AI-generated high-intensity interval training information: a cross-sectional comparative evaluation of quality and readability
Year: 2026
Type: article
Abstract: Background AI chatbots are increasingly used for health and exercise information. High-intensity interval training (HIIT) is a common exercise topic, yet the quality, transparency, and readability of chatbot-generated HIIT information have not been systematically compared under a standardized testing protocol. Objective To compare selected dimensions of quality, transparency, and readability in HIIT-related responses generated by ChatGPT, Copilot, Gemini, and Perplexity, and to evaluate readability against a prespecified sixth-grade benchmark for public-facing health information. Methods This cross-sectional comparative content analysis was conducted in August 2026. Google Trends was used to identify HIIT-related search terms worldwide over the preceding 5 years. Of 29 candidate terms, 14 were retained after predefined screening, and each term was submitted once to each of four chatbots, yielding 56 responses. Two reviewers, both holding doctoral degrees in sports science, independently evaluated all responses using DISCERN, EQIP, the JAMA benchmark criteria, and GQS, which assessed complementary dimensions of information quality, usability, transparency, and overall usefulness. Inter-rater reliability was assessed before adjudication. Six readability indices were calculated. Because the same 14 queries were evaluated across all four chatbots, overall differences were tested using Friedman tests; significant results were followed by paired Wilcoxon signed-rank tests with Holm correction. Kendall’s W was reported as the overall effect-size measure. Results Perplexity-generated responses had the highest median scores for DISCERN [51.50 (47.50, 53.75)], EQIP [75.00 (70.00, 80.00)], JAMA [1.00 (1.00, 1.00)], and GQS [4.00 (4.00, 4.00)]. Friedman tests showed significant differences across chatbots for DISCERN (χ 2 = 27.98, p < 0.001, W = 0.666), EQIP (χ 2 = 25.74, p < 0.001, W = 0.613), JAMA (χ 2 = 35.40, p < 0.001, W = 0.843), and GQS (χ 2 = 21.15, p < 0.001, W = 0.504). None of the four chatbots met the prespecified sixth-grade readability benchmark across the evaluated indices. Conclusion Under this specific testing protocol, HIIT-related responses generated by the four chatbots differed in the evaluated dimensions of information quality and transparency, with Perplexity-generated responses receiving higher median scores on all four assessment instruments. These scores should not be interpreted as direct evidence of factual accuracy, citation accuracy, or clinical safety, which were not independently assessed. None of the evaluated chatbot-generated responses met the prespecified readability benchmark.
Source: Frontiers in Public Health
Authors: Zheng Zhang, Xiang-Yan Ruan, Kai-Tuo Zhu
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: Yancheng Institute of Technology
OpenAlex ID: W7223348831