Exercising for maintaining health and preventing diseases is well documented in the literature (Pedersen & Saltin, 2015). Moreover, physical activity is an important variable for public health economics, since it is inversely associated with the costs of health procedures, drug prescriptions, and chronic diseases (Bueno et al., 2016).

In this scenario, an important aspect for the success of intervention programs aimed at promoting physical activity is the identification of factors that can limit participation and adherence. Lack of time and motivation are frequently mentioned barriers among adults (Rech et al., 2018). Therefore, adherence to exercise should be considered when planning actions based on the characteristics of the programs offered (Lovato et al., 2015). Thus, investigating different intervention strategies with a better time/efficiency ratio, such as high-intensity aerobic exercise, seems relevant.

It is known that affective responses to exercise are relevant to increasing adhesion. However, the intensity of exercise can modulate these responses, with low- and moderate-intensity exercise generating positive affective responses, while intensities above the second metabolic threshold are associated with negative affective responses (Camacho et al., 2014; Ekkekakis et al., 2011). It is possible that activities that cause high negative affect increase the risk of discontinuity in exercise programs.

Moreover, mood state can be directly associated with quality of life, and this quality of life can be improved with exercise. Since exercise, when performed at a controlled intensity, has an important role in the control and improvement of mood state (Laux et al., 2018).

The literature indicates that audiovisual stimuli, such as videos and music, can positively influence affective responses to exercise (Almeida et al., 2021; Bird et al., 2019; Stork et al., 2015). Music can hold attention and unlock several emotions that alter or regulate mood state (Thakare et al., 2017).

Despite the presence of audiovisual stimuli in exercise spaces, no studies have investigated effective responses to high-intensity aerobic exercise when combined with audiovisual stimuli. This investigation can provide new perspectives by examining the comparative effects of auditory (music) and audiovisual (music video) stimuli on mood responses following high-intensity aerobic exercise. This context has received little or no empirical attention to date.

Therefore, the present study aimed to analyze the influence of listening to music and watching video clips on mood responses among young adults following a high-intensity aerobic exercise session. The results of this study may inform intervention strategies to preserve engagement in physical exercise programs, guide health professionals in implementing more effective aerobic training programs, and potentially reduce the risks associated with physical inactivity and non-communicable chronic diseases. Thus, exercise physiologists, physical education professionals, and sports scientists whose research focuses on investigating psycho-affective responses to aerobic exercise may benefit. Furthermore, endurance sports enthusiasts may also benefit from the information this research may produce, given the known difficulties of engaging in physical exercise programs.

The comparisons between conditions (C, M, and VC) showed no significant differences for confusion (A), fatigue (C), anger (D), tension (E), and vigor (F) (p>0.05). Regarding depression (B), a statistically significant difference was found only at rest (pre-exercise), with M showing significantly higher values than C and VC (p<0.05).

On the other hand, comparisons within sessions revealed statistical differences at several points. Fatigue (C) was significantly higher after exercise in all three conditions compared with pre-exercise and after recovery (p<0.05). This also occurred with confusion (A), which remained high after exercise compared with after recovery in C and VC (p<0.05). Tension (E) was significantly lower after recovery, in VC, when compared to pre-exercise and after exercise (p<0.05), and significantly lower, in M, after recovery when compared to pre-exercise.

The present study aimed to analyze the influence of listening to music and watching video clips on mood responses among young adults following a high-intensity aerobic exercise session. The main findings indicate that there were no differences among conditions (control, music, and video clips) for confusion, fatigue, anger, tension, and vigor (p>0.05). Specifically, for depression, a statistical difference was found in pre-exercise (rest) values, with M showing higher values than C and VC. However, this effect did not persist after exercise nor after recovery.

The literature indicates an association between physical activity and mood, suggesting that the intensity of exercise can modulate this response. It is speculated that moderate intensity exercise improves mood state. In contrast, high-intensity exercise can lead to negative responses, and this variation can be more related to the construct of depression than anxiety (Peluso & Andrade, 2005).

The association between mood state and exercise has been approached in some studies. Laux et al. (2018), for instance, examined the effects of a high-intensity, strength-and-metabolic-functional training session on the mood state of young adults. The results showed improvements in aspects related to tension and anger in both training models, as well as in depression during the strength session, when comparing pre- and post-training moments. This does not corroborate the present study’s findings. On the other hand, Bigliassi et al. (2012) investigated the effects of music on psychophysiological variables in adults during exercise on a cyclergometer at 60% of heart rate reserve. They reported that at this intensity, music was unable to influence variables such as rate of perceived exertion and mood state.

It is considered that music can exert an ergogenic and distractive effect during exercise, mainly at moderate intensities, with adequate intervals, and self-selection (Thakare et al., 2017). Bird et al. (2019) found that audiovisual stimuli can improve affect, perception, and exercise performance at the ventilatory threshold on a cyclergometer. Almeida et al. (2021) found that watching video clips while exercising attenuated displeasure after a high-intensity cyclergometer session (10% above anaerobic threshold). Beyond the insertion of audiovisual stimuli, it is important to analyze that the type of exercise can also be considered an intervening factor in positive affect and in the identification of the individual with the exercise, and that positive affect is inversely associated with the regulations of motivation (da Cruz Silva et al., 2019).

From a physiological standpoint, acute aerobic exercise demonstrates a clear intensity-dependent response, where high-intensity exercise typically causes greater acute physiological stress (lower heart rate variability, higher lactate) and potential temporary reductions in mood (tension/fatigue), while moderate- to low-intensity exercise often produces immediate improvements in mood (vigor) and faster post-exercise autonomic recovery (Zhang & Wang, 2025; Steptoe & Cox, 1988; Mazzeo & Marshall, 1987; Urhausen et al., 1994). In the present study, this may be overlapping with the potential modulating effects of auditory and audiovisual stimuli on mood responses.

From a psychophysiological point of view, at exercise intensities exceeding the ventilatory threshold, for example, attentional resources are progressively allocated to interoceptive signals, including respiratory effort, muscle pain, and fatigue, thus reducing the cognitive regulation capacity of affective responses. Consequently, as exercise intensity increases, individuals tend to shift from dissociative to associative attentional strategies, directing their focus to bodily sensations such as muscle fatigue and respiratory effort (Ekkekakis, 2003; Ekkekakis et al., 2011). Furthermore, the influence of music appears to diminish at higher exercise intensities, as physiological signals, including fatigue and shortness of breath, increasingly dominate attentional capacity (Karageorghis & Priest, 2012).

Consequently, at high exercise intensities, attentional resources are increasingly guided by interoceptive signals associated with physiological effort, such as fatigue, dyspnea, and muscle discomfort, thereby limiting the effectiveness of external stimuli, including music or video, in modulating affective responses.

Despite not finding differences between conditions, it is important to note that immediately after exercise, fatigue was higher in all three conditions than pre-exercise and post-recovery (Figure 2C). Exercise-induced acute fatigue is a multifactorial phenomenon involving peripheral and central mechanisms that emerge in response to increased physiological exertion. At the muscular level, the accumulation of metabolites such as inorganic phosphate, hydrogen ions, and adenosine diphosphate during high-intensity exercise impairs excitation-contraction coupling and reduces force production. Furthermore, alterations in calcium metabolism, including reduced release from the sarcoplasmic reticulum and decreased myofibrillar sensitivity to calcium, further compromise contractile function. Concomitantly, central mechanisms also contribute to fatigue, as the progressive reduction of voluntary neural impulses from the central nervous system limits the recruitment of motor units. This process is partially mediated by the activation of muscle afferents from groups III and IV, which respond to metabolic and mechanical disturbances and exert an inhibitory influence on central motor output. Moreover, the depletion of high-energy substrates, such as phosphocreatine and glycogen, exacerbates the decline in performance. Collectively, these peripheral and central factors interact to induce acute fatigue during exercise, ultimately reducing the ability to maintain muscle work (Gandevia, 2001; Enoka & Duchateau, 2008; Amann, 2011; Spriet, 2014).

This also occurred with confusion (Figure 2A), which was significantly higher immediately after exercise in C and M, compared to post-recovery, a difference that did not occur in VC. Moreover, in VC, tension (Figure 2E) was lower after recovery than pre-exercise or immediately after exercise. Such analysis suggests that the audiovisual response can specifically influence variables such as confusion and tension.

This effect can be partially explained by the fact that exercising while watching music videos can generate lower levels of tension and confusion than listening to music alone, due to greater attentional distraction and multisensory engagement. Audiovisual stimuli simultaneously engage visual and auditory pathways, promoting the dissociation of interoceptive signals such as fatigue, discomfort, and respiratory effort. This greater capacity for dissociation can improve affective responses and reduce negative mood states during exercise. Furthermore, visual content can increase emotional engagement and perceptual absorption, thus reducing cognitive load and limiting attentional focus on physiological effort. Consequently, the integration of visual and auditory stimuli appears to increase attentional dispersion and improve psychological responses during exercise compared to isolated auditory stimulation (Jones et al., 2014; Hutchinson et al., 2015; Bigliassi et al., 2019; Terry et al., 2020).

The type and intensity of the exercise used in the present study differ from those reported in the literature (Hayakawa et al., 2000; Laux et al., 2018; Bird et al., 2019; Geraldes et al., 2019). Stork et al. (2015) examined whether listening to self-selected music could reduce the adverse effects of performing a sprint interval training session. The authors observed performance improvement, but not in perceptions of affect or motivation. Another factor that modifies mood responses to exercise is the physiological induction of hypoxia, as well as the acute effect of moderate exercise performed in hypoxia. Souza et al. (2015) verified that moderate exercise, performed in hypoxia, increased post-exercise anxiety and aggravated mood state. In this scenario, Giampá et al. (2018) reported changes in the dimensions of the BRUMS during hypoxia, including increases in fatigue and mental confusion and a decrease in vigor.

At exercise intensities exceeding the ventilatory threshold, for example, attentional resources are progressively allocated to interoceptive signals, including respiratory effort, muscle pain, and fatigue, thus reducing the cognitive regulation capacity of affective responses. Consequently, as exercise intensity increases, individuals tend to shift from dissociative to associative attentional strategies, directing their focus to bodily sensations such as muscle fatigue and respiratory effort. Furthermore, the influence of music appears to diminish at higher exercise intensities, as physiological signals, including fatigue and shortness of breath, increasingly dominate attentional capacity.

Consequently, at high exercise intensities, attentional resources are increasingly guided by interoceptive signals associated with physiological effort, such as fatigue, dyspnea, and muscle discomfort, thereby limiting the effectiveness of external stimuli, including music or video, in modulating affective responses.

It is concluded that listening to music and/or watching video clips did not affect the mood state of young adults after a high-intensity cycle ergometer aerobic exercise session. The characteristics of high-intensity exercise and its physiological demands outweigh the effects of audiovisual distractions on focus transfer during this exercise modality.

No.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

This research does not receive external funding.

The authors declare no conflict of interest.