ArticleFrontiers in psychology2026
A taxonomic framework of music-based intervention components to guide research: recommendations from the NIH U24 music research networks.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: As research on music and health expands across disciplines, inconsistent terminology limits communication, replication, and mechanistic discovery. The absence of a shared lexicon obscures comparisons across studies and hinders identification of the specific music-based intervention (MBI) components and mechanistic pathways responsible for change in health-related outcomes. Objective: To address this gap, a multidisciplinary working group representing four U.S. National Institutes of Health (NIH) U24 Music Research Networks developed and validated the Music-Based Intervention (MBI) Taxonomy. Methods: Using an iterative, expert consensus process, the working group developed an initial framework of MBI components, terms, and definitions. The draft taxonomy was subsequently validated, interrogated, and refined through a two-part systematic review of 200 studies, including 100 clinical MBI and 100 laboratory-based music studies, using a phased, iterative, consensus-based process. Results: The resulting taxonomy provides a hierarchical framework comprising seven core constructs of MBIs: Music Features; Person-Specific Music Factors; Engagement Mode; Music Delivery Factors; Social and Environmental Contexts; Music Selection; and Additional Non-Music Components. Across these constructs the taxonomy specifies components, subcomponents, recommended terminology, and definitions to support the conceptualization and description of mechanistic music research. Conclusion: The MBI Taxonomy aims to improve the consistency and transparency of MBI components, support mechanistic study conceptualization and design, and facilitate replication and cross-study synthesis. As a comprehensive meta-framework, it serves as a foundational tool to help overcome disciplinary silos and foster a more cohesive, rigorous body of mechanistic music research.
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