Evidence map›Paper›PMID 40421074›Full record

ReviewPatient preference and adherence2025

Assessing Previous Strategies and Presenting a Novel Smart Glasses to Enhance Adherence to Amblyopia Therapy in Children.

Saeed Aljohani

Abstract readReview
In one paragraph

Review in Patient preference and adherence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

1 author.

Saeed AljohaniDepartment of Optometry, College of Applied Medical Sciences, Qassim University, Qassim, Buraydah, Saudi Arabia.ORCID 0000-0002-5373-023X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amblyopia treatment in children, often involving patching or atropine, faces significant challenges with adherence. Adherence to patching is often poor due to discomfort and psychosocial factors such as social stigma, while adherence data for atropine treatment remains scarce, hindering a clear understanding of patients' adherence in real-world settings. This review assesses both traditional methods and alternative strategies aimed at improving adherence, including Bangerter filters, binocular therapies, intermittent occlusion, and perceptual learning. While these alternatives help reduce the treatment burden, they do not consistently outperform conventional methods in improving visual outcomes and still face notable adherence challenges, especially in older children. Educational interventions, such as cartoons and motivational tools, show promise in improving adherence, especially in low-adherence populations, but their long-term effectiveness has yet to be established. Digital therapies such as Luminopia and CureSight represent promising alternatives to traditional amblyopia treatments, with preliminary evidence indicating improvements in visual outcomes and adherence. Nevertheless, further research is necessary to determine their efficacy and how they compare to established methods. Building on this, we hypothesize that the AmblySmart glasses, a novel technology that integrates with smart devices, could further improve adherence by linking treatment to children's screen time. However, further studies are needed to investigate this technology's effectiveness and practicality compared to traditional methods. Overall, this review highlights the importance of developing innovative approaches to optimize adherence and improve treatment outcomes in amblyopic children.

Indexed as

adherenceamblyopiaatropinepatchingreviewtreatment outcome

Identifiers

PMID40421074
PMCPMC12104753

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.