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Abstract

Objective:

This study aimed to analyse the acute effects of active breaks (AB) on vigilance among sports college students in Portugal.

Methods:

Thirty-two students following a sports degree programme (24 males and 8 females, aged 20.7 ± 2.5 years) voluntarily participated in this crossover randomised study. All participants completed a psychomotor vigilance task (PVT), a reaction time task widely used to assess sustained attention and alertness, during a regular class session. Afterwards, they attended a 60-minute lecture before repeating the PVT. A 10-minute break ensued: half the participants remained seated passively (control condition), while the other half performed moderate-intensity individual calisthenic exercises (AB condition). A three-way repeated-measures analysis of variance (ANOVA) was conducted to examine the effects of condition (control vs AB), time-on-task (minutes 1–5) and assessment moment (baseline, pre, post).

Results:

No significant main effects of condition were found; however, a significant three-way interaction between condition, time and assessment moment (p = .025) was evident. Post hoc comparisons revealed a significant increase in reaction time from minute 3 to minute 5 during the post-intervention PVT in the AB condition, suggesting potential fatigue or reduced vigilance over time.

Conclusion:

These findings indicate that ABs may transiently affect vigilance, but their effects may not persist during prolonged tasks. Further research is warranted to determine the optimal parameters for implementing ABs in an academic context, particularly among physically active populations.

Keywords

Attention brain performance cognitive performance physical activity vigilance

Introduction

In higher education, teaching typically involves the division of course units into 2- or 3-hour lecture-style sessions focused on different issues. This traditional approach to learning often fosters a passive attitude among students, negatively affecting their attention span and ability to retain information (Windschitl, 1999; Young et al., 2009). Such passivity hinders deeper learning (Cejnar, 2015; Lammers and Murphy, 2002) and may lead to reduced concentration levels (Young et al., 2009). Studies suggest that attention tends to wane after 10–30 minutes on a task (Horgan, 2003; Stuart and Rutherford, 1978), due to limitations in central executive function (Diamond, 2015). Attention is not a singular process however but comprises various functions, including alertness, sustained attention, alternating attention, divided attention and selective attention (Cid and Ferro, 2017). The capacity to maintain attention on a task over time is known as vigilance (Davies and Parasuraman, 1982; Parasuraman, 1988) relying on selective attention to appropriately respond (quickly and accurately) to relevant stimuli (Oken et al., 2006; Sarter et al., 2001).

Given the challenge of sustaining high levels of vigilance¹ over extended periods, alongside its significance for the learning process (Betts et al., 2006), various researchers propose varying the level of stimulation during lectures to counteract vigilance decline (Young et al., 2009). Research on this topic has indicated the potential benefits of incorporating brief periods of physical exercise (PE) to help restore vigilance. Recent advances in neuroscience have considerably enhanced understanding of the links between PE, cognitive performance and brain structure and function (Donnelly et al., 2016). Studies examining these relationships have shown promising results, with no evidence of detrimental effects (Donnelly et al., 2016; Erickson et al., 2019; Valkenborghs et al., 2019). For instance, research has demonstrated that attentional performance following a single session of PE is associated with increased activation in attention control-related brain regions such as middle and superior frontal gyrus and the superior parietal lobe (Basso and Suzuki, 2017).

In addition, a single, short session of PE has been linked to changes in brainwave activation (Crabbe and Dishman, 2004). Each type of brainwave is linked to specific states of consciousness and mood (Allen et al., 2004; Crabbe and Dishman, 2004). Beta waves typically occur during normal daily activity and in situations requiring mental focus or involving stress, whereas alpha waves are associated with wakeful, relaxation, effortless engagement and heightened alertness. Notably, increased alpha activity following PE has been correlated with reduced cortical activation, reflecting states of fatigue, relaxation or decreased anxiety (Boutcher, 1993; Dalsgaard and Secher, 2007). Furthermore, in a small-scale preliminary study (Contreras-Jordán et al., 2022), activation in frontal, parietal and occipitotemporal regions was observed during PE, suggesting a potential enhancement in cognitive performance.

Research conducted with high school students and young adults has demonstrated the positive effects of active breaks on vigilance. For instance, Tine (2014) used the D2 test of attention and found that participants aged 17–21 years showed improved selective attention after completing a 12-minute aerobic exercise active breaks. Akatsuka et al. (2015), employing a go/no-go task, observed enhanced inhibitory control after a 15-minute session of moderate-intensity aerobic active breaks in a group of 10 participants (mean age of 19.8 years). Similarly, Rosa-Guillamón and Carrillo-López (2023) reported improved attention outcomes in two distinct 10-minute Active-Breaks groups compared to a control group, suggesting that active breaks conducted outside the classroom may yield superior results. However, all these studies allocated participants to separate control and experimental groups, preventing the same individuals from experiencing both conditions, as would occur in crossover studies. Moreover, the strongest evidence supporting cognitive benefits of exercise has emerged from studies involving children aged 6–13 years (Donnelly et al., 2016; Erickson et al., 2019; Sibley and Etnier, 2003), adults over 50 years (Angevaren et al., 2008; Erickson et al., 2019) and individuals with dementia or cognitive impairments (Erickson et al., 2019; Karssemeijer et al., 2017; Law et al., 2020). As a result, substantial gaps remain in understanding how acute PE affects cognition, particularly during early childhood (under 6 years), adolescence (approximately 14–17 years) and young-to-middle adulthood (18–50 years) (Erickson et al., 2019; Stillman et al., 2020).

While there is generally a trend towards positive associations between active breaks and cognitive performance, findings have not been consistently favourable, and positive outcomes often vary between studies (Contreras-Jordán et al., 2022; Donnelly et al., 2016). Indeed, a systematic review together with a meta-analysis on this topic (Infantes-Paniagua et al., 2021) concluded that there is insufficient evidence supporting the positive effects of active breaks on students’ attention. The heterogeneity of study designs and measurement approaches, as well as the limited number of studies conducted in educational settings, likely account for the inconclusive results. Nonetheless, investigating the relationship between active breaks and cognitive performance is particularly important in the further and higher education systems, where a significant proportion of the day is devoted to cognitively demanding activities (Sibley and Etnier, 2003). Furthermore, students of this age are typically nearing the completion of their cognitive and hormonal maturation, thereby minimising any potential confounding influences arising from pubertal development (Best, 2010). The aim of the present study was to employ a crossover design to examine the impact of moderate to high-intensity aerobic active breaks on vigilance in students enrolled in higher education. It was hypothesised that students would demonstrate greater vigilance under active breaks conditions than under a control condition.

Methods

Participants

Seventy third-year students enrolled in a Sport and Leisure degree programme at the Polytechnic Institute of Viana do Castelo (Portugal) were invited to participate in this study. However, 38 declined to take part at the outset, leaving 32 (24 young men and 8 women, mean age 20.7 ± 2.5 years) who agreed to participate (see Figure 1).

Figure 1.

Flowchart of the included sample.

The following criteria determined eligibility for inclusion: (1) provision of signed informed consent; (2) good health status, with no medical contraindications or musculoskeletal injuries; (3) regular attendance at the first author’s classes and (4) participation in all evaluation sessions. Participants were excluded if they scored below 4 on any item of the Hooper index.

The aims, assessments and procedures were clearly explained to all participants, emphasising voluntarily the nature of participation and the right to withdraw at any time. Anonymity and confidentiality were also guaranteed. The study complied with the ethical standards outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Instituto Politécnico de Viana do Castelo (CECSVS2024/02/iv).

Measures

The Hooper index

The Hooper questionnaire, developed by Sue Hooper et al. (1995), was created to monitor overtraining and recovery. It has since become a widely used tool to assess athletes’ and students’ well-being, providing insight into levels of fatigue, stress and recovery (Clemente et al., 2020a, 2020b). The questionnaire evaluates fatigue, sleep, stress and soreness (DOMS – delayed-onset muscle soreness), each of which is rated on a seven-point Likert-type scale (1–7). For stress, fatigue and muscle soreness, a score of 1 indicates very low levels, and 7 indicates very high levels, whereas for sleep quality, 1 represents very good sleep, and 7 represents very poor sleep. Thus, higher overall scores reflect greater fatigue and reduced well-being.

In the present study, the Hooper Questionnaire was administered at the beginning of each experimental session, defined as a 60-minute lecture period forming part of participants’ regular academic schedule. A time within students’ regular academic schedule was chosen so that all participants were in an adequate state of recovery from physical activity prior to performing the vigilance task. Responses were checked only to verify that no participant reported a value below 4 on any of the four items. This cutoff point, although not specified in the original Hooper Index manual (Hooper and Mackinnon, 1995), was adopted based on its more general application in sports and exercise research (Saw et al., 2016), where values below 4 are generally interpreted as indicative of good wellness and readiness to perform. This conservative threshold was used to ensure that participants began each session in an adequate recovery condition, minimising potential confounding effects due to fatigue or poor recovery.

The Rate of Perceived Exertion

Most methods used to quantify effort are based on heart rate measurement. However, Foster et al. (2001) have proposed an alternative and widely validated approach known as session Rating of Perceived Exertion (RPE). The method involves rating the overall intensity of an exercise bout using a modified 10-point scale, offering a simple, individualised and mode-independent way of quantifying training load without the need for expensive equipment (Rodríguez-Marroyo and Antoñan, 2015). The session RPE method has been validated across a wide range of sports, age groups and levels of expertise (Haddad et al., 2017), demonstrating strong correlations with objective physiological measures such as the percentage of time at heart rate peak, the percentage of heart rate reserve and the percentage of peak maximal oxygen uptake (Herman et al., 2006). It has proven effective for monitoring steady-state, interval and team sport exercises, as well as different resistance training protocols aimed at strength, hypertrophy and power (Singh et al., 2007). In this study, the 10-point Foster et al. (2001) scale was used to assess responses to the question ‘how hard was the training session?’. Higher scores indicated greater perceived exertion.

Psychomotor Vigilance Task

The psychomotor vigilance task was administered using a smartphone application (Vigilance Buddy). All the smartphones used were identical (iPhone SE), and the application operated in full-screen mode to eliminate potential distractions. The task consisted of a visual stimulus–response task, in which a timer appeared on the screen following a random interval ranging from 2,000 ms to 10,000 ms. Participants were instructed to tap the centre of the screen as soon as the timer began. Each test comprised a single 5-minute block, with the number of responses per participant being determined by their individual reaction speed. The mean reaction times recorded for each participant were used for subsequent analysis. The psychomotor vigilance task has been shown to be a valid and reliable measure of sustained attention and reaction time across a range of age groups (Arsintescu et al., 2019; Kay et al., 2013).

Procedure

The present study employed a crossover design in which the same 32 students participated in both the control condition and the experimental condition (16 individuals per condition, as illustrated in Figure 2). During the first session, the aims and procedures of the study were described, and consent forms were distributed. Students were also given the opportunity to ask questions and familiarise themselves with the psychomotor vigilance task. This task was completed using smartphones as described earlier (eight devices were available and used simultaneously across classes). By the time data collection commenced, care was taken to ensure that all participating students were comfortable with the app and fully understood the task requirements.

Figure 2.

Crossover study design.

Data collection took place over two class sessions of 3 hours each, held 1 week apart. Within each class, only the students who had consented to participate in the study remained in the classroom during the break period. The non-participating students followed their usual routine outside the classroom. In the first of the two experimental sessions, half of the participating students were randomly assigned to the control condition, in which they took a passive 10-minute seated break. The other half formed the experimental condition and engaged in a 10-minute active break consisting of moderate-to-vigorous calisthenic exercises performed in the classroom. Random allocation was achieved by drawing lots: pieces of paper with the participants’ names were placed in a bag, and 16 names were drawn to determine the group that would begin with the active break.

In the second session, the groups switched conditions, so that each participant experienced both the passive and the active-break conditions. This crossover approach helped to minimise potential order effects. All sessions were conducted by the first author (AFS), who holds a doctoral degree in sports sciences. It is important to note, however, that although randomisation and counterbalancing took place, the possibility of residual order effects cannot be excluded.

Active breaks

The active interval session was implemented in the same classroom. The 10-minute routine began with 1-minute on-the-spot running, followed by 8 minutes of calisthenic exercises and concluded with 1-minute walking around the room as a cooldown. During the 8-minute callisthenics segment, two sets of eight exercises were performed, each lasting 30 seconds. These exercises included alternating lunges, skipping, squats, jumping jacks, walkouts, lunges with left knee lift, low-impact burpees and lunges with right-knee lift. At the end of the 10-minute session, the students were instructed to report their RPE, with a target range of 5–7 being expected.

Data analysis

Statistical analysis calculated measures of central tendency and dispersion (arithmetic means and standard deviation). Prior to the main analyses, assumptions of normality and homogeneity of variances were tested using the Kolmogorov-Smirnov and Levene’s tests, respectively. Normality tests confirmed that the data were normally distributed, justifying the use of parametric tests such as the t-test and analysis of variance (ANOVA). The order in which participants took part in the different conditions (starting in the control or experimental condition) was not considered in the analysis; thus, data from both conditions were treated based solely on condition type (Control Condition vs Active-Breaks Condition).

This study utilised a crossover randomised design, including two within-subject factors – Effort Condition (Control vs Active-Breaks) and Time-on-Task (five consecutive 1-minute blocks of the psychomotor vigilance task) – and one between-subject factor – Assessment Moment (Baseline, Pre and Post). Each participant performed the psychomotor vigilance task under both Effort Conditions (Control and Active-Breaks) across three Assessment Moments: (1) Baseline – at the beginning of the class; (2) Pre – after 60 minutes of class and (3) Post – following a 10-minute break. All the assessments were conducted in the same classroom.

To analyse the reaction time data, a three-way mixed repeated-measures ANOVA was conducted using SPSS (version 29.0, IBM Corp., Armonk, NY, USA), with Effort Condition and Time-on-Task as within-subject factors, and Assessment Moment as a between-subject factor. The Greenhouse-Geisser correction was applied whenever the assumption of sphericity was violated. Significant effects and interactions were further explored through post hoc analyses or pairwise comparisons when appropriate.

In addition, paired-sample t-tests were used to compare average reaction times between conditions specifically at the post moment (Control Condition 3 vs Active Breaks 3), to evaluate the acute effect of the 10-minute active break. The same t-tests were applied to participants’ RPE to assess differences in perceived intensity between conditions. Effect sizes were reported as Cohen’s d for t-tests (with thresholds of 0.2 = small, 0.5 = medium and >0.8 = large) and partial eta squared (ηp²) for ANOVAs. The significance level was set at p < .05 for all statistical analyses.

Results

To ensure that the intensity of effort remained consistent across both sessions in this crossover study, RPE values were recorded and compared. As shown in Figure 3, the RPE averaged around 6.13 ± 0.81 and 6.19 ± 0.75 points to experimental sessions (session 2 and 3, respectively, as shown in Figure 2), and no statistically significant differences were observed between the two sessions.

Figure 3.

Rate of Perceive Exertion (RPE) in the first and second experimental groups.

Multivariate tests revealed no significant main effect of Condition, Wilks’ Lambda = 0.979, F(2, 61) = 0.649, p = .526, and no significant Condition × Assessment Moment interaction, Wilks’ Lambda = 0.934, F(2, 61) = 2.156, p = .125. However, a significant main effect of Time was identified, Wilks’ Lambda = 0.851, F(4, 59) = 2.588, p = .046, indicating that the dependent variable varied significantly across the 5 minutes of assessment, regardless of condition or assessment moment. No significant interactions were found for Time × Assessment Moment, Wilks’ Lambda = 0.946, F(4, 59) = 0.834, p = .509, or for Condition × Time, Wilks’ Lambda = 0.820, F(8, 55) = 1.506, p = .177. Importantly, a significant three-way interaction between Condition, Time and Assessment Moment was found, Wilks’ Lambda = 0.738, F(8, 55) = 2.441, p = .025, suggesting that the pattern of change over time differed depending on both the intervention phase and the assessment moment (as shown in Figures 4 -6).

Figure 4.

Mean Reaction time (RT) (±SE) as a function of Group Condition 1 (baseline), time-on-task and Group × time-on-task.

Figure 5.

Mean Reaction time (RT) (±SE) as a function of Group Condition 2 (after 60 minutes of class), time-on-task and Group × time on task.

Figure 6.

Mean Reaction time (RT) (±SE) as a function of Group Condition 3 (following a 10-minute break), time-on-task and Group × time-on-task.

Figure 7.

Mean and standard deviation of reaction time performance in the pre- and post-test in control condition (control group) and experimental condition (active-break group).

Mauchly’s test of sphericity indicated violations of the sphericity assumption for all within-subjects factors (Condition, Time and Condition × Time), with all p-values < .001. Therefore, Greenhouse-Geisser and Huynh-Feldt corrections were applied to adjust the degrees of freedom for subsequent within-subjects analyses. Between-subjects effects revealed no significant differences across assessment groups, Condition: F(1, 62) = 2.050, p = .157, suggesting that the observed effects were primarily attributable to within-subject factors and their interactions.

To further explore the significant three-way interaction, pairwise comparisons with Bonferroni adjustment were performed. Results indicated that the only statistically significant difference occurred during the post-intervention moment in the experimental (Active-Breaks) condition, where a significant increase in reaction time was observed between minute 3 and minute 5 (mean difference = –37.188 ms, p = .005, 95% CI: −66.641 to −7.736). This finding suggests a time-dependent decline in vigilance performance following the active-break intervention. No other significant differences were detected between time points across the baseline or pre-intervention, nor within the control condition. Overall, these findings support the interpretation that although the active-break condition may initially enhance cognitive performance, its beneficial effects appear to diminish towards the end of the task period, as reflected by increased reaction times indicative of cognitive fatigue.

Discussion

This study aimed to examine the effect of moderate- to high-intensity aerobic active breaks on reaction time among sport science college students. Although overall differences between pre- and post-test were marginal (p = .07, d = 0.21), a significant three-way interaction was identified, indicating that reaction time performance varied depending on the condition, moment of assessment and time-on-task. Specifically, reaction time significantly increased from minute 3 to minute 5 during the post-intervention period in the active breaks condition, suggesting a decline in vigilance performance towards the end of the task. One potential explanation for the lack of broader significant effects lies in the composition of our sample, which consisted of students enrolled in a sports science course. Given their higher physical fitness, two issues may have arisen: (1) despite the use of the subjective perceived exertion scale (RPE) to monitor effort, the exercise intensity may not have reached the intended moderate level, and/or (2) participants’ habitual engagement in physical activity may have resulted in superior baseline cognitive performance, leaving less room for improvement.

PE induces numerous physiological changes, including elevations in core body temperature, increased cortical blood flow, elevated heart rate and greater release of catecholamines (McMorris et al., 2015). These responses are associated with heightened activation and arousal, affecting cortical excitability (Langner and Eickhoff, 2013; Oken et al., 2006). Consequently, these mechanisms may underpin the observed variations in psychomotor vigilance task performance in response to exercise intensity. Notably, research has shown that peak vigilance performance occurs at light to moderate levels of exercise intensity, suggesting an optimal range for maximising cognitive performance during physical activity. Moreover, recent findings indicate that such effects may be mediated by neurotransmitter and neurotrophic factor release, as well as changes in cerebral blood flow during exercise (Ceylan et al., 2023; Da Cunha et al., 2023; Hötting and Röder, 2013). This underscores the complex interaction between physical exertion and cognitive function and the need for further research to elucidate the mechanisms linking exercise intensity and cognition. In this study, a time-dependent effect was identified following the active breaks, as reaction time increased significantly from minute 3 to minute 5 during the post-intervention assessment. This suggests that, despite potential initial cognitive benefits, a fatigue-related decline in vigilance performance may emerge over time.

Research exploring the effects of active breaks among adolescents and young adults is limited (Erickson et al., 2019; Stillman et al., 2020). Among the few existing studies, findings on the impact of moderate-to-vigorous physical activity on cognition are mixed, largely due to the lack of rigorous experimental designs and consistent definitions of physical activity and cognitive measures, highlighting the need for further research during young and early adulthood (Erickson et al., 2019; Kao et al., 2018). As brain maturation continues through adolescence and early adulthood (Lebel and Beaulieu, 2011; Stillman et al., 2020), developmental factors may contribute to the variability observed across studies.

The primary factors influencing the effects of active breaks have been identified as (1) effort intensity; (2) timing and duration of the activity; (3) the nature of the cognitive task administered and (4) participants’ physical fitness levels (Fernández and Víllora, 2021). Considering intensity, the most prominent pattern reported is an inverted U-shaped relationship, suggesting that moderate intensity yields the greatest effect (Lambourne and Tomporowski, 2010; McMorris and Hale, 2012). However, other studies have suggested that cognitive benefits may also occur at very light, light and moderate intensities, while hard or maximal intensities show limited benefit (Brown et al., 2017; Chang et al., 2012). Although moderate intensity is typically reported as a characteristic of active breaks, there remains a lack of consensus on how it should be defined and measured (Erickson et al., 2019). In the present study, exercise intensity was monitored using the RPE scale to achieve the target intensity, though no objective measures were used. While some studies have implemented active breaks sessions lasting 10 minutes – as in the present study – others suggest that sessions lasting 11–20 minutes produce greater cognitive benefits (Cabral et al., 2019; Schmidt et al., 2019; Van Den Berg et al., 2019; Wilson et al., 2016). Sessions shorter than 11 minutes or longer than 20 minutes appear to yield smaller effects (Chang et al., 2012). However, our findings suggest that even within a 10-minute active break session, the cognitive benefits may not be sustained throughout the subsequent vigilance task, as shown by the performance decline observed in the final minutes of the psychomotor vigilance task.

It is important to note that all participants in this study were physically active sports science students, which may have contributed to a ceiling effect, reducing the likelihood of observing further cognitive improvements following the active breaks. This may also explain the late-task decline in vigilance performance observed in the post-intervention assessment.

Limitations

This study has some limitations. First, exercise intensity was not measured using objective methods such as accelerometery or heart rate monitoring. However, the RPE scale is a widely recognised tool for monitoring load (Clemente et al., 2022; Impellizzeri et al., 2004; Lima et al., 2020). Second, the use of a convenience sample may have influenced the findings. Third, although the study design incorporated randomisation, counterbalancing and temporal spacing between sessions, no specific statistical analysis was conducted to test for order effects, nor were data collapsed across session order as recommended by Müller et al. (2021). This represents a limitation in controlling for potential order effects. Moreover, the fact that the significant effect observed occurred only in the final minutes of the post-intervention task limits the generalisability of conclusions regarding the impact of active breaks and suggests the need for future research employing extended task durations and more refined measures of cognitive load. Finally, the dual role of the researcher as both a teacher and an investigator during class sessions, combined with the absence of blinding and independent observation to verify intervention fidelity, may have introduced bias and affected participants’ engagement and performance in the vigilance task.

Conclusion

Although only marginal differences were observed between conditions in the pre-post active break comparison, a significant three-way interaction revealed that post-condition reaction times worsened progressively during the vigilance task following the active break. This suggests that while active breaks may initially enhance alertness or engagement, these effects may not be sustained throughout extended tasks and can even result in fatigue-induced declines in performance. Furthermore, although the present study’s findings contribute to the existing body of research on the effects of active breaks on students’ behaviour, it is important to note that active breaks also play a role for educators, as they can be integrated with active learning methodologies such as cooperative learning to enhance student engagement in the learning process. However, it is crucial for teachers to receive training in particular active break methodologies to ensure their effective implementation. The present findings therefore call for a more nuanced understanding of how active breaks are implemented – particularly regarding session duration, intensity and cognitive task demands – to prevent potential negative effects on sustained attention. While the potential benefits of incorporating active breaks into college classes remain promising, especially with respect to short-term arousal and engagement, caution is warranted when generalising their advantages to all cognitive tasks or durations, particularly among highly fit populations.

Footnotes

Acknowledgements

We thank the students who volunteered to participate in this study.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Use of AI

No artificial intelligence tools were used in the preparation or conduct of this study or the preparation of this paper.

ORCID iDs

Ana Filipa Silva

Rui Miguel Silva

Francisco Tomás González-Fernández

Notes

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The effects of active breaks on sustained attention among sports college students

Abstract

Objective:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Participants

Measures

The Hooper index

The Rate of Perceived Exertion

Psychomotor Vigilance Task

Procedure

Active breaks

Data analysis

Results

Discussion

Limitations

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

Use of AI

ORCID iDs

Notes

References