\cite{Boldt_1996}Discussion
\cite{Biagini_2012}\cite{Atan2013}
\cite{kravitz2007}
\cite{szabo1999}
\cite{Mokrane_1998}
The average calculated low-frequency power percentage values from the ECG were higher for the group that performed with music than the group that performed without music. The p-value calculated from the associated one-way ANOVA was less than 0.05, indicating that there is a statistical significance between the mean low-frequency value between the music and non-music groups. Many athletes utilize music during exercise or resistance training for higher amounts of mental stimulation, or to get “pumped up”, which many believe can increase athletic performance or delay the effects of fatigue on the body. It was expected to see an increased low-frequency power percentage value in the group that listened to music compared to the group that didn’t have music, as this would be indicative of an increased sympathetic branch activation of the autonomic nervous system. It would make sense to see increased sympathetic activation in those individuals who are preparing mentally to work out as the sympathetic system of the autonomic nervous system is responsible for mobilizing blood from the internal organs to somatic muscles to prepare for movement, provide supplemental energy to the body, and facilitate oxygen uptake.6
The average heart rate, measured in beats per minute, was higher in the group that listened to music compared to the group that performed without music. This result was expected and goes hand-in-hand with the previous result explaining that the group with music had an increased low-frequency power percentage value, as this potentially indicates an increased sympathetic system activation. Increased sympathetic activation should illustrate an increased heart rate, due to the fact that the sympathetic system controls the release of catecholamines at the sinus node which causes an increase in heart rate in response to stress or excitation. 8 The p-value from the one-way ANOVA comparing the average heart rate of the music and non-music group was less than 0.05, indicating statistical significance between the groups. However, it is worth mentioning that the music group had an average heart rate of 79 beats per minute, while the non-music group had an average heart rate of 76 beats per minute. The change between groups of 3 bpm is not much of a considerable change, and it is very hard to attribute the minor difference in heart rate between groups to solely the music stimulus. There could be many other factors of the environmental condition that could cause a change in heart rate as the laboratory setting is very foreign to many of the participants, there were outside noises from other individuals that could be startling, as well as just natural physiological phenomena occurring like anxiety or nervousness that can adjust heart rate.
During the goniometer test, the average person held the weight at 90 degrees for nearly 56 seconds longer when listening to music. This was predicted since listening to music can distract the psychological fatigue allowing the the subject to hold a weight longer without changing the 10 degrees.9 There was a higher standard deviation in the subjects when listening to music than those who were not listening to music. This data was affected by the one large outlier seen in Figure 4 as the subject did not know they were supposed to just give up when they felt very fatigued and in this case, the subject held waiting for the researchers to tell them to stop. As for the controls (those who listened to no music both time) they had an average time holding the weight for 86 seconds and the second time in they had an average time of 78 seconds. This helped support our data as there was not any significant difference between the two trials in the control group. The slight difference in time could have been due to subjects not trying as hard the second trial.
Only 12 subjects participated in the study making this experiment difficult to suggest proper accuracy and significant data. Also, there were 4 controls in this experiment that performed the activities in silence both times they came in. The environmental conditions in the experiment were possibly distracting and the equipment was difficult for some users to use to their full potential. Specifically, the hand dynamometer was very uncomfortable for many users especially those with smaller hands, making them not able to squeeze as hard as possible due to pain rather than fatigue. A more ergonomic handle could possibly help the subjects complete this task with more accuracy. Also, the second time participants came in, they had learned the experiment and knew they could "cheat" the hand dynamometer and not give a 100% max grip strength in the beginning. Also, the conditions were not ideal due to many distractions going on in the lab, which could possibly change the ECG measures. Working in a silent room when people were not listening to music would help improve the accuracy of the data. The EMG measurement could have also been performed in a better manner. A more telling way to use the EMG for this study would have been to use it alongside the goniometer test, where the participants were pushed to a point of fatigue. By doing this the amount of muscle recruitment during a physically taxing event would be more easily shown. While the study used only the curl of a 15 pound weight, which did not push any of the participants to a point of fatigue. Moreover, a more ideal representation to demonstrate fatigue would have been to use the EMG with isometric contractions in order to run a frequency analysis, as this could demonstrate the body progressively recruiting slow-twitch fibers as fatigue affects the body.