The participants remained seated for a short period of time (10 minutes), as they answered the BRUMS (pre). This tool (BRUMS) represents a questionnaire to assess mood states, consisting of 24 adjectives (0–16 Likert Scale) stratified among 6 mood domains: anger, confusion, depression, fatigue, tension, and vigor (22). Additionally, all equipment (heart rate monitor and pedometer—DigiWalker SW200; Yamax, San Antonio, TX, USA) were placed at this time, and they could spread sunblock (Sundown; Johnson & Johnson, New Brunswick, NJ, USA), repellent (Repelex, São Paulo, Brazil), drink water, and use the restroom if they judged necessary.
During the next 10 minutes, the participants remained lying and HRV analysis (described below) was performed, with a complete period of silence (when randomly selected songs from PM were inserted at this time).
The warm-up period consisted of 2 minutes of walking in a self-selected rhythm, plus 6 minutes of jogging at a heart rate of 130 b·min−1 (allowing ±5 b·min−1 variations), plus 2 minutes of running at a heart rate of 150 b·min−1 (allowing ±5 b·min−1 variations) (adapted from Skof and Strojnik) (34) in a synthetic space to run in circles (radius = ±10 m) in the middle of the track.
The participants had to run 5 km as fast as they could, and for every lap (400 m) + 200 final meters they had to check their HR and RPE (15-point scale), answering the following question: “how hard is the task at this moment?” (5); during all tests, the values very, very light (number 7), and very, very hard (number 19) were used as anchoring points (27). They were also given instructions regarding the importance of the veracity of the reported RPE, highlighting the fact that answering a lower or higher value of perception would not influence their performance. In addition, the time of each lap + 200 final meters was recorded with a digital chronometer (Ipod; Apple, Inc.) by the same evaluator. For music conditions (PM, SM, FM, and CS), all tracks were played using the same previous device (another Ipod; Apple, Inc.) placed in a clamp (HammerHead; São José, Santa Catarina, Brazil) with a headset (Model ONB-M80; Onbongo), keeping the volume at 75 (±5) dB measured directly on the ear with a decibel meter (Icel DL-4020, Brazil).
After 5 km of running, the subjects had to lie down immediately and stay there for a period of 10 minutes, collecting HRV information in a complete period of silence. For music condition (CS), all tracks (Enya, May It Be, New Age Style—110 b·min−1; Bach, Air, On the G String, Classic Style—106 b·min−1; Hilary Stagg, Pleasant Dreams, New Age Style—95 b·min−1) were chosen following style recommendations (41), and the track order was always the same.
Finally, the participants remained seated and answered the BRUMS (post). All equipment was withdrawn, and they could drink water, use the restroom, and eat a cereal bar (Nestlé—banana taste; Nestlé S.A., Vevey, Canton of Vaud, Switzerland). At the end of 30 minutes, they answered the CR10 (10 points) concerning their general perception of effort in the whole period of running.
Heart Rate Variability: Running Test
The HRV was collected during the resting and recovery periods to assess the impact of music on vagal withdrawal and the recovery process, respectively. The last 5 minutes of the resting period and the total time exposure of the recovery period were considered for analysis; a strong digital filter was applied to smooth possible noise (detailed explanations regarding data analyses are better described below).
For the time domain, the root mean square of successive differences (RMSSD—vagal nerve index) and SD of R-R intervals (SDNN—global activity of autonomous system) were included (13). Fast Fourier transform was used to estimate the density of power specter, and this variable was quantified by 2 band pass: low frequency (LF)—0.04–0.15 Hz (global activity of autonomous systems) and high frequency (HF)—0.15–0.40 Hz (vagal nerve index) (13). Furthermore, a nonlinear analysis using a Poincaré plot was performed, presenting SD1 (parasympathetic) and SD2 (global activity) as extra indexes (14).
Heart Rate Variability: Functional Near-Infrared Spectroscopy
These same variables were obtained in the neuroimaging test to measure the influence of music on emotional responses through the autonomous system. The total music time was considered for HRV parameters, and a strong digital filter was applied to smooth possible noise. The HRV process was the same as described under the section Heart Rate Variability: Running Test.
A moving average window of 0.5 seconds was applied to each dependent variable of fNIRS and subsequently analyzed using software developed on Excel 2010 (Microsoft Office, Redmond, WA, USA) to stratify, account, share by voxels and PFC area/side (medial PFC [mPFC]—voxels 7, 8, 9, and 10; right dorsolateral PFC [RdlPFC]—voxels 11, 12, 13, 14, 15, and 16; left dorsolateral PFC [LdlPFC]—voxels 1, 2, 3, 4, 5, and 6), interpreting values to O2Hb and HHb. The difference between the neuroimaging test and baseline was represented in a.u.·μM−1. The difference between O2Hb and HHb was calculated previously (Diffbase) and during each track (Difftest). Finally, the difference of these differences (Difftest − Diffbase = activation) was considered as PFC activation (24), representing the increase of O2Hb, the decrease of HHb, or mutual considerations. The spectral and temporal components of HRV were analyzed with the Kubios HRV Software (Biosignal Laboratory, University of Kuopio, Finland).
Functional Near-Infrared Spectroscopy: Statistical Analysis
Data smoothing was performed through outlier identification, and subsequent replacement was performed applying multiple imputation in 6 cells for all variables. After descriptive statistics, data normality and homogeneity were checked using Shapiro-Wilk's test and Levene's test, respectively. The variables mPFC, LR, SAM (VAL and ACT), HF, LF, and SD1 presented Gaussian distribution and scalar dispersion (presented as mean ± SD), with possible parametric assessments (1-way ANOVA, followed by Bonferroni's post hoc test for multiple comparisons).
Nevertheless, RdlPFC, LdlPFC, and RMSSD did not show the same distribution (presented as median and interquartile range). In addition, the high dispersion does not allow any transformation (log10, square, etc.), enabling only nonparametric evaluations (Kruskal-Wallis' test followed by Mann-Whitney U-test when necessary). Furthermore, SDNN and SD2 initially had a nonparametric distribution, but the log10 transformations through computing variable were capable of correcting this dispersion, allowing parametric comparisons, as described above. The paired comparisons between the off period (PM condition) and music insertion were performed through paired Student's t-test for parametric variables (cerebral activation and HRV analysis) and Wilcoxon test for nonparametric variables. The significance level was set at p ≤ 0.05.
Five-Kilometer Run: Statistical Analyses
Data smoothing was performed through outlier identification, and subsequent replacement was performed applying multiple imputation in 8 cells for all variables. After descriptive statistics, data normality and homogeneity were checked using Shapiro-Wilk's test and Levene's test, respectively. Psychological variables (BRUMS, TRIMP, and FS) were processed through nonparametric analysis because of high dispersion (zero or negative values) and nonscalar distribution (presented as median and interquartile range). These variables were compared between PRE and POST moments using the Wilcoxon test and at each moment using the Kruskal-Wallis test followed by Mann-Whitney U-test when necessary.
HRV variables were analyzed through parametric methods (independent Student's t-test), only computing variables for RMSSD and SD1 (log10). This allowed a quick and standard correction. We have also conducted a temporal analysis for RMSSD, which was compared through repeated-measure ANOVA followed by Tukey's post hoc test (30-second windows). Rate of perceived exertion, HR, and LP were compared through repeated-measure ANOVA followed by Tukey's post hoc test for multiple comparisons (13 analyses), and the number of steps and total time were compared using 1-way ANOVA followed by Bonferroni's post hoc test. The significance level was set at p ≤ 0.05.
The smallest worthwhile change was used for total time in an attempt to check the possibility of the effect being prejudicial, trivial, or beneficial. The chances in the test were classified as <1% almost surely not, 1–5% very unlikely, 5–25% unlikely, 25–75% possible, 75–95% likely, 95–99% very likely, and >99% almost sure. When the chance of increase and decrease were both higher than 5%, the final examination was considered undetermined. The outcome description had the following criteria: first values mean the benefits, second values mean the trivial effect, and third values mean the prejudicial effect (first/second/third) (39).
Functional Near-Infrared Spectroscopy
The 3 studied areas of the PFC were activated during music exposure without differences between the experimental conditions (Table 1): mPFC (F = 0.605; p = 0.615), RdlPFC (χ2 = 2.53; p = 0.471), and LdlPFC (χ2 = 0.67; p = 0.880). Nevertheless, the silence time applied after PM showed a significant increase in the medial and right dorsolateral areas of the PFC (residual effects during 5 off minutes) (mPFC: Z score Δ = −1.78, p = 0.042; RdlPFC: Z score Δ = −1.70, p = 0.049; LdlPFC: Δ = −1.53, p = 0.125). Also, Kruskal-Wallis' test did not shown significant differences between PFC regions compared between the experimental conditions (PM: χ2 = 0.53, p = 0.762; SM: χ2 = 0.42, p = 0.807; FM: χ2 = 1.94, p = 0.379; CS: χ2 = 0.36, p = 0.835).
The chosen songs for PM, SM, and FM presented satisfactory enjoyment, pleasure, and activation levels (LR: 8.07 ± 1.87 a.u., VAL: 6.33 ± 1.62 a.u., ACT: 5.93 ± 1.02 a.u. for PM; LR: 7.60 ± 1.51 a.u., VAL: 6.67 ± 1.59 a.u., ACT: 5.47 ± 1.73 a.u. for SM; LR: 8.03 ± 1.33 a.u., VAL: 6.60 ± 1.18 a.u., ACT: 5.77 ± 1.54 a.u. for FM) without differences between them (LR: F = 0.39, p = 0.672; VAL: F = 0.20, p = 0.817; ACT: F = 1.67, p = 0.201). Nonetheless, CS showed significant differences compared with the other 3 for LR and ACT (LR: 5.53 ± 2.26 a.u., F = 6.56, p = 0.012; VAL: 5.60 ± 1.35 a.u., F = 1.64, p = 0.190; ACT: 3.23 ± 1.67 a.u., F = 3.13, p = 0.033).
The HRV analysis provided useful information regarding the emotional response to music. The following variables did not show any significant difference between the experimental conditions: SDNN: F = 2.14, p = 0.105; LF: F = 1.36, p = 0.262; HF: F = 1.52, p = 0.217; SD2: F = 2.26, p = 0.091. However, SD1 (general F = 2.95; p = 0.040) and RMSSD (general χ2 = 10.67; p = 0.14) demonstrated important and significant differences when CS (SD1: 35.33 ± 10.58 ms; RMSSD: 52 [20.1] ms) was compared with SM (SD1: 27.52 ± 7.76 ms, p = 0.014; RMSSD: 37 [12.2] ms, Z score Δ = −2.69, p = 0.006) and FM (SD1: 29.53 ± 9.55 ms, p = 0.048; RMSSD: 39 [9.4] ms, Z score Δ = −2.38, p = 0.017). Additionally, the paired comparisons between music exposure for PM and the off period (5 minutes of silence) did not show any differences for SDNN (t = −0.25; p = 0.801), RMSSD (Z = −0.56; p = 0.572), LF (t = −0.78; p = 0.443), HF (t = 0.62; p = 0.545), SD1 (t = 0.92; p = 0.369), and SD2 (t = −0.31; p = 0.358).
Psychological Analysis: Five-Kilometer Run
Analyses concerning personality were considered very consistent (low dispersion) (4 choleric, 3 melancholic, 1 phlegmatic, 7 sanguine). Eleven participants were classified as extroverted (choleric and sanguine) and 8 were considered unstable (choleric and melancholic). This feature might be influenced by the physical practice, which makes unlikely further stratifications.
The 6 BRUMS' domains demonstrated no differences between any experimental conditions regarding the PRE period (anger: χ2 = 2.61, p = 0.624; confusion: χ2 = 4.11, p = 0.391; depression: χ2 = 0.52, p = 0.901; fatigue: χ2 = 2.03, p = 0.730; tension: χ2 = 3.34, p = 0.501; vigor: χ2 = 0.98, p = 0.912) or POST period (anger: χ2 = 2.09, p = 0.718; confusion: χ2 = 3.27, p = 0.513; depression: χ2 = 0.77, p = 0.942; fatigue: χ2 = 1.43, p = 0.838; tension: χ2 = 5.70, p = 0.222; vigor: χ2 = 0.76, p = 0.943). Therefore, music exposure was not capable of modulating mood after 5 km of running, as presented in Figure 4. The general perception of effort (CR10) demonstrated no differences according to the experimental conditions after 5 km of running, which indicates a similar psychological stress (p > 0.05).
Heart Rate Variability: Five-Kilometer Run
The general analysis of whole resting (before) and recovery (after) periods has shown a significant difference for PM and CS according to some parasympathetic indices. The music application before running did not affect the general RMSSD (CO: 63.1 ± 14.81 ms vs. PM: 54.5 ± 21.82 ms; t = 1.60; p = 0.120), SDNN (CO: 79.14 ± 22.63 ms vs. PM: 81.17 ± 22.12 ms; t = −0.24; p = 0.805), LF (CO: 61.65 ± 15.60 n.u. vs. PM: 61.52 ± 14.31 n.u.; t = 0.02; p = 0.981), HF (CO: 38.41 ± 15.59 n.u. vs. PM: 38.38 ± 14.31 n.u.; t = −0.01; p = 0.990), SD2 (CO: 101.14 ± 28.56 ms. vs. PM: 106.63 ± 28.50 ms; t = −0.52; p = 0.603), but this was capable of influencing the nonlinear index of vagal nerve SD1 (CO: 41.97 ± 9.61 ms vs. PM: 38.52 ± 15.58 ms; t = 19.32; p = 0.002). Moreover, temporal analysis of RMSSD according to motivational music application presented very interesting outcomes (less parasympathetic values) as shown in Figure 5.
During recovery, calm songs not only demonstrated an effect for SDNN (CO: 50.96 ± 16.60 ms vs. CS: 45.42 ± 13.93 ms; t = −0.99; p = 0.331) and SD2 (CO: 67.58 ± 28.70 ms. vs. CS: 59.54 ± 24.25 ms; t = −0.82; p = 0.414) but also for RMSSD (CO: 4.78 ± 1.87 ms vs. CS: 7.69 ± 4.60 ms; t = 12.1; p < 0.001), LF (CO: 88.93 ± 5.55 n.u. vs. CS: 82.24 ± 10.11 n.u.; t = −2.22; p = 0.034), HF (CO: 11.09 ± 5.46 n.u. vs. CS: 17.75 ± 8.12 n.u.; t = 2.21; p = 0.035), SD1 (CO: 3.4 ± 1.3 ms vs. CS: 5.36 ± 3.22 ms; t = 11.86; p < 0.001). Similarly, a temporal analysis of RMSSD after running showed a visual distribution of vagal activation through 2 experimental conditions (higher parasympathetic values) (CO and CS—Figure 6).
Psychophysiological and Performance Responses: Five-Kilometer Run
No HR or RPE differences were found for any protocol during all lap assessments. This represents a similar psychophysiological stress when analyzed in an isolated way. Nonetheless, the first 2 laps were dissimilar for SM and FM compared with the other 3 (CO, PM, and CS).
In addition, the final performance was considered the same despite the visual differences (CO: 1639 ± 143 seconds; PM: 1605 ± 148 seconds; SM: 1560 ± 146 seconds; FM: 1565 ± 144 seconds; CS: 1636 ± 150 seconds; F = 0.415; p = 0.797). Posterior analyses involving SWC showed a higher probability to achieve benefit by applying music as an ergogenic aid for 5 km of running (PM vs. CO: 39/60/2—possible; SM vs. CO: 89/11/0—likely; FM vs. CO: 85/15/0—likely). Second, the number of steps was considered similar (CO: 4543 ± 668 steps; PM: 4491 ± 584 steps; SM: 4476 ± 577 steps; FM: 4510 ± 605; CS: 4550 ± 647 steps; F = 0.041; p = 0.977). The psychophysiological comparisons for 5 km of running are demonstrated in Figure 7.
The purpose of this study was to investigate how music could aid in 5 km of running (psychophysiological, performance, and recovery parameters), applied at different times, and then verify the cerebral and emotional response to such external stimulus. Thus, music was capable of activating the PFC area (medial and dorsolateral areas—left and right), evoking emotional responses (pleasure and activation), decreasing vagal tonus before running (as an indicator of preparation), increasing the same parasympathetic index after running (accelerating recovery process), improving initial speed up to 800 m, and providing a likely chance to achieve a better performance. Furthermore, it is important to note that this study accomplished a very real training situation (5 km of running—open space and self-selected songs), making the present findings useful for further applications.
Initially, music demonstrated an interesting activation in the 3 studied PFC areas (mPFC, RdlPFC, and LdlPFC), without differences between the experimental conditions. The PFC has been widely studied because of its great neural network with parallel areas (amygdala, hippocampus, cingulate cortex, and parietal cortex), representing an important link for external stimuli analysis. These cited areas indicate that chosen songs in some manner were capable of inducing emotional or memory responses. Also, the cingulate cortex and parietal cortex have been known for extensive performance monitoring and higher order sensory processing, respectively (30,40). However, such an emotional link was expected through music exposure, but residual effects with higher activation at 5 minutes during the silence period showed that music may induce at least medium-term benefits before physical tasks. These data agree with León-Carrión et al. (23) who tested a very similar task with 30 healthy subjects (15 men; 25.84 ± 7.62 years), applying a variety of film clips carrying emotional features (17 in.) and demonstrated higher HbO2 during stimuli cessation (off period).
Furthermore, SAM and HRV analyses provided an accompanying result. Self-Assessment Manikin has shown that CS conditions were considered calm compared with the other 3 conditions (PM, SM, and FM), and the RMSSD and SD1 (parasympathetic indices) presented higher values for CS compared with SM and FM. These results suggest an intriguing effect of music in modulating the vagal nerve through unconscious and conscious cortical areas in humans, decreasing stress through higher pituitary action (↓ interleukin-6, ↓ epinephrine, ↓ dehydroepiandrosterone) (7).
The music was not considered as a sufficient stimulus to alter psychological states due to the high intensity of the exercise. These outcomes contrast with other important studies involving open-loop exercises and probably this difference may indicate a more prominent psychological effect of music exposure for free exercises compared with time-trial protocols (38), which could create a previous calculus inside trained minds, inserting these participants in a parallel world of closed-loop exercise. As a result of this, the final outcome of this technique would be blocked from external influences (31,35). The first 800 m were markedly faster for SM and FM. Thereafter, similar responses were evident among all experimental conditions. Theoretically, the auditory stimulus is expected to prolong time to exhaustion by acting to limit the processing of fatigue-related cues through the efferent nervous system. Initially, participants were affected by music because they needed a time period to process all afferent information regarding peripheral receptors. As soon as the brain realized the exercise intensity, a mechanism called attentional switching occurred by directing attention to the most important signals (15,33). It decreased the effects of music on running performance because they have focused on internal processes instead of external influences.
This study has shown a real emotional response through music exposure before and after 5 km of running. The ability to increase arousal and to calm down was clearly demonstrated. Such an effect represents a very significant tool for arousal and recovery control without side effects. Several studies have applied music at previous times (during warm-up), finding controversial outcomes. This is because of the different methods applied (exercise models) (11,16), which must be taken into account for final recommendations.
Additionally, improvements in recovery using music are really rare (9), and the parasympathetic reduction after high-intensity cyclic exercise (maximum aerobic; ∼30 minutes), represents a simple and effective strategy for HRV manipulation through the sensory system. Also, the chosen songs were accepted by runners, and all components of music (tempo, melody, harmony, and style) were probably able to affect the arousal state through Conrad's proposed mechanism (7), decreasing physiological responses through arousal control.
Despite HR and RPE stabilization, the first and second lap showed significant improvements for music conditions SM and FM, and this represents an energy economy as shown previously by several studies (2,26). Despite PM indicating only a 39% chance of benefit and diminished vagal tonus for running, no differences were found for any variables with this experimental condition. It is considered likely that such a previous strategy was quickly covered by the physical exercise (more important peripheral cues for the central nervous system rather than residual effects of music). The same purpose may have happened in medium and final laps for SM and FM because of the curve distribution (Figure 7). Interestingly, SM and FM have very similar features, and despite asynchronous songs being tested, synchronous mechanisms could be aimed by runners unconsciously (20), making SM and FM more similar than expected.
A series of limitations in this study exist because of the research model (field applied research—outdoor). First of all, the weather represents one of the most important problems of this study because it is impossible to control. Second, asynchronous music can induce lesser outcomes than synchronous, but this strategy can fit better to real life (amateur runners using music for training). Third, the lap count may have created a kind of distraction factor (mainly for music conditions), making exercise more pleasurable, but losing knowledge of the end point. Finally, sedative songs (CS) did not follow the self-selected method applied for the other conditions (PM, SM, and FM). As a result of this, current outcomes regarding CS might be different according to varied cultures.
We conclude that music conditions are capable of activating the PFC (medial and dorsolateral—left and right), causing greater residual effects, increasing vagal withdrawal (HRV analysis) before running, improving parasympathetic recovery (HRV analysis) after running, and decreasing initial lap times (first and second laps).
Enhancing performance or accelerating recovery is a common goal for coaches and athletes. Our study has demonstrated an interesting application of self-selected music for running, which is considered a simple method to achieve a variety of improvements. First of all, coaches and athletes are able to use music during training session as a means by which to improve running performance. During high-load microcycles when the necessity of motivation and enthusiasm is high, music-related interventions may aid runners to accomplish better results. Based on the current outcome, runners can also use music before competitions with a moderate chance of 39% to achieve benefits by applying music as an ergogenic aid. Finally, exercisers can use sedative auditory stimuli to accelerate fast-recovery (10 minutes) and prevent cardiac-related complications. For further replications, many aspects must be taken into consideration, including indoor track assessments (weather control), whole superficial cerebral analysis (fMRI or EEG), even during exercise (12), and synchronous music using online software to achieve real synchronization during cyclic exercises (39).
The authors thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for graduate scholarships and Professor Jim Waterhouse for his meaningful contributions.
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Keywords:Copyright © 2015 by the National Strength & Conditioning Association.
prefrontal cortex; sensory aids; exercise