Abstract

Approximately 1 in 4 adults will develop hallux valgus (HV). Up to 80% of adult Internet users reference online sources for health-related information. Overall, with the high prevalence of HV combined with the numerous treatment options, we believe patients are likely turning to Internet search engines for questions relevant to HV. Using Google's people also ask (PAA) or frequently asked questions (FAQs) feature, we sought to classify these questions, categorize the sources, as well as assess their levels of quality and transparency. On October 9, 2022, we searched Google using these 4 phrases: "hallux valgus treatment," "hallux valgus surgery," "bunion treatment," and "bunion surgery." The FAQs were classified in accordance with the Rothwell Classification schema and each source was categorized. Lastly, transparency and quality of the sources' information were evaluated with the Journal of the American Medical Association's (JAMA) Benchmark tool and Brief DISCERN, respectively. Once duplicates and FAQs unrelated to HV were removed, our search returned 299 unique FAQs. The most common question in our sample was related to the evaluation of treatment options (79/299, 26.4%). The most common source type was medical practices (158/299, 52.8%). Nearly two-thirds of the answer sources (184/299; 61.5%) were lacking in transparency. One-way analysis of variance revealed a significant difference in mean Brief DISCERN scores among the 5 source types, F(4) = 54.49 (P < .001), with medical practices averaging the worst score (12.1/30). Patients seeking online information concerning treatment options for HV search for questions pertaining to the evaluation of treatment options. The source type encountered most by patients is medical practices; these were found to have both poor transparency and poor quality. Publishing basic information such as the date of publication, authors or reviewers, and references would greatly improve the transparency and quality of online information regarding HV treatment. Level V, mechanism-based reasoning.

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