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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 9, 2024
Date Accepted: Jul 31, 2025

The final, peer-reviewed published version of this preprint can be found here:

Parallel Corpus Analysis of Text and Audio Comprehension to Evaluate Readability Formula Effectiveness: Quantitative Analysis

Ahmed A, Leroy G, Kauchak D, Barai P, Harber P, Rains SA

Parallel Corpus Analysis of Text and Audio Comprehension to Evaluate Readability Formula Effectiveness: Quantitative Analysis

J Med Internet Res 2025;27:e69772

DOI: 10.2196/69772

PMID: 41037781

PMCID: 12490814

Parallel Corpus Analysis of Text and Audio Comprehension: Evaluating Readability Formula Effectiveness

  • Arif Ahmed; 
  • Gondy Leroy; 
  • David Kauchak; 
  • Prosanta Barai; 
  • Philip Harber; 
  • Steven A. Rains

ABSTRACT

Background:

Health literacy, the ability to understand and act on health information, is critical for patient outcomes and healthcare system effectiveness. While plain language guidelines enhance text-based communication, audio-based health information remains underexplored, despite the growing use of virtual assistants and smart devices in healthcare. Traditional readability formulas, such as Flesch-Kincaid, provide limited insights into the complexity of health-related texts and fail to address challenges specific to audio formats. Factors like syntax and semantic features significantly influence comprehension and retention across modalities.

Objective:

This study investigates features that affect comprehension of medical information delivered via text or audio formats. We also examine existing readability formulas and their correlation with perceived and actual difficulty of health information for both modalities.

Methods:

We developed a parallel corpus of health-related information that differed in delivery format: text or audio. We used text from BMJ Lay Summary (N=193), WebMD (N=40), Patient Instruction (N=40), Simple Wikipedia (N=243), BMJ Journal (N=200). Participants (N = 487) read or listened to a health text and then completed a questionnaire evaluating perceived difficulty of the text measured using a 5-point Likert scale and actual difficulty measured using multiple-choice and true-false questions (comprehension) as well as free recall of information (retention). Questions were generated by generative AI (ChatGPT 4.0 ). Underlying syntactic, semantic, and domain specific features, as well as common readability formulas were evaluated for their relation to information difficulty.

Results:

Text versions were perceived as easier than audio, with BMJ Lay Summary scoring 1.76 vs. 2.1 and BMJ Journal 2.59 vs. 2.83 (lower is easier). Comprehension accuracy was higher for text across all sources (e.g., BMJ Journal: 76% vs. 58%; Patient Instructions: 86% vs. 66%). Retention was better for text, with significant differences in exact word matching for Patient Instructions and BMJ Journal. Longer texts increased perceived difficulty in text but reduced free recall in both modalities (-0.23, -0.25 in audio). Higher content word frequency improved retention (0.23, 0.21) and lowered perceived difficulty (-0.20 in audio). Verb-heavy content eased comprehension (-0.29 in audio), while nouns and adjectives increased difficulty (0.20, 0.18). Readability formulas outcomes were unrelated to comprehension or retention, but correlated with perceived difficulty in text (e.g., Smog Index: 0.334 correlation).

Conclusions:

Text was more effective for conveying complex health information, but audio can be suitable for easier content. In addition, several textual features affect information comprehension and retention for both modalities. Finally, existing readability formulas did not explain actual difficulty. This study highlighted the importance of tailoring health information delivery to content complexity by using appropriate style and modality.


 Citation

Please cite as:

Ahmed A, Leroy G, Kauchak D, Barai P, Harber P, Rains SA

Parallel Corpus Analysis of Text and Audio Comprehension to Evaluate Readability Formula Effectiveness: Quantitative Analysis

J Med Internet Res 2025;27:e69772

DOI: 10.2196/69772

PMID: 41037781

PMCID: 12490814

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