Evidence map›Paper›PMID 38362247›Full record

SynthesisFrontiers in psychology2024

Caffeine intake and anxiety: a meta-analysis.

Chen Liu, Licheng Wang, Chi Zhang, Ziyi Hu, Jiayi Tang, Junxian Xue, Wenchun Lu

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 4 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

28 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

7 authors.

Chen LiuSchool of International Education, Xuzhou Medical University, Xuzhou, China.
Licheng WangSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Chi ZhangSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Ziyi HuSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Jiayi TangSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Junxian XueSchool of Public Health, Xuzhou Medical University, Xuzhou, China.
Wenchun LuSchool of Management, Xuzhou Medical University, Xuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The results from studies on relationship between caffeine intake and risk of anxiety remains controversial, so we conducted a meta-analysis to summarize the evidence about the association between caffeine intake and risk of anxiety. Relevant articles were identified by researching PubMed, Web of Science, Cochrane library, Embase, CNKI, WANFANG DATA, SinoMed and VIP from the inception to December, 2022. Three investigators independently sifted through the literature, extracted the data, and evaluated the quality of the included studies based on predetermined selection criteria and assessed articles with Risk of bias assessment tool for Cochrane systematic reviews and analytical cross-sectional study quality assessment tool from JBI PACES. After assessing the quality of the literature, meta-analysis was performed using Revman 5.4 and Stata 12.0. Data were obtained from eight articles, and 546 participants from 14 studies in eight articles from healthy populations were included in the caffeine-anxiety analyses. As the scales used to assess anxiety vary in the literature, we chose standardized mean difference as the outcome indicator. In terms of overall effect, the results of the meta-analysis showed that caffeine intake increased the risk of anxiety [SMD = 0.94, 95% Cl = (0.28, 1.60),

Indexed as

anxietycaffeinecoffeehealthy populationmeta-analysis

Identifiers

PMID38362247
PMCPMC10867825

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.