Abstract
This study explores exercisers’ use of self-selected music. Ten participants (seven female, three male) aged 26–58 years who exercised regularly took part in semi-structured interviews about their exercise and music use. Interviews explored how they sourced, selected and experienced music during exercise. The recorded data were transcribed and analysed using interpretative phenomenological analysis (IPA) to identify common patterns while also recognising individual experience. Four themes were identified: Taking control, referring to overcoming internal and external challenges through music; It’s all about me, involving self-identity and social positioning; Exercise-music literacy, concerning musical judgement and technological skills; and Embodiment, concerning body-music-hardware interactions and synchronisation. The results show examples of circumstances under which music provides exercisers with both positive and negative experiences. The findings contribute to understanding of the effects of music in exercise and demonstrate the individuality of preferences and usage.
Keywords
Exercise has therapeutic and preventative effects for physical health (Myers, 2008; Warburton, Charlesworth, Ivey, Nettlefold, & Bredin, 2010) and mental health (Donaghy, 2007; Stanton, Happell, & Reaburn, 2014). Governments in the UK, US and Australia recommend a minimum of 150 minutes weekly of moderate-intensity exercise (Weed, 2016), with UK guidelines also including two strength training sessions. Nevertheless, inactivity levels are high; in the UK, 39% of adults do not meet government recommendations, and 60% are unaware of them (British Heart Foundation, 2017). Evidence from clinical populations suggests that music played during exercise can improve adherence to exercise programmes (Alter et al., 2015; Mathews, Clair, & Kosloski, 2001). Music use may, therefore, help address inactivity.
Everyday music listening
Research into everyday music listening suggests listeners’ selection processes are dynamic, and dependent on sociocultural background, mood and environment (Bull, 2007; Heye & Lamont, 2010). Mood is related to concepts of affect and emotion, and all are central to music choice and listening. Following Juslin and Sloboda’s (2011) definitions, affect is used here as an umbrella term in relation to evaluative or valenced assessments, while emotion is used to mean short-term, intense and attributable to a specific cause, and mood is applied to longer-lasting states with non-specific cause. Terminology is maintained in participants’ quotes.
Responses to music involve complex interactions; Hargreaves, in his reciprocal feedback model (2012), presents physiological, cognitive and affective responses to music, interacting in varied ways, leading to individual musical preference. Although some listeners are more adept, or “musically sophisticated”, than others (Müllensiefen, Gingras, Musil, & Stewart, 2014), DeNora (2000) notes that all listeners are skilled at finding the “right” music for their general listening requirements. Skånland’s participants described an interactive process of choosing music that “felt right” (2013, p. 6), enabling reflection on their current mood. Listeners regulated affect and controlled their environments by creating a soundtracked private space so that, for example, stress-inducing crowds became “masses of fine people” (Skånland, 2013, p. 7). This suggests music affects how individuals draw meaning from their external environment, indicating interplay between extrinsic and intrinsic factors.
Heye and Lamont’s (2010) study of MP3 listening during travel similarly identified the personalisation of listening, creating a private environment that simultaneously enhanced awareness of surroundings. The varied choices among 422 participants were predicated on familiarity and liking. Bull also emphasised personalisation, describing it as “privatisation of public space” (2007, p. 4); technology confers control, allowing construction of a personalised, exclusionary environment.
The main contributors to music selection comprise individual and situational factors (Greb, Schlotz, & Steffens, 2017). Greb et al. (2017) explored self-chosen instances of everyday music listening collected through an online survey, with 5% of the 1761 instances across 587 participants involving listening to music during exercise. Selection of exercise music was predicated on wellbeing and motor synchronisation, although elsewhere synchronisation has been found not to be a priority in self-reports on music and exercise use (Hallett & Lamont, 2017). Krause, North, and Hewitt (2016) found that creating a particular level of arousal is key to choice of exercise music, supporting a link between psychological arousal and movement intensity.
Listening to music while exercising
The term “exercise” has been used inconsistently in the literature. Here, the definition proposed by Caspersen, Powell, and Christenson (1985), specifying structure and repetition of movement in the pursuit of physical fitness, is used. This definition includes a wide variety of activities in a range of settings, encompassing government recommendations, existing literature and participants’ own concepts. “Workout” is used to refer to any structured session meeting Casperson et al.’s definition of exercise.
As shown above, listeners have distinct criteria for selecting music in different contexts (Bull, 2007; DeNora, 2000) and exercise introduces particular requirements, most notably for motivation, arousal and, in some cases, distraction (Hallett & Lamont, 2015). A theoretical model for the motivational effect of music in exercise (Karageorghis, 2016) suggested that intrinsic musical features such as rhythm, alongside personal associations and cultural contexts, would, via motivation, affect arousal and rate of perceived exertion. This updated an earlier influential model proposed by Karageorghis, Terry, and Lane (1999) by incorporating greater reciprocity between factors.
Physical effort may be associated with both negative and positive affect (Bellows-Riecken, Mark, & Rhodes, 2013; Ekkekakis, 2009). For example, positive affect may arise from biological processes such as the release of cannabinoids (Boecker et al., 2008; Raichlen, Foster, Gerdeman, Seillier, & Giuffrida, 2012), or from psychosocial elements such as a sense of achievement or meeting social needs (Bellows-Riecken et al., 2013). Negative affect, on the other hand, is associated with feeling obligation, the exercise environment, and the discomfort of exertion (Bellows-Riecken et al., 2013; Ekkekakis, 2009). There is qualitative evidence that exercisers choose music to manage affect (Hallett & Lamont, 2015; Priest & Karageorghis, 2008). Additionally, media use, such as listening to music or watching TV during exercise, has a distracting effect from effort and may also create the perception that time spent exercising is passing more quickly (Hallett & Lamont, 2015).
Selecting music for exercise
Previous research has tended to use researcher- or peer-selected/rated music in controlled experiments, rather than participants’ own choices. For example, Bird, Hall, Arnold, Karageorghis, and Hussein (2016) used peer ratings to identify motivational musical stimuli for a study of music and affect in recreational exercisers. In cases of self-selection (where music is chosen by the listener rather than a researcher or instructor), constraints are sometimes employed; for example, in a study of performance, mood, feeling state (affect) and physiological outcomes, Terry, Karageorghis, Saha, and D’Auria (2012) offered elite triathlete participants 100 tracks to choose from to facilitate the necessary synchronisation of running cadence. These examples demonstrate the compromise between experimental control and participants’ varied music preferences.
Qualitative research has examined music listening in gyms, covering both other-selected and self-selected music. Kinnafick, Thogersen-Ntoumani, Shepherd, Wilson, Wagenmakers, and Shaw’s (2018) study of a High Intensity Interval Training (HIIT) intervention found that the other-selected music went unnoticed or was not engaged with, although evidence suggests other-selected music played in gyms can still be perceived positively even if not congruent with preferences (Hallett & Lamont, 2015; Priest & Karageorghis, 2008). The latter two studies included other-selected and self-selected music, but neither explored music use in other exercise contexts, such as outdoor running. Hallett and Lamont (2017) explored a broader range of exercise types and environments, but the analysis was quantitative. The present qualitative study was designed to be complementary, drawing a small number of participants from the same sample.
Technological opportunities
The possibilities for creating, curating and transporting large music libraries on small devices have increased rapidly over recent decades. Personal listening devices, or PLDs (Fligor, 2009; Peng, Tao, & Huang, 2007; Worthington et al., 2009), are ubiquitous and enable the creation of enjoyable, self-selected exercise accompaniment. Listeners tend not to use all available functions (Heye & Lamont, 2010), adopting simple processes to transfer music from computer to PLD, selecting specific artists or shuffling, but eschewing playlists and streaming (this may reflect limited streaming options and internet access at the time of the study). Krause and North (2016) found technological identity relates to practices such as streaming, and confidence with new listening technology influences how music is accessed (see also Heye & Lamont, 2010).
Disliked music may lead to workouts being curtailed (Priest & Karageorghis, 2008), reflecting Bellows-Riecken et al.’s (2013) findings that environment can provoke negative affect. Self-selection helps ensure that music heard is strongly liked. While there is substantial research into everyday PLD use (Bull, 2007; Greasley & Lamont, 2011; Heye & Lamont, 2010; Krause & North, 2016; Krause, North, & Hewitt, 2015), use of self-selected music in exercise contexts has received less coverage (Hallett & Lamont, 2015; Stork, Kwan, Gibala, & Martin Ginis, 2015). Pleasure and control during listening are linked (Krause & North, 2017) and technology facilitates control; the authors compared lack of control over broadcast music compared with using a PLD. Of their 6275 reported music-exposure locations, 61 were in gyms, and 182 of 6379 activities were exercise (including that outside of gyms). Mean arousal levels, and liking of and engagement with music heard, were higher when analysed for exercisers than for gym attendees, perhaps reflecting greater control over music choice away from a gym (e.g. running outside with a PLD). A qualitative approach could explore underpinning reasons.
Music choice reflects an intention to manage arousal levels (Krause et al., 2016; Laukka & Quick, 2013) and to support movement (Greb et al., 2017). These are quantitative survey studies with relatively little qualitative data, but the findings are consistent with the reciprocal feedback model of musical response (Hargreaves, 2012) where musical fit is judged in terms of genre and style. The model recognises the importance of highly individualised familiarity and reference systems. Preference for control over and personalisation of the auditory environment indicates that the best-fitting music in such circumstances reflects individual differences, and generic “exercise music” is therefore likely to entail some compromise. It is, however, unclear whether this is due to musical characteristics and associations, or whether control is itself an element of music enjoyment.
Little is known about how exercisers utilise music for their own aims outside laboratory settings where physiological and performance outcomes are the focus, and extensive searches found few qualitative studies of music use in exercise. This study aimed to understand better the factors influencing exercise music choices by exploring the experiences of exercisers. A qualitative approach offered scope to extend understanding beyond previous gym-based literature, allowing participants to describe their priorities. The research questions were: (a) how do exercisers experience the use of music during their workouts? and (b) how do they make choices regarding their music use during workouts?
Method
The study design used interpretative phenomenological analysis (IPA), because it focuses on subjective experience of and meaning for the individual, rather than assuming a knowable truth (Langdridge, 2007). The emphasis is on individual narratives, with different participants ascribing different meanings to similar situations, while the approach still recognises commonalities. IPA’s ontology draws heavily on Heidegger’s Dasein (Heidegger, 1962), which argues for an intersubjective “being-in-the-world”, meaning individual existence and experience is centred on interaction with the environment and others. Merleau-Ponty’s (1996) emphasis on the body as conduit for intersubjectivity is also influential (Smith, Flowers, & Larkin, 2009). Throughout interactions, people strive to derive meaning from their experiences, ranging from simple, literal meanings to implications for their identity and life’s meaning (Smith, 2019).
IPA’s epistemology involves attempting to understand a participant’s world, conveying their experience (Larkin, Watts, & Clifton, 2006). Phenomenology refers to the interpretation of phenomena, the content of Dasein (Heidegger, 1962). However, since only the individual can access their own full Dasein, it is inevitable that hermeneutics affect interpretation. As a result, researchers bring their own Dasein to their interpretation, as do readers of resulting research papers (and, indeed, reviewers and editors).
IPA maintains an ideographic approach, basing findings on scrutiny of individuals’ descriptions of their worlds, which are carefully compared to identify similarities and differences. This does not exclude generalisation, but the process of generalising involves gradual, careful building (Smith et al., 2009). This contrasts with the nomothetics of quantitative analysis by which generalisations are made through statistical tests, prioritising group rather than individual. IPA emphasises individual participant experiences while maintaining recognition and acknowledgement of limitations.
Design
Smith et al.’s approach to IPA (2009) was followed, as it has strong connections with applied psychology and scope to discover deep, rich meanings within data. Semi-structured interviews explored music use in exercise as individuals experience it. Reflexivity, a fundamental element of IPA, considered the influence of researcher experience and preference on data interpretation. The project was carried out by the first author, including all data collection and analysis. The second author provided supervision throughout, checking the resulting themes.
Participants
Ten participants were interviewed using semi-structured interview schedules, their data transcribed, and analysis carried out following the suggested procedures of Smith et al. (2009). Smith et al. consider three to six participants an adequate sample for IPA research; this slightly larger sample size offered scope to explore a broader range of experience. The multiple perspectives provide triangulation through contrasting viewpoints (Smith et al., 2009). All participants had previously completed an online survey on their use of music in exercise (Hallett & Lamont, 2017) and provided email contact details to indicate willingness to participate in a semi-structured interview. Opportunity sampling was carried out using the contact details provided while also checking survey responses to ensure participants with a variety of sporting experience, achievements, music preferences and music usage levels were invited. Potential participants were emailed until a sufficient number had been recruited, as many who were initially approached did not respond. Eight participants took part in face-to-face interviews, and two further participants were interviewed by phone. The sample consisted of 10 adults (seven women and three men, Mage = 38.4, SD = 8.96, age range: 26–58 years), who participated in running, walking, fitness classes, swimming, cycling, gym-based workouts and team sports, although music was not universally listened to during all activities. The most popular activities were running, walking and cardiovascular machine use. Frequencies are shown in Table 1.
Exercise activities among participants.
Ethical approval was given by Keele University Ethics Review Panel. Participants provided informed consent prior to semi-structured interviews. Forms were electronically submitted for the telephone semi-structured interviews, and hard copies were used for face-to-face semi-structured interviews.
Interview schedules and process
A generic outline semi-structured interview schedule (see Appendix) was used, with notes added for each interview referring to survey responses to explore these further. For example, one participant had mentioned exercising when the gym music was “too loud” and this was noted so it could be explored. Interviews lasted 37–86 minutes, with a mean duration of 60min 12s (SD = 14min 2s). The interview schedules were used flexibly, and responses were explored on an ad hoc basis. Participants were encouraged to lead the interview, focusing on what they felt was important about their experiences of using music during exercise. Interviews were recorded using an Olympus VN-4100PC digital recorder and transcribed using ExpressScribe software. Participants have been assigned pseudonyms.
Analytical process
The semi-structured interview and transcription process provided opportunities to reflect on the data, and observations were noted directly after interviews and during transcription. Transcripts were printed, read, re-read and the recordings listened to again. Colour-coded notes were made in the margins according to the initial stages outlined by Smith et al. (2009), highlighting key points, exploring language use and investigating deeper meanings. Possible themes were identified and examples noted. This process was carried out on each transcript individually, with commonalities among transcripts also identified.
A mindmap of possible themes was annotated with quotes colour-coded according to participant; busy, “rainbow-coloured” areas indicated common themes. Using this framework, pertinent quotes were collated, and themes and subthemes refined, noting frequencies and possible overlaps, to produce four main themes. These reflected commonalities among participants, with individual experiences related to themes in different ways, enabling similarities to be considered alongside individual experience.
Results
Four main themes were identified: Taking control involved using music to induce or maintain positive affect, create a personalised exercise environment and achieve control; It’s all about me emphasised individuality, differentiation and personalisation of listening through associations, life stage and motivations; Exercise-music literacy reflected competence to source and select music suited to one’s needs, and to convey it to a PLD; and Embodiment concerned body-music interactions and synchronisation. These themes and their subthemes are shown in Table 2, along with an indication of which transcripts they appeared in. Quotations from participants for each theme are presented in Tables 3 to 6, and cross-referenced from the text.
Themes, subthemes and representation among participants.
Quotations for “Taking control”.
Quotations for ‘It’s all about me”.
Quotations for “Exercise-music literacy”.
Quotations for “Embodiment”.
Taking control
This theme involved overcoming barriers by creating the “right” individualised environment. Supporting quotations are presented in Table 3. These include subthemes of internal challenges such low self-efficacy and negative feelings about training, external challenges such as uncontrollable environmental factors, and the recognition of achieving control via music.
Internal challenges relating to self-efficacy, tiredness and boredom were overcome with music. Andrew (Q1) described how the “lure” of listening to music helped overcome reluctance to begin a run and to take control of the exercise session, avoiding the temptation to cut it short. Steven also stated that music helped him complete longer runs (Q2) and that this was not feasible without music, which appeared to have facilitated self-efficacy. Ruth had originally tried to run with music, expecting it to help overcome a sense of effort (Q3), but abandoned it for various reasons (see Q22 and Q35 in subsequent sections) in favour of a music-free, counting strategy used to dissociate (Q4). Ruth’s focus on something other than physical activity, in order to dissociate, is comparable to other participants’ use of music.
Both Charlotte (Q5) and Belinda (Q6) used music to set short-term endurance-related targets. Charlotte challenged herself to continue at high intensity for the remainder of a track, while Belinda did not allow herself to check progress on a machine display until a song finished. Belinda described this as using music intentionally to “distract”, possibly relating to a strategy to manage rowing, which she found “dull”. In Charlotte’s case, the dissociation helped overcome tiredness.
Participants also used music to help them gain control over external challenges, particularly other-selected music broadcast in gyms. Both Katie (Q7) and Sarah (Q8) described listening to music on their PLDs to create a private “bubble”, shutting out music imposed on them in favour of their own selection. Katie, in contrast to the “bubble” described in Q7, vividly expressed the experience of being in a spin class where she disliked the other-selected music, conveying an unpleasant lack of control of the environment (Q9). While the classes may have been designed to replicate a “fun” club environment, Katie was experiencing something very different. This was not applicable to all her classes; she also noted managing exercise intensity through familiarity with class music she liked (Q8).
Steven and Andrew described particularly positive experiences of achieving control through combining running and music; for Steven (Q11), this related to mood, and elevated feelings of confidence and self-belief, while Andrew (Q12) described being in a “zone” where enjoying the music helped him complete a longer run.
It’s all about me
This theme concerns music use to reflect self-identity, to differentiate from others and construct a social identity through music-invoked memories. There were three subthemes: differentiation from others; life stage and context; and connections with others. Supporting quotes are provided in Table 4.
Participants emphasised their individuality when talking about their exercise music. Sophie stated that she was less reliant on music than many of her friends (Q13), while Katie described herself as not an “album type” (Q14). Charlotte considered herself unusual in not synchronising to the beat of the music (Q15). Steven had found an online source for music mixes; having initially described the creator as a “kindred spirit” because of shared unusual musical preferences (Q16), he described excluding tracks that he considered unsuitable, attributing this to “personal taste”.
Participants drew on an autobiographical sense of self through music to promote positive affect. Sophie described escaping from current responsibilities to take time for herself, running to tracks that brought back memories of university (Q17). Belinda also listened to exercise music playlists that evoked memories of university, involving “exercise, socialising and feeling good” (Q18) and interactions with university friends. Sarah used music to remind her of social connections (Q19), choosing a track selected for her by her husband, “Only Losers Take the Bus”, to encourage herself to persevere with more challenging sections of her runs and gaining a sense of his encouragement from it. This reinforcement of identity and social ties was purely for the individual; music was listened to through headphones, perhaps indicating a private reassurance of identity, rather than articulating identity through group listening.
These examples show music choice reflecting a sense of self, carried over to an exercising-self differentiated from known and unknown others. Participants constructed their identities socially while simultaneously promoting their individuality, conveying both who they were and the social worlds to which they belonged.
Exercise-music literacy
Exercise-music literacy might be defined as “the wide range of skills and competencies that people develop to seek out, comprehend, evaluate and use music to make soundtracks to increase quality and/or enjoyment of exercise” – an adaptation of Zarcadoolas, Pleasant, and Greer’s definition of health literacy (2006). Creating playlists requires technological competency alongside musical judgement to select appropriate exercising tracks. This theme shows how music is selected to address workout quality through regulating physical output, for example selecting more upbeat or intense music for faster running. It reflects preparation for exercise. Within the theme, there were subthemes of musical judgement, where preferences were recognised and music selected for its potential to enhance exercise, and of technological competence to manage electronic music libraries and PLDs. Supporting quotations are provided in Table 5.
Participants were able to explain their preferences for particular music for exercise. Katie’s description of “pumping house” having too little musical variation, and requirement for “peaks and troughs” (Q20) are examples. However, Ruth’s approach of choosing music she enjoyed in other settings (Q21) resulted in a running playlist that was unsatisfactory, and she found the tracks irritating in the different context (Q22). Ruth was highly engaged with music, and a trained musician who listened to popular and classical music radio stations frequently. The irritation and annoyance she described may have related to music raising her arousal levels, since she likened her running to meditation.
Amanda (Q23) and Charlotte (Q24) also had classical music training, but responded very differently to remixes of classical repertoire played in aerobics classes; Amanda enjoyed them as they were consistent with her identity as a “classical person”, while Charlotte described how they did not feel “right”, attributing this to her familiarity with the original scoring. These differences demonstrate varied preference even with a similar musical background, and how identity is constructed in relation to the music in different ways.
Participants selected music for both higher and lower intensity. Steven described “speeding up” with music, attributing an emotional affect to its increasing intensity, although he struggled to articulate this, stating “It’s difficult to pinpoint” (Q25). Martin, in contrast, selected music where the “mood” would lead to him walking more slowly to avoid pain from underlying medical conditions (Q26).
Technological competence included using computer software and PLD functions to manage libraries and source and create tracks. Participants described finding suitable exercise music from a range of sources. Andrew browsed new music using Amazon, Spotify and iTunes, while Martin’s lack of funds drew him to sites such as InSound, which at the time offered free downloads. Amanda, Charlotte and Sophie described an evolutionary process of building playlists, changing them periodically to incorporate new discoveries, and removing tracks that they had tired of while keeping preferred tracks. Charlotte used shuffle (Q27) to avoid creating associations between certain tracks and locations on her running route.
Not all participants were technologically adept; Belinda, despite being able to pinpoint the pertinent musical qualities of her preferred exercise tracks, described difficulties in transferring the CD tracks to a new phone (Q28). Consequently, she continued to use an old phone with an unreliable battery in order to continue listening to preferred tracks during exercise. Other participants were more adept with technology, with Martin sourcing new music from music e-newsletter links (Q29) and Amanda using music software to merge preferred music with pre-recorded spoken training instructions for a Couch to 5k programme (Q30).
Embodiment
This theme encompasses the described experiences of internalising the music and beat entrainment. Hardware, in contrast, was often experienced as problematic, externalised and inconvenient, with the exception of Charlotte. In contrast to exercise-music literacy, where the focus is preparation, embodiment refers to experience once exercise is underway. Supporting quotes are provided in Table 6.
Andrew described how he needed the music to “really get in your head” (Q31), while Steven referred to “endorphins” and “physicality” of music (Q32), inferring body-music interaction, and Belinda talked about familiar music becoming “part of you” (Q33). These quotes suggest an experience of internalising the music. Nevertheless, entrainment of the beat sufficiently to synchronise with it presented challenges. Belinda considered her selection of synchronous music “a stroke of luck” (Q34), while Ruth, having selected tracks based primarily on liking, subsequently found the beats per minute were not the “right pace” (Q35). Katie felt she needed to run with the beat, but the tempo of selected songs did not correspond to her running cadence, leading to stumbling (Q36). Sarah described how she found herself unable to run in time to music even when using music designed to be the appropriate tempo, and noted she was also unable to clap in time to music (Q37). The latter indicates possible beat deafness (Phillips-Silver et al., 2011); while difficulties synchronising running could reflect inappropriate tempo, clapping is not similarly constrained. Sarah presented herself and her feet as different entities – “I’m trying … and my feet” – and there is a sense that her feet are autonomous, refusing to be controlled by the rest of her. This may also reflect a struggle to translate cognitive intention into physical action, and contrasts with the participants describing music as internalised.
Hardware was often described negatively. Despite PLDs being able to transport huge libraries, providing flexibility for exercise music choice, they were experienced as inconvenient. Sophie referred to the “extra bulk of carrying that thing” (Q38) while Ruth described irritation with headphones that fell out (Q39), which had contributed to her abandoning listening to music while running. Other participants expressed concern with wires getting in the way in races (Steven, Q40), or becoming tangled while lifting weights (Amanda, Q41). The distinction between these comments and those of Charlotte was marked. As an early adopter of new listening technology, Charlotte described her journey with different devices and how her well-used iPod had become permanently marked by salt from perspiration which could not be removed (Q42); she had become engrained in her device.
Discussion
Ten participants who exercised regularly were interviewed to explore their experiences of using music during exercise as well as their selection and curation practices. Interpretative phenomenological analysis (IPA) was used to analyse the transcripts, and four main themes were identified. These were Taking control, It’s all about me, Exercise-music literacy and Embodiment. Findings indicated sophisticated, personalised music selection and use applies in exercise, much as in other contexts. There were some frustrations and limitations in optimising music use, particularly regarding technological issues and synchronising activity to the beat.
Experiences of exercise music use
The themes incorporated similarities found across experiences and practices which were nevertheless highly individualised. Participants used identical expressions; for example, Katie and Sarah both referred to creating a “bubble” through listening to music, constructing a controllable environment in the context of an uncontrollable one. This echoes Bull’s findings regarding the use of personal listening devices (PLDs), and his description of the “privatised auditory bubble” (2005, p. 344). Music was also used to create a distraction, enabling dissociation from feelings of effort or boredom to gain control over internal challenges. This supports the findings of Priest and Karageorghis (2008) and Hallett and Lamont (2015), where dissociation was practised by participants using music or other media to distract from the effort of exercise.
Music was described as being “inside” the participant and sensed as embodied. The interaction of activity and music led participants to experience increased confidence and enjoyment; the motivation, arousal and reduced rate of perceived exertion identified as consequences of listening to music (Karageorghis, 2016) are evident here. Despite commonalities of purpose and experience, participants’ processes and preferences were very individual. This suggests that tailoring, rather than a homogenised approach, is likely to help generate optimum benefits.
The embodiment theme reflected participants experiencing music as “absorbed” and describing it as being within them. However, participants struggled to select and synchronise to an entrained beat, despite evidence suggesting listeners are adept at choosing music (DeNora, 2000; Skånland, 2013). This may be due to discrepancies in the beat as, for example, current dance music is typically 130 to 140 beats per minute (bpm), at least 20 to 30 bpm too slow for a typical running cadence. Sarah described using music intended for synchronous running, but this ranged from 150 to 170 bpm, or a cadence of 75 to 85 strides per minute (a stride represents two steps, one left and one right). Hafer, Silvernail, Hillstrom, and Boyer (2016) found an average running cadence among their participants of 84.3 +/- 5.2 strides per minute, corresponding with 168.6 +/-10.4 bpm, so 170 bpm may have been too slow (although Sarah described difficulties with non-exercise entrainment such as clapping along at concerts). Evidence suggested that synchronising arises through luck (Belinda), is struggled with (Katie and Sarah), or no attempt is made (Charlotte). While Greb et al. (2017) and Hallett and Lamont (2017) found that intentional synchronisation was not uncommon, findings here suggest otherwise (although the sample is small, and generalisation was not intended). Exercisers experienced some difficulty synchronising. This suggests that studies using synchronised movement may need to assist participants to achieve this, rather than assuming movement matches a headphone-delivered beat. Additionally, there is scope to increase awareness among exercisers of cadence and beats per minute.
Music selection
Practices of selecting music traversed themes, drawing on social identity (It’s all about me), musical characteristics and accessibility of technology (Exercise-music literacy). These indicated complex processes of selection, consistent with Hargreaves’ reciprocal feedback model (2012) and Karageorghis’ model of the motivational effect of music in exercise (2016). Participants could usually explain aspects of musical content underpinning their selections. This is consistent with the Goldsmiths Musical Sophistication Index (Müllensiefen et al., 2014), demonstrating that selection is characterised by recognition of specific intrinsic musical devices rather than based on a “hunch”. Several participants described specific social memories influencing their music choices, notably university (Belinda, Sophie and Steven); this related to self-identification within the private listening space created by headphone use, contributing to positive affect. Simultaneously, lack of awareness of others’ individual exercise music choices, and a belief that broadcast gym music and class music represented typical listening practices, may have contributed to assumptions of being unusual in the It’s all about me theme.
Selection was not always successful; Ruth’s choices from non-exercise contexts did not “fit” her running, although it is not clear whether this was the “wrong” music, or whether any music would have been over-arousing. Exercise-music literacy was not universal, particularly regarding technology. While technological challenges were pronounced in Belinda’s struggles to rip CDs to a new phone, participants did not generally use their devices in particularly complex ways. This supports the earlier findings of Heye and Lamont (2010), despite recent advances in technology. Playlists were used, but participants reported having only one or two. Shuffle and manual scrolling/skipping were favoured as controls. Given that participants used self-selected music for a variety of different activities, it might be expected that they would compile playlists for particular types of exercise; this was not the case.
Music and the technology to play it were perceived as separate; music became embodied, while technology was often an inconvenience, although some participants were able to exploit it, such as Amanda’s blending of spoken running programme instructions with her preferred music. The range of capabilities is consistent with that found by Krause and North (2016). The exercise-music literacy theme encompasses both technology and choice of music, but within a relatively narrow context of exercise, rather than across everyday listening contexts. Lack of capacity to select music of appropriate tempo for a cadence is less relevant in non-exercise listening, and could be related to awareness, or simply reflect lack of time or concern among exercisers. It was surprising that Bluetooth headphones were not more widely used to avoid wires. Better designed, unobtrusive, widely available technology may enhance the exercise experience.
Reflexive considerations
This section provides reflexivity from the first author, who carried out the project under the supervision of the second author. Reflexivity is important in IPA, and Willig’s framework of personal and epistemological reflexivity (Willig, 2008) is used here. I am interested in both music and exercise, having studied music at postgraduate level and worked as an exercise-to-music and gym instructor. I am a keen runner of average ability, and my knowledge of running subculture conferred familiarity with technical terms and some of the events mentioned. This inadvertently led to assumptions; Charlotte’s discussion of a particular marathon I had also taken part in was an important reminder of individuality, as she enjoyed a section that many runners, including myself, struggled with. My previous research indicated that my own approach to music use in exercise – multiple tailored playlists for different activities, often incorporating synchronisation – was unusual. Awareness that participants were likely to have quite different approaches to myself piqued my curiosity and I was keen to find out how and why they might contrast.
Epistemologically, geography affected who could be interviewed face-to-face. Although two interviews were carried out by telephone, the sound quality made transcription more difficult and meant facial expressions and body language could not help guide the interviews. Recruitment, as with many such studies, was difficult and I had met six of the 10 participants previously, although I did not know any of them well.
Limitations and future research
This study is historically positioned with particular technologies and music choices available. Fitness watch and smartwatch music control now reduces the need to carry additional devices. Bluetooth headphones were available at the time of the interviews, but were not used by the participants in this study. These devices could facilitate a more satisfactory experience with hardware than reported by some participants. Technology, music-using behaviours and preferences of exercisers are dynamic, and future research should document changes. While the present study reveals numerous interesting individual experiences, the individuality of the transcripts suggests that there are more practices among exercisers to be discovered and described. Given the dynamic nature of the field, a full picture of music use remains elusive. Additionally, all participants were UK-based, and practices may vary culturally.
There is scope to increase knowledge through further qualitative work, particularly using the grounded theory method (Charmaz, 2012; Glaser & Strauss, 1967; Strauss & Corbin, 1994) to build a more comprehensive model of music use in exercise with scope to develop music-based interventions for a range of exercise outcomes. Individual differences were evident not only among the participants, but between participants and the researcher carrying out data collection and analysis. Further exploration could extend examples.
Better understanding of exercise-music literacy would also be useful. While this study presents examples of different competence levels, also documented by Krause and North (2016), there is not currently a measurement tool. Quantified exercise-music literacy might explain variance in intervention studies and assist with further understanding of individual differences. Additionally, deploying factors such as associative properties of music might make exercise more enjoyable through generating positive affect; as yet, there is little evidence to connect enjoyment and adherence. Future research should explore this, as well as examine the potential for music enjoyment to assist with adherence.
Conclusion
The findings here underline the individuality of self-selected music use in exercise, particularly regarding personal associations. Some exercisers were frustrated by limitations in using technology and finding the best music for their needs, particularly for synchronous activity, while others had developed ways of enhancing their exercise with highly tailored, competently curated music collections. Comments regarding personalising the environment, and regarding responses to broadcast music, are particularly relevant for the fitness industry, where classes should be motivating and background music can be obtrusive (Hallett & Lamont, 2015). Overall, indications are that exercisers are adept at using music to increase enjoyment of workouts, but that greater understanding of technology and music characteristics could enhance this further.
Footnotes
Appendix
The outline below was used as a starting point for each semi-structured interview schedule. It was tailored and augmented to reflect participants’ survey responses so that these could be explored in further depth.
Exercise generally
Talk about music in exercise
Dissociation or focus? [i.e. is music used to dissociate from activity or complement it?]
Shared references in classes? [i.e. does music have similar cultural meaning to different attendees in the same exercise class?]
Where other people choose it
Where it’s worked particularly well
Where it hasn’t worked particularly well
Music outside exercise – is it different?
Acknowledgements
We are grateful to Keele University for providing a Studentship for the first author, which supported this project.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by a Keele University Studentship awarded to the first author.
