Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study.

BACKGROUND: Diabetes patient associations and diabetes-specific patient groups around the world are present on social media. Although active participation and engagement in these diabetes social media groups has been mostly linked to positive effects, very little is known about the content that is shared on these channels or the post features that engage their users the most. OBJECTIVE: The objective of this study was to analyze (1) the content and features of posts shared over a 3-year period on 3 diabetes social media channels (Facebook, Twitter, and Instagram) of a diabetes association, and (2) users' engagement with these posts (likes, comments, and shares). METHODS: All social media posts published from the Norwegian Diabetes Association between January 1, 2017, and December 31, 2019, were extracted. Two independent reviewers classified the posts into 7 categories based on their content. The interrater reliability was calculated using Cohen kappa. Regression analyses were carried out to analyze the effects of content topic, social media channel, and post features on users' engagement (likes, comments, and shares). RESULTS: A total of 1449 messages were posted. Posts of interviews and personal stories received 111% more likes, 106% more comments, and 112% more shares than miscellaneous posts (all P<.001). Messages posted about awareness days and other celebrations were 41% more likely to receive likes than miscellaneous posts (P<.001). Conversely, posts on research and innovation received 31% less likes (P<.001), 35% less comments (P=.02), and 25% less shares (P=.03) than miscellaneous posts. Health education posts received 38% less comments (P=.003) but were shared 39% more than miscellaneous posts (P=.007). With regard to social media channel, Facebook and Instagram posts were both 35 times more likely than Twitter posts to receive likes, and 60 times and almost 10 times more likely to receive comments, respectively (P<.001). Compared to text-only posts, those with videos had 3 times greater chance of receiving likes, almost 4 times greater chance of receiving comments, and 2.5 times greater chance of being shared (all P<.001). Including both videos and emoji in posts increased the chances of receiving likes by almost 7 times (P<.001). Adding an emoji to posts increased their chances of receiving likes and being shared by 71% and 144%, respectively (P<.001). CONCLUSIONS: Diabetes social media users seem to be least engaged in posts with content topics that a priori could be linked to greater empowerment: research and innovation on diabetes, and health education. Diabetes social media groups, public health authorities, and other stakeholders interested in sharing research and innovation content and promoting health education on social media should consider including videos and emoji in their posts, and publish on popular and visual-based social media channels, such as Facebook and Instagram, to increase user engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12913-018-3178-7.

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.

Design and implementation of a prototype with a standardized interface for transducers in ambient assisted living.

Solutions in the field of Ambient Assisted Living (AAL) do not generally use standards to implement a communication interface between sensors and actuators. This makes these applications isolated solutions because it is so difficult to integrate them into new or existing systems. The objective of this research was to design and implement a prototype with a standardized interface for sensors and actuators to facilitate the integration of different solutions in the field of AAL. Our work is based on the roadmap defined by AALIANCE, using motes with TinyOS telosb, 6LoWPAN, sensors, and the IEEE 21451 standard protocol. This prototype allows one to upgrade sensors to a smart status for easy integration with new applications and already existing ones. The prototype has been evaluated for autonomy and performance. As a use case, the prototype has been tested in a serious game previously designed for people with mobility problems, and its advantages and disadvantages have been analysed.

Development of robust behaviour recognition for an at-home biomonitoring robot with assistance of subject localization and enhanced visual tracking.

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.

Understanding Emotional Dynamics in Autism Social Media Communities.

Searches for autism on social media have soared, making it a top topic. Social media posts convey not only plain text, but also sentiments and emotions that provide insight into the experiences of the autism community. While sentiment analysis categorizes overall sentiment, emotion analysis provides nuanced insights into specific emotional states. The objective of this study is to identify emotions in posts related to autism and compare the emotions specifically contained in posts that include the hashtag #ActuallyAutistic with those that do not. METHODS: We extracted a sample of X' posts related to autism and used DistilBERT to assign one out of six emotions (sadness, joy, love, anger, fear, surprise) to each post. RESULTS: We have analyzed a total of 414,287 posts, 98,602 (23.8%) of those included the hashtag #ActuallyAutistic. The most common expressed emotion was joy, which was expressed in 52.5% of the posts, followed by sadness, identified in 28.6% of the posts. 12% of the posts expressed fear, 4.9% reflected anger, 1.1% showed love, and 0.9% expressed surprise. Posts tagged as #ActuallyAutistic showed less joy (27.1% vs. 60.4% in posts without this hashtag, p<0.001) and more sadness (52.7% vs. 21.1% in those without the hashtag, p<0.001). CONCLUSIONS: The use of the hashtag #ActuallyAutistic is associated with a different emotional tone, characterized by less joy and more sadness. These results suggest the need for greater support and acceptance towards the autistic community, both online and in society in general. Insights from our study can be valuable for policy makers, health, educational or other programmes aiming at enhancing well-being, inclusiveness, improve services, and create a more compassionate and understanding atmosphere for autistic people.

What Do Autistic People Discuss on Twitter? An Approach Using BERTopic Modelling.

Social media provide easy ways to autistic individuals to communicate and to make their voices heard. The objective of this paper is to identify the main themes that are being discussed by autistic people on Twitter. We collected a sample of tweets containing the hashtag #ActuallyAutistic during the period 10/02/2022 and 14/09/2022. To identify the most discussed topics, BERTopic modelling was applied. We manually grouped the detected topics into 6 major themes using inductive content analysis: 1) General aspects of autism and experiences of autistic individuals; 2) Autism awareness, pride and funding; 3) Interventions, mostly related to Applied Behavior Analysis; 4) Reactions and expressions; 5) Everyday life as an autistic (lifelong condition, work, housing…); and 6) Symbols and characteristics. The majority of tweets were presenting general aspects and experiences as autistic individuals; raising awareness; and about their dissatisfaction with some interventions. The identification of autistic individuals' main discussion themes could help to develop meaningful public health agendas and research involving and addressed to autistic individuals.

Diabetes on Twitter: A Sentiment Analysis.

BACKGROUND: Contents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The objective of this study is to analyze the sentiment expressed in messages on diabetes posted on Twitter. METHOD: Tweets including one of the terms "diabetes," "t1d," and/or "t2d" were extracted for one week using the Twitter standard API. Only the text message and the number of followers of the users were extracted. The sentiment analysis was performed by using SentiStrength. RESULTS: A total of 67 421 tweets were automatically extracted, of those 3.7% specifically referred to T1D; and 6.8% specifically mentioned T2D. One or more emojis were included in 7.0% of the posts. Tweets specifically mentioning T2D and that did not include emojis were significantly more negative than the tweets that included emojis (-2.22 vs -1.48, P < .001). Tweets on T1D and that included emojis were both significantly more positive and also less negative than tweets without emojis (1.71 vs 1.49 and -1.31 vs -1.50, respectively; P < .005). The number of followers had a negative association with positive sentiment strength ( r = -.023, P < .001) and a positive association with negative sentiment ( r = .016, P < .001). CONCLUSION: The use of sentiment analysis techniques on social media could increase our knowledge of how social media impact people with diabetes and their families and could help to improve public health strategies.

Preferences and interests of diabetes social media users regarding a health-promotion intervention.

BACKGROUND: Nowadays, rapid and accessible participatory research on diabetes can be carried out using social media platforms. The objective of this study was to identify preferences and interests of diabetic social media users regarding a health-promotion intervention targeting them. METHODS: Social media followers of the Norwegian Diabetes Association were invited to participate in the creation of a health-promotion intervention on diabetes by expressing their opinions through an online questionnaire posted on Facebook, Twitter, and Instagram. The questionnaire asked participants about their demographics and preferences regarding type of health content: format, frequency, and channels to deliver content. Questions regarding the perceived quality of diabetes-related information and satisfaction with content on social media were also included. RESULTS: The questionnaire was answered by 346 participants: 332 (96%) of those were reached via Facebook, 66.5% of respondents (n=230) identified themselves as women, 54% (n=187) as individuals diagnosed with type 1 diabetes, and 71% (n=235) were aged 30-64 years. The preferred type of content was "research and innovation on diabetes", selected by 78.0% of the respondents. "Text format" was the choice for 93.4%, and 97.3% would prefer to find health-promotion content on Facebook. There was heterogeneity in the desired frequency of this content. In a scale ranging from 0 to 100, the perceived quality of diabetes-related information on social media was 62.0±1.2 and satisfaction with such content 61.9±1.3. CONCLUSION: The approach used in this study was successful in reaching and involving participants quickly, and could also potentially increase diabetes patients' engagement and satisfaction with health-promotion interventions, enhance their sense of community, and thus help people attain healthier lifestyles. It is a limitation that our sample might not have been fully representative, as the most interested social media users might have chosen to participate.

Gamification strategy on prevention of STDs for youth.

Sexually transmitted diseases (STDs) and especially chlamydia is a worrying problem among North-Norwegian youngsters. Gamified web applications should be valued for sexual health education, and thus STDs prevention, for their potential to get users engaged and involved with their healthcare. Aiming to achieve that youngsters become more aware of STDs we have developed "sjekkdeg.no", a gamified web application focused on sexual health targeting North-Norwegian youngsters. Gamification techniques like avatars, achievement-based gifts and social network sharing buttons have been implemented in the site that includes educational content on sexual health and a STDs symptom checker. Preliminary results show that the game-style web app could be useful to encourage users to learn more on sexual health and STDs and thus changing their risky behaviors and preventing sexually transmitted diseases.