SynthesisFrontiers in public health2026
Work-related musculoskeletal disorders among gig-based food delivery workers: a systematic review and meta-analysis.
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. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Occupational injuries and health risks among food delivery riders in China: research progress and prevention strategies.Frontiers in public health · 2026Pooled it
- Predation, acceleration, and loss of control: a multilevel theoretical framework for algorithmic time politics and the occupational health of platform workers.Frontiers in public health · 2026Article
- Developmental or evaluative? Understanding the impact of algorithmic evaluation on gig workers' thriving at work.Frontiers in psychology · 2026Article
- The impact of perceived algorithmic management on employees' physical and mental health: a chain mediation analysis of frustrated work autonomy and workplace AI replacement anxiety.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The digital economy has spurred gig work, especially in food delivery, which grew during COVID-19. However, gig workers face occupational hazards like traffic accidents, poor ergonomics, and unsafe conditions, leading to work-related musculoskeletal disorders (WMSDs). Studies show high rates of back and neck pain among delivery riders due to physical strain, repetitive motions, and long hours. WMSDs reduce productivity and increase healthcare costs. This review examines WMSD prevalence, risk factors, and related issues like accidents and violence among food delivery workers. Methods: We searched for relevant articles up to June 2025 from PubMed, Scopus, and Web of Science. Two independent reviewers extracted data from the selected studies, including baseline information, outcomes, and prevalence of WMSDs. All data analyses were performed using R version 4.3.3. Results: After removing 1,013 duplicate records, we retained 1,279 for screening. Following a thorough review, we identified 23 eligible entries for inclusion in our study. As per our analysis, delivery workers face high injury prevalence: lower back (43%), shoulder (39%), neck (30%), upper back (24%), and RTA (25%). Risk factors include gig economy systemic vulnerabilities, prolonged static postures, vibration exposure, open-door vehicles, and dangerous traffic practices. Different forms of violence (physical, verbal, and psychological) affected delivery workers, while exploitation and discrimination were particularly evident among minorities. Conclusion: This review demonstrated a high burden of WMSDs among delivery workers, who face serious hazards like injuries, accidents, and violence due to precarious gig economy conditions, time pressures, and poor safety measures. This study provides the first quantitative pooled estimates of WMSD prevalence among food delivery workers, along with an additional narrative synthesis of traffic accidents and workplace violence.
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Registered trials
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.