Optimizing Music Therapy for Depression Treatment: Investigating the Relationship Between Music Preferences and Mental Health

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Abstract: Music therapy is a promising adjunct to depression care, yet effects likely vary with everyday listening. In a cross-sectional online survey (N = 736), we examined associations between preferred genres, self-reported typical tempo (beats per minute; BPM), and engagement mode (composing/playing/exploring vs. purely receptive) with single-item anxiety and depression (0–10). After prespecified cleaning (e.g., implausible BPM < 30 or > 300 excluded) and listwise analysis, we estimated OLS models with HC3 standard errors; genre differences used one-way ANOVA (inference restricted to adequately sized groups), reporting ω² and 95% CIs. Typical BPM showed small, positive linear associations with both anxiety and depression; the quadratic term was null and model R² values were modest. Genre effects were small at the omnibus level, and only a limited subset of pairwise contrasts remained significant after correction (e.g., Metal vs. Classical/R&B; Video-game music vs. Classical for anxiety). Active engagement showed selective, small associations (composition with lower depression) that warrant experimental tests. Findings, derived from a non-clinical, correlational dataset, argue for individualised use of music in practice and for prospective trials that manipulate engagement type and tempo within genres.
Keywords: Music Therapy, Depression, Anxiety, Music Preferences, Beats Per Minute (BPM), Active Music Engagement, Mental Health
APA Citation: Yinyue Fang (2026). Optimizing Music Therapy for Depression Treatment: Investigating the Relationship Between Music Preferences and Mental Health. International Journal of Public Health and Medical Research, 6(3), 70-82. https://doi.org/10.62051/ijphmr.v6n3.10

References

  1. Aalbers, S., Fusar-Poli, L., Freeman, R. E., Spreen, M., Ket, J. C. F., Vink, A. C., Maratos, A., Crawford, M. J., Chen, X.-J., & Gold, C. (2017). Music therapy for depression. Cochrane Database of Systematic Reviews, 2017(11), CD004517. https://doi.org/10.1002/14651858.CD004517.pub3
  2. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300.
  3. Bernardi, L., Porta, C., & Sleight, P. (2006). Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music: The importance of silence. Heart, 92(4), 445–452. https://doi.org/10.1136/hrt.2005.064600
  4. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
  5. De Witte, M., da Silva Pinho, A., Stams, G.-J. J. M., Moonen, X., Bos, A. E. R., & van Hooren, S. (2022). Music therapy for stress reduction: A systematic review and meta-analysis. Health Psychology Review, 16(1), 134–159. https://doi.org/10.1080/17437199.2020.1846580
  6. Erkkilä, J., Punkanen, M., Fachner, J., Ala-Ruona, E., Pöntiö, I., Tervaniemi, M., Vanhala, M., & Gold, C. (2011). Individual music therapy for depression: Randomised controlled trial. The British Journal of Psychiatry, 199(2), 132–139. https://doi.org/10.1192/bjp.bp.110.085431
  7. Fancourt, D., & Finn, S. (2019). What is the evidence on the role of the arts in improving health and well-being? A scoping review (HEN Synthesis Report 67). WHO Regional Office for Europe.
  8. Gustavson, D. E., Stallings, M. L., Chrencik, E. C., Cornew, J. M., Klein, J. W., & Lense, M. D. (2021). Mental health and music engagement: Review, framework, and guidelines for future studies. Translational Psychiatry, 11, 92. https://doi.org/10.1038/s41398-021-01483-8
  9. Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722. https://doi.org/10.3758/BF03192961
  10. Koelsch, S. (2014). Brain correlates of music-evoked emotions. Nature Reviews Neuroscience, 15(3), 170–180. https://doi.org/10.1038/nrn3666
  11. Koelsch, S. (2020). A coordinate-based meta-analysis of music-evoked emotions. NeuroImage, 223, 117350. https://doi.org/10.1016/j.neuroimage.2020.117350
  12. Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256. https://doi.org/10.1037/0022-3514.84.6.1236
  13. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714
  14. Sharman, L., & Dingle, G. A. (2015). Extreme metal music and anger processing. Frontiers in Human Neuroscience, 9, 272. https://doi.org/10.3389/fnhum.2015.00272
  15. Tang, Q., Huang, Z., Zhou, H., & Ye, P. (2020). Effects of music therapy on depression: A meta-analysis of randomized controlled trials. PLOS ONE, 15(11), e0240862. https://doi.org/10.1371/journal.pone.0240862
  16. Tukey, J. W. (1949). Comparing individual means in the analysis of variance. Biometrics, 5(2), 99–114.
  17. Welch, B. L. (1947). The generalization of Student’s problem when several different population variances are involved. Biometrika, 34(1–2), 28–35.
  18. Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18(5), 459–482. https://doi.org/10.1002/cne.920180503