Sonification of Arm Movements in Stroke Rehabilitation - A Novel Approach in Neurologic Music Therapy

Abstract

Gross motor impairments are common after stroke, but efficient and motivating therapies for these impairments are scarce. We present an innovative musical sonification therapy, especially designed to retrain patients' gross motor functions. Sonification should motivate patients and provide additional sensory input informing about relative limb position. Twenty-five stroke patients were included in a clinical pre-post study and took part in the sonification training. The patients' upper extremity functions, their psychological states, and their arm movement smoothness were assessed pre and post training. Patients were randomly assigned to either of two groups. Both groups received an average of 10 days (M = 9.88; SD = 2.03; 30 min/day) of musical sonification therapy [music group (MG)] or a sham sonification movement training [control group (CG)], respectively. The only difference between the two protocols was that in the CG no sound was played back during training. In the beginning, patients explored the acoustic effects of their arm movements in space. At the end of the training, the patients played simple melodies by coordinated arm movements. The 15 patients in the MG showed significantly reduced joint pain (F = 19.96, p < 0.001) in the Fugl-Meyer assessment after training. They also reported a trend to have improved hand function in the stroke impact scale as compared to the CG. Movement smoothness at day 1, day 5, and the last day of the intervention was compared in MG patients and found to be significantly better after the therapy. Taken together, musical sonification may be a promising therapy for motor impairments after stroke, but further research is required since estimated effect sizes point to moderate treatment outcomes.

Keywords: music-supported therapy; neuroplasticity; neurorehabilitation; sonification; stroke.

Figures

Figure 1

The 3D space defined by a wooden frame. Pitch was mapped onto the y-axis ranging from c′ at the bottom to a′ at the top. Brightness was mapped onto the x-axis from the left (dull) to the right (bright) and volume onto the z-axis with loudness increasing with increasing proximity. Positions in the x–z plane were labeled for ease of instruction.

Figure 2

Movement smoothness at day 1, day 5, and the last day of the intervention was compared in the treatment group (MG) patients and found to be significantly better after the therapy. Shown here are the kernel density estimates of the MG smoothness measures at day 1 (red line), day 5 (green line), and the last day of the intervention (blue line; see Materials and Methods and Results for details) with the smoothness index shown on the x-axis and the density estimation on the y-axis. Medians of the corresponding distribution are depicted as dashed lines in corresponding color.

Figure 3

Fugl–Meyer joint pain subscale results. Taking into account the unequal regression slopes of CG and MG, the Johnson–Neyman technique estimates the two groups’ post-test difference (triangles, dashed straight line) from the joint pretest values and determines upper and lower confidence intervals (CI; crosses, dotted lines). The triangles and crosses do not represent individual data points but estimated values. The CIs and the solid straight line at y = 0, confine the area where the two groups differed significantly in their post-test Fugl–Meyer Joint pain score. Compared to CG patients, MG patients with a pretest score below 20 seemed to benefit from the additional sonification therapy.