research on pop music

July 26, 2012

Is Pop Music Evolving, or Is It Just Getting Louder?

By John Matson

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American

Music just ain’t what it used to be. At least, that’s the stereotypical lament of each receding generation of music listeners. It’s also one way to read a new study on the evolution of pop music in the past half-century.

A group of researchers undertook a quantitative analysis of nearly half a million songs to look for widespread changes in music’s character over the years. The findings, published online July 26 in Scientific Reports, show that some trends do emerge over the decades—none of them necessarily good. ( Scientific American and Scientific Reports are both parts of Nature Publishing Group.)

The researchers based their analysis on the Million Song Dataset, a publicly available 280-gigabyte file that provides a sort of background sketch—name, duration, tempo, and so on—of songs from nearly 45,000 artists. Of the million songs therein, 464,411 came out between 1955 and 2010 and include data on both the sonic characteristics and the year of release.

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Joan Serrà, a postdoctoral scholar at the Artificial Intelligence Research Institute of the Spanish National Research Council in Barcelona, and his colleagues examined three aspects of those songs: timbre (which “accounts for the sound color, texture, or tone quality,” according to Serrà and his colleagues); pitch (which “roughly corresponds to the harmonic content of the piece, including its chords, melody, and tonal arrangements”); and loudness (more on that below).

After peaking in the 1960s, timbral variety has been in steady decline to the present day, the researchers found. That implies a homogenization of the overall timbral palette, which could point to less diversity in instrumentation and recording techniques. Similarly, the pitch content of music has shriveled somewhat. The basic pitch vocabulary has remained unchanged—the same notes and chords that were popular in decades past are popular today—but the syntax has become more restricted. Musicians today seem to be less adventurous in moving from one chord or note to another, instead following the paths well-trod by their predecessors and contemporaries.

Finally, it comes as no surprise that music has gotten louder. A piece of music’s loudness is an intrinsic characteristic of the recording, not to be confused with the listener-controlled volume. “Basically, the audio signal, when recorded and stored, is physically bounded to be between certain values (+1 and –1 volts in original recording systems),” Serrà explained in an email. “You can record signals fluctuating between –0.2 and +0.2 or between –0.6 and +0.6 (positive and negative fluctuations are necessary to make the loudspeaker membrane move). That's the intrinsic loudness level we're talking about.”

For years audiophiles have decried the “loudness wars”—the gradual upping of recorded music’s loudness over time, in an apparent effort to grab listeners’ attention. Loudness comes at the expense of dynamic range—in very broad terms, when the whole song is loud, nothing within it stands out as being exclamatory or punchy. ( This two-minute YouTube video does a great job of demonstrating how excessive loudness saps richness and depth from a recording.) Indeed, Serrà and his colleagues found that the loudness of recorded music is increasing by about one decibel every eight years.

It’s an interesting study, and it seems to support the popular anecdotal observation that pop music of yore was better, or at least more varied, than today’s top-40 stuff. (A recent study also found that song lyrics are darker and more self-focused than they used to be.) But I did wonder if there was a selection bias in play here. The Million Song Dataset, huge as it is, may not provide a representative slice of pop music, especially for old songs. Its contents are heavily weighted to modern music: the database contains only 2,650 songs released between 1955 and 1959, but nearly two orders of magnitude more—177,808 songs—released between 2005 and 2009. That’s because it draws on what’s popular now, as well as what has been digitized and made available for download . And the songs of yesteryear that people enjoy today (as oldies) may not be the same ones that people enjoyed when those songs first came out.

Let’s assume for the moment that the trends identified in the new study—especially the homogenous timbres and restricted pitch sequences—are bad. Then the rare song that bucked those trends, offering up novel melodies and sonic textures, would stand out as being good. Therefore, that song would have a better chance than its contemporaries of surviving the test of time—that is, a better chance of finding itself digitized and widely played some 50 years after its release, thereby boosting its odds of inclusion in the Million Song Dataset. Meanwhile, the blander tunes of decades past would have faded into analog obscurity. The relatively few old songs in such a database, then, would tend to be more sonically interesting than the average song of today, and any analysis comparing old songs to new would likely reflect that. So I wondered if part of what this study is telling us is that bland music can fool us, but not for long.

Serrà acknowledged in an email that a bias due to the “test of time” effect is possible but argued that its influence should be small. For instance, he noted, the long-term patterns and trends that he and his colleagues identified also hold over relatively short—and relatively recent—time periods (say, 1997 to 2007), where the “test of time” effect should be minimal. “The same happens with close and not-so-recent time periods (e.g., 1960 and 1968), where both years could partly incorporate such an effect,” he wrote. “Since the trend is consistent in short time spans where you assume the ‘test of time’ bias is minimal and, furthermore, the trend is also consistent for longer time spans, we can assume it is a general trend and, thus, that the ‘test of time’ effect is really small.”

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The Evolution Of Earworms: Researchers Track History Of Pop Music

For the last half century, pop music has evolved from blues to disco to hip-hop. By digitally analyzing more than 17,000 songs, a team at Queen Mary University of London has released a study showing how pop music has changed throughout history.

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Articles on Pop music

Displaying 1 - 20 of 152 articles.

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Samuel Murray , University of Liverpool

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Beatriz Ilari , University of Southern California and Lynne Snyder , University of Southern California

Publishing Taylor Swift’s flight information: Is it stalking or protected free speech?

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Ben Shapiro’s hip-hop hypocrisy and white male grievance lands him on top of pop music charts for a brief moment

A.D. Carson , University of Virginia

The ghosts of the past: Pop music is haunted by our anxieties about the future

Alexander Carpenter , University of Alberta

I’ve researched Clara Bow – it’s no wonder the actress inspired Taylor Swift’s new album

Jennifer Voss , De Montfort University

Billy Joel is back for an encore − but why did he wait so long to turn the lights back on?

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With Slut! Taylor Swift joins a long history of women fighting slut-shaming in their writing

Lucy Thompson , Aberystwyth University

In 2003, one in four Aussie households owned Innocent Eyes. Delta Goodrem deserves a place in our music history

Kate Pattison , RMIT University

How did Taylor Swift get so popular? She never goes out of style

The beautiful pessimism at the heart of Jimmy Buffett’s music

Drew M. Dalton , Dominican University

Taylor Swift’s Eras Tour is a potent reminder that the internet is not real life

Aarushi Bhandari , Davidson College

Astrud Gilberto spread bossa nova to a welcoming world – but got little love back in Brazil

Mario Higa , Middlebury

How Chinese superfans became a force of nationalist activism in the name of their ‘idols’

Yan Wang , Lancaster University

Empowerment, individual strength and the many facets of love: why I fell for Tina Turner

Leigh Carriage , Southern Cross University

Eurovision under the shadow of war: how the 2023 contest highlighted humanitarianism, empathy and solidarity

Jess Carniel , University of Southern Queensland

How to win Eurovision: the secret code of the contest’s winning lyrics

Glenn Fosbraey , University of Winchester

Inspiration, influence and theft: what the Ed Sheeran case can tell us about 70 years of pop music

Jadey O'Regan , University of Sydney

‘Who the hell is Edgar?’ – a viral Eurovision song about Edgar Allan Poe evokes a strange history of mediums and creative possession

Hannah Lauren Murray , The University of Melbourne

Related Topics

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Associate Professor, Music Industry, RMIT University

Senior Lecturer in Popular and Contemporary Music, Newcastle University

Associate professor in Humanities, University of Southern Queensland

Senior Lecturer in Communication, University of Technology Sydney

Lecturer in the Sociology of Gender, Macquarie University

Assistant Teaching Professor of Music, Penn State

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Professor of Music, Australian Catholic University

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Professor of Cultural Studies, Director, Graduate Program in Gender Studies and Feminist Research, McMaster University

Casual lecturer, United States Studies Centre, University of Sydney

Associate Professor in American Literature, University of Leeds

Lecturer in Popular Music Studies, University of Leeds

Director, Institute for Popular Music, University of Rochester

Senior Lecturer in Commercial Music Performance, University of Westminster

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I am currently working as a Data Fellowship Coach at Multiverse , helping to build an outstanding alternative to university and corporate training through professional apprenticeships. I work with individuals from a variety of organisations to help them develop and apply Data Analysis skills in their roles.

Prior to that I was a Research Fellow in the Birmingham Centre for Media and Cultural Research at Birmingham City University . My research explored contemporary popular music reception practices and the role of digital, data and internet technologies on the business and cultural environments of music consumption. In the main this research was built around the development of The Harkive Project , an online, crowd-sourced method of generating data from music consumers about their everyday relationships with music and technology. I was also the co-Managing Editor of Riffs: Experimental Writing on Popular Music , a member of the PEC-funded Live Music Research team, and the Project Coordinator for the AHRC-funded Songwriting Studies Network .

Outside of work, I continue to build on my 20+ years of working in the business of popular music, working as digital catalogue manager for Static Caravan Recordings and as a musician and recording artist with Independent Country .

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Whether you know it or not, you’ve likely encountered A.I. — artificial intelligence — in your music consumption over the past year. Maybe it was Ghostwriter releasing a song with a fake “Drake” and “the Weeknd” in collaboration that took over the internet last year. Or maybe it was Drake himself rapping as “Tupac” and “Snoop Dogg” during the recent Kendrick Lamar beef . Or maybe it was a new track by the country superstar Randy Travis, who suffered a stroke in 2013, and hasn’t sung a song since.

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The power of nostalgia: Age and preference for popular music

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Marketers need evidence to help them select music to promote their products. Ethnicity, social class and/or personality type can distinguish individual music tastes, but age and nostalgia may be the largest determinant of all (North, American Journal of Psychology, 123 , 199–208, 2010 ). Research into listener preference for music from different eras has found conflicting results. Papers generally agree that it takes an inverse U shape, but disagree on the era for which people are most nostalgic. The seminal paper found a peak for music released when listeners were 23 years of age (Holbrook & Schindler, Journal of Consumer Research, 16 , 119–124, 1989 ), a follow-up 9 years of age (Hemming, Musicae Scientiae, 17 , 293–304, 2013 ), and 19 years of age (Holbrook & Schindler, Musicae Scientiae, 17 , 305–308, 2013 ). This paper attempts to correct the issues raised by Holbrook & Schindler ( Musicae Scientiae, 17 , 305–308, 2013 ) by improving the representativeness of the sample and introducing a new analysis technique, the two-lines test. This paper finds support for Holbrook & Schindler, but with a slightly younger age peak of roughly 17 years. Additionally, the larger sample allows investigation of differences by generation, which reveals differences that may be caused by their different current age, and so the relationship with, and interplay of nostalgia and music. The central conclusion of the paper is that people do exhibit a preference for music released during their late adolescence/early adulthood. When targeting consumers of a narrow age demographic, music released during this time is more likely to be preferred than any other.

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1 Background

Considerable attention has been dedicated to understanding the development and nature of music preferences (see Colley, 2008 ; Dunn et al., 2012 ). Findings suggest that age, gender, ethnic background, social class and/or personality type can distinguish individual music tastes (e.g. LeBlanc et al., 1996 ; Rentfrow & Gosling, 2003 ; Savage, 2006 ). Several studies suggest that age may be the largest predictor of music preferences (e.g. Baur et al., 2012 ; North, 2010 ). Common findings are that younger listeners are fans of Rock and Pop, while older listeners more often turn to Classical and Jazz (LeBlanc et al., 1996 ; Savage, 2006 ).

In further investigating the influence of age, Holbrook and Schindler ( 1989 ) found that preferences for popular music are developed during a critical period of an individual’s life and that these preferences tend to stay stable over time. The authors regressed a standardized dependent variable of musical preference on an interactive predictor variable termed “song-specific age”, which is a measure of a respondent’s age at the time that various musical stimuli (i.e. songs) reached popularity in the Billboard Top 10 charts. Two important findings were revealed. First, preferences for popular music and the age someone is when the song was released (conceptualized as “song-specific age” or SSA) follow an inverted U-shape relationship. Second, preferences for popular music peak when an individual is around 23.5 years old. It was concluded that preferences for popular music reflect the tastes acquired during the period of late adolescence to early adulthood. That is, people have highest preferences for music from when they are around 23.5 years old, and lower preferences for music that was released before or after this approximate age.

This study was later replicated with a larger German sample (Hemming, 2013 ). However, a conflicting preference peak of 8.59 was found, which led the author to conclude that there was no empirical support for the original study. The original authors argued that the replication study did support the original findings due to a number of faults in the replication (Holbrook & Schindler, 2013 ). Specifically, outliers, most commonly at the tails of the data, should have been removed as they reflect very small sample sizes. Further, the distribution of the replication data was skewed to the right and would have been better suited to a cubic rather than a quadratic equation. Additionally, the cleaning of data points consisting of fewer than 50 individual ratings was arbitrary and resulted in the removal of large parts of data. After these criticisms were resolved in a re-analysis, the preference peak increased to 19.23. Therefore, the preference peak was maintained as occurring “during late adolescence or early adulthood”. This conflict forms the core of the present research, which is an additional replication of these studies.

Calls for external validity were only in part answered by Hemming’s replication. The original study only gathered data from a convenience sample of 108 respondents and could be expanded to a larger, representative sample. As alluded to by all authors, there are more advanced techniques for examining the relationship between popular music preferences and age, such as two linear regressions rather than a traditional quadratic regression. One alternative is Simonsohn’s ( 2019 ) two-lines test, which was developed as a more valid method for detecting a true U-shaped relationship. The test fits two linear regressions for low and high values of x and determines that a U-shape relationship is present if both slopes are significant and of opposite sign. It also establishes a data-driven breakpoint through a “Robin-Hood algorithm”, which tests two linear regressions across the data, and then “steals” observations from the “richer” (i.e. more statistically powerful) line until both regressions are as statistically powerful as possible (for more details, see Simonsohn, 2019 ). While Holbrook and Schindler ( 1996 ) had applied two linear regressions through piece-wise linear modelling when examining the generational effect in preferences for films, they had not revisited it in the context of preferences for popular music.

Music consumption has evolved considerably, even since Hemming’s replication in 2013, most notably through the rapid rise of music streaming. By way of illustration, the number of users of any music subscription service has increased from 18 million in 2013 to 341 million in 2019 (nearly an 1800% increase). According to industry reports, music streaming is frequently used by 89% of surveyed respondents, while streaming engagement is increasing globally among all ages (International Federation of the Phonographic Industry, 2020 ). With on-demand access to millions of songs, and sophisticated algorithms to provide personally curated music suggestions, there is greater opportunity for listeners to discover a wider range of music, both new and old. Therefore, a shift in the preference peak or even from an inverted U-shaped relationship altogether is not improbable. This research aims to examine whether a preference for popular music of a specific era still exists and whether it still follows a non-monotonic relationship with age.

2.1 Participants

We recruited a large sample through an online panel provider, Toluna. The sample was comprised of 1,036 participants ranging between 18–84 years of age (mean age = 48.6, SD = 17.6, female = 53%). Data collection was conducted through an online survey in February 2019, which also gathered music genre preferences and music media usage data for another study. As shown in Table 1 , our sample resembles the general US population as per census data (U.S. Census Bureau Population Division, 2019 ).

2.2 Research design and task

Thirty-four songs were selected from the 1950 to 2016 Billboard Top 10 charts in two-year intervals. Song selection excludes those in the top three to avoid the influence of potential outliers, i.e. songs that are very popular or well-known. These songs are intended to be representative of their respective year in the Billboard Top 10. All songs were trimmed to 30-s excerpts which captured the main essence of the song (part of the verse and chorus). Respondents were exposed to each 30 s excerpt in an individually randomized order. Preference was assessed on an 11-point scale, starting at 0: “I dislike it a lot” and 10: “I like it a lot”, with a midpoint included at 5: “I neither like nor dislike”. A ‘Don’t know” option was also provided. Respondents were required to listen to at least 10 s of each song before moving on to the next song. They could rate each song at any time but could not advance to the next until at least the 11 th second. The full list of songs appears in the Appendix, Table 2 .

We first calculated the independent variable: song-specific age (SSA). This is a measure of a respondent’s age when each of the 34 songs featured in the Billboard Top 10. It is calculated by subtracting each respondents’ age from the year that each song was featured in the Billboard charts. For example, if a respondent was born in 1970, their SSA is 30 for the song that was in the 2000 Billboard charts. SSA may also be a negative number, which indicates that a song was released before that respondent’s birth (e.g. a respondent born in 2000 has an SSA of -30 for the 1970 Billboard Top 10 Song). As in the original papers, the same SSA consists of different songs due to respondents’ age differences. A respondent born in 1970 will have an SSA of 30 for the song released in 2000, while a respondent born in 1980 will have the same SSA for the 2010 Billboard song. Initial SSA calculations produced 132 unique SSAs ranging from -51 to + 81, with no missing values. Next, individual ratings were standardized by subtracting the mean and dividing by the standard deviation. We then aggregated individual ratings by SSA and calculated the mean standardized rating for each to form the dependent variable. SSA was further recoded as a deviation from their own mean to minimize multicollinearity between the two independent variables. We then regressed mean standardized musical preference on SSA and SSA squared to determine whether the original inverted U-shaped relationship was present. We have also used Simonsohn’s ( 2019 ) two-lines test on both the aggregated and disaggregated data and summarize these.

Upon visual examination, standardized musical preference and song-specific age follows an inverted U-shaped relationship. Due to absolute differences in respondents’ age, there were fewer old and young respondents in the sample, and so the extreme low and high SSAs may not be reliable. We removed any SSAs consisting of fewer than 100 individual ratings, and any outlying individual standardized ratings that were identified as using the interquartile method, which leaves 104 SSAs ranging from -38 to 65 for re-analysis. The resulting regression plot for this trimmed data is shown in Fig.  1 .

Relationship between song-specific age and standardized musical preference (preference = -0.00029 * SSA 2  + 0.0059* SSA + 0.16)

Multiple regression reveals a strong relationship between popular music preferences and song-specific age (R 2  = 0.71, F(2, 101) = 125.8, p  < 0.001). This was significant for both song-specific age (t(102) = 4.0, p  < 0.001) and song-specific age squared (t(102) =—15.4, p  < 0.001). In this instance, peak popular music preferences occurred at 10 years of age. However, the quadratic model fits poorly towards the peak of the observed values, which is affecting the preference peak calculation.

Holbrook and Schindler ( 2013 ) suggest applying a cubic model (shown in Fig.  2 ). The cubic model provides a modest increase in explained variance (R 2  = 0.71 for the quadratic model to R 2  = 0.75 for the cubic), and the preference peak rises to 17.6 years of age.

Cubic relationship between song-specific age and standardized musical preference (preference = -0.0000028* SSA 3 * -0.00018* SSA 2  + 0.0088* SSA + 0.10)

We now turn our attention to a two linear regression solution, which is Simonsohn’s ( 2019 ) two-lines test. An inverted U-shaped relationship was detected for popular music preference and song-specific age. Line one was significant and positive for the low values of x (b = 0.01, z = 11.97, p  < 0.0001), while the second line was also significant and negative for high values of x (b = -0.02, z = -30.31, p  < 0.0001, see Fig.  3 ). The breakpoint indicates that popular music preferences peak when respondents are about 16.8 years old. Visually, this age corresponds with the approximate peak of the observed values. While previously the quadratic regressions failed to provide a reasonable fit towards the peak of the observed values, the two-lines test appears to provide a more feasible age peak.

Two-lines test of the relationship between song-specific age and standardized musical preference

We also calculated an individual peak for each respondent. When running the two-lines test across the set of individuals, only 136 of 1036 tests (13%) detected a statistically significant result at p  < 0.05 for both lines, with a peak preference in music released when respondents were 13.4 years of age (SD = 18.8, median = 14.7), somewhat dissimilar to the previous findings. If the significance level is changed to p  < 0.1 ( n  = 189, 18% of the sample), the new mean for peak preferences is detected at 13 years of age (SD = 19.4, median 14.7), compared with 16.8 (SD = 22.6, median 16.5) for all respondents, regardless of significance of the test. When inspecting a histogram of the breakpoints detected at p  < 0.1, (see Fig.  4 ), we see that the mean does not reflect the data, as it is clustered in the range representing the late teenage years. The mean calculated by using all detected breakpoints appears to be more reflective of even the subset of significantly detected breakpoints, and so is our preferred peak in musical preference.

Histograms of detected breakpoints

When inspecting a scatter plot of the peaks (see Fig.  5 ), there are two key patterns to note. The first is that the overall shape of the scatter reflects the relative years of music heard by each respondent. If all respondents reported a peak at a similar age, we would see that the points were arranged in a roughly horizontal line. This is not the case. Second, for respondents with birth year roughly 1970 onwards, the peak in preference has the possibility of being negative, that is, they can prefer songs from before they were born more than others.

Scatter and histograms of peak musical preference as detected (or otherwise) by the two-lines test

There are three potential causes for these patterns. The first is that for older respondents, most of the music encountered in this study will have been released long after their young adulthood, and for younger respondents, a great deal of the music will have been released long before they were born: there may be recency effects interacting with nostalgia, or the increased availability of music across all parts of life. The second is that by representing an entire era of music through just a few specific songs, we open ourselves to the variation in quality of the songs themselves, and the variation in musical preference that exists in the population. The third potential cause is that the influence of nostalgia holds different levels of influence over individuals, and individuals vary in their interest in and enthusiasm for, new and old music, or indeed music in general.

5 Discussion

Our replication generalizes Holbrook and Schindler’s ( 1989 ) original findings concerning the development of popular music preferences. The relationship between popular music preferences and song-specific age is still curvilinear and inverted U. In our analysis, preferences for popular music peak at 16.7 using Simonsohn’s two-lines test, or 16.8 using the two-lines test across individuals. That is, individuals prefer music from their mid-to-late teens most, and prefer music released earlier or later in their lives least. While the original study’s preference peak at 23.5 years of age was classified as late adolescence or early adulthood, our research finds a preference peak that occurs earlier, more closely aligned with mid-to-late adolescence, or the beginnings of adulthood. Given the quantum shifts in music consumption and increased accessibility of music offered by digitalization and streaming, a lowering of the peak is conceivable. Increased exposure to music at a younger age and greater availability to music of all ages could be contributing factors to this shift in preferences. Additional research is required to uncover why preferences for popular music are peaking at a younger age. However, understanding these generational differences requires further work and remains an avenue of investigation for future researchers.

Our research further demonstrates the varying effects of model choice. That is, the researcher’s decision to use a quadratic, cubic or interrupted regression, will notably impact the location of the curve-maxima. By applying Simonsohn’s ( 2019 ) robust two-lines test, we validate that the relationship between popular music preferences and song-specific age is still an inverted U. Further, a more plausible preference peak of 16.8 is obtained: and, that it varies by individual, as shown in the individual-level analysis which finds an average peak of 17 years when considering all detected peaks. Perhaps, future work should exclude preferences for tracks released before respondents were born, as this appears to muddy results – and while we acknowledge that fashion moves in cycles of rediscovery and rebirth, people are unlikely to hold actual nostalgia (as opposed to mere admiration) for eras they didn’t experience.

Our research is not without its limitations. First, musical stimuli are limited to popular music, which means we cannot generalize our findings to more diverse musical selections. Selecting a single song to represent a year introduces noise into the data: the songs chosen are pop, as they are likely to have been heard by the entire community, but individual genres or artists differ in their popularity. Naturally, a more diversified musical corpus can be considered in future work. Additionally, we can only conclude that popular music from one’s mid-to-late teens is preferred more than music released before or after. We are unable to understand if this is even the most enjoyed music, only that it is more preferred than popular music released before or after this period of adolescence. We were also unable to validate Hemming’s ( 2013 ) German replication, as our scope is solely the USA. Observation of this preference peak in different countries, especially those that are non-Western, is required for generalizability. Calls for a longitudinal design were unanswered by our research, which again is an area that should certainly be considered in future work. One potential solution could be exploiting behavioural music data rather than claimed preferences. This may facilitate observation of music preferences individually and how changes play out over time.

We confirm the original study and conclude that preferences for popular music peak during early adolescence or mid-to-late teens, and that newer or older tracks do not command this same level of affection. However, rather than one definitive preference peak, we find evidence for variation in peak preference, with the bulk of people’s preferences occurring in the teenage years. Nearly three decades later, we validate Holbrook and Schindler’s original research concerning the development of popular music preferences. Their seminal work appears to be a cultural phenomenon rather than an artefact of a single study.

Data availability

The data for this paper were collected by Toluna, a third-party panel provider, during February 2019, across the USA. The authors jointly analysed the data, and it is currently stored in a Dropbox folder managed by the Ehrenberg-Bass Institute for Marketing Science.

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Davies, C., Page, B., Driesener, C. et al. The power of nostalgia: Age and preference for popular music. Mark Lett 33 , 681–692 (2022). https://doi.org/10.1007/s11002-022-09626-7

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The healing power of music: a promising new avenue for cardiovascular health

Introduction.

Cardiovascular diseases are commonly treated using non-pharmacological interventions, which are often the preferred first-line approach. Unfortunately, there appears to be a lack of utilization and understanding regarding the most effective non-pharmacological options available. The reasons for this knowledge gap are not entirely clear; however, the pharmaceutical industry's influence could be a contributing factor. Additionally, many non-pharmacological interventions for cardiovascular health may not be well-received by patients due to the burden they impose. For example, interventions such as smoking cessation, dietary changes, and exercise can be labor-intensive and unpleasant. A promising new non-pharmacological intervention for cardiovascular disease is the use of music.

Music has long been a source of comfort, inspiration, and entertainment for people around the world. But recent research suggests that it may have even more profound benefits, particularly when it comes to cardiovascular health. Music has been shown to impact the cardiovascular system through various mechanisms. One proposed mechanism is the effect of music on the autonomic nervous system (ANS). Studies have shown that music can modulate ANS activity, resulting in decreased heart rate and blood pressure ( 1 ). This effect may be mediated by the release of endogenous opioids, which have been shown to be involved in the modulation of ANS activity ( 2 ). Classical music with a slow beat has been shown to reduce heart rate and blood pressure, while classical music with a fast beat has been found to increase heart rate and blood pressure ( 3 ). These effects can be attributed to the ability of music to affect the release of hormones such as adrenaline and cortisol, which are associated with the sympathetic nervous system response, or oxytocin, which is associated with the parasympathetic nervous system response ( 4 ). In addition to its effects on the ANS, music has also been shown to impact the endothelial function of blood vessels. The endothelium is a layer of cells that lines the inside of blood vessels and is involved in the regulation of blood flow and vascular tone. Dysfunction of the endothelium is a hallmark of cardiovascular disease and is associated with increased risk of cardiovascular events. Studies have shown that music can improve endothelial function, possibly through the release of nitric oxide, a key regulator of vascular tone ( 5 ).

One of the most widely recognized clinical uses of music in the context of cardiovascular disease is the utilization of music therapy as part of cardiac rehabilitation. Music therapy has demonstrated efficacy in enhancing rehabilitation tolerance, elevating mood, and promoting adherence to rehabilitation exercises ( 6 ). Typically, the application of music therapy is directed towards either reducing anxiety and stress or fostering a motivational environment for rehabilitation. Randomized clinical trials have demonstrated a significant improvement in various health-related outcomes for patients that undergo cardiac rehabilitation with music therapy compared to patients who undergo cardiac rehabilitation without music therapy ( 7 ). Additionally, music therapy in post-cardiac surgery patients may decrease pain and anxiety ( 8 ). Finally, using music to diminish pain reduces the use of analgesic medication and reduces adverse effects of opioid analgesics such as falls and delirium.

In recent years, the use of music in invasive cardiac procedures has gained prominence as an effective non-pharmacological intervention. Hospitals can be a frightening place for patients, and the thought of undergoing a cardiac procedure can be incredibly anxiety-inducing. It's no surprise that patients often feel overwhelmed and stressed when faced with this daunting experience. Researchers have demonstrated significant reductions in anxiety and pain levels among adult patients who received music interventions before, during, and after surgery ( 9 ). Additionally, various types of music have resulted in improved patient satisfaction during surgical procedures ( 10 ). The effects of music listening on patients during cardiac catheterization were investigated and music was found to have a significant relaxing and calming effect for the patients ( 11 ).

The ensuing section pertains to the exploration of music's role within the domain of heart failure management. Heart failure management requires a multidisciplinary team and clinical trials typically focus on improving survival and decreasing exacerbations. However, heart failure is associated with many comorbidities such as depression, anxiety, and poor sleep quality. Despite the recognized negative impact of these comorbidities on patients' overall well-being and treatment outcomes, they are often overlooked or under-addressed in clinical practice. From a quality-of-life point of view, listening to music has been shown to improve various aspects such as anxiety, depression and sleep quality ( 12 ). Despite the growing evidence for the benefit of music therapy in heart failure, there are still some challenges to its implementation in clinical practice. One major issue is the lack of standardized protocols for music therapy, making it difficult to compare studies and establish best practices. Additionally, there is a need for more research on the optimal timing, frequency, and duration of music therapy interventions in heart failure.

The healing power of music has emerged as a promising new avenue for improving cardiovascular health. With numerous studies pointing towards its efficacy, music has demonstrated its ability to reduce stress, anxiety and even lower blood pressure and heart rate. Furthermore, music may contribute to the improvement of endothelial function, a critical element in upholding cardiovascular health. The potential of music therapy as a complementary intervention in managing cardiovascular diseases is indeed impressive and calls for more in-depth exploration. By incorporating music as a non-pharmacological intervention in cardiovascular disease management, healthcare providers can potentially improve outcomes and enhance the quality of life for their patients. The incorporation of music into the domain of cardiovascular health has unveiled a realm of novel opportunities, holding the promise of remarkable advancements in the foreseeable future.

Funding Statement

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

The internal medicine residency training program of McGill University has provided funding for the article processing fees related to this article.

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SB: Conceptualization, Data curation, Writing – original draft, Writing – review & editing.

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Cam’ron’s CNN Interview Goes Off the Rails When Asked About Diddy: ‘Who Booked Me for This?’

Killa also took the opportunity to promote his Pink Horse Power libido supplement.

By Michael Saponara

Michael Saponara

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Cam’ron can turn any interview into a viral moment — just ask Bill O’Reilly. The Dipset rapper appeared on CNN on Monday night (May 20), but claimed he didn’t know he was being booked to talk about Sean “ Diddy ” Combs and the assault allegations made against him.

Diddy Shares Apology Over Disturbing Cassie Ventura Hotel Video: ‘I’m Truly Sorry…

The interview quickly went left when host Abby Phillip asked Cam about recognizing Diddy in the video of the “I’ll Be Missing You” artist physically assaulting his then-girlfriend Cassie in a Los Angeles hotel in 2016 that CNN obtained last week.

Phillip’s facial expression seemingly gave away she knew the interview was headed down a disastrous path, but she continued to attempt to get Cam’ron’s thoughts on Diddy’s apology video, in which Combs called his actions “inexcusable.”

“The apology ain’t for me, it’s for Cassie,” Cam replied. “He ain’t do nothing for me. What I think about it don’t matter. You need to ask Cassie if she accepts the apology. I said what I said.”

While CNN replayed a heartfelt clip from It Is What It Is about Cam’ron asking Ma$e why he had him sign with The Notorious B.I.G. instead of Diddy, Cam took the chance to promote his Pink Horse Power libido supplement.

He slugged back a cup of the intimacy juice. “Sorry, I’m going to get some cheeks after this Horse Power joint,” Cam said, admitting he was headed to see a woman after the interview.

After one final attempt to pry information and give Cam a chance to speak on Diddy, the “Oh Boy” rapper snapped and asked who booked him to appear on CNN before wrapping up the three-minute segment.

“Who’s the talent agent for this joint? You think I be sitting around watching what Diddy do? I didn’t know this was a Diddy joint that y’all invited me to,” he fired back. “Who booked me for this joint?”

However, a source familiar with the situation tells Billboard that the rapper’s rep was “fully aware and briefed” about the topic ahead of Cam’ron’s interview with Phillip on NewsNight .

“I just felt you tried to use me, pause,” he said. “I don’t know why they ain’t watch 60 Minutes . They ain’t watch Bill O’Reilly? That’s what I do on them joints… You call me on CNN for the bulls–t, I’ma give you the bulls–t.”

Watch Cam’ron’s CNN appearance and his response to it on It Is What It Is below.

So Cam'ron went on CNN tonight and it was an absolute disaster pic.twitter.com/xm3tCe9O7N — philip lewis (@Phil_Lewis_) May 21, 2024

View this post on Instagram A post shared by @mr_camron

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