Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being.

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.

Longitudinal associations of social support, everyday social interactions, and mental health during the COVID-19 pandemic.

Main effect models contend that perceived social support benefits mental health in the presence and the absence of stressful events, whereas stress-buffering models contend that perceived social support benefits mental health especially when individuals are facing stressful events. We tested these models of how perceived social support impacts mental health during the COVID-19 pandemic and evaluated whether characteristics of everyday social interactions statistically mediated this association - namely, (a) received support, the visible and deliberate assistance provided by others, and (b) pleasantness, the extent to which an interaction is positive, flows easily, and leads individuals to feel understood and validated. 591 United States adults completed a 3-week ecological momentary assessment protocol sampling characteristics of their everyday social interactions that was used to evaluate between-person average values and within-person daily fluctuations in everyday social interaction characteristics. Global measures of perceived social support and pandemic-related stressors were assessed at baseline. Psychiatric symptoms of depression and anxiety were assessed at baseline, at the end of each day of ecological momentary assessment, and at 3-week follow-up. Consistent with a main effect model, higher baseline perceived social support predicted decreases in psychiatric symptoms at 3-week follow-up (ß = -.09, p = .001). Contrary to a stress-buffering model, we did not find an interaction of pandemic-stressors × perceived social support. The main effect of perceived social support on mental health was mediated by the pleasantness of everyday social interactions, but not by received support in everyday social interactions. We found evidence for both main effects and stress-buffering effects of within-person fluctuations in interaction pleasantness on daily changes in mental health. Results suggest the importance of everyday social interaction characteristics, especially their pleasantness, in linking perceived social support and mental health.

Mental health during the COVID-19 pandemic: Impacts of disease, social isolation, and financial stressors.

BACKGROUND: Although research shows that the Covid-19 pandemic has led to declines in mental health, the existing research has not identified the pathways through which this decline happens. AIMS: The current study identifies the distinct pathways through which COVID-induced stressors (i.e., social distancing, disease risk, and financial stressors) trigger mental distress and examines the causal impact of these stressors on mental distress. METHODS: We combined evidence of objective pandemic-related stressors collected at the county level (e.g., lack of social contact, infection rates, and unemployment rates) with self-reported survey data from over 11.5 million adult respondents in the United States collected daily for eight months. We used mediation analysis to examine the extent to which the objective stressors influenced mental health by influencing individual respondents' behavior and fears. RESULTS: County-level, day-to-day social distancing predicted significantly greater mental distress, both directly and indirectly through its effects on individual social contacts, worries about getting ill, and concerns about finances. Economic hardships were indirectly linked to increased mental distress by elevating people's concerns about their household's finances. Disease threats were both directly linked to mental distress and indirectly through its effects on individual worries about getting ill. Although one might expect that social distancing from people outside the home would have a greater influence on people who live alone, sub-analyses based on household composition do not support this expectation. CONCLUSION: This research provides evidence consistent with the thesis that the COVID-19 pandemic harmed the mental well-being of adults in the United States and identifies specific stressors associated with the pandemic that are responsible for increasing mental distress.

Scaling up analogical innovation with crowds and AI.

Analogy-the ability to find and apply deep structural patterns across domains-has been fundamental to human innovation in science and technology. Today there is a growing opportunity to accelerate innovation by moving analogy out of a single person's mind and distributing it across many information processors, both human and machine. Doing so has the potential to overcome cognitive fixation, scale to large idea repositories, and support complex problems with multiple constraints. Here we lay out a perspective on the future of scalable analogical innovation and first steps using crowds and artificial intelligence (AI) to augment creativity that quantitatively demonstrate the promise of the approach, as well as core challenges critical to realizing this vision.

Eliciting and receiving online support: using computer-aided content analysis to examine the dynamics of online social support.

BACKGROUND: Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. OBJECTIVE: The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. METHODS: Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. RESULTS: Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=-.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=-.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). CONCLUSIONS: Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities.

Effectiveness of shared leadership in Wikipedia.

OBJECTIVE: The objective of the paper is to understand leadership in an online community, specifically, Wikipedia. BACKGROUND: Wikipedia successfully aggregates millions of volunteers' efforts to create the largest encyclopedia in human history. Without formal employment contracts and monetary incentives, one significant question for Wikipedia is how it organizes individual members with differing goals, experience, and commitment to achieve a collective outcome. Rather than focusing on the role of the small set of people occupying a core leadership position, we propose a shared leadership model to explain the leadership in Wikipedia. Members mutually influence one another by exercising leadership behaviors, including rewarding, regulating, directing, and socializing one another. METHOD: We conducted a two-phase study to investigate how distinct types of leadership behaviors (transactional, aversive, directive, and person-focused), the legitimacy of the people who deliver the leadership, and the experience of the people who receive the leadership influence the effectiveness of shared leadership in Wikipedia. RESULTS: Our results highlight the importance of shared leadership in Wikipedia and identify trade-offs in the effectiveness of different types of leadership behaviors. Aversive and directive leadership increased contribution to the focal task, whereas transactional and person-focused leadership increased general motivation. We also found important differences in how newcomers and experienced members responded to leadership behaviors from peers. APPLICATION: These findings extend shared leadership theories, contribute new insight into the important underlying mechanisms in Wikipedia, and have implications for practitioners who wish to design more effective and successful online communities.

Visual copresence and conversational coordination.

Pickering & Garrod's (P&G's) theory of dialogue production cannot completely explain recent data showing that when interactants in referential communication tasks have different views of a physical space, they accommodate their language to their partner's view rather than mimicking their partner's expressions. Instead, these data are consistent with the hypothesis that interactants are taking the perspective of their conversational partners.