Evidence map›Paper›PMID 42707490›Full record

SynthesisFrontiers in public health2026

Digital health interventions for reducing occupational burnout in nurses: a systematic review and meta-analysis.

Yange Yang, Jinpeng Wen, Hejia Wan, Qiaoju Yang, Jiayi Guan, Lijun Min, Songbo Jia, Zhenzhen Wang, Joston Gary

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Frontiers in public health, 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

9 authors.

Yange Yang *School of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Jinpeng Wen *School of Journalism and Communication, South China University of Technology, Guangzhou, China.
Hejia Wan *School of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Qiaoju YangSchool of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Jiayi GuanSchool of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Lijun MinSchool of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Songbo JiaSchool of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Zhenzhen WangSchool of Nursing, Henan University of Chinese Medicine (Wisdom Health Nursing School), Zhengzhou, Henan, China.
Joston Gary *Department of Management and Engineering, Linköping University, Linköping, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To systematically evaluate and meta-analyze the effectiveness of digital health interventions (DHIs) in reducing occupational burnout among nurses and nursing staff compared with usual care, waitlist control, or non-digital interventions. Methods: Following PRISMA 2020 guidelines, six electronic databases (PubMed/MEDLINE, CINAHL, Embase, Web of Science, PsycINFO, and Scopus) were searched from January 2015 to March 2025 for randomized controlled trials and quasi-experimental studies. Risk of bias was assessed using Cochrane RoB 2 and JBI checklists. Random-effects meta-analysis using the DerSimonian-Laird method, pre-specified subgroup and sensitivity analyses, publication-bias assessment, and GRADE certainty assessment were performed. Results: Thirty-seven studies encompassing approximately 8,450 nurses and nursing staff across 14 countries were included, of which 28 provided data for quantitative synthesis. The pooled standardized mean difference indicated a statistically significant moderate reduction in burnout ( Conclusion: DHIs, particularly structured and guided web-based CBT/ACT programs, were associated with moderate reductions in occupational burnout among nurses and nursing staff. Early evidence for AI-tailored interventions is promising but requires independent replication. The findings support the integration of evidence-based DHIs into broader workforce well-being strategies that combine individual support with organizational action on the structural determinants of burnout. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261365184.

Indexed as

Burnout, ProfessionalDigital HealthNursesCognitive Behavioral TherapyHumansacceptance and commitment therapyartificial intelligenceburnoutcognitive behavioral therapydigital healthmHealthnursesoccupational health

Identifiers

PMID42707490
PMCPMC13547474

What OpenQuestion holds

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Registered trials

None linked

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.