The Effect of Enumeration of Self-Relevant Words on Self-Focused Attention and Repetitive Negative Thoughts

Self-focused attention refers to awareness of self-referent, internally generated information. It can be categorized into dysfunctional (i.e., self-rumination) and functional (self-reflection) aspects. According to theory on cognitive resource limitations (e.g., Moreno, 2006), there is a difference in cognitive resource allocation between these two aspects of self-focused attention. We propose a new task, self-relevant word (SRW) enumeration, that can aid in behaviorally identifying individuals’ use of self-rumination and self-reflection. The present study has two purposes: to determine the association between self-focus and SRW enumeration, and to examine the effect of dysfunctional SRW enumeration on repetitive negative thinking. One hundred forty-six undergraduate students participated in this study. They completed a measure of state anxiety twice, before and after imagining a social failure situation. They also completed the SRW enumeration task, Repetitive Thinking Questionnaire, Short Fear of Negative Evaluation Scale, and Rumination-Reflection Questionnaire. A correlational analysis indicated a significant positive correlation between self-reflection and the number of SRWs. Furthermore, individuals high in self-reflection had a tendency to pay more attention to problems than did those high in self-rumination. A significant positive correlation was found between self-rumination and the strength of self-relevance of negative SRWs. Through a path analysis, we found a significant positive effect of the self-relevance of negative SRWs on repetitive negative thinking. Notably, however, the model that excluded self-rumination as an explanatory variable showed a better fit to the data than did the model that included it. In summary, SRW enumeration might enable selective and independent detection of the degree of self-reflection and self-rumination, and therefore should be examined in future research in order to design new behavioral procedures

Cultural Adaptation of the Actionable Health App Evaluation in Japan: Protocol for a Web-Based Modified Delphi Expert Consensus Study

BackgroundWith an increase in both the number of mental health disorders people are experiencing and the difficulty in accessing mental health care, the demand for accessible mental health care services has increased. The use of mobile devices has allowed people to receive care in their daily lives without restrictions on time or location. However, the majority of publicly available mobile health apps are not evidence-based, and the top-rated apps are not always safe or user-friendly and may not offer clinically beneficial results. ObjectiveThis study aims to create a cultural adaptation of the American Psychiatric Association’s comprehensive app evaluation framework in Japan using a web-based modified Delphi expert consensus. MethodsA web-based modified Delphi study includes developing the Japanese version of the comprehensive app evaluation framework and 3 Delphi rounds. In the first round, our working group sends a questionnaire to the panelists, who then complete it. In the second and third rounds, the working group sends a questionnaire and a summary of the panelists’ answers based on each of the previous rounds. The panelists answer the questionnaires based on this summary. The summarization procedure is automated to help reduce the biases that can be generated when panelists’ answers are summarized and when the panelists receive them. The working group sends only the result of the summarization with the next round’s questionnaire. All interactions between the working group and the panelists will be conducted on Qualtrics (Qualtrics Japan LLC), a questionnaire platform. To culturally validate the comprehensive mental health app evaluation framework, participants from the following three categories will be recruited in Japan: (1) researchers, (2) practitioners, and (3) app developers. ResultsThis study received funding from a crowdfunding campaign in Japan (April 2023). The Delphi study began in January 2023 and will be completed in December 2023. We had already completed the translation of the 105 original app evaluation item questions by December 2022. ConclusionsWhile the need for treatment using mental health apps is increasing, no framework that can be used to develop a centralized database for health apps is available or accessible, and no consensus has been reached among stakeholders in Japan about an appropriate framework. The results of the web-based modified Delphi method presented in this paper may provide direction for the development and use of mental health apps in the future among the relevant stakeholders. Furthermore, this study will enhance recognition of the framework among researchers, clinicians, mental health app developers, and users, in addition to devising new instruments to help users or practitioners efficiently choose the right app for their situations. International Registered Report Identifier (IRRID)PRR1-10.2196/4446

Emotion-injecting prompt for large language model chatbot

This paper introduces a new use of large Language Models (LMs) that provide an emotional feedback experience while chatting. The proposed prompt creates a chatbot that infers emotional experiences, evaluates emotional parameters, and then replies to users considering these parameters. This prompted chatbot can help construct an environment in which we train emotional conversation such as customer service, especially in the situation dealing with difficult customer