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

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

SUBMITTER: Fulmer R

PROVIDER: S-EPMC6315222 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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

Fulmer Russell R Joerin Angela A Gentile Breanna B Lakerink Lysanne L Rauws Michiel M

JMIR mental health 20181213 4

<h4>Background</h4>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 ...[more]

PMID: 30545815

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Project description:BackgroundArtificial intelligence-based chatbots are emerging as instruments of psychological intervention; however, no relevant studies have been reported in Latin America.ObjectiveThe objective of the present study was to evaluate the viability, acceptability, and potential impact of using Tess, a chatbot, for examining symptoms of depression and anxiety in university students.MethodsThis was a pilot randomized controlled trial. The experimental condition used Tess for 8 weeks, and the control condition was assigned to a psychoeducation book on depression. Comparisons were conducted using Mann-Whitney U and Wilcoxon tests for depressive symptoms, and independent and paired sample t tests to analyze anxiety symptoms.ResultsThe initial sample consisted of 181 Argentinian college students (158, 87.2% female) aged 18 to 33. Data at week 8 were provided by 39 out of the 99 (39%) participants in the experimental condition and 34 out of the 82 (41%) in the control group. On an average, 472 (SD 249.52) messages were exchanged, with 116 (SD 73.87) of the messages sent from the users 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 differences between the experimental and control groups were found from the baseline to week 8 for depressive and anxiety symptoms. However, significant intragroup differences demonstrated that the experimental group showed a significant decrease in anxiety symptoms; no such differences were observed for the control group. Further, no significant intragroup differences were found for depressive symptoms.ConclusionsThe students spent a considerable amount of time exchanging messages with Tess and positive feedback was associated with a higher number 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.

Project description:BackgroundThe need for scalable delivery of mental health care services that are efficient and effective is now a major public health priority. Artificial intelligence (AI) tools have the potential to improve behavioral health care services by helping clinicians collect objective data on patients' progress, streamline their workflow, and automate administrative tasks.ObjectiveThe aim of this study was to determine the feasibility, acceptability, and preliminary efficacy of an AI platform for behavioral health in facilitating better clinical outcomes for patients receiving outpatient therapy.MethodsThe study was conducted at a community-based clinic in the United States. Participants were 47 adults referred for outpatient, individual cognitive behavioral therapy for a main diagnosis of a depressive or anxiety disorder. The platform provided by Eleos Health was compared to a treatment-as-usual (TAU) approach during the first 2 months of therapy. This AI platform summarizes and transcribes the therapy session, provides feedback to therapists on the use of evidence-based practices, and integrates these data with routine standardized questionnaires completed by patients. The information is also used to draft the session's progress note. Patients were randomized to receive either therapy provided with the support of an AI platform developed by Eleos Health or TAU at the same clinic. Data analysis was carried out based on an intention-to-treat approach from December 2022 to January 2023. The primary outcomes included the feasibility and acceptability of the AI platform. Secondary outcomes included changes in depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder-7) scores as well as treatment attendance, satisfaction, and perceived helpfulness.ResultsA total of 72 patients were approached, of whom 47 (67%) agreed to participate. Participants were adults (34/47, 72% women and 13/47, 28% men; mean age 30.64, SD 11.02 years), with 23 randomized to the AI platform group, and 24 to TAU. Participants in the AI group attended, on average, 67% (mean 5.24, SD 2.31) more sessions compared to those in TAU (mean 3.14, SD 1.99). Depression and anxiety symptoms were reduced by 34% and 29% in the AI platform group versus 20% and 8% for TAU, respectively, with large effect sizes for the therapy delivered with the support of the AI platform. No group difference was found in 2-month treatment satisfaction and perceived helpfulness. Further, therapists using the AI platform submitted their progress notes, on average, 55 hours earlier than therapists in the TAU group (t=-0.73; P<.001).ConclusionsIn this randomized controlled trial, therapy provided with the support of Eleos Health demonstrated superior depression and anxiety outcomes as well as patient retention, compared with TAU. These findings suggest that complementing the mental health services provided in community-based clinics with an AI platform specializing in behavioral treatment was more effective in reducing key symptoms than standard therapy.Trial registrationClinicalTrials.gov NCT05745103; https://classic.clinicaltrials.gov/ct2/show/NCT05745103.

Project description:BackgroundAnxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.ObjectiveThis review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.MethodsWe searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis.ResultsOf the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.ConclusionsWearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies' results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

Project description:BackgroundAnxiety disorders have the highest prevalence of all psychiatric disorders in China. Medication and psychotherapy are two main treatment approaches for this group of disorders, and when used in combinations are significantly more beneficial than medication alone. The resources are insufficient. The availability of psychotherapy is low due to the limited resources. Artificial intelligence (AI)-assisted psychotherapy offers an opportunity to develop an efficient and standardized psychotherapy model and improve the availability of psychotherapy, which is key to improve the clinical efficacy of anxiety disorder treatments.ObjectivesThe present protocol aims to determine whether medication plus AI-assisted psychotherapy has greater efficacy than medication alone in the treatment of anxiety disorders.MethodsWe will recruit patients in eight hospitals in China. Seven hundred and eight patients with anxiety disorders will be randomly allocated on a 1:1 basis to either medication plus AI-assisted psychotherapy group, or medication alone group. We have built an AI psychotherapy robot named XIAO AN. In this study we will deliver psychotherapy to patients in the medication plus AI-assisted psychotherapy group. Patients will be assessed at baseline and at the end of week 2, 4, 8, and 12. Follow-up assessments will be conducted at 3 and 6 months posttreatment. The primary outcome is change of Hamilton Anxiety Rating Scale (HAMA) score from baseline the end of 12-week treatment. A secondary efficacy outcome will be improvement in treatment at an early stage (score reduction in HAMA ≥25% after 2 weeks of treatment). Other measurements include Hamilton Depression Scale, Clinical Global Impression, Treatment Emergent Symptom Scale, Social Disability Screening Schedule, Insomnia Severity Index and so on. Scales will be assessed by independent raters who are blind to treatment allocation and analyses will be conducted by a statistician who is also blind to treatment allocation.DiscussionThis will be the first multicentered randomized controlled single-blind trial in China to assess the efficacy of medication plus AI-assisted psychotherapy compared with medication alone for anxiety disorders. The study has the potential to address the limitations of the limited availability of psychotherapy, and to augment the efficacy of the treatment of anxiety disorders in China.

Project description:BACKGROUND:Depression and anxiety symptoms are common among university students, but many do not receive treatment. This is often because of lack of availability, reluctance to seek help, and not meeting the diagnostic criteria required to access services. Internet-based interventions, including smartphone apps, can overcome these issues. However, a large number of apps are available, each with little evidence of their effectiveness. OBJECTIVE:This study aims to evaluate for the first time the effectiveness of a self-guided mobile app, Feel Stress Free, for the treatment of depression and anxiety symptoms in students. METHODS:A web-based randomized controlled trial compared a cognitive behavioral therapy (CBT)-based mobile app Feel Stress Free with a wait-list control. University students self-identified as experiencing symptoms of anxiety or depression and were randomized to 6 weeks of intervention (n=84) or control (n=84), unblinded. The app is self-guided and incorporates behavioral relaxation activities, mood tracking and thought challenging, and minigames. Participants completed the Hospital Anxiety and Depression Scale online at baseline and every fortnight. RESULTS:At week 6, the primary end point, there was evidence that the Feel Stress Free app reduced depression symptoms (mean difference -1.56; 95% CI -2.67 to -0.44; P=.006) but only very weak evidence that it reduced anxiety symptoms (mean difference -1.36; 95% CI -2.93 to 0.21; P=.09). At week 4, there was evidence to support the effectiveness of the intervention for anxiety symptoms (mean difference -1.94; 95% CI -3.11 to -0.77; P=.001) and, though weaker, depression symptoms (mean difference -1.08; 95% CI -2.12 to -0.04; P=.04). At week 6, 83% (34/41) of participants indicated that they were using the app weekly or more frequently. CONCLUSIONS:The Feel Stress Free app is a promising mobile intervention for treating symptoms of anxiety and depression in students and overcomes many of the barriers to traditional CBT. Further research is needed to establish its effectiveness at and beyond 6 weeks. TRIAL REGISTRATION:ClinicalTrials.gov NCT03032952; https://clinicaltrials.gov/ct2/show/NCT03032952.

Project description:BACKGROUND:Symptoms of anxiety co-occur in a variety of disorders including in depressive episodes of bipolar disorder and in patients with thyrotoxicosis. Treatment of refractory bipolar disorder with supraphysiologic doses of levothyroxine (L-T4) has been shown to improve the phenotypic expression of the disorder and is associated with an increase of circulating thyroid hormones. However, it might be associated with somatic and mental adverse effects. Here we report the investigation of the influence of treatment with supraphysiologic doses of L-T4 on symptoms of anxiety in patients with refractory bipolar depression. METHODS:Post-hoc analysis from a 6-week, multi-center, randomized, double-blind, placebo-controlled study of the effects of supraphysiologic L-T4 treatment on anxiety symptoms in bipolar depression. Anxiety symptoms were measured weekly with the Hamilton anxiety/somatization factor (HASF) score of the Hamilton Depression Rating Scale (HAMD) and the State- and Trait Anxiety Inventory (STAI). RESULTS:Treatment of both groups was associated with a significant reduction in anxiety symptoms (p?<?0.001) with no statistical difference between groups (LT-4: from 5.9 (SD?=?2.0) at baseline to 3.7 (SD?=?2.4) at study end; placebo: from 6.1 (SD?=?2.4) at baseline to 4.4 (SD?=?2.8) at study end; p?=?0.717). Severity of anxiety at baseline did not show a statistically significant correlation to the antidepressive effect of treatment with supraphysiologic doses of L-T4 (p?=?0.811). Gender did not show an influence on the reduction of anxiety symptoms (females: from 5.6 (SD?=?1.7) at baseline to 3.5 (SD?=?2.4) at study end; males: from 6.1 (SD?=?2.3) at baseline to 4.0 (SD?=?2.4) at study end; p?=?0.877). CONCLUSIONS:This study failed to detect a difference in change of anxiety between bipolar depressed patients treated with supraphysiologic doses of L-T4 or placebo. Comorbid anxiety symptoms should not be considered a limitation for the administration of supraphysiologic doses of L-T4 refractory bipolar depressed patients. Trial registration ClinicalTrials, ClinicalTrials.gov identifier: NCT01528839. Registered 2 June 2012-Retrospectively registered, https://clinicaltrials.gov/ct2/show/study/NCT01528839.

Project description:ObjectivePoor diet can be detrimental to mental health. However, the overall evidence for the effects of dietary interventions on mood and mental well-being has yet to be assessed. We conducted a systematic review and meta-analysis examining effects of dietary interventions on symptoms of depression and anxiety.MethodsMajor electronic databases were searched through March 2018 for all randomized controlled trials of dietary interventions reporting changes in symptoms of depression and/or anxiety in clinical and nonclinical populations. Random-effects meta-analyses were conducted to determine effect sizes (Hedges' g with 95% confidence intervals [CI]) for dietary interventions compared with control conditions. Potential sources of heterogeneity were explored using subgroups and meta-regression analyses.ResultsResults: Sixteen eligible randomized controlled trials (published in English) with outcome data for 45,826 participants were included; the majority of which examined samples with nonclinical depression (n = 15 studies). Nonetheless, dietary interventions significantly reduced depressive symptoms (g = 0.162, 95% CI = 0.055 to 0.269, p = 0.003). Similar effects were observed among high-quality trials (g = 0.171, 95% C.I.=0.057 to 0.286, p=0.003) and when compared with both inactive (g = 0.114, 95% C.I.=0.008 to 0.219, p=0.035) and active controls (g = 0.224, 95% C.I.= 0.052 to 0.397, p = 0.011). No effect of dietary interventions was observed for anxiety (k = 11, n = 2270, g = 0.085, 95% C.I. = -0.031 to 0.202, p=0.151). Studies with female samples observed significantly greater benefits from dietary interventions, for symptoms of both depression and anxiety.ConclusionsDietary interventions hold promise as a novel intervention for reducing symptoms of depression across the population. Future research is required to determine the specific components of dietary interventions that improve mental health, explore underlying mechanisms, and establish effective schemes for delivering these interventions in clinical and public health settings.RegistrationPROSPERO Online Protocol: CRD42018091256.

Project description:BackgroundChronic fatigue syndrome (CFS) is a complex disease of unknown etiology and mechanism. The purpose of this study was to investigate the effect of Prolong Life with Nine Turn Method (PLWNT) Qigong exercise on CFS focusing on fatigue, sleep quality, depression, and anxiety.MethodsA total of 90 participants diagnosed with CFS were randomly assigned into two parallel groups: PLWNT and cognitive behavioral therapy (CBT). The participants in the PLWNT or CBT group participated in qigong exercise or cognitive behavior education program, respectively, once a week in-person and were supervised online during the remaining 6 days at home, over 12 consecutive weeks. The primary outcome was fatigue (Multi-dimensional Fatigue Inventory 20 [MFI-20]), and secondary outcomes were sleep quality (Pittsburgh Sleep Quality Index [PSQI]), anxiety, depression (Hospital Anxiety and Depression Scale [HADS]), and changes in the Neuropeptide Y (NPY) of peripheral blood.ResultsThe within-group comparisons of the PLWNT and CBT groups revealed significant improvement in both groups in MFI-20, PSQI, and HADS scores (P < 0.05). No significant difference were found between the PLWNT and CBT groups, even though the effective rate of the PLWNT group was 62.22%, which is slightly than 50.00% of the CBT group. The fatigue scores in the PLWNT group were positively correlated with sleep degree (r = 0.315) and anxiety degree (r = 0.333), only anxiety degree (r = 0.332) was found to be positively correlated with fatigue in the CBT group. The analysis of peripheral blood showed that NPY decreased after PLWNT intervention but increased significantly in the CBT.ConclusionThe PLWNT qigong exercise has potential to be an effective rehabilitation method for CFS symptoms including fatigue, sleep disturbance, anxiety, and depression. Future studies should expand study sample size for in-depth investigation to determine the optimal frequency and intensity of PLWNT qigong intervention in CFS patients. The study was registered in the ClinicalTrials.gov database on April 12, 2018, with registration number NCT03496961.

Project description:Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time.The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression.In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, "Depression in College Students," as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2).Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants' comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy.Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT.

Project description:BackgroundOur previous studies found the high prevalence of depression and anxiety among Chinese cancer patients, and many empirical studies have been conducted to evaluate the effects of psychological interventions on depression and anxiety among Chinese cancer patients. This study aimed to conduct a meta-analysis in order to assess the effects of psychological interventions on depression and anxiety in Chinese adults with cancer.MethodsThe four most comprehensive Chinese academic database- CNKI, Wanfang, Vip and CBM databases-were searched from their inception until January 2014. PubMed and Web of Science (SCIE) were also searched from their inception until January 2014 without language restrictions, and an internet search was used. Randomized controlled studies assessing the effects of psychological interventions on depression and anxiety among Chinese adults with cancer were analyzed. Study selection and appraisal were conducted independently by three authors. The pooled random-effects estimates of standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated. Moderator analysis (meta-regression and subgroup analysis) was used to explore reasons for heterogeneity.ResultsWe retrieved 147 studies (covering 14,039 patients) that reported 253 experimental-control comparisons. The random effects model showed a significant large effect size for depression (SMD = 1.199, p < 0.001; 95% CI = 1.095-1.303) and anxiety (SMD = 1.298, p < 0.001; 95% CI = 1.187-1.408). Cumulative meta-analysis indicated that sufficient evidence had accumulated since 2000-2001 to confirm the statistically significant effectiveness of psychological interventions on depression and anxiety in Chinese cancer patients. Moderating effects were found for caner type, patients' selection, intervention format and questionnaires used. In studies that included lung cancer, preselected patients with clear signs of depression/anxiety, adopted individual intervention and used State-Trait Anxiety Inventory (STAI), the effect sizes were larger.ConclusionsWe concluded that psychological interventions in Chinese cancer patients have large effects on depression and anxiety. The findings support that an adequate system should be set up to provide routine psychological interventions for cancer patients in Chinese medical settings. However, because of some clear limitations (heterogeneity and publication bias), these results should be interpreted with caution.

Dataset Information

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

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

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