Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial (Preprint)
Open accessPreprint — published as 10.2196/mental.9782 · Paper page
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.
AI snapshot
Written from the paper’s full text.
TL;DR
In a randomized controlled trial with 74 college students, the integrative psychological AI chatbot Tess reduced anxiety symptoms in both 2-week and 4-week access groups and reduced depression symptoms in the 2-week daily-check-in group compared with an information-only control. Tess users reported higher satisfaction and engagement, but the study used a nonclinical sample, had small groups, and did not collect follow-up data.
Key findings
- Depression (PHQ-9): a statistically significant difference was found between the control group and group 1 (Tess for 2 weeks with daily check-ins), with group 1 showing reduced depressive symptoms while the control group’s symptoms increased; the Results section reports P=.02, while the Abstract reports P=.03.
- Anxiety (GAD-7): significant differences were found between the control group and both Tess groups, with group 1 at P=.045 and group 2 at P=.02 showing reduced anxiety symptoms, whereas the control group did not.
- PANAS: a statistically significant difference was found between the control group and group 1 (P=.03), suggesting Tess impacted scores.
- Satisfaction and engagement: 86% (43/50) of Tess participants were overall satisfied versus 60% (14/24) of controls; 80% (40/50) learned something new from Tess versus 43% (10/24) from the eBook; test groups exchanged 14,238 messages total, with group 1 averaging 283 messages (SD 147.6) and group 2 averaging 286 messages (SD 104.6). The reported effect size for depression was Cohen d=0.68.
Methods
- Randomized controlled trial: 75 participants signed consent and 74 completed; participants were recruited from 15 universities across the United States and randomized to an information-only control group (NIMH eBook; n=24), Tess group 1 (2 weeks unlimited access with daily check-ins; n=24), or Tess group 2 (4 weeks unlimited access with biweekly check-ins; n=26).
- Measures: Web-based surveys including PHQ-9, GAD-7, and PANAS at baseline and 2 to 4 weeks later (T2); baseline mean PHQ-9 scores were 8.17 (control), 6.67 (group 1), and 7.04 (group 2), and baseline mean GAD-7 scores were 9.46, 6.71, and 7.5, respectively.
- Analysis: SPSS was used; the paper describes a multivariate analysis of covariance in the Abstract and a multivariate analysis of covariance/MANOVA in the Methods, with an alpha level of .05 and post hoc Tukey tests.
Limitations
- The paper states that generalizability is limited because socioeconomic status was not formally assessed, recruitment via social media may not represent all Tess users, group sizes were limited, no follow-up data assessed whether benefits were sustained, and the nonclinical sample had low baseline depression and anxiety scores, requiring further research in clinical populations.
- The paper also states that the control group experienced a slight increase in anxiety and depression symptoms, suggesting the eBook was not sufficient support; emotion identification was limited to language; and system errors related to natural language processing or emotion mismatch could not be addressed during the study.
- Evident from the text: the condition was not masked for service providers (Tess), and the paper reports inconsistent P values for the PHQ-9 result (Abstract P=.03 vs Results P=.02).
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 6 cited sources checked · 0 supported · 0 flagged
- Finding
Tess also affected PANAS scores relative to the control group.
“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.”
Quote in the paperNot yet classified - Finding
The study found a significant reduction in depression symptoms for the 2-week Tess group compared with the control group.
“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.”
Quote in the paperNot yet classified - Finding
The authors conclude that AI can be a cost-effective and accessible therapeutic agent and that integrative psychological AI is a feasible option for delivering support.
“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.”
Quote in the paperNot yet classified - Finding
Both Tess test groups showed significant reductions in anxiety symptoms compared with the control group.
“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.”
Quote in the paperNot yet classified - Finding
The results section reports that 74 participants completed the study and attrition was 0% in the test group and less than 1% in the control group.
“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).”
Quote in the paperNot yet classified
- Finding
The paper reports that Tess may reduce symptoms of depression and anxiety by delivering CBT-based interventions in conversational form.
“The results support and expand on findings from a previous randomized controlled trial [10] and demonstrate that psychological AI has the potential to reduce symptoms of depression and anxiety by delivering CBT-based interventions in the form of conversations.”
Quote in the paperNot yet classified0 of 1 sources checked- cites [10] Fitzpatrick KK Darcy A Vierhile M Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial JMIR Ment Health 2017 06 06 4 2 e19 10.2196/mental.7785 28588005 v4i2e19 28588005 PMC5478797 source
Not checked in the free run.
- Finding
Participants who interacted with Tess reported higher engagement and overall satisfaction than those in the control group.
“Participants who interacted with Tess displayed higher levels of engagement and overall satisfaction than those from the control group.”
Quote in the paperNot yet classified - Method
Participants completed baseline and follow-up web-based surveys including the PHQ-9, GAD-7, and PANAS.
“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).”
Quote in the paperNot yet classified - Method
The trial used a randomized controlled design with 75 participants recruited from 15 universities in the United States.
“In this randomized controlled trial, 75 participants were recruited from 15 universities across the United States.”
Quote in the paperNot yet classified - Method
Tess is described as a psychological AI chatbot that delivers brief conversations as integrative mental health support, psychoeducation, and reminders.
“Tess is a psychological AI chatbot designed by X2AI Inc to deliver brief conversations in the form of integrative mental health support, psychoeducation, and reminders.”
Quote in the paperNot yet classified - Method
The intervention groups received unlimited access to Tess for either 2 weeks or 4 weeks, while the control group received an information-only NIMH eBook on depression among college students.
“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.”
Quote in the paperNot yet classified - Background
Computer-assisted therapy and a conversational CBT chatbot are described as less-intensive and more cost-effective alternatives for treating depression and anxiety.
“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.”
Quote in the paperNot yet classified - Method§ Methods
The authors say the study used methodology developed to closely parallel Fitzpatrick et al. to compare Tess with prior conversational-agent work.
“The methodology of our study was developed to closely parallel that of Fitzpatrick et al [10] to further contribute to the emergence of AI as a supportive mental health tool.”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fitzpatrick et al [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Background
Mental health care barriers for students include cost, location, availability, and stigma.
“Students in need of mental health care face many barriers including cost, location, availability, and stigma.”
Quote in the paperNot yet classified - Method
The study aimed to assess whether the integrative psychological AI Tess was feasible and effective for reducing self-identified depression and anxiety symptoms in college students.
“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.”
Quote in the paperNot yet classified - Method
Tess is not intended to replace a therapist and is meant to be an adjunct to therapy.
“Tess serves as a therapeutic tool or resource that can be used as an adjunct to therapy that supports an integrative approach and is not intended to replace the role of a therapist.”
Quote in the paperNot yet classified - Method
The PHQ-9 was used to assess depressive symptom severity over the previous two weeks.
“Depressive symptom severity was assessed using the PHQ-9 [34]. The PHQ-9 is a self-report instrument with nine items and asks participants about depressive symptoms experienced in the last two weeks.”
Quote in the paperNot yet classified0 of 1 sources checked- cites [34] Kroenke K Spitzer RL Williams JB The PHQ-9: validity of a brief depression severity measure J Gen Intern Med 2001 09 16 9 606 13 11556941 jgi01114 11556941 PMC1495268 source
Not checked in the free run.
- Method
The study's control condition received an NIMH eBook that provides evidence-based information and resources for college students with depression.
“Participants in the information-only control group were provided with an electronic link to the NIMH’s eBook on depression among college students [33]. The eBook is free for the public and provides evidence-based information and resources to help students identify, and seek treatment for, symptoms of depression.”
Quote in the paperNot yet classified0 of 1 sources checked- cites [33] National Institute of Mental Health Nimh.nih 2017 2017-03-29 Depression in College Students https://www.nimh.nih.gov/health/publications/depression-and-college-students/index.shtml
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Method
The PANAS was used as a 20-item self-report measure of current positive and negative affect.
“The Positive and Negative Affect Schedule (PANAS) [36] is a 20-item self-report measure of current positive and negative affect.”
Quote in the paperNot yet classified0 of 1 sources checked- cites [36] Watson D Clark LA Tellegen A Development and validation of brief measures of positive and negative affect: the PANAS scales J Pers Soc Psychol 1988 06 54 6 1063 70 3397865 3397865 source
Not checked in the free run.
- Method
The chatbot adapts its interventions to user-reported moods and can offer different therapies such as CBT, mindfulness-based therapy, emotionally focused therapy, ACT, motivational interviewing, self-compassion therapy, and interpersonal psychotherapy.
“During the study, Tess delivered interventions rooted in a variety of psychological modalities such as CBT, mindfulness-based therapy, emotionally focused therapy, acceptance and commitment therapy, motivational interviewing, self-compassion therapy, and interpersonal psychotherapy.”
Quote in the paperNot yet classified - Method
The GAD-7 was used to assess anxiety symptom severity over the previous two weeks.
“Anxiety symptom severity was assessed using the 7-item Generalized Anxiety Disorder (GAD-7) [35]. The GAD-7 is a self-report measure with seven items. Similar to the PHQ-9, the GAD-7 evaluates anxiety symptoms experienced during the previous two weeks.”
Quote in the paperNot yet classified0 of 1 sources checked- cites [35] Spitzer RL Kroenke K Williams JB Löwe B A brief measure for assessing generalized anxiety disorder: the GAD-7 Arch Intern Med 2006 05 22 166 10 1092 7 10.1001/archinte.166.10.1092 16717171 166/10/1092 16717171 source
Not checked in the free run.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering collection and assessment of psychiatric scale data and other effectiveness outcomes.
“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).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
Split into 2 atoms — 0 confirmed, 1 partially supported, 1 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured12 of the included studies presented data on treatment dropout; online or remote psychotherapy interventions reported a dropout rate between 0% and 61%, psychoeducation between 4% and 60%, and no intervention between 5% and 34%.
“12 of the included studies (41, 73–83) presented data on treatment dropout, online or remote psychotherapy interventions reported a dropout rate between 0% and 61%, psychoeducation a dropout rate between 4% and 60% and no intervention a dropout rate between 5% and 34%.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
Methods > Ethics and Informed Consent reports 0% attrition in the Tess intervention groups and one dropout among 24 participants in the eBook control group, approximately 4%. This trial provides dropout data that could contribute to the cited ranges, but it does not report results for 12 studies or establish the full ranges for psychotherapy, psychoeducation, and no intervention. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA minority of users, around 30% or less, thought that the tool did not understand them or found their interactions unhelpful or bothersome.
“They also reported that a minority of users, around 30% or less, thought that the tool did not understand them or found their interactions unhelpful or bothersome (73, 74, 76–78, 80–82, 84, 90).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The Results > Analysis section reports that 11 of 50 Tess participants (22%) said the bot failed to understand some responses or became confused. It also reports content problems, but does not establish that 30% or fewer found their interactions unhelpful or bothersome; those descriptions are not reported as measured outcomes. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPrevious systematic reviews and meta-analyses have shown significant reductions in symptoms for depression and other mental disorders while using mental health apps, some of which included CBT or gamification elements.
“Additionally, previous systematic reviews and meta-analyses have shown significant reductions in symptoms for depression and other mental disorders while using mental health apps, some of which included cognitive behavioral therapy (CBT) or gamification elements [26,36,37,40,41].”
From Examining the effectiveness of gamification in mental health apps for depression: Systematic review and meta-analysis (2021) · cites this paper as [36]
The Introduction (Background; page unavailable) says CBT-delivering psychological AI has reduced depression and anxiety symptoms, and the Discussion (Comparison With Prior Work; page unavailable) reports significant reductions with Tess. But this source is a randomized trial, not a systematic review or meta-analysis of mental health apps, and the cited passages do not establish the claim about gamification elements. (LLM, conf=0.84)
Experimental — accuracy not yet measuredWhen used enough, chatbots can have positive mental health outcomes.
“When used enough, there can be positive mental health outcomes16–19.”
From Loneliness and suicide mitigation for students using GPT3-enabled chatbots (2024) · cites this paper as [17]
Eight studies reported ethnicity; in most studies between 31% and 91% of participants identified as White.
“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.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
In terms of allegiance bias, most included articles developed and studied their own AI tools instead of AI tools developed by other researchers.
“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).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The NMA for main effects considered n=6 RCTs.
“The NMA considered n=6 RCTs (41, 73, 74, 76–78).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The NMA for anxiety considered n=4 RCTs.
“The NMA considered n=4 RCTs (73, 74, 76, 78).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The meta-analysis assessing impact of AI-augmented interventions on mental health symptoms considered n=10 studies presenting pre- and post-intervention outcomes.
“The meta-analysis assessing the impact of AI-augmented interventions on mental health symptoms considered n=10 studies (41, 73–78, 80, 84, 90) presenting pre- and post-intervention outcomes.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The meta-analysis for depression considered n=7 studies presenting pre- and post-intervention depression score outcomes.
“The meta-analysis assessing the impact of AI-augmented interventions for the management of depression symptoms considered n=7 studies (74, 76–78, 80, 84, 90) presenting pre- and post-intervention depression score outcomes.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The NMA for depression considered n=4 RCTs.
“The NMA considered n=4 RCTs (74, 76–78).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The meta-analysis for anxiety considered n=4 studies presenting pre- and post-intervention anxiety score outcomes.
“The meta-analysis assessing the impact of AI-augmented interventions for the management of anxiety symptoms considered n=4 studies (73, 74, 76, 78)presenting pre- and post-intervention anxiety score outcomes.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
Aside from AI-self-guided eCBT (n=6) and psychoeducation (n=4), other interventions and comparators were reported in three or fewer studies.
“Aside from AI-self-guided eCBT [n=6 (73, 74, 77, 78, 84, 90)] and psychoeducation [n=4 (74, 76–78)], other interventions and comparators were reported in three or fewer studies”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The NMA for dropout considered n=6 RCTs.
“The NMA considered n=6 RCTs (41, 73, 74, 76–78).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
The therapeutic AI agents or chatbots identified by our systematic search (Wysa, Woebot, TEO, Tess, XiaoE, and Sexpert) were effective at delivering self-guided therapy content by communicating intelligently with their users, promoting a significant reduction in mental health symptom severity with high user satisfaction.
“For instance, the therapeutic AI agents or chatbots identified by our systematic search (i.e., Wysa, Woebot, TEO, Tess, XiaoE, and Sexpert) were effective at delivering self-guided therapy content by communicating intelligently with their users, promoting a significant reduction in mental health symptom severity with high user satisfaction (73, 74, 76–78, 82, 84, 90).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [76]
A growing number of studies report effectiveness of chatbots in improving symptoms of anxiety.
“thereby improving symptoms of anxiety [10,23-25]”
The GAD-7 is a 7-item self-report scale assessing anxiety symptoms over the past 2 weeks, with total scores categorized as none (0-4), mild (5-9), moderate (10-14), and severe (≥15).
“The 7-item Generalized Anxiety Disorder scale (GAD-7) [34] is a 7-item self-report scale based on a 4-point Likert scale, ranging from 0 (not at all) to 3 (nearly every day).”
Overall, our quantitative and qualitative findings are aligned with recent chatbot evaluation studies and will inform our design decisions for future developments.
“Overall, our quantitative and qualitative findings are aligned with recent chatbot evaluation studies [10,23-25] and will inform our design decisions for future developments.”
A growing number of studies report effectiveness of chatbots in reducing perceived stress.
“as well as their effectiveness in reducing perceived stress [16-25]”
From Engagement and effectiveness of a healthy-coping intervention via chatbot for university students during the COVID-19 pandemic: Mixed methods proof-of-concept study (2021) · cites this paper as [23]
The Discussion’s Principal Findings report reduced anxiety and depression symptoms among students using Tess, but not reduced perceived stress. The Methods’ Measures section lists depression, anxiety, and positive and negative affect measures; a search for “perceived stress” found no assessment or finding for that outcome. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe effectiveness of health care conversational agents was assessed in 8 studies.
“The effectiveness of health care conversational agents was assessed in 8 studies [47,52,57,61,70,75,81,84].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [81]
Eight studies noted the effectiveness of conversational agents for mental health applications.
“Eight studies noted the effectiveness of conversational agents for mental health applications [57,61,64,67,75,80,81,84].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [81]
The conversational agent Tess by Fulmer et al initiated a statistically significant improvement in depression and anxiety compared with the control group.
“The conversational agent Tess by Fulmer et al [81] initiated a statistically significant improvement in depression and anxiety compared with the control group.”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [81]
Psychological conditions were the most commonly addressed type of condition, the focus of 3 studies.
“In the reviewed articles, psychological conditions were the most commonly addressed type of condition, which was the focus of 3 studies [52-54].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Four studies found high levels of engagement with the conversational agent or reported the conversational agent to be engaging.
“Four studies found high levels of engagement with the conversational agent or reported the conversational agent to be engaging [50,52,53,58].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
In 7 studies, user experience was assessed and was generally positive regarding acceptability, understanding, comprehensibility, interaction rates, or content relevance.
“In 7 studies, user experience was assessed. User experience was generally positive regarding the acceptability, understanding of the conversational agents, comprehensibility of the systems’ responses, interaction rates, or content relevance [51,53,54,56-59].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
The remaining 4 studies used a written format of interaction modality, with both input and output in written form.
“The remaining 4 studies use a written format of interaction modality, which means that both input and output were in a written form [51-53,57].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Two RCTs found that interaction with conversational agents led to decreased symptoms of depression and anxiety compared with control groups.
“Two RCTs reported on health-related outcomes and found that interaction with the conversational agents led to decreased symptoms of depression and anxiety compared with the control groups [52,53].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
In 3 papers, students served as main study participants.
“In 3 papers, students served as main study participants [52,53,59].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Among the included studies, natural language processing was the most frequently used AI technique, followed by speech recognition, machine learning, natural language understanding, neural networks, and artificial intelligence markup language.
“Natural language processing represented the most used technique [50-53,55,56,59] before speech recognition (including speech-to-text and text-to-speech) [50,54,56,58,59], machine learning [53,54,59], natural language understanding [54,59], neural networks [54,59] and artificial intelligence markup language [56,57], as shown in Table 3.”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Emotion algorithms were used in one included study.
“The following techniques were used in one study each: deep learning [59], natural language generation [54], emotion algorithms [53], and decision trees [52].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Five included studies relied on external sources for the artificial intelligence system.
“A total of 4 studies developed the artificial intelligence system internally [50,51,57,59], and 5 studies relied on external sources [52-54,56,58].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Two conversational agents were commercially available, and one was available for free on Android app store.
“Two conversational agents were commercially available [52,53], and 1 was available for free on Android app store [56].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
Four included studies were classified as AI.
“Four studies were classified as AI [53,56-58].”
From Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (2020) · cites this paper as [53]
In Fulmer et al [29], chatbots improved positive and negative affect at the 2-week follow-up.
“While one study concluded that chatbots improved the positive and negative affect at the 2-week follow-up [29], another study did not find any significant influence of chatbots at the 2-week follow-up [28].”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [29]
The chatbot in [29] was more advanced than the one in [28]; it depended on artificial intelligence and machine learning to generate responses to users.
“Specifically, the chatbot in the first study [29] was more advanced than the one in the second study [28]; it depended on artificial intelligence and machine learning to generate responses to users, and this makes it more humanlike and lets users feel more socially connected [5].”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [29]
Fulmer et al found a statistically significant difference favoring chatbot use over information on positive and negative affect at the 2-week follow-up (P=.03).
“Fulmer et al [29] found a statistically significant difference favoring chatbot use over information on positive and negative affect at the 2-week follow-up (P=.03).”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [29]
The first study assessed the effect of the chatbot on positive and negative affect together, while the other study examined positive affect and negative affect separately.
“With regards to the second difference, while the first study assessed the effect of the chatbot on positive and negative affect together [29], the other study examined the effect of the chatbot on positive affect and negative affect separately [28].”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [29]
Participants reported being satisfied with the conversational agent interventions offered.
“Generally, from studies that reported user experience outcomes (n=11), participants reported being satisfied with the conversational agent interventions offered [22,23,25,26,29,30,32].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
Five controlled studies reported significant posttreatment improvements in psychological distress in the intervention group compared with a no treatment or information control group.
“Of the 13 studies included, 5 controlled studies reported significant posttreatment improvements in psychological distress in the intervention group compared with a no treatment or information control group [23,25,26,31,34].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
Significant improvements were observed on measures of depression, psychological distress, anxiety, fear of heights, and positive affect.
“Significant improvements were observed on measures of depression [25,26,31], psychological distress [34], anxiety [26], fear of heights [23] and positive affect [26,34].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
Greater engagement with the conversational agent resulted in greater reductions in psychological distress.
“Generally, greater engagement with the conversational agent resulted in greater reductions in psychological distress [22,26,28,29,33].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
The use of conversational agents for treating mental health problems appears to be limited but is growing quickly, with 5 of the included studies published in 2018 alone.
“The use of conversational agents for treating mental health problems appears to be limited but is growing quickly, with 5 of the included studies published in 2018 alone [23,24,26,29,34].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
Three studies reported that participants found the conversational agent interventions available and accessible.
“In addition, 3 studies reported that participants found the conversational agent interventions available and accessible [26,32,33].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
This provides some support for the utility of conversational agents in treating mild-to-moderate psychological distress in adults.
“This provides some support for the utility of conversational agents in treating mild-to-moderate psychological distress in adults [23,25,26,31,34].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
Participants reported that they found the agent empathic, liked the interactivity, the agent's personality, the agent's ability to form a relationship, and the agent's ability to learn from input.
“Participants reported that they found the agent empathic [26], that they liked the interactivity [30], the agent’s personality [22,25], the agent’s ability to form a relationship [28,33], and the agent’s ability to learn from input [26].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
The predominant challenges to intervention with a conversational agent included repetitive content, limitations in the agents ability to understand or respond appropriately, a shallow or superficial relationship, the sound and quality of the agents voice, and specific intervention tools or content.
“The predominant challenges to intervention with a conversational agent included repetitive content [22,25,26,28,29,33], limitations in the agents ability to understand or respond appropriately [22,25,26,29], a shallow or superficial relationship [28,33], the sound and quality of the agents voice [32], and specific intervention tools or content [25,29].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
A large proportion of agents were eclectic interventions comprising a variety of strategies and psychoeducation drawing on a range of therapeutic orientations.
“Finally, a large proportion of agents were eclectic interventions comprising a variety of strategies and psychoeducation drawing on a range of therapeutic orientations [22,26,29,33].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [26]
Using an identical search strategy, the authors updated the review in January 2020 and identified a further 11 studies; most recent studies were aimed at mental health self-management (n=5).
“Using an identical search strategy, we updated this review in January 2020 and identified a further 11 studies. Similar to the previous review, most recent studies were aimed at mental health self-management (n=5) [10-12].”
From A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study (2020) · cites this paper as [11]
The depression symptom reduction, converted to Patient Health Questionnaire-9 equivalent, in the experimental group (1.5 points over 4 weeks) was comparable to that reported in the trial of the X2AI tool (0.9 points over 4 weeks).
“Of note, the depression symptom reduction, when converted to the Patient Health Questionnaire - 9 items equivalent, in the experimental group in our trial (1.5 points over 4 weeks) was comparable to that reported in the trial of the X2AI tool (0.9 points over 4 weeks) [51].”
AI users highlighted positive aspects of including AI, such as accessibility and empathy.
“AI users highlighted positive aspects of including AI, such as accessibility and empathy [78,86].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [78]
The remaining controlled intervention studies were classified as "good" quality investigations as they met most of the criteria.
“The remaining controlled intervention studies were classified as “good” quality investigations as they met most of the criteria [78,86].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [78]
The largest proportion of included conversational agents (12/31, 39%) addressed mental health issues.
“The largest proportion of them (12/31, 39%) addressed mental health issues [13,32-42]”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [34]
The most frequently raised issue with conversational agents (9 studies) was poor understanding because of limited vocabulary, voice recognition accuracy, or error management of word inputs.
“The most frequently raised issue with conversational agents (9 studies) was poor understanding because of limited vocabulary, voice recognition accuracy, or error management of word inputs [13,32-37,41,52].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [34]
Users reported liking reminders and assistance in forming routines, accountability, facilitated learning, and ease of learning and use.
“Other features of the agents that users reported liking were the reminders and assistance in forming routines [37,48] and that the agents provided accountability [13,34,48], facilitated learning [13,34,37], and were easy to learn and use [8,15].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [34]
Users in 4 studies reported liking that the agent had a personality and/or showed empathy, whereas users in other studies reported disliking the lack of personal connection or had difficulty empathizing with the agent or reported disliking its limited conversation and responses.
“Similarly, users in 4 studies reported liking that the agent had a personality and/or showed empathy [13,32,34,42], whereas users in other studies reported disliking the lack of personal connection or had difficulty in empathizing with the agent [35,37,50] or reported disliking its limited conversation and responses [35,56].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [34]
Feedback from users in 5 studies expressed a preference for interactivity; users in 1 study liked the interactivity of the chatbot, and users in 4 studies desired greater interactivity or relational skills.
“Feedback from users in 5 studies expressed a preference for interactivity, with users in 1 study noting that they liked the interactivity of the chatbot [35,37], and users in the other 4 studies expressed a desire for greater interactivity or relational skills in the conversational agent [14,32,34,53].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [34]
CBT is seldom used due to pervasive and persistent problems with access, cost, and stigma.
“However, CBT is seldom used due to pervasive and persistent problems with access, cost, and stigma [15-18].”
From Chatbot-Delivered Cognitive Behavioral Therapy in Adolescents With Depression and Anxiety During the COVID-19 Pandemic: Feasibility and Acceptability Study (2022) · cites this paper as [17]
Conversational agents or chatbots that deliver CBT via a text-based, semiautomated algorithm are known to reduce mild-to-moderate depressive symptoms in nonclinical populations.
“Conversational agents or “chatbots” that deliver CBT via a text-based, semiautomated algorithm are known to reduce mild-to-moderate depressive symptoms in nonclinical populations [17,20-23].”
From Chatbot-Delivered Cognitive Behavioral Therapy in Adolescents With Depression and Anxiety During the COVID-19 Pandemic: Feasibility and Acceptability Study (2022) · cites this paper as [17]
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Sorted by citations
Lattie, E. G., Adkins, E. C., Winquist, N., Stiles-Shields, C., Wafford, Q. E., & Graham, A. K. (2019) · Journal of Medical Internet Research · doi:10.2196/12869 · Cited by 843 · see citing works
My Chatbot Companion - a Study of Human-Chatbot Relationships
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BackgroundConversational Agents in Health Care: Scoping Review and Conceptual Analysis
Tudor Car, L., Dhinagaran, D. A., Kyaw, B. M., Kowatsch, T., Joty, S., Theng, Y.-L., & Atun, R. (2020) · Journal of Medical Internet Research · doi:10.2196/17158 · Cited by 553 · see citing works
“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].”
BackgroundMethodInfluentialThe Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review
Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020) · Journal of Medical Internet Research · doi:10.2196/20346 · Cited by 626 · see citing works
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“Specifically, the chatbot in the first study [29] was more advanced than the one in the second study [28]; it depended on artificial intelligence and machine learning to generate responses to users, and this makes it more humanlike and lets users feel more socially connected [5].”
InfluentialThe impact of ChatGPT on higher education
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Artificially intelligent chatbots in digital mental health interventions: a review
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Digital interventions for the treatment of depression: a meta-analytic review
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Physicians’ perceptions of chatbots in health Care: cross-sectional web-based survey
Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/12887 · Cited by 384 · see citing works
D’Alfonso, S. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.005 · Cited by 408 · see citing works
Haque, M. D. R., & Rubya, S. (2023) · JMIR mHealth and uHealth · doi:10.2196/44838 · Cited by 404 · see citing works
The Personalization of Conversational Agents in Health Care: Systematic Review
Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D., Briatore, A., & Coiera, E. (2019) · Journal of Medical Internet Research · doi:10.2196/15360 · Cited by 332 · see citing works
“While most studies used quasi-experimental study designs, only two used randomized controlled trials [35,44].”
MethodPerceptions and opinions of patients about mental health chatbots: scoping review
Abd-Alrazaq, A. A., Alajlani, M., Ali, N., Denecke, K., Bewick, B. M., & Househ, M. (2021) · Journal of Medical Internet Research · doi:10.2196/17828 · Cited by 301 · see citing works
Conversational agents in the treatment of mental health problems: mixed-method systematic review
Gaffney, H., Mansell, W., & Tai, S. (2019) · JMIR Mental Health · doi:10.2196/14166 · Cited by 275 · see citing works
Sauerbrei, A., Kerasidou, A., Lucivero, F., & Hallowell, N. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02162-y · Cited by 322 · see citing works
Liu, H., Peng, H., Song, X., Xu, C., & Zhang, M. (2022) · Internet Interventions · doi:10.1016/j.invent.2022.100495 · Cited by 296 · see citing works
“The results were consistent with some of the previous studies (Fitzpatrick et al., 2017; Fulmer et al., 2018), in which there were shorter intervention periods of 2 and 4 weeks.”
ResultSchachner, T., Keller, R., & v Wangenheim, F. (2020) · Journal of Medical Internet Research · doi:10.2196/20701 · Cited by 267 · see citing works
Greer, S., Ramo, D., Chang, Y.-J., Fu, M., Moskowitz, J., & Haritatos, J. (2019) · JMIR mHealth and uHealth · doi:10.2196/15018 · Cited by 265 · see citing works
Artificial intelligence and chatbots in psychiatry
Pham, K. T., Nabizadeh, A., & Selek, S. (2022) · Psychiatric Quarterly · doi:10.1007/s11126-022-09973-8 · Cited by 241 · see citing works
The Opportunities and Risks of Large Language Models in Mental Health
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Loneliness and suicide mitigation for students using GPT3-enabled chatbots
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He, Y., Yang, L., Qian, C., Li, T., Su, Z., Zhang, Q., & Hou, X. (2023) · Journal of Medical Internet Research · doi:10.2196/43862 · Cited by 213 · see citing works
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Life Crafting as a Way to Find Purpose and Meaning in Life
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Artificial Intelligence for Chatbots in Mental Health: Opportunities and Challenges
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Coaching With Artificial Intelligence: Concepts and Capabilities
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Artificial Intelligence for Health Professions Educators
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Toward fairness in AI for people with disabilities SBG@a research roadmap
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“The total scores are separated into the following four categories to determine the anxiety symptom levels: none (0-4), mild (5-9), moderate (10-14), and severe (≥15) [23,24].”
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Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.
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MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling
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Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview
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Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot
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“Additionally, previous systematic reviews and meta-analyses have shown significant reductions in symptoms for depression and other mental disorders while using mental health apps, some of which included cognitive behavioral therapy (CBT) or gamification elements [26,36,37,40,41].”
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Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine
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College Student Perspectives of Telemental Health: a Review of the Recent Literature
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Bonding With Bot: User Feedback on a Chatbot for Social Isolation
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Artificial intelligence for psychotherapy: a review of the current state and future directions
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Artificial intelligence in paediatric radiology: Future opportunities
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Why would you use medical chatbots? interview and survey
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Chatbot, as Educational and Inclusive Tool for People with Intellectual Disabilities
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Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges
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Therapeutic Alliance in Online and Face-to-face Psychological Treatment: Comparative Study
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“%) were from the United States of America [3,4,24,30,32-34 ,42-49 42-49].”
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Artificial intelligence and counseling: Four levels of implementation
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- Authors
- Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M.
- Affiliations
- Northwestern University (US)
- xAI (United States) (US)
- Saxion (NL)
- Corresponding author
- Fulmer, Russell
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2018-02-28
- Volume
- 5
- Issue
- 4
- Pages
- e64-e64
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC6315222
- OpenAlex
- W2889335577
- Cited by
- 715
- References
- 41
- Citations by year
- 2026: 138 · 2025: 161 · 2024: 114 · 2023: 104 · 2022: 68 · 2021: 61 · 2020: 51 · 2019: 18
- FWCI
- 41.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- depressive symptoms, anxiety symptoms, university students, cognitive behavioral therapy, digital mental health interventions, PHQ-9, GAD-7, PANAS, feasibility, treatment efficacy, student mental health
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