Patients choose music with high energy, danceability, and lyrics in analgesic music listening interventions

Self-selected music is the best predictor of a successful outcome in music interventions, but the reasons behind this are unclear. One suggestion is that patients choose different types of music compared to experimenters. To explore this suggestion, the current study identified specific pieces of music that were used in previous studies for pain management using a scoping review, and analyzed each track in terms of the Spotify audio features of energy, danceability, instrumentalness, valence, and tempo. Music was categorized depending on whether it was chosen by the patient from an unlimited choice (PUC), a limited choice (LC), or chosen by the experimenter (EC), so that comparisons could be made between groups. One-way analyses of variance (ANOVAs) identified that PUC music was significantly higher in energy and danceability, and lower in instrumentalness, compared to LC or EC music. A logit ordinal regression demonstrated that as people are given more freedom to choose music to reduce their pain, they increasingly choose music that is higher in energy and danceability, and more likely to contain lyrics. This study also demonstrates the impact of allowing patients to choose music from an unlimited range compared to choosing from a limited range of music.

Automated summary

This study found that self-selected music is the best predictor of successful outcomes in music interventions. Patients tend to choose music with higher energy and danceability compared to music chosen by experimenters or from a limited selection.