Open Access

Spontaneous Entrainment of Running Cadence to Music Tempo

  • Edith Van Dyck1Email author,
  • Bart Moens1,
  • Jeska Buhmann1,
  • Michiel Demey1,
  • Esther Coorevits1,
  • Simone Dalla Bella2 and
  • Marc Leman1
Sports Medicine - Open20151:15

DOI: 10.1186/s40798-015-0025-9

Received: 28 November 2014

Accepted: 2 July 2015

Published: 14 July 2015

The Erratum to this article has been published in Sports Medicine - Open 2015 1:30

Abstract

Background

Since accumulating evidence suggests that step rate is strongly associated with running-related injuries, it is important for runners to exercise at an appropriate running cadence. As music tempo has been shown to be capable of impacting exercise performance of repetitive endurance activities, it might also serve as a means to (re)shape running cadence. The aim of this study was to validate the impact of music tempo on running cadence.

Methods

Sixteen recreational runners ran four laps of 200 m (i.e. 800 m in total); this task was repeated 11 times with a short break in between each four-lap sequence. During the first lap of a sequence, participants ran at a self-paced tempo without musical accompaniment. Running cadence of the first lap was registered, and during the second lap, music with a tempo matching the assessed cadence was played. In the final two laps, the music tempo was either increased/decreased by 3.00, 2.50, 2.00, 1.50, or 1.00 % or was kept stable. This range was chosen since the aim of this study was to test spontaneous entrainment (an average person can distinguish tempo variations of about 4 %). Each participant performed all conditions.

Results

Imperceptible shifts in musical tempi in proportion to the runner’s self-paced running tempo significantly influenced running cadence (p < .001). Contrasts revealed a linear relation between the tempo conditions and adaptation in running cadence (p < .001). In addition, a significant effect of condition on the level of entrainment was revealed (p < .05), which suggests that maximal effects of music tempo on running cadence can only be obtained up to a certain level of tempo modification. Finally, significantly higher levels of tempo entrainment were found for female participants compared to their male counterparts (p < .05).

Conclusions

The applicable contribution of these novel findings is that music tempo could serve as an unprompted means to impact running cadence. As increases in step rate may prove beneficial in the prevention and treatment of common running-related injuries, this finding could be especially relevant for treatment purposes, such as exercise prescription and gait retraining.

Key Points

  • Music tempo can spontaneously impact running cadence.

  • A basin for unsolicited entrainment of running cadence to music tempo was discovered.

  • The effect of music tempo on running cadence proves to be stronger for women than for men.

Background

Approximately 56 % of recreational runners sustain a running-related injury each year [1]. About 50 % of all running-related injuries occurs at the knee and is most often due to the inability of the lower extremity joints to adequately control the loads applied during initial stance [24]. A number of strategies designed to reduce loads to these joints have been suggested, with one of the most common ones applying an increased step rate. Subtle increases in step rate have for instance been shown to substantially reduce the loading to the hip and knee joints during running and may therefore prove beneficial in the prevention and treatment of common running-related injuries [5]. However, less is known about the specific strategies that can be employed to change step rate. In this study, a novel strategy using music as a tool to impact step rate is examined. The means by which music might serve as an adequate tool for manipulating running cadence is discussed below.

A great deal of runners exercise while listening to music. This should not come as a surprise, since music listening during sport activities is believed to capture attention [6], distract from fatigue and discomfort [7], prompt and alter mood states [8, 9], enhance work output [10, 11], increase arousal [12], relieve stress [13], stimulate rhythmic movement [14], and evoke a sense of power and produce power-related cognition and behaviour [15]. Simpson and Karageorghis [16], for instance, examined the effect of music on a 400-m sprint performance while controlling for pre-performance mood. It was shown that music resulted in better sprint performance compared to the no music control. In another study, Styns et al. [17] observed that participants walked faster with music than with metronome ticks, while Bood et al. [18] showed that time to exhaustion was significantly longer with acoustic stimuli than without when participants were asked to run to exhaustion on a treadmill. Results of studies such as these suggest that music could be applied to physical activities, such as walking or running, with a considerable positive effect.

The idea that music can serve as a strategy for coping with physical exertion has been linked to the parallel processing model, which focuses on the limited human attention capacity [19, 20]. This implies that the focus of an exerciser is shifted to external events in an effort to reduce the perception of neural exertion signals coming from the muscles, joints, and cardiopulmonary systems [21]. However, it appears that external musical cues can only be the focus of attention in the case of low-to-moderate physiological awareness and perceived exertion. When the workload becomes too high, the exerciser’s attention is typically shifted towards the painful or fatiguing effects of the exercise [19, 20, 2224]. In general, music has shown to be most effective to exert ergogenic and distractive effects when it is used to accompany self-paced exercise [8, 2527]. In addition, it is believed that particularly motivational music can successfully uplift mood state and increase work capacity [9, 28, 29].

Besides the motivational factor, exercise that is repetitive in nature is believed to benefit mostly from music that is synchronized with the tempo of the exerciser’s movements; endurance can be extended, and performers exercise at higher intensities when moving in synchrony with musical stimuli [29]. It has been suggested that this effect of synchronized music is due to its ability to reduce the metabolic cost of exercise by enhancing neuromuscular or metabolic efficiency [28, 30]. Regular corporeal patterns demand less energy to imitate, due to the lack of timely adjustments within the kinetic pattern but also because of an increased level of relaxation resulting from the precise expectancy of the forthcoming movement [31]. As such, a point of reference is created that is able to attract and swiftly entrain recurring motor pattern efficiency [30, 32]. Synchronization is typically understood as an intentional mechanism, which is highly task constrained [33]. Most previous research on the impact of synchronized music on exercise performance generally focused on instructed or imposed synchronization, e.g. [12, 1618, 29]. However, it is also the case that synchronization can occur spontaneously [33]. Previous studies have highlighted the natural or spontaneous predisposition of humans to respond to rhythmical qualities of music [34, 35], but much less is known about the capabilities of exercisers, and especially runners, to spontaneously synchronize with the tempo of musical stimuli. Yet, spontaneous entrainment of one tempo with another is only believed to occur when the strength of the coupling is able to overcome possible contrasts in natural movement period or tempo [36]. For a given coupling strength, unintentional entrainment only occurs within a specific range of period differences, reflecting the system’s entrainment basin [33, 3740].

The effect of music on repetitive endurance activities also depends on the specific tempo of the musical stimulus. Waterhouse et al. [41] revealed that cyclists’ covered distance, power, and pedal cadence increased when faster music was presented, while slowing down the music tempo resulted in decreases of these measures. Edworthy and Waring [8] explored the effect of music tempo (and loudness) on treadmill running and demonstrated that an increase in the tempo, and to a lesser extent the loudness of the stimulus, resulted in an increase in running speed. In the light of findings such as those described above, it is quite plausible that music tempo could also serve as a means to influence running cadence. And as a link between step rate and hip and knee joint loading has been established before [5], results of this study could be particularly relevant with regard to the prevention and treatment of running-related injuries.

The aim of this study was to validate the impact of music tempo on running cadence. We hypothesized that recreational runners would adapt their self-paced running cadence to imperceptible changes in musical tempi and, thus, entrain spontaneously with the music tempo. Furthermore, we believed that the degree of entrainment would decrease with increasing changes in music tempo and, thus, that a basin for unintentional entrainment of running cadence to music tempo exists. As it has been shown that unintentional coordination typically manifests as relative or intermittent coordination (i.e. movements are attracted to a 0 or 180° but are not phase locked) [36, 37, 42], rather than phase-locked steps, entrainment refers to the amount of steps taken in a tempo sufficiently close to the music tempo (max. 1 % difference between running cadence and music tempo). Besides, since previous research often reported better results for women compared to men regarding music-to-movement coordination [11, 43], we expected female participants to display larger levels of entrainment. Finally, as it has been demonstrated that only when physiological awareness and perceived exertion are relatively low that music can distract from fatigue and discomfort [19, 22], the relationship between the level of entrainment and the degree of perceived exertion was examined.

Methods

Ethics Statement

The study was approved by the Ethics Committee of the Faculty of Arts and Philosophy of Ghent University, and all procedures followed were in accordance with the statements of the Declaration of Helsinki. In addition, all participants signed a form to declare that they participated voluntarily; that they had received sufficient information concerning the tasks, the procedures, and the technologies used; that they had the opportunity to ask questions; and that they were aware of the fact that running movements were measured, for scientific and educational purposes only.

Participants

To establish sample size, a power analysis for a repeated-measures design was conducted using G*Power 3.1.9.2 [44]. Based on the effect sizes reported in comparable studies [16, 18, 28], the analysis indicated that minimally 14 participants for an α of 0.05 and a power of 0.80 would be required. Sixteen healthy adult participants (nine females) took part in the study. The test group consisted of recreational runners with an average age of 22.25 years (SD = 2.14), a mean body mass of 66.56 kg (SD = 9.32), and an average height of 1.74 m (SD = 0.10), who reported to be fit to run about 10 km. The majority (62.50 %) had received musical training (Fisher’s exact test showed no significant association between gender and musical background, χ 2(1) = 2.05, p = .30). All participants reported that running is an activity that forms a part of their lives, with varying degrees of frequency (12.50 % runs multiple times a week; 56.25 % runs about once a week; 31.25 % runs about once a month; 0 % runs about once a year or not at all). Of all participants, 50 % reported to typically train with music, 32.25 % generally runs without music, and 18.75 % runs both with and without musical accompaniment.

Stimuli

Previous research indicated that the natural running cadence for recreational runners lies somewhere between 130 and 200 steps per minute (SPM) [45]. On that account, a music database consisting of songs in the tempo range of 130–200 beats per minute (BPM) was created. A group of 19 students from Ghent University, all recreational runners, were asked to provide a list of at least ten songs they believed to be motivational to run to. From that specific list of music, the database for the experiment was created. In total, 117 songs with a clear beat and correct tempo range were pre-selected (see Table 1). In the course of the selection process, it was verified that the tempo of each song remained stable throughout the entire track. Using Audacity software (http://audacity.sourceforge.net), intros without clear beats were cut from the stimuli. BeatRoot [46] was applied to track the beats of each song in order to ensure that only songs between 130 and 200 BPM were included, while ReplayGain was used to normalize perceived loudness and minimize possible imbalances in sound pressure level.
Table 1

List of musical stimuli

ID

Artist

Song

Label(s)

Year published

Tempo (BPM)

1

Epica

Illusive Consensus

Transmission

2003

132

2

Gregory Porter

On My Way to Harlem (Radio Edit)

Motema

2012

138

3

Interpol

Slow Hands

Matador

2004

139

4

The Supremes

I Hear a Symphony

Motown

1965

139

5

Van Halen

Ain’t Talkin’ ‘Bout Love

Warner Bros

1978

139

6

Combichrist

Electrohead

Out of Line/Metropolis

2007

140

7

dEUS

The Soft Fall

PIAS

2012

140

8

P!nk

Who Knew

LaFace

2006

140

9

Noisettes

Never Forget You

Mercury/Vertigo

2009

141

10

Rammstein

Benzin

Motor

2005

142

11

Royksopp

Tricky Tricky

Astralwerk/EMI

2009

142

12

Deftones

My Own Summer (Shove It)

Maverick/Warner Bros

1997

143

13

16 Horsepower

Outlaw Song

Jetset

2006

144

14

Coldplay

In My Place

Parlophone

2002

144

15

The Hickey Underworld

Future Words

PIAS

2009

145

16

ABBA

Waterloo (English Version)

Polar/Epic

1973

146

17

Steppenwolf

Born to Be Wild

Dunhill/RCA

1967

146

18

The Sisters of Mercy

Alice

Merciful Release

1982

146

19

School Is Cool

The World Is Gonna End Tonight

Not on label

2011

147

20

Tom Odell

I Know

Columbia/In the Name Of

2012

147

21

Trixie Whitley

Irene

Unday Records

2013

147

22

Aphex Twin

Flim

Warp/Sire/WEA

1997

148

23

Bruce Springsteen

Dancing In the Dark

Columbia

1984

148

24

Nneka

Heartbeat

Yo Mama’s Recording

2008

148

25

Alt-J

Breezeblocks

Infectious

2012

149

26

Marco Borsato

Ik leef niet meer voor jou

Polydor

1995

149

27

A Perfect Circle

Thinking of You

Virgin

2000

150

28

Editors

An End Has a Start

Kitchenware/FADER

2007

150

29

Florence and The Machine

Dog Days Are Over

Island

2009

150

30

Guns N’ Roses

It’s So Easy

Geffen Records/Interscope

1987

150

31

Katy Perry

E.T.

Capitol

2010

150

32

Pearl Jam

Lightning Bolt

Monkeywrench/Republic

2013

151

33

The Killers

Spaceman

Island/Vertigo

2008

151

34

Bloc Party

Flux

Wichita/Vice

2007

152

35

Elton John

Saturday Night’s Alright (For Fighting)

MCA/DJM

1973

152

36

P!nk

Are We All We Are

RCA

2012

152

37

De Staat

Sweatshop

Cool Green Recordings

2011

153

38

Ike & Tina Turner

Nutbush City Limits

United Artists

1973

153

39

Kings of Leon

Sex On Fire

RCA

2008

153

40

OutKast

B.O.B.

LaFace/Arista

2000

153

41

The Black Eyed Peas

Pump It

Interscope

2005

153

42

Massive Attack

Teardrop

Circa/Virgin

1998

154

43

Kaiser Chiefs

Never Miss a Beat

B-Unique/Universal

2008

155

44

Morphine

Honey White

Rykodisc

1995

155

45

The Pipettes

Your Kisses Are Wasted On Me

Memphis Industries/Cherrytree

2006

155

46

The Strokes

Juicebox

RCA

2006

155

47

Hooverphonic

Mad About You (Orchestra Version)

Columbia

2012

156

48

Nirvana

In Bloom

DGC

1991

156

49

The Van Jets

Ricochet

Belvédère

2005

156

50

Air

Surfing On a Rocket

Virgin

2004

157

51

Millencolin

No Cigar

Epitaph

2000

157

52

The Beach Boys

Surfin’ USA

Capitol

1963

157

53

Shaggy

Boombastic

Virgin

1995

158

54

Jones & Stephenson

The First Rebirth (Original Mix)

Prolekult

1994

159

55

Kings of Leon

California Waiting

RCA/HandMeDown

2003

159

56

Michael Sembello

Maniac

Warner Bros

1983

159

57

OutKast

Hey Ya! (Radio Mix Club Mix)

LaFace

2003

159

58

Beyonce

Halo

Columbia

2008

160

59

Birdman & Lil Wayne

Stuntin’ Like My Daddy (Street)

Cash Money/Universal

2006

160

60

Customs

Justine

Noisesome/EMI

2009

160

61

Mastodon

Spectrelight

Reprise/Roadrunner

2011

160

62

TNGHT

Higher Ground

Warp/LuckyMe

2012

160

63

P.O.D.

Alive

Atlantic

2001

161

64

Queens of the Stone Age

Little Sister

Interscope

2005

161

65

‘T Hof Van Commerce

Baes (Radio Edit)

Plasticine

2012

162

66

Black Sabbath

Paranoid

Vertigo

1970

162

67

Blondie

One Way or Another

Chrysalis

1978

162

68

Karate

Ice or Ground

Southern

2002

162

69

Moby

Feeling So Real

Mute/Elektra

1995

162

70

Orchestral Manoeuvres In the Dark

Electricity

Factory

1979

162

71

U96

Love Religion (Video Edit)

Guppy/Motor

1995

162

72

Wham!

Wake Me Up Before You GoGo

Columbia

1984

162

73

Bomfunk MC’s

Freestyler

Sony Music Finland/Epidrome

1999

163

74

Jamaica

Cross the Fader

Downtown

2011

164

75

Midlake

Antiphon

Bella Union

2013

164

76

Muse

Survival

Helium 3/Warner Music Group

2012

164

77

Sugababes

About You Now

Island

2007

164

78

Ella Fitzgerald

A-Tisket, A-Tasket

Golden Options

2008

165

79

Ike & Tina Turner

River Deep Mountain High

Philes

1966

165

80

Green Day

Boulevard of Broken Dreams

Reprise

2004

166

81

Pixies

Where Is My Mind

4 AD

1988

166

82

Rammstein

Mann gegen Mann

Universal

2005

166

83

Arctic Monkeys

Do I Wanna Know

Domino

2013

170

84

Chet Faker

I’m Into You

Opulent/Remote Control

2012

170

85

Joy Division

Disorder

Factory

1979

170

86

Panic! At the Disco

I Write Sins Not Tragedies

Fueled by Ramen/Decaydance

2005

170

87

Queens of the Stone Age

No One Knows

Interscope

2002

170

88

The All-American Rejects

My Paper Heart

Doghouse/DreamWorks

2002

170

89

Foo Fighters

The Pretender

Roswell/RCA

2007

172

90

Netsky

Love Has Gone

Hospital

2012

172

91

Paramore

Misery Business

Fueled by Ramen

2007

172

92

The Streets

Fit But You Know It

Locked On/679

2004

172

93

DJ Fresh

Hot Right Now (Radio Edit)

Ministry of Sound

2012

174

94

Interpol

A Time To Be So Small

Matador

2004

174

95

Kanye West

Homecoming (feat. Chris Martin)

Roc-A-Fella/Def Jam

2008

174

96

Rudimental

Waiting All Night (feat. Ella Eyre)

Asylum

2013

174

97

Kelis & Andre 3000

Millionaire

Virgin

2004

176

98

Technohead

I Wanna Be a Hippy

Mokum

1995

177

99

Komatsu

Comin’

Lighttown Fidelity

2011

178

100

Mo’ Horizons

Pe Na Estrada (Radio Edit)

Agogo

2008

178

101

Tony Bennett & Lady Gaga

The Lady Is a Tramp

Sony Music Entertainment

2011

179

102

One Direction

Kiss You

Syco/Columbia

2012

180

103

Red Hot Chili Peppers

Can’t Stop

Warner Music

2002

182

104

The Pointer Sisters

I’m So Excited

Planet

1982

184

105

Ok Go

Don’t Ask Me

Capitol

2002

186

106

Joan Jett & The Blackhearts

I Love Rock ‘N Roll

RAK

1975

188

107

Wheatus

Teenage Dirtbag

Columbia

2000

188

108

Absynthe Minded

Pretty Horny Flow

Abeille Musique

2008

190

109

Eminem

Berzerk

Aftermath Entertainment/Shady/Interscope

2013

190

110

Macklemore & Ryan Lewis

Thrift Shop (feat. Wanz)

Macklemore LLC/ADA

2012

190

111

Roxette

The Look

EMI

1988

190

112

Isbells

As Long As It Takes

Zeal

2009

197

113

Beyonce

Crazy In Love (feat. Jay-Z)

Columbia/Music World

2003

198

114

Rihanna

Pon de Replay

Def Jam

2005

198

115

Gorillaz

Stylo (Radio Edit) [feat. Mos Def & Bobby Womack]

Parlophone/Virgin

2010

200

116

Wallace Vanborn

Atom Juggler

PIAS

2010

200

117

Linkin Park

In the End

Warner Bros

2000

210

Apparatus

Participants were equipped with two iPods (fourth generation), one attached at each ankle. Using the Sensor Monitor Pro application on the iPods, data from accelerometers and gyroscopes was streamed wirelessly at 100 Hz to the main processing computer. A Wi-Fi hotspot (TP-Link N750) with special 3-dB gain antennas for longer range was used for maintaining a stable connection between the computer and sensors. Some minimal jitter and lag in the data stream were neutralized using a 500-ms buffer before processing.

Incoming sensor data was processed by a customized version of D-Jogger [47], a music alignment framework that selects and tempo-adapts music to runners’ gait frequencies using kinematic sensor input (Additional file 1). Music tempi were manipulated using a phase vocoder, which time stretches music without pitch modification. D-Jogger was adapted to match the experimental protocol (detect running cadence, playback tempo-matched music to this reference, increase or decrease music tempo). The system logged all data and calculations in real time. Finally, the resulting auditory stimuli were sent back to the participant using a Sennheiser HDR130 audio transmitter (with a range of up to 100 m). The participant perceived the music through Sennheiser HD60 headphones connected to the transmitter (attached to the upper arm). The delay due to the wireless audio transmission was negligible.

Experimental Procedure and Set-up

The experiment took place in the Flanders Sports Arena of Ghent, Belgium. In order to select motivational music adapted to each runner’s personal taste, participants performed the Brunel Music Rating Inventory 2 (BMRI-2) test [48] at the start of the experiment. In this test, they were asked to rate all items of the music database by answering six questions about the motivational aspects of each song. Each item referred to an action, a time, a context, and a target (e.g. “The rhythm of this song would motivate me during a running exercise”) [49]. Participants responded on a seven-point Likert scale anchored by 1 (“strongly disagree”) and 7 (“strongly agree”). Afterwards, participants filled out a questionnaire on personal background, music education, and sports training. At the same time, for each participant individually, the 20 songs that had obtained the highest scores during the BMRI-2 test were loaded into the D-Jogger system.

Subsequently, participants were equipped with the iPods, the wireless headphone, and the audio transmitter. Each participant was asked to run on a 200-m running track for four laps continuously, for 12 times. Participants were instructed to run at their own comfortable tempo. No information was distributed concerning the real purpose of the experiment, and all participants ran in solo conditions. After each set of four laps, a break of approximately 5 min was introduced to enable the participant to recover sufficiently. Meanwhile, they were asked to indicate how heavy the effort had been during the exercise. This was rated on a Rating of Perceived Exertion (RPE) Scale [50], ranging from 6 (“no exertion at all”) to 20 (“maximal exertion”).

To get acquainted with the experimental set-up, the first set of four laps consisted of a practice set during which no music was played. Each of the 11 following four-lap sequences consisted of (1) a lap without music, (2) a lap with tempo-matched music, and (3) two laps with tempo-changed music. In the first lap, the participant ran at his/her self-paced cadence without musical accompaniment. In the second lap, music with a tempo matching the cadence assessed during the final 20 s of the previous lap was played. The musical stimulus consisted of the song that obtained the highest score during the BMRI-2 test with a tempo that differed maximally 5 % from the running cadence of the participant. After the song was selected, its tempo was adjusted to exactly match the mean running cadence. Finally, during the third and fourth laps, the tempo of the music was adjusted according to one of the 11 tempo-changed conditions.

In each of the 11 four-lap sequences, a different condition was tested. During the two final laps with tempo-changed music, the music tempo was adjusted to either −3.00, −2.50, −2.00, −1.50, −1.00, 0.00, +1.00, +1.50, +2.00, +2.50, or +3.00 % of its original one, played during the second lap. This range was chosen since an average person can distinguish tempo variations from about 4 % [51] and since the aim of this study was to test spontaneous or unintentional entrainment. The different conditions were randomized over the experiment in such a way that each participant performed all conditions but no participants performed the conditions in the same order. To ensure that they were not aware of the actual objective, participants filled out a questionnaire regarding their perception of the purpose of the experiment at the end. Responses did not indicate that they were aware of the experiment’s real purpose.

Data Analysis

Cadence Adaptation

Running cadence was calculated using the iPods’ acceleration data. In order to check the degree of cadence increase/decrease, running cadence (SPM) recorded during the laps with tempo-changed music (tempo-changed laps or TCL) was compared to the cadence captured during the lap with tempo-matched music (tempo-matched lap or TML) and will be further referred to as cadence adaptation. As the tempo was gradually shifting during that period, the first 5 s of the laps with tempo-changed music was discarded. The final 20 s of those laps was also ignored as participants possibly altered their running behaviour due to the anticipated ending of the final lap (e.g. slowing down or speeding up).
$$ \mathrm{Cadence}\ \mathrm{adaptation}\ \left(\%\right) = \frac{\mathrm{avg}\left(\mathrm{S}\mathrm{P}\mathrm{M}\_\mathrm{T}\mathrm{C}\mathrm{L}\right)}{\mathrm{avg}\left(\mathrm{S}\mathrm{P}\mathrm{M}\_\mathrm{T}\mathrm{M}\mathrm{L}\right)} $$

Entrainment

A second measure of interest concerned the percentage of tempo-entrained steps during the laps with tempo-changed music. A step taken in a tempo sufficiently close to the music tempo (max. 1 % difference between SPM and BPM) at that specific moment is regarded as a tempo-entrained step. The tempo entrainment score is the percentage of tempo-entrained steps of the total amount of steps.

Results

Running Cadence

This study tested whether the changes in music tempo would affect running cadence. A Kolmogorov-Smirnov test (KS test) showed that the assumption of normality was met, D(161) = 0.04, p > .05. A 11 × 2 × 2 repeated measures ANOVA with tempo condition as within-subject factor and gender and musical training as between-subject factors revealed a significant main effect of condition on cadence adaptation, F(10, 40) = 6.50, p < .001. Contrasts revealed a linear relation between condition and cadence adaptation, F(1, 4) = 94.56, p < .001, r 2 = .96. The evolution of cadence adaptation over the different conditions is shown in Fig. 1.
https://static-content.springer.com/image/art%3A10.1186%2Fs40798-015-0025-9/MediaObjects/40798_2015_25_Fig1_HTML.gif
Fig. 1

Mean tempo and cadence adaptation for the different conditions. Data presented is mean ± SE

There was no significant effect of gender, indicating rather similar levels of cadence adaptation for males and females, F(1, 4) = 6.51, p = .06, r 2 = .62. However, there was a significant interaction effect between tempo condition and gender, F(10, 40) = 3.40, p < .01. As can be seen in Fig. 2, although for both males and females running cadence increased (or decreased) with increases (or decreases) in music tempo, these adjustments were more pronounced for women than for men. In addition, there was no significant effect of musical training, F(1, 4) = 6.48, p = .06, r 2 = .62, which indicated that participants without musical training displayed similar levels of cadence adaptation as participants with a musical background. Finally, no significant interaction effect was found between musical training and tempo condition, F(10, 40) = 1.79, p = .10 (see Fig. 3).
https://static-content.springer.com/image/art%3A10.1186%2Fs40798-015-0025-9/MediaObjects/40798_2015_25_Fig2_HTML.gif
Fig. 2

Interaction plot of estimated marginal means calculated for cadence adaptation at both gender levels

https://static-content.springer.com/image/art%3A10.1186%2Fs40798-015-0025-9/MediaObjects/40798_2015_25_Fig3_HTML.gif
Fig. 3

Interaction plot of estimated marginal means calculated for cadence adaptation at both musical background levels

Entrainment Basin

In order to trace a possible basin for entrainment, the effect of the conditions on the level of tempo entrainment was tested. KS tests showed that the entrainment values were significantly non-normal, D(161) = 0.15, p < .001. Friedman’s ANOVA showed a significant effect of condition on tempo entrainment, χ 2(10) = 19.27, p < .05. Wilcoxon tests were used to follow up this finding, and all conditions were compared against the control condition (0 % of tempo change). A Bonferroni correction was applied, and all effects are thus reported at a .005 level of significance. It appeared that, compared to the control condition (Median (Mdn) = 74.25), tempo entrainment was significantly lower in the +2.50 % condition ((Mdn = 12.48), Z = −2.92, r 2 = .53) and tended to be lower in the +3.00 % ((Mdn = 14.01), Z = −2.41, p = .016, r 2 = .36) and −3.00 % conditions ((Mdn = 6.97), Z = −2.48, p = .013, r 2 = .38). Figure 4 represents the mean tempo entrainment for every single condition.
https://static-content.springer.com/image/art%3A10.1186%2Fs40798-015-0025-9/MediaObjects/40798_2015_25_Fig4_HTML.gif
Fig. 4

Entrainment basin displaying mean tempo entrainment for the different conditions. Data presented is mean ± SE

It is noteworthy that the entrainment basin did not differ significantly between females and males (see Fig. 5). However, the mean level of entrainment appeared to be higher for females as compared to their male counterparts. When testing this assumption, a Mann-Whitney test indeed revealed significantly higher levels of tempo entrainment for female participants (Mdn = 60.05) compared to their male counterparts (Mdn = 39.10), U = 10.00, Z = −2.28, p < .05, r 2 = .32. It was also tested whether a link between musical training and entrainment could be found. However, no significant difference was found between participants with (Mdn = 50.73) or without musical background (Mdn = 38.24) regarding their level of entrainment, U = 18.00, Z = −1.30, p = .19, r 2 = .11.
https://static-content.springer.com/image/art%3A10.1186%2Fs40798-015-0025-9/MediaObjects/40798_2015_25_Fig5_HTML.gif
Fig. 5

Interaction plot of estimated marginal means calculated for tempo entrainment at both gender levels

Perceived Exertion

It was also checked whether the level of entrainment could be related to the degree of perceived exertion. For this purpose, a two-tailed Spearman’s correlation test was performed on entrainment values and ratings on the RPE scale. However, no significant relationship between perceived exertion and entrainment was found, r s = −.04, p = .58.

Discussion

The aim of this study was to examine whether music tempo could serve as a means to influence running cadence. Results indeed unveiled a significant relationship between imperceptible alterations in music tempo, in proportion to recreational runners’ self-paced running cadence, and cadence adaptation. In other words, faster music resulted in an increase, while slower music led to a decrease in running cadence. This effect can be explained through the idea of a sensorimotor mechanism that aligns footfall to musical beats. Adjustment of the footfalls to the beats relies on a phase-error correction mechanism of expected sensory outcomes [52]. Consequently, our study confirms results of previous research stressing the effect of music tempo on exercise performance [8, 11, 41, 53, 54]. This particular study also extends preceding research, as in this case, the effect on running cadence was tested using imperceptible changes in musical tempi with no explicit instructions regarding entrainment with the music. In contrast, in past research, participants were generally instructed to couple movement to music. Even if this was not the case, employed tempo variations usually proved to be too large to be unnoticeable. For example, Waterhouse, Hudson, and Edwards [41] compared cycling performance to normal, fast (increase of 10 %), and slow music (decrease of 10 %). Edworthy and Waring [8] examined treadmill-running behaviour when listening to music with a tempo of either 200 or 70 BPM, while Karageorghis et al. [53] employed tempi of 80, 120, and 140 BPM in their study on walking. In contrast, a maximum deviation of 3 % from the original music tempo was implemented in this particular study, as the amount of variation in tempo that an average person can distinguish is situated around 4 % [51]. Consequently, novel insights were presented in this study, as it was shown that recreational runners are able to adapt their running cadence (up to 2 % of the original cadence) to tempo changes in music (up to 3 % of the original tempo) without being aware of this attunement and without being instructed to do so. This finding supports the notion that an individual tends to synchronize spontaneously to an auditory rhythm occurring in the environment [37, 39, 52] and is in agreement with the natural predisposition of humans to respond to rhythmical qualities of music [34, 35].

It was also tested whether a basin for spontaneous entrainment of running cadence to music tempo could be found. Previous research has suggested that a range of period differences exists over which entrainment of movements of an individual with an environmental rhythm generally occurs and that beyond this range the occurrence of unintentional coordination is highly unlikely [33, 3640]. Results indeed revealed a significant decrease in the level of entrainment in combination with increasing deviations from the original music tempo. The degree of entrainment with the tempo of the music dropped significantly as soon as tempo increases of 2.50 % were introduced but also tended to drop at decreases of 3.00 %. This could be explained by the fact that when deviations (especially increases) from the original, self-selected, and thus comfortable running tempo got larger, the effort required from the runner increased and at a certain point probably required too much effort, resulting in significantly lower levels of entrainment. As such, our results are in line with the idea of an entrainment basin for spontaneous coordination [33, 3640]. However, our findings also contrast with those of Mendonça et al. [54], showing that for uninstructed synchronization of walking to music, participants did not adapt their step frequency to music that differed 5 to 10 % above and under their nominal step frequency, while they did adjust when synchronization was instructed. This could imply that a wider basin might be found for instructed entrainment to music tempo, while spontaneous entrainment occurs only when smaller deviations from the original tempo are introduced. But this is subject to some speculation and might benefit from further research.

Music is believed to only successfully distract from fatigue and discomfort when physiological awareness and perceived exertion are relatively low [19, 20, 2224]. Therefore, in order to control for possible effects of perceived exertion, after each set of four laps, a break of approximately 5 min was introduced. Besides, the relationship between the degree of perceived exertion and the level of entrainment was also examined in the analysis. Nevertheless, no significant relationship between perceived exertion and entrainment was found. This could be due to the fact that, in general, participants did not perceive the task as extremely light or exceptionally hard but mostly rated their perceived exertion as intermediate. A reason for this might be that runners ran at their comfort tempo and no large shifts in the tempo of the music were incorporated in the study, but it might also be partly due to the introduction of the breaks after each condition. Besides, most previous research demonstrating decreasing levels of influence of music on attentional processes at higher exercise intensities tested this effect using asynchronous music, e.g. [19, 20, 2224]. Whether this also applies to synchronous music still remains rather unclear, although, in their study on the effect of synchronous music on treadmill running, Terry et al. [29] did indicate lower levels of perceived exertion, assessed at moderate-to-high work intensities, for synchronous music compared to the no-music control. Yet, the magnitude of the differences in rating of perceived exertion proved to be rather small.

Another hypothesis referred to gender. We expected female participants to exhibit larger levels of entrainment in comparison with their male counterparts. Indeed, significantly higher levels of tempo entrainment were observed for females. In addition, although the effect of the music tempo on running cadence was unveiled for both males and females, changes in running cadence as a result of deviations in music tempi were more pronounced for female runners than for male ones, which suggests that women were more influenced by tempo changes than men. These findings resonate with the general belief that women are more responsive to musical stimuli [11, 41, 34, 55].

One should bear in mind that the current study focused on self-paced running, and thus, the type of exercise under study concerned one that is of low-to-moderate intensity. When studying activities with higher levels of intensity, music might not have a comparable effect on the exercisers’ performance, as when high workloads are undertaken, the exerciser’s attention could be shifted towards the painful or fatiguing effects of the exercise [19, 20, 2224]. However, although most previous research on high-intensity exercise did not show any remarkable effects of music tempo, exemplary studies that have unveiled such effects do exist as well. In a study by Rendi, Szabo, and Szabo [10], for example, where exercisers were asked to perform a 500-m rowing sprint, in which physiological awareness is high, it was shown that fast-tempo music increased arousal and, in turn, performance, even during high-intensity sprints, while music with a slow tempo did not generate such stimulating effects. Further exploration of the impact of music tempo on sport activities with high workloads would be beneficial.

It could also be questioned whether spontaneous, thus uninstructed, entrainment is generally more beneficial with regard to exercise performance than instructed entrainment. It could be suggested that when synchronization is spontaneous, it may require less attentional resources, thus leading to even more important benefits (e.g. leaving free attentional resources to realize other tasks). Besides, exercise training could be simplified when instruction would prove to be redundant. On the other hand, it has been indicated that instructed synchronization is a form of active attentional manipulation, which has been shown to have more positive effects, at least in the form of perceived exertion and exercise efficiency [12, 28]. However, as this question has not been solved yet, the discussion whether spontaneous synchronization is more beneficial compared to instructed (or even imposed) synchronization should be unravelled in future studies.

In this particular study, recreational runners were tested. However, since music is believed to be more beneficial for recreational compared to trained exercisers [56], different results might have been obtained if competitive runners were tested. Previous research on treadmill running indicated that less trained exercisers might depend to a greater extent on the positive feeling states generated by music, while trained exercisers generally tend to focus on the tasks and specifics of their training [57, 58]. Furthermore, as (either recreational or professional) runners do not typically tend to run distances of 800 m consecutively, interrupted by short brakes, it might be interesting to investigate whether the effect of music tempo is sustained over the course of longer, interrupted distances. Whether the entrainment basin for recreational runners would differ from that of professional runners and whether its effects are sustained over longer distances could be tested in future research.

Conclusions

To conclude, it was unveiled that music tempo could serve as an unprompted means to re(shape) running cadence of recreational runners. This influence was shown to have a certain range, which suggests that maximal effects of music tempo can only be obtained up to a certain level of tempo change and proved to be stronger for female compared to male runners. As modifying step rate may prove beneficial in the prevention and treatment of common running-related injuries, this novel finding could be especially relevant for treatment purposes, such as exercise prescription and gait retraining.

Notes

Declarations

Acknowledgements

The authors wish to thank the Flanders Sports Arena of Ghent, for making it possible to use their running track. The authors also acknowledge BeatHealth (contract #610633), a collaborative project funded by the European Commission under the Seventh Framework Programme, and the Methusalem project, awarded by the Flemish Government, for funding this study.

Authors’ Affiliations

(1)
IPEM, Department of Arts, Music and Theatre Sciences, Ghent University
(2)
EuroMov, Movement 2 Health Laboratory (M2H), University of Montpellier

References

  1. van Gent RN, Siem D, van Middelkoop M, van Os AG, Bierma-Zeinstra SM, Koes BW. Incidence and determinants of lower extremity running injuries in long distance runners: a systematic review. Br J Sports Med. 2007;41:469–80.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Taunton JE, Ryan MB, Clement DB, McKenzie DC, Lloyd-Smith DR, Zumbo BD. A retrospective case-control analysis of 2002 running injuries. Br J Sports Med. 2002;36:95–101.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Ferber R, Noehren B, Hamill J, Davis I. Competitive female runners with a history of iliotibial band syndrome demonstrate atypical hip and knee kinematics. J Orthop Sports Phys Ther. 2010;40:52–8.View ArticlePubMedGoogle Scholar
  4. Noehren B, Davis I, Hamill J. ASB clinical biomechanics award winner 2006 prospective study of the biomechanical factors associated with iliotibial band syndrome. Clin Biomech. 2007;22:951–6.View ArticleGoogle Scholar
  5. Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, Ryan MB. Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc. 2011;43:296–302.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Priest DL, Karageorghis CI. A qualitative investigation into the characteristics and effects of music accompanying exercise. Eur Phys Educ Rev. 2008;14:347–66.View ArticleGoogle Scholar
  7. Yamashita S, Twai K, Aktmoto T, Sugawara J, Kono I. Effects of music during exercise on RPE, heart rate and the autonomic nervous system. J Sports Med Phys Fitness. 2006;46:425–30.PubMedGoogle Scholar
  8. Edworthy J, Waring H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics. 2006;49:1597–610.View ArticlePubMedGoogle Scholar
  9. Shaulov N, Lufi D. Music and light during indoor cycling. Percept Motor Skills. 2009;108:597–607.View ArticlePubMedGoogle Scholar
  10. Rendi M, Szabo A, Szabó T. Performance enhancement with music in rowing sprint. Sport Psychol. 2008;22:175–82.Google Scholar
  11. Priest DL, Karageorghis CI, Sharp NCC. The characteristics and effects of motivational music in exercise settings: the possible influence of gender, age, frequency of attendance, and time of attendance. J Sports Med Phys Fitness. 2004;44:77–86.PubMedGoogle Scholar
  12. Lim HBT, Karageorghis CI, Romer LM, Bishop DT. Psychophysiological effects of synchronous versus asynchronous music during cycling. Med Sci Sports. 2014;46:407–13.Google Scholar
  13. Särkämö T, Tervaniemi M, Laitinen S, Forsblom A, Soinila S, Mikkonen M, et al. Music listening enhances cognitive recovery and mood after middle cerebral artery stroke. Brain. 2008;131:866–76.View ArticlePubMedGoogle Scholar
  14. 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.View ArticlePubMedGoogle Scholar
  15. Hsu DY, Huang L, Nordgren LF, Rucker DD, Galinsky AD. The music of power: perceptual and behavioral consequences of powerful music. Soc Psychol Personal Sci. 2014; doi:10.1177/1948550614542345
  16. Simpson SD, Karageorghis CI. The effects of synchronous music on 400-m sprint performance. J Sports Sci. 2006;24:1095–102.View ArticlePubMedGoogle Scholar
  17. Styns F, van Noorden L, Moelants D, Leman M. Walking on music. Hum Mov Sci. 2007;26:769–85.View ArticlePubMedGoogle Scholar
  18. Bood RJ, Nijssen M, van der Kamp J, Roerdink M. The power of auditory-motor synchronization in sports: enhancing running performance by coupling cadence with the right beats. Plos One. 2013; doi:10.1371/journal.pone.0070758
  19. Rejeski WJ. Perceived exertion: an active or passive process? J Sport Exerc Psychol. 1985;7:371–8.Google Scholar
  20. 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.PubMedGoogle Scholar
  21. Tenenbaum G. A social-cognitive perspective of perceived exertion. In: Tenenbaum G, Eklund R, editors. Handbook of sport psychology. 3rd ed. Hoboken: Wiley; 2007. p. 560–77.View ArticleGoogle Scholar
  22. Tenenbaum G. The study of perceived and sustained effort: concepts, research findings, and future directions. In: Hackfort D, Duda J, Lidor R, editors. Handbook of research on applied sport psychology. Morgantown: Fitness Information Technology; 2005. p. 335–49.Google Scholar
  23. 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.View ArticleGoogle Scholar
  24. Hutchinson JC, Tenenbaum G. Attention focus during physical effort: the mediating role of task intensity. Psychol Sport Exerc. 2007;8:233–45.View ArticleGoogle Scholar
  25. Karageorghis CI. The scientific application of music in sport and exercise. In: Lane AM, editor. Sport and exercise psychology. London: Hodder Education; 2008. p. 109–37.Google Scholar
  26. Cohen SL, Paradis C, LeMura LM. The effects of contingent-monetary reinforcement and music on exercise in college students. J Sport Behav. 2007;30:146–60.Google Scholar
  27. Elliott D, Carr S, Orme D. The effect of motivational music on sub-maximal exercise. Eur J Sport Sci. 2005;5:97–106.View ArticleGoogle Scholar
  28. Karageorghis CI, Mouzourides D, Priest DL, Sasso T, Morrish D, Whalley C. Psychophysical and ergogenic effects of synchronous music during treadmill walking. J Sport Exerc Psychol. 2009;31:18–36.PubMedGoogle Scholar
  29. Terry PC, Karageorghis CI, Mecozzi Saha A, D’Auria S. Effects of synchronous music on treadmill running among elite triathletes. J Sci Med Sport. 2012;15:52–7.View ArticlePubMedGoogle Scholar
  30. Kenyon GP, Thaut MH. Rhythm-driven optimization of motor control. Recent Res Dev Biomech. 2003;1:29–47.Google Scholar
  31. Smoll FL, Schultz RW. Accuracy of motor behaviour in response to preferred and nonpreferred tempos. J Hum Mov Stud. 1982;8:123–38.Google Scholar
  32. Rossignol S, Melvill-Jones G. Audiospinal influences in man studied by the H-reflex and its possible role in rhythmic movement synchronized to sound. Electroencephalogr Clin Neurophysiol. 1976;41:83–92.View ArticlePubMedGoogle Scholar
  33. Richardson MJ, Marsh KL, Schmidt RC. Effects of visual and verbal interaction on unintentional interpersonal coordination. J Exp Psychol: Human Percept Perform. 2005;31:62–79.Google Scholar
  34. 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.View ArticlePubMedGoogle Scholar
  35. Large EW. On synchronizing movements to music. Hum Mov Sci. 2000;19:527–66.View ArticleGoogle Scholar
  36. von Holst E. Relative coordination as a phenomenon and as a method of analysis of central nervous system function. In: Martin R, editor. The collected papers of Erich von Holst, The behavioral physiology of animal and man, vol. 1. Coral Gables: University of Miami Press; 1973. p. 33–135.Google Scholar
  37. Lopresti-Goodman SM, Richardson MJ, Silva PL, Schmidt RC. Period basin of entrainment for unintentional visual coordination. J Mot Behav. 2008;40:3–10.View ArticlePubMedGoogle Scholar
  38. Schmidt RC, Richardson MJ. Dynamics of interpersonal coordination. Berlin: Springer; 2008.View ArticleGoogle Scholar
  39. Schmidt RC, Richardson MJ, Arsenault CA, Galantucci B. Visual tracking and entrainment to an environmental rhythm. J Exp Psychol: Human Percept Perform. 2007;33:860–70.Google Scholar
  40. Strogatz SH. Nonlinear dynamic and chaos: with applications to physics, biology, chemistry, and engineering. Cambridge: Perseus Books; 1994.Google Scholar
  41. Waterhouse J, Hudson P, Edwards B. Effects of music tempo upon submaximal cycling performance. Scand J Med Sci Sports. 2010;20:662–9.View ArticlePubMedGoogle Scholar
  42. Kelso JAS. Dynamic patterns: the self-organization of brain and behavior. Cambridge: MIT Press; 1995.Google Scholar
  43. Van Dyck E, Moelants D, Demey M, Deweppe A, Coussement P, Leman M. The impact of the bass drum on human dance movement. Music Percept. 2013;30:349–59.View ArticleGoogle Scholar
  44. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175–91.View ArticlePubMedGoogle Scholar
  45. Karageorghis CI, Terry PC, Lane AM, Bishop DT, Priest DL. The BASES expert statement on use of music in exercise. J Sports Sci. 2012;30:953–6.View ArticlePubMedGoogle Scholar
  46. Dixon S. Evaluation of the audio beat tracking system BeatRoot. J New Music Res. 2007;36:39–50.View ArticleGoogle Scholar
  47. Moens B, Muller C, van Noorden L, Franek M, Celie B, Boone J, et al. Encouraging spontaneous synchronisation with DJogger, an adaptive music player that aligns movement with music. Plos One. In press.
  48. Karageorghis CI, Priest DL, Terry PC, Chatzisarantis NLD, Lane AM. Development and 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.View ArticlePubMedGoogle Scholar
  49. Ajzen I, Fishbein M. Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychol Bull. 1977;84:888–918.View ArticleGoogle Scholar
  50. Borg G. Borg’s perceived exertion and pain scales. Human Kinetics: Champaign; 1998.Google Scholar
  51. Levitin D. This is your brain on music: the science of a human obsession. New York: Dutton; 2006.Google Scholar
  52. Repp BH, Su YH. Sensorimotor synchronization: a review of recent research (2006-2012). Psychon Bull Rev. 2013;20:403–52.View ArticlePubMedGoogle Scholar
  53. Karageorghis C, Jones L, Low D. Relationship between exercise heart rate and music tempo preference. Res Quart Exerc Sport. 2006;77:240–50.View ArticleGoogle Scholar
  54. Mendonça C, Oliveira M, Fontes L, Santos J. The effect of instruction to synchronize over step frequency while walking with auditory cues on a treadmill. Hum Mov Sci. 2014;33:33–42.View ArticlePubMedGoogle Scholar
  55. Pellett TL. Children’s stereotypical perceptions of physical activities: a K–12 analysis. Percept Motor Skills. 1994;79:1128–30.View ArticlePubMedGoogle Scholar
  56. Karageorghis CI, Priest DL. Music in the exercise domain: a review and synthesis (Part II). Int Rev Sport Exerc Psychol. 2012;5:67–84.PubMed CentralView ArticlePubMedGoogle Scholar
  57. Brownley KA, McMurray RG, Hackney AC. Effects of music on physiological and affective responses to graded treadmill exercise in trained and untrained runners. Int J Psychophysiol. 1995;19:193–201.View ArticlePubMedGoogle Scholar
  58. Mohammadzadeh H, Tartibiyan B, Ahmadi A. The effects of music on the perceived exertion rate and performance of trained and untrained individuals during progressive exercise. Facta Univ Phys Educ Sport. 2008;6:67–74.Google Scholar

Copyright

© Van Dyck et al. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.