Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with "small world" network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.

Prevalence of online sexual harassment and online bullying: a nationwide survey among high school students in Denmark.

Background: Using data from a nationwide survey among high school students in Denmark, the aim of the current study is to measure the prevalence of online bullying and online sexual harassment and assess gender and age differences in exposure. Methods: We used data from the Danish National Youth Study 2019, which is a nationwide web survey among high school students, including general, commercial, preparatory and technical high schools. Data were collected from January to April 2019 through a self-administered questionnaire in the classroom. A total of 29,086 students participated (response rate: 66%). The survey included questions about online sexual harassment (victimization and perpetration) and online bullying. Results: Approximately 11% of students reported receiving sexually offensive inquiries online, and about 10% received a sexual image/video of others without the subjects' consent. Additionally, 4% experienced that other people shared a sexual image/video of them without their consent within the last 12 months. The most common type of online bullying was feeling ignored by others online (25%), followed by someone spreading rumors or writing nasty things about them (13%), and receiving threats/unpleasant messages (12%). Gender results were mixed.

Governing the misconduct of OTA platforms: A tripartite evolutionary game analysis considering the collaborative supervision of airlines and consumers.

Online travel agency (OTA) platforms frequently engage in unfair behaviors that infringe on the legitimate rights and interests of consumers and airlines in the ticket sale market. Effective governance of the OTA platforms' misconduct has become an urgent topic. In order to address the governance dilemma of OTA platforms' misconduct, a tripartite evolutionary game model considering the collaborative supervision between airlines and consumers is constructed. This study analyzes the evolutionary path and stable strategy of the three participants, airlines, platforms and consumers by numerical simulation. The results show that some actions, such as airlines' strict control of ticket sales resources and high fines on the platform, reducing the cost of customers' rights protection, and effectively guiding online public opinion, can benefit airlines and consumers and enhance their willingness to cooperate in supervision. Legitimate consumer rights protection not only brings negative public opinion and image loss to airlines, but also to platforms, which can force airlines to impose stricter constraints on platforms and force platforms to strengthen self-restraint. Therefore, a market mechanism instead of government regulatory that can effectively suppress platforms misconduct should be established to promote platforms self-regulation through a collaborative effort between airlines and consumers. Some special measures that guide the interests of three participators are also provided.

Promoting digital inclusion for enhanced resident wellbeing: an examination of aged residential care facility websites.

AIM: Digital inclusion has a positive impact on health and wellbeing through fostering connectivity and access to information. In Aotearoa New Zealand, 4% of older adults live in aged residential care (ARC) facilities and are vulnerable to social isolation. This study explored whether ARC facilities provide opportunities to socially connect online and whether the COVID-19 pandemic affected the provision of these opportunities. METHOD: Information on technology and internet provision from 558 ARC facilities was extracted from facilities' own or related websites in 2019 and 2021. ARC facilities were categorised according to whether they provided digital devices, internet access and internet-based leisure activities, or made no reference to technology. RESULTS: In 2019, 392 (70%) of 558 ARC facilities publicised availability of internet-based technologies for residents; however, only 46 (8%) mentioned providing access to computer devices. In 2021 (during the pandemic), there was a small increase to 421 (76%) and 54 (10%) of facilities respectively. Facilities mentioning internet-based leisure activities were 63 (11%) in 2019 and 55 (10%) in 2021. Those not referring to technology had reduced from 166 (30%) in 2019 to 137 (24%) in 2021. CONCLUSION: Few ARC facilities enabled residents to fully access the digital world, even after periods of isolation due to COVID-19. Aged care providers could be more proactive by providing internet access and digital learning opportunities.

Cognitive functioning and sustained internet use amid the COVID-19 pandemic: longitudinal evidence from older adults in Switzerland.

This study aimed to investigate the relationship between pre-pandemic objective and subjective cognitive functioning and sustained Internet use during the pandemic among older adults in Switzerland. Data from 1299 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2019/2020 and a supplementary technology use questionnaire during the pandemic in 2021 were used. Cognitive functioning was assessed in 2019/2020 through objective measures (delayed and immediate memory, verbal fluency) and self-rated memory. Sustained Internet use was defined as having used the Internet at least once in the past seven days in 2019/2020 and reporting daily or weekly use in 2021. We found that 73.1% of respondents consistently used Internet between 2019/2020 and 2021. Using multivariable probit regression models controlling for sociodemographic and health variables, we found that higher global cognition z-scores, especially in immediate and delayed memory, were associated with a higher likelihood of sustained Internet use. Additionally, respondents with good, very good, or excellent self-rated memory were more likely to sustain their Internet use. These findings highlight the potential critical role of cognitive health in shaping older adults' digital engagement, suggesting that cognitive assessments and training should be further considered in digital literacy initiatives for this population.

ChatGPT and Google Assistant as a Source of Patient Education for Patients With Amblyopia: Content Analysis.

BACKGROUND: We queried ChatGPT (OpenAI) and Google Assistant about amblyopia and compared their answers with the keywords found on the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) website, specifically the section on amblyopia. Out of the 26 keywords chosen from the website, ChatGPT included 11 (42%) in its responses, while Google included 8 (31%). OBJECTIVE: Our study investigated the adherence of ChatGPT-3.5 and Google Assistant to the guidelines of the AAPOS for patient education on amblyopia. METHODS: ChatGPT-3.5 was used. The four questions taken from the AAPOS website, specifically its glossary section for amblyopia, are as follows: (1) What is amblyopia? (2) What causes amblyopia? (3) How is amblyopia treated? (4) What happens if amblyopia is untreated? Approved and selected by ophthalmologists (GW and DL), the keywords from AAPOS were words or phrases that deemed significant for the education of patients with amblyopia. The "Flesch-Kincaid Grade Level" formula, approved by the US Department of Education, was used to evaluate the reading comprehension level for the responses from ChatGPT, Google Assistant, and AAPOS. RESULTS: In their responses, ChatGPT did not mention the term "ophthalmologist," whereas Google Assistant and AAPOS both mentioned the term once and twice, respectively. ChatGPT did, however, use the term "eye doctors" once. According to the Flesch-Kincaid test, the average reading level of AAPOS was 11.4 (SD 2.1; the lowest level) while that of Google was 13.1 (SD 4.8; the highest required reading level), also showing the greatest variation in grade level in its responses. ChatGPT's answers, on average, scored 12.4 (SD 1.1) grade level. They were all similar in terms of difficulty level in reading. For the keywords, out of the 4 responses, ChatGPT used 42% (11/26) of the keywords, whereas Google Assistant used 31% (8/26). CONCLUSIONS: ChatGPT trains on texts and phrases and generates new sentences, while Google Assistant automatically copies website links. As ophthalmologists, we should consider including "see an ophthalmologist" on our websites and journals. While ChatGPT is here to stay, we, as physicians, need to monitor its answers.

Using Short-Form Videos to Get Clinical Trial Newcomers to Sign Up: Message-Testing Experiment.

BACKGROUND: Recruiting participants for clinical trials poses challenges. Major barriers to participation include psychological factors (eg, fear and mistrust) and logistical constraints (eg, transportation, cost, and scheduling). The strategic design of clinical trial messaging can help overcome these barriers. While strategic communication can be done through various channels (eg, recruitment advertisements), health care providers on the internet have been found to be key sources for communicating clinical trial information to US adults in the social media era. OBJECTIVE: This study aims to examine how communication source (ie, medical doctors and peers) and message framing of TikTok videos (ie, psychological and logistical framing) influence clinical trial-related attitudes, perceptions, and sign-up behaviors under the guidance of the integrated behavioral model. METHODS: This study used a 2 (source: doctor vs peer) × 2 (framing: psychological vs logistical) between-participant factorial design web-based experiment targeting adults in the United States who had never participated in clinical trials (ie, newcomers). A Qualtrics panel was used to recruit and compensate the study respondents (n=561). Participants viewed short-form videos with doctors or peers, using psychological or logistical framing. The main outcome measures included perceived source credibility, self-efficacy, attitude toward clinical trial participation, behavioral intention, and sign-up behavior. Structural equation modeling was used to analyze the direct and indirect effects of message factors on the outcome variables. Source (doctor=1; peer=0) and framing (psychological=1; logistical=0) were dummy-coded. RESULTS: Doctor-featured messages led to greater perceived source credibility (ß=.31, P<.001), leading to greater self-efficacy (95% CI 0.13-0.30), which in turn enhanced behavioral intention (95% CI 0.12-0.29) and clinical trial sign-up behavior (95% CI 0.02-0.04). Logistical barrier-framed messages led to greater self-efficacy (ß=-.09, P=.02), resulting in higher intention to participate in clinical trials (95% CI -0.38 to -0.03) and improved sign-up behavior (95% CI -0.06 to -0.004). Logistical barrier-framed messages were also directly associated with an increased likelihood of signing up for a clinical trial (ß=-.08, P=.03). The model accounted for 21% of the variance in clinical trial sign-up behavior. Attitude did not significantly affect behavioral intention in this study (ß=.08, P=.14), and psychological and logistical barrier-framed messages did not significantly differ in attitudes toward clinical trial participation (ß=-.04, P=.09). CONCLUSIONS: These findings advance our understanding of how people process popular message characteristics in short-form videos and lend practical guidance for communicators. We encourage medical professionals to consider short-form video sites (eg, TikTok and Instagram Reels) as effective tools for discussing clinical trials and participation opportunities. Specifically, featuring doctors discussing efforts to reduce logistical barriers is recommended. Our measuring of actual behavior as an outcome is a rare and noteworthy contribution to this research.

Development of an Internet-based Product-related Child Injury Textual Data Platform (IPCITDP) in China.

Background: Internet-based media stories provide valuable information for emerging risks of product-related child injury prevention and control, but critical methodological challenges and high costs of data acquisition and processing restrict practical use by stakeholders. Methods: We constructed a data platform through literature reviews and multi-round research group discussions. Developed components included standard search strategies, filtering criteria, textual document classification, information extraction standards and a keyword dictionary. We used ten thousand manually labelled media stories to validate the textual document classification model, which was established using the Bidirectional Encoder Representation from Transformers (BERT). Multiple information extraction methods based on natural language processing algorithms were adopted to extract data for 29 structured variables from media stories. They were evaluated through manual validation of 1000 media stories about product-related child injury. We mapped the geographic distribution of media sources and media-reported product-related child injury events. Results: We developed an internet-based product-related child injury textual data platform, IPCITDP, consisting of four layers - automatic data search and acquisition, data processing, data storage, and data application - concerning product-related child injury online media stories in China. Each layer occurred daily. External validation demonstrated high performance for the BERT classification model we established (accuracy = 0.9703) and the combined information extraction strategies (accuracy >0.70 for 25 variables). As of 31 December 2023, IPCITDP collected 35 275 eligible product-related child injury reports from 13 261 news media websites or social media platform accounts which were geographically located across all 31 mainland Chinese provinces and covered over 97% of prefecture-level cities. The injury cases in IPCITDP were typically reported several months or years earlier than official announcements about the product-related child injury risks. Our data platform added data concerning 15 supplementary variables that the national product-related injury surveillance system lacks. Two examples demonstrate the value of IPCITDP in supplementing existing data and providing early epidemiological detection of emerging signals concerning product-related child injury: magnetic beads and electric self-balancing scooters. Conclusions: Our data platform provides injury data that can support early detection of new product-related child injury characteristics and supplement existing data sources to reduce the burden of product-related injury among Chinese children.

SIGEPS: A System for Managing the Health Internship Program.

The healthcare sector's demand for a skilled workforce necessitates effective management of internship and residency programs. This study introduces the System for Managing the Health Internship Program (SIGEPS), aimed at addressing internship and residency management challenges in Joinville's municipal health network. The method of this study focused on developing a computational solution with applied and exploratory objectives. The research adopted a User-Centered Development approach, involving seven stages from scope determination to system implementation. Key stages included selecting Google's free technological tools, developing forms for information input, integrating these into a Google Sheets spreadsheet, and creating a website for schedule display. The primary result was the successful development of SIGEPS, which automated the process from initial requests to scheduling and analysis of healthcare institutions and displayed schedules online. The system offers several advantages over previous models, such as concurrent scheduling by multiple employees, process automation, enhanced information transparency, and improved efficiency with significant time savings. Future enhancements include integrating decision support systems and enabling data analysis reports through dashboards to further reduce scheduling efforts and enhance professional efficiency. This study demonstrates the feasibility of developing and implementing a free computational solution using the Google platform for managing internships and residencies in the healthcare sector.

An Online Visual Elicitation Tool to Facilitate Conversations About Supportive Care in the Context of Cognitive Impairment.

Older adults with dementia are often left out of decision-making about their care. We developed and evaluated the use of an online visualization tool to foster discussions about future care preferences. The study provides valuable insights on methods for facilitating conversations about complex topics in the context of cognitive decline.

Developing an Open-Source, User-Friendly, OWASP-Compliant Architecture for Healthcare Web Application Testing.

Nowadays, web applications are fundamental in the healthcare sector. However, with the widespread use of this technology, risks related to cybersecurity attacks also increase. To mitigate this phenomenon, every 3-4 years, the nonprofit foundation Open Worldwide Application Security Project (OWASP) compiles a top 10 ranking of the most critical web application security risks. Along with the top 10 Web Application Security Risks, OWASP also provides the Web Security Testing Guide, which offers comprehensive guidelines for conducting security tests. This guide includes suggestions for specific tools to use when performing different tests, among other valuable insights. However, the use of these recommended tools can be costly and can require advanced technical skills and a deep understanding of security best practices and web technologies. In addition, since the OWASP work on web security is generic, it would be useful to restrict and adapt it to the healthcare area. This would help in reducing the overhead when dealing with the needed tools. The goal of this study is to make web application security assessment in healthcare more accessible by developing tools that simplify the process and makes it user- friendly. Before developing such tools, an in-depth feasibility study must be conducted to verify the existence of open-source libraries to carry out the necessary testing procedures. It will be also necessary to identify how tools could be simplified and enhanced when focusing on healthcare.

A Comparative Study of Access Analysis Service Utilization on Japanese Medical Institutions' Websites with GDPR-Compliant Cases.

The browsing history of a medical institution's website can potentially reveal or identify information about the health condition of the website visitor through browser cookies and fingerprints. In Japan, although the Personal Information Protection Law was revised in April 2022, the use of access analysis services to collect browsing history on medical institution websites has not been investigated. Therefore, this study investigates the actual usage of access analysis services on Japanese medical institution websites and compares it with the current situation in France, which follows the General Data Protection Regulation. The results revealed that the larger the size of the hospital, the higher the percentage of adoption of access analytics services in Japan. However, the implementation of a system for obtaining consent for the use of access analysis in Japan was found to be poor compared to that of French medical institutions. While access analysis tools are used in the websites of several medical institutions in Japan, the implementation of the process of obtaining consent to acquire browsing history is poor.

Social and Web Data Framework for Understanding Loneliness.

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework's utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.

The Tromsø Study: Lessons Learnt About Searching for Information About Health and Illness.

INTRODUCTION: Norway has a high use of e-health. METHODS: This paper summarizes and discusses the published data from the Tromsø 7 Study, conducted between 2015 and 2016, focusing on e-health utilization in the Norwegian population aged 40 and above. RESULTS: More than half of the participants reported using the Internet for health purposes. The main channels for obtaining information were search engines, apps, social media platforms, and online videos. The respondents frequently acted upon the information obtained online, and online health information influenced decisions regarding healthcare utilization and treatment management. Most respondents indicated a positive reaction to the information found online. CONCLUSIONS: The Tromsø 7 Study highlights the widespread utilization of e-health in Norway. The study also emphasizes the significant impact of e-health on individuals' decision-making processes related to their health. The findings suggest that the use of e-health overall does not replace the use of traditional health services, but rather functions as a supplement. Most respondents report positive reactions to online health information, highlighting the importance and relevance of e-health in modern healthcare practices.

Usability Evaluation of a Web-Based Remote Patient Monitoring Application: An Ongoing Study.

This study aims to discover problems and user experiences in a new released version of Sleepiz web application using heuristic evaluation and eye-tracking retrospective think-aloud performed by domain experts and end users. The web application is designed to support healthcare professionals in decision-making and monitoring of elderly people diagnosed with chronic respiratory diseases. Identification of usability problems and user experiences might contribute to improve the platform and will be reported to the developers.

A Web-Based Questionnaire Builder to Facilitate form Management for the Electronic Data Capture with REDCap.

REDCap, a popular platform for building surveys for electronic data capture, offers two methods for creating questionnaires: an interactive web interface to modify single questions and an upload method to import entire questionnaires. Both methods present limitations in terms of usability and time needed for different tasks. We propose a browser-based web application to design and manage REDCap questionnaires using a What-You-See-Is-What-You-Get approach. The application provides a user-friendly interface for a comprehensive overview of all imported questionnaires, and three distinct views cater to different aspects of the questionnaire design process. The questionnaires can be imported and exported through the REDCap CSV format and thus integrate seamlessly into its environment. REDCapQB represents a significant advancement in questionnaire design and management, offering researchers a powerful and user-friendly tool for electronic data capture in translational research studies within the REDCap ecosystem.

Exploring Greek Population Attitudes Towards Using Health-Related Websites for Managing Their Health.

The last few years the Internet has evolved into a prominent information source for many people worldwide. Latest research has shown that an ever increasing number of citizens and patients go online in order to access health information and seek support in managing their health, including understanding their condition, adopting life-saving lifestyle adjustments and keeping up with treatment or aftercare guidelines. Due to this rise on the demand of online health information, health-related sites have increased substantially, with each one of them striving to maintain the most comprehensive and reliable source of health and medical information on the Internet. This paper presents a survey conducted among Greek population aiming at exploring participants general attitudes towards using the Internet to access health information as well as their views regarding a specific Greek health-related website, namely Iatronet. To this end, an online Greek version of eHealth Impact Questionnaire has been used which was developed using RedCAP platform.

Using Natural Language Processing on Expert Panel Discussions to Gain Insights for Recruitment, Retention and Intervention Adherence for Online Social Support Interventions on a Stage II-III Clinical Trial Among Hispanic and African American Dementia Caregivers.

We applied natural language processing (NLP) to a corpus extracted from 4 hours of expert panel discussion transcripts to determine the sustainability of a Stage II-III clinical trial of online social support interventions for Hispanic and African American dementia caregivers. Prominent topics included Technology/hard to reach populations, Training younger populations, Building trust, Privacy and security issues, Simplification of screening questions and recruitment procedures, Understanding participants' needs, Planning strategies and logistics, Potential recruitment places, Adjusting intervention size downwards to engage elderly participants, Targeting different generations, Internet-based interventions by age range, and Providing step-by-step instructions and an overview of the entire research process during recruitment. The application of NLP to qualitative data on a dementia caregiving clinical trial provides useful insights for recruitment, retention, and adherence to guidelines for such interventions serving Hispanic and African American dementia caregivers.

Information Platform for Antibiotics.

Antibiotic stewardship is continuously evolving to incorporate results from novel research, clinical findings, and specialist recommendations. Numerous dedicated information sources, including web-based solutions, are available to keep medical practitioners informed. However, the provided information is often extensive, requiring users to extract the relevant facts. This study aimed to deliver an information platform that provides references, links, and information in a straightforward and engaging manner. Implementing a high-fidelity prototype prioritized medical and patient-oriented functionalities, structured around questions and quizzes. Additionally, the platform offers access to professional references, such as official healthcare guidelines and scientific articles. The development process adhered to design principles and included user testing with established usability measures (SUS, Nielsen's heuristics), resulting in satisfactory scores from IT experts and somewhat lower scores from users. Although designed to cater to a broader range of users, more work is needed to improve usability for the general public.