ArticleFrontiers in psychology2026
The conflict between need and fear: how privacy concerns moderate the influence of depression on university students' acceptance of AI music therapy.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: AI-driven music therapy offers a promising, accessible digital intervention for the growing mental health crisis in universities. The "Deficiency Compensation Hypothesis" suggests that depression may drive students toward such digital help-seeking. However, the inherent data sensitivity of AI tools triggers the "Privacy Calculus," potentially inhibiting adoption. This study investigates the interplay between depression severity, privacy concerns, and the intention to use AI music therapy among university students. Methods: A cross-sectional survey was conducted with 612 university students in China. The study measured depression levels (PHQ-8), AI-specific privacy concerns, perceived usefulness, and intention to use. A hierarchical regression model with moderation analysis was employed to examine whether privacy concerns weaken the association between distress and help-seeking motivation. Results: Participants exhibited mild depression on average (PHQ-8 Mean = 6.07). Regression analysis revealed that depression positively predicted the intention to use AI music therapy ( Conclusion: The findings highlight a critical paradox in digital mental health: while depressive symptoms are positively associated with students' intention to seek AI-based help, privacy fears can significantly attenuate this association. For highly privacy-sensitive individuals, the need for therapeutic relief is overridden by the fear of surveillance. Consequently, developers and universities must prioritize "privacy by design" and transparent trust mechanisms, rather than relying solely on algorithmic precision, to ensure these tools can serve as effective emotional support for vulnerable students.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.