How Artificial Intelligence for Healthcare Look Like in the Future?

Research on artificial intelligence (AI) for healthcare gained interest in recent years. However, the use of AI in daily clinical practice is still rare. We created and distributed an online survey among professionals working within the health informatics field to explore their views. The provided answers were classified into referring or not to: 1) Application areas; 2) Medical specialities; 3) Specific technologies; 4) Use cases; 5) Citizens involvement; and 6) Challenges. We received 42 valid responses. With regard to the sentiment of the answers, 71,4% were classified by the AFINN tool as being positive. In light of the open question, 76,2% of the respondents referred to possible applications areas. They think the most frequent uses will be for diagnostic, decision making and treatment. 54,8% of respondents referred to use cases, being personalized care and daily practice the most popular scenarios. 28,6% mentioned citizens' involvement, and 23,8% medical specialities in which AI might be used. There is a mostly positive attitude towards the application of AI in healthcare, in particular regarding its future use for realising routine tasks. From these results, we conclude that research should further focus on realising AI-based applications for relieving health professionals from repetitive tasks and optimize healthcare processes

Social media chatbot for increasing physical activity: usability study

Fully automated self-help interventions integrated with social media chatbots could serve as highly cost-effective physical activity promotion tools for a large population. We have developed MYA, a Telegram-based chatbot for increasing physical activity. The objective of this study was to assess the usability of MYA. To identify usability issues, we recruited volunteers and asked them to interact with MYA and to answer the Chatbot Usability Questionnaire. Thirty volunteers participated in the study, 83.3% agreed MYA was welcoming during initial setup and 63.3% agreed MYA was very easy to use. MYA was perceived as realistic and engaging, easy to navigate, and its responses were useful, appropriate, and informative (all 53.3%). However, 63.3% of respondents agreed MYA failed to recognize most of their inputs, and 43.3% claimed it would be easy to get confused when using MYA. Although the results are encouraging, it remains unclear if a social media chatbot can motivate people to increase their physical activity. MYA has the potential to do that, with improvements in functionalities like challenge personalization. The efficacy of these approaches should be studied in a clinical trial.publishedVersio

Usability Testing of a Social Media Chatbot for Increasing Physical Activity Behavior

Digital interventions for increasing physical activity behavior have shown great potential, especially those with social media. Chatbots, also known as conversational agents, have emerged in healthcare in relation to digital interventions and have proven effective in promoting physical activity among adults. The study’s objective is to explore users’ experiences with a social media chatbot. The concept and the prototype development of the social media chatbot MYA were realized in three steps: requirement analysis, concept development, and implementation. MYA’s design includes behavior change techniques effective in increasing physical activity through digital interventions. Participants in a usability study answered a survey with the Chatbot Usability Questionnaire (CUQ), which is comparable to the Systems Usability Scale. The mean CUQ score was below 68, the benchmark for average usability. The highest mean CUQ score was 64.5 for participants who thought MYA could help increase their physical activity behavior. The lowest mean CUQ score was 40.6 for participants aged between 50 and 69 years. Generally, MYA was considered to be welcoming, very easy to use, realistic, engaging, and informative. However, some technical issues were identified. A good and diversified user experience promotes prolonged chatbot use. Addressing identified issues will enhance users’ interaction with MYA.publishedVersio

Exploring the Evolution of Social Media in Mental Health Interventions: A Mapping Review

Background: With the rise of social media, social media use for delivering mental health interventions has become increasingly popular. However, there is no comprehensive overview available on how this field developed over time. Objectives: The objective of this paper is to provide an overview over time of the use of social media for delivering mental health interventions. Specifically, we examine which mental health conditions and target groups have been targeted, and which social media channels or tools have been used since this topic first appeared in research. Methods: To provide an overview of the use of social media for mental health interventions, we conducted a search for studies in four databases (PubMed; ACM Digital Library; PsycInfo; and CINAHL) and two trial registries (Clinicaltrials.gov; and Cochranelibrary.com). A sample of representative keywords related to mental health and social media was used for that search. Automatic text analysis methods (e.g., BERTopic analysis, word clouds) were applied to identify topics, and to extract target groups and types of social media. Results: A total of 458 studies were included in this review (n=228 articles, and n=230 registries). Anxiety and depression were the most frequently mentioned conditions in titles of both articles and registries. BERTopic analysis identified depression and anxiety as the main topics, as well as several addictions (including gambling, alcohol, and smoking). Mental health and women's research were highlighted as the main targeted topics of these studies. The most frequently targeted groups were “adults” (39.5%) and “parents” (33.4%). Facebook, WhatsApp, messenger platforms in general, Instagram, and forums were the most frequently mentioned tools in these interventions. Conclusions: We learned that research interest in social media-based interventions in mental health is increasing, particularly in the last two years. A variety of tools have been studied, and trends towards forums and Facebook show that tools allowing for more content are preferred for mental health interventions. Future research should assess which social media tools are best suited in terms of clinical outcomes. Additionally, we conclude that natural language processing tools can help in studying trends in research on a particular topic.publishedVersio

Is There a Weekly Pattern for Health Searches on Wikipedia and Is the Pattern Unique to Health Topics?

Published version. Source at http://doi.org/10.2196/jmir.5038.Background: Online health information–seeking behaviors have been reported to be more common at the beginning of the workweek. This behavior pattern has been interpreted as a kind of “healthy new start” or “fresh start” due to regrets or attempts to compensate for unhealthy behavior or poor choices made during the weekend. However, the observations regarding the most common health information–seeking day were based only on the analyses of users’ behaviors with websites on health or on online health-related searches. We wanted to confirm if this pattern could be found in searches of Wikipedia on health-related topics and also if this search pattern was unique to health-related topics or if it could represent a more general pattern of online information searching—which could be of relevance even beyond the health sector. Objective: The aim was to examine the degree to which the search pattern described previously was specific to health-related information seeking or whether similar patterns could be found in other types of information-seeking behavior. Methods: We extracted the number of searches performed on Wikipedia in the Norwegian language for 911 days for the most common sexually transmitted diseases (chlamydia, gonorrhea, herpes, human immunodeficiency virus [HIV], and acquired immune deficiency syndrome [AIDS]), other health-related topics (influenza, diabetes, and menopause), and 2 nonhealth-related topics (footballer Lionel Messi and pop singer Justin Bieber). The search dates were classified according to the day of the week and ANOVA tests were used to compare the average number of hits per day of the week. Results: The ANOVA tests showed that the sexually transmitted disease queries had their highest peaks on Tuesdays (P<.001) and the fewest searches on Saturdays. The other health topics also showed a weekly pattern, with the highest peaks early in the week and lower numbers on Saturdays (P<.001). Footballer Lionel Messi had the highest mean number of hits on Tuesdays and Wednesdays, whereas pop singer Justin Bieber had the most hits on Tuesdays. Both these tracked search queries also showed significantly lower numbers on Saturdays (P<.001). Conclusions: Our study supports prior studies finding an increase in health information searching at the beginning of the workweek. However, we also found a similar pattern for 2 randomly chosen nonhealth-related terms, which may suggest that the search pattern is not unique to health-related searches. The results are potentially relevant beyond the field of health and our preliminary findings need to be further explored in future studies involving a broader range of nonhealth-related searches

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

Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas

Motivation: Digital therapeutics (DTX), i.e., health interventions that are provided through digital means, are increasingly available for use; in some countries, physicians can even prescribe selected DTX following a reimbursement by health insurances. This results in an increasing need for methodologies to consider and monitor DTX’s negative consequences, their risks to patient safety, and possible adverse events. However, it is completely unknown which aspects should be subject to surveillance given the missing experiences with the tools and their negative impacts. Objective: Our aim is to develop a tool—the DTX Risk Assessment Canvas—that enables researchers, developers, and practitioners to reflect on the negative consequences of DTX in a participatory process. Method: Taking the well-established business model canvas as a starting point, we identified relevant aspects to be considered in a risk assessment of a DTX. The aspects or building blocks of the canvas were constructed in a two-way process: first, we defined the aspects relevant for discussing and reflecting on how a DTX might bring negative consequences and risks for its users by considering ISO/TS 82304-2, the scientific literature, and by reviewing existing DTX and their listed adverse effects. The resulting aspects were grouped into thematic blocks and the canvas was created. Second, six experts in health informatics and mental health provided feedback and tested the understandability of the initial canvas by individually applying it to a DTX of their choice. Based on their feedback, the canvas was modified. Results: The DTX Risk Assessment Canvas is organized into 15 thematic blocks which are in turn grouped into three thematic groups considering the DTX itself, the users of the DTX, and the effects of the DTX. For each thematic block, questions have been formulated to guide the user of the canvas in reflecting on the single aspects. Conclusions: The DTX Risk Assessment Canvas is a tool to reflect the negative consequences and risks of a DTX by discussing different thematic blocks that together constitute a comprehensive interpretation of a DTX regarding possible risks. Applied during the DTX design and development phase, it can help in implementing countermeasures for mitigation or means for their monitoring

Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter

Background: On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people’s perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition. Objective: The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk’s disclosure. Methods: We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms “Aspergers” or “Aspie.” The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool. Results: We extracted a total of 227 popular tweets (34 posted the week before Musk’s announcement and 193 posted the week after). We classified 210 (92.5%) of the tweets as neutral, 13 (5.7%) tweets as informative, and 4 (1.8%) as containing misinformation. Both informative and misinformative tweets were posted after Musk’s disclosure. Popular tweets posted before Musk’s disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement. Conclusions: The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition.publishedVersio

Personalized Digital Solutions for Mental Health

Introduction: Mental health is one of the major global concerns in the field of healthcare. The emergence of digital solutions is proving to be a great aid for individuals suffering from mental health disorders. These solutions are particularly useful and effective when they are personalized. The objective of this paper is to understand the personalization factors and the methods that have been used to collect information to personalize the digital mental health solutions. Methods: This paper builds on a previous review that analyzed the personalization of digital solutions in mHealth, and expands on the extracted information for the specific case of mental health. Results: Ten mental health digital solutions have been analyzed. The paper focuses on targeted conditions, personalization factors and the methods used for collecting personalization factors. Discussion: The analyzed mental health digital solutions cover a wide range of health conditions. It is remarkable that most articles do not explicitly mention the factors used to personalize the solution. Among the solutions that mention them, there is a great diversity of factors utilized, such as age, gender, user preferences, and subjective behavior. The authors point out the methods for obtaining data to personalize the solutions, including in-app questionnaires, self-reports, and usage data of the solutions. Conclusions: The analysis of current mental health digital solutions emphasizes the need to create guidelines for designing personalized digital solutions for mental health

Covid-19-related misinformation on social media: a systematic review

Source at https://www.who.int/publications/journals/bulletin/. Objective - To review misinformation related to coronavirus disease 2019 (COVID-19) on social media during the first phase of the pandemic and to discuss ways of countering misinformation. Methods - We searched PubMed®, Scopus, Embase®, PsycInfo and Google Scholar databases on 5 May 2020 and 1 June 2020 for publications related to COVID-19 and social media which dealt with misinformation and which were primary empirical studies. We followed the preferred reporting items for systematic reviews and meta-analyses and the guidelines for using a measurement tool to assess systematic reviews. Evidence quality and the risk of bias of included studies were classified using the grading of recommendations assessment, development and evaluation approach. The review is registered in the international prospective register of systematic reviews (PROSPERO; CRD42020182154). Findings - We identified 22 studies for inclusion in the qualitative synthesis. The proportion of COVID-19 misinformation on social media ranged from 0.2% (413/212 846) to 28.8% (194/673) of posts. Of the 22 studies, 11 did not categorize the type of COVID-19-related misinformation, nine described specific misinformation myths and two reported sarcasm or humour related to COVID-19. Only four studies addressed the possible consequences of COVID-19-related misinformation: all reported that it led to fear or panic. Conclusion Social media play an increasingly important role in spreading both accurate information and misinformation. The findings of this review may help health-care organizations prepare their responses to subsequent phases in the COVID–19 infodemic and to future infodemics in general