Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies

AI Chatbots for Psychological Health for Health Professionals: Scoping Review

Background

Health professionals face significant psychological burdens including burnout, anxiety, and depression. These can negatively impact their well-being and patient care. Traditional psychological health interventions often encounter limitations such as a lack of accessibility and privacy. Artificial intelligence (AI) chatbots are being explored as potential solutions to these challenges, offering available and immediate support. Therefore, it is necessary to systematically evaluate the characteristics and effectiveness of AI chatbots designed specifically for health professionals.

Objective

This scoping review aims to evaluate the existing literature on the use of AI chatbots for psychological health support among health professionals.

Methods

Following Arksey and O'Malley's framework, a comprehensive literature search was conducted across eight databases, covering studies published before 2024, including backward and forward citation tracking and manual searching from the included studies. Studies were screened for relevance based on inclusion and exclusion criteria, among 2465 studies retrieved, 10 studies met the criteria for review.

Results

Among the 10 studies, six chatbots were delivered via mobile platforms, and four via web-based platforms, all enabling one-on-one interactions. Natural language processing algorithms were used in six studies and cognitive behavioral therapy techniques were applied to psychological health in four studies. Usability was evaluated in six studies through participant feedback and engagement metrics. Improvements in anxiety, depression, and burnout were observed in four studies, although one reported an increase in depressive symptoms.

Conclusions

AI chatbots show potential tools to support the psychological health of health professionals by offering personalized and accessible interventions. Nonetheless, further research is required to establish standardized protocols and validate the effectiveness of these interventions. Future studies should focus on refining chatbot designs and assessing their impact on diverse health professionals.

The use of virtual reality-assisted interventions on psychological well-being and treatment adherence among kidney transplant recipients: A randomized controlled study

This randomized controlled trial aims to investigate the effects of educational and psychological interventions on the health outcomes of patients suffering from chronic diseases. We recruited 372 patients and randomly assigned them to one of two intervention arms during the trial, which lasted for a year. Both groups participated in a 12-month intervention program, where the intervention group received health education and supportive psychological therapy utilizing virtual reality (VR) technology, while the control group received conventional health education guidance. Statistical analysis showed that compared to the control group, the intervention group demonstrated significant improvements (p < 0.05) in depression assessment scores, compliance scores, and Barthel functional scoring. However, the two groups had no significant difference in the incidence of complications and health knowledge mastery. Additionally, the intervention group had fewer hospitalization days than the control group, with statistically significant differences. The research results prove that targeted intervention effectively improves medication adherence, patient awareness, and reduces patient hospitalization days, which is particularly important for managing chronic diseases.

Effectiveness of mental health chatbots in depression and anxiety for adolescents and young adults: a meta-analysis of randomized controlled trials

BACKGROUND

The mental health chatbot is dedicated to providing assistance to individuals grappling with the complexities of depression and anxiety.

OBJECTIVE

The study aimed to evaluate the effectiveness of the mental health chatbot in alleviating symptoms of depression and anxiety among adolescents and young adults.

METHODS

A systematic review framework was employed with a protocol pre-registered on Prospero (CRD42023418877). Databases were systematically searched, including PubMed, ACM Digital Library, Embase, Cochrane and IEEE. Data synthesis was conducted narratively, and meta-analysis was performed by pooling data from the original studies.

RESULTS

Ten randomized controlled trials focused on an acute population, mainly females and university students. Chatbots designed for daily conversations and mood monitoring, using cognitive behavioral therapy techniques, showed efficacy in treating depression (95% CI = -1.09 to -0.23; p = .003). However, it is essential to highlight that these interventions utilizing chatbots for mental health were not found to be efficacious in managing symptoms of anxiety (95% CI = -0.56 to 0.4; p = .74).

CONCLUSIONS

Evidence supports the effectiveness of mental health chatbots in treating depression, but further exploration and refinement are needed to optimize their efficacy in managing anxiety.

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation.

Trends and associated factors of mental health diagnoses in Catalan Primary Care (2010-2019)

BACKGROUND

The prevalence of mental health disorders has significantly increased in recent years, posing substantial challenges to healthcare systems worldwide, particularly primary care (PC) settings. This study examines trends in mental health diagnoses in PC settings in Catalonia from 2010 to 2019 and identifies associated sociodemographic, clinical characteristics, psychopharmacological treatments, and resource utilization patterns.

METHODS

Data from 947,698 individuals without prior severe mental illness, derived from the Data Analytics Program for Health Research and Innovation (PADRIS), were analyzed for this study. Sociodemographic data, diagnoses, and resource utilization were extracted from electronic health records. Descriptive statistics, chi-square tests, Mann-Whitney tests, and a multivariate binary logistic regression were employed to analyze the data.

RESULTS

Over the study period, 172,112 individuals (18.2%) received at least one mental health diagnosis in PC, with unspecified anxiety disorder (40.5%), insomnia (15.7%) and unspecified depressive disorder (10.2%) being the most prevalent. The prevalence of these diagnoses increased steadily until 2015 and stabilized thereafter. Significant associations were found between mental health diagnoses, female sex, lower socioeconomic status, higher BMI, and smoking status in a multivariate binary logistic regression.

CONCLUSIONS

This study highlights a growing burden of stress-related mental health diagnoses in PC in Catalonia, driven by demographic and socioeconomic factors. These findings may be indicative of broader trends across Europe and globally. Addressing this rising prevalence requires innovative approaches and collaborative strategies that extend beyond traditional healthcare resources. Engaging stakeholders is essential for implementing effective, sustainable solutions that promote mental health in Catalonia and potentially inform similar initiatives worldwide.

A review on the efficacy of artificial intelligence for managing anxiety disorders

Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

BACKGROUND

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

OBJECTIVE

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

METHODS

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

RESULTS

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

CONCLUSIONS

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

Anxious and depressive symptoms and health-related quality of life in a cohort of people who recently attempted suicide: A network analysis

BACKGROUND

Suicide is an international health concern with immeasurable impact from the perspective of human and social suffering. Prior suicide attempts, anxious and depressive symptoms, and relatively lower health-related quality of life (HRQoL) are among the most replicated risk factors for suicide. Our goal was to visualize the distribution of these features and their interconnections with use of a network analysis approach in individuals who recently attempted suicide.

METHODS

Individuals with a recent suicide attempt were recruited from nine University Hospitals across Spain as part of the SURVIVE cohort study. Anxious and depressive symptoms, and perceived HRQoL were included in the network analysis. Network structures were estimated with the EBICglasso model. Centrality measures and bridge symptoms connecting communities were explored. Subnetworks comparing younger and older individuals, and women and men were analyzed.

RESULTS

A total of 1106 individuals with a recent suicide attempt were included. Depressed mood was the symptom with the greatest influence in the overall network, followed by anxiety symptoms such as feeling nervous, worrying, restless, and having difficulties to relax. Perceived general health was associated with increased suicidal ideation in the whole sample. Older people showed a specific connection between perceived general health and depressed mood.

LIMITATIONS

The cross-sectional design does not allow determination of established causality.

CONCLUSIONS

Depressed mood was the core network's symptom and, therefore, an important target in the management and prevention of suicide. HRQoL had more influence on the network of older populations, in which it should be a primary focus.

Adapting and Evaluating an AI-Based Chatbot Through Patient and Stakeholder Engagement to Provide Information for Different Health Conditions: Master Protocol for an Adaptive Platform Trial (the MARVIN Chatbots Study)

BACKGROUND

Artificial intelligence (AI)-based chatbots could help address some of the challenges patients face in acquiring information essential to their self-health management, including unreliable sources and overburdened health care professionals. Research to ensure the proper design, implementation, and uptake of chatbots is imperative. Inclusive digital health research and responsible AI integration into health care require active and sustained patient and stakeholder engagement, yet corresponding activities and guidance are limited for this purpose.

OBJECTIVE

In response, this manuscript presents a master protocol for the development, testing, and implementation of a chatbot family in partnership with stakeholders. This protocol aims to help efficiently translate an initial chatbot intervention (MARVIN) to multiple health domains and populations.

METHODS

The MARVIN chatbots study has an adaptive platform trial design consisting of multiple parallel individual chatbot substudies with four common objectives: (1) co-construct a tailored AI chatbot for a specific health care setting, (2) assess its usability with a small sample of participants, (3) measure implementation outcomes (usability, acceptability, appropriateness, adoption, and fidelity) within a large sample, and (4) evaluate the impact of patient and stakeholder partnerships on chatbot development. For objective 1, a needs assessment will be conducted within the setting, involving four 2-hour focus groups with 5 participants each. Then, a co-construction design committee will be formed with patient partners, health care professionals, and researchers who will participate in 6 workshops for chatbot development, testing, and improvement. For objective 2, a total of 30 participants will interact with the prototype for 3 weeks and assess its usability through a survey and 3 focus groups. Positive usability outcomes will lead to the initiation of objective 3, whereby the public will be able to access the chatbot for a 12-month real-world implementation study using web-based questionnaires to measure usability, acceptability, and appropriateness for 150 participants and meta-use data to inform adoption and fidelity. After each objective, for objective 4, focus groups will be conducted with the design committee to better understand their perspectives on the engagement process.

RESULTS

From July 2022 to October 2023, this master protocol led to four substudies conducted at the McGill University Health Centre or the Centre hospitalier de l'Université de Montréal (both in Montreal, Quebec, Canada): (1) MARVIN for HIV (large-scale implementation expected in mid-2024), (2) MARVIN-Pharma for community pharmacists providing HIV care (usability study planned for mid-2024), (3) MARVINA for breast cancer, and (4) MARVIN-CHAMP for pediatric infectious conditions (both in preparation, with development to begin in early 2024).

CONCLUSIONS

This master protocol offers an approach to chatbot development in partnership with patients and health care professionals that includes a comprehensive assessment of implementation outcomes. It also contributes to best practice recommendations for patient and stakeholder engagement in digital health research.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05789901; https://classic.clinicaltrials.gov/ct2/show/NCT05789901.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54668.

Conversational agents for depression screening: A systematic review

OBJECTIVE

This work explores the advances in conversational agents aimed at the detection of mental health disorders, and specifically the screening of depression. The focus is put on those based on voice interaction, but other approaches are also tackled, such as text-based interaction or embodied avatars.

METHODS

PRISMA was selected as the systematic methodology for the analysis of existing literature, which was retrieved from Scopus, PubMed, IEEE Xplore, APA PsycINFO, Cochrane, and Web of Science. Relevant research addresses the detection of depression using conversational agents, and the selection criteria utilized include their effectiveness, usability, personalization, and psychometric properties.

RESULTS

Of the 993 references initially retrieved, 36 were finally included in our work. The analysis of these studies allowed us to identify 30 conversational agents that claim to detect depression, specifically or in combination with other disorders such as anxiety or stress disorders. As a general approach, screening was implemented in the conversational agents taking as a reference standardized or psychometrically validated clinical tests, which were also utilized as a golden standard for their validation. The implementation of questionnaires such as Patient Health Questionnaire or the Beck Depression Inventory, which are used in 65% of the articles analyzed, stand out.

CONCLUSIONS

The usefulness of intelligent conversational agents allows screening to be administered to different types of profiles, such as patients (33% of relevant proposals) and caregivers (11%), although in many cases a target profile is not clearly of (66% of solutions analyzed). This study found 30 standalone conversational agents, but some proposals were explored that combine several approaches for a more enriching data acquisition. The interaction implemented in most relevant conversational agents is text-based, although the evolution is clearly towards voice integration, which in turns enhances their psychometric characteristics, as voice interaction is perceived as more natural and less invasive.

A text-based conversational agent for asthma support: Mixed-methods feasibility study

Objective

Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates 'Brisa', a chatbot designed to improve asthma patients' self-assessment and self-management.

Methods

We recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to 'Brisa' via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements.

Results

74% of participants engaged with 'Brisa' at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation.

Conclusions

The study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.

A chatbot-delivered intervention for optimizing social media use and reducing perceived isolation among rural-living LGBTQ+ youth: Development, acceptability, usability, satisfaction, and utility

Background

Lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) youth are at higher risk of isolation and depression than their heterosexual peers. Having access to tailored mental health resources is a documented concern for rural living LGBTQ+ youth. Social media provides access to connections to a broader and like-minded community of peers, but it also is a vehicle for negative interactions. We developed REALbot, an automated, social media-based educational intervention to improve social media efficacy, reduce perceived isolation, and bolster connections for rural living LGBTQ+ youth. This report presents data on the acceptability, feasibility, and utility of REALbot among its target audience of rural living LGBTQ+ youth.

Methods

We conducted a week-long exploratory study with a single non-comparison group of 20 rural-living LGBTQ+ youth aged 14-19 recruited from social media to test our Facebook- and Instagram-delivered chatbot. We assessed pre- and post-test scores of social media self-efficacy, social isolation (4-item Patient-Reported Outcomes Measurement System - PROMIS), and depressive symptoms (Patient Health Questionnaire, Adolescent Version - PHQ-A). At post-test, we assessed acceptability (User Experience Questionnaire - UEQ-S), usability (Chatbot Usability Questionnaire -CUQ and Post-Study Satisfaction and Usability Questionnaire -PSSUQ), and satisfaction with the chatbot (Client Satisfaction Questionnaire - CSQ), along with two open-ended questions on 'likes' and 'dislikes' about the intervention. We compared pre- and post-test scores with standard univariate statistics. Means and standard deviations were calculated for usability, acceptability, and satisfaction. To analyze the responses to post-test open-end questions, we used a content analysis approach.

Results

Acceptability of REALbot was high with UEQ-S 5.3 out of 7 (SD = 1.1) and received high usability scores with CUQ and PSSUQ (mean score (M) = 78.0, SD = 14.5 and M = 86.9, SD = 25.2, respectively), as well as high user satisfaction with CSQ (M = 24.9, SD = 5.4). Themes related to user 'likes' and 'dislikes' were organized in two main categories: usability and content provided. Participants were engaged with the chatbot, sending an average of 49.3 messages (SD = 43.6, median = 30). Pre-/post- changes in scores of perceived isolation, depressive symptoms and social media self-efficacy were not significant (p's > 0.08).

Conclusion

REALbot deployment was found to be feasible and acceptable, with good usability and user satisfaction scores. Participants reported changes from pre- to post-test in most outcomes of interest and effect sizes were small to medium. Additional development and a formal evaluation of feasibility and engagement with behavioral targets is warranted.

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.

Can digital health researchers make a difference during the pandemic? Results of the single-arm, chatbot-led Elena+: Care for COVID-19 interventional study

Background

The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information.

Methods

The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples T-tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time. To investigate the mediating role of behavioral activation (i.e., users setting behavioral intentions and reporting actual behaviors) we use mixed-effect regression models. Free-text entries were analyzed qualitatively.

Results

Results show strong demand for publicly available lifestyle coaching during the pandemic, with total downloads (N = 7'135) and 55.8% of downloaders opening the app (n = 3,928) with 9.8% completing at least one subtopic (n = 698). Greatest areas of health vulnerability as assessed with screening measures were physical activity with 62% (n = 1,000) and anxiety with 46.5% (n = 760). The app was effective in the treatment of mental health; with a significant decrease in depression between first (14 days), second (28 days), and third (42 days) assessments: F2,38 = 7.01, p = 0.003, with a large effect size (η2G = 0.14), and anxiety between first and second assessments: t54 = 3.7, p = <0.001 with a medium effect size (Cohen d = 0.499). Those that followed the coaching program increased in net promoter score between the first and second assessment: t36 = 2.08, p = 0.045 with a small to medium effect size (Cohen d = 0.342). Mediation analyses showed that while increasing number of subtopics completed increased behavioral activation (i.e., match between behavioral intentions and self-reported actual behaviors), behavioral activation did not mediate the relationship to improvements in health outcome assessments.

Conclusions

Findings show that: (i) there is public demand for chatbot led digital coaching, (ii) such tools can be effective in delivering treatment success, and (iii) they are highly valued by their long-term user base. As the current intervention was developed at rapid speed to meet the emergency pandemic context, the future looks bright for other public health focused chatbot-led digital health interventions.