{
  "abstract": "Background\n\nPrivacy concerns represent a primary barrier to the adoption of large language model (LLM) applications, particularly in sensitive domains such as health care. Although a growing body of survey-based research has examined user privacy concerns with LLMs, no standardized instrument exists for measuring this construct.Objective\n\nThis study aimed to develop and validate a concise, standardized questionnaire for assessing user privacy concerns with LLM-based systems, suitable for use by researchers and practitioners such as AI application developers and health care professionals.Methods\n\nA literature review was conducted to identify privacy-related domains relevant to LLM use. A draft questionnaire was developed and subjected to content validity review by 8 experts. The finalized instrument was administered via Qualtrics to adults aged 18 years or older who were aware of LLM-based applications, recruited through the University of Pittsburgh, LinkedIn, and the Pitt+Me research participant registry between April 2025 and July 2026. Internal consistency reliability and factor structure were assessed, with factor structure examined in a split-sample design, in which exploratory factor analysis (EFA) was conducted on a random one-third of the sample and confirmatory factor analysis (CFA) on the holdout two-thirds.Results\n\nAll questionnaire items achieved acceptable content validity (item-level content validity index >0.80; average scale-level content validity index=0.964). A total of 603 responses were collected. After preprocessing, the final sample comprised 361 participants. Most participants were younger than 35 years of age (198/361, 54.8%), female (230/361, 63.7%), and White (285/361, 78.9%); 88.1% (318/361) held at least a bachelor's degree. The 6-item questionnaire showed high internal consistency (Cronbach α=0.90; McDonald ω=0.90). EFA (n=121) supported a single dominant factor with all standardized loadings ≥0.70. CFA (n=240) showed excellent fit (comparative fit index=0.996; Tucker-Lewis index=0.993; root mean square error of approximation=0.046; standardized root mean square residual=0.057). A 2-factor model separating the transparency- and control-related items fit significantly better than the 1-factor model (Δχ²₁=8.9; P=.003), but the 2 factors were correlated at r=0.83, indicating closely related facets. Because the Fornell-Larcker criterion was met for the transparency and control subscale but not for data use and risk salience, the 6-item total score remains the recommended primary metric.Conclusions\n\nThe LLM privacy concern assessment is a concise, psychometrically sound 6-item instrument that measures privacy concern with LLM-based systems via 2 correlated subdimensions (transparency and control; data use and risk salience) summarized by a single total score. It enables standardized measurement across studies and practical privacy assessment in real-world LLM applications.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Health Information Management, School of Health and Rehabilitation Sciences, University of Pittsburgh, 3396 Fifth Avenue, Pittsburgh, PA, 15213, United States, 1 412-383-6653."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Leming Zhou",
      "orcid": "0000-0003-4398-0267"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongtao Wang",
      "orcid": "0009-0008-4333-6440"
    },
    {
      "affs": [
        0
      ],
      "name": "Yi Xu",
      "orcid": "0009-0003-9034-7461"
    },
    {
      "affs": [
        0
      ],
      "name": "I Made Agus Setiawan",
      "orcid": "0000-0001-8383-8471"
    },
    {
      "affs": [
        0
      ],
      "name": "Haomin Hu",
      "orcid": "0000-0001-9200-062X"
    },
    {
      "affs": [
        0
      ],
      "name": "Andi Saptono",
      "orcid": "0000-0002-0933-8150"
    },
    {
      "affs": [
        0
      ],
      "name": "Yong K Choi",
      "orcid": "0000-0001-7882-4358"
    },
    {
      "affs": [
        0
      ],
      "name": "Bambang Parmanto",
      "orcid": "0000-0002-4907-8402"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a3v17en3yvdcz.bib",
  "cited_by": 0,
  "doi": "10.2196/102910",
  "fulltext_url": "https://europepmc.org/articles/PMC13686613",
  "id": "a3v17en3yvdcz",
  "issns": [
    "2292-9495"
  ],
  "landing_url": "https://europepmc.org/article/MED/42906530",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3312|Privacy, Security, and Data Protection"
    ]
  ],
  "openalex": "W7223128054",
  "pdf_url": "https://europepmc.org/articles/PMC13686613?pdf=render",
  "pmcid": "PMC13686613",
  "pmid": "42906530",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a3v17en3yvdcz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42906530",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42906530"
    }
  ],
  "subjects": [],
  "title": "Development and Validation of the Large Language Model Privacy Concern Assessment Questionnaire: Psychometric Evaluation Study on a Brief Instrument for Assessing User Privacy Concerns With Large Language Model Applications.",
  "title_text": "Development and Validation of the Large Language Model Privacy Concern Assessment Questionnaire: Psychometric Evaluation Study on a Brief Instrument for Assessing User Privacy Concerns With Large Language Model Applications.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/a3v17en3yvdcz",
  "venue": "JMIR human factors",
  "versions": []
}