Music genre and volume do not modulate energy intake,short-term satiety,and mood states: A randomized controlled trial

Abstract

This study was designed to investigate whether background music genre and volume can alter energy intake, short-term satiety, and mood states in women with normal body weight. Participants (N = 35) were tested using a randomized, crossover design consisting of five conditions: the control day on which no music was playing (CON), 60-dB Western classical (60 dB C), 80-dB Western classical (80 dB C), 60-dB rock (60 dB R), and 80-dB rock (80 dB R) music. The four music conditions were from 15 min before lunch and during an ad libitum lunch until the end of the meal. The participants were first asked to report visual analog scale (VAS) scores on sensory outcomes, lunch was served, and the energy intake of the individuals and their profile of mood states (POMS) were measured. Overall, mean VAS scores were similar between the groups on all of the test days (p > .05). Listening to different music genres and volumes did not exert a significant effect on energy intake (p > .05). The only mood state that differed significantly was confusion-bewilderment (p = .017). Neither music genre nor volume influence short-term energy intake and satiety-related VAS outcomes.

Keywords

music genre music volume energy intake satiety mood appetite

Eating behavior is a complex set of actions which involves personal, nutritional, social, and environmental factors (De Castro, 1996). A large body of evidence has indicated that over-nutrition is one of the most effective factors in inducing weight gain and obesity (Fogel et al., 2020; Kheirollahpour et al., 2020; Sutin et al., 2016). As obesity is known to be one of the fundamental causes of diabetes, cardiovascular diseases, and cancer, modifying the main determinants of eating behavior could decrease the development of obesity and obesity-related diseases (Davis et al., 2020; Poeta et al., 2019). The study of the effects of environmental factors on eating behavior has become increasingly substantial as the outcomes obtained from these studies help to build healthier food cues and nutritional habits (Paquet, 2019).

Environmental factors such as the presence of other people (Hetherington et al., 2006), location (Markovski et al., 2017), and television viewing (Braude & Stevenson, 2014) appear to influence food choice and food intake. Recently, the effect of listening to music during eating has been examined in a few studies. Data collected from participants’ home records showed that the presence of music was related to higher food intake in college students (Stroebele & de Castro, 2006). Another retrospective study examined the influence of immediate eating environment determinants and suggested that the presence of music had a positive relationship with energy intake in Australian adults (Lock et al., 2016). On the contrary, two laboratory studies found no change in total energy intake in overweight or normal adolescents when eating alone with background music (Mekhmoukh et al., 2012; Péneau et al., 2009).

Beyond the presence of music, the relationship between the volume of music and food intake has been examined in previous studies. McCarron and Tierney (1989) concluded that the loudness of music, characterized by increased volume, enhanced the amount of consumed soft drinks (cola, orange, and lemon) during an experiment under naturalistic conditions among male and female subjects (median consumptions were 0, 250, and 500 mL at 0, 70, and 90 dB, respectively). In a different study, high volume levels (88 dB) increased the number of ordered alcoholic beverages (2.6 drinks when environmental music was 72 dB vs. 3.4 drinks at 88 dB) and decreased the average amount of time spent by the patrons in a bar setting (Guéguen et al., 2008). On the contrary, a randomized crossover study showed that energy intake during an ad libitum lunch did not differ between settings with no music, 60 dB music, and 90 dB music in normal weight and overweight or obese men (Mamalaki et al., 2017). Mamalaki et al. (2017) concluded that other parameters related to environmental conditions such as presence of other people, location, or alcohol consumption could be more influential on eating behavior. In addition, the possible effect of music type per se is also mentioned in the same study.

Research studies on the effects of music genre on human behavior have become increasingly popular over the past decade. Listening to different music genres, namely, rock music or classical music, has been shown to result in recalling more “rock items” than “classical items” when rock music was played (Yeoh & North, 2010), higher mental workload, standard deviation of speed, and frequency of lane crossing under the influence of rock music (Wen et al., 2019) and significantly better performance of medical students during the laparoscopic exercise when exposed to classical music rather than rock music (Nees et al., 2021). These studies attracted attention to the point that different cognitive functions could be influenced by different music genres. Due to the fact that eating is an intrinsic human routine and is affected by the manipulating cognitions present at the time of an eating period (Schachter & Gross, 1968), studies evaluating the influence of music on food intake have also attracted attention. Previous studies examining the effects of instrumental, nonclassical music on eating behavior have confirmed the hypothesis that fast tempo music (122 ± 19.6 mean beats per minute [bpm]) results in increased eating speed (mean 4.40 ± 1.57 bites/min; Roballey et al., 1985) and, likewise, fast music played on a piano (132 bpm) decreased drinking time (McElrea & Standing, 1992). In a different study, it was reported that when slow-tempo music was played, customers stayed at a restaurant longer than when fast music was played (Caldwell & Hibbert, 1999). On the contrary, a recent study reported that slow music (65 bpm) played in a quasi-naturalistic cafeteria setting prolonged meal duration in comparison to fast music (165 bpm) but did not result in increased food intake (Mathiesen et al., 2022).

Caldwell and Hibbert (2002) concluded that music preference, which was measured by asking participants how much they liked the music being played, was a better determinant of actual time spent dining in comparison with music tempo. In accordance with Caldwell and Hibbert’s (1999) finding, a recent study showed that eating duration was longer with slow-tempo music and that the presence of music increased eating duration in comparison with eating in silence regardless of music tempo (Mathiesen et al., 2020).

More specifically, music genre has been shown to alter taste perception in different studies. Interestingly, the same musical piece recomposed in four genres—classical, jazz, hip-hop, and rock—was shown to influence flavor pleasantness and overall impression of food stimuli (Fiegel et al., 2014). Accordingly, the jazz stimulus resulted in significantly increased enjoyment in food stimuli in comparison with hip-hop stimulus. Further studies are needed to explain the relationship between different music genres and food intake in terms of taste perception, flavor pleasantness, and impression of the food.

Collectively, results from these studies suggest that rate or the amount of food/drink consumed can be modified by environmental music due to its volume, genre, or tempo. Although the underlying mechanism of the relationship between environmental music and food intake is relatively unknown, the rate of the food intake appeared to have a central role. Different studies reported that slower eating rate can induce more pronounced anorexigenic gut hormone release (Kokkinos et al., 2010) and help to reduce energy intake (Andrade et al., 2008) than faster eating rate. However, a recent systematic review and meta-analysis examining the relationship between music and food intake in nine articles reported that music-related features did not significantly moderate the link between music and food intake. It was emphasized that more experimental studies are required within this context due to the lack of sufficiently explored relationship between music and food intake (Cui et al., 2021). Furthermore, most of the studies in the literature have primarily focused on eating patterns in public places like restaurants or bars (Caldwell & Hibbert, 1999; Guéguen et al., 2008), with the impact of music on food consumption not being adequately examined through randomized controlled trials. Therefore, the present study aimed to determine the effect of both background music volume and genre on energy intake during an open buffet meal. As subjective satiety outcomes are affected by external factors and are associated with subsequent food intake, the participants’ satiety outcomes using a visual analog scale (VAS) were also measured (Herman & Polivy, 2008). Since Rentfrow and Gosling (2003) revealed four music-preference dimensions for 14 music genres and classical and rock music were classified in different dimensions, classical and rock music were selected as different music genres in the current study. It was hypothesized that higher music volume and the rock music genre would lead to higher energy intake and increased VAS scores associated with lower satiety and higher desire for food intake in comparison with lower music volume and Western classical music during an open buffet lunch.

Emotional perspective is also an important contributor when evaluating the effect of music on food intake. Foods can induce emotional responses when eaten (Osdoba et al., 2015) but the effects of background music on this response and the relationship between music, food intake, and mood states are relatively unknown. Few studies have examined the effect of the emotion induced by listening to music on the participant’s tasting experience. For instance, beer was liked more and tasted sweeter when the participants listened to music associated with positive emotion (Reinoso-Carvalho et al., 2019). Moreover, specific types of music (discharge, diversion, or solace) were shown to be effective in alleviating food intake, depending upon the emotion exerted (van den Tol et al., 2022). Examination of emotions in response to food consumption was carried out using the profile of mood states (POMS), which has been shown to measure the influence of experimental manipulations on various emotional responses (Kien et al., 2013; Mento et al., 2017). Hence, the second aim of this study was to determine whether or not different genres and volumes of music change mood states such as tension-anxiety, depression-dejection, anger-hostility, vigor-activity, fatigue-inertia, and confusion-bewilderment in participants during an ad libitum lunch. Within this context, it was hypothesized that higher music volume and rock music would elevate the emotional responses of POMS.

Materials and methods

Participants

Thirty-five female participants were recruited from Hacettepe University, Ankara, Turkey and the surrounding community through poster advertisements and announcements. A questionnaire examining general health and nutritional habits was used for screening all volunteers. Inclusion criteria were healthy women between the ages of 18 and 30 years who were nonsmokers, not dieting, not diagnosed with any metabolic disease, and no hearing loss. Participants were excluded if they had a measured body mass index (BMI) <18 or >25 kg/m². Participants could not be professional athletes, possess food allergies, or have extreme dislikes for specific foods or be pregnant or lactating. None of the participants were taking medications known to influence appetite or body weight. Regular meal consumption was another inclusion criterion for participants. In addition, participants with extreme dislikes for any specific genre of music (including rock and classical music) were identified by the questionnaire and excluded from the study. Participants were excluded if they scored >9 on Beck’s Depression Inventory due to predisposition to depression. Participants’ body weight, height, and body compositions in terms of lean body mass (kg) and fat percentage (%) were measured with a Tanita MC-980 monitor (Tanita Corp., Tokyo, Japan). As menstrual period may influence appetite and satiety ratings, test days were organized 1 week before menstruation for all participants. All participants were asked to record their food frequency and to fulfill a 24-hr dietary recall prior to every experiment day. Similarities in dietary energy intake and food choices from food frequency were taken into consideration for choosing participants. The Non-Interventional Clinical Studies Ethics Board of Hacettepe University (GO18/1097–16) provided ethical approval for the study on November 27, 2018. Written informed consent to participate was taken from participants after the completion of checking the inclusion criteria.

Procedure

This study was a crossover study conducted at the Nutrition Laboratory in the Department of Nutrition and Dietetics, Hacettepe University, Ankara, Turkey. The experimental setting was stable for all of the experiment days and room temperature was maintained at 21°C throughout the study protocol. The participants (N = 35) were divided into two groups: a Monday group (n = 17) and a Friday group (n = 18) according to the day on which they participated in the study. The participants attended the laboratory five times in total, once a week in their respective groups. In the laboratory, there was a large cupboard against the wall at the front of the room and at the top of the cupboard there was a portable speaker (JBL Xtreme 3: Portable Speaker, Harman International Industries, Incorporated, Stamford, USA) used for distributing the music to the laboratory environment. The experimental protocol of this study used the European consensus on postprandial studies evaluating appetite measures and eating behaviors (Blundell et al., 2010). The study was carried out in a random order and at least 1 week apart in five trials: a control day on which no music was playing (CON), a 60-dB Western classical day (Vivaldi’s Four Seasons, played by Itzhak Perlman with the London Philharmonic Orchestra, a 2015 CD, with an average tempo of 117 bpm) (60 dB C), an 80-dB Western classical day (with the same music) (80 dB C), a 60 dB rock day (Queen’s Greatest Hits, a 2011 CD, average tempo of 128 bpm) (60 dB R), and an 80 dB rock music day (with the same music) (80 dB R) which were played from 15 min before lunch until the end of lunch. In this study, Western classical and rock models were chosen as these genres had previously been compared in different studies and had produced different outcomes regarding the influence of music types on diverse cognitive parameters (Nees et al., 2021; Wen et al., 2019; Yeoh & North, 2010). The decibel (dB) is a relative unit measuring sounds pressure or forcefulness; 60 to 70 dB of music is classified as soft and 80 to 90 dB is classified as loud (Staum & Brotons, 2000). In this study, 60 and 80 dB were chosen to test two distinct volumes. The nutrition laboratory where all the tests were performed was an isolated room at a remote location to provide a well-controlled environment for all experiments. Hence, throughout the study period no environmental sound or noise could be detected or interacted with the played music. A portable speaker was used to distribute the music to the laboratory environment. A decibel meter (Extech SL130G; Flir Systems, Inc.) was used to measure the actual music volume throughout the experiments. The participants sat 2 m away from the speaker to ensure that the music was distributed equally. The decision to establish a distance of 2 m was determined by using the decibel meter. The desired decibels were reached by increasing or decreasing the music volume setting on the speaker. The serving table for the lunch was a separate dining table at a moderately close distance to where the participants sat. On each test day, the same amounts and types of food were served and the buffet items were identical. The same portion sizes, serving cutlery, and serving bowls were used on all the test days. Participants were allowed to eat lunch for 30 min and were instructed to eat lunch until they were comfortably satisfied. They were informed that the topic of the research was to examine their energy intake on different test days rather than to measure the musical effects on appetite. A pretest was conducted by researchers to determine what decibel levels were appropriate, and how to set up the laboratory environment for participants to hear music at the same sound level.

In order to assure homogeneity, participants were instructed to consume a similar meal on the previous evening. Participants were instructed to have dinner comprising a bowl of vegetable soup (200 mL), grilled meat (60 g), salad (200 g), white bread (50 g) (a total of 622 kcal), and adequate water. On the experimental days, the participants arrived at 8 am after fasting for 12 hr, VAS and POMS were given to them. They settled at the same place every test day. They started to complete the VAS immediately after they arrived, until 2 pm every 15 min. In the morning they were served breakfast consisting of two thin slices of white bread (50 g), a slice of cheddar cheese (30 g), and one cup of tea at 8:30 am (a total of 260 kcal). The participants were asked to consume the full breakfast within 15 min. Additional food or beverages were not served until the open buffet lunch. At 12 pm, an ad libitum buffet-style lunch was served at the dining area, where the designated music was played. During the lunch, the participants completed the POMS. Lunch consisted of pasta and soft drinks and participants were informed about the menu at the enrollment stage of the study. The recipe for the pasta was as follows: 500 g pasta was boiled in hot water with 50 g of sunflower oil. The pasta was then added to 300 g ready-to-eat Napolitano sauce and mixed until homogeneously distributed. The nutritional value of 100 g of this recipe was 287 kcal, 44.8 g carbohydrates, 8.0 g protein, and 8.2 g fat. The nutritional value of 100 mL of fruit juices (orange, peach, and mixed) was between 63 and 90 kcal, with 12.7–20.0 g carbohydrates. At 12:30 pm, the music was turned off and the buffet was taken away from the laboratory. The participants continued to complete their VAS and, at 2 pm, they left the room while being given all the measurements. They were not allowed to eat or drink anything between 12:30 pm and 2 pm.

Throughout the study period, participants were in the same room and were allowed to read books or use laptops silently throughout the experiment. Social interaction was mostly limited. The energy and macronutrient intakes of the participants were measured by weighing the amounts of food and drink consumed and converting these values into energy (kcal) and macronutrients based on the manufacturers’ labeling.

VAS

The VAS was used to assess hunger, satiety, prospective food consumption, amount of food they could consume, and desire for sugary foods throughout the study period (Flint et al., 2000). Appetite ratings were recorded on a 100 mm VAS with words anchored at each end describing the extremes of a unipolar question (Flint et al., 2000). The questions were as follows: for hunger: “I am not hungry at all”/“I have never been more hungry”; for satiety: “I am not sated at all”/“I have never been more sated”; for prospective food consumption: “I cannot consume any food at all”/“I have never wanted to consume food that much”; for desire for a sugary snack: “I do not want to consume a sugary snack at all”/“I have never wanted to consume a sugary snack that much” and for amount of food: “I can only have a small amount of food”/“I can eat a large amount of food.” The baseline VAS scores were measured before breakfast at 8 am. After breakfast, participants filled in VAS questionnaires every 15 min until lunch and afterwards.

POMS

POMS is used to assess the effects of experimental manipulations on various conditions (Lieberman et al., 1986). Briefly, the scores of six identifiable mood or affective factors as “tension-anxiety,” “depression-dejection,” “anger-hostility,” “vigor-activity,” “fatigue-inertia,” and “confusion-bewilderment” were calculated by adding the responses that defined the mood factor. The responses defining each mood were as follows: 0 = not at all, 1 = a little, 2 = moderately, 3 = quite a bit, and 4 = extremely. Participants were asked to complete the POMS 15 min after starting lunch and to complete it within 15 min (Costa et al., 2020). They were asked to consider how the music made them feel during the time they ate their lunch while the music was playing in the background.

Statistical analyses

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22 (SPSS Inc., Chicago, IL, USA). The primary outcome of this trial was to assess the effects of different music volume and genre on energy and macronutrient intakes during an ad libitum lunch. For the primary outcome, data were analyzed using a general linear model, analysis of variance (ANOVA). Where ANOVA was significant, Bonferroni post hoc analysis was used for comparisons between conditions. The secondary outcome variables were participants’ VAS and POMS scores. For the VAS, data were analyzed using a repeated measures analysis of covariance (ANCOVA) with the baseline measurement as the covariate. Subjects and test day were included in the procedure, in addition to the music/time interaction. Data on the area under the curve (AUC) for VAS were obtained using GraphPad Prism version 6 (GraphPad Software Inc., La Jolla, CA, USA). Baseline values were added as covariates in the AUC data. Data are given as mean ± standard error of the mean unless otherwise stated; p < .05 was considered as statistically significant. Partial eta-squared (η²_p) effect sizes were reported where values between .01 and .06 were considered as small effect, values between .06 and .14 as medium effect, and values higher than .14 were considered a large effect (Cohen, 1988). Power analysis indicated that at least 25 subjects per condition were required in order to estimate a minimum effect size of 10% (for energy intake differences) for comparisons between treatment arms (yielding a power of .80 and alpha of .05).

Results

All of the participants completed the study successfully on each test day. All participants’ data were analyzed for each musical condition. Participants were 21.6 ± 1.50 years of age, with an average BMI of 21.7 ± 2.04 kg/m², and had a waist circumference of 73.7 ± 6.73 cm. Body composition data for the participants were 41.9 ± 5.52 kg lean body mass and 25.5 ± 4.18 % fat.

Figure 1 shows the mean VAS-rated hunger (a), satiety (b), prospective food consumption (c), amount of food that could be consumed (d), and desire for sugary foods (e) for the total study period. Baseline values were similar between the test days (p > .05). VAS scores showed that both breakfast and lunch significantly influenced VAS scores (p < .001). However, neither music genre nor volume affected VAS scores during the test days: for hunger, F(26.25, 1,109.051) = 0.822, p = .723, η²_p = .019; for satiety, F(29.895, 1,263.071) = 1.222, p = .298, η²_p = .026; for prospective food consumption, F(4.028, 170.182) = 1.003, p = .408, η²_p = .023; for amount of food that could be consumed, F(23.635, 998.577) = 0.808, p = .728, η²_p = .019; and for desire for a sugary snack, F(18.096, 764.565) = 0.558, p = .930, η²_p = .013. No interaction was detected between music genre or volume and time. In addition, the AUC data of the VAS scores did not show a significant difference between the groups (Table 1): for hunger, F(4, 110) = 0.080, p = .988, η²_p = .003; for satiety, F(4, 110) = 0.099, p = .983, η²_p = .004; for prospective food consumption, F(4, 110) = 0.135, p = .969, η²_p = .005; for amount of food that could be consumed, F(4, 110) = 0.085, p = .987, η²_p = .003; and for desire for a sugary snack, F(4, 110) = 0.446, p = .775, η²_p = .016.

Figure 1.

Mean VAS Scores (±SEM) During the Test Days. (a) VAS-Rated Hunger, (b) VAS-Rated Satiety, (c) VAS-Rated Prospective Food Consumption, (d) VAS-Rated Amount of Food That Could Be Consumed, (e) VAS-Rated Desire for a Sugary Snack. A Light Breakfast was Served at 8 am, Immediately After Recording Baseline VAS Scores. Lunch was Served at 12 pm. Repeated Measures Indicated That There Were No Statistically Significant Differences Between the Study Groups for Hunger, Satiety, Prospective Food Consumption, Amount of Food That Could be Consumed, or Desire for a Sugary Snack.

Table 1.

AUC Data of VAS Scores During the 5 Test Days.

VAS question	Study groups					F	p	η²_p
VAS question	CON	60 dB C	80 dB C	60 dB R	80 dB R	F	p	η²_p
Hunger	818.6 ± 26.3	851.3 ± 26.1	811.8 ± 26.3	819.5 ± 26.3	781.5 ± 27.0	0.080	.988	.003
Satiety	1,196.6 ± 31.1	1,228.5 ± 31.8	1,262.3 ± 31.8	1,253.37 ± 31.6	1,269.16 ± 31.5	0.099	.983	.004
Prospective consumption	873.2 ± 27.4	901.0 ± 27.9	833.7 ± 26.9	890.5 ± 28.8	833.4 ± 27.4 ± 26.9	0.135	.969	.005
Amount of food that could be consumed	821.0 ± 25.0	853.5 ± 25.6	819.0 ± 25.0	842.9 ± 25.8	791.2 ± 24.8	0.085	.987	.003
Desire for sugary snack	686.2 ± 16.2	731.7 ± 16.9	687.6 ± 16.2	733.2 ± 16.9	736.4 ± 16.9	0.446	.775	.016

Note. AUC: area under curve; VAS: visual analogue scale; SEM: standard error of the mean; CON: the control day on which no music was playing.

Mean AUC data of VAS scores (±SEM) recorded during lunch, N = 35. Analysis of variance (ANOVA) indicated that there were no statistically significant differences between the study groups.

Total and individual energy intakes from the pasta and drink during the open buffet lunch are shown in Figure 2. Total energy intake and energy intake from pasta and drink were similar for all groups, F(4, 169) = 0.642, p = .633, η²_p = .015; F(4, 169) = 0.637, p = .637, η²_p = .015, and F(4, 169) = 0.999, p = .410, η²_p = .023, respectively. Table 2 shows the POMS scores that were recorded during the lunch. Mood states of tension-anxiety, depression-dejection, anger-hostility, vigor-activity, and fatigue-inertia did not exhibit different scores between the test days (p = .633). Interestingly, the confusion-bewilderment component of POMS was significantly different between groups (p = .017) and had a medium effect size (η²_p = .068). Post-hoc analysis showed that confusion-bewilderment was significantly lower on the 80 dB R test day in comparison with CON, 60 dB C, 80 dB C, and 60 dB R (p = .016).

Figure 2.

Mean Energy Intake Scores (±SEM) During the Test Days. ANOVA Indicated That There Were no Statistically Significant Differences Between the Study Groups for Total Energy, Energy From Pasta, or Energy From Beverages.

Table 2.

POMS Data of the Participants.

Mood	Study groups					F	P	η²_p
Mood	CON	60 dB C	80 dB C	60 dB R	80 dB R	F	P	η²_p
Tension-anxiety	9.09 ± .088	8.02 ± 0.69	8.09 ± 0.87	8.21 ± 0.69	8.09 ± 1.04	0.276	.893	.006
Depression-dejection	7.51 ± 1.33	5.60 ± 1.20	5.69 ± 1.03	7.38 ± 1.33	4.77 ± 0.90	1.054	.381	.024
Anger-hostility	6.14 ± 1.16	4.17 ± 0.92	5.54 ± 1.03	6.38 ± 1.13	4.63 ± 0.89	0.495	.495	.020
Vigor-activity	13.97 ± 0.79	13.11 ± 0.93	11.31 ± 0.97	12.02 ± 0.89	12.37 ± 1.07	1.195	.315	.028
Fatigue-inertia	7.00 ± 0.83	6.03 ± 0.71	5.97 ± 0.79	6.53 ± 0.97	4.77 ± 0.68	1.083	.367	.025
Confusion-bewilderment^a	7.40 ± 0.69^a	4.97 ± 0.54^a	5.34 ± 0.65^a	5.47 ± 0.64^a	4.60 ± 0.57^b	3.092	.017	.068
Total Score	23.17 ± 4.16	15.69 ± 3.58	19.31 ± 3.66	21.94 ± 4.53	14.49 ± 3.21	0.971	.425	.022

Note. POMS: profile of mood states; SEM: standard error of the mean; CON: the control day on which no music was playing.

Mean POMS scores (±SEM) recorded during lunch.

Analysis of variance (ANOVA) indicated that confusion-bewilderment component of POMS was significantly different (p = .017).

Discussion

The current study was designed to evaluate whether participants’ short-term energy intake, satiety-related subjective sensory outcomes, and mood states can be influenced by listening to different genres and volumes of background music. No significant effect of background Western classical or rock music played at 60 or 80 dB was observed on the energy intake and satiety-related VAS scores of the participants. Although this is the first study to have investigated the effects of music genre and volume within the same study design, the outcomes obtained from this experiment exhibited similar results to those of other studies examining the influence of background music on energy intake in laboratory settings (Mamalaki et al., 2017; Mekhmoukh et al., 2012; Péneau et al., 2009). Other studies that reported significantly increased food or drink intake with different background music genres or volumes appear to have been conducted in natural settings (McElrea & Standing, 1992; Roballey et al., 1985). A recent systematic review and meta-analysis that included studies conducted primarily in private locations or laboratories also reported that music-related features such as volume, tempo, and genre did not show a moderating effect between music and food intake (Cui et al., 2021). The present study provides additional evidence regarding the effects of background music on appetite and satiety-related measures with respect to experimental settings, the style of music played, the sound level, and how the music is heard.

In the current study, participants were tested in a controlled laboratory environment in order to measure food intake, subjective satiety response, and mood states more accurately. However, music has been one of the most important environmental components in dining environments and some of the previous research conducted in this field has therefore been focused on consumers’ eating attitudes in restaurants or bars. In a laboratory setting, the participants are guided to perform specific activities for the research (for instance, filling in the forms or recalling the eating time) in a controlled environment which can suppress eating behavior but natural settings such as restaurants, bars, or dining places lack of this “controlled atmosphere” and allow participants to act without any guidance and consideration about their eating behavior. This may result in consuming more food in natural environments. Therefore, one explanation of the controversial outcomes related to the influence of background music on food intake obtained from these studies could be the different settings of the experiments. Stroebele and de Castro (2006) reported that there was a tendency to consume higher amounts of food and fat when music was played in restaurants according to the participants’ dietary intake data gathered by using diet-diary method. This result was similar to a previous finding in which individuals were tested in a service environment by measuring their drink consumption (Stafford & Dodd, 2013). One of the suggested mechanisms that associate increased consumption with background music in service environments is the distraction of participants from their meals (Kaiser et al., 2016). Accordingly, participants can be distracted by the influence of music, their perception of satiety modifies, and thus consumption increases. It is possible that the participants in the current study may have been unresponsive to these factors as they performed the study in a laboratory setting. This could be an important contributor to the observed results in the present study and other studies that were conducted in similar settings.

Due to the sophisticated nature of music, many of its features, such as genre, can also affect eating behavior. In the present study, the two different music genres played were classical and rock music. These genres were selected as they have previously appeared to evoke distinct emotions and induce different feelings (Rentfrow & Gosling, 2003). However, these two distinct music genres did not influence the subjective VAS scores or food intake in the current study. Fiegel et al. (2014), reported that listening to jazz and hip-hop music changed the overall impression for food stimuli but classical music did not exert this effect. Fiegel et al. (2014), suggested that music familiarity was considered as an influential factor for this outcome. For instance, a known and enjoyed music may become less pleasant if it is listened more frequently. Consequently, this might interact with food stimuli and affect the overall impression. In the present study, candidates with extreme dislikes for any particular type of music were excluded but familiarity with the played music genres was not questioned. It was considered that this situation may help to generate a study group whose musical taste consisted of different aspects of musical stimuli that resembles real life. As the two musical pieces on the CDs used in this study were well known and popular, it is also possible that the participants felt familiarity with the background music and could not display apparent differences in food intake. This situation can be explained by Berlyne’s theory of aesthetics which states that individuals’ pleasure in response to a stimulus will increase with increased complexity and pleasure starts to decline after increased complexity (Messinger, 1998). It is noteworthy that the overall impression of food stimuli and the measurement of actual energy intake are two distinct parameters and they can indicate different outcomes. It would therefore be interesting to explore the interaction between food stimuli, energy intake, and familiar or unfamiliar musical pieces in future studies in order to understand the exact links.

Although a universal consensus has not been established on the definition of music genre, studies in this field enable to categorize music to different dimensions (Zentner et al., 2008). Based on 3,500 individuals’ musical preference, Rentfrow and Gosling (2003) reported that rock music belonged to an intense and rebellious musical dimension whereas classical music belonged to the reflective and complex dimension. These dimensions were differentiated according to their complexity, emotional valence, and energy levels. The musical pieces used in this study (Vivaldi’s Four Seasons as classical music and Queen’s Greatest Hits as rock music) were selected as they belong to distinct music genres with different dimensions. The emotional dimensions of background music such as valence and arousal have been shown to influence food preferences; as Reinoso-Carvalho et al. (2019) showed that participants liked beer more when listening to music associated with positive emotion, whereas the same beer was rated as more bitter when the participants listened to music associated with negative emotion. Similarly, another study found that cookies tasted with a pleasant background music were reported as better than those tasted with an unpleasant background music when presented in differing orders (Ziv, 2018). In the current study, participants’ positive or negative feelings related to the played musical pieces were not questioned, although potential participants with extreme dislikes for any specific genre of music were excluded from the study. The null results of this study can be attributable to this approach but a different study also did not identify a relationship between listening to songs with different emotion-arousing potential and the amount of food consumed (Kaiser et al., 2016). More experimental studies are therefore needed to confirm the present findings related to the emotional dimensions of music and food intake.

Volume, another component of music stimulus, might also play a role in eating behavior. Previous research examining this association reported that high-volume music increased drink consumption (Guéguen et al., 2008). Although the two volumes studied in the present research were reported to be clearly audible and most frequently played in bar or restaurant settings (Mamalaki et al., 2017), they did not exert a significant difference on food intake or subjective VAS response. Once again, the absence of significant outcomes in the case of the current study can be attributable to the experimental setting. Novak et al. (2010) reported that classical music and ambient noise in the range of 62 to 67 dB in a restaurant increased the dining pleasure and overall consumer satisfaction in 19- to 27-year-old patrons. Although the amount of food consumed was not measured in that study, the specified music volumes indicate that relatively lower music volume with ambient noise was associated with overall dining pleasure. In accordance with these results, one study reported that 55 dB of music volume led to increased sales of healthy foods, whereas 70 dB of music volume led to increased purchase of unhealthy foods (Biswas et al., 2019). Future complementary studies should continue to assess the influence of the wide range of music volumes on eating behavior in different experimental settings. It is also important to note that in order to clarify the relationship between music volumes and eating behavior, parameters related to both pleasantness and actual food intake should be measured simultaneously.

Particular aspects of hearing music such as differences between using earplugs, headphones, or listening to music without individual hearing devices have not been investigated rigorously. Kaiser et al. (2016) compared the conditions of eating when listening to background music through loudspeakers to headphones but no difference was found in food intake. Mekhmoukh et al. (2012) examined the effect of music on meal intake in overweight and normal-weight adolescents while they listened to their MP3 audio device during the meal and suggested that how the music was heard could be a confounding factor. Indeed, the previous studies examining the link between music and eating behavior have used different approaches regarding this issue. In the current study and another study carried out in a laboratory setting (Mamalaki et al., 2017), the music was played through an impersonal device, whereas in other studies it was played through individual devices (Kantono et al., 2016; Péneau et al., 2009). These discrepancies are difficult to explain as these studies introduced controversial results about the influence of listening music on food intake. In addition, a retrospective study reporting increased energy intake with the presence of music did not specify how the music was listened to (Lock et al., 2016). Further studies are therefore needed to determine whether the way in which the music is heard could be influential on eating behavior.

This study also hypothesized that having lunch with different genres of music being played would alter the mood state of the participants. Interestingly, the confusion and bewilderment component of POMS was significantly lower on the 80 dB R test day. It is difficult to interpret this finding as none of the previous studies exploring the effect of music on eating behavior have measured POMS. Even so, the current results did not indicate a significant difference in food intake and VAS parameters. This result can therefore simply be attributable to the participants’ prior familiarity with the music. It can be suggested that participants might have felt more comfortable with 80 dB R and that this in turn decreased their confusion-bewilderment in a laboratory environment. It is also possible that 80 dB R would have resembled the natural environment of participants. Indeed, all of the total scores of POMS were lower on the 4 test days with background music.

The crossover design and the controlled laboratory setting constitute the strengths of the present study but there are also limitations worth noting. Although the laboratory setting enables the observed parameters to be recorded more accurately, this was different from a complex real-world setting. For instance, throughout the study period, the participants were asked to limit their social interaction as much as possible. In addition, this study involved only a restricted number of female participants of a particular age range in order to set up a more homogenized population. Conducting similar experiments with larger sample sizes including both women and men or with different clinical groups could therefore help to extend the current findings to the general population. Furthermore, in order to elucidate the mechanisms of why and how music can affect eating behavior, future studies should measure further biochemical and psychological aspects of musical effect along with nutritional status.

Conclusion

The background music genre and volume during lunch did not influence satiety-related subjective VAS scores or food intake in the present study. Music surrounds our lives in a variety of ways in different shapes and forms. For many of us, music is an indispensable element that helps to increase happiness, strengthen emotions, and focus attention in several activities. Hence, at some point it is inevitable to neglect the effects of ambient music on eating behavior. As the need for efficient strategies for healthy eating behavior is evident as self-regulation of eating behavior becomes increasingly difficult in modern societies, future studies are needed to achieve deeper insights into the interactions between music components and nutrition.

Footnotes

Acknowledgements

The authors thank all the participants involved in this research.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Cansu Çetin

Aslı Akyol

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