Contents

• Introduction
• Literature Review
• Methods
• Results
• Conclusions
• Limitations and future directions

Introduction

• Factors influencing food intake

  • Internal (hormones, Drewnowski & Bellisle, 2003; blood glucose; Mayer, 1996; genes, de Castro, 2004b, 2004a)

  • External (environmental factors; Bell & Pliner, 2003, p. 22003; de Castro & Plunkett, 2002; Rozin & Tuorila, 1993; Stroebele & De Castro, 2004; Wansink, 2004)

    • Environmental factors: eating environments (e.g., atmospherics, the effort of obtaining food, social interactions, and eating distractions) and food environments (e.g., its salience, structure, and size)

• Music & food intake

  • An indirect path (arousal & distraction; sensation transference effect)
  • Inconsistent findings
  • Potential moderators

Literature Review

• Volume of Music

  • Louder music is related to higher consumption of beverage and alcohol (Guéguen et al., 2008; McCarron & Tierney, 1989; Stafford & Dodd, 2013)

  • No effect of volume on food intake (Stroebele & de Castro, 2006)

• Type of music

  • Rock music increases consumption of alcohol (Lindman et al., 1987)

  • Classical music seems to be helpful for reducing intake of savory foods (Hussain et al., 2020)

  • Spend more money on restaurant when surrounded with classical or jazz music (North et al., 2003)

  • Classical music can increase food enjoyment (Novak et al., 2010)

    • Food enjoyment is positively related to food intake (Robinson et al., 2012)

Literature Review

• Speed of music

  • High tempo songs may elicit high energy intake (Milliman, 1986; Roballey et al., 1985; Wansink & Van Ittersum, 2012)

    • Tempo is related to the speed of food intake, which is relevant to the amount of food intake (Ohkuma et al., 2015)
    • Chewing intensity (the number of bites) increases with higher speed music (Roballey et al., 1985)

  • Slow music decreases eating rate and increases expense (Caldwell & Hibbert, 2002; Milliman, 1986)

• Sample size (Stroebele & de Castro, 2006)

• Experimental setting (Stroebele & de Castro, 2006)

Methods

Literature Search

• Time period: prior to December 2020

• Databases

  • PsycINFO, Web of Science, PubMed, and ProQuest Dissertations & Theses (PQDT)
  • Additional: Google Scholar

• Keywords (music-related)

  • music OR musical experience OR musical preference OR musical skills OR musical abilities OR auditory discrimination OR music interventions OR effects of music OR musicians OR lullaby OR sing OR song OR listening OR harp OR auditory stimulation

• Keywords (food intake-related)

  • food intake OR eating OR food consumption OR ingestion OR food choice OR diet OR appetite OR satiety OR satiation OR expected satiety OR appetite OR hunger OR fullness OR sensory specific satiety OR energy intake OR food behavio*r OR eating behavio*r

Methods

Selection Criteria

• Inclusion criteria

  • The article must have been published in English;
  • The study must have measured the amount of food consumed by participants/energy intake;
  • Surveys and experiments were both included;
  • Ages of participants are not limited.

• Exclusion criteria

  • Were only abstracts without sufficient information to code;
  • Were theoretical review papers;
  • Duplicated a sample from another study;
  • Were studies that combined two or more approaches (e.g., music plus videos).

Methods

Selection Criteria

• English articles only

  • Language restrictions won’t lead to systematic bias (Morrison et al., 2012).

• Unpublished articles (dissertations)

  • Rare overestimating treatment effect (Schmucker et al., 2017)
  • “Time, effort and cost”
  • Easy access, low bias and complete demonstration of findings (Moyer et al., 2010)
  • A better representation of missing study (Ferguson & Brannick, 2012)

Methods

Coding of Studies (two coders; adequate intercoder reliability)

• Name of the author
• Publication year;
• Type of publication (journal or dissertation);
• Country (America or Europe);
• Experimental design (between group, within group or mixed design);
• Type of music (pop, classical, rock or other);
• Volume of music (high volume: higher or equals 68dB; low volume: lower than 68dB);
• Source of sample (high school, university, clinical context or other source);

Methods

Coding of Studies

• Meal duration (autonomous or controlled);
• Type of food (solid, liquid or mixed);
• Experimental setting (lab, natural environment or other settings);
• Number of participants;
• Number and proportion of male participants;
• Proportion of white participants;
• Mean of age and body mass index (BMI) of participants;
• Number of participants, mean and standard deviation of food intake for two groups (i.e., music-on group vs. music-off group).

Methods

Coding of Studies

• Calculation of effect sizes (Hedge’s g)

  • Effect size calculator

    • M & SD
    • SEM, F, df, Chi-square, beta

  • Directions of g

    • Positive
    • Negative

Methods

Quality Assessment

• Appraisers

  • Two students

• Tool

  • An adapted scale (Rhodes et al., 2019)
  • 6 items (sample item: “Did the study describe the participant eligibility criteria?”)
  • 6 aspects of study quality (e.g., sampling, power, validity)
  • Each response scale ranges from 1-6 (1-2 = low, 3-4 = medium, 5-6 = high)
  • Methodological appraisal score (score/36*100)

Methods

Data Analyses

• R 4.0.0 (Team, 2020) & metafor (Viechtbauer, 2010)

  • Three-level random-effects model (Assink & Wibbelink, 2016; Cheung, 2014; Van den Noortgate et al., 2013)

  • Heterogeneity is caused by three levels of variance

    • The sampling variance of effect sizes (Lv1)
    • The variance between effect sizes of the same sample (Lv2)
    • Variance between studies included in our analysis (Lv3)

Methods

Data Analyses

• R 4.0.0 (Team, 2020) & metafor (Viechtbauer, 2010)

  • Outlier detection

    • Baujat plot and the influential case diagnostics (Viechtbauer, 2010; Viechtbauer & Cheung, 2010)

  • Overall effect size (Cohen, 1992)

    • 0.20 (small)
    • 0.50 (medium)
    • 0.80 (large)

Methods

Data Analyses

• R 4.0.0 (Team, 2020) & metafor (Viechtbauer, 2010)

  • Heterogeneity

    • Q statistics

  • Publication bias

    • Funnel plot

      • Plot’s symmetry

    • Begg’s rank test (Begg & Mazumdar, 1994)

      • A non-significant p value indicates insufficient publication bias

Results

Flow Diagram of Search Strategy and Study Selection

Results

Descriptive Characteristics of Included Studies

• Publication dates (1989 to 2020)
• 631 participants (252 males and 379 females)
• 41 effect sizes: -0.78 (Hussain et al., 2020) to 3.15 (McCarron & Tierney, 1989)

Quality Assessment of Included Studies

• Quality appraisal scores: 50 to 100.0 %

Outlier Detection

• 3 outliers (number of effect size = 25: McCarron & Tierney, 1989; number of effect size = 39 & 40: Mekhmoukh et al., 2012)

Results

Quality Assessment Scoring by Study

Results

Outlier Detection - Baujat Plots

Results

Overall Analysis

• 8 studies with 38 effect sizes: -0.78 (Hussain et al., 2020) to 2.12 (Mekhmoukh et al., 2012)

• Overall effect size

  • g = 0.191 (95% CI: -.003, .386; SE = 0.10, t = 1.99, p = .054)
  • Q(37) = 71.46 (p < .001)

Publication Bias

• No evidence for publication bias (Kendall’s tau = 0.0712, p = .530)

Moderator Analysis

• BMI [F(1, 21) = 5.11, p = .035; β₁ = .114, p < .05]

Results

Funnel Plot for Checking Publication Bias

Conclusions

• The relationship between music and food intake was small and positive

• BMI was the only significant moderator

  • Positive relationship
  • Underlying mechanism

• Why were other moderators not significant?

  • Coding problems

    • Music type (precise or rough)
    • Tempo (no enough information)

  • Experimental settings

    • Restaurants vs. private places or labs

Limitations and future directions

• Limited number of included articles

• Eating in restaurants vs. randomized controlled trials

References

References

References