Evidence map›Paper›PMID 40904827›Full record

SynthesisFrontiers in neurology2025

Effects of TCM aromatherapy on post-stroke depression: a meta-analysis of randomized controlled trials.

Jing Chen, Yaxuan Yan, Jindan Lv

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jing ChenThe Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, Guangzhou, China.
Yaxuan YanThe Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, Guangzhou, China.
Jindan LvThe Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Traditional Chinese Medicine(TCM) aromatherapy has been gradually applied to patients with post-stroke depression (PSD), but uncertainty remains. To systematically evaluate the effectiveness and safety of TCM aromatherapy in the treatment of post-stroke depression, we aim to conclude by synthesizing randomized controlled trials (RCTs). Methods: We searched the following databases in English and Chinese: China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature (CBM), Wanfang Database, VIP Database, Chaoxing, PubMed, and The Cochrane Library for randomized controlled trials on the effects of TCM aromatherapy intervention in patients with post-stroke depression. The search period was from the inception of each database to February 20, 2025. After evaluation according to the Cochrane Handbook for Systematic Reviews of Interventions. Meta-analysis was performed using RevMan 5.4. Results: Ten RCTs involving 614 patients were included. The meta-analysis indicates that, compared to the control group, TCM aromatherapy can ameliorate depressive symptoms in patients with PSD [SMD = 0.53, 95% CI (0.19, 0.87), Conclusion: The results of this study indicate that TCM aromatherapy alone or combined with other therapies appears to be effective in improving depression symptoms of stroke survivors, but high-quality evidence evaluating TCM aromatherapy for PSD is still needed. Systematic reviews registration: This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database in February 2025. The registration number is CRD42025642863.

Indexed as

essentail oilmeta-analysispost-stroke depressiontherapytraditional Chinese medicine aromatherapy

Identifiers

PMID40904827
PMCPMC12403004

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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.