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Influence of music on performance and psychophysiological responses during moderate-intensity exercise preceded by fatigue
Physiology & Behavior 139 (2015) 274–280
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Influence of music on performance and psychophysiological responses during moderate-intensity exercise preceded by fatigue Joao P. Lopes-Silva a, Adriano E. Lima-Silva a,⁎, Romulo Bertuzzi b, Marcos D. Silva-Cavalcante a,b a Sport Science Research Group, Department of Physical Education and Sports Science, Federal University of Pernambuco, Alto do Reservatório Street, Bela Vista, Vitória de Santo Antão, Pernambuco 55608-680, Brazil b School of Physical Education and Sport (GEDAE-USP), University of São Paulo, Mello de Moraes street, 65, Butanta, São Paulo 05508900, Brazil
H I G H L I G H T S • • • •
Time to exhaustion is not influenced by the music. Prior fatigue reduces performance and listening to music cannot reverse this effect. Listening to music seems draw the attentional focus away from internal sensations of fatigue. Listening to music distracted feelings of fatigue, but the decision to finish the exercise is based on RPE.
a r t i c l e
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Article history: Received 26 June 2013 Received in revised form 13 November 2014 Accepted 14 November 2014 Available online 20 November 2014 Keywords: Time to exhaustion Perceived exertion Associative thoughts Listening to music
a b s t r a c t Purpose: We examined the effects of listening to music on time to exhaustion and psychophysiological responses during moderate-intensity exercise performed in fatigued and non-fatigued conditions. Methods: Fourteen healthy men performed moderate-intensity exercise (60% Wmax) until exhaustion under four different conditions: with and without pre-fatigue (induced by 100 drop jumps) and listening and not listening to music. Results: Time to exhaustion was lower in the fatigued than the non-fatigued condition regardless listening to music. Similarly, RPE was higher in the fatigued than the non-fatigued condition, but music had no effect. On the other hand, listening to music decreased the associative thoughts regardless of fatigue status. Heart rate was not influenced by any treatment. Conclusion: These results suggest that listening to music changes attentional focus but is not able to reverse fatigue-derived alteration of performance. © 2014 Elsevier Inc. All rights reserved.
1. Introduction In the last decade, a centrally regulated system model has been proposed to explain exercise performance, in which the metabolic rate during exercise is regulated by the central nervous system in a dynamic, non-linear and integrative manner, to ensure that a catastrophic failure of homeostasis does not occur [1–3]. In this model, peripheral afferent signals are all integrated by the brain and used to generate the rating of perceived exertion (RPE). The rate at which RPE increases during opened-loop, constant-load exercises (in which exercise intensity cannot be reduced) seems to be set at the beginning of the exercise and
⁎ Corresponding author at: Department of Physical Education and Sports Science, Federal University of Pernambuco Alto do Reservatório Street, Bela Vista, Vitória de Santo Antão, Pernambuco 55608-680, Brazil. Tel.: +55 819593980. E-mail addresses: [email protected] (J.P. Lopes-Silva), [email protected] (A.E. Lima-Silva), [email protected] (R. Bertuzzi), [email protected] (M.D. Silva-Cavalcante).
http://dx.doi.org/10.1016/j.physbeh.2014.11.048 0031-9384/© 2014 Elsevier Inc. All rights reserved.
regulated as a function of the remaining time to exhaustion [1]. Thus, the time to exhaustion during a given exercise bout can be predicted by the rate of increase of RPE [4–8]. It is interesting to note that some psychological manipulation techniques alter the RPE response during opened-loop exercise [9,10]. For example, Potteiger et al. [9] have shown that fast, upbeat music, classical music or self-selected music during exercise at 70% VO2max reduced peripheral, central and overall RPE compared with the no-music condition. Similarly, Nethery [10] found that listening to music during exercise at 50% VO2max reduced the RPE compared to control or sensorydeprived conditions. Surprisingly, none of these studies verified whether the reduction in RPE led to an increase in exercise capacity, i.e., in time to exhaustion. Therefore, whether the reduction in RPE when listening to music during opened-loop, moderate-intensity exercise is converted into a higher performance remains unknown. The effects of listening to music on RPE have been explained through the model of parallel processes [11]. This model suggests the existence of competition in the cognition of information, where information
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derived from many different sources during exercise competes for focal awareness. In accordance with this model, listening to music would exert an influence only at low-to-moderate exercise intensities (b80% VO2max), and in this situation, external stimuli would be able to compete against weaker internal cues for attentional focus. However, internal cues (e.g., pH, lactate, VO2, ventilation, respiratory rate) become stronger at higher exercise intensities (N80% VO2max) and would predominate over external stimuli for attentional focus, suggesting that listening to music would have no effect on RPE at this intensity [10]. Nevertheless, while it seems an attractive model to explain the effect of listening to music on RPE, this model has not been widely and thoroughly tested. A prediction of this model could be experimentally tested if a moderateintensity exercise is performed in a pre-fatigued condition, in which the internal cues would compete more strongly with the music for attentional focus than in a non-pre-fatigued condition. Therefore, if the model is correct, these intensified afferent signals would be more powerful at controlling attentional focus than the music would. The present study was designed to verify the influence of listening to music on the time to exhaustion and psychophysiological responses during opened-loop, moderate-intensity exercise, performed in fatigued and non-fatigued conditions. First, we hypothesized that RPE would be greater during the fatigued compared with the non-fatigued condition, leading to a reduction in the time to exhaustion. Second, because the internal afferent signals coming from pre-fatigued muscles may be stronger than the distraction of listening to music, we hypothesized that listening to music would have no influence on either performance or psychophysiological responses in the fatigued condition. 2. Materials and methods 2.1. Participants Fourteen healthy, physically active men participated in this study. The main characteristics of the participants are described in Table 1. The protocol, benefits and risks were explained, and written consent was obtained from each participant. The study's procedures were previously approved by Ethics Committee of the Federal University of Alagoas. 2.2. Experimental design The participants reported to the laboratory on six different occasions. During the first visit, participants underwent anthropometric measurements and performed an incremental test for maximal power output determination (Wmax). One hour after the incremental test, the participants performed a familiarization test at 60% Wmax until exhaustion. At least 72 h after the incremental test, participants performed the following tests, in a counterbalanced order and 7 days apart: (1) one test with pre-fatigue at 60% Wmax listening to music (FAD-MUS); (2) one test with pre-fatigue at 60% Wmax listening to no music (FADNOMUS); (3) one test at 60% Wmax without pre-fatigue and listening to music (NOFAD-MUS); and (4) one test at 60% Wmax without prefatigue and listening to no music (NOFAD-NOMUS). All tests were performed at the same time of day to minimize any circadian variance. The participants were asked to refrain from vigorous physical activities, caffeine and alcohol 48 h before each test.
Table 1 Mean (±SD) for age and anthropometric measurements of the participants. Values Age (years) Height (cm) Weight (kg) Body fat (%)
24.0 140 74.6 15.2
± ± ± ±
1.7 73 5.9 5.5
275
2.3. Anthropometric measurements The weight and height of each subject were measured using a digital scale (0.1 kg accuracy) and a stadiometer (0.1 cm accuracy), respectively. The percentage of body fat mass was estimated from three measurements of skinfold thickness (biceps, abdominal and thigh on the right side with the subject in a standing position) using a Harpenden caliper (Quinton Instruments Company, Washington), according to Jackson and Pollock [12]. While more sophisticated methods such as bodpod, hydrostatic weighing, DEXA and deuterium dilution provide more precise estimative of body composition, skinfold thickness also provide satisfactory measurement (standard error of the estimate ~0.0077%) [12]. 2.4. Incremental test The incremental test was performed on an electrical magnetic braked cycle ergometer (Ergo-Fit 167, Ergo-Fit GmbH & Co., Pirmasens, Germany). The test was started with a 3-min warm-up at 50 W, followed by 25 W increases every minute until exhaustion. The participants were required to maintain a pedal frequency of approximately 70 rpm. The Wmax was determined as the highest power output reached during last complete stage. Exhaustion was assumed when the participants could not sustain a pedal frequency higher than 65 rpm. 2.5. Experimental test Before each test, the participants performed a standardized 5-min warm-up at 10% Wmax. Immediately after the warm-up, the power output was adjusted to 60% Wmax, and the participants cycled at 70 rpm until exhaustion. Exhaustion was identified using the same criteria adopted in the incremental test. In the music conditions, a portable MP3 player was turned on after warm-up. In the non-music conditions, headphones were connected, but the portable MP3 player remained off during the entire test [13, 14]. In the pre-fatigued tests, ~ 60 min before the test, the participants performed a validated protocol to induce fatigue (see below) [15,16]. The following variables were measured at intervals of 5 min and at exhaustion during all the tests: heart rate (HR), overall RPE, leg RPE and associative thoughts. HR was measured using a cardio frequency meter (Polar S810i heart rate monitor, Polar Electro OY, Kempele, Finland). RPE was measured using the Borg 15-point scale [17]. The participants were asked to report a peripheral RPE based on discomfort from joints and muscles of the legs (RPElegs) and an overall RPE based on discomfort from the whole body experienced during exercise (RPEoverall). The individual RPEoverall and RPElegs values were regressed against absolute time (min), and the percentage of the time to volitional exhaustion (% time to exhaustion). The slope of RPE against time and % time to exhaustion were computed using a least squares fitting procedure. Furthermore, the percentage of associative thoughts (attentional focus) was assessed with a 10-cm bipolar line [18]. The reliability and validity of these scales have been reported in previous studies [19,20]. 2.6. Music details A specialized disc jockey (DJ) selected pop and rock music tracks based on the recommendations of Karageorghis, Terry, and Lane [21]. The participants listened to upbeat, fast music (N 120-140 bpm) through headphones connected to a portable MP3 player set at approximately 80 decibels [22]. Each track lasted approximately 4 min, and the same track sequence was applied during NOFAD-MUS and FAD-MUS [18]. The song titles, artists and track durations used were (1) Destination Calabria, Alex Gaudino, 3:02 min; (2) Dreams, Van Halen, 5:01 min; (3) Voyage, voyage, Desireless, 4:10 min; (4) Bound for Glory, Angry Anderson, 4:13 min; (5) Rise Up, Yves Larock, 2:53 min; and (6) Lay Down Your Guns, Jimmy Barnes, 3:56 min. Participants completed the Brunel Music Rating Inventory-2 (BMRI-2) immediately after completion
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of the experimental conditions that included music to ascertain which musical aspects (rhythm, style, melody, tempo, instrument and beat) were most motivational in their performance [23]. 2.7. Fatigue protocol To induce a pre-fatigued condition, the participants performed a protocol consisting of 100 drop jumps approximately 60 min before FAD-MUS and FAD-NOMUS. The participants drooped 100 times from a 40-cm platform down to a 90° knee angle before jumping upward as high as possible. There was a 20-s rest period for recovery between each drop jump [15,16]. To verify the effectiveness of this protocol to induce fatigue, a bilateral maximum voluntary isometric contraction (MVC) of the quadriceps muscle (knee extension) was performed immediately before (MVCpre) and after the 100 drop jumps (MVCpos). The force produced during MVCs was measured using a load cell (EMG SYSTEM DO BRASIL LTDA). After MVCpos, the participants rested for 30 minutes by sitting in a chair. Thereafter, the participants walked to the cycle ergometer and performed the experimental test. 2.8. Muscle damage assessment To ensure that each session started without any trace of muscle soreness induced by the previous session, we used a 7-day washout period. In addition, the delayed-onset muscle soreness (DOMS) was subjectively assessed immediately before each experimental test using a seven-point Likert scale [24]. The participants were asked to rate the overall level of DOMS felt in both legs (i.e., buttocks, groin, thighs, hamstrings, calves and shins) during the past 12 waking hours according to the following verbal anchors: (0) a complete absence of soreness; (1) a light pain felt only when touched/a vague ache; (2) a moderate pain felt only when touched/a slight persistent pain; (3) a light pain when walking up or down stairs; (4) a light pain when walking on a flat surface/painful; (5) a moderate pain, stiffness or weakness when walking/very painful; and (6) a severe pain that limited the ability to move. Finally, the baseline MVC values were compared between the four conditions to determine whether the experimental sessions began with similar initial force levels because force generation is impaired by muscle soreness [16]. 2.9. Statistical analysis The data are shown as the means ± SD, unless otherwise noted. A two-way analysis of variance with repeated measures (music/ no-music × fatigued/non-fatigued) followed by a Bonferroni adjustment was used to compare the time to exhaustion, MVC, RPE, HR, associative thoughts and slopes of RPE. A three-way analysis of variance with repeated measures (music/no-music × fatigued/non-fatigued × time) followed by a Bonferroni adjustment was used to compare RPE, HR and associative thoughts as a function of time. When assumptions of sphericity were violated, the critical value of F was adjusted using the Greenhouse-Geisser epsilon value from the Mauchley test of sphericity. Scores for music, rhythm, style, melody, tempo, instrument and beat were compared between the NOFAD-MUS and the FAD-MUS conditions using the paired t-test. Significance was accepted at p b .05. 3. Results 3.1. Fatigue protocol The MVC immediately after the fatigue protocol was significantly lower (p b .05) than at the baseline for both the FAD-NOMUS and FAD-MUS conditions (Table 2), but there was no differences between these two conditions (p N .05), suggesting that the protocol was equally effective at reducing muscle force and inducing fatigue in both conditions.
Table 2 Maximum voluntary isometric contraction (MVC) immediately before and after the fatigue protocol.
FAD-NOMUS FAD-MUS
MVCpre (kgf)
MVCpos (kgf)
117.9 ± 30 124.5 ± 23
99.6 ± 35.9* 104.8 ± 28.3*
FAD-NOMUS: pre-fatigue condition listening to no music, FAD-MUS: pre-fatigue condition listening to music. Data are reported as the mean ± SD. *Significantly lower than before fatigue protocol (p b .05).
3.2. Muscle damage assessment and MVC before experimental tests When the individuals were asked to answer the DOMS questionnaire, only one participant reported a light pain when walking up or down stairs, while all the other participants reported a complete absence of soreness. Furthermore, no difference (p N .05) was found in MVCpre between the four conditions (NOFAD-NOMUS: 123.2 ± 31.1; NOFAD-MUS: 123.4 ± 30; FAD-NOMUS: 117.9 ± 30; and FAD-MUS: 124.5 ± 23 kgf), suggesting that all the participants began the experimental tests without any clear signal of fatigue caused by the prior experimental session. 3.3. Music ratings Scores for individual components of rhythm, style, melody, tempo and beat were not different between NOFAD-MUS and FAD-MUS (Fig. 1). Participants reported that all of the music's aspects rhythm (p N .05), style (p N .05), melody (p N .05), tempo (p N .05), instrument (p N .05) and beat (p N .05) were equally motivating in both conditions. 3.4. Time to exhaustion The time to exhaustion was significantly lower in the FAD-NOMUS and FAD-MUSIC than in the NOFAD-NOMUSIC and NOFAD-MUSIC conditions (p b .05), but there was no main effect of music (p N .05) (Fig. 2). In addition, there was no fatigue × music interaction (p N .05). 3.5. Psychophysiological responses There was a main effect of fatigue on mean RPEoverall (p b .05), but there was no significant effect of music (p N .05) or the music × fatigue interaction on RPEoverall (p N .05) (Table 3). The mean RPEoverall values were significantly higher in both fatigued conditions than the nonfatigued conditions (Table 3). RPEoverall increased significantly over time in all conditions (p b .05). There was a main effect of fatigue (p b .05), but there was no effect of music (p N .05) or the interaction between fatigue × music on RPEoverall (p N .05) (Fig. 3). When the slope of RPEoverall was plotted against the exercise time (absolute) (or the percentage of total exercise time completed (relative), there was no difference between the conditions (Table 3). In addition, there was no significant difference in RPEoverall at the exhaustion point between the four conditions (Fig. 3). Similar to RPEoverall, there was a main effect of fatigue on mean RPElegs (p b .05), but without any effect of music (p N .05) or the interaction between music × fatigue (p N .05) (Table 3). RPElegs increased significantly over time in all conditions (p b .05). Similar to RPEoverall, there was a main effect of fatigue (p b .05), but not music (p N .05) or the interaction between music × fatigue (p N .05), on RPElegs (Fig. 4). When the slope of RPElegs was plotted against the absolute exercise time, there was no significant difference between the conditions (p N .05). However, when RPElegs slope was plotted against the percentage of the time to exhaustion, there was a main effect of fatigue (p b .05), without any effect of music (p N .05) or the music × fatigue interaction (p N .05) (Table 3). There was no significant difference in RPElegs at exhaustion between the four conditions (Fig. 4).
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Fig. 1. Mean scores ± SEM of individual components of the BMRI-2 for the non-fatigued listening to music (NOFAD-MUS) and fatigued listening to music (FAD-MUS) conditions.
There was a main effect of music on mean associative thoughts (p b .05), but there was no main effect of fatigue (p N .05) or the music × fatigue interaction (p N .05) (Table 3). The associative thoughts increased significantly over time in all conditions (p b .05) (Fig. 5), and the values were lower in conditions with than without music (p b .05). However, there was no effect of fatigue (p N .05) or the music × fatigue interaction (p N .05) on the increase in associative thoughts over time. The mean HR was similar between all the conditions (p N .05) and increased significantly with time (p b .05).
4. Discussion The main findings of the present study are (1) the pre-fatigued condition reduced time to exhaustion and increased RPE during moderateintensity exercise, whereas listening to music did not to reduce or reverse the impairment caused by fatigue and (2) listening to music had a more pronounced effect on associative thoughts, reducing the attentional focus regardless of the fatigue condition.
Fig. 2. Time to exhaustion. Values are represented as the mean ± SEM. NOFAD-NOMUS: non-fatigued and listening to no music; NOFAD-MUS: non-fatigued and listening to music; FAD-NOMUS: fatigued and listening to no music; FAD-MUS: fatigued and listening to music. *Significantly lower than NOFAD-NOMUS and NOFAD-MUS.
4.1. Locomotor muscle fatigue and indirect markers of muscle damage The 100 drop-jump protocol induced a significant reduction (− 16.5%) in the MVC of the knee extensor in both NOFAD-MUS and FAD-MUS. This protocol was designed to induce mainly peripheral fatigue [15,16]. The magnitude of locomotor muscle fatigue found in the present study was similar to those reported in previous studies using identical eccentric exercise protocols [16,25,26]. These results suggest that our fatigue protocol was effective at inducing a pre-fatigued condition. In addition, there was no significant difference in MVC or DOMS between conditions at the beginning of each experimental session. These two indirect markers of exercise-induced muscle damage suggest that a 7-day washout was appropriated and sufficient to guarantee a full recovery from the last exercise session.
4.2. Time to exhaustion We found that performing constant-load exercise (60% Wmax) after fatiguing jumps resulted in a significant decrease in time to exhaustion (−18.7%). This result corroborates the findings of previous studies that showed a significant reduction in time to exhaustion after a fatiguing exercise [16,27–29]. Marcora et al. [16] reported a reduction in the time to exhaustion (− 15.2%) during a constant-load exercise (80% Wmax) after an identical eccentric exercise protocol (100 drop-jump test), when compared with a control condition. Similarly, Eston et al. [27] reported a significant reduction in the time to exhaustion (− 40.4%) during a constant-load test (75% VO2max) after participants had performed an incremental exercise test, when compared with a control condition. Therefore, the time to exhaustion in moderateintensity exercise performed in a fatigued condition is significantly reduced compared to a non-fatigued condition, indicating that the fatigue protocol used in the present study induced fatigue satisfactorily. While time to exhaustion was reduced in the pre-fatigued condition, listening to music did not reverse the deleterious effects of fatigue on performance, corroborating previous studies that showed no effect of listening to music when participants performed exercise at any level of fatigue [14,30]. For example, Lima-Silva et al. [14] showed that during a 5-km running race, the time to cover the first 1.5 km and the total time were lower when the music was introduced in the first 1.5 km compared to control (no music), while the time to cover the last 1.5 km and the total time were not different between the control condition and the condition with music introduced in the last 1.5 km, suggesting that the runners could be distracted from undesirable feelings only
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Table 3 RPEoverall and RPElegs (average, absolute and relative slopes), heart rate and associative thoughts during the exercise. Data are mean ± SD.
RPEoverall average (units) RPElegs average (units) RPEoverall slope (unit min−1) RPElegs slope (unit min−1) RPEoverall slope (unit %−1) RPElegs slope (unit %−1) HR (bpm) Associative thoughts average (%)
NOFAD-NOMUS
NOFAD-MUS
FAD-NOMUS
13.1 6.1 0.351 0.288 0.101 0.072 153 73.3
12.8 5.7 0.324 0.289 0.084 0.073 154 57.4
14.4 7.1 0.338 0.275 0.075 0.063 155 73.9
± ± ± ± ± ± ± ±
2.4 1.3 0.127 0.138 0.444 0.020c 7 21.9
± ± ± ± ± ± ± ±
2.8 1.4 0.125 0.097 0.036 0.023c 9 25.1b
± ± ± ± ± ± ± ±
2.6a 1.3a 0.224 0.096 0.041 0.026 9 18.8
FAD-MUS 14.5 6.8 0.461 0.327 0.086 0.062 155 63.6
± ± ± ± ± ± ± ±
2.2a 1.4a 0.243 0.198 0.031 0.028 2 23.1b
NOFAD-NOMUS: non-fatigued and listening to no music; NOFAD-MUS: non-fatigued and listening to music; FAD-NOMUS: fatigued and listening to no music; FAD-MUS: fatigued and listening to music. a Significantly higher than NOFAD-NOMUS and NOFAD-MUS (p b .05). b Significantly lower than NOFAD-NOMUS and FAD-NOMUS (p b .05). c Significantly higher than FAD-NOMUS and FAD-MUS (p b .05).
during the first part of the race, when metabolic and physiological afferent signals were less intense. In addition, Tenenbaum et al. [30] showed that when the participants listened to music during a high-intensity exercise (90% VO2max), in which physiological and metabolic signals are intensified, performance was not affected by listening to music, suggesting that the internal cues were stronger than the capacity of distraction from external stimuli. Therefore, our results suggest that when exercise begins with a pre-fatigued condition, the metabolic and physiological signals from fatigued muscles become more powerful, disabling any potential effect of listening to music. On the other hand, although some studies have suggested that during moderate-intensity exercise listening to music may increase the performance [9,10,30], we did not observe any increase in time to exhaustion when subjects listened to music, even when performed without pre-fatigue. Some studies have evaluated the influence of listening to music on performance during either opened- or closed-loop, high-intensity exercises and have found controversial and inconclusive results [14,30–33]. For example, Tenebaum et al. [30] showed that listening to music during running at 90% VO2max had no influence on the time to exhaustion. Similarly, Nakamura et al. [33] found that the total distance covered during constant-load exercise performed at critical power (207 ± 53 W) was not increased when listening to music (preferred or non-preferred music) compared to control. However, Atkinson et al. [31] showed an improvement in performance during a 10-km cycling time trial when athletes listened to music compared with no
As hypothesized, listening to music during the fatigued condition did not change the RPE response during the exercise, suggesting that there was a predominance of the physiological over psychological factors in the fatigued condition. This result is consistent with findings from Nethery [10], who observed no reduction in RPE during highintensity exercise (N80% VO2max) when listening to music. It has been suggested that the integration of sensory cues derived from both cardiopulmonary (e.g., respiratory rate, heart rate) and metabolic factors (e.g., blood lactate level, mechanical strain) indirectly and unconsciously influence the perception of effort during exercise [34]. Furthermore, the extent to which these sensory cues can influence RPE may depend on the intensity of exercise [35,36]. During low-intensity, long-duration exercise (b 80% VO2max), peripheral signals contribute substantially to generate the RPE, while during high-intensity, short-duration exercise (N80% VO2max), cardiopulmonary signals also become important [35]. We observed that HR was similar between all conditions, while
Fig. 3. RPEoverall for non-fatigued listening to music (NOFAD-MUS), non-fatigued listening to no music (NOFAD-NOMUS), fatigued listening to music (FAD-MUS) and fatigued listening to no music (FAD-NOMUS). *FAD-NOMUS and FAD-MUS are significantly higher than NOFAD-NOMUS and NOFAD-MUS at the same time point (p b .05). §Significantly higher than preceding values (p b .05). Data are mean ± SEM.
Fig. 4. RPElegs for non-fatigued and listening to no music (NOFAD-NOMUS), non-fatigued and listening to music (NOFAD-MUS), fatigued and listening to no music (FAD-NOMUS) and fatigued and listening to music (FAD-MUS). *FAD-NOMUS and FAD-MUS are significantly higher than NOFAD-NOMUS and NOFAD-MUS at the same time point (p b .05). § Significantly higher than preceding values (p b .05). Data are mean ± SEM.
music. These controversial findings may be have been caused by methodological issues, such as differences between exercise protocols (closed-loop versus opened-loop exercise), as well as aspects related to the music, such as socio-cultural influences, age, music tempo and musical preference. Further studies should account for these issues when investigating music's effects on performance. 4.3. Psychophysiological responses
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perceived to generate the attentional focus [30]. However, in the present study, the reduction in associative thoughts was not accompanied by a reduction in RPE or an increase in the time to exhaustion, suggesting that listening to music distracts from feelings of fatigue caused by exercise, but the decision to terminate the exercise is ultimately a conscious behavior based on the perception generated in the subconscious homeostatic control system [38]. In other words, listening to music was able to distract the participants from sensations of pain and fatigue caused by exercise, but the perception of exertion was decisive in terminating the exercise. 5. Conclusion
Fig. 5. Associative thoughts in the non-fatigued and listening to no music (NOFADNOMUS), non-fatigued and listening to music (NOFAD-MUS), fatigued and listening to no music (FAD-NOMUS) and fatigued and listening to music (FAD-MUS) conditions. *NOFAD-NOMUS and FAD-NOMUS are significantly higher than NOFAD-MUS and FADMUS at the same time point (p b .05). §Significantly higher than preceding values (p b .05). Data are mean ± SEM.
listening to music did not reduce RPE in the pre-fatigued condition, suggesting that the RPE response may have been mainly influenced by afferent signals coming from pre-fatigued muscles. Unlike our findings, other studies have shown that listening to music during moderate-intensity exercise without fatigue decreases RPE [9, 10]. For example, Potteiger et al. [9] have shown that fast, upbeat music, classical music or self-selected music during exercise at 70% VO2max reduced the peripheral, central and overall RPE compared to the non-music condition. Similarly, Nethery [10] found that listening to music during exercise at 50% VO2max reduced RPE compared with control or sensory-deprived conditions, while during exercise at 80% VO2max, RPE remained unchanged. Although there is no clear explanation for the differences between our study and others [9,10], the different exercise protocols (until exhaustion versus 20 min) may explain these differences. In fact, in a study conducted by Coquart and Garcin [37], in which the participants performed constant-load running tests at 90% of maximal aerobic velocity with known and unknown durations, RPE was lower when the total duration was unknown beforehand, suggesting that the knowledge of the end point influences the RPE response during exercise. Therefore, some studies that demonstrated a decreased RPE during constant-load exercises when listening to music informed the participants of the duration of the test beforehand [9,10]. Instead, in the present study, participants were informed that they should cycle until exhaustion, which could have affected the effects of music on RPE response. In contrast to RPE, associative thoughts were reduced in the music conditions compared with the non-music conditions. The associative thoughts were reduced by music irrespective of the presence of prefatigue. These results corroborate a previous study that demonstrated a reduction in associative thoughts when listening to music [14,22]. In fact, Lima-Silva et al. [14] showed that when music was introduced in the first 1.5 km of a 5-km running time trial, the associative thoughts were reduced during the first kilometer, but associative thoughts were not reduced when the music was introduced in the last 1.5 km of the trial. The effects of listening to music on associative thoughts can be explained by a parallel processing model [11], which predicts that the sensations derived from many different sources compete for focal awareness. The pleasurable external cues provided by music may compete with internal cues arising from physiological and metabolic alterations, occupying an important part of information that is consciously
In summary, our results show that pre-fatigue induces an increase in RPE and a reduction in time to exhaustion during moderate-intensity exercise, and listening to music is not able to reverse these alterations. However, listening to music can reduce associative thoughts, suggesting that music can distract participants from uncomfortable physical feelings caused by exercise, although not to the extent of altering performance. Acknowledgments João Lopes-Silva and Marcos Silva-Cavalcante are grateful to CAPES for their scholarship. References [1] Noakes TD. Linear relationship between the perception of effort and the duration of constant load exercise that remains. J Appl Physiol 2004;96:1571–2. [2] Noakes TD, St. Clair Gibson A. Logical limitations to the “catastrophe” models of fatigue during exercise in humans. Br J Sports Med 2004;38:648–9. [3] Noakes TD, St. Clair Gibson A, Lambert EV. From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans. Br J Sports Med 2004;38:511–4. [4] St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med 2004;38:797–806. [5] Crewe H, Tucker R, Noakes TD. The rate of increase in rating of perceived exertion predicts the duration of exercise to fatigue at a fixed power output in different environmental conditions. Eur J Appl Physiol 2008;103:569–77. [6] Tucker R. The anticipatory regulation of performance: the physiological basis for pacing strategies and the development of a perception-based model for exercise performance. Br J Sports Med 2009;43:392–400. [7] Lima-Silva AE, De-Oliveira FR, Nakamura FY, Gevaerd MS. Effect of carbohydrate availability on time to exhaustion in exercise performed at two different intensities. Braz J Med Biol Res 2009;42:404–12. [8] Lima-Silva AE, Pires FO, Bertuzzi RCM, Lira FS, Casarini DH, Kiss MAPDM. Low carbohydrate diet affects the oxygen uptake on kinetics and rating of perceived exertion in high intensity exercise. Psychophysiology 2010;48:277–84. [9] Potteiger JA, Schroeder JM, Goff KL. Influence of music on ratings of perceived exertion during 20 minutes of moderate intensity exercise. Percept Mot Skills 2000;91: 848–54. [10] Nethery VM. Competition between internal and external sources of information during exercise: influence on RPE and the impact of the exercise load. J Sports Med Phys Fitness 2002;42:172–8. [11] Rejeski WJ. Perceived exertion: an active or passive process? J Sport Psychol 1985;7: 371–8. [12] Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr 1978;40:497–504. [13] Chtourou H, Chaouachi A, Hammouda O, Chamari K, Souissi N. Listening to music affects diurnal variation in muscle power output. Int J Sports Med 2012;33:43–7. [14] Lima-Silva AE, Silva-Cavalcante MD, Pires FO, Bertuzzi R, Oliveira RO, Bishop D. Listening to music in the first, but not the last 1.5 km of a 5-km running trial alters pacing strategy and improves performance. Int J Sports Med 2012;33:813–8. [15] Skurvydas A, Jascaninas J, Zachovajevas P. Changes in height of jump, maximal voluntary contraction force and low-frequency fatigue after 100 intermittent or continuous jumps with maximal intensity. Acta Physiol Scand 2000;169:55–62. [16] Marcora SM, Bosio A, Morree HM. Locomotor muscle fatigue increases cardiorespiratory responses and reduces performance during intense cycling exercise independently from metabolic stress. Am J Physiol Regul Integr Comp Physiol 2008;294: 874–83. [17] Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 1982;14: 377–81. [18] Tammen VV. Elite middle and long distance runners associative dissociative coping. J Appl Sport Psychol 1996;8:1–8. [19] Doherty M, Smith PM, Hughes MG, Collins D. Rating of perceived exertion during high-intensity treadmill running. Med Sci Sports Exerc 2001;33:1953–8.
280
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[20] Razon S, Basevitch I, Land W, Thompson B, Tenenbaum G. Perception of exertion and attention allocation as a function of visual and auditory conditions. Psychol Sport Exerc 2009;10:636–43. [21] Karageorghis CI, Terry PC, Lane AM. Development and validation of an instrument to assess the motivational qualities of music in exercise and sport: the Brunel music rating inventory. J Sports Sci 1999;17:713–24. [22] Edworthy J, Waring H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics 2006;49:1597–610. [23] Karageorghis CI, Priest DL, Terry PC, Chatzisarantis NL, Lane AM. Redesign and initial validation of an instrument to assess the motivational qualities of music in exercise: the Brunel Music Rating Inventory-2. J Sports Sci 2006;24:899–909. [24] Vickers AJ. Time course of muscle soreness following different types of exercise. BMC Musculoskelet Disord 2001;23:2–5. [25] Skurvydas A, Dudoniene V, Kalvenas A, Zuoza A. Skeletal muscle fatigue in longdistance runners, sprinters and untrained men after repeated drop jumps performed at maximal intensity. Scand J Med Sci Sports 2002;12:34–9. [26] Nielsen JS, Madsen K, Jorgensen LV, Sahlin K. Effects of lengthening contraction on calcium kinetics and skeletal muscle contractility in humans. Acta Physiol Scand 2005;184:203–14. [27] Eston R, Faulkner J, St.Clair Gibson A, Noakes T, Parfitt G. The effect of antecedent fatiguing activity on the relationship between perceived exertion and physiological activity during a constant load exercise task. Psychophysiology 2004;44:779–86. [28] Marcora SM, Bosio A. Effect of exercise-induced muscle damage on endurance running performance in humans. Scand J Med Sci Sports 2007;17:662–71 [17, 662–671].
[29] Davies RC, Rowlands AV, Eston RE. Effect of exercise-induced muscle damage on ventilatory and perceived exertion responses to moderate and severe intensity cycle exercise. Eur J Appl Physiol 2009;107:11–9. [30] Tenenbaum G, Lidor R, Lavyan N, Morrow K, Tonnel S, Gershgoren A, et al. The effect of music type on running perseverance and coping with effort sensations. Psychol Sport Exerc 2004;5:89–109. [31] Atkinson G, Wilson D, Eubank M. Effects of music on work-rate distribution during a cycling time trial. Int J Sports Med 2004;25:611–5. [32] Lim HB, Atkinson G, Karageorghis CI, Eubank MR. Effects of differentiated music on cycling time trial. Int J Sports Med 2009;30:435–42. [33] Nakamura PM, Pereira G, Papini CB, Nakamura FY, Kokubun E. Effects of preferred and nonpreferred music on continuous cycling exercise performance. Percept Mot Skills 2010;110:257–64. [34] Hampson DB, St. Clair Gibson A, Lambert MI, Noakes TD. The influence of sensory cues on the perception of exertion during exercise and central regulation of exercise performance. Sports Med 2001;31:935–52. [35] Robertson RJ. Central signals of perceived exertion during dynamica exercise. Med Sci Sports Exerc 1982;14:390–6. [36] Pires FO, Lima-Silva AE, Bertuzzi R, Casarini DH, Kiss MAPDM, Lambert MI, et al. The influence of peripheral afferent signals on the rating of perceived exertion and time to exhaustion during exercise at different intensities. Psychophysiology 2011;48:1284–90. [37] Coquart JB, Garcin M. Knowledge of the endpoint: effect on perceptual values. Int J Sports Med 2008;29:976–9. [38] St Clair Gibson A, Baden DA, Lambert MI, Lambert EV, Harley YXR, Hampson D, et al. The conscious perception of the sensation of fatigue. Sports Med 2003;33:167–76.
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Physiology & Behavior journal homepage: www.elsevier.com/locate/phb
Influence of music on performance and psychophysiological responses during moderate-intensity exercise preceded by fatigue Joao P. Lopes-Silva a, Adriano E. Lima-Silva a,⁎, Romulo Bertuzzi b, Marcos D. Silva-Cavalcante a,b a Sport Science Research Group, Department of Physical Education and Sports Science, Federal University of Pernambuco, Alto do Reservatório Street, Bela Vista, Vitória de Santo Antão, Pernambuco 55608-680, Brazil b School of Physical Education and Sport (GEDAE-USP), University of São Paulo, Mello de Moraes street, 65, Butanta, São Paulo 05508900, Brazil
H I G H L I G H T S • • • •
Time to exhaustion is not influenced by the music. Prior fatigue reduces performance and listening to music cannot reverse this effect. Listening to music seems draw the attentional focus away from internal sensations of fatigue. Listening to music distracted feelings of fatigue, but the decision to finish the exercise is based on RPE.
a r t i c l e
i n f o
Article history: Received 26 June 2013 Received in revised form 13 November 2014 Accepted 14 November 2014 Available online 20 November 2014 Keywords: Time to exhaustion Perceived exertion Associative thoughts Listening to music
a b s t r a c t Purpose: We examined the effects of listening to music on time to exhaustion and psychophysiological responses during moderate-intensity exercise performed in fatigued and non-fatigued conditions. Methods: Fourteen healthy men performed moderate-intensity exercise (60% Wmax) until exhaustion under four different conditions: with and without pre-fatigue (induced by 100 drop jumps) and listening and not listening to music. Results: Time to exhaustion was lower in the fatigued than the non-fatigued condition regardless listening to music. Similarly, RPE was higher in the fatigued than the non-fatigued condition, but music had no effect. On the other hand, listening to music decreased the associative thoughts regardless of fatigue status. Heart rate was not influenced by any treatment. Conclusion: These results suggest that listening to music changes attentional focus but is not able to reverse fatigue-derived alteration of performance. © 2014 Elsevier Inc. All rights reserved.
1. Introduction In the last decade, a centrally regulated system model has been proposed to explain exercise performance, in which the metabolic rate during exercise is regulated by the central nervous system in a dynamic, non-linear and integrative manner, to ensure that a catastrophic failure of homeostasis does not occur [1–3]. In this model, peripheral afferent signals are all integrated by the brain and used to generate the rating of perceived exertion (RPE). The rate at which RPE increases during opened-loop, constant-load exercises (in which exercise intensity cannot be reduced) seems to be set at the beginning of the exercise and
⁎ Corresponding author at: Department of Physical Education and Sports Science, Federal University of Pernambuco Alto do Reservatório Street, Bela Vista, Vitória de Santo Antão, Pernambuco 55608-680, Brazil. Tel.: +55 819593980. E-mail addresses: [email protected] (J.P. Lopes-Silva), [email protected] (A.E. Lima-Silva), [email protected] (R. Bertuzzi), [email protected] (M.D. Silva-Cavalcante).
http://dx.doi.org/10.1016/j.physbeh.2014.11.048 0031-9384/© 2014 Elsevier Inc. All rights reserved.
regulated as a function of the remaining time to exhaustion [1]. Thus, the time to exhaustion during a given exercise bout can be predicted by the rate of increase of RPE [4–8]. It is interesting to note that some psychological manipulation techniques alter the RPE response during opened-loop exercise [9,10]. For example, Potteiger et al. [9] have shown that fast, upbeat music, classical music or self-selected music during exercise at 70% VO2max reduced peripheral, central and overall RPE compared with the no-music condition. Similarly, Nethery [10] found that listening to music during exercise at 50% VO2max reduced the RPE compared to control or sensorydeprived conditions. Surprisingly, none of these studies verified whether the reduction in RPE led to an increase in exercise capacity, i.e., in time to exhaustion. Therefore, whether the reduction in RPE when listening to music during opened-loop, moderate-intensity exercise is converted into a higher performance remains unknown. The effects of listening to music on RPE have been explained through the model of parallel processes [11]. This model suggests the existence of competition in the cognition of information, where information
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derived from many different sources during exercise competes for focal awareness. In accordance with this model, listening to music would exert an influence only at low-to-moderate exercise intensities (b80% VO2max), and in this situation, external stimuli would be able to compete against weaker internal cues for attentional focus. However, internal cues (e.g., pH, lactate, VO2, ventilation, respiratory rate) become stronger at higher exercise intensities (N80% VO2max) and would predominate over external stimuli for attentional focus, suggesting that listening to music would have no effect on RPE at this intensity [10]. Nevertheless, while it seems an attractive model to explain the effect of listening to music on RPE, this model has not been widely and thoroughly tested. A prediction of this model could be experimentally tested if a moderateintensity exercise is performed in a pre-fatigued condition, in which the internal cues would compete more strongly with the music for attentional focus than in a non-pre-fatigued condition. Therefore, if the model is correct, these intensified afferent signals would be more powerful at controlling attentional focus than the music would. The present study was designed to verify the influence of listening to music on the time to exhaustion and psychophysiological responses during opened-loop, moderate-intensity exercise, performed in fatigued and non-fatigued conditions. First, we hypothesized that RPE would be greater during the fatigued compared with the non-fatigued condition, leading to a reduction in the time to exhaustion. Second, because the internal afferent signals coming from pre-fatigued muscles may be stronger than the distraction of listening to music, we hypothesized that listening to music would have no influence on either performance or psychophysiological responses in the fatigued condition. 2. Materials and methods 2.1. Participants Fourteen healthy, physically active men participated in this study. The main characteristics of the participants are described in Table 1. The protocol, benefits and risks were explained, and written consent was obtained from each participant. The study's procedures were previously approved by Ethics Committee of the Federal University of Alagoas. 2.2. Experimental design The participants reported to the laboratory on six different occasions. During the first visit, participants underwent anthropometric measurements and performed an incremental test for maximal power output determination (Wmax). One hour after the incremental test, the participants performed a familiarization test at 60% Wmax until exhaustion. At least 72 h after the incremental test, participants performed the following tests, in a counterbalanced order and 7 days apart: (1) one test with pre-fatigue at 60% Wmax listening to music (FAD-MUS); (2) one test with pre-fatigue at 60% Wmax listening to no music (FADNOMUS); (3) one test at 60% Wmax without pre-fatigue and listening to music (NOFAD-MUS); and (4) one test at 60% Wmax without prefatigue and listening to no music (NOFAD-NOMUS). All tests were performed at the same time of day to minimize any circadian variance. The participants were asked to refrain from vigorous physical activities, caffeine and alcohol 48 h before each test.
Table 1 Mean (±SD) for age and anthropometric measurements of the participants. Values Age (years) Height (cm) Weight (kg) Body fat (%)
24.0 140 74.6 15.2
± ± ± ±
1.7 73 5.9 5.5
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2.3. Anthropometric measurements The weight and height of each subject were measured using a digital scale (0.1 kg accuracy) and a stadiometer (0.1 cm accuracy), respectively. The percentage of body fat mass was estimated from three measurements of skinfold thickness (biceps, abdominal and thigh on the right side with the subject in a standing position) using a Harpenden caliper (Quinton Instruments Company, Washington), according to Jackson and Pollock [12]. While more sophisticated methods such as bodpod, hydrostatic weighing, DEXA and deuterium dilution provide more precise estimative of body composition, skinfold thickness also provide satisfactory measurement (standard error of the estimate ~0.0077%) [12]. 2.4. Incremental test The incremental test was performed on an electrical magnetic braked cycle ergometer (Ergo-Fit 167, Ergo-Fit GmbH & Co., Pirmasens, Germany). The test was started with a 3-min warm-up at 50 W, followed by 25 W increases every minute until exhaustion. The participants were required to maintain a pedal frequency of approximately 70 rpm. The Wmax was determined as the highest power output reached during last complete stage. Exhaustion was assumed when the participants could not sustain a pedal frequency higher than 65 rpm. 2.5. Experimental test Before each test, the participants performed a standardized 5-min warm-up at 10% Wmax. Immediately after the warm-up, the power output was adjusted to 60% Wmax, and the participants cycled at 70 rpm until exhaustion. Exhaustion was identified using the same criteria adopted in the incremental test. In the music conditions, a portable MP3 player was turned on after warm-up. In the non-music conditions, headphones were connected, but the portable MP3 player remained off during the entire test [13, 14]. In the pre-fatigued tests, ~ 60 min before the test, the participants performed a validated protocol to induce fatigue (see below) [15,16]. The following variables were measured at intervals of 5 min and at exhaustion during all the tests: heart rate (HR), overall RPE, leg RPE and associative thoughts. HR was measured using a cardio frequency meter (Polar S810i heart rate monitor, Polar Electro OY, Kempele, Finland). RPE was measured using the Borg 15-point scale [17]. The participants were asked to report a peripheral RPE based on discomfort from joints and muscles of the legs (RPElegs) and an overall RPE based on discomfort from the whole body experienced during exercise (RPEoverall). The individual RPEoverall and RPElegs values were regressed against absolute time (min), and the percentage of the time to volitional exhaustion (% time to exhaustion). The slope of RPE against time and % time to exhaustion were computed using a least squares fitting procedure. Furthermore, the percentage of associative thoughts (attentional focus) was assessed with a 10-cm bipolar line [18]. The reliability and validity of these scales have been reported in previous studies [19,20]. 2.6. Music details A specialized disc jockey (DJ) selected pop and rock music tracks based on the recommendations of Karageorghis, Terry, and Lane [21]. The participants listened to upbeat, fast music (N 120-140 bpm) through headphones connected to a portable MP3 player set at approximately 80 decibels [22]. Each track lasted approximately 4 min, and the same track sequence was applied during NOFAD-MUS and FAD-MUS [18]. The song titles, artists and track durations used were (1) Destination Calabria, Alex Gaudino, 3:02 min; (2) Dreams, Van Halen, 5:01 min; (3) Voyage, voyage, Desireless, 4:10 min; (4) Bound for Glory, Angry Anderson, 4:13 min; (5) Rise Up, Yves Larock, 2:53 min; and (6) Lay Down Your Guns, Jimmy Barnes, 3:56 min. Participants completed the Brunel Music Rating Inventory-2 (BMRI-2) immediately after completion
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of the experimental conditions that included music to ascertain which musical aspects (rhythm, style, melody, tempo, instrument and beat) were most motivational in their performance [23]. 2.7. Fatigue protocol To induce a pre-fatigued condition, the participants performed a protocol consisting of 100 drop jumps approximately 60 min before FAD-MUS and FAD-NOMUS. The participants drooped 100 times from a 40-cm platform down to a 90° knee angle before jumping upward as high as possible. There was a 20-s rest period for recovery between each drop jump [15,16]. To verify the effectiveness of this protocol to induce fatigue, a bilateral maximum voluntary isometric contraction (MVC) of the quadriceps muscle (knee extension) was performed immediately before (MVCpre) and after the 100 drop jumps (MVCpos). The force produced during MVCs was measured using a load cell (EMG SYSTEM DO BRASIL LTDA). After MVCpos, the participants rested for 30 minutes by sitting in a chair. Thereafter, the participants walked to the cycle ergometer and performed the experimental test. 2.8. Muscle damage assessment To ensure that each session started without any trace of muscle soreness induced by the previous session, we used a 7-day washout period. In addition, the delayed-onset muscle soreness (DOMS) was subjectively assessed immediately before each experimental test using a seven-point Likert scale [24]. The participants were asked to rate the overall level of DOMS felt in both legs (i.e., buttocks, groin, thighs, hamstrings, calves and shins) during the past 12 waking hours according to the following verbal anchors: (0) a complete absence of soreness; (1) a light pain felt only when touched/a vague ache; (2) a moderate pain felt only when touched/a slight persistent pain; (3) a light pain when walking up or down stairs; (4) a light pain when walking on a flat surface/painful; (5) a moderate pain, stiffness or weakness when walking/very painful; and (6) a severe pain that limited the ability to move. Finally, the baseline MVC values were compared between the four conditions to determine whether the experimental sessions began with similar initial force levels because force generation is impaired by muscle soreness [16]. 2.9. Statistical analysis The data are shown as the means ± SD, unless otherwise noted. A two-way analysis of variance with repeated measures (music/ no-music × fatigued/non-fatigued) followed by a Bonferroni adjustment was used to compare the time to exhaustion, MVC, RPE, HR, associative thoughts and slopes of RPE. A three-way analysis of variance with repeated measures (music/no-music × fatigued/non-fatigued × time) followed by a Bonferroni adjustment was used to compare RPE, HR and associative thoughts as a function of time. When assumptions of sphericity were violated, the critical value of F was adjusted using the Greenhouse-Geisser epsilon value from the Mauchley test of sphericity. Scores for music, rhythm, style, melody, tempo, instrument and beat were compared between the NOFAD-MUS and the FAD-MUS conditions using the paired t-test. Significance was accepted at p b .05. 3. Results 3.1. Fatigue protocol The MVC immediately after the fatigue protocol was significantly lower (p b .05) than at the baseline for both the FAD-NOMUS and FAD-MUS conditions (Table 2), but there was no differences between these two conditions (p N .05), suggesting that the protocol was equally effective at reducing muscle force and inducing fatigue in both conditions.
Table 2 Maximum voluntary isometric contraction (MVC) immediately before and after the fatigue protocol.
FAD-NOMUS FAD-MUS
MVCpre (kgf)
MVCpos (kgf)
117.9 ± 30 124.5 ± 23
99.6 ± 35.9* 104.8 ± 28.3*
FAD-NOMUS: pre-fatigue condition listening to no music, FAD-MUS: pre-fatigue condition listening to music. Data are reported as the mean ± SD. *Significantly lower than before fatigue protocol (p b .05).
3.2. Muscle damage assessment and MVC before experimental tests When the individuals were asked to answer the DOMS questionnaire, only one participant reported a light pain when walking up or down stairs, while all the other participants reported a complete absence of soreness. Furthermore, no difference (p N .05) was found in MVCpre between the four conditions (NOFAD-NOMUS: 123.2 ± 31.1; NOFAD-MUS: 123.4 ± 30; FAD-NOMUS: 117.9 ± 30; and FAD-MUS: 124.5 ± 23 kgf), suggesting that all the participants began the experimental tests without any clear signal of fatigue caused by the prior experimental session. 3.3. Music ratings Scores for individual components of rhythm, style, melody, tempo and beat were not different between NOFAD-MUS and FAD-MUS (Fig. 1). Participants reported that all of the music's aspects rhythm (p N .05), style (p N .05), melody (p N .05), tempo (p N .05), instrument (p N .05) and beat (p N .05) were equally motivating in both conditions. 3.4. Time to exhaustion The time to exhaustion was significantly lower in the FAD-NOMUS and FAD-MUSIC than in the NOFAD-NOMUSIC and NOFAD-MUSIC conditions (p b .05), but there was no main effect of music (p N .05) (Fig. 2). In addition, there was no fatigue × music interaction (p N .05). 3.5. Psychophysiological responses There was a main effect of fatigue on mean RPEoverall (p b .05), but there was no significant effect of music (p N .05) or the music × fatigue interaction on RPEoverall (p N .05) (Table 3). The mean RPEoverall values were significantly higher in both fatigued conditions than the nonfatigued conditions (Table 3). RPEoverall increased significantly over time in all conditions (p b .05). There was a main effect of fatigue (p b .05), but there was no effect of music (p N .05) or the interaction between fatigue × music on RPEoverall (p N .05) (Fig. 3). When the slope of RPEoverall was plotted against the exercise time (absolute) (or the percentage of total exercise time completed (relative), there was no difference between the conditions (Table 3). In addition, there was no significant difference in RPEoverall at the exhaustion point between the four conditions (Fig. 3). Similar to RPEoverall, there was a main effect of fatigue on mean RPElegs (p b .05), but without any effect of music (p N .05) or the interaction between music × fatigue (p N .05) (Table 3). RPElegs increased significantly over time in all conditions (p b .05). Similar to RPEoverall, there was a main effect of fatigue (p b .05), but not music (p N .05) or the interaction between music × fatigue (p N .05), on RPElegs (Fig. 4). When the slope of RPElegs was plotted against the absolute exercise time, there was no significant difference between the conditions (p N .05). However, when RPElegs slope was plotted against the percentage of the time to exhaustion, there was a main effect of fatigue (p b .05), without any effect of music (p N .05) or the music × fatigue interaction (p N .05) (Table 3). There was no significant difference in RPElegs at exhaustion between the four conditions (Fig. 4).
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Fig. 1. Mean scores ± SEM of individual components of the BMRI-2 for the non-fatigued listening to music (NOFAD-MUS) and fatigued listening to music (FAD-MUS) conditions.
There was a main effect of music on mean associative thoughts (p b .05), but there was no main effect of fatigue (p N .05) or the music × fatigue interaction (p N .05) (Table 3). The associative thoughts increased significantly over time in all conditions (p b .05) (Fig. 5), and the values were lower in conditions with than without music (p b .05). However, there was no effect of fatigue (p N .05) or the music × fatigue interaction (p N .05) on the increase in associative thoughts over time. The mean HR was similar between all the conditions (p N .05) and increased significantly with time (p b .05).
4. Discussion The main findings of the present study are (1) the pre-fatigued condition reduced time to exhaustion and increased RPE during moderateintensity exercise, whereas listening to music did not to reduce or reverse the impairment caused by fatigue and (2) listening to music had a more pronounced effect on associative thoughts, reducing the attentional focus regardless of the fatigue condition.
Fig. 2. Time to exhaustion. Values are represented as the mean ± SEM. NOFAD-NOMUS: non-fatigued and listening to no music; NOFAD-MUS: non-fatigued and listening to music; FAD-NOMUS: fatigued and listening to no music; FAD-MUS: fatigued and listening to music. *Significantly lower than NOFAD-NOMUS and NOFAD-MUS.
4.1. Locomotor muscle fatigue and indirect markers of muscle damage The 100 drop-jump protocol induced a significant reduction (− 16.5%) in the MVC of the knee extensor in both NOFAD-MUS and FAD-MUS. This protocol was designed to induce mainly peripheral fatigue [15,16]. The magnitude of locomotor muscle fatigue found in the present study was similar to those reported in previous studies using identical eccentric exercise protocols [16,25,26]. These results suggest that our fatigue protocol was effective at inducing a pre-fatigued condition. In addition, there was no significant difference in MVC or DOMS between conditions at the beginning of each experimental session. These two indirect markers of exercise-induced muscle damage suggest that a 7-day washout was appropriated and sufficient to guarantee a full recovery from the last exercise session.
4.2. Time to exhaustion We found that performing constant-load exercise (60% Wmax) after fatiguing jumps resulted in a significant decrease in time to exhaustion (−18.7%). This result corroborates the findings of previous studies that showed a significant reduction in time to exhaustion after a fatiguing exercise [16,27–29]. Marcora et al. [16] reported a reduction in the time to exhaustion (− 15.2%) during a constant-load exercise (80% Wmax) after an identical eccentric exercise protocol (100 drop-jump test), when compared with a control condition. Similarly, Eston et al. [27] reported a significant reduction in the time to exhaustion (− 40.4%) during a constant-load test (75% VO2max) after participants had performed an incremental exercise test, when compared with a control condition. Therefore, the time to exhaustion in moderateintensity exercise performed in a fatigued condition is significantly reduced compared to a non-fatigued condition, indicating that the fatigue protocol used in the present study induced fatigue satisfactorily. While time to exhaustion was reduced in the pre-fatigued condition, listening to music did not reverse the deleterious effects of fatigue on performance, corroborating previous studies that showed no effect of listening to music when participants performed exercise at any level of fatigue [14,30]. For example, Lima-Silva et al. [14] showed that during a 5-km running race, the time to cover the first 1.5 km and the total time were lower when the music was introduced in the first 1.5 km compared to control (no music), while the time to cover the last 1.5 km and the total time were not different between the control condition and the condition with music introduced in the last 1.5 km, suggesting that the runners could be distracted from undesirable feelings only
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Table 3 RPEoverall and RPElegs (average, absolute and relative slopes), heart rate and associative thoughts during the exercise. Data are mean ± SD.
RPEoverall average (units) RPElegs average (units) RPEoverall slope (unit min−1) RPElegs slope (unit min−1) RPEoverall slope (unit %−1) RPElegs slope (unit %−1) HR (bpm) Associative thoughts average (%)
NOFAD-NOMUS
NOFAD-MUS
FAD-NOMUS
13.1 6.1 0.351 0.288 0.101 0.072 153 73.3
12.8 5.7 0.324 0.289 0.084 0.073 154 57.4
14.4 7.1 0.338 0.275 0.075 0.063 155 73.9
± ± ± ± ± ± ± ±
2.4 1.3 0.127 0.138 0.444 0.020c 7 21.9
± ± ± ± ± ± ± ±
2.8 1.4 0.125 0.097 0.036 0.023c 9 25.1b
± ± ± ± ± ± ± ±
2.6a 1.3a 0.224 0.096 0.041 0.026 9 18.8
FAD-MUS 14.5 6.8 0.461 0.327 0.086 0.062 155 63.6
± ± ± ± ± ± ± ±
2.2a 1.4a 0.243 0.198 0.031 0.028 2 23.1b
NOFAD-NOMUS: non-fatigued and listening to no music; NOFAD-MUS: non-fatigued and listening to music; FAD-NOMUS: fatigued and listening to no music; FAD-MUS: fatigued and listening to music. a Significantly higher than NOFAD-NOMUS and NOFAD-MUS (p b .05). b Significantly lower than NOFAD-NOMUS and FAD-NOMUS (p b .05). c Significantly higher than FAD-NOMUS and FAD-MUS (p b .05).
during the first part of the race, when metabolic and physiological afferent signals were less intense. In addition, Tenenbaum et al. [30] showed that when the participants listened to music during a high-intensity exercise (90% VO2max), in which physiological and metabolic signals are intensified, performance was not affected by listening to music, suggesting that the internal cues were stronger than the capacity of distraction from external stimuli. Therefore, our results suggest that when exercise begins with a pre-fatigued condition, the metabolic and physiological signals from fatigued muscles become more powerful, disabling any potential effect of listening to music. On the other hand, although some studies have suggested that during moderate-intensity exercise listening to music may increase the performance [9,10,30], we did not observe any increase in time to exhaustion when subjects listened to music, even when performed without pre-fatigue. Some studies have evaluated the influence of listening to music on performance during either opened- or closed-loop, high-intensity exercises and have found controversial and inconclusive results [14,30–33]. For example, Tenebaum et al. [30] showed that listening to music during running at 90% VO2max had no influence on the time to exhaustion. Similarly, Nakamura et al. [33] found that the total distance covered during constant-load exercise performed at critical power (207 ± 53 W) was not increased when listening to music (preferred or non-preferred music) compared to control. However, Atkinson et al. [31] showed an improvement in performance during a 10-km cycling time trial when athletes listened to music compared with no
As hypothesized, listening to music during the fatigued condition did not change the RPE response during the exercise, suggesting that there was a predominance of the physiological over psychological factors in the fatigued condition. This result is consistent with findings from Nethery [10], who observed no reduction in RPE during highintensity exercise (N80% VO2max) when listening to music. It has been suggested that the integration of sensory cues derived from both cardiopulmonary (e.g., respiratory rate, heart rate) and metabolic factors (e.g., blood lactate level, mechanical strain) indirectly and unconsciously influence the perception of effort during exercise [34]. Furthermore, the extent to which these sensory cues can influence RPE may depend on the intensity of exercise [35,36]. During low-intensity, long-duration exercise (b 80% VO2max), peripheral signals contribute substantially to generate the RPE, while during high-intensity, short-duration exercise (N80% VO2max), cardiopulmonary signals also become important [35]. We observed that HR was similar between all conditions, while
Fig. 3. RPEoverall for non-fatigued listening to music (NOFAD-MUS), non-fatigued listening to no music (NOFAD-NOMUS), fatigued listening to music (FAD-MUS) and fatigued listening to no music (FAD-NOMUS). *FAD-NOMUS and FAD-MUS are significantly higher than NOFAD-NOMUS and NOFAD-MUS at the same time point (p b .05). §Significantly higher than preceding values (p b .05). Data are mean ± SEM.
Fig. 4. RPElegs for non-fatigued and listening to no music (NOFAD-NOMUS), non-fatigued and listening to music (NOFAD-MUS), fatigued and listening to no music (FAD-NOMUS) and fatigued and listening to music (FAD-MUS). *FAD-NOMUS and FAD-MUS are significantly higher than NOFAD-NOMUS and NOFAD-MUS at the same time point (p b .05). § Significantly higher than preceding values (p b .05). Data are mean ± SEM.
music. These controversial findings may be have been caused by methodological issues, such as differences between exercise protocols (closed-loop versus opened-loop exercise), as well as aspects related to the music, such as socio-cultural influences, age, music tempo and musical preference. Further studies should account for these issues when investigating music's effects on performance. 4.3. Psychophysiological responses
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perceived to generate the attentional focus [30]. However, in the present study, the reduction in associative thoughts was not accompanied by a reduction in RPE or an increase in the time to exhaustion, suggesting that listening to music distracts from feelings of fatigue caused by exercise, but the decision to terminate the exercise is ultimately a conscious behavior based on the perception generated in the subconscious homeostatic control system [38]. In other words, listening to music was able to distract the participants from sensations of pain and fatigue caused by exercise, but the perception of exertion was decisive in terminating the exercise. 5. Conclusion
Fig. 5. Associative thoughts in the non-fatigued and listening to no music (NOFADNOMUS), non-fatigued and listening to music (NOFAD-MUS), fatigued and listening to no music (FAD-NOMUS) and fatigued and listening to music (FAD-MUS) conditions. *NOFAD-NOMUS and FAD-NOMUS are significantly higher than NOFAD-MUS and FADMUS at the same time point (p b .05). §Significantly higher than preceding values (p b .05). Data are mean ± SEM.
listening to music did not reduce RPE in the pre-fatigued condition, suggesting that the RPE response may have been mainly influenced by afferent signals coming from pre-fatigued muscles. Unlike our findings, other studies have shown that listening to music during moderate-intensity exercise without fatigue decreases RPE [9, 10]. For example, Potteiger et al. [9] have shown that fast, upbeat music, classical music or self-selected music during exercise at 70% VO2max reduced the peripheral, central and overall RPE compared to the non-music condition. Similarly, Nethery [10] found that listening to music during exercise at 50% VO2max reduced RPE compared with control or sensory-deprived conditions, while during exercise at 80% VO2max, RPE remained unchanged. Although there is no clear explanation for the differences between our study and others [9,10], the different exercise protocols (until exhaustion versus 20 min) may explain these differences. In fact, in a study conducted by Coquart and Garcin [37], in which the participants performed constant-load running tests at 90% of maximal aerobic velocity with known and unknown durations, RPE was lower when the total duration was unknown beforehand, suggesting that the knowledge of the end point influences the RPE response during exercise. Therefore, some studies that demonstrated a decreased RPE during constant-load exercises when listening to music informed the participants of the duration of the test beforehand [9,10]. Instead, in the present study, participants were informed that they should cycle until exhaustion, which could have affected the effects of music on RPE response. In contrast to RPE, associative thoughts were reduced in the music conditions compared with the non-music conditions. The associative thoughts were reduced by music irrespective of the presence of prefatigue. These results corroborate a previous study that demonstrated a reduction in associative thoughts when listening to music [14,22]. In fact, Lima-Silva et al. [14] showed that when music was introduced in the first 1.5 km of a 5-km running time trial, the associative thoughts were reduced during the first kilometer, but associative thoughts were not reduced when the music was introduced in the last 1.5 km of the trial. The effects of listening to music on associative thoughts can be explained by a parallel processing model [11], which predicts that the sensations derived from many different sources compete for focal awareness. The pleasurable external cues provided by music may compete with internal cues arising from physiological and metabolic alterations, occupying an important part of information that is consciously
In summary, our results show that pre-fatigue induces an increase in RPE and a reduction in time to exhaustion during moderate-intensity exercise, and listening to music is not able to reverse these alterations. However, listening to music can reduce associative thoughts, suggesting that music can distract participants from uncomfortable physical feelings caused by exercise, although not to the extent of altering performance. Acknowledgments João Lopes-Silva and Marcos Silva-Cavalcante are grateful to CAPES for their scholarship. References [1] Noakes TD. Linear relationship between the perception of effort and the duration of constant load exercise that remains. J Appl Physiol 2004;96:1571–2. [2] Noakes TD, St. Clair Gibson A. Logical limitations to the “catastrophe” models of fatigue during exercise in humans. Br J Sports Med 2004;38:648–9. [3] Noakes TD, St. Clair Gibson A, Lambert EV. From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans. Br J Sports Med 2004;38:511–4. [4] St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med 2004;38:797–806. [5] Crewe H, Tucker R, Noakes TD. The rate of increase in rating of perceived exertion predicts the duration of exercise to fatigue at a fixed power output in different environmental conditions. Eur J Appl Physiol 2008;103:569–77. [6] Tucker R. The anticipatory regulation of performance: the physiological basis for pacing strategies and the development of a perception-based model for exercise performance. Br J Sports Med 2009;43:392–400. [7] Lima-Silva AE, De-Oliveira FR, Nakamura FY, Gevaerd MS. Effect of carbohydrate availability on time to exhaustion in exercise performed at two different intensities. Braz J Med Biol Res 2009;42:404–12. [8] Lima-Silva AE, Pires FO, Bertuzzi RCM, Lira FS, Casarini DH, Kiss MAPDM. Low carbohydrate diet affects the oxygen uptake on kinetics and rating of perceived exertion in high intensity exercise. Psychophysiology 2010;48:277–84. [9] Potteiger JA, Schroeder JM, Goff KL. Influence of music on ratings of perceived exertion during 20 minutes of moderate intensity exercise. Percept Mot Skills 2000;91: 848–54. [10] Nethery VM. Competition between internal and external sources of information during exercise: influence on RPE and the impact of the exercise load. J Sports Med Phys Fitness 2002;42:172–8. [11] Rejeski WJ. Perceived exertion: an active or passive process? J Sport Psychol 1985;7: 371–8. [12] Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr 1978;40:497–504. [13] Chtourou H, Chaouachi A, Hammouda O, Chamari K, Souissi N. Listening to music affects diurnal variation in muscle power output. Int J Sports Med 2012;33:43–7. [14] Lima-Silva AE, Silva-Cavalcante MD, Pires FO, Bertuzzi R, Oliveira RO, Bishop D. Listening to music in the first, but not the last 1.5 km of a 5-km running trial alters pacing strategy and improves performance. Int J Sports Med 2012;33:813–8. [15] Skurvydas A, Jascaninas J, Zachovajevas P. Changes in height of jump, maximal voluntary contraction force and low-frequency fatigue after 100 intermittent or continuous jumps with maximal intensity. Acta Physiol Scand 2000;169:55–62. [16] Marcora SM, Bosio A, Morree HM. Locomotor muscle fatigue increases cardiorespiratory responses and reduces performance during intense cycling exercise independently from metabolic stress. Am J Physiol Regul Integr Comp Physiol 2008;294: 874–83. [17] Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 1982;14: 377–81. [18] Tammen VV. Elite middle and long distance runners associative dissociative coping. J Appl Sport Psychol 1996;8:1–8. [19] Doherty M, Smith PM, Hughes MG, Collins D. Rating of perceived exertion during high-intensity treadmill running. Med Sci Sports Exerc 2001;33:1953–8.
280
J.P. Lopes-Silva et al. / Physiology & Behavior 139 (2015) 274–280
[20] Razon S, Basevitch I, Land W, Thompson B, Tenenbaum G. Perception of exertion and attention allocation as a function of visual and auditory conditions. Psychol Sport Exerc 2009;10:636–43. [21] Karageorghis CI, Terry PC, Lane AM. Development and validation of an instrument to assess the motivational qualities of music in exercise and sport: the Brunel music rating inventory. J Sports Sci 1999;17:713–24. [22] Edworthy J, Waring H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics 2006;49:1597–610. [23] Karageorghis CI, Priest DL, Terry PC, Chatzisarantis NL, Lane AM. Redesign and initial validation of an instrument to assess the motivational qualities of music in exercise: the Brunel Music Rating Inventory-2. J Sports Sci 2006;24:899–909. [24] Vickers AJ. Time course of muscle soreness following different types of exercise. BMC Musculoskelet Disord 2001;23:2–5. [25] Skurvydas A, Dudoniene V, Kalvenas A, Zuoza A. Skeletal muscle fatigue in longdistance runners, sprinters and untrained men after repeated drop jumps performed at maximal intensity. Scand J Med Sci Sports 2002;12:34–9. [26] Nielsen JS, Madsen K, Jorgensen LV, Sahlin K. Effects of lengthening contraction on calcium kinetics and skeletal muscle contractility in humans. Acta Physiol Scand 2005;184:203–14. [27] Eston R, Faulkner J, St.Clair Gibson A, Noakes T, Parfitt G. The effect of antecedent fatiguing activity on the relationship between perceived exertion and physiological activity during a constant load exercise task. Psychophysiology 2004;44:779–86. [28] Marcora SM, Bosio A. Effect of exercise-induced muscle damage on endurance running performance in humans. Scand J Med Sci Sports 2007;17:662–71 [17, 662–671].
[29] Davies RC, Rowlands AV, Eston RE. Effect of exercise-induced muscle damage on ventilatory and perceived exertion responses to moderate and severe intensity cycle exercise. Eur J Appl Physiol 2009;107:11–9. [30] Tenenbaum G, Lidor R, Lavyan N, Morrow K, Tonnel S, Gershgoren A, et al. The effect of music type on running perseverance and coping with effort sensations. Psychol Sport Exerc 2004;5:89–109. [31] Atkinson G, Wilson D, Eubank M. Effects of music on work-rate distribution during a cycling time trial. Int J Sports Med 2004;25:611–5. [32] Lim HB, Atkinson G, Karageorghis CI, Eubank MR. Effects of differentiated music on cycling time trial. Int J Sports Med 2009;30:435–42. [33] Nakamura PM, Pereira G, Papini CB, Nakamura FY, Kokubun E. Effects of preferred and nonpreferred music on continuous cycling exercise performance. Percept Mot Skills 2010;110:257–64. [34] Hampson DB, St. Clair Gibson A, Lambert MI, Noakes TD. The influence of sensory cues on the perception of exertion during exercise and central regulation of exercise performance. Sports Med 2001;31:935–52. [35] Robertson RJ. Central signals of perceived exertion during dynamica exercise. Med Sci Sports Exerc 1982;14:390–6. [36] Pires FO, Lima-Silva AE, Bertuzzi R, Casarini DH, Kiss MAPDM, Lambert MI, et al. The influence of peripheral afferent signals on the rating of perceived exertion and time to exhaustion during exercise at different intensities. Psychophysiology 2011;48:1284–90. [37] Coquart JB, Garcin M. Knowledge of the endpoint: effect on perceptual values. Int J Sports Med 2008;29:976–9. [38] St Clair Gibson A, Baden DA, Lambert MI, Lambert EV, Harley YXR, Hampson D, et al. The conscious perception of the sensation of fatigue. Sports Med 2003;33:167–76.