Evidence map›Paper›PMID 33681868›Full record

ArticleASSETS. Annual ACM Conference on Assistive Technologies2020

TableView: Enabling Eficient Access to Web Data Records for Screen-Magnifier Users.

Hae-Na Lee, Sami Uddin, Vikas Ashok

Abstract read
In one paragraph

Article in ASSETS. Annual ACM Conference on Assistive Technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hae-Na LeeStony Brook University.
Sami UddinOld Dominion University.
Vikas AshokOld Dominion University.

Funding

Space-Compacting Magnification Augmented with Natural Gestures and Keyboardless Text Entry for Low Vision Smartphone InteractionR01EY030085 · NEI · STATE UNIVERSITY NEW YORK STONY BROOK · PI RAMAKRISHNAN, I.V. · 2020 to 2022
$1.1M
NEI NIH HHS R01 EY030085
6 · The paper itself

Abstract

People with visual impairments typically rely on screen-magnifier assistive technology to interact with webpages. As screen-magnifier users can only view a portion of the webpage content in an enlarged form at any given time, they have to endure an inconvenient and arduous process of repeatedly moving the magnifier focus back-and-forth over different portions of the webpage in order to make comparisons between data records, e.g., comparing the available fights in a travel website based on their prices, durations, etc. To address this issue, we designed and developed TableView, a browser extension that leverages a state-of-the art information extraction method to automatically identify and extract data records and their attributes in a webpage, and subsequently presents them to a user in a compactly arranged tabular format that needs significantly less screen space compared to that currently occupied by these items in the page. This way, TableView is able to pack more items within the magnifier focus, thereby reducing the overall content area for panning, and hence making it easy for screen-magnifier users to compare different items before making their selections. A user study with 16 low vision participants showed that with TableView, the time spent on panning the data records in webpages was significantly reduced by 72.9% (avg.) compared to that with just a screen magnifier, and 66.5% compared to that with a screen magnifier using a space compaction method.

Indexed as

Empirical studies in accessibilityHuman-centered computing → Accessibility technologieslow visionscreen magnifierusabilityweb accessibility

Identifiers

PMID33681868
PMCPMC7936724

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.