RESEARCH ARTICLE
Optimizing beat synchronized running to
music
Jeska BuhmannID1*, Bart Moens1, Edith Van Dyck1, Dobromir Dotov2, Marc Leman1
1 IPEM, Ghent University, Ghent, Belgium, 2 LIVELab, McMaster University, Hamilton, Canada
* jeska.buhmann@ugent.be
Abstract
The use of music and specifically tempo-matched music has been shown to affect running
performance. But can we maximize the synchronization of movements to music and does
maximum synchronization influence kinematics and motivation? In this study, we explore
the effect of different types of music-to-movement alignment strategies on phase coher-
ence, cadence and motivation. These strategies were compared to a control condition
where the music tempo was deliberately not aligned to the running cadence. Results show
that without relative phase alignment, a negative mean asynchrony (NMA) of footfall timings
with respect to the beats is obtained. This means that footfalls occurred slightly before the
beat and that beats were anticipated. Convergence towards this NMA or preferred relative
phase angle was facilitated when the first music beat of a new song started close to the
step, which means that entrainment occurred. The results also show that using tempo and
phase alignment, the relative phase can be manipulated or forced in a certain angle with a
high degree of accuracy. Ensuring negative angles larger than NMA (step before beat)
results in increased motivation and decreasing cadence. Running in NMA or preferred rela-
tive phase angles results in a null effect on cadence. Ensuring a positive phase angle with
respect to NMA results in higher motivation and higher cadence. None of the manipulations
resulted in change in perceived exhaustion or a change in velocity. Results also indicate
that gender plays an important role when using forced phase algorithms: effects were more
pronounced for the female population than for the male population. The implementation of
the proposed alignment strategies and control of beat timing while running opens possibili-
ties optimizing the individual running cadence and motivation.
Introduction
Sports and exercise activities are generally believed to benefit from music listening. Under par-
ticular conditions music has been shown to capture attention, raise spirits, trigger a range of
emotions, alter or regulate mood, evoke memories, increase work output, heighten arousal,
induce states of higher functioning, reduce inhibitions, and encourage rhythmic movement [1,
2]. Effects of music during exercise can even be enhanced when certain types of music are con-
sidered [3, 4], especially when a certain level of synchrony between the musical stimuli and the
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December 6, 2018
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OPEN ACCESS
Citation: Buhmann J, Moens B, Van Dyck E, Dotov
D, Leman M (2018) Optimizing beat synchronized
running to music. PLoS ONE 13(12): e0208702.
https://doi.org/10.1371/journal.pone.0208702
Editor: Ramesh Balasubramaniam, University of
California Merced, UNITED STATES
Received: February 19, 2018
Accepted: November 21, 2018
Published: December 6, 2018
Copyright: © 2018 Buhmann et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data files and
scripts are available via https://doi.org/10.5281/
zenodo.1467592.
Funding: This research has been supported by
BeatHealth (contract #610633), a collaborative
project funded by the European Commission under
the Seventh Framework Programme: https://
cordis.europa.eu/project/rcn/110990_en.html. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
listener’s movements occurs [5–8]. A much-researched topic concerns the synchrony between
music and running, measured by beat and footfall as markers of the rhythm that drives syn-
chronized running. However, synchronizing one’s footfall with the musical beat is not a
straightforward endeavor. Some people easily synchronize, while others need an instruction to
synchronize [9]. Some music facilitates movement (such as groove [10]) and running (such as
activating music), while other music is less effective [3, 4]. In short, the use of music and specif-
ically tempo-matched music has been shown to affect running performance, but it is an open
question whether we can maximize the synchronization of movements to music and whether
this maximum synchronization somehow influences kinematics and motivation.
In this context, we define synchronization as the stable maintenance over time of the senso-
rimotor coupling between beat and footfall. We define entrainment as the process that realizes
the sensorimotor coupling, more specifically the process that brings the (perceived) beat and
the (performed) footfall towards a stable timing. Rather than through pull and push forces (as
in a dynamical system) it is straightforward to assume that prediction error minimization is a
mechanism for entrainment [11]. For an in depth explanation of the factors that determine the
strength of the sensorimotor coupling and entrainment, see Leman [12].
The tempo of running is expressed in steps per minute (SPM), i.e. cadence, and for music it
is expressed in beats per minute (BPM). The tempo of running, or cadence, refers to the fre-
quency of steps, whereas speed (expressed in km/h) refers to the total distance travelled in a
certain amount of time. In previous studies, the preferred exercise intensity was often mea-
sured before the actual experiment and the music tempo was generally rather coarsely matched
to a subject’s spontaneous or comfort tempo (e.g., within a 10% range of the assessed cadence)
[13, 14]. However, such an approach disregards the fact that the comfort tempo of an individ-
ual might be different at the time of the test, or that it might fluctuate during the test period.
As a result, the contrast between the tempo of the music and the tempo of the exercise perfor-
mance is likely to become too considerable to enable spontaneous entrainment. For that rea-
son, we measure comfort tempo in the first part of each running task. In addition, a study by
Van Dyck et al. [15] unveiled that uninstructed synchronization of running cadence to musical
tempo occurs spontaneously when the tempo of the music does not deviate more than 2.5%
from the initial running cadence. This finding highlights that conditions of entrainment yield
affordances for sensorimotor adaptation to be effective, provided that the tempo of the music
can be matched to the person’s comfort tempo with high accuracy. Other research that
addressed the particular relations between entrainment conditions and sensorimotor adapta-
tion is, however, scarce [12, 16].
Another aspect that is likely to be important in terms of spontaneously manipulating run-
ning cadence is the anticipation effect reported in sensorimotor synchronization studies
(SMS). For example, people typically tend to tap a little bit before the auditory stimulus, which
is indicated by a slightly negative phase angle of the tap relative to the click, also referred to as
negative mean asynchrony (NMA) [11]. They do this, presumably, because the sensing of the
tapping has a longer delay than the sensing of the click [17]. Synchronization at brain level
therefore means tapping before the click. Being able to continuously manipulate the exact
moment of the beat might therefore prove to be a very accurate way to match and influence
people’s running capacity.
In the present study we focus on very precise sensorimotor (beat-footfall) alignment strate-
gies during running. In fact, some of those strategies manipulate only the timing of the beat
and it is expected that the timing of the footfall is somehow influenced by this manipulation.
Moens et al. [16] describe four different strategies that manipulate sensorimotor alignment.
However, these alignment strategies were not yet compared with each other within a single
study, nor with the same musical test samples. Therefore, the purpose of this study is to
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contribute to a high definition sensorimotor alignment strategy that accounts for each person’s
sensorimotor ability. In particular, we aim at comparing different beat-footfall alignments
using a randomized experimental setup where different strategies for supporting such align-
ments are applied. We hypothesized that strategies employing phase alignment to adjust the
time of the musical beat relative to the time of the footfall during running would result in the
highest level of sensorimotor synchronization and have a bigger effect on kinematics (cadence
and speed) compared to strategies using period alignment instead. In addition, we expect that
a high degree of synchronization would have an effect on motivation.
While our main aim is to discover whether different music-to-movement alignment strate-
gies affect kinematics (cadence and speed) and/or motivation in distinct ways, we also want to
address possible gender differences in entrainment capacity. The study by Van Dyck et al. [15]
unveiled higher levels of entrainment for female compared to male runners. Other research
shows that, when people are requested to rate the motivational qualities of musical excerpts,
women pay closer attention to the rhythmical qualities of the stimuli compared to their male
counterparts [18]. In addition, musical preference seems to affect women differently than
men: when female runners listen to highly preferred stimuli, they tend to perform better than
when listening to non-preferred music. In comparison, musical preference does not seem to
affect the performance of male exercisers [19]. From the above-described findings, we
expected to uncover differences between men and women regarding their ability to entrain
and synchronize with the beats of the music. Such differences would be particularly relevant in
terms of designing a more individualized music-technology approach.
Materials and methods
Subjects
To establish sample size, a power analysis for a repeated-measures design was conducted using
GPower 3.1.9.2 [20]. Based on a small effect size (.25) with alpha set at .05 and power at .95, it
was estimated that at least 28 participants would be required. In total, 36 healthy, adult partici-
pants (19 males) took part in the study. All participants were recreational runners (Mage =
31.22 years; SDage = 8.13 years) and indicated to be capable of running 30 minutes continu-
ously. Of all participants, 38.89% were trained in music (Pearson Chi-Square test showed no
significant relation between gender and musical background, χ2(1) = 1.22, p = .27). In addi-
tion, about half of them (55.56%) reported to generally run without music, 22.22% indicated to
usually run with music, and 22.22% ran both with and without musical accompaniment. The
study was approved by the Ethics Committee of the Faculty of Arts and Philosophy of Ghent
University and was in accordance with the statements of the Declaration of Helsinki. Written
informed consent was obtained from all the participants before the start of the experiment.
Experimental design
Stimuli.
A music database consisting of music tracks in the tempo range of 120–200 BPM
(the range of natural running cadence) was created. The database included musical stimuli
from a previous running experiment [15]. Using the Brunel Music Rating Inventory-2 (BMRI-
2) test [21], all stimuli were rated as highly motivational for running. Additional tracks were
selected to ensure complete coverage of the tempo range. In total, 43 tracks with clear beat infor-
mation were selected. The tempo stability throughout each entire track was validated and intros
lacking clear beats were cut from the stimuli using Audacity (http://audacity.sourceforge.net).
BeatRoot [22] was applied to track the beats and tempo of each music track, while Adobe Audi-
tion (http://www.adobe.com) was used to normalize perceived loudness and minimize possible
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imbalances in sound pressure level using the ITU-R BS.1770-3 standard at -23 LUFS which is
commonly used in audio broadcasts [23].
Apparatus.
Data was collected using a 7" tablet (Panasonic FZ-M1) running Windows
8.1, which was strapped to a backpack. In addition, a pair of sensors, headphones, and a man-
agement computer was employed. The tablet operated as the main hub that handled incoming
sensor data and provided the musical stimuli.
To detect footfall instants, participants were equipped with two iPods (4th generation); one
attached at each ankle. Using the Sensor Monitor Pro application on the iPods, data from
accelerometers and gyroscopes was streamed wirelessly to the tablet at a sampling rate of 100
Hz. Step timings were extracted from the signal using an approach based on Pappas et al. [24].
Speed measurements were performed using a sonar system (MaxBotix LV-MaxSonar-EZ:
MB1010) connected to the tablet through a Teensy 3.1 micro-controller. It detected marker
rods of 1.90 m high, placed at a regular interval of 10 m around the running track. Through
computation of the time it took to cover each interval, absolute speed was determined. The
analogue signal was sampled at 30 Hz and digitized using the Teensy.
The wireless connection between the tablet, iPods, and management computer was provided
through a Wi-Fi router (TP-Link M5360), firmly strapped to the backpack, ensuring reliable
communication between all crucial components. The management computer was applied to
initiate the experimental sessions and to monitor sensor data in real-time. Musical tempi were
manipulated using a phase vocoder based on the technology of E´lastique Pitch of ZPlane.de
[25]. The phase vocoder manipulates tempo in real-time without modifying pitch using a com-
bination of frequency and time-domain methods. The system logged all data and calculations in
real-time. Music tempo was adapted based on the selected alignment strategy (for the imple-
mentation of the music alignment strategies, see Moens et al. [16]). Finally, the aligned music
was sent back to the participant using Sennheiser HD60 headphones connected to the tablet.
Procedure.
All experiments took place in the Flanders Sports Arena of Ghent, Belgium.
After being equipped, participants were asked to run on a 200 m running track for five minutes
continuously, and this for six consecutive times. In each of the six 5-minute runs, a different
alignment strategy was tested and it was ensured that all orders could occur only once.
Participants were instructed to run at their own comfortable pace. No information was dis-
tributed concerning the purpose of the experiment and all participants ran in solo conditions.
After each 5-minute run, participants were allowed to take a break for several minutes in
which they rated their perceived exertion (RPE) on the Borg Scale [26]. This way we could
determine whether fatigue was influenced by the different music alignment strategies. In addi-
tion, they rated the level of physical enjoyment on the 8-item version of the Physical Activity
Enjoyment Scale (PACES) [27, 28], a single factor scale to assess the level of enjoyment during
a physical activity in adults across exercise modalities.
Each of the 5-minute runs started with 25 seconds of silence, followed by five musical
excerpts of equal length (55 s) with an original tempo approaching the average cadence of the
last seven footsteps. Musical tempo was then manipulated based on the selected alignment
strategy, presented in the following section.
Conditions or alignment strategies.
In the control alignment strategy (S0), music and
running performance behave in a completely allochronic fashion (in our case: music is played
20 BPM faster/slower than the assessed running cadence). This strategy is used as a control
condition to compare against five other music-to-movement alignment strategies.
Two strategies (S1, S2) involve the alignment of the music tempo to the runner’s cadence.
The tempo matching occurs either at the beginning of a song only (S1), or continuously
throughout the exercise (S2). Tempo-matching alone, however, does not consider the exact
matching of the musical beats to the footfall instants; hence it neglects the phase (or the time
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between musical beat and footfall) while it maintains the period (for the musical beat and foot-
fall, independently). In the adaptive tempo conditions, participants were still able to change
their timing with respect to the beats as it takes several steps to determine the runners’ cadence.
Three strategies (S3, S4, S5) involve the alignment of the music tempo of the runner’s cadence
using relative phase angle manipulation. The relative phase angle expresses the footfall-beat time
as a segment of the previous beat-period, which is defined as having 360˚. Steps and beats are
recurring events, hence cyclic in nature. Hence, the difference in timing between a beat and the
nearest footfall can be expressed with a relative phase angle (-180˚ to +180˚), where a 0˚ angle
indicates that the beat and footfall instant coincide exactly. Alignment strategies that employ
such phase manipulation minimize the relative phase angle between the beat and footfall instant,
and therefore, they drive the alignment to any desired relative phase angle. In S3 the relative
phase angle is minimized to 0˚ once, at the beginning of the exercise. The tempo of the music is
however adapted continuously. The final two strategies (S4, S5) involve continuous relative phase
angle adaptation starting at the beginning of the exercise. S4 guides the runner towards a prede-
fined phase angle by adjusting the phase and hence tempo of the music at each step, thus at dis-
crete timing intervals. In previous research, feedback from participants indicated that such a
music adaptation sometimes felt unnatural or forced due to sudden tempo changes. Hence a new
strategy is introduced (S5) based on adaptive oscillators [29], which results in continuous relative
phase alignment towards a predefined relative phase angle using smoother tempo adaptations.
The first experiment in the present study intended that S4 and S5 guided the runner
towards a 0˚ relative phase angle, or a perfect synchrony between footfalls and musical beats.
However, after the experiment it became clear that an inaccuracy in the calibration led both
alignment strategies towards -70˚ instead of 0˚. This resulted in musical beats occurring after
the footfall, while they were intended to occur simultaneously. For clarity, we henceforth refer
to S4 and S5 as strategies with a configurable relative phase angle. A second (follow-up) experi-
ment was then performed to assess the influence of our initially intended S4 [0˚] and S5 [0˚]
strategies, which is reported subsequently. Based on the results of the first experiment, we also
added an additional condition, which forced a +30˚ angle (beat before the step), and this is
also reported later in this paper. In the discussion we consider the results from both experi-
ments. A summary of the different strategies is provided in Table 1.
Although we are aware of the phenomenon of NMA in SMS studies, little is known about
the magnitude of such a NMA for running and whether this is similar for all participants. We
therefore decided to use 0˚ as a target relative phase and discuss our results taking into account
the knowledge on NMA.
Measurements
Cadence and speed. We examined the effect of the different alignment strategies on kine-
matic parameters such as cadence and speed. Average cadence and speed values during music
playback are compared to those in the preceding 25 seconds of silence. The resulting depen-
dent variables are expressed in percentages, where zero indicates no difference, while a nega-
tive or positive value indicates a respective decrease or increase in cadence or speed compared
to the silent part of the condition.
Synchronization and phase angles.
The level of synchronicity with the music, or rather,
the stability and timing of the relation between a runner’s footfall and the musical beat, is typi-
cally represented by the resultant vector or R. The length of this vector is a measure of tempo
entrainment, ranging from zero to one with one representing perfect entrainment [30]. In addi-
tion, the angle of the resultant vector represents the average relative phase angle and reveals
whether footfall instants occur before the beat is played (negative phase) or after (positive phase).
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The resultant vector represents an average of a time period, and is calculated based on a distribu-
tion of individual phase angles or timing differences between each step and the closest beat [31].
Each relative phase angle is calculated with the formula below, where St refers to a step at time t.
B1 is the time of the beat that occurred before St and B2 is the time of the first beat after St.
φ ¼ 360  St Results
Synchronization
A Friedman’s ANOVA showed a main effect of the strategy on resultant vector length, χ2(5) =
147.705, p < .001. Wilcoxon tests were used to follow up this finding. A Bonferroni correction
was applied and so all effects are reported at a .003 level of significance. Results reveal that all
six strategies differ with respect to the resultant vector length, except S2 versus S3 and S4
[-70˚] versus S5 [-70˚]. All comparisons are summarized in Table 2.
The circular Wheeler-Watson Mardia test was used to verify that all phase angle distribu-
tions were significantly different and thus not homogenous (W = 5113.6, p < .001), meaning
the alignment strategies had a significant influence on the relative phase angles.
Cadence
One of the dependent variables of interest is the change in cadence from running in silence to
running with music. A 2x6 mixed-design ANOVA test with gender (male, female) as between-
subjects variable and condition (S0 to S5 [-70˚]) as within-subjects variable revealed a signifi-
cant main effect of the strategy on the change in cadence, F(5,170) = 16.46, p < .001. Contrasts
revealed that for S4 [-70˚], F(1, 34) = 17.23, p < .001, r = .58, and S5 [-70˚], F(1, 34) = 29.48, p
< .001, r = .68, running cadence decreased significantly more (M = -1.81%, SE = 0.37, and M
= -2.15%, SE = 0.34 respectively) compared to S0 (M = -0.53%, SE = 0.27).
There was no significant main effect of gender, indicating that on average there were no dif-
ferences in change in cadence between male (M = -1.06%, SE = 0.15) and female participants
(M = -0.93%, SE = 0.22), F(1,34) < 1, p = .78, r = .05.
However, an interaction effect between the strategy and the gender of the participant was
observed, F(5, 170) = 4.97, p < .001, indicating that the change in cadence differed between
men and women for different strategies. Contrasts were performed, revealing interaction
effects between gender x S0 x S4 [-70˚], F(1, 34) = 9.10, p = .005, r = .46, and gender x S0 x S5
[-70˚], F(1, 34) = 6.40, p = .016, r = .40. This indicated that although, for both males and
Table 2. Significant differences in phase coherence (resultant vector length R).
Comparisons (Mdn)
z
pa
r
S0 (0.03) vs. S1 (0.65)
-5.232
< .001
-.62
S0 (0.03) vs. S2 (0.80)
-5.232
< .001
-.62
S0 (0.03) vs. S3 (0.80)
-5.232
< .001
-.62
S0 (0.03) vs. S4 [-70˚] (0.94)
-5.232
< .001
-.62
S0 (0.03) vs. S5 [-70˚] (0.94)
-5.232
< .001
-.62
S1 (0.65) vs. S2 (0.80)
-3.268
.001
-.39
S1 (0.65) vs. S3 (0.80)
-3.991
< .001
-.47
S1 (0.65) vs. S4 [-70˚] (0.94)
-5.232
< .001
-.62
S1 (0.65) vs. S5 [-70˚] (0.94)
-5.232
< .001
-.62
S2 (0.80) vs. S3 (0.80)
-0.644
.519
-.08
S2 (0.80) vs. S4 [-70˚] (0.94)
-5.185
< .001
-.61
S2 (0.80) vs. S5 [-70˚] (0.94)
-5.059
< .001
-.60
S3 (0.80) vs. S4 [-70˚] (0.94)
-5.122
< .001
-.60
S3 (0.80) vs. S5 [-70˚] (0.94)
-5.001
< .001
-.59
S4 [-70˚] (0.94) vs. S5 [-70˚] (0.94)
-0.055
.956
-.01
a p values were calculated with Wilcoxon signed-rank tests comparing all six alignment strategies with each other.
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females, cadence decreased substantially during S4 [-70˚] and S5 [-70˚] compared to S0, this
decrease was more pronounced for female runners.
Speed
No main effect of the type of strategy on change in speed was uncovered, F(5, 165) = 1.02, p =
.407, nor was there a significant main effect of gender, F(1, 33) = 1.36, p = .25, r = .20. Besides,
there was no interaction effect between strategy and gender, F(5, 165) = 2.16, p = .061.
Motivation and perceived exertion
No significant differences were revealed for perceived exertion (RPE) with a Friedman’s
ANOVA, χ2(5) = 6.64, p = .25.
Wilcoxon signed-rank tests (comparing each strategy with S0) were performed on the
scores of the PACES scale. A Bonferroni correction was applied and so all effects are reported
at a .01 level of significance. The motivational scores were higher for S5 [-70˚] (Mdn = 71.38)
compared to S0 (Mdn = 67.25), z = -2.662, p = .008, r = -.31. None of the other strategies dis-
played significant differences in motivation when compared to the allochronic strategy (S0).
Table 3 summarizes the motivational comparisons. The median value for S5 [-70˚] is not that
different from S1-4. The reason, however, that ratings for S5 [-70˚] are significantly different
from S0, while the other alignment strategies are not, is due to differences in ranking: a higher
percentage of the ratings where in favor of S5 [-70˚] over S0 than in the other comparisons.
Follow-up experiment
The data analysis of the first experiment showed surprising results of S4 and S5, leading to an
investigation of the apparatus’ calibration. As we noted earlier, S4 and S5 were initially aimed to
obtain a 0˚ phase synchronization. However, the system incorrectly forced a -70˚ relative phase
angle. We believed that the non-intended forced -70˚ angle was the main cause of the observed
cadence decrease and motivational increase for these strategies. Therefore, it was decided to do
a follow-up experiment introducing correct 0˚ strategies. As the procedure and apparatus is
almost identical, we only elaborate on the differences and results for cadence and motivation.
Materials and methods
Tests took place at the same location of the first experiment. We recruited 11 of the initial par-
ticipants (6 female, 5 male, Mage = 39.27 years; SDage = 9.82 years) to have a similar population
and to be able to compare both experiments.
Strategies S0, S1 and S2 were identical to the initial experiment. The -70˚ strategies (S3 [-70˚],
S4 [-70˚], S5 [-70˚]) were replaced by their intended counterparts (S3 [0˚] S4 [0˚] and S5 [0˚]),
where the beats coincide with the footfalls as initially intended. One additional strategy was added
Table 3. Differences in motivation (PACES ratings).
Comparisons (Mdn)
z
pa
r
S0 (67.25) vs. S1 (69.88)
-.718
.487
-.08
S0 (67.25) vs. S2 (73.19)
-1.987
.048
-.23
S0 (67.25) vs. S3 (73.31)
-1.327
.196
-.15
S0 (67.25) vs. S4 [-70˚] (71.75)
-1.581
.109
-.18
S0 (67.25) vs. S5 [-70˚] (71.38)
-2.662
.008
-.31
a p values were calculated with Wilcoxon signed-rank tests comparing five alignment strategies with the allochronic
control condition (S0).
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to explore possible inverse effects of the negative phase angle, namely S5 [+30˚]. This strategy
placed the beat slightly before the footfall, implying that the musical beats preceded the runner.
For methodological reasons, this S5 [+30˚] strategy occurred at the end of the experiment and
was optional, to exclude influences of potential dropouts on our comparison with the earlier
experiment. A summary of the different strategies is provided in Table 1.
Results
All 11 participants completed all seven conditions. In total, two out of 77 trials were invalid
due to sensor errors. Given our initial experimental results, this section focuses on phase
angles, cadence, and motivation.
Phase angles and synchronization.
As for the initial experiment, a repeated-measures
ANOVA showed a main effect of the strategy on resultant vector length, F(6,54) = 103.794, p
< .001. The circular Wheeler-Watson Mardia test was used to verify that all phase angle distri-
butions were significantly different and thus not homogeneous (W = 1664.7, p < .001), mean-
ing that the alignment strategies had a significant influence on the relative phase angles.
Cadence.
In order to confirm that the retrieved effect on cadence from the initial experi-
ment was due to the phase angle being targeted at -70˚, we expected to find no effects on
cadence for S4 and S5 targeted at 0˚. Therefore, as in the initial experiment, a 2x6 mixed-
design ANOVA test with gender (male, female) as between-subjects variable and condition
(S0 to S5 [0˚]; excluding S5 [+30˚]) as within-subjects variable was performed. Indeed, no
main effect of the strategy on the change in cadence was revealed, F(5,40) = 1.210, p = .322. In
addition, no significant main effect of gender was observed, F(1,8) = 4.128, p = .077, r = .58,
nor an interaction effect between the strategy and the gender of the participant, F(5, 40) =
1.149, p = .351.
When we included S5 [+30˚], and performed a 2x7 mixed-design ANOVA, we did find a
main effect on cadence, F(6,48) = 3.693, p = .004, and a small but significant gender effect, F
(1,8) = 5.412, p = .048, r = .64. No gender x strategy interaction effect was found, F(6,48) =
1.306, p = .273.
Concerning the impact on cadence of different target phase angles in the configurable
phase angle strategy (S5), additional tests were performed. To compare S5 [-70˚] from our ini-
tial experiment with S5 [0˚] from the follow-up experiment an independent samples t-test was
executed. This test revealed that the decrease in cadence for S5 [-70˚] was significantly larger
(M = -2.15%, SE = 0.34) than for S5 [0˚], (M = -0.09%, SE = 0.51), t(45) = -3.034, p = .004, r =
.41. Furthermore, a paired samples t-test was used to compare S5 [+30˚] with S5 [0˚] from the
follow-up experiment. Results showed that the change in cadence for S5 [+30˚] was signifi-
cantly larger and even positive (M = 0.68%, SE = 0.59) compared to S5 [0˚], (M = -0.40%,
SE = 0.44), t(9) = -2.970, p = .016, r = .70.
Motivation.
Wilcoxon signed-rank tests (comparing each strategy with S0) were per-
formed on the scores of the PACES scale. A Bonferroni correction was applied and so all
effects are reported at a .008 level of significance. Only the motivational scores for S5 [+30˚]
showed a trend towards being significantly higher (Mdn = 44.00) than for S0 (Mdn = 38.00), z
= -2.661, p = .008, r = -.57. No other differences were found. Table 4 summarizes the motiva-
tional comparisons.
Discussion
The goal of the present study is whether we maximize the synchronization of perceived musical
beat and footfall during running, and whether this maximum synchronization influences kine-
matics and motivation. Table 5 summarizes the results, showing the different phase angle
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distributions for conditions and experiments. We then consider the effects of synchronization,
cadence, and motivation. The last section discusses possible gender differences for these effects.
Preferred phase angles, synchronization, and phase locking
The data from the different alignment strategies reveal differences and preferences in synchro-
nization behavior. Fig 1 provides a visual summary of the different strategies that were tested
in terms of timing information of the step versus the beat (i.e. the relative phase angle) and the
synchronization stability or phase coherence (i.e. resultant vector length).
In Fig 1, condition S0 clearly visualizes what the control condition was designed for, i.e.
that runners don’t synchronize, nor phase-lock (because they cannot). S0 can thus provide a
baseline or comparison for later tests.
Since strategy S1, S2, and S3 did not use continuous relative phase angle manipulation, they
gave a good idea of the (average over song) relative phase angle when runners ran with music
at their (previously measured) preferred tempo. Once the song starts, the tempo remains con-
stant (S1), or is continuously adapted to match the runners’ preferred tempo, which may
change during the song (S2 and S3). These conditions allowed for self-selected relative phase
Table 4. Differences in motivation (PACES ratings) for follow-up experiment.
Comparisons (Mdn)
z
pa
r
S0 (38.00) vs. S1 (42.00)
-1.721
.085
-.37
S0 (38.00) vs. S2 (42.00)
-1.736
.083
-.37
S0 (38.00) vs. S3 (41.00)
-0.949
.343
-.20
S0 (38.00) vs. S4 [0˚] (41.00)
-1.961
.050
-.42
S0 (38.00) vs. S5 [0˚] (42.00)
-1.661
.097
-.35
S0 (38.00) vs. S5 [+30˚] (44.00)
-2.661
.008
-.57
a p values were calculated with Wilcoxon signed-rank tests comparing six alignment strategies with the allochronic
control condition (S0).
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Table 5. Circular descriptives of the phase angle distributions for all strategies. Initial experiment data is abbreviated as I.E., while follow-up experiment data is abbre-
viated as F.U.E. For each alignment strategy the distribution of relative phase angles is described, using the following parameters: the mean angle ø representing the mean
direction, the resultant vector length R and the circular variance CV, angular deviation s (dispersion around the mean), circular skewness b (asymmetry) and the circular
kurtosis k (peak load). See [30] for more information.
Alignment Strategy
S0
S1
S2
S3
S4–70˚
S4 0˚
S5–70˚
S5 0˚
S5 30˚
Short name
Allochronic Music
Fixed Tempo
Continuous
Tempo
Adaptation
Continuous
Tempo
Adaptation, in
phase start
Forced Phase
Coherence—
Algorithmic
Forced Phase Coherence—
Adaptive Oscillator
Forced phase angle start / goal
-70˚
0˚
-70˚
0˚
-70˚
0˚
+30˚
Experiment
I.E.
F.U.E.
I.E.
F.U.E.
I.E.
F.U.E.
I.E.
F.U.E.
I.E.
F.U.E.
I.E.
F.U.E.
F.U.E.
N Steps
26326
7246
26445
7285
26275
7451
26199
6709
25980
7466
25773
7419
6772
N Songs
180
55
180
55
180
55
180
50
180
55
180
55
50
Mean relative phase angle ø
138˚
165˚
-13.7˚
-17.7˚
-8.04˚
3.61˚
-22.1˚
-39.2˚
-70.3˚
-1.95˚
-75.5˚
-9.66˚
17.4˚
Resultant Vector Length
j
R
j
0.0034
0.0085
0.424
0.402
0.196
0.136
0.671
0.667
0.911
0.912
0.925
0.936
0.895
Circular Variance CV
0.997
0.991
0.576
0.598
0.804
0.864
0.329
0.333
0.0891
0.0877
0.0747
0.0645
0.105
Angular Deviation s
1.4
1.4
1.1
1.1
1.3
1.3
0.81
0.82
0.42
0.42
0.39
0.36
0.46
Circular Skewness b
0.00048
-0.0006
0.02
0.03
-0.014
-0.035
-0.031
0.024
-0.0049
0.0074
0.0011
0.02
0.011
Circular Kurtosis k
-0.0016
0.0026
0.22
0.19
0.062
0.04
0.46
0.36
0.74
0.74
0.79
0.82
0.74
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angles. Since S2 and S3 used an adaptive tempo strategy, there was less freedom to deviate
from the initial relative phase angle between footfall and beat (for S2 this relative phase angle is
random, for S3 it is around -70˚ or 0˚ for initial and follow-up experiment).
Of particular interest was condition S1, where, due to the fixed tempo of the song, the high-
est degree of freedom was implemented with regard to the selection of the relative phase angle.
Each song started at a random relative phase angle (random time with respect to footfall), but
the distribution ended up with a clear clustering around negative relative phase angles (-14º
and -18 º for initial and follow-up experiment respectively). In addition, there was a large
spread in resultant vector lengths: in some cases people’s footfalls showed a constant timing
relative to the timing of the beats for a song (the dots on or near the circle perimeter, represent-
ing a high resultant vector length), in other cases this relation was less stable, or even almost
absent (the dots close to the center of the circle, representing a low resultant vector length).
This indicates that there was a convergence during the song from the starting angle towards
the final angle. The average resulting relative phase angle, as indicated by the arrows in Fig 1
Fig 1. Circular scatter plots of resultant vectors. Each dot represents the resultant vector (both angle and length) per song per participant. A negative angle indicates that
during that specific song, the footfall (averaged over the song) occurred before the musical beat (runner is first), while a positive angle indicates that the footfall occurred
after the beat (music is first). The distance from the center indicates the resultant vector length. The closer the dot is to the circle perimeter, the higher the phase coherence,
i.e. the more steps were taken towards this average phase angle. The arrows indicate the resultant vector length and average phase angle of the complete distribution of
songs, thus providing a general overview of all participants’ behavior to all songs, in a specific condition. The color and form of each dot indicate whether the data point
was from the initial or the follow-up experiment, which is denoted in the legend of the figures.
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and the resultant vector length value (length of the arrow in Fig 1, see also Table 5), show a pre-
ferred relative phase angle around -15˚. This confirms the preference for a slight negative
mean asynchrony, and that runners prefer the footfalls occurring just prior to the beats.
In S2, the tempo of the music was continuously matched to the tempo of the runner. The
figure shows high amounts of synchronization (runners consistently keeping their initial rela-
tive phase) but with a slight clustering around the same negative relative phase angles. This
indicates that phase attraction towards the preferred relative phase angle found in S1 still hap-
pened with adaptive tempo conditions, but it is less pronounced and it occurs only when start-
ing close to this preferred angle.
S3 further elaborated on this finding. The blue dots on Fig 1.S3 indicate data from the fol-
low-up experiment, where each song started around 0˚, that is, the beat is perfectly in sync
with the footfall. These participants deviated towards -40˚, thus increasing their relative phase
angle. For the initial experiment, we discovered a reverse effect: participants started in -70˚
and finished in on average -22˚, thus reducing their relative phase angle. This indicates that
there is a ‘phase attractor’ between -20˚ and -40˚, and that when the starting relative phase was
close to these angles, participants tended to get phase-locked around their preferred relative
phase angle.
Note that this attractor effect was not clearly visible in S2 as there was no clear clustering
around these relative phase angles. In this condition, the starting relative phase angle was ran-
dom. To explain why runners starting in these random relative phase angles were not attracted
to their own preferred angle, we refer to a mathematical model which is used in movement
coordination: the HKB model [34]. This model shows an attraction basin around the preferred
relative phase angle, but if a relative phase angle deviates too far from this preferred relative
phase angle, the attraction is not present. This could explain why there are high synchroniza-
tion scores, or resultant vector lengths, at all different relative phase angles. Additionally, when
participants were eventually attracted towards this preferred angle, another issue arose: since
the songs lasted only 55 seconds, it is likely that runners did not have enough time to converge
towards their preferred relative phase angle. Fig 1.S2, visualizes this phenomenon with lower
resultant vector lengths between -90º and 0˚ (compared to S1), which can be attributed to this
time-consuming convergence. A transition from one relative phase angle to a completely dif-
ferent relative phase angle results in a wide distribution and thus a lower resultant vector
length. We assume that if the song would have been longer, the same clustering as in S1 and S3
might have been discovered in S2 for starting relative phase angles close to the preferred rela-
tive phase angles.
S1 and S3, both the initial and the follow-up experiments, clearly revealed a negative mean
asynchrony (NMA), i.e. the participants showed a tendency to anticipate the beats (from -40˚
to -14˚). This is in accordance with other studies concerning sensorimotor synchronization.
On average, the runners put their feet down 40 to 15 ms before they perceive the musical beat.
This asynchrony could be explained by the fact that the tactile signal takes longer than the
audio signal to reach the brain. To synchronize in the brain, the tactile signal needs to occur
before the audio signal, hence the asynchrony of about -15º in relative phase. This is in line
with the nerve-conduction hypothesis, where peripheral processing time is dependent on the
distance of feedback (tactile or auditory) to the brain [17] and the sensory accumulator model
assuming that synchrony is established at the level of central representations [35]. Another
group of explanations is based on the onset computation or P-center hypothesis [36]: if an
event (e.g., a tap or a footstep) is extended over time, the perceptual center (P-center) differs
from the onset of the event. For tapping, it is suggested that rather than the initial surface con-
tact, the moment of peak force is the meaningful target in timing control [37].
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In terms of attraction towards the NMA or preferred phase angle, we found that our results
from S1—S3 are in line with Haken-Kelso-Bunz (HKB) model [34]. This model depicts inter-
limb coordination and phase transitions between different states. It shows a strong attraction
basin around the 0˚ phase angle and a secondary attractor near 180˚ (antiphase), inducing phase
transitions from the current angle towards the closest attractor. The model also shows that the
strength of this attraction force decreases with increasing movement frequency, and at higher
tempi (such as running at a higher cadence than 150 SPM) the secondary antiphase attractor dis-
appears and only relative phases close to 0˚ get attracted. This is remarkably similar to the find-
ings of S1 through S3 (although with a rotation towards the NMA or preferred relative phase
angle of the participants): (1) if the starting phase differs enough from the NMA, no attraction is
present when both systems operate at the same frequency (music is matched to cadence) and (2)
when the starting phase is close to the NMA, participants converge towards their NMA.
S4 and S5 were designed to keep a constant relation between the moments of the footfall
and the beat. This is clearly illustrated in Fig 1 by the concentrations of dots on the circle
perimeter near the target phase angle that was implemented in these strategies: -70˚ in the ini-
tial experiment (in red), 0˚ (in blue) and +30˚ (in green) in the follow-up experiment. This
indicates that the software works as configured, and that we can force users towards a certain
relative phase angle. Later sections of the discussion will look into possible kinematic and
motivational effects of certain relative phase angles.
To summarize, we found that the three conditions allowing self-selected relative phase angles
show that the preferred angle does not approximate 0˚, but is slightly negative–following the
NMA displayed in sensorimotor synchronization experiments [11, 38]. We also found indica-
tions for an attraction force towards this preferred relative phase angle, especially when the initial
relative phase was close to the preferred relative phase. When the initial starting relative phase
was not near to this attractor, a stable relative phase was kept at this angle. Hence, to reach the
preferred self-selected relative phase angle, the starting relative phase is important. If we want to
design future music-to-movement alignment strategies that support the runners’ natural and
individual interactive behavior with music, not only should the technique be capable of selecting
music that matches the preferred tempo of the runner, it should also be able to apply the preferred
relative phase angle (as measured for instance with a fixed tempo strategy like S1 and fine-tuned
with a strategy similar to S3) as a target relative phase angle in strategies similar to S4 and S5.
Effect on cadence
The combined results of the initial and the follow-up experiment indicate that strategy S1, S2,
and S3 have no significant effect on cadence when compared to the allochronic music strategy
(S0). Runners can be phase-locked, i.e. maintain a stable footfall-to-beat relation (seen best in
S1, S2 and S3), and in general running with these strategies reflects a natural preference for a
slightly negative phase angle relation (NMA). In contrast, our main results do show an effect
of the alignment strategy on cadence with S4 and S5. This is further elaborated on with the fol-
low-up experiment: the effect appears to be dependent on the target phase angle settings in the
strategy. Fig 2 shows the resulting cadence change based on the forced relative phase angle.
For this discussion, we group several conditions based on the forced relative phase angle:
(0) control or asynchronous music, (1) self-selected relative phase angles, (2) forced at -70˚, (3)
forced at 0˚, and (4) forced at +30˚. Fig 2 visualizes the effect of forced relative phase angles on
cadence.
There was no difference between self-selected angles and the control condition in terms of
the cadence change, meaning participants did not show any difference in running behavior in
their self-selected angle compared to allochronic music. There was however an effect of 0˚:
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cadence seemed to increase to a minor extent, while this increase was more pronounced at
+30˚. At -70˚, we noticed a clear decrease in cadence. Thus, to have a neutral null-effect, the
optimal relative phase angle would be the preferred relative phase angle as found in S1 and S3,
similar to our earlier findings on synchronization.
The decrease in cadence observed in S4 and S5 at -70º could be explained by the fact that
the beats were continuously perceived approximately 70 ms after the moment of the footfall.
The participant was therefore constantly urged to lengthen the duration of his or her next step
to reach the preferred -20˚ attractor. We theorize that, in order to deal with the perceived sen-
sorimotor error, the brain continuously adapts in order to try to minimize the perceived senso-
rimotor error (i.e., obtain NMA). Although that error persists (due to our matching
algorithm), the entrainment (or brain-driven sensorimotor error minimization) persists as
well, and the outcome is a decrease in cadence. Conversely, the introduction of the beat prior
to the footfall instant (positive relative phase angle) might rather induce a feeling of ’being
late’, which could in turn stimulate the runner to speed up. This was indeed observed in the
follow-up experiment, where S5 at +30˚ resulted in an increase in cadence compared to S0.
To further explore these results, a correlation analysis was performed to see a potential rela-
tion between the relative phase angle and the cadence change. This analysis only considers the
data of conditions where phase manipulation was used (S4 [-70˚], S4 [0˚], S5 [-70˚], S5 [0˚]
and S5 [+30˚]).
A Kolmogorov-Smirnov test indicated that the data were not normally distributed (p <
.01), hence a Spearman’s Rho correlation test was performed. We assumed that participants
would have tried to reach their preferred relative phase angle around -20˚, hence a one-tailed
test was performed as this assumption implies that a negative relative phase angle would result
Fig 2. Cadence change compared to initial silence over time. Conditions are grouped based on the forced relative phase angle to improve visual representation. Self-
selected relative phase angles include S1 through S3, Forced at -70˚ are S4 [-70˚] and S5 [-70˚], forced at 0˚ include S4 [0˚] and S5 [0˚], and finally forced at +30˚ is S5
[+30˚]. Self-selected relative phase angles induce little to no cadence change compared to the control condition, while the -70˚, 0˚ and +30˚ conditions have an effect over
time. Error bars represent 1 SE. The error bars are noticeably higher in the 0˚ and +30˚ conditions, as the amount of participants (N = 11) for the follow-up experiment
was lower than the amount of participants in the initial experiment (N = 39).
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in a lower cadence and vice versa. We uncovered a positive correlation between the relative
phase angle and cadence change (r = .293, p < .001, n = 681), showing that a connection
between cadence change and the forced relative phase angle indeed exists.
Furthermore, the influence of the relative phase angle appeared to build up over time. Fig 2
shows the evolution of cadence change for different strategies over the five songs. When look-
ing at the average cadence change over time, it was evident that the influence was not immedi-
ate, but increased over time. The lowering-cadence effect seemed to stop around 2.2%
slowdown (similar to the tempo basin found by Van Dyck et al. [15]), while the cadence
increasing effect was less pronounced. This shows that the relative phase angle manipulation
effect was ’limited’ towards the influence on cadence.
Motivation
In the initial experiment, as well as in the follow-up experiment, similar results were obtained
when comparing the enjoyment ratings of the different strategies to S0. The strategies that did
not significantly affect running cadence (S1, S2, S3, S4 [0˚], and S5 [0˚]) were not rated differ-
ently from S0. However, in the two conditions that affected cadence most (S5 [-70˚] and S5
[+30˚]) a small but significantly higher enjoyment rate, or a tendency towards such an effect,
was observed. According to literature on gaming [39] and exergames [40] the balance between
skill and challenge is crucial for being intrinsically self-rewarding. When challenged at a level
that the gamer perceives as pleasant, he or she is more likely to have a positive experience. As
such, it could be suggested that a small deviation from a preferred cadence, such as realized by
continuously manipulating the relative phase angle to a non-preferred phase angle (S5–70˚
and +30˚), might imply a more challenging or rewarding running exercise. A challenge that is
not too demanding for the runner’s skills (note that no significant increases in RPE were
observed) could result in a more pleasant and rewarding experience than when the runner is
not challenged or manipulated to change his or her preferred running behavior. According to
Fritz et al. [41] moving in synchrony with music can evoke a sense of agency, or a feeling of
’being in control’. Combined with a certain amount of physical exertion, this sense of agency
might contribute to a perceived positivity bias [42]. As such, our results seem to suggest that
finding a new phase angle balance induces such feelings of agency. Similar to the skill-chal-
lenge balance [39, 40], being in and out of balance with a slightly demanding phase angle
seems to more positively impact enjoyment levels compared to maintaining a preferred, less
demanding, phase angle. Alternatively, error minimization towards NMA can be seen as a reg-
ulatory mechanism to homeostasis (an equilibrium state consisting of predictive, expressive
and effort processes) [12].
Fig 3 summarizes our findings concerning the influence of forced relative phase angles. We
note 3 regions: the neutral ’self-selected’ or negative mean asynchrony near -20˚ (footfalls
slightly before beats), the cadence increasing larger angles between -10˚ and 30˚ (steps simulta-
neous or after the beats) and the lower angles between -40˚ and -90˚ (steps noticeably before
the beats). The effects of running in these regions are shown in the legend.
Gender differences
Several studies gave direct and indirect proof or reason to expect differences in how men run,
entrain, and synchronize with beats of the music, compared to women [15, 18, 19]. And
although no main effect of gender was found on the change in cadence, some interesting inter-
action effects were uncovered that are worth discussing. Below, we compare the running
behavior of men and women from the perspective of preferred phase angle and synchroniza-
tion, cadence, and motivation.
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Gender differences: Preferred phase angle and synchronization.
Analysis showed that
there is no significant difference between genders for average phase angles in all different con-
ditions. However, in the fixed music condition (S1), where participants had the most freedom
to self-select relative phase angles (and converge to their NMA), we noticed a slight difference
in distributions for males versus females. The relative phase angle distribution was slightly
more asymmetrical and wider than that for females, indicating a larger spread in relative phase
angles and a less consistent synchronization. When looking at the phase coherence or synchro-
nization of these angles (expressed by the resultant vector length), more pronounced differ-
ences emerge: women tend to reach higher synchronization scores than men in the fixed
music (S1) condition. Fig 4 shows the distribution of the resultant vector lengths for S1.
Analysis with independent t-tests confirmed that there was indeed a significant difference
in the resultant vector length for S1 (fixed tempo) for males (M = 0.57, SD = 0.27) compared
to females (M = 0.68, SD = 0.27), t(233) = -2.964, p = .003. Females thus obtained higher syn-
chronization scores. No other conditions showed such differences; perhaps because these con-
ditions (S2-S5) allowed less freedom to self-select phase angles.
Using a resultant vector length of .75 as a threshold to determine running in phase coher-
ence or not (similar to [4]), we see 52% of songs in phase coherence for women versus 23% for
men in the S1 (fixed tempo) condition.
Gender differences: Cadence.
A study on running to music [15] reported differences
between male and female participants: women showed significantly higher levels of tempo
entrainment than men. This is in line with research by Karageorghis [18], who found that
women pay more attention to the rhythmic characteristics of music than men do. These find-
ings make it interesting to look at the before mentioned forced-phase influence on cadence
with the gender as a grouping factor.
Fig 3. Summary of the effects of running in a forced relative phase angle.
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The main results showed an interaction effect between condition (alignment strategy) and
gender (F(5, 170) = 4.97, p < .001) for the cadence change, indicating that the change in cadence
differed between men and women for the different strategies. More in depth analysis showed a
more pronounced change in cadence between the strategies for female runners (see Fig 5). It
seems that, compared to their male counterparts, women demonstrated lower levels in cadence
change with S4 [-70˚] and S5 [-70˚], and higher ones when running with S1, S2, and S3.
A correlation test was performed on the forced relative phase angle conditions to test the
significance of the forced phase angle on cadence change. Fig 6 illustrates the dataset. Both
male and female distributions were non-normal (p < .01) as determined by the Kolmogorov-
Smirnov test. The Spearman’s Rho correlation test showed a difference in correlation coeffi-
cients for the different genders. While the male population showed a slightly positive and sig-
nificant correlation between relative phase angle and cadence change (r = .124, p < .01,
N = 363), the female population demonstrated a much higher correlation coefficient at a
higher level of significance (r = .449, p < .001). These results indicate that females might
indeed be more susceptible towards the influence of relative phase angles on cadence.
Gender differences: Motivation.
With respect to gender, our interest was to find out if
there are differences in entrainment capacity and effects on cadence. For completeness sake,
we also report that no significant differences were observed for the enjoyment ratings
(PACES) between men and women: not in general, and not when the data were inspected per
strategy.
Fig 4. Resultant vector length or phase coherence for all songs in the S1 (fixed music) condition. A high level of phase coherence is observed for
most of the women, whereas the flatter distribution of the resultant vector length for men indicates that a higher percentage of men did not maintain a
phase-locked running behavior over the duration of a song.
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Conclusions
Music-to-movement alignment strategies make it possible for everyone to keep running in
synchrony with a musical stimulus, without consciously manipulating your own behavior to
Fig 5. Cadence change compared to initial silence, split on gender. Conditions are grouped based on the forced relative phase angle to improve visual representation.
Self-selected angles include S1 through S3, Forced at -70° are S4 [-70˚] and S5 [-70˚], Forced at 0° include S4 [0˚] and S5 [0˚], and finally Forced at +30° is S5 [+30˚]. Both
the control and self-selected phase angles induced little to no cadence change, while the -70˚ and +30˚ conditions had an effect that was more pronounced for females.
Running at 0˚ phase angle seemed to have neutral effects for the males but a cadence increasing effect for females.
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Fig 6. Correlations between forced relative phase angle and cadence change. The effect is more visible for the female than the male population.
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align with the music. Our study shows that strategies implementing a continuous relative
phase alignment achieve the highest level of synchronization (as intended) and are therefore
perfectly capable of spontaneously manipulating running behavior. A change in cadence is in
fact induced by deviating the relative phase angle continuously from a preferred phase angle
that is typically slightly negative: a more negative phase angle (step before beat) causes a slow-
down in cadence and a positive phase angle (step after beat) leads to an increase in cadence.
Not only was cadence affected by phase angles that diverged from the runners’ preferred
phase angles, also enjoyment levels were affected. Although differences in enjoyment were not
very obvious, small, slightly significant increases in enjoyment were reported for the strategies
that impacted cadence. Spontaneously guiding runners a little away from their preferred run-
ning behavior could add just enough challenge to their exercise, resulting in a more rewarding
experience [39, 40].
The general preference for the footfalls to occur prior to the beats suggests that future
music alignment strategies could be improved by considering the negative mean asynchrony
(NMA) rather than 0˚ as a reference point. A question for future research might be whether
the size of the NMA is task- and/or user dependent. For example, is human anticipation of a
beat different when running compared to tapping? Are there differences between people and
why? With respect to the latter, our results indicate that gender plays a role when using forced
phase algorithms: effects of phase coherence and change in cadence were more pronounced
for females compared to their male counterparts.
Practical applications
Music-to-movement alignment strategies enable us to continuously and closely follow a per-
son’s behavioral response to music. This is of great value for sports and rehabilitation pro-
grams where music-based biofeedback is employed to improve individual performance [43].
In the future, the aim is to further improve our alignment strategies and introduce musical
beats either slightly before or after the predicted footfalls. Such strategies could open up possi-
bilities to spontaneously (and imperceptibly) optimize cadence and step size [44].
Acknowledgments
The authors also wish to thank the Flanders Sports Arena of Ghent, for giving us access to
their indoor running track.
Author Contributions
Conceptualization: Bart Moens, Dobromir Dotov, Marc Leman.
Formal analysis: Jeska Buhmann.
Funding acquisition: Marc Leman.
Investigation: Jeska Buhmann, Edith Van Dyck.
Methodology: Jeska Buhmann, Bart Moens, Edith Van Dyck.
Project administration: Edith Van Dyck.
Software: Bart Moens, Dobromir Dotov.
Supervision: Edith Van Dyck.
Writing – original draft: Jeska Buhmann.
Writing – review & editing: Jeska Buhmann, Bart Moens, Edith Van Dyck, Marc Leman.
Optimizing beat synchronized running to music
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Optimizing beat synchronized running to music
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Optimizing beat synchronized running to music.
Buhmann, Jeska,Moens, Bart,Van Dyck, Edith,Dotov, Dobromir,Leman, Marc