Evidence map›Paper›PMID 39650790›Full record

ArticleInternational journal of general medicine2024

Mapping Knowledge Landscapes and Evolving Trends of Clinical Hypnotherapy Practice: A Bibliometrics-Based Visualization Analysis.

Fei-Yi Zhao, Li Li, Peijie Xu, Gerard A Kennedy, Zhen Zheng, Yan-Mei Wang, Wen-Jing Zhang, Li-Ping Yue, Yuen-Shan Ho, Qiang-Qiang Fu and 1 more

Abstract read
In one paragraph

Article in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
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

11 authors.

Fei-Yi Zhao *Department of Nursing, School of International Medical Technology, Shanghai Sanda University, Shanghai, 201209, People's Republic of China.
Li Li *Shanghai Changning Center for Disease Control and Prevention, Shanghai, 200335, People's Republic of China.
Peijie Xu *School of Computing Technologies, RMIT University, Melbourne, VIC, 3000, Australia.
Gerard A Kennedy *School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia.ORCID 0000-0002-4419-050X
Zhen ZhengSchool of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia.
Yan-Mei WangShanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China.
Wen-Jing ZhangShanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China.
Li-Ping YueDepartment of Nursing, School of International Medical Technology, Shanghai Sanda University, Shanghai, 201209, People's Republic of China.
Yuen-Shan HoSchool of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, People's Republic of China.
Qiang-Qiang FuYangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, People's Republic of China.ORCID 0000-0002-2369-1508
Russell ConduitSchool of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia.ORCID 0000-0001-9356-6844

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Increasing interest in hypnotherapy's application for a wide range of health conditions has spurred a rise in global research and publications. This study aims to visualize development patterns and current research hotspots in clinical hypnotherapy practice using scientometric methods, and to predict future research directions based on the keyword trending topics analysis. Methods: Data on hypnotherapy applications and mechanisms in clinical settings between 1994 and 2023 were gathered from Scopus, Web of Science, and PubMed, followed by analysis and visualization using the VOSviewer, Results: A total of 1,549 publications were examined, indicating a steady annual increase with an average growth rate of 8.5%, reaching a high of 134 publications in 2022. The United States was the primary research hub. Collectively, 1,464 distinct institutions involving 3,195 scholars contributed to this research theme. Collaboration was predominantly confined to the same country, institution, and/or research team. High-frequency keywords included "Pain", "Irritable Bowel Syndrome (IBS)", and "Anxiety". Systematic review and/or meta-analysis have emerged as favored research methods. fMRI and EEG were commonly used techniques for exploring the neuropsychological mechanisms underlying hypnotherapy. "Self-Hypnosis", "Virtual Reality", and "Meditation" were predicted as trending topics, indicating that patients' self-managed hypnosis practice, virtual reality hypnotherapy, and exploration of the variations in mechanisms between meditation and hypnotherapy might be emerging topics and/or future key research directions within the current field. Conclusion: The use of hypnotherapy for diverse clinical issues, particularly pain, IBS, and comorbid anxiety, is garnering global attention. The evidence-based approach is widely used to assess the quality of clinical evidence for hypnotherapy. Researchers are keen on innovating traditional hetero-hypnosis, with a shift towards more cost-effective self-hypnosis and immersive virtual reality hypnotherapy. Promoting and reinforcing collaborative research efforts across countries, institutions, and teams is warranted.

Indexed as

anxietyCitespacecomplementary and alternative medicinehypnosisirritable bowel syndromepainscientometricsVOSviewer

Identifiers

PMID39650790
PMCPMC11625437

What OpenQuestion holds

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Registered trials

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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.