Evidence map›Paper›PMID 40055942›Full record

ReviewFuture science OA2025

Computer vision syndrome: a comprehensive literature review.

Fares Kahal, Ahmad Al Darra, André Torbey

Abstract readReview
In one paragraph

Review in Future science OA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

3 authors.

Fares KahalFaculty of Medicine, Syrian Private University, Damascus, Syria.ORCID 0000-0003-4607-8316
Ahmad Al DarraFaculty of Medicine, Syrian Private University, Damascus, Syria.ORCID 0009-0002-6671-5562
André TorbeyFaculty of Medicine, Syrian Private University, Damascus, Syria.ORCID 0000-0003-4115-4690

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computer Vision Syndrome is a growing health concern in the digital age, with a reported prevalence of 69.0%. It is caused by screen-related, environmental, ergonomic, and physiological factors, affecting diverse demographics. The COVID-19 pandemic significantly amplified CVS due to increased screen time for remote work, online learning, and social media use, with studies reporting symptoms in up to 74% of individuals. Unique visual challenges from digital screens, including reduced clarity and glare, exacerbate symptoms like dry eyes and discomfort, especially in those with uncorrected vision. Understanding CVS is crucial for mitigating its impact through effective prevention and management strategies. This study explores the causes, diagnosis, management, and prevention strategies of CVS by synthesizing recent findings from optometry, occupational health, digital health, and ergonomics. It also highlights emerging trends such as AI, wearables, and augmented reality while providing practical management strategies. A narrative review of literature from 2014 to 2024 was conducted, focusing on PubMed-indexed, peer-reviewed articles, including meta-analyses and systematic reviews, with priority given to recent, highly cited studies.

Indexed as

Artificial intelligence (AI) in eye healthcomputer vision syndromedigital healthprevalenceprevention and managementrisk factorssymptomswearable technologies

Identifiers

PMID40055942
PMCPMC11901492

What OpenQuestion holds

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LicenceCC BY-NC
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.