Sensorimotor synchronization to music reduces pain

doi:10.21203/rs.3.rs-1424205/v1

Download PDF

Article

Sensorimotor synchronization to music reduces pain

https://doi.org/10.21203/rs.3.rs-1424205/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Pain-reducing effects of music listening are well-established, but the effects are small and their clinical relevance questionable. Recent theoretical advances, however, have proposed that synchronizing to music, such as clapping, tapping or dancing, has evolutionarily important social effects that are associated with activation of the endogenous opioid system (which supports both analgesia and social bonding). Thus, active sensorimotor synchronization to music could have stronger analgesic effects than simply listening to music. In this study, we show that sensorimotor synchronization to music significantly amplifies the pain-reducing effects of music listening. Using pressure algometry to the fingernails, pain stimuli were delivered to n = 59 healthy adults either during music listening or silence, while either performing an active tapping task or a passive control task. Compared to silence without tapping, music with tapping (but not simply listening to music) reduced pain with a large, clinically significant, effect size (d = 0.93). Simply tapping without music did not elicit such an effect. Our analyses indicate that both attentional and emotional mechanisms drive the pain-reducing effects of sensorimotor synchronization to music, and that tapping to music in addition to merely listening to music may enhance pain-reducing effects in both clinical contexts and everyday life.

Pain-reducing effects of music listening are among the most widely studied and most replicable effects of music in clinical settings^1,2. For example, a recent meta-analysis by Kuhlmann et al.², with 7385 patients in 92 randomized controlled trials, reported that music listening interventions significantly reduce pain in surgical patients. However, across four different meta-analyses, the pain-reducing effects of music listening had only small to moderate effect sizes (and were inconsistent among the studies analyzed), which calls the clinical relevance of music for pain relief into question^1–4. Notably, almost all of the studies included in the aforementioned meta-analyses used passive listening interventions (the study by Lee reports 10 music therapy studies using an active intervention such as group singing³), and to the best of our knowledge no study has compared pain-reducing effects between passive music listening and active engagement in music. The lack of research comparing pain-reducing effects between these two forms of music listening (i.e active vs. passive) is an important shortcoming because recent advances have proposed that active sensorimotor synchronization to music is associated with the activation of the endogenous opioid system (EOS), including the release of endorphins^5,6. Notably, EOS activation supports both analgesia and social bonding⁶. Hence, recent evolutionary accounts have posited that the human capacity to synchronize movements to a musical pulse in a group (such as group singing, clapping, drumming, dancing) is an evolutionarily adaption because it stimulates social bonding^5–9. However, to the best of our knowledge, the hypothesis of an EOS activation by sensorimotor synchronization to music has never been investigated. Accordingly, we set up a test with the premise that if sensorimotor synchronization to music indeed activates the EOS, then it should have larger pain-reducing effects than simply listening to music (pain-reduction is commonly used as a proxy of EOS activation^5–12.

The experimental design is illustrated in Fig. 1. We applied specific amounts of pain to the fingernails through the use of pressure algometry^13–15. The experiment used a 2x2 within-subjects design in which participants either listened to music or underwent a silent control period (henceforth referred to as the within-subjects factor Condition) and either performed an active foot tapping task, or a passive control task with no movement (henceforth referred to as the within-subjects factor Task). As the dependent variable, perceived pain was rated on a scale ranging from 1 to 9 (1 = very little, 5 = medium, 9 = very strong). In addition, to pain ratings, participants provided ratings of their emotional state in terms of pleasantness as well as arousal, and then rated their familiarity with the music (also on scales ranging from 1 to 9). Emotion ratings were obtained to explore whether the mechanisms driving pain-reducing effects of sensorimotor synchronization to music include emotion (based on our previous work on music-evoked emotions¹⁶ and previous research showing that modulatory effects of music on pain were mediated by the pleasantness of the emotions induced¹⁷. At the end of the experiment, participants also rated their preference for the music on a scale ranging from 1 to 9 (see Method). Familiarity and preference ratings were obtained to elucidate possible contributions of these factors on pain reduction¹⁸.

In accordance with the well-established pain-reducing effects of music listening in clinical settings^1–4, we expected perceived pain to be reduced while listening to music compared with the silent control condition (independent of the task, i.e. independent of whether participants were tapping or not). Furthermore, consistent with the well-established effects of attention on pain^19–22, we expected pain-reducing effects of active tapping compared with no tapping (independent of the condition, i.e., independent of whether music or silence was presented), because active tapping requires increased attention on the part of the participants and thus distracts from perceived pain. Most importantly, we investigated whether participants felt less pain while actively tapping to music compared to passively listening to music, which would reveal, for the first time, analgesic effects of sensorimotor synchronization to music.

Figure 2 shows the mean data of the perceived pain, dependent on Condition (listening to music, or silent control condition), and Task (performing the active tapping task, or the passive control task). A linear mixed effects (LME) analysis indicated significant main effects of both Condition (β = -0.72, SE = 0.20, p = .027), and Task (β = -0.20, SE = 0.07, p = .005), reflecting that the perceived pain was reduced while listening to music (compared to silence; see red vs. blue violin plots in Fig. 2), and while performing the active tapping task (compared with the passive control task; see darker vs. lighter colors in Fig. 2). Importantly, the pain-reducing effect of the active tapping task was descriptively larger while listening to music (mean difference = 0.28 scale points, SE = 0.08, d = 0.46) than during silence (mean difference = 0.12 scale points, SE = 0.09, d = 0.17). To explore this result pattern in detail, we conducted a simple slope analysis with Task as predictor and Condition as moderator. This simple slope analysis revealed a significant effect of the Task only while listening to music (β = -0.29, SE = 0.10, p = .005) but not during silence (β = -0.12, SE = 0.10, p = .225). Consequently, the difference in perceived pain was most pronounced between music with tapping vs. silence without tapping (mean difference = 0.93 scale points, SE = 0.13, see dashed grey horizontal lines in Fig. 2), with a large effect size of d = 0.93 (effect sizes are interpreted according to Cohen’s convention²³).

Emotional mechanisms underlying the pain-reducing effect of sensorimotor synchronization to music. To test our assumption that the mechanisms driving the pain-reducing effect of sensorimotor synchronization to music include emotion, we computed two additional LME analyses. For detailed information on the experimental design and data analysis for both analyses see Supplementary Information. The first included only music trials and the factors Task, Preference and Familiarity. The dependent variable was perceived pain. Figure 3 shows the preference effect on the perceived pain for the music trials only, separately for the active tapping task and the passive control condition. Consistent with the first analysis reported above, our second analysis also revealed a significant main effect of Task (β = -0.28, SE = 0.10, p = .006) with reduced perceived pain for the active vs. passive task. Importantly, this analysis further revealed a significant main effect of Preference (β = -0.14, SE = 0.03, p < .001), indicating that pain ratings decreased with increasing preference for the music excerpts. The interaction of the factors Task and Preference was not significant (β = -0.04, SE = 0.05, p = .453) and notably, no effect of the covariate Familiarity was observed (β = 0.03, SE = 0.02, p = .222).

The second LME analysis on involved emotional mechanisms examined the effect of Condition and Task on the perceived pleasantness. Results revealed a significant main effect of Condition (β = 1.02, SE = 0.25, p = .009), reflecting that perceived pleasantness was higher while listening to music (compared to silence; see red vs. blue violin plots in Fig. 4). In addition, this LME analysis revealed a trend for an interaction of Condition and Task (p = .073), indicating that the perceived pleasantness of the active tapping task was descriptively larger while listening to music (mean difference = 0.18 scale points, SE = 0.08, d = 0.30) than during silence (mean difference = 0.06 scale points, SE = 0.11, d = − 0.07). The difference in perceived pleasantness between music with tapping vs. silence without tapping (mean difference = 1.08 scale points, SE = 0.16, see dashed grey horizontal line in Fig. 4) had a large effect size²³ of d = 0.86. The main effect of Task was not significant (β = 0.06, SE = 0.07, p = .391). A descriptive analysis of arousal ratings is provided in Supplementary Table S1.

Our results show that participants felt less pain while actively tapping to music, compared with merely listening to music. This finding indicates that sensorimotor synchronization to music has analgesic effects. Compared to music listening without tapping, tapping to music elicited a moderate pain-reducing effect, and compared to the silent control condition without tapping, tapping to music elicited a large, clinically significant²³ pain-reducing effect (d = 0.93). Our observation of a reduction of perceived pain while passively listening to music (compared with silence) is well in accordance with previously reported pain-reducing effects of music listening in clinical settings^1–4. Likewise, our finding of a significant reduction of perceived pain while actively tapping (compared to the passive control task, independent of whether music or silence was presented) is consistent with the well-established analgesic effects of distracting attention from a painful stimulus^19–22. Notably, our findings are consistent with the hypothesis that sensorimotor synchronization to music reduces pain by virtue of EOS activation⁶, thus supporting psychological accounts on the effects of movement synchronization to music on social bonding^5–9, trust^24,25, and cooperation²⁶, as well as evolutionary accounts on the adaptive utility of music due to the promotion of social bonding by coordinated, synchronized musical activity⁸.

We also investigated potential mechanisms underlying the analgesic effects of sensorimotor synchronization to music. So far, the (causal) mechanisms underlying the pain-reducing effects of (passive) music listening are not fully understood, but previous work has implicated attentional and emotional mechanisms as the two main candidates^27–29. Our study was purposefully set up in a way that both music perception (compared to silence) and the active tapping task (compared to the passive control task) likely distracted participants’ attention away from the pain stimulus³⁰. Hence, tapping to music would have the strongest attention-capturing effect, resulting in the largest pain-reducing effects. However, the finding that tapping in silence did not significantly reduce pain (compared with silence without tapping) indicates that the attentional distraction by the tapping cannot alone explain the pain reducing effects of tapping to music.

In addition, we hypothesized that sensorimotor synchronization to music would activate the EOS. Because EOS activation has well-documented effects on analgesia and pleasure^16,31, emotional mechanisms are likely involved in the analgesic effects of sensorimotor synchronization to music. To test this assumption, we analyzed participants’ ratings of their emotional state in terms of their perceived pleasantness, preference for the music, and familiarity with the music. Familiarity was included in this analysis due to a possible association between familiarity of music and (musical) preference, e.g it had been reported that the (dopaminergic) reward network is activated more strongly by familiar than unfamiliar music^18,32,33. Results showed that perceived pain was significantly reduced when listening to strongly preferred music (unaffected by familiarity), consistent with previous research on preference in the context of pain-reducing effects of music (e.g., participants tolerate painful stimuli significantly longer when listening to their own preferred music^34–37. Moreover, our findings indicate that tapping to music was perceived as more pleasant than merely listening to music, suggesting that it was more rewarding to tap to the music, also consistent with a stronger EOS activation.

In conclusion, our findings demonstrate that sensorimotor synchronization to music significantly impacts pain perception and can amplify the well-established pain-reducing effect of merely listening to music. Our results have wide-ranging implications for the use of music in clinical settings (as an adjunct treatment of pain)³⁸, in music therapy (e.g. for chronic pain)³⁹, and in everyday life (for the management of acute pain). Moreover, our findings support the recent social bonding hypothesis of music, and thus the significance of music in the evolution of humans⁶. Finally, our results shed new light on the mechanisms underlying pain reduction with music, showing that such effects are driven by social bonding, attention, emotion, and preference.

Participants. A naive random sample 59 participants was included in the data analysis (age range 19 to 35 years, M = 22.15 years, SD = 3.22, 29 females), based on an a-priori sample size estimation using G*Power (version 3.1.9.6)⁴⁰ which indicated that for the detection of a small to medium effect size (d = 0.4)²³ with an α-level of 0.05 and a statistical power of 90% (one-tailed), a sample of n = 59 is required. Exclusion criteria were use of any prescription drugs, psychiatric or neurological disease, hearing impairment, and history of substance dependence (according to self-report). Participants did not consume alcohol nor any medicine for the treatment of pain at least 24 hours prior to the experiment. None of the participants had musical anhedonia according to the Barcelona Music Reward Questionnaire(M = 81.06, SD = 11.37)⁴¹. The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the Regional Committee for Medical and Health Research Ethics for Western Norway (Reference Number: 2019/1031). The study was registered as a clinical trial at ClinicalTrials.gov (registration number NCT05267795), and the trial was first posted on 04/03/2022. All participants provided written informed consent before enrollment and received a monetary compensation of 200 NOK following participation in the experiment.

Stimuli. Music stimuli were presented with an average sound pressure of approximately 60 dB SPL over Beyerdynamic DT 770-PRO 250 Ohm headphones. 10 instrumental music experts were selected, each 30 seconds long (song characteristics are provided in Supplementary Table S2). Each excerpt was played twice during the experimental task, once during the active task and once during the passive task. Stimuli were delivered using the Matlab-based toolbox Cogent 2000 (version 1.33).

Pressure Algometry. Pain was applied by the experimenter using a Wagner Force One Pressure Algometer (Wagner Instruments, Greenwich, USA). The equipment head (used to apply the pressure) had a diameter of 12mm and a surface area of 113mm². Pain stimuli were delivered to index, middle and ring fingers of both hands. For each participant, pain thresholds were determined separately for each of the six fingers used for pain delivery, prior to the actual experiment. The average value of these pain thresholds was then computed, and for the actual experiment, only 50% of the participant’s average pain threshold was delivered as pain stimulus in each of the 40 experimental trials (e.g., if a participant had an average pain threshold of 5 kg, then a pressure stimulus of 2.5 kg was applied in the actual experiment). During the entire experiment, the applied pressure was measured with a 10 Hz sampling rate using MESURgauge Plus by Mark-10 (version 2.0.5). The statistical analysis of these data showed that neither the applied pressure, nor the duration of applied pressure, differed significantly for the within-subjects factors Condition (music, silence; pressure intensity: F(1,58) = 0.33, p = .568; pressure duration: F(1,58) = 1.62, p = .209), nor Task (active, passive; pressure intensity: F(1,58) = 0.33, p = .568; pressure duration: F(1,58) = 0.66, p = .419). Likewise, no significant interaction between Condition and Task was observed for the pressure intensity (F(1,58) = 1.00, p = .321), nor for pressure duration (F(1,58) < 0.01, p > .999). Thus, we can exclude the possibility that the pain ratings of participants were simply an artifact of faulty stimulus delivery (note that the experimenter was also blinded to avoid any systematic differences of pain stimulus delivery between the experimental trial types). For further information see Supplementary Information and Supplementary Table S3.

Procedure. 40 experimental trials were delivered, 10 for each experimental trial type (Music Active, Music Passive, Silence Active, Silence Passive). The experimenter was blinded during the entire experimental procedure. Each trial started with an instruction screen where participants were either instructed to tap their right foot like a metronome (active tapping task) or to relax (passive control task). Then, participants pressed the space bar and either music started to play, or a silent period started, and participants either had to tap or relax (depending on the trial type), while looking at a fixation cross in the middle of the screen. After 20 seconds the experimenter applied pressure with the Algometer to a fingernail for 10 seconds. Then (after the pain application had stopped), participants indicated the pain they felt at the end of the pain application on a 9-point scale (1 = very little, 5 = medium, 9 = very strong), followed by a pleasantness rating (1 = very uncomfortable, 5 = medium, 9 = very comfortable) and an arousal rating (1 = very calm, 5 = medium, 9 = very activated). In music trials, participants also rated to which extent they were familiar with the music (1 = not at all, 5 = partially known, 9 = well known). Participants were aware that the experimenter could not see their ratings (so that they would not feel influenced in any way). Each trial lasted about one minute (thus, the experiment had a duration of approximately 40 minutes). After completing all 40 experimental trials, participants listened to the 10 music excerpts again and indicated their preference for each excerpt on a 9-point scale (1 = strongly disliked, 5 = medium, 9 = strongly liked).

Experimental Design & Data Analysis. Data analysis was computed using R (version 4.0.4) including the R Stats, R Base, and ggplot 2 (version 3.3.3)⁴² packages.Figures were graphically edited with CorelDraw Graphics Suite version 21.0.0.593 (Corel Corporation, Ottawa, Ontario, Canada).We conducted an LME analysis on single trial level, as this analysis allowed to reduce interindividual variance introduced by participants and music excerpts, and decrease the risk for false positives⁴³. The analysis was applied by the use of the lme4package (version 1.1-26)⁴⁴. The model included the two categorical fixed-effects factors Condition (music [+0.5] and silence [-0.5]) and Task (active [+0.5] and passive [-0.5]) as well as their interaction as predictors for the perceived pain in each individual experimental trial (rated on a scale ranging from 1 to 9). Furthermore, the model included the Participants and music Excerpts as random-effects factors. The model was estimated by using a restricted maximum likelihood approach⁴⁵. The α-level for significance was 0.05. We estimated degrees of freedom and p-values with the Satterthwaite approximation implemented in the lmerTest package (version 3.1)⁴⁶. We used the R package influence.ME (version 0.9-9)⁴⁷ to test the data for statistical outliers on the participant level. No outliers were detected, and the model was conducted with a sample of 59 participants. Especially in clinical contexts the lack of statistical significance does not necessarily mean that a treatment has no effect^48,49. Therefore, and to comprehensively explore the pattern of results, we resolved the interaction between Condition and Task with a simple slope LME analysis implemented in the R package jtool (version 2.1.2)⁵⁰. In addition, we calculated Cohen's d effect size²³ by the use of the R package lsr (version 0.5)⁵¹ because this effect size measure is taken as one of the most important indicators of clinical significance⁴⁹.

Data availability

Data supporting the findings of this study will be provided by the senior author upon reasonable request.

Code availability

The R scripts used for the analyses, statistics and visualizations presented in this article will be provided by the senior author upon reasonable request.

Acknowledgements

The current project was funded by a grant from the Research Council of Norway (RCN, Nr. 260576) awarded to S.K., and the Trond Mohn Stiftelse (TMS) / Bergens Forskningsstiftelse (BFS).

Author information

Affiliations

Department for Biological and Medical Psychology, University of Bergen, Postboks 7807, Bergen, Norway

Lucy M. Werner, Stavros Skouras and Stefan Koelsch

Department of Biological Psychology, Institute for Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Lucy M. Werner & Laura Bechtold

Department of Psychosocial Science, University of Bergen, Postboks 7807, 5020 Bergen, Norway

Ståle Pallesen

Contributions

L.W conducted the experiment, analyzed the results and wrote the original draft. S.S conceived the experiment, analyzed the results and reviewed the original draft. L.B analyzed the results and reviewed the original draft. S.P contributed to the methodology and reviewed the original draft. S.K. conceived the experiment, provided resources and supervision and essentially reviewed and edited the original draft.

Competing interests

The authors declare no competing interests.

Cepeda, M. S., Carr, D. B., Lau, J. & Alvarez, H. Music for pain relief. Cochrane Database Syst. Rev 2, (2006).
Kuhlmann, A. Y. R. et al. Meta-analysis evaluating music interventions for anxiety and pain in surgery. Br J Surg 105, 773–783, doi:10.1002/bjs.10853 (2018).
Lee, J. H. The effects of music on pain: a meta-analysis. Journal of music therapy 53, 430–477 (2016).
Martin-Saavedra, J. S., Vergara-Mendez, L. D., Pradilla, I., Velez-van-Meerbeke, A. & Talero-Gutierrez, C. Standardizing music characteristics for the management of pain: A systematic review and meta-analysis of clinical trials. Complement Ther Med 41, 81–89, doi:10.1016/j.ctim.2018.07.008 (2018).
Granot, R. in The Routledge Companion to Music Cognition 101–111 (Routledge, 2017).
Tarr, B., Launay, J. & Dunbar, R. I. Music and social bonding:“self-other” merging and neurohormonal mechanisms. Front. Psychol. 5, 1096 (2014).
Launay, J., Tarr, B. & Dunbar, R. I. M. Synchrony as an Adaptive Mechanism for Large-Scale Human Social Bonding. Ethology 122, 779–789, doi:10.1111/eth.12528 (2016).
Savage, P. E. et al. Music as a coevolved system for social bonding. Behavioral and Brain Sciences, 1–36 (2020).
Tarr, B., Launay, J. & Dunbar, R. I. Silent disco: dancing in synchrony leads to elevated pain thresholds and social closeness. Evol Hum Behav 37, 343–349, doi:10.1016/j.evolhumbehav.2016.02.004 (2016).
Cohen, E. E., Ejsmond-Frey, R., Knight, N. & Dunbar, R. I. Rowers' high: behavioural synchrony is correlated with elevated pain thresholds. Biol. Lett. 6, 106–108 (2010).
Dunbar, R. I., Kaskatis, K., MacDonald, I. & Barra, V. Performance of music elevates pain threshold and positive affect: implications for the evolutionary function of music. Evol. Psychol. 10, 147470491201000403 (2012).
Fritz, T. H. et al. Musical Agency during Physical Exercise Decreases Pain. Front Psychol 8, doi:ARTN 231210.3389/fpsyg.2017.02312 (2018).
Graven-Nielsen, T., Wodehouse, T., Langford, R., Arendt‐Nielsen, L. & Kidd, B. Normalization of widespread hyperesthesia and facilitated spatial summation of deep‐tissue pain in knee osteoarthritis patients after knee replacement. Arthritis Rheum. 64, 2907–2916 (2012).
Romundstad, L., Stubhaug, A., Niemi, G., Rosseland, L. A. & Breivik, H. Adding propacetamol to ketorolac increases the tolerance to painful pressure. Eur. J. Pain 10, 177–183 (2006).
Walton, D. et al. Reliability, standard error, and minimum detectable change of clinical pressure pain threshold testing in people with and without acute neck pain. J. Orthop. Sports Phys. Ther. 41, 644–650 (2011).
Koelsch, S. Brain correlates of music-evoked emotions. Nat. Rev. Neurosci. 15, 170–180 (2014).
Roy, M., Peretz, I. & Rainville, P. Emotional valence contributes to music-induced analgesia. Pain 134, 140–147 (2008).
Jancke, L. Music, memory and emotion. J Biol 7, 21, doi:10.1186/jbiol82 (2008).
Birnie, K. A., Chambers, C. T. & Spellman, C. M. Mechanisms of distraction in acute pain perception and modulation. Pain 158, 1012–1013, doi:10.1097/j.pain.0000000000000913 (2017).
Legrain, V. et al. A neurocognitive model of attention to pain: behavioral and neuroimaging evidence. Pain 144, 230–232, doi:10.1016/j.pain.2009.03.020 (2009).
Mccaul, K. D. & Malott, J. M. Distraction and Coping with Pain. Psychol Bull 95, 516–533, doi:Doi 10.1037/0033-2909.95.3.516 (1984).
Valet, M. et al. Distraction modulates connectivity of the cingulo-frontal cortex and the midbrain during pain—an fMRI analysis. Pain 109, 399–408 (2004).
Cohen, J. Statistical power analysis for the behavioral sciences. 2nd edn, (L. Erlbaum Associates, 1988).
Knight, S., Spiro, N. & Cross, I. Look, listen and learn: Exploring effects of passive entrainment on social judgements of observed others. Psychology of Music 45, 99–115, doi:10.1177/0305735616648008 (2017).
Launay, J., Dean, R. T. & Bailes, F. Synchronization Can Influence Trust Following Virtual Interaction. Exp Psychol 60, 53–63, doi:10.1027/1618-3169/a000173 (2013).
Wiltermuth, S. S. & Heath, C. Synchrony and Cooperation. Psychol Sci 20, 1–5, doi:10.1111/j.1467-9280.2008.02253.x (2009).
Howlin, C. & Rooney, B. The cognitive mechanisms in music listening interventions for pain: A scoping review. J. Music Ther. 57, 127–167 (2020).
Lunde, S. J., Vuust, P., Garza-Villarreal, E. A. & Vase, L. Music-induced analgesia: how does music relieve pain? Pain 160, 989–993 (2019).
Villarreal, E. A. G., Brattico, E., Vase, L., Ostergaard, L. & Vuust, P. Superior Analgesic Effect of an Active Distraction versus Pleasant Unfamiliar Sounds and Music: The Influence of Emotion and Cognitive Style. Plos One 7, doi:ARTN e2939710.1371/journal.pone.0029397 (2012).
Jancke, L., Kuhnis, J., Rogenmoser, L. & Elmer, S. Time course of EEG oscillations during repeated listening of a well-known aria. Front Hum Neurosci 9, 401, doi:10.3389/fnhum.2015.00401 (2015).
Le Merrer, J., Becker, J. A., Befort, K. & Kieffer, B. L. Reward processing by the opioid system in the brain. Physiol Rev 89, 1379–1412, doi:10.1152/physrev.00005.2009 (2009).
Pereira, C. S. et al. Music and emotions in the brain: familiarity matters. PLoS One 6, e27241, doi:10.1371/journal.pone.0027241 (2011).
Zatorre, R. J. & Salimpoor, V. N. From perception to pleasure: music and its neural substrates. Proc Natl Acad Sci U S A 110 Suppl 2, 10430–10437, doi:10.1073/pnas.1301228110 (2013).
Garcia, R. L. & Hand, C. J. Analgesic effects of self-chosen music type on cold pressor-induced pain: Motivating vs. relaxing music. Psychol Music 44, 967–983, doi:10.1177/0305735615602144 (2016).
Mitchell, L. A., MacDonald, R. A. & Brodie, E. E. A comparison of the effects of preferred music, arithmetic and humour on cold pressor pain. Eur. J. Pain 10, 343–351 (2006).
Mitchell, L. A. & MacDonald, R. A. An experimental investigation of the effects of preferred and relaxing music listening on pain perception. J. Music Ther. 43, 295–316 (2006).
Mitchell, L. A., MacDonald, R. A. & Knussen, C. An investigation of the effects of music and art on pain perception. Psychol. Aesthet. Creat. Arts 2, 162 (2008).
Nilsson, U. The anxiety- and pain-reducing effects of music interventions: a systematic review. AORN J 87, 780–807, doi:10.1016/j.aorn.2007.09.013 (2008).
Bradt, J. et al. The impact of music therapy versus music medicine on psychological outcomes and pain in cancer patients: a mixed methods study. Support Care Cancer 23, 1261–1271, doi:10.1007/s00520-014-2478-7 (2015).
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).
Mas-Herrero, E., Marco-Pallares, J., Lorenzo-Seva, U., Zatorre, R. J. & Rodriguez-Fornells, A. Individual differences in music reward experiences. Music Percept. 31, 118–138 (2012).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, 2016).
Yarkoni, T. The generalizability crisis. Behav. Brain Sci., 1–37 (2020).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).
Luke, S. G. Evaluating significance in linear mixed-effects models in R. Behav. Res. Methods 49, 1494–1502 (2017).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 26 (2017).
Nieuwenhuis, R., Te Grotenhuis, H. & Pelzer, B. Influence. ME: tools for detecting influential data in mixed effects models. R J. 4, 38–47 (2012).
Nuzzo, R. Scientific method: statistical errors. Nature 506, 150–152 (2014).
Page, P. Beyond statistical significance: clinical interpretation of rehabilitation research literature. Int. J. Sports Phys. Ther. 9, 726–736 (2014).
Long, J. A. jtools. R package version 2.1.2 (2021).
Navarro, D. Learning statistics with R: A Tutorial for Psychology Students and Other Beginners. (University of Adelaide, 2015).

No competing interests reported.

SupplementaryInformationPainMusic.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Sensorimotor synchronization to music reduces pain

Status:

Version 1

Abstract

Figures

Introduction

Results

Discussion

Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1