How much can music listening habits tell us about mental health? An interpretable machine learning benchmark using genre preference and self-reported anxiety, depression, insomnia, and obsessive–compulsive symptoms
Year: 2026
Type: article
Abstract: Background Music listening is widely believed to index mental health, and this belief underwrites applications inferring psychological state from listening records. We estimated how much information musical preference and listening behavior carry about self-reported distress, and tested whether classification accuracy reported for a widely reused public dataset reflects discriminative skill or class imbalance. Methods Cross-sectional secondary analysis of the Music and Mental Health (MxMH) survey ( n = 736), a self-selected online convenience sample collected in 2022 with no probability sampling frame, no design weights, and no measurement of gender, geography, education, income, or clinical history. In total, 42 predictors covering genre exposure, favorite genre, streaming platform, listening hours, tempo preference, and musical engagement were modeled against four single-item, unvalidated self-anchored distress ratings, a secondary composite index, and a self-attributed judgment of whether music improves mental health. Elastic net, support vector machine, random forest, and gradient boosting models were compared against mean-prediction and majority-class baselines using nested cross-validation, corrected t -tests, permutation nulls, and Shapley additive explanations. Results Out-of-sample explanatory power was small. Random forests achieved cross-validated R 2 values of 0.068 for depression, 0.058 for the composite index, 0.032 for anxiety, and 0.006 or below for insomnia and obsessive–compulsive ratings, exceeding the mean-prediction baseline for all outcomes except insomnia ( p = 0.157). Permutation tests separated depression, anxiety, the composite, and obsessive–compulsive ratings from noise, but not insomnia ( p = 0.198). For the self-attributed effect of music, the best classifier reached 74.7% accuracy against a majority-class baseline of 74.5% ( p = 0.896), while balanced accuracy peaked at 0.551 and the Matthews correlation coefficient at 0.135. Conclusion Listening habits carry a real but very small signal about self-reported distress, and apparent accuracy on this dataset is an artifact of class imbalance. Musical preference is not a usable individual-level marker of psychological state. These estimates come from a single, heavily reused, non-representative convenience sample with unvalidated single-item outcomes and no demographic weighting variables, so they characterize this archive rather than any defined population and require independent replication before any specific effect size is treated as settled. Dataset: MxMH survey, Kaggle.
Source: Frontiers in Psychology
Authors: Yakun Li, Binxin Ren
Institution: Sichuan Normal University
Language: English
Topic: Music Therapy and Health
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223661900