Evidence map›Paper›PMID 38463538›Full record

ArticleASSETS. Annual ACM Conference on Assistive Technologies2023

Screen Magnification for Readers with Low Vision: A Study on Usability and Performance.

Meini Tang, Roberto Manduchi, Susana Chung, Raquel Prado

Open access · goldAbstract read
In one paragraph

Article in ASSETS. Annual ACM Conference on Assistive Technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Reading with Screen Magnification: Eye Movement Analysis Using Compensated Gaze Tracks.Proceedings. Eye Tracking Research & Applications Symposium · 2024
    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

4 authors at 2 institutions in 1 country.

Meini TangUniversity of California, Santa Cruz, USA.
Roberto ManduchiUniversity of California, Santa Cruz, USA.
Susana ChungUniversity of California, Berkeley, USA.
Raquel PradoUniversity of California, Santa Cruz, USA.
University of California, Santa Cruz · USUniversity of California, Berkeley · US

Funding

Gaze-contingent computer screen magnification control for people with low visionR01EY030952 · NEI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI MANDUCHI, ROBERTO · 2020 to 2023
$1.4M
NEI NIH HHS R01 EY030952
6 · The paper itself

Abstract

We present a study with 20 participants with low vision who operated two types of screen magnification (lens and full) on a laptop computer to read two types of document (text and web page). Our purposes were to comparatively assess the two magnification modalities, and to obtain some insight into how people with low vision use the mouse to control the center of magnification. These observations may inform the design of systems for the automatic control of the center of magnification. Our results show that there were no significant differences in reading performances or in subjective preferences between the two magnification modes. However, when using the lens mode, our participants adopted more consistent and uniform mouse motion patterns, while longer and more frequent pauses and shorter overall path lengths were measured using the full mode. Analysis of the distribution of gaze points (as measured by a gaze tracker) using the full mode shows that, when reading a text document, most participants preferred to move the area of interest to a specific region of the screen.

Indexed as

readingscreen magnificationvisual impairment

Identifiers

PMID38463538
PMCPMC10923554
OpenAlexW4387793515

What OpenQuestion holds

Textmetadata
LicenceTDM
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.