Evidence map›Paper›PMID 38155842›Full record

ArticleGeneral psychiatry2023

Uncovering potential distinctive acoustic features of healing music.

Yue Ding, Jiaqi Jing, Qihui Guo, Jiajia Zhou, Xinyao Cheng, Xiaoya Chen, Lihui Wang, Yingying Tang, Qing Fan

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PubMed Full text at PMC Publisher
In one paragraph

Article in General psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yue DingShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.ORCID 0000-0003-3662-8265
Jiaqi JingShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Qihui GuoShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Jiajia ZhouShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Xinyao ChengShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Xiaoya ChenShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Lihui WangShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Yingying TangShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Qing FanShanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Music therapy is a promising complementary intervention for addressing various mental health conditions. Despite evidence of the beneficial effects of music, the acoustic features that make music effective in therapeutic contexts remain elusive. Aims: This study aimed to identify and validate distinctive acoustic features of healing music. Methods: We constructed a healing music dataset (HMD) based on nominations from related professionals and extracted 370 acoustic features. Healing-distinctive acoustic features were identified as those that were (1) independent from genre within the HMD, (2) significantly different from music pieces in a classical music dataset (CMD) and (3) similar to pieces in a five-element music dataset (FEMD). We validated the identified features by comparing jazz pieces in the HMD with a jazz music dataset (JMD). We also examined the emotional properties of the features in a Chinese affective music system (CAMS). Results: The HMD comprised 165 pieces. Among all the acoustic features, 74.59% shared commonalities across genres, and 26.22% significantly differed between the HMD classical pieces and the CMD. The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46% of the features. The potential healing-distinctive acoustic features were identified as the standard deviation of the roughness, mean and period entropy of the third coefficient of the mel-frequency cepstral coefficients. In a three-dimensional space defined by these features, HMD's jazz pieces could be distinguished from those of the JMD. These three features could significantly predict both subjective valence and arousal ratings in the CAMS. Conclusions: The distinctive acoustic features of healing music that have been identified and validated in this study have implications for the development of artificial intelligence models for identifying therapeutic music, particularly in contexts where access to professional expertise may be limited. This study contributes to the growing body of research exploring the potential of digital technologies for healthcare interventions.

Indexed as

mental healthpsychotherapy

Identifiers

PMID38155842
PMCPMC10753712

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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 OpenQuestion graph.