Online Patient Education Materials Related to Lipoprotein(a): Readability Assessment (Preprint)

Abstract

BACKGROUND Lipoprotein (a) (Lp(a)) is a highly proatherogenic lipid fraction that is a clinically significant risk modifier. Patients wanting to learn more about Lp(a) are likely to use online patient educational materials (OPEM). However, the readability of OPEM may exceed the health literacy of the general public. OBJECTIVE This study aims to assess the readability of online patient education materials related to Lp(a). We hypothesized that the readability of these online materials would exceed the 6th grade level recommended by the American Medical Association (AMA). METHODS Using an online search engine, we queried the top 20 search results from 10 commonly used Lp(a)-related search terms to identify a total of 200 websites. We excluded duplicate websites, advertised results, research journal articles, or non-patient-directed materials, such as those intended only for health professionals or researchers. Grade-level readability was calculated using 5 standard readability metrics (Automated Readability Index, SMOG Index, Coleman Liau Index, Gunning Fog Score, Flesch Kincaid score) to produce robust point (mean) and interval (confidence interval) estimates of readability. Generalized estimating equations were used to model grade-level readability by each search term, with the 5 readability scores nested within each OPEM. RESULTS A total of 27 unique websites were identified for analysis. The average readability score for the aggregated results was 12.2 grade level (95% CI, 10.9798 - 13.3978). OPEM were grouped into 6 categories by primary source: industry, lay press, research foundation and non-profit organizations, university or government, clinic, and other. The most readable category was OPEM published by universities or government agencies (9.0, 95% CI 6.8-11.3). The least readable OPEM on average were the ones published by the lay press (13.0, 95% CI 11.2-14.8). All categories exceeded the 6th grade reading level recommended by the AMA. CONCLUSIONS Conclusions: Lack of access to readable OPEM may disproportionately affect patients with low health literacy. Ensuring that online content is understandable by broad audiences is a necessary component of increasing the impact of novel therapeutics and recommendations regarding Lp(a).

Evaluation of Prompts to Simplify Cardiovascular Disease Information Using a Large Language Model

“…are inaccessible for the general public. 1 Artificial intelligence (AI) chatbots powered by large language models (LLMs) are a potential source of public-facing CVD information. 2,3 Generative language models present risks related to information quality but also opportunities for producing accessible information about CVD at scale, which could advance the American Heart Association's 2020 impact goals related to health literacy.…”