23.2: A Theoretical Model for Music Applications in Sport

Karageorghis (2016) published a theoretical model detailing the antecedents, moderators, and consequences of music use in exercise and sport (see Figure 23.1). Herein, we will focus solely upon the implications of the model for sport participation and training. Moreover, we will not discuss every detail of the model; the interested reader is referred to the original 2016 publication and also to chapter 2 of the Karageorghis (2017) text. The model embraced several aspects of previous models (e.g., Karageorghis et al., 1999; Terry & Karageorghis, 2006), but took an ecumenical and fully integrative approach, particularly in relation to proposed antecedents and moderators. Moreover, the model is heuristic in nature and not mechanistic, meaning that it provides a fisheye view to facilitate fluid understanding of what is, in actuality, a highly complex array of relationships, processes, and neural mechanisms.

Figure 23.1. A Theoretical Model of the Antecedents, Moderators, and Consequences of Music Use in the Exercise and Sport Domain

Note.Republished with permission of Taylor & Francis Informa UK Ltd – Books, from Sport and exercise psychology (2nd ed., p. 301), by A. M. Lane (Ed.), London, UK: Routledge. Copyright © 2016; permission conveyed through Copyright Clearance Center.

The antecedents or precursors are the intrinsic and extrinsic qualities of music, moderators are those factors that influence the strength of the relationship between a musical stimulus and an athlete’s responses to it (e.g., age and attentional style), and consequences relate to the main outcomes associated with music use during sport-related activities (e.g., arousal regulation, reduced rating of perceived exertion [RPE], or enhanced recovery). The model is predicated on ~50 years of empirical research (see Karageorghis & Priest, 2012a, 2012b; Karageorghis & Terry, 1997; Lucaccini & Kreit, 1972; Smirmaul, 2017; Terry et al., 2020, for reviews).

The model includes variables relating to athletes themselves, the nature of the task in which they are engaged, and the specifics of the sporting context (see Figure 23.1). Moreover, both individual and group-based training are considered. Given that the model is heuristic in nature and thus embraces a broad range of factors, only segments of it can be tested in any single empirical study. The complexity of relationships represented within the model is such that individual hypotheses are not made explicit in pictorial form; rather these are summarized in narrative form (see Karageorghis, 2016). The model postulates a series of reciprocal interactions and feedback loops among the antecedents, moderators, and consequences of music use. Input, in this instance, music, is identified, coded, and moderated by a broad range of personal and situational factors.

To touch on intrinsic musical factors (see Antecedents in Figure 23.1), the temporal aspects of music such as tempo, rhythm, and meter can bear strong influence on an athlete’s level of activation. Contrastingly, aspects such as modality (e.g., major vs. minor) and harmony (how notes are combined) may be salient in terms of evoking affective responses (Juslin, 2013). Rhythm and tempo will lead to differential responses depending on the age or personality profile of athletes and the nature of the task in which they are engaged (e.g., Franěk et al., 2014; Karageorghis et al., 2019; Liljeström et al., 2013).

It is thought that extraverts are likely to prefer stimulative music (cf. Eysenck, 1967; McCown et al., 1997), which is characterized by a fast tempo (> 120 bpm), prominent rhythmical features, and exaggerated bass tones. Along similar lines, the melodic and harmonic qualities of music will lead to differential responses depending on an athlete’s cultural background. Lyrics contain semantic information and their effects on the athlete will depend on the way they receive them as well as the relevance of lyrics to a given athletic task (Karageorghis, 2017; Sanchez et al., 2014).

Among the extrinsic properties of music, iconic cues pertain to how structural elements of a piece of music relate to the tone of certain emotions. For example, music that is slow and soft may sound “relaxing” because there are intrinsic commonalities with sedation and restiveness (see e.g., North & Hargreaves, 2008). Because such cues are grounded in the structure of music, it is expected that the same music should hold similar “iconic meaning” for different athletes (i.e., irrespective of their ethnic or cultural background). Cultural associations are often forged through the mass media and are, therefore, likely to be relevant to large sections of the population. Taking the earlier-cited example of Vangelis’s Chariots Of Fire, the piece immediately conjures images of athletes striding over the sands at St. Andrews, Scotland and of striving for Olympic glory. Such imagery is fused in the collective consciousness due to the immediate associations with Hugh Hudson’s Oscar-winning movie.

The model depicts a reciprocal relationship between personal and situational factors given that, in a sport setting, the music should be functional or carefully coordinated with the tasks and specifics of the session (cf. Kodzhaspirov et al., 1986). Moderators such as personal preferences and attentional style (e.g., associative vs. dissociative; see Hutchinson & Karageorghis, 2013) will interact with the social environment to determine an athlete’s response to music. There is a wealth of empirical evidence showing that gender and age moderate athletes’ response to pieces of music (e.g., Crust, 2008; Karageorghis et al., 2010, Karageorghis, Bigliassi et al., 2018). There is a tendency for women to rate the rhythmical qualities and danceability of music more highly than men; nonetheless, men tend to value the importance of cultural associations to a greater degree than women, and prefer styles that might be described as “heavier” (e.g., Colley, 2008; Hallett & Lamont 2017).

To touch on the consequences (see the right-hand side of Figure 23.1), the two strongest and most consistent appear first (psychological and psychophysical), followed by behavioral consequences, and finally, psychophysiological consequences, that are the least consistent (see Karageorghis & Priest, 2012b; Terry et al., 2020). Several empirical studies show that athletes appear to experience several of the consequences in unison. For example, appropriate music use can result in enhanced affect that is coupled with greater work output (Karageorghis et al., 2010; Olson et al., 2015; Terry et al., 2012).

In Figure 23.1, you will notice the inclusion of a feedback loop from the consequences back to the music factors. This relates to how the consequences that an athlete might experience influence their future music-selection decisions, and how this process is moderated by personal factors and situational factors. Accordingly, the model predicts that an athlete’s response to music will be evaluated by them with reference to the moderator factors, and that their evaluation will serve to shape future selection decisions. The implication is that an athlete is more likely to reselect pieces of music that are deemed to lead to positive consequences and vice versa.