Predictors of lung cancer patients' behavioral intention to use virtual reality for treatment preparedness and shared decision-making: a Unified Theory of Acceptance and Use of Technology-based perception study.

Safa Elkefi, Steven K Feiner, Alicia K Matthews, Luis M Rocha, Carlos Gershenson-Garcia, Roberta Scheinmann

Abstract read

In one paragraph

Article in JAMIA open, 2026. 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

6 authors.

Safa ElkefiSchool of System Science & Industrial Engineering, Watson College of Engineering, Binghamton University, Binghamton, NY 13850, United States.

Steven K FeinerDepartment of Computer Science, Columbia University, New York, NY 10027, United States.

Alicia K MatthewsCollege of Nursing, RUSH University, Chicago, IL 60612, United States.

Luis M RochaSchool of System Science & Industrial Engineering, Watson College of Engineering, Binghamton University, Binghamton, NY 13850, United States.

Carlos Gershenson-GarciaSchool of System Science & Industrial Engineering, Watson College of Engineering, Binghamton University, Binghamton, NY 13850, United States.

Roberta ScheinmannSchool of Nursing, Columbia University, New York, NY 10032, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: We examined the intention of lung cancer patients to use virtual reality (VR) to support shared decision-making and treatment preparedness and identified predictors of this intention using the Unified Theory of Acceptance and Use of Technology (UTAUT). Methods: Two hundred cancer patients completed a survey. Logistic regression was used to identify the predictors of VR use for decision-making. Results: 89.5% were willing to use VR; willing participants were younger than those who were not willing (60.8 ± 15.3 years; Conclusions: Findings indicate high receptivity to VR and highlight actionable levers, including usability, family engagement, assistance, and instructional supports, for implementation in oncology care.

Indexed as

digital healthlung canceroncologytechnology acceptance modelvirtual reality

Identifiers

PMID42798801

DOI10.1093/jamiaopen/ooag178

PMCPMC13613060

What OpenQuestion holds

Textmetadata

Read underepoch 390

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.