METHODS: YouTube videos were systematically acquired with 4 search terms. The top 50 videos per search term by the number of views were stored in a YouTube account. A set of inclusion/exclusion criteria were applied, videos were assessed for viewing characteristics, a 4-point scoring system (0-3) was applied to evaluate QOI in 10 predetermined domains, and a 3-point scoring system (0-2) was applied to evaluate COI. Descriptive statistical analyses and intrarater and interrater reliability tests were performed.
RESULTS: Strong intrarater and interrater reliability scores were observed. Sixty-three videos from the top 58 most-viewed DPs were viewed 1,395,471 times (range, 414-124,939). Most DPs originated from the United States (20%), and orthodontists (62%) uploaded most of the videos. The mean number of reported domains was 2.03 ± 2.40 (out of 10). The mean overall QOI score per domain was 0.36 ± 0.79 (out of 3). The "Placement of miniscrews" domain scored highest (1.23 ± 0.75). The "Cost of miniscrews placement" domain scored the lowest (0.03 ± 0.25). The mean overall QOI score per DP was 3.59 ± 5.64 (out of 30). The COI in 32 videos was immeasurable, and only 2 avoided using technical words.
CONCLUSIONS: The QOI related to temporary anchorage devices contained within videos provided by DPs through the YouTube Web site is deficient, particularly in the cost of placement. Orthodontists should be aware of the importance of YouTube as an information resource and ensure that videos related to temporary anchorage devices contain comprehensive and evidence-based information.
METHODS: Using thematic analysis based on three frameworks, 120 posted messages and comments were examined from MyEndosis Facebook group-a support group for women with endometriosis from January to July 2014.
RESULTS: Results showed the issues discussed were (a) personal struggles, (b) medication and treatment, (c) alternative medication, (d) side effects, and (e) medication recommended by doctors. While using this social medium, users found (a) emotional support, (b) esteem support, (c) information support, (d) network support, and (e) tangible assistance in their engagement with others.
CONCLUSION: The analysis suggested that users' interactions were structured around information, emotion, and community building, which many doctors and nurses were not aware of. The group was shaped as a social network where peer users share social support, cultivate companionship, and exert social influence.
Methods: This open label comparative design study randomized health professional clinicians to receive "practice points" on tendinopathy management via Twitter or Facebook. Evaluated outcomes included knowledge change and self-reported changes to clinical practice.
Results: Four hundred and ninety-four participants were randomized to 1 of 2 groups and 317 responders analyzed. Both groups demonstrated improvements in knowledge and reported changes to clinical practice. There was no statistical difference between groups for the outcomes of knowledge change (P = .728), changes to clinical practice (P = .11) or the increased use of research information (P = .89). Practice points were shared more by the Twitter group (P social media posts are as effective as longer posts for improving knowledge and promoting behavior change. Twitter may be more useful in publicizing information and Facebook for encouraging course completion.
OBJECTIVE: The aim of this study was to explore public sentiments and emotions toward the LSSR and identify issues, fear, and reluctance to observe this restriction among the Indonesian public.
METHODS: This study adopts a sentiment analysis method with a supervised machine learning approach on COVID-19-related posts on selected media platforms (Twitter, Facebook, Instagram, and YouTube). The analysis was also performed on COVID-19-related news contained in more than 500 online news platforms recognized by the Indonesian Press Council. Social media posts and news originating from Indonesian online media between March 31 and May 31, 2020, were analyzed. Emotion analysis on Twitter platform was also performed to identify collective public emotions toward the LSSR.
RESULTS: The study found that positive sentiment surpasses other sentiment categories by 51.84% (n=1,002,947) of the total data (N=1,934,596) collected via the search engine. Negative sentiment was recorded at 35.51% (686,892/1,934,596) and neutral sentiment at 12.65% (244,757/1,934,596). The analysis of Twitter posts also showed that the majority of public have the emotion of "trust" toward the LSSR.
CONCLUSIONS: Public sentiment toward the LSSR appeared to be positive despite doubts on government consistency in executing the LSSR. The emotion analysis also concluded that the majority of people believe in LSSR as the best method to break the chain of COVID-19 transmission. Overall, Indonesians showed trust and expressed hope toward the government's ability to manage this current global health crisis and win against COVID-19.
OBJECTIVE: This study aimed to evaluate the utility and usability of ScreenMen.
METHODS: This study used both qualitative and quantitative methods. Healthy men working in a banking institution were recruited to participate in this study. They were purposively sampled according to job position, age, education level, and screening status. Men were asked to use ScreenMen independently while the screen activities were being recorded. Once completed, retrospective think aloud with playback was conducted with men to obtain their feedback. They were asked to answer the System Usability Scale (SUS). Intention to undergo screening pre- and postintervention was also measured. Qualitative data were analyzed using a framework approach followed by thematic analysis. For quantitative data, the mean SUS score was calculated and change in intention to screening was analyzed using McNemar test.
RESULTS: In total, 24 men participated in this study. On the basis of the qualitative data, men found ScreenMen useful as they could learn more about their health risks and screening. They found ScreenMen convenient to use, which might trigger men to undergo screening. In terms of usability, men thought that ScreenMen was user-friendly and easy to understand. The key revision done on utility was the addition of a reminder function, whereas for usability, the revisions done were in terms of attracting and gaining users' trust, improving learnability, and making ScreenMen usable to all types of users. To attract men to use it, ScreenMen was introduced to users in terms of improving health instead of going for screening. Another important revision made was emphasizing the screening tests the users do not need, instead of just informing them about the screening tests they need. A Quick Assessment Mode was also added for users with limited attention span. The quantitative data showed that 8 out of 23 men (35%) planned to attend screening earlier than intended after using the ScreenMen. Furthermore, 4 out of 12 (33%) men who were in the precontemplation stage changed to either contemplation or preparation stage after using ScreenMen with P=.13. In terms of usability, the mean SUS score of 76.4 (SD 7.72) indicated that ScreenMen had good usability.
CONCLUSIONS: This study showed that ScreenMen was acceptable to men in terms of its utility and usability. The preliminary data suggested that ScreenMen might increase men's intention to undergo screening. This paper also presented key lessons learned from the beta testing, which is useful for public health experts and researchers when developing a user-centered mobile Web app.
OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.
METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.
RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.
CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.