Effects of physical activity breaks on cognitive function in undergraduate students: a pilot study

Abstract

Physical activity is known to have several cognitive benefits. As a result, many teachers introduce short bouts of physical activity (active breaks) during their lessons. However, active breaks are less common in the university context, where students tend to remain passive during lessons. Therefore, there is a paucity of literature on active breaks and their benefits for undergraduate students. This study aimed to investigate the effects of a 10-min active break at moderate intensity on vigilance in undergraduate students by using a psychomotor vigilance task. Twenty-six undergraduate students (M_age = 23.36, SD = 1.98; 53.8% females) participated in this study and performed two conditions: control condition (passive condition) and experimental condition (active break). Results revealed that after receiving 10 min of moderate-intensity physical activity, the students improved their attention, as they responded faster to the psychomotor vigilance task than in the control condition. Considering these results, it seems that students can benefit from physical activity breaks by improving their vigilance. However, as this conclusion is based on a pilot study, we should be cautious in interpreting these results. Further research into this finding is needed.

Keywords:

• Acute physical activity
• exercise breaks
• undergraduates
• sustained attention
• physical education

REVIEWING EDITOR:

• Pier Luigi Sacco, Humanities, IULM University, Milano, Italy

SUBJECTS:

• Teachers & Teacher Education
• Higher Education
• Physical Education

Introduction

The literature demonstrates that our brains learn better with methods that encourage students to be active in the lessons (Schmidt et al., 2016). One of the main concerns about students’ passive role in traditional masterclass-based teaching is that their overall attention is negatively affected (Windschitl, 1999), which in turn could decrease students’ learning (Janssen et al., 2014). Therefore, additional efforts are needed to use new teaching methods that enhance the active participation of students. An example could be the integration of active breaks (i.e. short bouts of physical activity during or between lessons) within the school day, which may lead to improvements in students’ attention, knowledge acquisition, and cognitive performance (Hogan et al., 2013; Van den Berg et al., 2016). Such cognitive performance refers to the cognitive/executive functioning [i.e. ‘a set of general control processes that regulate one’s thoughts and behavior’ (Miyake & Friedman, 2012, p. 8)] that includes several mental abilities, such as reasoning, thinking, remembering, problem-solving, decision making, attention, and learning (Fisher et al., 2019).

Physical activity is known to have several cognitive benefits, which in turn are positively associated with academic performance (Donnelly et al., 2016). Several studies with children reveal an association between physical activity and cognition in general (Chaddock et al., 2011; Howie et al., 2014), and attention in particular (Infantes-Paniagua et al., 2021; Ma et al., 2015; Mahar, 2011; Rudasill et al., 2010). The ability to sustain attention over time and to respond appropriately to infrequent stimuli is commonly known as sustained attention or vigilance (Davies & Parasuraman, 1982). It is widely accepted in neuroimaging studies (Drummond et al., 2005) that maintaining high vigilance performance in vigilance tasks for any length of time is very demanding (Oken et al., 2006; Sarter et al., 2001). Low levels of vigilance result in slower reaction times, response anticipation, a decrease in performance with time-on-task, or even failure to detect the target. Researchers suggest that this decline in performance over time reflects the loss of attentional resources (Helton & Warm, 2008; Warm et al., 1996, 2008).

Previous studies have demonstrated a positive relationship between vigilance and academic performance at several educational levels, including adolescents (Steinmayr et al., 2010; Trautmann & Zepf, 2012) and university students (Infantes-Paniagua et al., 2021). This positive association implies better learning, as observed in a recent study with university students (Fenesi et al., 2018). For this reason, several researchers recommend increasing activation levels through physical activity during lessons to compensate for the decrease in vigilance. Some authors (e.g. Horgan, 2003) agree that the demands during the lessons change every 10–15 min, which strongly suggests the importance of vigilance to respond to different situations and changes during the school day (Goh et al., 2016). Therefore, developing active breaks at moderate intensity levels during the lessons could restore vigilance levels and maintain academic performance at its best (Infantes-Paniagua et al., 2021).

However, it seems that not all active breaks have the same effects on vigilance. Several authors have tried to find the ideal characteristics of active breaks to achieve greater improvements in cognition. Some of these characteristics that have been studied are the duration (min) and intensity of physical activity (low, moderate, or vigorous), which are moderators in the effects of active breaks. The appropriate duration of active breaks is an ongoing debate in the literature. However, it seems that at least 10 min of physical activity is needed to achieve positive changes and improvements in children’s vigilance (Janssen et al., 2014; Martínez-López et al., 2020). According to the extant literature, there is a peak in reaction time performance (using simple and choice reaction time tasks) during moderate intensity exercise, that is, between 60 and 80% of the VO₂ max (Brisswalter et al., 2002).

In addition, exercise at moderate intensity improves vigilance, motivation, and cognitive performance (Davranche & Audiffren, 2004; Owen et al., 2018a), as well as increases the metabolic load (increased dehydration, changes in plasma levels of certain neurotransmitters, etc.; Acevedo & Ekkekakis, 2006; Kenney et al., 2013; Wilmore et al., 1995). On the contrary, low or vigorous exercise results in performance deterioration or a lack of significant changes in cognition (Brisswalter et al., 2002).

Given the positive effects of moderate-intensity exercise on sustained attention or vigilance, it is of interest that researchers investigate these effects which in turn exert a major impact on performance during the lessons (Breslau et al., 2010; Chang et al., 2012; Duncan et al., 2007; Rabiner et al., 2016; Tomporowski et al., 2011). However, to the best of our knowledge, there is a paucity of studies measuring vigilance performance after a short bout of physical exercise in an academic context, especially in the university context (Budde et al., 2008; Gallotta et al., 2012, 2015). This is rather surprising as most studies with adults have shown a positive effect of acute exercise on vigilance (e.g. Chang & Etnier, 2009). Therefore, improvements in the functionality of the attentional set could lead to improvements in memory and knowledge acquisition (Dagenbach & Carr, 1994).

This study aimed to examine the effects of a 10-min active break at moderate intensity on vigilance in undergraduate students by using a Psychomotor Vigilance Task (PVT). According to previous literature, which has demonstrated a positive relationship between physical activity, brain function, and cognitive performance (e.g. Mahar, 2011; Owen et al., 2018b), we expected active breaks to improve students’ sustained attention or vigilance.

Methods

Study design

The present study was based on the within-participants design with the factor of effort condition (control condition and experimental condition) and time-on-task (10-min physical activity in which students performed motor games based on coordination skills, locomotor skills (running, jumping, sliding…) and stability skills (balancing, bending, turning…). The university students were selected for convenience and performed both conditions (control and experimental). To investigate the effects of a 10-min active break at moderate intensity, those in the control condition were asked to maintain their ordinary routines during the class, while those in the experimental condition performed an active break.

Participants

A total of 26 undergraduate students, 12 males and 14 females (M_age = 23.36, SD = 1.98) participated in this pilot study, both in the experimental and control conditions. Most of the students had a moderate level of physical fitness, with a VO₂ max of 53.13 mL·kg⁻¹·min⁻¹ (±5.41) (see Table 1 for further information). They were recruited from the Faculty of Education in Albacete (University of Castilla-La Mancha, Spain) using flyers. All participants were healthy, reported normal or corrected to normal vision, and had no history of neurological or physical disorders.

Table 1. Descriptive information on the participants’ characteristics (mean ± SD).

	Participants (n = 15)
Age (years)	23.36 ± 1.98
VO2 max (mL·kg^−1·min^−1)	53.13 ± 5.41
Walking MET (min/week)	240.18 ± 229.78
Moderate MET (min/week)	3960.12 ± 1911.23
Vigorous MET (min/week)	1200.34 ± 1605.12
Heart rate mean (control condition)	71.91 ± 14.37
Heart rate mean (experimental condition)	112.86 ± 17.81

Instruments

Heart rate and VO₂ max

Polar Team ProTM hardware and software (Polar Electr, Corp., Finland) were used to register heart rate during the sessions. The Polar Team Pro™ Sensor incorporates an integrated GPS (10 Hz), a heart rate frequency sensor, and a system of three micro-electrical-mechanical components (i.e. accelerometer, gyroscope, and digital compass; 200 Hz). This sensor must be used with the Polar Team Pro™ soft strap. Heart rate data was simultaneously recorded by the classroom teacher using an iPad™ at the time of the intervention. Heart rate data was immediately measured after the active break session. The formula (208 − [0.7 × age]) proposed by Tanaka et al. (2001) was used to obtain the estimated threshold of the participants.

Due to the complexity of obtaining the exact VO₂ max in the educational context, the recommendation to perform the multi-stage 20 m shuttle run test (Léger et al., 1988) was considered. According to Lang et al. (2018), the VO₂ max could be estimated from the number of stages (periods) of the test and the age of the students using the following formula: $${\text{VO}}_2\mathrm{\text{max}}=31.025+3.238\cdot \text{stage}-3.248\cdot \text{stage +}0.1536\cdot \text{stage}\cdot \text{age}$$

Psychomotor vigilance task

The participants’ mobile phone was used to present the stimuli in the PVT (Wilkinson & Houghton, 1982). Mobile phone notifications were blocked beforehand. The center of the mobile phone screen was placed ∼30–40 cm from the participant’s head and at eye level. In the PVT, participants were asked to touch the screen as soon as they saw the timer in the center of the screen. There was nothing on the screen until the timer randomly appeared. Such timer, shown in Figure 1, started at the speed of a real stopwatch and could be presented on the screen after a random time interval ranging from 2000 to 10,000 ms. After touching the screen, participants received their result in ms during a 300 ms intertrial, and then the next trial (i.e. timer) began. Verbal and written instructions were given to participants before the start of the PVT in each session. They were asked to fixate their attention on the center of the screen, not to move their eyes, and to respond as quickly as possible (while avoiding anticipation errors).

Figure 1. Example of one trial of the psychomotor vigilance task (PVT).

The duration of the task in each condition was 10 min (Loh et al., 2004). The first five trials of the PVT were discarded from the analysis and used to verify that participants had understood the PVT. Trials with Reaction Times below 100 ms were assumed to represent anticipation errors and were discarded from the analysis. Participants completed 74.33 (±7.14) and 71.21 (±8.61) trials in the control and experimental conditions respectively. Accuracy and reaction time (corrected items) were extracted for each condition achieved by each participant.

Procedure

Two weeks before the intervention, the following measures were registered: anthropometric data, current sports habits, level of physical activity, addictions, and medical conditions that might interfere with physical activity. We followed the guidelines of the American College of Sports Medicine (2010) to ensure the safety of the participants. Students provided their verbal consent before developing the study. All participants were treated according to the American Psychological Association guidelines to ensure the anonymity of their responses. The study was conducted in accordance with the ethical principles of the Helsinki Declaration of 1964 for research involving human subjects and was approved by the Hospital Research Ethics Committee of [city of approval] [reference: 2020/07/076].

One week before intervention, participants performed the 20 m shuttle run test following Léger’s protocol for predicting VO₂ max (Léger et al., 1988; Léger & Gadoury, 1989). They had to run between two lines 20 m apart while keeping up with audio signals from a pre-recorded CD. The initial speed was 8.5 km/h, and this was increased by 0.5 km/h every minute, indicating the next stage. The last stage recorded as completed was used to predict VO₂ max. Heart rate was assessed using the Polar M430 with a wristband with an H10 heart rate monitor (Polar Electro Oy, Kempele, Finland).

To counteract possible bias, physical activity levels and VO₂ max were used as reference indicators to initially divide the sample into the control and experimental conditions. The experimental condition performed the active break intervention in a sports hall (first floor), while the control condition was in a classroom on the second floor, continuing with their ordinary routine teaching.

Participants completed two sessions (experimental and control condition) on separate weeks, but on the same day of the week (Wednesday from 10 to 12 am) and location. The participant’s heart rate was monitored during both sessions to control the intensity of the session, especially in the experimental condition.

To conduct the study, all participants received a 25-min masterclass. After that, those in control condition read a scientific paper on physical activity and cognition (10 min). Meanwhile, participants in the experimental condition performed 10 min of physical activity at 60–80% of VO₂ max. After participating in these tasks, all students completed the PVT in 10 min. Finally, they attended the rest of the master class for 15 min. One week later, students in the control condition participated in the experimental condition and vice versa, but there was no washout period between the two conditions. See Figure 2 for a graphical description of the procedure.

Figure 2. Schematic representation of the experiment (see text for full description).

Statistical analysis

This pilot experiment was based on a within-participants design with a factor of effort condition (control condition and experimental condition) and time-on-task (one 10-min physical activity with motor games and coordinative exercises). These 10 min were divided into 1-min slots to examine the time course of reaction time in the PVT. Two-way repeated measures ANOVA was used to analyze the reaction time data. Heart rate data was analyzed using paired-sample t-tests. Effect sizes are indicated by Cohen’s d for t-tests (small, d = 0.2; medium, d = 0.5; and large, d = 0.8) and partial eta squared for ANOVAs (Lakens, 2013). The Greenhouse-Geisser correction was applied when sphericity was violated (Jennings & Wood, 1976). The level of significance was set at p < 0.05 for all analyses.

Results

Heart rate

A paired sample t-test confirmed that the students had a lower heart rate during the control condition (70.81 bmp ± 14.89) compared to the experimental condition (112.86 bmp ± 15.30), t (16) = −20.15, p < .001, d = −2.78.

Reaction times

A repeated measures analysis of variance (ANOVA) on the mean of participants’ reaction times with the factors of the session (control and experimental condition) and task duration (10 min) revealed a significant main effect of the session, F (1, 16) = 7.96, p = .01, η² = .34. As shown in Figure 3, participants responded faster in the experimental condition (318 ms) than in the control condition (373.55 ms). There was also a significant main effect of time-on-task, F (9, 135) = 7.76, p = .001, η² = .34. The interaction between the factors of the session and time-on-task was not significant F < 1.

Figure 3. Psychomotor vigilance task. Participants’ mean reaction time (±SE) and individual performance as a function of effort condition.

Discussion

This pilot study aimed to examine the effects of 10-min activity breaks on vigilance in undergraduate students using a PVT. To observe these possible effects, we compared the results of the PVT (based on sustained attention) between the control and experimental conditions.

According to our expectations, participants in the experimental condition significantly improved their vigilance after receiving 10 min of physical activity in comparison with the control condition, as they responded faster on the PVT after the active breaks. This improvement in vigilance following physical activity breaks has also been observed in previous research with children (Ma et al., 2015; Mahar, 2011) and undergraduate students, as Niedermeier et al. (2020) demonstrated that 10 min of physical activity increased their visual attention. This finding suggests that even short bouts of physical activity help to improve vigilance in undergraduate students, which in turn may help to improve their knowledge acquisition and learning (Janssen et al., 2014). On the contrary, Wilson et al. (2016) found no effect on students’ sustained attention, although these authors consider that active breaks are effective in increasing students’ physical activity levels. In this line, Bailey et al. (2014) found no effect on concentration performance after moderate coordinative exercise (i.e. moderate intensity exercise involving bilateral movements requiring coordination). Thus, cognitive effects might be mainly moderated by the intensity of the exercise rather than by the type of exercise. Accordingly, Van den Berg et al. (2016) did not find significant differences regarding the effects of three types of exercise on cognition: aerobic, coordination, and strength exercises.

In addition, our results showed a lower heart rate during the control condition compared to the experimental condition. Thus, participants performed worse in the control condition than in the experimental condition. Higher vigilance performance was found in the experimental condition at moderate intensities. In this sense, according to an inverted U-shape activation hypothesis (e.g. Tomporowski, 2003), our data suggest that the intensity of the exercise used in our study did not exceed the ‘critical point’ activation at any time (Chmura et al., 2002). In terms of intensity, previous studies have also found that moderate intensity exercise has beneficial effects on cognitive skills (Owen et al., 2018a) whereas low to moderate intensity does not appear to have such positive effects (Van den Berg et al., 2016).

Conclusions

The implementation of active breaks has gained popularity in schools due to their cognitive benefits. However, other educational settings, such as the university context, have received little attention in the study of active breaks and cognition. Therefore, this work could be a starting point in this area of knowledge. Although we should be cautious in interpreting the results of this pilot study, it seems that the implementation of short active breaks at moderate intensity during classes in the university context may well improve cognitive function through vigilance performance.

Despite the novelty of this study, we would like to acknowledge some limitations that could be addressed in future research. It would be valuable to increase the sample and include both undergraduates and students from other educational levels, which would give us a broader view of it and bring to light new knowledge about the effect of active breaks. Also, as we only measure the acute effect of active breaks in this study, it would be interesting to integrate active breaks into the curriculum to observe their long-term effects on students’ cognition. Furthermore, as active breaks seem to have a positive effect on attention and cognition, future research could assess their effect on academic performance. Another limitation of this study could be the instrument. Although the PVT is appropriate for measuring attention, other instruments, such as the encephalograph would provide us with more information about the success of active breaks in terms of cognition as we would have data on brain connectivity.

Finally, we would like to mention the didactic implications of this study. In the university courses, lessons usually last 60 min or more. Therefore, an active break in the middle of the lesson can be developed to improve students’ attention. Also, it would be useful to review what has been explained through physical movement. For example, the teacher can associate some physical movements (e.g. jumping) with any verbal or visual stimuli related to the curricular content of a subject (e.g. types of bones). However, active breaks can also be implemented through mechanical activities (e.g. jumping for 30 s). Although the implementation of active breaks may seem complex, it is possible to implement them with undergraduate students is possible as no special resources, spaces, or materials are needed. Active breaks also help to promote physical activity, which tends to decrease as students become older.

Ethical approval

All procedures were performed in accordance with the ethical standards of the institutional research committee and the Helsinki Declaration of 1964 and its subsequent amendments, or equivalent ethical standards. The study was approved by the University Ethics Committee (No. 2020/07/076) and all participants gave their informed consent to take part in the study.

Acknowledgments

None.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by the Junta de Comunidades de Castilla-La Mancha and the ERDF [Reference: SBPLY/19/180501/000147].

Notes on contributors

Juan Carlos Pastor-Vicedo

Juan Carlos Pastor-Vicedo has a Ph.D. in Sports Sciences. He works as a lecturer at the University of Castilla-La Mancha (Albacete). His main research line is related to talent development and neuroeducation.

María Pilar León

María Pilar León is PhD in Physical Education. She has a postdoctoral fellowship at the University of Castilla-La Mancha. Her current research mostly focuses on children’s body image, body expression, and motor creativity, although she also contributes to research in other areas such as active breaks and giftedness through funded projects.

Francisco Tomás González-Fernández

Francisco Tomás González-Fernández as Francisco currently works as professor in the department of Physical Education and Sports, Faculty of Sport Sciences. The current research interests are: cognitive performance in sports, youth sports performance, decision-making in sports and Soccer.

Alejandro Prieto-Ayuso

Alejandro Prieto-Ayuso has a PhD in Physical Education. He works as a lecturer at the University of Castilla-La Mancha (Cuenca). His main research line is related to gifted students in Physical Education and talent development in sports.