The Recursive Relationship Between Personalized Music Choice and Adolescent Mental Wellness: An Empirical Study

Overview

Music streaming has enabled music choice to transition from a collective social listening experience to a personalized one. This research, for the first time, relates the two facets of music choice when in a specific mental wellness state with the impact of music chosen on mental wellness. Adolescents (N = 80) were asked the kind of music they would choose when in specific mental wellness states. They were also asked about the impact different types of music had on them. It was found that there is a recursive relationship between music choice and music impact in various mental wellness states. For example, in a sad/hurt state, listeners choose to listen to slow and soft music, which in turn makes them sadder; in a happy/excited state listeners choose to listen to loud and fast music, which increases excitement. This recursive relationship reinforced by the trend towards music personalization may exacerbate mood volatility with the possibility of pushing the listener to become excessively happy (leading to risky decisions, such as substance abuse) or excessively sad (with mental health challenges like depression). Future research to develop a user-centered adaptive mobile health app that technologically augments the listener's chosen music in each mental state, with other songs from their library can be an effective mood regulation strategy. Given the ready availability of music streaming on mobile phones, and with self-recorded mental wellness data now available in iOS17, this strategy can become an essential continuous mental health intervention strategy to support adolescent mental health.

Awards (2)

• American Psychological Association: Complimentary student affiliate memberships
• American Psychological Association: Third Award of $500 $500

Competition history

• ISEF 2024 Behavioral and Social Sciences · Entry BEHA048 · Los Angeles, California, United States

Resources

• ISEF abstract page ↗

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