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

Abstract

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. CLINICALTRIAL International Standard Randomized Controlled Trial Number: ISRCTN61214172; https://doi.org/10.1186/ISRCTN61214172.

Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review

“…The studies included a diverse sample of adults (n=22), college students and young adults (n=4), anonymous users (n=2), and adolescents (n=1) ( 28 – 33 , 41 , 73 – 94 ). Eight studies reported on the ethnicity of the participants ( 30 , 32 , 41 , 74 , 76 , 77 , 80 , 83 ), and in most of the studies between 31% and 91% of the participants identified as White. The studies focused on a population with moderate psychiatric symptoms including depression, anxiety, obsessive-compulsive disorder (OCD), insomnia, stress, body dysmorphic disorder (BDD), opioid misuse, social anxiety disorder (SAD), and bipolar personality disorder.…”

“…Among the included studies, the domains that commonly presented a high risk of bias were allegiance bias (n=14) and selective reporting (n=8). In terms of allegiance bias, because of the scarcity and novelty of AI-augmented interventions and other AI algorithms, most of the included articles developed and studied their own AI tools, instead of AI tools developed by other researchers ( 29 , 33 , 73 , 74 , 76 , 79 , 80 , 82 – 87 , 90 ). The studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering the collection and assessment of psychiatric scale data and other effectiveness outcomes ( 29 , 76 , 79 , 85 – 88 , 92 ).…”

“…In terms of allegiance bias, because of the scarcity and novelty of AI-augmented interventions and other AI algorithms, most of the included articles developed and studied their own AI tools, instead of AI tools developed by other researchers ( 29 , 33 , 73 , 74 , 76 , 79 , 80 , 82 – 87 , 90 ). The studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering the collection and assessment of psychiatric scale data and other effectiveness outcomes ( 29 , 76 , 79 , 85 – 88 , 92 ). Other sources of bias, such as adherence, selection, confounding or information bias commonly presented a low risk of bias ( Supplementary Material 2 ).…”

“…The sub-group analysis showed that AI-self-guided eCBT had a significant medium effect size, AI-guided eCBT effect had a non-significant effect, and AI-modified eCBT, AI-self-guided ACT and Combined had a significant large effect size in the reduction of mental health symptoms ( Figure 3A ). The NMA considered n=6 RCTs ( 41 , 73 , 74 , 76 – 78 ). The head-to-head comparisons against AI-self-guided eCBT determined that this intervention was significantly more effective than AI chatbot with a large effect and psychoeducation with a medium effect size for the reduction of mental health symptoms.…”

Desenvolvimento de uma tecnologia digital de apoio à terapia cognitivo-comportamental

“…Além destes, diversos outros estudos têm sido conduzidos nos últimos anos focados no uso de aplicativos, serious games e realidade virtual na solução de problemas psicológicos, como: Fobias Específicas (Barreto et al, 2019), Transtorno por Uso de Substâncias Psicoativas (Whittaker et al, 2016;Scott, Dennis, & Gustafson, 2017), Esquizofrenia (Cardoso, Marques, Queirós, & Rodrigues, 2014), Transtorno Depressivo e Transtornos de Ansiedade (Fulmer, Joerin, Gentile, Lakerink, & Rauws, 2018;Cormack et al, 2019), dentre outros. Apesar dos resultados promissores relatados por tais estudos, a proporção de tecnologias aplicadas à saúde aumentou de forma desenfreada (Martínez-Pérez, Torre-Díez, & López-Coronado, 2013;Anthes, 2016), o que culmina em outro problema: a reduzida base empírica no desenvolvimento destas aplicações tecnológicas.…”