Social Media Content of Idiopathic Pulmonary Fibrosis Groups and Pages on Facebook: Cross-sectional Analysis

Current Landscape and Future Directions of Patient Education in Adults with Interstitial Lung Disease

Background

Understandable, comprehensive, and accessible educational materials for patients with interstitial lung disease (ILD) are lacking. Patients consistently ask for improved access to evidence-based information about ILD. Nonetheless, few research studies focus directly on developing and evaluating interventions to improve patient knowledge.

Objective

We describe the current landscape of patient education in ILD, identify gaps in current approaches to information delivery, and provide frameworks to address these challenges through novel educational tools.

Methods

A literature review was conducted in collaboration with a medical librarian (M.R.D.) in April 2022 using Ovid MEDLINE (1946-), Embase (1947-), Cochrane Central (1993-), and CINAHL (1961-). Search terms included "interstitial lung disease," "pulmonary fibrosis," "patient education," and "information seeking behavior" (see the data supplement for full search terms). Reference lists from selected articles were used to identify additional studies.

Results

Currently, patient education is commonly combined with exercise regimens in pulmonary rehabilitation programs in which benefits of the educational component alone are unclear. Few studies investigate improving knowledge access and acquisition for patients with ILD and their caregivers regarding self-management, oxygen use, and palliative care plans. Online distribution of health information through social media runs the risk of being unregulated and outdated, although it is an avenue of increasing accessibility.

Conclusion

By expanding access to novel ILD-specific education programs and accounting for social determinants of health that impact healthcare access, patient education has the potential to become more attainable, improving patient-centered outcomes. Further research into optimal development, delivery, and efficacy testing of patient education modalities in ILD is warranted.

Role of the internet of medical things in care for patients with interstitial lung disease

PURPOSE OF REVIEW

Online technologies play an increasing role in facilitating care for patients with interstitial lung disease (ILD). In this review, we will give an overview of different applications of the internet of medical things (IoMT) for patients with ILD.

RECENT FINDINGS

Various applications of the IoMT, including teleconsultations, virtual MDTs, digital information, and online peer support, are now used in daily care of patients with ILD. Several studies showed that other IoMT applications, such as online home monitoring and telerehabilitation, seem feasible and reliable, but widespread implementation in clinical practice is lacking. The use of artificial intelligence algorithms and online data clouds in ILD is still in its infancy, but has the potential to improve remote, outpatient clinic, and in-hospital care processes. Further studies in large real-world cohorts to confirm and clinically validate results from previous studies are needed.

SUMMARY

We believe that in the near future innovative technologies, facilitated by the IoMT, will further enhance individually targeted treatment for patients with ILD by interlinking and combining data from various sources.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BACKGROUND

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

OBJECTIVE

This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

METHODS

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

CONCLUSIONS

Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Patient Perceptions of Retinal Detachment Management and Recovery through Social Media

PURPOSE

Social media support groups can provide accessibility to advice and emotional regarding medical topics, such as retinal detachment repair, but this is almost universally provided by laypersons. We sought to determine how topics related to retinal detachment repair are associated with various emotional responses and the spread of misinformation, as identified through an online social media support group.

METHODS

Retrospective observational study of the largest Facebook support group for retinal detachment from 03/19/2021 to 07/19/2021. Members of the support group that posted during the study period. Comments were coded by content (Pre-procedural, Peri-procedural Post procedural, Repeat procedures) and participant response (Emotional responses, Asking for medical advice, and Misinformation). Associations between content and responses were examined using Pearson's chi-squared test, two-sample t-test, and linear regression.

RESULTS

Posts that included written comments from the study period were analyzed. Negative emotional responses appeared in 30% of posts and positive emotional responses were in 16% of posts. Misinformation was more likely to appear in pre-procedure posts (5.3% versus 1.4%, p = .03). Negative emotional responses trended towards being more common in topics related to repeat procedures (40% vs 28%), although this did not reach statistical significance (p = .06).

CONCLUSIONS

Surgeons should be aware that patients frequently express negative experiences on this forum, asked for medical advice, even in the post-operative period, and that these posts generated high engagement. Misinformation may be propagated in support groups, though less commonly with regard to post-procedural questions.

Content Analysis of Idiopathic Pulmonary Fibrosis-related Information on Twitter

Background

Information regarding idiopathic pulmonary fibrosis (IPF) on the internet is often outdated, inaccurate, and potentially harmful. Twitter is a social media platform that allows users to post content in the form of "tweets".

Objective

We sought to assess the prevalence of inaccurate information regarding IPF on Twitter. We hypothesized that foundations and medical organizations would be the least likely to post inaccurate information and that inaccurate tweets would have higher user engagement.

Methods

All tweets posted between 2011 and 2019 were gathered using "snscrape" on Python 3.8 while searching for the phrase "idiopathic pulmonary fibrosis". Quantitative analysis was performed to describe trends in IPF-related tweet frequency over time. A subset of tweets made between 2018 and 2019 was screened for verifiable medical statements, which were then analyzed for accuracy compared with contemporary clinical practice guidelines, with descriptive statistics reported. Logistic regression was used to compare tweet accuracy and recommendation of nonindicated therapies across sources, with adjustment for tweet age and character count. Wilcoxon rank-sum tests were used to determine if user engagement (favorites, retweets, and replies) differed between accurate and inaccurate tweets.

Results

A total of 16,787 tweets were identified between 2011 and 2019. Between 2018 and 2019, 4,861 tweets were included, of which 1,612 (33%) contained verifiable medical statements. Tweets from sources other than foundations or medical organizations were more likely to contain inaccurate information and to recommend nonindicated therapies in both unadjusted and adjusted analyses. News and media sources had the highest odds of communicating potentially harmful information in both adjusted (odds ratio [OR], 12.00; 95% confidence interval [CI], 5.87-27.16) and unadjusted (OR, 11.62; 95% CI, 5.70-26.21) analyses when compared with foundations and medical organizations. Tweets containing inaccurate information had significantly lower numbers of favorites and retweets (P < 0.001 for both).

Conclusion

Misinformation regarding IPF is present on Twitter and is more often presented by news and media sources. Medically inaccurate tweets displayed less user engagement than accurate tweets. This differs from findings on IPF-related information on YouTube and Facebook, which may reflect differences in both author and consumer qualities across social media platforms.

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

BACKGROUND

Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States.

OBJECTIVE

This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System.

METHODS

The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey.

RESULTS

Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level.

CONCLUSIONS

Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.