Abstract Background Privacy 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 This 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 A 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 All 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 (Δ χ 2 1 =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 The 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.
Uptake of mild cognitive impairment (MCI) and dementia screening among community-dwelling older adults remains suboptimal globally and is shaped by limited dementia literacy, fear, stigma, and difficulties navigating services. Virtual reality (VR)–assisted education may enhance engagement, but older adults’ experiences of such education and the contextual conditions shaping screening intentions require further understanding. This study aims to explore community-dwelling older adults’ understanding, concerns, and attitudes regarding MCI and screening after exposure to a community-based VR-assisted cognitive health education intervention and to examine their perceived information, motivation, and behavioral skills related to screening using the information-motivation-behavioral skills (IMB) model. Twenty-two community-dwelling older adults were recruited from participating communities between August 23 and September 17, 2025, using purposive sampling with attention to variation in age and educational attainment, supplemented by snowball sampling. All participants completed a 40-minute VR-assisted cognitive health education session comprising a 10-minute immersive 360° video delivered through a Meta Quest 3 head-mounted display followed by 30 minutes of nurse-led face-to-face education. Four researchers coded the original Mandarin transcripts; themes and subthemes were derived inductively from the data and subsequently mapped to the IMB model. Sixteen data-derived subthemes were subsequently mapped to the 3 domains of the IMB model. Information-related accounts encompassed misunderstanding and lack of knowledge about MCI, the relationship between MCI and dementia, knowledge acquisition and clarification, timing and psychological readiness, information overload, attention to peripheral details, and uncertain awareness of cognitive health status. Motivation-related accounts included fear and stigma associated with screening, perceived reduction in stigma and greater screening acceptability, reconciliation with aging, low self-prioritization, and a pessimistic outlook on treatment. Behavioral-skills accounts included low confidence in self-management, knowledge to action, community-based referral and navigation, and difficulty changing established habits. Across domains, participants’ accounts suggested that greater understanding and screening acceptability did not necessarily translate into action when practical navigation and referral support were lacking. Participants perceived VR-assisted cognitive health education as engaging and helpful for making MCI-related information more concrete, but their accounts also indicated that screening intentions may not translate into action without clear, trusted navigation and referral support. These findings describe participants’ experiences and perceived barriers and facilitators rather than estimating intervention effectiveness.
