Evidence map›Paper›PMID 39882495›Full record

SynthesisBulletin of the World Health Organization2025

Health workers' adoption of digital health technology in low- and middle-income countries: a systematic review and meta-analysis.

Minmin Wang, Kepei Huang, Xiangning Li, Xuetong Zhao, Laura Downey, Sondus Hassounah, Xiaoyun Liu, Yinzi Jin, Minghui Ren

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Bulletin of the World Health Organization, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Article
  8. Review
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  12. Accelerating Community Engagement: Measuring Results.International journal of public health · 2025
    Article
  13. Review
  14. Article
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.

Minmin WangDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing100191, China.
Kepei HuangDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing100191, China.
Xiangning LiSchool of Public Health, Imperial College London, London, England.
Xuetong ZhaoSchool of Public Health, Peking University, Beijing, China.
Laura DowneyGeorge Institute for Global Health, University of New South Wales, Sydney, Australia.
Sondus HassounahSchool of Public Health, Imperial College London, London, England.
Xiaoyun LiuChina Center for Health Development Studies, Peking University, Beijing, China.
Yinzi JinDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing100191, China.
Minghui RenDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing100191, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To conduct a systematic review and meta-analysis of the facilitators of and barriers to the acceptance and use of digital health technology by health workers in low- and middle-income countries. Methods: We searched several databases for relevant articles published until 25 April 2024. We extracted data on four unified theories of acceptance and use of technology factors (performance expectancy, effort expectancy, social influence and facilitating conditions) and six additional factors (attitude, habit, incentive, risk, trust and self-efficacy); how these affected the outcomes of behavioural intention and actual use; and the strength of association if reported. We conducted a meta-analysis of the quantitative studies. Findings: We reviewed 36 publications, 20 of which were included in our meta-analysis. We observed that performance expectancy was the most frequently reported facilitator (in 21 studies; 58.3%) and that lack of facilitating conditions was the most cited barrier (10; 27.8%). From our meta-analysis, trust ( Conclusion: Our approach of clustering the facilitators of and barriers to the acceptance and use of digital health technology from the perspective of health workers highlighted the importance of creating an enabling ecosystem. Supportive infrastructure, tailored training programmes and incentive policies should be incorporated in the implementation of digital health programmes in low- and middle-income countries.

Indexed as

Attitude of Health PersonnelDeveloping CountriesDigital TechnologyHealth PersonnelDigital HealthHumans

Identifiers

PMID39882495
PMCPMC11774224

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.