Author(s) :

Angela Chen.

Volume/Issue :

Abstract :

Mental health disorders, such as anxiety, depression, and insomnia, have become increasingly prevalent. This paper explores the relationship between mental health conditions and music-related factors using a dataset which includes variables such as music genres and listening hours. The study employs two types of statistical models—ordinal logit regression and count regression—to investigate the impact of music genres, listening habits, and demographic variables on mental health and music consumption patterns. In the ordinal logit analysis, key predictors, including classical music preferences and favored genres, were positively associated with improvements in mental health, while listening to music while working and certain genres, such as R&B, negatively affected mental health outcomes. The count-oriented regression models, both negative binomial and Poisson models, used to assess the factors influencing daily music listening time, revealed that listening while working and being a composer were strongly associated with increased listening time, while being an instrumentalist decreased it. Psychological conditions like obsessive-compulsive disorder and insomnia were positively correlated with music listening hours, suggesting a potential coping mechanism for these conditions. Overall, this study provides valuable insights into how music preferences, mental health conditions, and listening behaviors interact, offering evidence-based recommendations for integrating music therapy into mental health treatments.