Evidence map›Paper›PMID 42111593›Full record

ReviewFrontiers in psychology2026

Pandemic fatigue and associated factors: a meta-analysis using the COM-B model.

Jin Zhao, Jiayu Wang, Luo Jiang, Fuxian Li, Lili Ji, Hui Jin

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review 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.

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

6 authors.

Jin ZhaoKey Laboratory of Environmental Medicine Engineering, Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Jiayu WangNanjing University of Chinese Medicine, Nanjing, China.
Luo JiangKey Laboratory of Environmental Medicine Engineering, Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Fuxian LiKey Laboratory of Environmental Medicine Engineering, Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Lili JiJiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Hui JinKey Laboratory of Environmental Medicine Engineering, Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pandemic fatigue during the prolonged COVID-19 crisis undermines sustained engagement with protective measures and public health messaging. Existing studies provide heterogeneous prevalence estimates and disparate lists of correlates but lack a unified, theory-driven synthesis. Methods: Searches were conducted in six databases up until March 2026. This study synthesized the prevalence of pandemic fatigue and factors associated with it during COVID-19 using the COM-B model and TDF theory. Results: Twenty-three cross-sectional studies ( Conclusion: Pandemic fatigue is common and was associated with diverse capability-, opportunity-, and motivation-related factors. A COM-B-informed interpretation suggests multi-level strategies that combine skills building, supportive environments, and psychosocial support.

Indexed as

COM-B modelCOVID-19meta-analysispandemic fatigueTDF

Identifiers

PMID42111593
PMCPMC13149464

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.