Bedtime Music for Sleep Problems in Older Adults With Dementia: A Feasibility Study

Sleep problems are highly prevalent in elderly persons with dementia. Poor sleep constitutes a major problem as it causes distress and may aggravate symptoms of dementia. Music has been proposed as a potential sleep aid, and in this study, we assessed the feasibility and effect of bedtime music listening for improving sleep problems in older adults with dementia. We used a within-subject design including 40 participants. Participants and caregivers evaluated the feasibility and sleep improvement after the intervention period. We measured sleep objectively with wrist actigraphy (total sleep time and sleep efficiency). In the intervention period, participants listened to music for 30 minutes every night at bedtime. We developed sleep playlists of different genres, and participants could choose the one they liked the best. We found that the music intervention was well-liked, and sleep improvement was observed in approximately half of the participants. Wrist actigraphy showed no significant changes in total sleep time or sleep efficiency. Music listening at bedtime could provide a safe, comfortable and low-cost intervention for sleep problems among elderly persons with dementia, the intervention is feasible, but more research is needed to determine the effect on sleep outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved

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

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

The audio features of sleep music: Universal and subgroup characteristics

Throughout history, lullabies have been used to help children sleep, 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 elucidated the characteristics of music associated with sleep by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists at the global streaming platform Spotify. Compared to music in general, we found that sleep music was softer and slower; it was more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was present in sleep music, which clustered into six distinct subgroups. Strikingly, three of the subgroups included popular tracks that were faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of the audio features of sleep music and highlight the individual variation in the choice of music used for sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset, advancing our understanding of how humans use music to regulate their behaviour in everyday life

Number of occurrences of each of the top 20 genre categories in the Sleep Playlist Dataset.

Number of occurrences of each of the top 20 genre categories in the Sleep Playlist Dataset.</p

Playlist exclusion criteria.

(DOCX)</p