[This corrects the article DOI: 10.2196/69351.].

Background The prevalence of anxiety and depression is increasing globally, outpacing the capacity of traditional mental health services. Digital mental health interventions (DMHIs) provide a cost-effective alternative, but user engagement remains limited. Integrating artificial intelligence (AI)–powered conversational agents may enhance engagement and improve the user experience; however, with AI technology rapidly evolving, the acceptability of these solutions remains uncertain. Objective This study aims to examine the acceptability, engagement, and usability of a conversational agent–led DMHI with human support for generalized anxiety by exploring patient expectations and experiences through a mixed methods approach. Methods Participants (N=299) were offered a DMHI for up to 9 weeks and completed postintervention self-report measures of engagement (User Engagement Scale [UES]; n=190), usability (System Usability Scale [SUS]; n=203), and acceptability (Service User Technology Acceptability Questionnaire [SUTAQ]; n=203). To explore expectations and experiences with the program, a subsample of participants completed qualitative semistructured interviews before the intervention (n=21) and after the intervention (n=16), which were analyzed using inductive thematic analysis. Results Participants rated the digital program as engaging (mean UES total score 3.7; 95% CI 3.5-3.8), rewarding (mean UES rewarding subscale 4.1; 95% CI 4.0-4.2), and easy to use (mean SUS total score 78.6; 95% CI 76.5-80.7). They were satisfied with the program and reported that it increased access to and enhanced their care (mean SUTAQ subscales 4.3-4.9; 95% CI 4.1-5.1). Insights from pre- and postintervention qualitative interviews highlighted 5 themes representing user needs important for acceptability: (1) accessible mental health support, in terms of availability and emotional approachability (Accessible Care); (2) practical and effective solutions leading to tangible improvements (Effective Solutions); (3) a personalized and tailored experience (Personal Experience); (4) guidance within a clear structure, while retaining control (Guided but in Control); and (5) a sense of support facilitated by human involvement (Feeling Supported). Overall, the DMHI met participant expectations, except for theme 3, as participants desired greater personalization and reported frustration when the conversational agent misunderstood them. Conclusions Incorporating factors critical to patient acceptability into DMHIs is essential to maximize their global impact on mental health care. This study provides both quantitative and qualitative evidence for the acceptability of a structured, conversational agent–driven digital program with human support for adults experiencing generalized anxiety. The findings highlight the importance of design, clinical, and implementation factors in enhancing engagement and reveal opportunities for ongoing optimization and innovation. Scalable models with stratified human support and the safe integration of generative AI have the potential to transform patient experience and increase the real-world impact of conversational agent–led DMHIs. Trial Registration ISRCTN Registry ISRCTN 52546704; https://www.isrctn.com/ISRCTN52546704

Escalating global mental health demand exceeds existing clinical capacity. Scalable digital solutions will be essential to expand access to high-quality mental healthcare. This study evaluated the effectiveness of a digital intervention to alleviate mild, moderate and severe symptoms of generalized anxiety. This structured, evidence-based program combined an Artificial Intelligence (AI) driven conversational agent to deliver content with human clinical oversight and user support to maximize engagement and effectiveness. The digital intervention was compared to three propensity-matched real-world patient comparator groups: i) waiting control; ii) face-to-face cognitive behavioral therapy (CBT); and iii) remote typed-CBT. Endpoints for effectiveness, engagement, acceptability, and safety were collected before, during and after the intervention, and at one-month follow-up. Participants (n=299) used the program for a median of 6 hours over 53 days. There was a large clinically meaningful reduction in anxiety symptoms for the intervention group (per-protocol (n=169): change on GAD-7 = -7.4, d = 1.6; intention-to-treat (n=299): change on GAD-7 = -5.4, d = 1.1) that was statistically superior to the waiting control, non-inferior to human-delivered care, and was sustained at one-month follow-up. By combining AI and human support, the digital intervention achieved clinical outcomes comparable to human-delivered care while significantly reducing the required clinician time. These findings highlight the immense potential of technology to scale effective evidence-based mental healthcare, address unmet need, and ultimately impact quality of life and economic burden globally. ### Competing Interest Statement Chief Investigator (EMa) and other investigators (CEP, EMi, GW, MPE, EC, SL, AS, CH, JY, MB, LM, SM, RC, VT, AC, AW, AB) are employees of ieso Digital Health Limited (the company funding this research) or its subsidiaries. None of these authors had a direct financial incentive related to the results of this study or the publication of the manuscript. ### Clinical Trial ISRCTN ID: 52546704 ### Funding Statement This research was funded by ieso Digital Health Ltd. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: NHS Research Ethics Committee (REC) West of Scotland REC 4 gave ethical approval for this research (IRAS ID: 327897) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Owing to the potential risk of patient identification, and following data privacy policies at ieso and DHC, individual-level data are not available. Aggregated data are available upon request, subject to a data-sharing agreement with ieso and DHC. Data requests should be sent to the corresponding author and will be responded to within 30 days.

Objective: Understanding patient responses to psychotherapy is important in developing effective interventions. However, coding patient language is a resource-intensive exercise and difficult to perform at scale. Our aim was to develop a deep learning model to automatically identify patient utterances during text-based internet-enabled Cognitive Behavioural Therapy and to determine the association between utterances and clinical outcomes. Method: Using 340 manually annotated transcripts we trained a deep learning model to categorize patient utterances into one or more of five categories. The model was used to automatically code patient utterances from our entire data set of transcripts (∼34,000 patients), and logistic regression analyses used to determine the association between both reliable improvement and engagement, and patient responses. Results: Our model reached human-level agreement on three of the five patient categories. Regression analyses revealed that increased counter change-talk (movement away from change) was associated with lower odds of both reliable improvement and engagement, while increased change-talk (movement towards change or self-exploration) was associated with increased odds of improvement and engagement. Conclusions: Deep learning provides an effective means of automatically coding patient utterances at scale. This approach enables the development of a data-driven understanding of the relationship between therapist and patient during therapy.

BACKGROUND:It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).METHODS:Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session. Latent Markov modelling was used on these data to define depressive states and explore transition probabilities between states. Clinical outcomes and patient demographics were compared between patients starting at different depressive states.RESULTS:A model with seven depressive states emerged as the best compromise between optimal fit and interpretability. States loading preferentially on cognitive/affective v. somatic symptoms of depression were identified. Analysis of transition probabilities revealed that patients in cognitive/affective states do not typically transition towards somatic states and vice-versa. Post-hoc analyses also showed that patients who start in a somatic depressive state are less likely to engage with or improve with therapy. These patients are also more likely to be female, suffer from a comorbid long-term physical condition and be taking psychotropic medication.CONCLUSIONS:This study presents a novel approach for depression sub-typing, defining fluid depressive states and exploring transitions between states in response to CBT. Understanding how different symptom profiles respond to therapy will inform the development and delivery of stratified treatment protocols, improving clinical outcomes and cost-effectiveness of psychological therapies for patients with depression.

The transcultural adaptation involved the following steps:ü Validity assessment was conducted by a multidisciplinary committee of experts ü Consisted of both a quantitative analysis (calculation of content validity coefficients CVC) and a qualitative analysis (assessment of the experts' comments and suggestions). Cross-Cultural Adaptation of The Internet Gaming Disorder Scale-Short-Form (IGDS9-SF) to the Brazilian Context ResultsThe transcultural adaptation of the scale followed the proposed protocol and the content validity coefficients was satisfactory (≥ 0.83) for all the structures and equivalences assessed.

IMPORTANCE Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for measuring the delivery of psychotherapy. OBJECTIVE To use a deep learning model applied to a large-scale clinical data set of cognitive behavioral therapy (CBT) session transcripts to generate a quantifiable measure of treatment delivered and to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS All data were obtained from patients receiving internet-enabled CBT for the treatment of a mental health disorder between June 2012 and March 2018 in England. Cognitive behavioral therapy was delivered in a secure online therapy room via instant synchronous messaging. The initial sample comprised a total of 17 572 patients (90 934 therapy session transcripts). Patients self-referred or were referred by a primary health care worker directly to the service. EXPOSURES All patients received National Institute for Heath and Care Excellence-approved disorder-specific CBT treatment protocols delivered by a qualified CBT therapist. MAIN OUTCOMES AND MEASURES Clinical outcomes were measured in terms of reliable improvement in patient symptoms and treatment engagement. Reliable improvement was calculated based on 2 severity measures: Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7), corresponding to depressive and anxiety symptoms respectively, completed by the patient at initial assessment and before every therapy session. RESULTS Treatment sessions from a total of 14 899 patients (10 882 women) aged between 18 and 94 years (median age, 34.8 years) were included in the final analysis. We trained a deep learning model to automatically categorize therapist utterances into 1 or more of 24 feature categories. The trained model was applied to our data set to obtain quantifiable measures of each feature of treatment delivered. A logistic regression revealed that increased quantities of a number of session features, including change methods (cognitive and behavioral techniques used in CBT), were associated with greater odds of reliable improvement in patient symptoms (odds ratio, 1.11; 95% CI, 1.06-1.17) and patient engagement (odds ratio, 1.20, 95% CI, 1.12-1.27). The quantity of nontherapy-related content was associated with reduced odds of symptom improvement (odds ratio, 0.89; 95% CI, 0.85-0.92) and patient engagement (odds ratio, 0.88, 95% CI, 0.84-0.92). CONCLUSIONS AND RELEVANCE This work demonstrates an association between clinical outcomes in psychotherapy and the content of therapist utterances. These findings support the principle that CBT change methods help produce improvements in patients' presenting symptoms. The application of deep learning to large clinical data sets can provide valuable insights into psychotherapy, informing the development of new treatments and helping standardize clinical practice.

We introduce and demonstrate the usefulness of a tool that automatically annotates therapist utterances in real-time according to the therapeutic role that they perform in an evidence-based psychological dialogue. This is implemented within the context of an on-line service that supports the delivery of one-to-one therapy. When combined with patient outcome measures, this tool allows us to discover the active ingredients in psychotherapy. In particular, we show that particular measures of therapy content are more strongly correlated with patient improvement than others, suggesting that they are a critical part of psychotherapy. As this tool gives us interpretable measures of therapy content, it can enable services to quality control the therapy delivered. Furthermore, we show how specific insights can be presented to the therapist so they can reflect on and improve their practice.

Background Common mental health problems affect a quarter of the population. Online cognitive–behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear. Aims This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet. Method Real-world clinical outcomes data were collected from 2211 NHS England patients completing a course of CBT delivered by a trained clinician via the internet. Logistic regression analyses were performed using patient and service variables to identify significant predictors of response to treatment. Results Multiple patient variables were significantly associated with positive response to treatment including older age, absence of long-term physical comorbidities and lower symptom severity at start of treatment. Service variables associated with positive response to treatment included shorter waiting times for initial assessment and longer treatment durations in terms of the number of sessions. Conclusions Knowledge of which patient and service variables are associated with good clinical outcomes can be used to develop personalised treatment programmes, as part of a quality improvement cycle aiming to drive up standards in mental healthcare. This study exemplifies translational research put into practice and deployed at scale in the National Health Service, demonstrating the value of technology-enabled treatment delivery not only in facilitating access to care, but in enabling accelerated data capture for clinical research purposes. Declaration of interest A.C., S.B., V.T., K.I., S.F., A.R., A.H. and A.D.B. are employees or board members of the sponsor. S.R.C. consults for Cambridge Cognition and Shire. Keywords: Anxiety disorders; cognitive behavioural therapies; depressive disorders; individual psychotherapy

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/outcome models in the UK's largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors' own group) who work in text processing for biomedicine and other areas. GATE is available online <1> under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis.

Semantic search over documents is about finding information that is not based just on the presence of words, but also on their meaning [1, 2]. This task is a modification of classical Information Retrieval (IR), but documents are retrieved on the basis of relevance to ontology concepts, as well as words. Nevertheless the basic assumption is quite similar - a document is characterized by the bag of tokens constituting its content, disregarding its structure. While the basic IR approach considers word stems as tokens, there has been considerable effort towards using word-senses or lexical concepts (see [3, 4]) for indexing and retrieval. In the case of semantic search, what is being indexed is typically a combination of words, ontological concepts conveying the meaning of some of these words (e.g. Cambridge is a location), and optionally relations between such concepts (e.g. Cambridge is in the UK) [1]. The latter enable somebody searching for documents about the UK to find also documents mentioning Cambridge.

This paper presents GATE Teamware--an open-source, web-based, collaborative text annotation framework. It enables users to carry out complex corpus annotation projects, involving distributed annotator teams. Different user roles are provided (annotator, manager, administrator) with customisable user interface functionalities, in order to support the complex workflows and user interactions that occur in corpus annotation projects. Documents may be pre-processed automatically, so that human annotators can begin with text that has already been pre-annotated and thus making them more efficient. The user interface is simple to learn, aimed at non-experts, and runs in an ordinary web browser, without need of additional software installation. GATE Teamware has been evaluated through the creation of several gold standard corpora and internal projects, as well as through external evaluation in commercial and EU text annotation projects. It is available as on-demand service on GateCloud.net, as well as open-source for self-installation.

This paper presents AnnoMarket, an open cloud-based platform which enables researchers to deploy, share, and use language processing components and resources, following the data-as-a-service and software-as-a-service paradigms. The focus is on multilingual text analysis resources and services, based on an opensource infrastructure and compliant with relevant NLP standards. We demonstrate how the AnnoMarket platform can be used to develop NLP applications with little or no programming, to index the results for enhanced browsing and search, and to evaluate performance. Utilising AnnoMarket is straightforward, since cloud infrastructural issues are dealt with by the platform, completely transparently to the user: load balancing, efficient data upload and storage, deployment on the virtual machines, security, and fault tolerance.

Cloud computing is increasingly being regarded as a key enabler of the 'democratization of science', because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research--GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost-benefit analysis and usage evaluation.

The growth of social media and other unstructured content on one hand, and Linked Open Data on the other, now pose a significant challenge for Semantic Web researchers wishing to design, implement, and evaluate semantic annotation algorithms on web scale datasets. Running experiments on standard servers is often too time consuming, whereas implementing these efficiently with MapReduce/Hadoop requires significant engineering skills. This paper presents a cloud-based semantic annotation and search infrastructure, which enables researchers to configure and run easily semantic annotation pipelines, while the infrastructure itself takes care of the distribution problem. The infrastructure also supports shared/crowdsourced gold-standard creation, performance evaluation, efficient text and annotation indexing, and semantic search. The paper also evaluates the performance gains achieved by using the cloud-based infrastructure and presents a cost/benefit analysis against existing similar services.