The Music That Helps People Sleep And The Reasons They Believe It Works
What we do know is that the tempo of the music you listen to before falling asleep plays a major role in determining whether said music is efficient in helping you sleep or not. Tempo is a term used to describe the speed of the music being played, and it’s generally measured in BPM (short for “beats per minute”). Normal resting heart usually beats at speeds that vary from 60 to 100 beats per minute, so the music used in studies was around 60 to 80 BPM. So, the end goal here is to choose music that goes in sync with how a normal resting heart is supposed to beat, so that the heart will eventually mimic the tempo of the music. Needless to say, not all types of music can put us in a relaxing mood, but why exactly does that happen? Researchers have looked into different kinds of music genres and studies the effects they have on the human body and mind.
Mental —Classifications of this level 2 theme were applied to comments in which the person aims to improve their mental state in advance of sleep with the use of music. Go for soothing songs that you like—specifically ones that have a slow rhythm of 60 to 80 BPM. The music streaming service Spotify, which recently polled its users' “sleep” playlists to identify the top 20 songs for shuteye, found that British singer Ed Sheeran is currently the most popular artist to snooze to. By the time Middleton released Sleep Better, a once-derided field was gaining legitimacy and sprawling in many directions. In the experimental wing, Basinski and Rich were suddenly being asked to perform sleep concerts for thousands of horizontal fans at major festivals Awakening Your Higher Mind like Le Guess Who in the Netherlands and Moogfest in North Carolina.
Yet another song illustrating being peaceful when sleeping, the narrator requests not to be woken up as it would spoil his day. Lennon, who wrote the song, enjoyed staying in bed even when he wasn’t asleep. Love —For some this habit was born of the enjoyment of listening to music before sleep, as opposed to indifferent passive musical experiences. This encompasses comments that point to music being used to regulate breathing; this also includes any mention of meditation practices. Relax –This level 2 theme contains cases where the participant used the term ‘relax’ or its synonyms. Additionally, any allusion to minimizing/combating stress and/or anxiety, this also includes claiming to be calmed or soothed.
All the quantitative data were subjected to statistical analysis using the party , ggplot2 , psych , and reshape2 , packages within R (version 3.4.0) . Linear regression and tree-based models were used to identify any meaningful relationships between sleep-related variables and other background variables . This form of theory-driven thematic analysis is a coding technique that prioritizes the minimization of subjective bias by setting out clear processes by which the analysis is to be conducted. The four-step process aims to reduce subjectivity at the points of both analysis and interpretation, by the requirement to have two independent ‘coders’ and a three stage process of theme development between them.
Using sleep music to relax your mind and body before bed has worked for many people looking to fall asleep quicker, but if you’re undecided on how it will benefit you, try out a monthly sleep music app. Slumber offers a monthly subscription for $7.99, along with yearly ($49.99) or lifetime ($249.99) access if you’re serious about your snoozing. In the first place and as already noted, we are unable to draw conclusions about the effectiveness of music on sleep physiology and underlying sleep mechanisms based on survey results alone. Research has shown that subjective and objective measures of sleep physiology are not always closely linked and hence the reported positive benefits of music on sleep may not be reflected in objective sleep measurements . The second limitation is that, due to the nature of the online survey, the subject matter and the methods of recruitment, we observed some sample bias in age, with a disproportionately high number of young respondents. However, we did see a broad range of participants with our youngest participants being 18 years old, and our oldest being 79 years old.