborators (2013; 2016a; 2016b) findings were based on the results from a RT task (i.e., PVT) where participants have to respond to every stimulus. Thus, an important issue remains unsolved: the potential association between aerobic fitness and the ability to detect and respond to relevant non-frequent target stimuli ignoring frequent irrelevant non-target stimuli, which, as noted above, has been considered one of the critical features to tax sustained attention. That is, there is still the question of whether the superior performance of high-fit individuals with respect to low-fit participants observed in an RT sustained attention task like the PVT would be also revealed in an ACC oddball task.

To address the aforementioned shortcoming of previous research, here, we compared performance of two groups of young adults differentiated in terms of aerobic fitness (high-fit and low-fit) in the RT PVT and an ACC oddball task. The 27′ duration of the oddball task allowed us to investigate the vigilance decrement as a function of aerobic fitness. It is important to note that previous studies have found significant vigilance decrements with even shorter task durations. For example, Nuechterlein and collaborators (1983) examined sustained attention performance in an oddball paradigm lasting only 8 min, and found a marked decrement in sensitivity over time. More importantly, Ariga & Lleras (2011) found a significant vigilance decrement in young adults after the first 10′ of the same oddball task used in the present study. A 5′ PVT presented before and after the oddball task was used to investigate the changes in RT performance as a function of time and aerobic fitness, and to try and replicate Luque-Casado et al.’s (2013; 2016a) previous results. A shorter (with respect to Luque-Casado et al., 2013) version of the PVT was used in order not overload to the experimental session, as the main focus of the study was the oddball task. Note also that previous research has tested the reliability of this shorter version of the PVT (Loh et al., 2004; Roach, Dawson & Lamond, 2006).

We expected that high-fit young adults would be faster in the PVT with respect to the low-fit participants. Similarly, we hypothesized that low-fit young adults would exhibit a larger accuracy and RT impairment along the time in both tasks than their high-fit counterparts.

Materials & Method

Results

Descriptive and fitness data

The t-tests for independent samples revealed significant differences between groups in all the incremental test parameters (i.e., maximum power output (W), t(42) = 5.437, p < .001, d = 1.67, relative power output (W kg⁻¹), t(42) = 5.488, p < .001, d = 1.69, VO₂ (mL min⁻¹ kg⁻¹), t(42) = 4.595, p < .001, d = 1.41, and HR (bpm), t(42) =  − 3.987, p < .001), d =  − 1.25. Data showed clear evidence of the difference in aerobic fitness between groups (see Table 1). There were no statistically significant differences between groups in any other descriptive data (all ps ≥ .5).

PVT

The ANOVA of participants’ mean RTs revealed a marginally significant main effect of aerobic fitness, F(1, 42) = 3.663, p = .052, ηp² = .08. The high-fit group responded faster overall than the low-fit group (323 ± 24 ms and 346 ± 46 ms, respectively). Neither the main effect of session, F < 1, nor the interaction between both factors reached statistical significance, F < 1.

Table 1:

Mean and standard deviation of descriptive and fitness data for the high-fit and low-fit groups.

	High-fit	Low-fit
Anthropometrical characteristics
Sample size (n)	22 (11 females)	22 (11 females)
Age (years)	22.3 ± 3.5	22.5 ± 3.3
Height (cm)	170 ± 7.8	168 ± 10.8
Weight (kg)	66.2 ± 9.9	67.2 ± 16.8
BMI (kg/m2)	22.7 ± 2.7	23.3 ± 3.9
Incremental test parameters
Power max at VAT (W)	195 ± 70.4	103.8 ± 34.9
Relative power at VAT (W/kg)	2.9 ± 1.1	1.6 ± 0.5
VO2at VAT (ml/kg/min)	35.9 ± 13.9	20.8 ± 6.3
Heart rate (bpm)	61.7 ± 12.7	79.2 ± 15.8

DOI: 10.7717/peerj.3831/table-1

Oddball task

Hits trials.

The analysis of ACC (Hits) for target-trials yielded a significant main effect of time-on-task, F(1, 42) = 14.207, p < .001, ηp² = .25, that was better qualified by the significant interaction between aerobic fitness (high-fit, low-fit) and time-on-task (block 1, block 2), F(1, 42) = 4.502, p = .040, ηp² = .09; see Fig. 1). The t-test for dependent samples between block 1 and block 2 reached significance for the low-fit group, t(21) = 4.069, p < .001, d = 0.48, showing a reduced ACC over time (81.2 ± 17 % and 72.4 ± 18 %, in block 1 and block 2, respectively). In contrast, the difference between blocks was not significant for the high-fit group, t(21) = 1.194, p = .246, d = 0.17), (82.3 ± 12 % and 80.1 ± 15 %, in block 1 and block 2, respectively). The analysis of RTs for target-trials yielded no significant main effects of time-on-task, F < 1, and aerobic fitness, F(1, 42) = 2.855, p = .099, ηp² = .06. The interaction between both factors was not significant either, F < 1.

Commission errors.

The maintenance of accuracy in target-trials during the task showed by high-fit participants could have been due to a mere higher response rate than low-fit participants. However, this would have resulted also in more commission errors (responses to non-targets). To address this issue, we analyzed participants’ mean ACC (commission errors) to non-targets. The ANOVA showed a main effect of time-on-task, F(1, 42) = 7.579, p = .009, ηp² = .15, with participants being more accurate in block 2 (96.3 ± 12% and 92.3 ± 11%, for high-fit and low-fit, respectively) than in block 1 (95.1 ± 12% and 88.8 ± 15%, for high-fit and low-fit, respectively). The main effect of aerobic fitness failed to reach statistical significance, F(1, 42) = 3.680, p = .062, ηp² = .08. There was no significant interaction between both factors F(1, 42) = 1.549, p = .220, ηp² = .03.

Discussion

In the present study, we investigated the role of aerobic fitness in the sustained attention capacity of young adults. Towards this aim, two groups of participants (high-fit and low-fit) completed the PVT and an oddball task.

The results of the PVT showed that high-fit young adults were faster than low-fit, replicating previous research (Luque-Casado et al., 2013; Luque-Casado et al., 2016a; Luque-Casado et al., 2016b). Interestingly, even though the statistical analysis showed a marginal (p = .053) effect, the 23 ms difference between high-fit and low-fit participants was larger than the 19 ms reported in Luque-Casado et al. (2013) and the 12 ms (in the first 10′ of the task) reported in Luque-Casado et al. (2016a). Taken together, the present and previous research from our laboratory supports the PVT as a suitable tool to measure group differences in terms of RT performance in a vigilance task involving high temporal uncertainty of target onset. Note also, that previous studies (e.g., Dorrian, Rogers & Dinges, 2005; Drummond et al., 2005; Lim & Dinges, 2008) showed the recruitment of the sustained attention brain network during the PVT, highlighting the sensitivity of the task to vigilance demands, over and above the time-on-task effect. Indeed, Drummond et al. (2005) found different activation patterns of cortical and subcortical brain regions during the fastest RTs of a PVT, relative to the slowest RTs. They suggested that the activity of these regions might reflect top-down modulation processes related to the capacity to maintain the focus on the task. In the present study, high-fit and low-fit participants appear to differ in their overall RT performance, but not in the effect of session. The lack of a session effect in the PVT might be due to the time break before the second PVT and even because of the presence of the oddball task between PVT sessions, which might have provoked a sort of “mind reset” (cf. Ariga & Lleras, 2011), preventing vigilance degradation with respect to the first PVT. Nevertheless, it is also possible that the PVT employed in the current manuscript was not demanding sustained attention enough to induce the RT increment from the first to the second PVT session.

Importantly, in the oddball task, the high-fit group maintained their ACC rate over the whole duration of the task, while the low-fit group suffered a significant decline of response ACC over time. This finding suggests that aerobic fitness is related to the capacity to focus on relevant infrequent stimuli ignoring irrelevant frequent stimuli. Interestingly, the lack of group differences in RT in the oddball task seems to indicate that the greater sustained attention capacity showed by high-fit participants was irrespective of the nature of the task, i.e., high-fit young adults seem to adapt their performance to the goals of each task, either speed or ACC. Note, though, that the task instruction (stressing ACC over RT) in the oddball task might also explain the absence of RT difference between groups.

The present findings could be interpreted in the context of the “cardiovascular hypothesis” which proposes that exercise, particularly aerobic exercise, results in measurable gains in cardiorespiratory fitness, leads to benefits at the cognitive and brain levels. These gains refer to an increased ability of the heart to deliver oxygen to the working muscles which is thought to be associated with changes in cerebral structure (Colcombe & Kramer, 2003), cerebral blood flow (Endres et al., 2003), and a brain-derived neurotrophic factor [BDNF, IGF-1] (Vaynman, Ying & Gomez-Pinilla, 2003). In turn, all these changes have themselves been shown to be associated with cognitive performance (Vaynman, Ying & Gomez-Pinilla, 2004; Hillman, Erickson & Kramer, 2008; Voss et al., 2013; McMorris, 2016). Thus, according to the cardiovascular fitness hypothesis, changes in aerobic fitness are necessary for the cognitive benefits of physical activity to be observed. Certainly, the higher levels of aerobic fitness of high-fit young adults in our study may be associated with changes in underlying physiological mechanisms that result in a greater capacity to maintain attention over time compared to low-fit young adults. However, it is important to note that several meta-analyses of available studies seriously question this hypothesis (Angevaren et al., 2008; Verburgh et al., 2014; Young et al., 2015). This means that the exact mechanisms that produce the observed enhanced cognitive performance in physically fit individuals are still unknown.

At this point, it is also important to note that the purpose of this study was not to establish a causal-effect relationship, but rather to investigate the potential association between sustained attention and aerobic fitness. Randomized controlled trials are needed to establish that cause–effect relationship with respect to sustained attention and aerobic fitness. Moreover, we believe that future studies should investigate complex organ interactions (e.g., brain-heart communication) as a mechanism contributing to the enhanced cognitive and brain function in physically fit individuals (see Perakakis et al., 2017, for a first approach to this issue).

Conclusions

To conclude, the current research contributes to further understand the positive relation between aerobic fitness and sustained attention capacity in young adults. Importantly, a profound knowledge of the factors that support and enhance sustained attention is especially critical for public health, as a sedentary lifestyle has been blamed for so many diseases and also deterioration in cognitive function and performance of many daily life activities.