Category: Statistics

Stud Health Technol Inform. 2026 May 7;335:153-154. doi: 10.3233/SHTI260074.

ABSTRACT

Recruiting patients for medical research requires a balance between ethical transparency and practical feasibility. We examined a two-stage re-consenting process for patients in the context of biobanks, capturing trends in patient engagement and preferences regarding future data use. Participation declined mainly at the opt-in stage, highlighting early procedural barriers, while among consenting, most participants allowed broad secondary use of their data without additional recontacting. In general, the results obtained support a transparent opt-out solution for data donation and secondary use of health data.

PMID:42119110 | DOI:10.3233/SHTI260074

Stud Health Technol Inform. 2026 May 7;335:131-136. doi: 10.3233/SHTI260069.

ABSTRACT

BACKGROUND: Serious games (SGs) with hand-tracking solutions are increasingly adopted in neurological rehabilitation. The Leap Motion Controller (LMC) is widely used in clinical and research contexts, while software-based systems, such as MediaPipe (MP), are emerging.

OBJECTIVES: This study compared the usability of MP- and LMC-based SGs.

METHODS: Seven SGs were developed to evaluate Grasp and Pinch gestures and tested with fifty healthy volunteers. Usability was assessed with the System Usability Scale (SUS) after each device, and an ad-hoc comparative questionnaire (LMC, MP, Neutral) collected preferences on several aspects.

RESULTS: Both systems achieved excellent SUS scores (MP: 88/100; LMC: 90/100), with no statistical difference in usability. The comparative questionnaire showed a stronger preference for LMC (63%) versus MP (17%), with 20% neutral.

CONCLUSION: Although both solutions demonstrated high usability, LMC was more frequently preferred in direct comparison. The SUS results of MP encourage further investigation, including large-scale tests with neurological patients.

PMID:42119105 | DOI:10.3233/SHTI260069

Stud Health Technol Inform. 2026 May 7;335:96-101. doi: 10.3233/SHTI260062.

ABSTRACT

BACKGROUND: Primary health care forms the backbone of comprehensive and patient-centered healthcare in Austria and Germany. Particularly in rural areas, there are increasing gaps in local patient care.

OBJECTIVES: Hybrid health care services should ensure multidisciplinary primary health care at the community level.

METHODS: A mixed-methods approach consisting of a semi-structured literature review and an online survey of mayors and heads of administration in municipalities in Upper Austria and Upper Bavaria (12/2025, N=757, n=78, rr=10.3%) was conducted.

RESULTS: An average level of digital maturity was identified in municipalities of Upper Austria and Upper Bavaria. Furthermore, the future establishment and expansion of digital health care services was identified as a key lever.

CONCLUSION: In the future, primary health care at the community level will be characterized by hybrid healthcare services.

PMID:42119098 | DOI:10.3233/SHTI260062

Stud Health Technol Inform. 2026 May 7;335:69-77. doi: 10.3233/SHTI260057.

ABSTRACT

BACKGROUND: Digitalization in healthcare promises efficiency gains, in the present case particularly addressing medication management. In mobile care, staff often spend substantial time traveling to collect patients’ health insurance cards, visit physicians to obtain prescriptions, pick up medication at pharmacies, and deliver it to patients. The Linked Care project developed an integrated IT solution to streamline medication reordering in mobile care settings.

OBJECTIVES: To evaluate whether an electronic system improves the efficiency and quality of the medication reordering process compared to usual care.

METHODS: A non-randomized controlled trial allocated nurses and caregivers to intervention (Linked Care) or control groups. Standardized and project-specific self-reported outcomes on workload (psychological stress), time expenditure, errors & information loss, and usability were obtained via online questionnaires. Data was analyzed using mixed-effects models as well as repeated measures ANOVA for trend analyses.

RESULTS: Workload was consistently lower in the intervention group, whereas time expenditure showed no significant differences. Errors and information loss improved significantly in the 6-month follow-up. Usability ratings ranged from “excellent” to “good”.

CONCLUSION: Using Linked Care was associated with reduced self-perceived workload (psychological stress) and improved error prevention, with high usability; however, self-perceived time savings were negligible.

PMID:42119093 | DOI:10.3233/SHTI260057

Stud Health Technol Inform. 2026 May 7;335:53-54. doi: 10.3233/SHTI260054.

ABSTRACT

While health technology assessment (HTA) aims to quantify the quality of digital health apps, app stores offer unstructured information on app quality to users like patients. This provides barriers for people with Parkinson’s disease (PD) and Alzheimer’s disease (AD) to identify high-quality apps. This study explores the discoverability of AD/PD-related apps and their quality information in app stores. We applied descriptive statistics and text mining such as large language models (LLMs) and topic modelling to analyze app descriptions and user reviews of 1237 apps. Only ∼2% of apps were discoverable as holding a CE-mark. Most apps were “Care Support”, followed by “Health & Wellness” and “Patient Monitoring” patient-facing categories. User reviews addressed “user experience”, “health improvement”, and “costs”. To help patients identify high-quality apps, quality information should be presented in a structured and trustworthy way. The use of user feedback for patient-reported measures should be explored in future work.

PMID:42119090 | DOI:10.3233/SHTI260054

Stud Health Technol Inform. 2026 May 7;335:14-21. doi: 10.3233/SHTI260048.

ABSTRACT

Misinformation increasingly undermines trust in childhood immunization among both the public and healthcare workers. Innovative, scalable educational approaches are needed to strengthen vaccine confidence across diverse settings. Objectives: To describe and evaluate a global initiative delivering asynchronous online vaccination courses, focusing on participant reach, completion, and qualitative experiences. We conducted a pilot evaluation using descriptive analysis of enrollment and completion data, combined with qualitative content analysis of voluntarily submitted open-text feedback. Courses were fully online, self-paced, accredited for continuing professional development, and implemented through international, local, and institutional partnerships. Between April 2024 and December 2025, seven courses were delivered. A total of 3,018 participants from 75 countries completed at least one course, with an overall completion rate of 31.5%. Sixty feedback entries from low-, middle-, and high-income settings were analyzed. Nine themes emerged, with differing emphases across contexts. Asynchronous online vaccination education can decentralize knowledge at scale, support trust-building and empowerment, and complement traditional immunization communication strategies.

PMID:42119084 | DOI:10.3233/SHTI260048

Anal Chem. 2026 May 12. doi: 10.1021/acs.analchem.6c00454. Online ahead of print.

ABSTRACT

Cerebrospinal fluid (CSF) orexin-A is the gold-standard biomarker for narcolepsy type 1 (NT1), however, conventional radioimmunoassays (RIA) often suffer from cross-reactivity and overestimation, fueling long-standing conundrum regarding orexin detection in peripheral blood. In this study, we developed an ultrasensitive LC-MS/MS method (LLOQ = 0.1 pg/mL) incorporating a streamlined one-step protein precipitation protocol coupled with acid-shielding and cocktail-protection strategies to mitigate severe nonspecific adsorption and enzymatic degradation. Verification in paired CSF and blood samples from narcolepsy patients and controls revealed that orexin-B and peripheral orexins remain consistently below the detection limit (<0.1 pg/mL), proving that previously reported ng/mL levels in blood are analytical artifacts. Our LC-MS/MS approach resolved a 50-fold quantitative overestimation by RIA and significantly improved diagnostic resolution for narcolepsy type 2 (AUC = 0.73, P < 0.05) where RIA failed to achieve statistical significance (P = 0.297). This study establishes a high-specificity analytical framework as a practical reference standard for refined sleep disorder diagnostics and confirms that CSF orexin-A remains the currently the most reliable clinical biomarker.

PMID:42118575 | DOI:10.1021/acs.analchem.6c00454

Cancer. 2026 May 15;132(10):e70430. doi: 10.1002/cncr.70430.

ABSTRACT

BACKGROUND: Diabetes is associated with an increased risk of cancer; however, few epidemiological studies of diabetes and cancer risk have focused on Hispanic/Latino adults. This study examined the associations between three time-varying measures of diabetes and cancer incidence among participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

METHODS: HCHS/SOL is a multi-site prospective cohort study of 16,415 Hispanic/Latino adults. Time-varying diabetes measures (assessed at visit 1: 2008-2011 and visit 2: 2014-2017) included diabetes status (no diabetes, pre-diabetes, and diabetes), glycemic control (no diabetes, pre-diabetes, controlled diabetes [hemoglobin A_1c (HbA_1c) <7.0%], and uncontrolled diabetes [HbA_1c ≥7.0%]), and insulin resistance (no diabetes, pre-diabetes + Homeostatic Model Assessment of Insulin Resistance [HOMA-IR] <3.0, pre-diabetes + HOMA-IR ≥3.0, diabetes + HOMA-IR <3.0, and diabetes + HOMA-IR ≥3.0). Incident cancers diagnosed from visit 1 through 2021 were identified through state cancer registry linkages; 715 cancers including 330 obesity-related cancers (ORCs) were diagnosed over a mean follow-up of 10.7 years. The authors used survey-weighted marginal structural Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each time-varying diabetes measure and overall cancer and ORC risk, adjusting for demographic, social, and behavioral characteristics.

RESULTS: Time-varying pre-diabetes and diabetes (vs. no diabetes) status were associated with cancer HRs of 1.82 (95% CI, 1.27-2.61) and 2.49 (95% CI, 1.65-3.74), respectively. HRs were further elevated among those with diabetes + HbA_1c ≥7.0% (HR, 3.12; 95% CI, 1.44-6.79) and those with diabetes + HOMA-IR ≥3.0 (HR, 2.78; 95% CI, 1.69-4.56). Associations were stronger for ORC risk; however, estimates were less precise.

CONCLUSION: Diabetes is associated with increased risk of cancer and ORC. Diabetes prevention and control may be additionally important for cancer prevention among Hispanic/Latino adults.

PMID:42118574 | DOI:10.1002/cncr.70430

JMIR AI. 2026 May 12;5:e84315. doi: 10.2196/84315.

ABSTRACT

BACKGROUND: Regulation of artificial intelligence (AI) has been slow relative to the pace of its integration into health care. Several AI diagnostic tools for diabetic retinopathy (DR) have already received Food and Drug Administration (FDA) clearance, making it a timely and concrete example for exploring public perspectives on regulatory approval. The scope of FDA regulation of AI tools is being explored, and public attitudes about regulatory oversight should inform these discussions and are explored in this paper. Prior research suggests that comfort, trust, and political orientation shape views on government regulation and emerging technologies, potentially affecting support for oversight of AI in health care.

OBJECTIVE: This study assessed the perceived importance of FDA approval for AI-supported clinical decision support tools, with DR as the use case. We explored how comfort with AI tool developers, trust in data sharing, political affiliation, and demographic characteristics relate to the importance of FDA approval among US adults.

METHODS: A national survey was conducted in 2023 using the NORC AmeriSpeak Panel, a probability-based sample including 1787 respondents, with a subset of 982 participants answering questions about a use case describing an AI tool for identifying DR. Participants rated the importance of FDA approval for such tools on a 4-point Likert scale, with responses dichotomized between high and low perceived importance. Logistic regression models assessed associations between this outcome and predictors including comfort with AI tool developers, trust in data sharing, political affiliation, and demographic characteristics.

RESULTS: Among the 982 respondents presented with the DR use case, 658 (67%) indicated that FDA approval was “fairly” or “very” important. Statistically significant factors associated with the outcome (“It is important that the AI tool is approved by the FDA”) included higher comfort with using the tool (odds ratio [OR] 1.44, 95% CI 1.11-1.87; P=.006), comfort with developers from private companies (OR 1.38, 95% CI 1.09-1.76; P=.008), and hospitals (OR 1.60, 95% CI 1.25-2.05; P<.001). Trust in responsible data sharing (OR 1.25, 95% CI 1.05-1.5; P=.01) and higher education (OR 1.64, 95% CI 1.02-2.62; P=.04) also predicted higher support. Lean or strong Republicans (OR 0.43, 95%CI 0.3-0.6; P<.001) and Independents (OR 0.63, 95% CI 0.42-0.96; P=.03) were less likely to view FDA approval as important, as were Black (OR 0.50, 95% CI 0.34-0.77; P<.001) and Hispanic (OR 0.57, 95% CI 0.38-0.86; P=.007) respondents compared with White respondents.

CONCLUSIONS: This study offers insights into public attitudes regarding FDA oversight of AI-based clinical decision support tools. Findings highlight how comfort, trust, and lower confidence from marginalized communities and some political groups shape perceived importance of FDA approval, offering a point for broader applications in health care AI governance. These factors should be better considered as health systems work to ensure trustworthy implementation of new AI technologies.

PMID:42118568 | DOI:10.2196/84315

JAMA Netw Open. 2026 May 1;9(5):e2611430. doi: 10.1001/jamanetworkopen.2026.11430.

ABSTRACT

IMPORTANCE: The physician-scientist workforce has been in decline for decades, and the shortage of physician-scientists from racial and ethnic groups that are underrepresented in medicine (URiM) is particularly acute. Medical school is a key developmental period during which research career intentions (RCI) may change, yet little is known about RCI evolution during this period.

OBJECTIVE: To evaluate factors associated with RCI among first-year medical students, overall and by URiM status.

DESIGN, SETTING, AND PARTICIPANTS: This is a cross-sectional analysis of baseline data from the ongoing Longitudinal Evaluation of Research Career Intentions Among Medical Students Study. Participants were first-year medical students enrolled during the 2024 to 2025 academic year at US medical schools accredited by the Liaison Committee on Medical Education. MD-PhD students were excluded.

EXPOSURES: The baseline survey collected information on sociodemographic factors, pre-medical school and first-year research experiences (eg, research education, research participation, mentorship, and authorship), medical school learning environment, and psychosocial characteristics.

MAIN OUTCOMES AND MEASURES: The primary outcome was RCI in year 1 of medical school, defined as intending to be significantly or exclusively involved in research during one’s medical career. Characteristics associated with RCI were evaluated using stability selection.

RESULTS: Among 1136 first-year medical student respondents (mean [SD] age, 24.7 [2.7] years; 790 female [69.5%]) at 134 US medical schools, 26.9% (306 students) reported RCI, with a slightly higher prevalence among URiM students than non-URiM students (246 students [28.2%] vs 60 students [23.9%]; standardized mean difference, 0.097). Prematriculation factors associated with RCI among all students included research participation (odds ratio [OR], 1.51; 95% CI, 1.13-2.00), conference presentation (OR, 1.56; 95% CI, 1.16-2.09), and manuscript authorship (OR, 1.38; 95% CI, 1.03-1.86). Postmatriculation factors included research participation (OR, 1.74; 95% CI, 1.31-2.30) and having a physician-scientist role model (OR, 1.72; 95% CI, 1.31-2.30). Factors uniquely associated with RCI among URiM students included prematriculation research experiences (OR, 1.51; 95% CI, 1.14-2.01) and presentation of research (OR, 1.57; 95% CI, 1.17-2.11) and postmatriculation manuscript authorship (OR, 1.84; 95% CI, 1.04-3.25). Among non-URiM students, only postmatriculation factors were uniquely associated with RCI, including having a research mentor (OR, 1.76; 95% CI, 1.34-2.31) and receiving education about physician-scientist work-life balance (OR, 1.63; 95% CI, 1.24-2.15).

CONCLUSIONS AND RELEVANCE: This cross-sectional analysis of baseline data from an ongoing cohort study found that RCI was prevalent among first-year medical students and was associated with characteristics and experiences prior to matriculation and during year 1. Some factors associated with RCI differed between URiM and non-URiM medical students, suggesting distinct pipelines to research career development. These findings highlight opportunities to support physician-scientist training through tailored education, exposure, and mentorship.

PMID:42118538 | DOI:10.1001/jamanetworkopen.2026.11430