Evaluating the readability, understandability, and quality of online materials about chest pain in children

Arslan, Derya; Tutar, Mahmut Sami; Kozanhan, Betül

doi:10.1007/s00431-020-03772-8

Cited by 12 publications

(20 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our study aims to assess the writing styles of web-based health resources on children's health through an integrated, holistic approach, that is, the development of machine learning models to evaluate whether the content and the writing style of a piece of web-based health educational material is more related to children's health promotion and education, or more for the general public. The underlying hypothesis of our study is that the content and writing style of high-quality web-based health educational resources vary with the intended readership, which is based on the principles of clinically developed guidelines for health educational resource assessment such as PEMAT [21][22][23] and health educational research findings in support of user-oriented health communication styles [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Xie¹,

Ji²,

Liu³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background Medical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education. Objective Using authoritative and highly influential web-based children’s health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children’s health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public. Methods The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline. Results Using five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI −0.016 to 0.1929), mean specificity (P=.02; 95% CI −0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI −0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI −0.1699 to 0.0681), mean specificity (P=.10; 95% CI −0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI −0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets. Conclusions We developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children’s health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Xie¹,

Ji²,

Liu³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…In 2020, Arslan et al found similar scores in a study in which the readability of online materials for chest pain in children was evaluated. [15] Our study showed that the information provided on the Internet is very difficult to read for a substantial proportion of the population. We used the PEMAT, which evaluates the overall understandability, [9] to determine whether patients may understand educational materials about flat foot.…”

Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

How understandable are the patient education materials about flat foot on the Internet for parents?

et al. 2023

View full text Add to dashboard Cite

Flat foot is a common reason for parents to visit orthopedic clinics. As the Internet has become an easy-search platform, parents often seek online educational materials before seeking out a professional. The aim of this study was to investigate the quality, readability, and understandability of such online materials for parents. An Internet search was performed for “flat foot” and “pes planus” using the Google search engine. The readability was evaluated using 6 different grading systems: Flesch Reading Ease Score, Flesch–Kincaid Grade Level, Simple Measure of Gobbledygook, Fry Readability score, Gunning Fog Index tests, and Automated Readability Index. The Patient Education Materials Assessment Tool test was used to assess the understandability. For quality assessment, the Journal of American Medical Association benchmark criteria and Health on the Net code were applied. One hundred nine websites were included and evaluated for readability, understandability, and quality. The mean readability grade for all websites was 10.5 ± 2.0. The mean Gunning Fog Index tests and Flesch–Kincaid Grade Level scores for all websites were 12.4 ± 2.2 and 9.7 ± 2.1 sequentially. The mean Coleman–Liau index score was 10.0 ± 1.5, and the average Fry Readability score was 9.9 ± 2.0. The automated readability index for all websites was 10.3 ± 2.5. The average Flesch Reading Ease score for all educational materials was 59.3 ± 10.1. The average Patient Education Materials Assessment Tool score for all educational materials was 81% (range, 70–87%). The mean Journal of American Medical Association benchmark criterion for all websites was 1.0, with a range from 1.0 and 2.0. Eighteen (16.5%) websites had Health on the Net certificates. Readability, understandability, and quality of patient education materials about flat feet on the Internet vary and are often worse than professional recommendations.

show abstract

“…There seems to be an evolution of content providers in terms of understandability and actionability as may be evidenced by the presence of lower scores in the older videos than in the newer videos and studies. There are no studies on the assessment of understandability and actionability of YouTube videos on HDN, however, there are many studies on other diseases/conditions like online cardiovascular disease risk calculators [ 20 ], type 2 diabetic risk calculators [ 21 ], diabetes mellitus [ 22 ], laryngectomy [ 23 ], clubfoot [ 24 ], chest pain in children [ 25 ], etc., including one more similar study by author on Hemophilia [ 26 ]. Most studies had similar good understandability scores, but poor actionability scores.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Understandability and Actionability of YouTube Videos on Hemolytic Disease of the Newborn

Kumar¹,

Mani²,

Sriranjitha³

et al. 2023

Cureus

View full text Add to dashboard Cite

IntroductionWith revolutions in Information Technology, information and misinformation are easier to be found online. YouTube is the largest and most commonly searched video content website in the world. It is assumed that, due to the coronavirus pandemic, most patients try to know about diseases through the internet and reduce the number of hospital exposures unless otherwise. In order to assess the understandability and actionability of such YouTube videos available freely online about the disease, Hemolytic disease of the newborn (HDN), this study was planned. MethodsThis is a cross-sectional study conducted with the first 160 videos available on May 14, 2021, with the search keyword "HDN" with a relevance filter and a duration of 4 to 20 minutes. The videos were further screened regarding the information content and language. These videos were assessed by three independent assessors using the patient educational materials assessment tool for audio-visual content.

show abstract

Evaluating the readability, understandability, and quality of online materials about chest pain in children

Cited by 12 publications

References 32 publications

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

How understandable are the patient education materials about flat foot on the Internet for parents?

Assessment of Understandability and Actionability of YouTube Videos on Hemolytic Disease of the Newborn

Contact Info

Product

Resources

About