Child Pain Matters: A Training Protocol for General Nursing Staff in an Infusion Center on Procedural Anxiety in Pediatric Patients with Crohn\u27s Disease and Ulcerative Colitis.

Procedural anxiety is a broad concept that encompasses fear, distress, and pain. Anxiety is the most critical factor affecting pain perception. There is a growing body of literature suggesting that early pain might have long-term consequences. There is also research evidence that has linked inadequately managed pain in the pediatric population to negative behavioral and physiological consequences later in life. Pediatric patients with inflammatory bowel disease (IBD) refers to Crohn’s disease and Ulcerative Colitis. These chronic conditions often require multiple and repeated medical procedures that may cause pediatric patients to experience procedural anxiety. Needle related procedures are any procedures involving the use of needles for medical purposes such as immunization, venipuncture, IV insertions, intramuscular, or subcutaneous injections. The literature and relevant theories are discussed. A proposed training protocol for nursing staff in an infusion center is presented and this author created resource handouts for nurses, parents, and caregivers. A social narrative written by this author is presented. Also included are distraction card easel prototypes developed by this author to be used as a distraction technique to reduce procedural anxiety. An illustrative case study is presented to show the application of psychological interventions in reducing procedural anxiety. The future utility of the protocol, adapting to individual differences, and future directions are discussed. Keywords: Procedural anxiety, pediatric, Inflammatory Bowel Disease (IBD), social narrative, distraction, needle related procedures, comfort positions, procedural support

Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context

Discovering Design Principles for Persuasive Systems: A Grounded Theory and Text Mining Approach

Persuasive systems aim to change users\u27 behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in information technology. In that regard, recent research aims at identifying system design principles that are specific to persuasive systems. In this article we extend the existing literature by discovering design principles for persuasive systems from a systematic analysis of users feedback from the actual use of persuasive systems. Specifically, we use grounded theory and text mining (topic modeling) to extract design concepts from online user reviews of mobile diabetes applications. Overall, the results extend existing findings by highlighting the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and structural features into persuasive system design

Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context

Discovering design principles for persuasive systems: A grounded theory and text mining approach

Persuasive systems aim to change users\u27 behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in information technology. In that regard, recent research aims at identifying system design principles that are specific to persuasive systems. In this article we extend the existing literature by discovering design principles for persuasive systems from a systematic analysis of users feedback from the actual use of persuasive systems. Specifically, we use grounded theory and text mining (topic modeling) to extract design concepts from online user reviews of mobile diabetes applications. Overall, the results extend existing findings by highlighting the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and structural features into persuasive system design

Predicting Big Movers Based on Online Stock Forum Sentiment Analysis

While social media sentiment has been proved to have predictive value for stock indices, it is intriguing to investigate if it is useful for predicting price changes for individual stocks. We focus on a special kind of stocks, big movers, i.e., stocks that undergo a drastic one-day price change, and a special kind of social media, online stock discussion forums. Based on an empirical study, our research shows that discussions during the days lead up to the big one-day price change do contain sentiments that can be used to predict big movers. The findings of our research have theoretical implications for future research on social media sentiment and practical implications for developing stock investment strategies

Health Risks of e-cigarettes: Analysis of Twitter Data Using Topic Mining

The recent rise of e-cigarettes and vaping products has increased concerns that another young generation may become addicted to nicotine. Recently, it becomes evident that several health issues are related to the use of e-cigarettes and vaping products. The objective of this paper is to understand and identify such health issues by collecting and analyzing social media data. The analysis reflects the most important themes and topics discussed by online user’s about e-cigarettes, vaping, and associated health issues. Using topic modeling techniques, we were able to identify several health issues related to the use of e-cigarettes and vaping products. These issues include lung diseases, coughing and breathing issues, heart related issues, throat burn, respiratory related risks, dizziness, addiction, bronchitis, and cancer

Perception of Bias in ChatGPT: Analysis of Social Media Data

In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics