Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial

By Russell Fulmer, Angela Joerin, B. Gentile, Lysanne Lakerink, Michiel Rauws. JMIR Mental Health, 2018

574 citations recorded by the source corpus.

Background Students in need of mental health care face many barriers including cost, location, availability, and stigma. Studies show that computer-assisted therapy and 1 conversational chatbot delivering cognitive behavioral therapy (CBT) offer a less-intensive and more cost-effective alternative for treating depression and anxiety. Although CBT is one of the most effective treatment methods, applying an integrative approach has been linked to equally effective posttreatment improvement. Integrative psychological artificial intelligence (AI) offers a scalable solution as the demand for affordable, convenient, lasting, and secure support grows. Objective This study aimed to assess the feasibility and efficacy of using an integrative psychological AI, Tess, to reduce self-identified symptoms of depression and anxiety in college students. Methods In this randomized controlled trial, 75 participants were recruited from 15 universities across the United States. All participants completed Web-based surveys, including the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Positive and Negative Affect Scale (PANAS) at baseline and 2 to 4 weeks later (T2). The 2 test groups consisted of 50 participants in total and were randomized to receive unlimited access to Tess for either 2 weeks (n=24) or 4 weeks (n=26). The information-only control group participants (n=24) received an electronic link to the National Institute of Mental Health’s (NIMH) eBook on depression among college students and were only granted access to Tess after completion of the study. Results A sample of 74 participants completed this study with 0% attrition from the test group and less than 1% attrition from the control group (1/24). The average age of participants was 22.9 years, with 70% of participants being female (52/74), mostly Asian (37/74, 51%), and white (32/74, 41%). Group 1 received unlimited access to Tess, with daily check-ins for 2 weeks. Group 2 received unlimited access to Tess with biweekly check-ins for 4 weeks. The information-only control group was provided with an electronic link to the NIMH’s eBook. Multivariate analysis of covariance was conducted. We used an alpha level of .05 for all statistical tests. Results revealed a statistically significant difference between the control group and group 1, such that group 1 reported a significant reduction in symptoms of depression as measured by the PHQ-9 (P=.03), whereas those in the control group did not. A statistically significant difference was found between the control group and both test groups 1 and 2 for symptoms of anxiety as measured by the GAD-7. Group 1 (P=.045) and group 2 (P=.02) reported a significant reduction in symptoms of anxiety, whereas the control group did not. A statistically significant difference was found on the PANAS between the control group and group 1 (P=.03) and suggests that Tess did impact scores. Conclusions This study offers evidence that AI can serve as a cost-effective and accessible therapeutic agent. Although not designed to appropriate the role of a trained therapist, integrative psychological AI emerges as a feasible option for delivering support. Trial Registration International Standard Randomized Controlled Trial Number: ISRCTN61214172; https://doi.org/10.1186/ISRCTN61214172.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/80788ea94d91b8bb48b4fe7966491b62c0464352

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Citations in the corpus

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020, background): “On average, the randomized controlled trials (RCTs) [9,13,34,37,46,47,49,53] and qualitative studies [41,48,56] evaluated were generally determined to have the highest quality and lowest risk of bias, with none of the other three study types meeting more than half of the criteria for the quality…”
Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey (2018, background): “Early research has demonstrated the benefits of using health care chatbots, such as helping with diagnostic decision support [15,16], promoting and increasing physical activity [17], and cognitive behavioral therapy for psychiatric and somatic disorders [18-24], which provide effective, acceptable, ”
Artificially intelligent chatbots in digital mental health interventions: a review (2021, background): “Although research suggests that users rate chatbots favorably [98], few studies have examined the perceptions of chatbots in the general population.”
Digital Mental Health Interventions for Depression, Anxiety, and Enhancement of Psychological Well-Being Among College Students: Systematic Review (2018, unknown): “Most studies included were focused on either universal prevention programs (n=36 [43,48,53,54,63-67,70-73,75-78,90,91, 94,95,97,99,100,102,106-108,110-112,114,118,119,125,126]), or on treatment intervention programs (n=22 [41,49,50,52, 58,69,74,79,80,83-86,88,89,92,98,113,117,120,122,127]).”
Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020, methodology): “Moreover, 19 studies allowed free text user inputs [45,48,51,56,57,60,61,66,69,70,72,74,77,78,80,81,85,86,89], and 4 studies used both fixed and free text user inputs [53,64,65,73].”
My Chatbot Companion - a Study of Human-Chatbot Relationships (2021, background): “Hence, while our study only provides insight into users ’ perceptions of the impact of an HCR, it nevertheless adds to the existing literature as to how relationships with artificial entities may support wellbeing and mental health (Fitzgerald et al., 2017; Fulmer et al., 2018, Ta et al., 2020).”
An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews (2022, unknown): “Several well-known chatbots such as Wysa [34], Woebot [47], Replika [51], Youper [52], and Tess [53] were discussed in prior literature.”
The Personalization of Conversational Agents in Health Care: Systematic Review (2019, methodology): “While most studies used quasi-experimental study designs, only two used randomized controlled trials [35,44].”