Unpacking the black box of voice therapy: Exploration of motor learning and gestural components used in the treatment of muscle tension voice disorder.

Abstract

Voice therapy is the recommended care for Muscle Tension Voice Disorder (MTVD). For optimal care, SLPs should base their decisions on the three elements of Evidence Based Practice (E3BP) (i.e. research, clinical judgement & client factors). However, SLPs working in voice report using an uneven mix of these elements and that limited high quality evidence is a barrier to E3BP (Chan, et al., 2013). A systematic review of voice therapy for MTVD was conducted, showing positive therapeutic effect. However, methodological limitations prevented strong conclusions about voice therapy. Foremost among the problems was lack of therapy content description. Based on the argument that research and clinical practice won’t advance without disaggregating voice therapy, two studies were designed to illuminate the contents of the ‘black box’ of voice therapy. Both studies used data from six video-recorded voice therapy sessions; two consecutive sessions for MTVD from three SLP-client pairs. SLP behaviour during the videos was analysed via deductive content analysis using two frameworks. The first framework (MLCF-modified) was based on the Motor Learning Classification Framework (MLCF) (Madill et al., 2019) and consisted of ten motor learning (ML) variables. The second framework (GFFCS-modified) was based on Kong’s (2015) gesture form and function classification system and consisted of eight gesture form and eight gesture function categories. Unpacking the black box of voice therapy: Exploration of motor learning and gestural components used in the treatment of muscle tension voice disorder. The two studies were strikingly similar. SLPs used all categories of the MLCFmodified and GFFCS-modified. The rate of SLP ML variables and gestures was high and distribution of types was similar across consecutive sessions and SLPs. This suggests that SLPs communicate large amounts of information during voice therapy and questions the extent to which SLPs modify therapy according to patient need. Unpacking the ‘black box’ of voice therapy is a complex project but one that will ultimately advance voice therapy and hopefully, lead to improved voice care for MTVD.