Evidence map›Paper›PMID 39775059›Full record

ArticlePLOS digital health2025

A mobile intervention to reduce anxiety among university students, faculty, and staff: Mixed methods study on users' experiences.

Sarah Livermon, Audrey Michel, Yiyang Zhang, Kaitlyn Petz, Emma Toner, Mark Rucker, Mehdi Boukhechba, Laura E Barnes, Bethany A Teachman

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
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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

9 authors.

Sarah LivermonDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, United States of America.ORCID https://orcid.org/0000-0002-7051-2883
Audrey MichelDepartment of Psychology, University of Virginia, Charlottesville, Virginia, United States of America.
Yiyang ZhangDepartment of Psychology, University of Virginia, Charlottesville, Virginia, United States of America.
Kaitlyn PetzDepartment of Psychology, University of Virginia, Charlottesville, Virginia, United States of America.
Emma TonerDepartment of Psychology, University of Virginia, Charlottesville, Virginia, United States of America.
Mark RuckerDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, United States of America.
Mehdi BoukhechbaDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, United States of America.
Laura E BarnesDepartment of Systems and Information Engineering, University of Virginia, Charlottesville, United States of America.
Bethany A TeachmanDepartment of Psychology, University of Virginia, Charlottesville, Virginia, United States of America.

Funding

SCH: INT: Context-Aware Micro-Interventions for Social AnxietyR01MH132138 · NIMH · UNIVERSITY OF VIRGINIA · PI BARNES, LAURA ELIZABETH, TEACHMAN, BETHANY A · 2022 to 2025
$1.3M
Using computational modeling to formalize an integrated psychosocial theory of lonelinessF31MH136730 · NIMH · UNIVERSITY OF VIRGINIA · PI Emma Rose Toner · 2024 to 2026
$68k
NIMH NIH HHS F31 MH136730NIMH NIH HHS R01 MH132138
6 · The paper itself

Abstract

Anxiety is highly prevalent among college communities, with significant numbers of students, faculty, and staff experiencing severe anxiety symptoms. Digital mental health interventions (DMHIs), including Cognitive Bias Modification for Interpretation (CBM-I), offer promising solutions to enhance access to mental health care, yet there is a critical need to evaluate user experience and acceptability of DMHIs. CBM-I training targets cognitive biases in threat perception, aiming to increase cognitive flexibility by reducing rigid negative thought patterns and encouraging more benign interpretations of ambiguous situations. This study used questionnaire and interview data to gather feedback from users of a mobile application called "Hoos Think Calmly" (HTC), which offers brief CBM-I training doses in response to stressors commonly experienced by students, faculty, and staff at a large public university. Mixed methods were used for triangulation to enhance the validity of the findings. Qualitative data was collected through semi-structured interviews from a subset of participants (n = 22) and analyzed thematically using an inductive framework, revealing five main themes: Effectiveness of the Training Program; Feedback on Training Sessions; Barriers to Using the App; Use Patterns; and Suggestions for Improvement. Additionally, biweekly user experience questionnaires sent to all participants in the active treatment condition (n = 134) during the parent trial showed the most commonly endorsed response (by 43.30% of participants) was that the program was somewhat helpful in reducing or managing their anxiety or stress. There was overall agreement between the quantitative and qualitative findings, indicating that graduate students found it the most effective and relatable, with results being moderately positive but somewhat more mixed for undergraduate students and staff, and least positive for faculty. Findings point to clear avenues to enhance the relatability and acceptability of DMHIs across diverse demographics through increased customization and personalization, which may help guide development of future DMHIs.

Identifiers

PMID39775059
PMCPMC11706487

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.