Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Most of the many technological adaptations of MI, however, have focused on delivering the action-oriented treatment, leaving its relational component unexplored or vaguely described. This study intends an early design of a conversational sequence that considers both technical and relational components of MI for a mental health concern. Objective: This case study aims to design a conversational sequence for a brief motivational interview to be delivered by a Web-based text messaging application (“chatbot”), and to investigate its conversational experience with graduate students in their coping with stress. Methods: A brief conversational sequence was designed with varied combinations of MI skills to follow the four processes of MI. A Web-based text messaging app, Bonobot, was built as a research prototype to deliver the sequence in an online conversation. A total of 30 full-time graduate students who self-reported stress in regard of their school life was recruited for a survey of demographic information and perceived stress (PSS-10), and a semi-structured interview. Interviews were transcribed verbatim and analyzed by Braun and Clarke’s thematic method. Themes that reflect the process, impact of, and needs for the conversational experience are reported. Results: Participants had a high level of perceived stress (M=22.5, SD=5.0). Our findings include themes as follows: Evocative Questions and Clichéd Feedback; Self-Reflection and Potential Consolation; and Need for Information and Contextualized Feedback. Participants particularly favored the relay of evocative questions, but were less satisfied with the agent-generated reflective and affirming feedback that filled in-between. Discussing the idea of change was a good means of reflecting on themselves, and some of Bonobot’s encouragements related to graduate school life were appreciated. Participants suggested the conversation provide informational support, as well as more contextualized feedback. Conclusions: A conversational sequence for a brief motivational interview was presented in this case study. Participant feedback suggests sequencing questions and MI-adherent statements can facilitate a conversation for stress management, which may encourage a chance of self-reflection. More diversified sequences, along with more contextualized feedback, should follow to offer a better conversational experience and to confirm any empirical effect. Clinical Trial: n/a

Both enterprises and their employees have globally experienced remote work at an unprecedented scale since the outbreak of COVID-19. As the pandemic becomes less of a threat, some companies have called their employees back to a physical office, citing issues related to working remotely, but many employees have refused to return. Thus, working in the metaverse has gained much attention as an alternative that could complement the weaknesses of completely remote work or even offline work. However, we do not know yet what benefits and drawbacks the metaverse has as a legitimate workspace, because there are few real cases of 1) working in the metaverse and 2) working remotely at such an unprecedented scale. Thus, this paper aims to identify real challenges and opportunities the metaverse workspace presents when compared to remote work by conducting semi-structured interviews and participatory workshops with various employees and company stakeholders (e.g., HR managers and CEOs) who have experienced at least two of three work types: working in a physical office, remotely, or in the metaverse. Consequently, we identified 1) advantages and disadvantages of remote work and 2) opportunities and challenges of the metaverse. We further discuss design implications that may overcome the identified challenges of working in the metaverse.

Many people gather online and form teams with strangers to collaborate on tasks. However, while intrateam trust and cohesion are critical for team performance, such characteristics take time to establish and are harder to build up through computer-mediated communication. Building on prior research that has shown that enhancing familiarity between members can help, we hypothesized that the use of a chatbot to support the familiarization of ad hoc teammates can help their collaboration. As such, we designed IntroBot, a chatbot that builds on an online discussion facilitator framework and leverages the social media data of users to assist their familiarization process. Through a between-subjects study (N=60), we found that participants who used IntroBot reported higher levels of trust, cohesion, and interaction quality, as well as generated more ideas in a collaborative brainstorming task. We discuss insights gained from our study, and present opportunities for the future of chatbot-assisted collaboration.

Background: Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). Objective: This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. Methods: The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. Results: The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001). Conclusions: This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

Algorithms are playing an increasingly important role in the production of news content as their computation capacity in manipulating large-scale data continues to grow. In this article, we present Personalized and Interactive News Generation System (PINGS), an algorithm-driven news generation system that is designed to provide personalized and interactive news for sports. We designed PINGS to generate baseball news based on the statistical importance of data and the direct manipulation of user interface components that alter the underlying algorithmic computation. We discuss the base-level algorithm framework for automated news content generation and describe the architecture of the system in terms of how it is designed to support the generation of personalized news stories. An evaluation revealed that the algorithm is capable of generating news stories that are significantly more interesting and pleasant to read than traditional baseball news articles.

Background: Diabetes mellitus requires continuous self-management to prevent complications. Patients frequently rely on online resources and mobile apps for diabetes-related information; however, these often lead to information overload, limited personalization, and difficulty in navigation. Generative artificial intelligence (AI) chatbots may address these challenges by providing accessible, personalized, and responsive guidance. Objective: This study aimed to explore the potential role of generative AI chatbots in diabetes management through a 2-part qualitative evaluation. Part 1 examined patients’ information needs, user experiences, and expectations. Part 2 investigated specialists’ perspectives on the practical utility of generative AI chatbots in supporting diabetes self-management. By incorporating perspectives from both patients and specialists, the study aimed to identify appropriate boundaries for the involvement of generative AI chatbots, reflecting the needs and expectations of both stakeholder groups. Methods: This study was conducted using DTalksBot, a generative AI chatbot powered by GPT-4 (OpenAI) and enhanced with retrieval-augmented generation. In Part 1, we aimed to understand the experiences, needs, and expectations of patients with diabetes. To achieve this, 24 participants engaged in structured chatbot sessions, completed postinteraction surveys, and participated in in-depth interviews. Data were analyzed using thematic and content analysis to identify patterns in user queries and experiences. In part 2, we invited 4 family medicine specialists to assess the accuracy of DTalksBot’s responses by reviewing conversation logs and to share expert insights on the future role of generative AI chatbots in diabetes management. Results: In part 1, a total of 24 patients submitted a total of 643 questions, which were categorized into 4 primary themes: personalized health advice and guidance (n=281, 44.6%), complications and comorbidities (n=174, 27.1%), medication and treatment exploration (n=111, 17.3%), and mental health management and support (n=30, 4.7%). Patients emphasized the advantages of generative AI chatbots over traditional information sources, including faster access to reliable content, reduced cognitive burden, and the ability to comfortably discuss sensitive topics. In part 2, specialists recognized the generative AI chatbots’ value in answering routine inquiries, but noted limitations in contextual accuracy, real-time data integration, and response personalization. Conclusions: Generative AI chatbots showed promise as complementary tools for diabetes self-management by offering accessible, reliable, and tailored support. This formative evaluation provides empirical evidence on how generative AI chatbots can address patient information needs and complement existing health care resources. To maximize utility, future generative AI chatbots need to integrate real-time health data, enhance contextual relevance, and align with clinical workflows to ensure safety, trust, and broader applicability.