Artificial intelligence chatbot for anxiety and depression in university students: a pilot randomized controlled trial

Background: The use of artificial intelligence based chatbots as an instrument of psychological intervention is emerging, however no studies have been reported in Latin America. Objective: The objective of the present study was to evaluate the viability, acceptability and potential impact of Tess, a chatbot, on symptoms of depression and anxiety in university students. Methodology: This was a pilot randomized controlled trial. The experimental condition used Tess for eight weeks and the control condition was assigned to a psychoeducation book on depression. Comparisons were conducted using Mann-Whitney U and Wilcoxon tests for depression symptoms, and Independent and Paired Samples t Tests to analyze anxiety symptoms. Results: The initial sample consisted of 181 Argentinian college students (87.2% female) ages 18 to33. A total of 39 (39%) participants in the experimental condition and 34 (41%) in the control group, provided data at week eight. There was an average of 472 (SD=249.52) messages exchanged and an average of 116 (SD=73.87) of the messages were sent from the user in response to Tess. A higher number of messages exchanged with Tess was associated with positive feedback (F2, 36=4.37; p =.02). No significant intergroup differences from baseline to week eight between the experimental group and the control group were found for depression and anxiety symptoms. However, significant intragroup differences demonstrated that the experimental group showed a significant decrease in anxiety symptoms; no differences were observed for the control group. No significant differences were found for depressive symptoms within the groups. Conclusions: Students engaged a considerable amount of time exchanging messages with Tess and positive feedback was associated with higher numbers of messages exchanged. The initial results show promising evidence for the usability and acceptability of Tess in the Argentinian population. Research on chatbots is still in its initial stages and further research is needed.Fil: Klos, María Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi". Grupo Vinculado CIIPME - Entre Ríos - Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi"; ArgentinaFil: Escoredo, Milagros. X2ai Inc.; Estados UnidosFil: Joerin, Angie. X2ai Inc.; Estados UnidosFil: Lemos, Viviana Noemí. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi". Grupo Vinculado CIIPME - Entre Ríos - Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi"; ArgentinaFil: Rauws, Michiel. X2ai Inc.; Estados UnidosFil: Bunge, Eduardo L.. Palo Alto University; Estados Unido

AI-based chatbot micro-intervention for parents: Meaningful engagement, learning, and efficacy

IntroductionMental health issues have been on the rise among children and adolescents, and digital parenting programs have shown promising outcomes. However, there is limited research on the potential efficacy of utilizing chatbots to promote parental skills. This study aimed to understand whether parents learn from a parenting chatbot micro intervention, to assess the overall efficacy of the intervention, and to explore the user characteristics of the participants, including parental busyness, assumptions about parenting, and qualitative engagement with the chatbot.MethodsA sample of 170 parents with at least one child between 2–11 years old were recruited. A randomized control trial was conducted. Participants in the experimental group accessed a 15-min intervention that taught how to utilize positive attention and praise to promote positive behaviors in their children, while the control group remained on a waiting list.ResultsResults showed that participants engaged with a brief AI-based chatbot intervention and were able to learn effective praising skills. Although scores moved in the expected direction, there were no significant differences by condition in the praising knowledge reported by parents, perceived changes in disruptive behaviors, or parenting self-efficacy, from pre-intervention to 24-hour follow-up.DiscussionThe results provided insight to understand how parents engaged with the chatbot and suggests that, in general, brief, self-guided, digital interventions can promote learning in parents. It is possible that a higher dose of intervention may be needed to obtain a therapeutic change in parents. Further research implications on chatbots for parenting skills are discussed

Computational Models of Classical Conditioning guest editors’ introduction

In the present special issue, the performance of current computational models of classical conditioning was evaluated under three requirements: (1) Models were to be tested against a list of previously agreed-upon phenomena; (2) the parameters were fixed across simulations; and (3) the simulations used to test the models had to be made available. These requirements resulted in three major products: (a) a list of fundamental classical-conditioning results for which there is a consensus about their reliability; (b) the necessary information to evaluate each of the models on the basis of its ordinal successes in accounting for the experimental data; and (c) a repository of computational models ready to generate simulations. We believe that the contents of this issue represent the 2012 state of the art in computational modeling of classical conditioning and provide a way to find promising avenues for future model development

Meta-Analysis of Parent Training Programs Utilizing Behavior Intervention Technologies

Behavioral Parent Training (BPT) traditionally occurs in face-to-face (FTF BPT). Recently, Behavioral Intervention Technology (BIT) has been developed to deliver BPT in lieu of or as an adjunct to FTF BPT using websites, computer software, smartphone applications, podcasts, pre-recorded sessions, and teletherapy. The present meta-analysis reviews BIT BPT randomized control and comparison studies to determine the overall efficacy of BITs, if the level of human support significantly effects BIT BPT treatment outcomes, and which populations BIT BPT are effective for, by analyzing the following study variables: socioeconomic status, race, and clinical population. The analyses indicated that, overall, BIT BPT is an effective treatment (g = 0.62), and did not indicate a significant difference between levels of human support (χ2 (3) = 4.94, p = 0.18). Analysis did indicate a significant difference between studies that used waitlist or education control groups, compared to studies that used active treatment controls (χ2 (1) = 12.90, p = 0.00). The analyses did not indicate a significant difference between clinical population, low socioeconomic status, and racial minority studies. These findings provide preliminary evidence that BIT BPT is effective for treating child and adolescent externalizing behavior in a variety of populations

Satisfacción y Tipo de Servicios de Salud Mental para Niños y Adolescentes

La satisfacción del usuario (SU) es actualmente considerada un factor importante en la evaluación de la calidad de los servicios de salud mental. No obstante, no ha sido todavía investigada en gran medida en los servicios de salud mental para niños, niñas y adolescentes (SSMNA). Nuestro objetivo en este estudio fue conocer el grado de satisfacción percibida por usuarios que recibieron atención en tres SSMNA privados de Buenos Aires, cada uno con una orientación teórica distinta. Utilizamos como instrumentos, cuestionarios de auto-informe que nos proveyeron datos para un análisis cuantitativo y cualitativo. Hallamos que los padres y madres que llevaron a sus hijos o hijas a los servicios de orientación cognitiva o sistémica, refirieron niveles de satisfacción similares entre sí y un mayor grado de satisfacción que los padres y madres cuyos hijos e hijas recibieron tratamiento psicodinámico

The Role of Human Support on Engagement in an Online Depression Prevention Program for Youth

Background: Depression is a significant public health problem for adolescents. The goal of this study was to evaluate the moderating role of human support in an online depression prevention program on both depression outcomes and overall engagement with the intervention. CATCH-IT is an Internet-based depression prevention program that has been shown to reduce symptoms for adolescents who report elevated depression symptom scores, compared to a health education (HE) control group. Participants in the CATCH-IT arm received human support (e.g., motivational interviewing, completed contacts). This study analyzes the moderating role of human support on depressive outcomes and engagement, and examines if engagement predicts depression outcomes. Methods: This secondary analysis consists of a randomized controlled trial for adolescents assigned to the CATCH-IT group. Mixed effects modeling, general linear models, and an exploratory multiple linear regression were used to explore the moderating relationship of human support between intervention and overall engagement. Study variables included depression outcomes (e.g., Center for Epidemiological Studies Depression Scale (CESD)), engagement components (e.g., modules completed, time on the site, and characters typed) and human support (e.g., motivational interviews and completed contacts.) Results: Results showed no significant relationship between contacts, motivational interviews, and depression scores. However, motivational interviews increased engagement with the intervention, such that those who received more motivational interviews completed significantly more modules, spent more time on the site, and typed more characters (p < 0.05). The number of contacts increased engagement with the intervention, and those who received more contacts spent more time on the site and typed more characters (p < 0.05). Exploratory multiple linear regression modeling demonstrated that male, African American/Black, and Hispanic/Latinx users were less engaged compared to other users. Lastly, engagement was not a significant predictor of depression outcomes (p > 0.05). Conclusions: The efficacy of CATCH-IT is not better explained by the degree to which participants received doses of human support from providers during the use of this online intervention. This may reveal the high potential of effective online interventions without the blended integration of human support for adolescents. To increase engagement of adolescents with an online depression prevention program, human support may be more efficient when utilizing MI rather than technical support

Editorial Introduction to Technological Approaches for the Treatment of Mental Health in Youth

Excerpt: [Note: In lieu of an abstract, this is an excerpt from the first page.] According to the World Health Organization (WHO), 10–20% of adolescents (10–19 years old) worldwide suffer from mental health conditions, with 50% starting at the age of 14 (World Health Organization 2020). Traditionally, mental health problems among youth have been addressed with psychotherapy conducted via face-to-face methods. However, many youth are actively seeking resources online for mental health support (Stephens et al. 2020; Rideout et al. 2018). Therefore, digital interventions can provide alternative methods to support youth patients while addressing and improving the limitations of face-to-face delivery formats. This has become more evident during the COVID-19 pandemic, where clinicians have been forced to use creative strategies, such as telehealth to reach their patients remotely. [...

Microinterventions produce immediate but not lasting benefits in mood and distress

Untreated depression remains one of the largest public health concerns. However, barriers such as unavailability of mental health providers and high cost of services limit the number of people able to benefit from traditional treatments. Though unsupported Internet interventions have proven effective at bypassing many of these barriers given their reach and scalability, attrition from interventions has been an ongoing concern. Microinterventions, or ultra-brief online tools meant to produce a rapid improvement in mood, may offer a way to provide the benefits of unsupported Internet interventions quickly, before attrition might occur. This study examined the immediate and lasting effects of three microinterventions (breathing exercises, thought records, and a pleasant activities selector) on mood and distress. Participants (N = 122) were randomized into three groups, each group completing two of the three microinterventions. Participants were asked to rate their mood and level of distress before and after completing the microintervention. Depression and perceived stress were assessed at baseline and at four weekly follow-ups. Although lasting effects were not found, a significant within-group reduction in distress and improvement in mood were observed immediately following the completion of the microintervention. This study demonstrates the potential benefits of microinterventions to individuals for their immediate needs vis-à-vis mood and distress

Mood management effects of brief unsupported internet interventions

ABSTRACTBackgroundMost users of unsupported Internet interventions visit that site only once, therefore there is a need to create interventions that can be offered as a single brief interaction with the user.ObjectiveThe main goal of this study was to compare the effect of a one-session unsupported Internet intervention on participants' clinical symptoms (depressive and anxiety symptoms) and related variables (mood, confidence and motivation).MethodA total of 765 adults residing in the United States took part in a randomized controlled trial. Participants were randomly assigned to one of five brief plain text interventions lasting 5–10min. The interventions designed to address depressive symptoms were: thoughts (increasing helpful thoughts), activities (increasing activity level), sleep hygiene, assertiveness (increasing assertiveness awareness), Own Methods (utilizing methods that were previously successful). They were followed-up one week after consenting.ResultsA main effect of time was observed for both depression (F(1, 563)=234.70, p<0.001) and anxiety (F(1, 551)=170.27, p<0.001). In all cases, regardless of assigned condition and Major Depressive Episode status, mean scores on both positive outcomes (mood, confidence and motivation) and negative outcome scores (depression and anxiety) improved over time.ConclusionsBrief unsupported Internet interventions can improve depressive symptoms at one-week follow-up. Further outcome data and research implications will be discussed