trust
among adults in the United States
The current standard for training brain-computer interface (BCI) machine learning models is user-specific. There is a high interest in developing generic models that are trained on data from other users to minimize BCI calibration time; however, this is limited by noisy, non-stationary brain signals and high inter-user variability. We investigate the trade-off between training data quality and quantity on P300 BCI performance in individuals with amyotrophic lateral sclerosis (ALS) with representative traditional machine learning (stepwise linear discriminant analysis, SWLDA) and deep learning (EEGNet) models. Results show that data quality and domain alignment are more critical than dataset size: user- specific models trained on significantly less data outperformed generic models; generic models trained on ALS data outperformed models trained on non-ALS data; block-averaging of features was mostly detrimental to EEGNet but beneficial to SWLDA; and accounting for inter-stimulus interval differences between ALS and non-ALS data had minimal effect. Our findings highlight the importance of individualized model tuning for reliable P300 BCIs. Symposium Presenter: Huda Haque Association between health literacy and cancer history with medical
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trust among adults in the United States
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Introduction: Health literacy, the ability to understand health information, shapes health-seeking behaviors. Among individuals with a history of cancer, low health literacy is associated with poor outcomes. In an era of disinformation, examining how health literacy and cancer history interact with trust can elucidate how patients make decisions.. Methods: We analyzed data from the Health Information National Trends Survey (HINTS), a nationally representative dataset, for the 2014, 2018, 2020, 2022, and 2024 cycles. Health literacy (Low: never, rarely, sometimes; High: always), personal cancer history (ever vs. never), and age (18-39, 40-64, 65+) were examined in relation to trust in doctors, family, and religious organizations for medical information (Low: not at all, a little, some; High: a lot). Survey-weighted logistic regression used jackknife replicate weights in R; age interactions were tested using Rao-Scott likelihood ratio tests. Latent class analysis (LCA) identified subgroups characterized by demographics, cancer history, and health literacy. Results: Higher health literacy was significantly associated with greater doctor trust (aOR 4.50, 95% CI 3.61-5.60), consistent across age groups and cycles. Cancer history was not associated with doctor trust, and trust did not vary by cycle. For family trust, associations differed by age (interaction p=0.03), with greater trust among younger adults with higher health literacy (18-39: aOR 2.02, 95% CI 0.91-4.51). Family trust declined in later cycles (2022 vs. 2014: aOR 0.58; 2024 vs. 2014: aOR 0.56), suggesting COVID-19-related shifts. Cancer survivors were less likely to trust religious organizations (aOR 0.61, 95% CI 0.43-0.87), and older adults were more likely. LCA identified five demographic profiles with distinct trust patterns. The younger, more educated class showed the highest physician trust and lowest religious trust, while the class including more Hispanic or Non-Hispanic Black members showed the strongest family trust. Cancer history was associated with physician trust within one class, suggesting context-dependent effects not captured by regression alone. Conclusion: These findings highlight how health literacy, cancer history, and demographic context jointly shape trust in health information sources. Targeted outreach strategies addressing these distinct trust profiles may promote informed health decision-making, particularly for cancer survivors and older adults.
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Duke University / 2026
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trust among adults in the United States