Assessing the Readability and Quality of Web-Based Resources on Exercise Stress Testing: Cross-Sectional Readability and Quality Analysis
Notice bibliographique
Résumé
Background: As internet usage continues to rise, an increasing number of individuals rely on online resources for health-related information. However, prior research has shown that much of this information is written at a reading level exceeding national recommendation, which may hinder patient comprehension and decision-making. The American Medical Association (AMA) recommends that patient-directed health materials be written at or below a 6th-grade reading level to ensure accessibility and promote health literacy. Despite these guidelines, studies indicate that many online health resources fail to meet this standard. The exercise stress test is a widely used diagnostic tool in cardiovascular medicine, yet no prior studies have assessed the readability and quality of online health information specific to this topic. Objective: This study aimed to evaluate the readability and quality of online resources on exercise stress testing and compare these metrics between academic and non-academic sources. Methods: A cross-sectional readability and quality analysis was conducted using Google and Bing to identify web-based patient resources related to exercise stress testing. Eighteen relevant websites were categorized as academic (n=7) or nonacademic (n=11). Readability was assessed using four established readability formulas: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), Simple Measure of Gobbledygook (SMOG), and Gunning Fog (GF). Website quality and reliability were evaluated using the modified DISCERN (mDISCERN) tool. Statistical comparisons between academic and nonacademic sources were performed using independent samples t tests. Results: The average FKGL, SMOG, and GF scores for all websites were 8.36, 8.28, and 10.14, respectively, exceeding the AMA-recommended 6th-grade reading level. Academic sources had significantly higher FKGL (9.1 vs. 7.9, P=.03), SMOG (8.9 vs. 7.9, P=.04), and lower FRE scores (57.6 vs. 65.3, P=.006) than nonacademic sources, indicating greater reading difficulty. The average GF scores for academic and nonacademic sources were 10.68 and 9.81, respectively, but this difference was not statistically significant. The quality of web resources, as assessed by mDISCERN, was classified as fair overall, with an average score of 29.44 out of 40 (74%). While academic and nonacademic websites had similar mDISCERN scores, areas such as source citation, publication dates, and acknowledgment of uncertainty were consistently lacking across all resources. Conclusions: Online resources on exercise stress testing are, on average, written at a reading level that exceeds the AMA's 6th-grade reading guideline, potentially limiting patient comprehension. Academic sources are significantly more difficult to read than nonacademic sources, though neither category meets the recommended readability standards. The quality of web-based resources was found to be fair but could be improved by ensuring transparency in sourcing and providing clearer, more comprehensive information. These findings underscore the need for improved accessibility and readability in online health information to support patient education and informed decision-making.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».