Evidence map›Paper›PMID 41281296›Full record

ArticleFrontiers in digital health2025

Simulated virtual reality experiences for predicting early treatment response in panic disorder.

Byung-Hoon Kim, Jae-Jin Kim, Junhyung Kim, Jiook Cha, Sang-Won Jeon, Kang-Seob Oh, Dong-Won Shin, Sung Joon Cho

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Byung-Hoon KimDepartment of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea.
Jae-Jin KimDepartment of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea.
Junhyung KimDepartment of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jiook ChaDepartment of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea.
Sang-Won JeonDepartment of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Kang-Seob OhDepartment of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Dong-Won ShinDepartment of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Sung Joon ChoDepartment of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Panic disorder (PD) is a disabling anxiety condition in which early improvement during treatment can predict better long-term outcomes. Objectives: This study investigated whether a newly developed virtual reality-based assessment tool, the Virtual Reality Assessment of Panic Disorder (VRA-PD), can help predict early treatment response in individuals with PD. Methods: In total, 52 participants, including 25 patients diagnosed with PD and 27 healthy individuals, were evaluated every 2 months over a 6-month period. Assessments included self-reported anxiety levels and heart rate variability measured during virtual reality scenarios, as well as standard clinical questionnaires. Patients with PD were further categorized based on their treatment progress into early responders ( Results: The model that combined virtual reality-based and conventional clinical data achieved higher accuracy (85%) and F1-score (0.71) than models using only clinical (accuracy: 77%, F1-score: 0.56) or only virtual reality data (accuracy: 75%, F1-score: 0.64). The most important predictors included anxiety levels during virtual scenarios, heart rate variability metrics, and scores from clinical scales such as the Panic Disorder Severity Scale and Anxiety Sensitivity Index. Conclusions: This study highlights the value of virtual reality-based assessments for predicting early treatment outcomes in PD. By providing ecologically valid and individualized measures, virtual reality may enhance clinical decision-making and support personalized mental healthcare.

Indexed as

anxietyearly treatment responseheart rate variabilitymachine learningpanic disordervirtual realityVR-based assessments

Identifiers

PMID41281296
PMCPMC12631630

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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.