The music that people use to sleep: universal and subgroup characteristics

Abstract

Sleep problems are increasing in modern society. Throughout history, lullabies have been used to soothe the sleep of children, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidate the characteristics of music used for sleep by extracting the features of a large number of tracks (N = 225,927) from 989 sleep playlists retrieved from the global streaming platform Spotify. We found that compared to music in general, music used for sleep is softer and slower; it is more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was found to be present in sleep music, which clustered into six distinct subgroups. Strikingly, three of these subgroups included popular mainstream tracks that are faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of sleep music and highlight the individual variation in the choice of music for facilitating sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset. This study can inform the clinical use of music and advance our understanding of how music is used to regulate human behaviour in everyday life.

Music we move to: Spotify audio features and reasons for listening

“…We hypothesised that a dance music dataset will exhibit higher levels of Spotify audio features such as danceability, energy, and valence compared to a baseline dataset. Additionally, in a similar vein to the work on sleep music by Scarratt et al [30], we expected to identify several subgroups of dance music, especially in light of the different ways and styles in which people move to music. Moreover, we explored other reasons why people listen to music for dancing, in particular with the aim of differentiating dance-related listening reasons from more general reasons.…”

“…This suggests that Spotify audio features of music people choose to listen to vary depending on the activity they engage with. For instance, one study investigated songs that are used for sleeping [30]. Such songs were found to be generally softer, slower and often instrumental compared to general music.…”

“…Additionally, as the Spotify API was used for analysis, songs that were not found on Spotify (N = 12) were also excluded. While the Spotify audio features can be found from MSSD dataset, to extract dance music dataset's Spotify audio features, we modified a Python script called "GeneralizedSpoti-fyAnalyser" provided by Ole Adrian Heggli [30]. Spotify audio features pertaining to energy, acousticness, danceability, valence, loudness, instrumentalness, speechiness, liveness and tempo were then gathered for both the baseline and dance music datasets.…”

“…Spotify audio features pertaining to energy, acousticness, danceability, valence, loudness, instrumentalness, speechiness, liveness and tempo were then gathered for both the baseline and dance music datasets. Note that, since Spotify does not provide extensive descriptions of how these features are calculated (as also discussed by Heggli and colleagues [30]), the exact meaning and calculation method of these features are debatable. Spotify does provide brief description of these features which can be found in S1 Text.…”

“…A second limitation relates to sample size. While previous research on different types of sleep music [30] collected data solely from Spotify, which thus allowed investigation of a substantial sample, the goal in the current study of investigating particular reasons associated with songs that people move to required a questionnaire method which limited the data for dance music to the number of respondents. Especially when RL dance ratings were divided according to the subgroups of dance music, the size of some of the subgroups did not allow for further interpretation.…”