Edited by: Isabelle Peretz, Université de Montréal, Canada
Reviewed by: Virginia Penhune, Concordia University, Canada; Alissa Fourkas, National Institutes of Health, USA
*Correspondence: Floris T. Van Vugt, Institute of Music Physiology and Musicians' Medicine, University of Music, Drama and Media Hanover, Emmichplatz 1, 30175 Hanover, Germany e-mail:
This article was submitted to the journal Frontiers in Human Neuroscience.
†These authors have contributed equally to this work.
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Motor impairments are among the most common and most disabling consequences of stroke (Ward and Cohen,
A variety of explanations has been advanced for the performance improvement and the neuroplastic changes due to music-supported therapy in these patients: the brain's use of auditory feedback, the novelty of the intervention, and increased motivation (Altenmüller et al.,
Furthermore, we hypothesized that greater engagement of patients in their rehabilitation would lead to an improved clinical outcome. Stroke victims often suffer from disturbances in motivation and mood (Caeiro et al.,
The present study aimed to test the potential for music as a tool to create pro-social engagement on the part of patients. In particular, we hypothesized that the aspect of music that might boost social participation is synchronized musical playing. That is, do patients benefit from producing sounds in synchrony? In order to specifically test for the effect of synchronization whilst keeping other factors constant, we divided our patient population into pairs. Some pairs played in synchrony during their therapy whilst others played in turn.
We asked the following questions. First, is playing together in synchrony associated with changes in functional motor outcome? Secondly, we asked whether playing in synchrony or in turn influenced patients' mood. Thirdly, we asked whether patients basic auditory-motor functioning (such as synchronizing to a metronome) was influenced by playing in synchrony or in turn.
We assigned patients to one of two groups in a randomized design. Both groups received music therapy in pairs and they played the same selection of finger exercises and children's songs. Patients received 10 therapy sessions of half an hour. The first three therapy sessions were individual and the remaining seven were in pairs. The patients were divided into two groups. Patients in one group played in synchrony (together group) whereas the patients in the other group played one after the other (in-turn group). All patients received therapy in groups of two. Prior to therapy (PRE) and after therapy (POST), all patients completed a battery of tests described below. In between the three individual sessions and the seven joint sessions, we included a short session of measurements (INTER).
We aimed at obtaining a representative sample of patients from the hospital population. Consequently, we were not able to maintain high homogeneity of patient selection. However, we feel that by making this choice, our results are maximally relevant to clinical practice. Inclusion criteria were:
light to moderate motor impairment in the upper extremity due to stroke (ischemia or hemorrhage); having residual voluntary movement capacity (practically, the patient was required to be able to move the arm and the index finger of the affected hand independently); age between 30 and 75 years; Barthel Index at least 25; right-handedness; less than 5 months had passed since patients' stroke at the time of inclusion in the study; being able to understand and agree with informed consent to participate.
Exclusion criteria were:
previous musical training for more than 4 years; psychiatric problems (as assessed by standard clinical investigation); cognitive impairment or aphasia.
Initially, 36 patients were identified that matched the inclusion criteria and provided informed consent to participate. However, six patients (17%) were released from the hospital prior to finishing our therapy program. Furthermore, two patients (6%) dropped out of therapy because they no longer felt therapy was effective. Our final sample consisted of 28 patients. Patients were assigned quasi-randomly to the groups. Patients were included two or three at a time, since insufficient patients were available to include all patients at the same time. A custom designed computer script was used to quasi-randomly assign patients to groups making sure that (1) the number of patients in the two groups were as close as possible, and (2) the two groups were as closely matched as possible for age, gender, Barthel index, and nine-hole pegboard test score. We report clinical data about these patients in Table
Number of patients | 14 | 14 | |
Sex (female/male) | 6/8 | 10/4 | Fisher exact test |
Age (years) | 65.6 (10.5) | 67.1 (11.8) | |
Handedness (R/L) | 14/0 | 13/1 | Fisher exact test |
Stroke type (Number of patients ischemia/hemorrhage) | 12/2 | 12/2 | Fisher exact test |
Affected hand (Number of patients R/L) | 9/5 | 8/6 | Fisher exact test |
Days since stroke (at PRE, days) | 40.6 (25.6) | 45.6 (29.9) | |
Lesion site (Number of patients with lesion at that site/Number of patients without lesion at that site) | |||
Left frontal | 2/12 | 3/11 | Fisher exact test |
Left temporal | 3/11 | 2/12 | Fisher exact test |
Left parietal | 1/13 | 0/14 | Fisher exact test |
Left occipital | 0/14 | 0/14 | Fisher exact test |
Left subcortical | 6/8 | 7/7 | Fisher exact test |
Right frontal | 2/12 | 2/12 | Fisher exact test |
Right temporal | 3/11 | 2/12 | Fisher exact test |
Right parietal | 1/13 | 1/13 | Fisher exact test |
Right occipital | 3/11 | 1/13 | Fisher exact test |
Right subcortical | 2/12 | 1/13 | Fisher exact test |
Barthel index PRE | 48.2 (15.0) | 48.9 (11.5) | |
Barthel index POST | 72.1 (14.4) | 67.7 (14.8) | |
Faces scale mood rating PRE | 2.42 (1.22) | 1.85 (1.23) | |
Therapy duration (days) | 18.2 (3.0) | 18.4 (5.1) |
Patients received 10 sessions of half an hour of piano training over the course of three to four weeks. The day before the first session and a day after the last session were dedicated to individual measurement sessions (PRE and POST), which are described in more details below.
The training program followed the same structure every day. In the beginning of the session, patients played simple finger exercises such as a five-tone scale up and down and other patterns with their paretic hand. Then patients learned to play one from a set of simple children's songs. If patients reached a sufficient level on one of the songs, they would be invited to learn additional songs from the set prepared by the therapist. See Supplementary Materials for more details about the contents of the music-supported therapy.
Each member in the patient pair played on their individual M-Key V2 MIDI controller keyboard that was chosen for its light touch. The two keyboards were connected through the M-Audio Midisport Uno MIDI-to-USB converter to a Linux laptop. The laptop ran a custom made C program that recorded the MIDI events and forwarded them to the software synthesizer Fluidsynth, which generated the sounds using a Steinway sound font. The program additionally changed the MIDI velocity value (loudness) to its maximum value. As a result, all sounds were maximally loud, regardless of how strong patients' keystroke was. This was done to prevent patients' typically very soft keystrokes from being inaudible. The sounds were then played through Creative Inspire T10 speakers (Creative Labs, Inc.) at a comfortable loudness level. The five keyboard keys used in the therapy were numbered. Songs and exercises were then written in a simplified musical notation as numbers in tabular form and presented visually to the patients as a memory aid (see Supplementary Materials for more information).
Patients played the piano with the hand of their affected extremity only. The therapist stood next to the patient and supported the patient's arm when so required. The patients were always encouraged to make as many of the movements by themselves as possible. For those patients who were more severely affected, the therapist initially pointed to the fingers or moved them gently, encouraging the patient to make the movements unassisted on the next trial. Throughout therapy, the therapist's aim was always to allow the patient to function as independently as possible instead of becoming dependent on the therapist.
In the together-condition, the two patients played different voices of the same musical materials (finger exercises or songs) in synchrony. The therapist indicated the tempo and started the patients at the same time. By contrast, in the in-turn group, patients always played one after the other and never in synchrony. While one patient was playing, the other patient waited.
The nine-hole pegboard test (9HPT) is a clinical test to assess fine motor control. The patients' task is to place nine small sticks one by one (pegs) in nine holes and take them out again (Mathiowetz et al.,
We investigated patient's finger tapping performance of the affected hand as a measure of fine motor control. Three different tapping conditions were measured: (1) paced thumb-to-index tapping, (2) index finger speed tapping, (3) middle finger speed tapping. The tests are described in detail in what follows. In all conditions, patients were seated comfortably at a table on which they rested their arm. In order to have a portable, flexible and yet maximally accurate measurement of finger tapping performance, we custom-designed a measurement device. Finger motion was recorded by a triaxial accelerometer (ADXL 335) attached gently to the patient's index or middle finger tip (depending on the task). Tap contact was measured by a force sensitive resistor (FSR SEN09375), which consisted of a small sheet whose electrical resistance changes upon contact in a way that depends on the contact force. Both sensors were read out by an Arduino Duemilanove experimentation board running a custom made C program to sample sensors at 3 kHz. The data was then transferred online over USB to a Linux laptop running a custom Python program allowing the therapist to preview the data. We made the blueprints of the device set up as well as the custom programs available online for free for future research groups to use (
In paced thumb-to-index tapping, patients were instructed to tap as regularly as possible in time with a metronome at 69 BPM (i.e., 1.15 Hz) during 60 s from the first tap (Calautti et al.,
In index finger speed tapping, we measured the maximum tapping rate and variability during approximately 14 s (measured from the first finger tap). Patients rested their elbow on the table and the patients' hand was palm down on the table. The fingers were held in a relaxed posture close to maximal extension but slightly bent so that the position could be sustained without muscular effort. No metronome was used in these speed tapping trials. The patients were instructed to tap as fast and as regularly as possible, lifting their finger at least 2 cm above the table on each cycle. The force sensor surface was placed on the table and the patients were instructed to tap on the same spot every time. In middle finger speed tapping, the procedure was the same as with index finger speed tapping but switching to the middle finger.
The raw data files containing the force trace over time were preprocessed using a custom developed python script (we do not report the accelerometer data here). The script discarded the first and last 0.5 s of data from the recordings and then converted the force sensor trace into Newtons using a previously established calibration table. We then smoothed the signal using a 160-sample Bartlett window (which amounted to approximately 53 ms at our sample rate). The script detected the tap onset landmarks (a sudden impact) when the force exceeded 0.05 Newton. Tap offsets (a release of contact from the tapping measurement surface) were defined as the time point when the force trace dropped below 0.05 Newton again at least 40 ms after the last tap onset. Similarly, the next onset was restricted to occur at least 75 ms after the last tap offset. All data files with their landmarks were furthermore visually inspected to ensure our method of analysis did not introduce any artifacts. In a number of cases the 0.05 Newton onset/offset threshold was adjusted manually to compensate for the fact that some patients tapped too softly or off the sensor surface. We furthermore recorded the maximal tapping force between subsequent tap onset and tap offsets; the intervals between adjacent onsets, which will be referred to as the inter-tap-intervals (ITIs) in what follows; and the duration between the tap onset and tap offset (tap dwell phase duration). Next, we discarded the ITIs that were larger than 2000 ms since these reflected pauses or interruptions in the patient's tapping behavior (such as asking the experimenter whether to continue tapping) instead of the patient's motor capacity. We also discarded ITIs shorter than 120 ms since there were disproportionately many as a result of double-tap recordings.
Patients' mood was established using the Profile of Mood States (POMS) (Lorr et al.,
In order to obtain a quick estimate of the development of a patient's mood throughout the therapy, we used a mood scale of faces (Kunin,
This study was performed in accordance with ethical guidelines proposed by the Medical University Hanover (MHH). The protocol was approved by the ethics board on 20 April 2011 (nr. 1056-2011).
We performed parametric ANOVA whenever the data quantity and distribution could reasonably be assumed to fulfill its assumptions. We detected deviations from sphericity using Mauchley's Test and whenever it was significant we applied the Greenhause-Geisser correction. In those cases, we indicated significance as pGG and omitted the uncorrected
We performed an ANOVA with time to complete the pegboard test as dependent variable and factors group (in-turn or together) and measurement (PRE or POST). There was a main effect of time point [
The PRE measurement of one patient (in the in-turn group) was invalid due to technical reasons and this patient was therefore removed from further analysis. We pooled the taps that were recorded before and after each therapy session and then computed the tapping speed and variability as follows. Speed was calculated as the median of the intervals (in ms). Variability was calculated by first discarding the taps that were 3 SD longer or shorter than the mean for that block, taking the standard deviation of the remaining intervals and then divided it by the mean for that block to obtain the coefficient-of-variation (CV in percent). We found no initial differences in tapping speed between the groups [
We performed an ANOVA on the log-transformed median tapping interval with factors session (PRE, POST, and the 10 therapy sessions) and group (in-turn, together). The main effect of group was not significant [
We performed the same ANOVA with log-transformed coefficient-of-variability (CV) as dependent measure. We found no effect of group [
Middle finger tapping was measured before (PRE) and after (POST) therapy. There were no differences in initial tapping speed [
An ANOVA with factors group (in-turn, together) and measurement session (PRE, POST) revealed no effect of group on middle finger tapping speed [
The same ANOVA was performed with tapping variability as dependent variable. We found no effect of group [
Two patients were eliminated from further analysis because during one session their tapping was too soft to be reliably assessed (both from the
For each factor of the POMS (depression/anxiety, fatigue, hostility, and vigor) we performed an ANOVA with factors group (together or in-turn) and time point (PRE, POST). We found a main effect of time point reflecting a reduction in depression [
Patients were asked to rate their own mood on the faces scale, both during the PRE and POST measurement sessions and during the therapy sessions. There were no differences in rating between the groups at the PRE measurement [Mann-Whitney U,
Patients were furthermore invited to rate how they experienced the therapy sessions. There was a tendency for the in-turn group to rate the first (individual) session more positive than the together group [Mann–Whitney U,
In the partner sympathy ratings, there were no differences in rating between the two groups in the first paired session (session 4) [Mann–Whitney U,
Our study was the first to implement music-supported therapy with pairs of patients instead of providing therapy to patients individually. We hypothesized that playing in synchrony would improve patient's social engagement and, through this greater engagement, improve their motor outcome. We controlled for potential benefits of patients sharing their musical rehabilitation experience (Overy,
Firstly, our results reveal that music-supported stroke rehabilitation can be effective not only when patients are treated individually (as in previous studies) but also in pairs. We found improvements in patients' fine motor control in the 9HPT and synchronization tapping. The finding that music-supported rehabilitation is effective in pairs has practical implications. Paired therapy could considerably reduce the time investment on the part of the therapists. Furthermore, patients showed reductions in depression and fatigue. This indicates that music may have a beneficial effect on mood, in line with previous findings in healthy participants (Seinfeld et al.,
Surprisingly, we found no clear improvements in index or middle finger tapping. This is in contrast to previous studies of music-supported therapy that reported improvements in finger tapping frequency (Schneider et al.,
The 9HPT showed a difference in rehabilitation outcome between patients playing in synchrony and patients playing in turn. Contrary to our hypothesis (that patients in the together group would show the greatest benefit), in this test we found a statistical trend for patients in the in-turn group to benefit more.
How could one explain that patients playing in-turn would show greater benefit than patients playing synchronously? We speculate that patients in the in-turn group may benefit from the opportunity to learn through observation. In healthy participants, seeing others perform a motor task leads to motor facilitation (Ménoret et al.,
An alternative explanation for our findings is that the simultaneously occurring sounds in the together-condition confused patients, preventing them from dissociating sounds that they self-generated from those that were generated by their partner. This could have prevented the motor system from learning from auditory feedback (Altenmüller et al.,
Furthermore, results indicate that patients in the in-turn group grew to like each other more over the course of therapy. This is contrary to previous findings where people moving in synchrony liked each other more than people who did not (Hove and Risen,
At the outset of this study we had hypothesized two causalities. First, playing in synchrony would increase social engagement on the part of the patients. Second, this greater social engagement would then increase motor rehabilitation outcome. We found no evidence for the first causality. Instead, patients performing in turn rated their partner higher in sympathy ratings. As for the second causality, groups performed mostly similar with perhaps a small advantage for the group playing in turn. This suggests that greater social engagement might indeed improve motor outcome, in line with previous studies.
A limitation of this study is that we have not tested a control group who did not receive any musical intervention. As a result, effects found here that do not differ between groups cannot strictly be attributed to the musical intervention. However, the advantage of this approach is that any differences between the groups are likely due to the principal experimental manipulation (playing together vs. playing in turn). Our patient sample was relatively small and heterogeneous and the exact lesion sites of the stroke were unknown to us. Future studies could correlate lesion localization maps to performance and functional motor outcome of patients undergoing music-supported therapy in order to establish which patient groups might benefit maximally from music-supported therapy.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This work was supported by the EBRAMUS, European Brain and Music Grant (ITN MC FP7, GA 238157). We are indebted to Britta Westner, M.A., for running a pilot study. We furthermore would like to thank Mr. Richter for valuable feedback about music-supported therapy. Also, Dr. Sabine Schneider kindly shared her knowledge of the previous implementation of the music-supported therapy that formed the basis for the current therapy program. We thank all the medical staff (doctors and nurses) in the Hessisch Oldendorf clinic for their cooperation and for indicating to us patients who might be suitable for inclusion in the study. Finally we wish to extend our gratitude to all the patients who devoted their time to participation in this study. We hope the therapy may benefit them in their daily lives.
The Supplementary Material for this article can be found online at: