Evidence map›Paper›PMID 40876320›Full record

ArticleJournal of anxiety disorders2025

Predicting anxiety symptoms through gaze-directed attention: A mobile eye-tracking study of adolescents during a real-world speech task.

Emily Hutchinson, Erica Huynh, Mary Woody, Dev Chopra, Amelia Lint, Enoch Du, Kristy Benoit Allen, Cecile Ladouceur, Jennifer Silk

Abstract read
In one paragraph

Article in Journal of anxiety disorders, 2025. 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

9 authors.

Emily HutchinsonUniversity of Pittsburgh, Department of Psychology, United States of America. Electronic address: emh142@pitt.edu.
Erica HuynhUniversity of Pittsburgh, Department of Psychology, United States of America.
Mary WoodyUniversity of Pittsburgh, Department of Psychiatry, United States of America.
Dev ChopraUniversity of Pittsburgh, Department of Psychology, United States of America.
Amelia LintUniversity of Pittsburgh, Department of Psychology, United States of America.
Enoch DuUniversity of Pittsburgh, Department of Psychology, United States of America.
Kristy Benoit AllenUniversity of Kansas, Department of Psychology, United States of America.
Cecile LadouceurUniversity of Pittsburgh, Department of Psychiatry, United States of America.
Jennifer SilkUniversity of Pittsburgh, Department of Psychology, United States of America.

Funding

Real-Time Assessment of Self-Injurious Thoughts and Behaviors in High-Risk Early Adolescent GirlsR01MH103241 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LADOUCEUR, CECILE D., SILK, JENNIFER S · 2015 to 2019
$3.4M
NIMH NIH HHS R01 MH103241
6 · The paper itself

Abstract

Adolescence is a key period in which anxiety symptoms dramatically increase, particularly among adolescent girls. Identifying potentially-modifiable risk factors that contribute to anxiety symptoms may be critical to mitigate anxiety symptoms and disorders among youth. Attention biases are one set of cognitive risk factors implicated in the development and maintenance of anxiety disorders. Yet previous research remains mixed as to what pattern of attention bias characterizes adolescents at-risk for developing anxiety. Further, prior literature has largely relied on indirect measures of attention (e.g., reaction time) and tasks with low ecological validity. The present study investigated how attention to social evaluative feedback contributed to concurrent and prospective anxiety symptoms in adolescent girls (n = 90, baseline M

Identifiers

PMID40876320
PMCPMC13523911

What OpenQuestion holds

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Registered trials

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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.