IntEgrating smoking cessation treAtment into usual online psychological care for people with common mEntal illness: Protocol for an online randomised feasibility and pilot study (ESCAPE digital).

BACKGROUND: In the UK, smoking prevalence in people with depression (34%) and anxiety (29%) is more than double that of the general population (13%). People who stop smoking improve their mental health with comparable effect sizes found for antidepressants. In England, online psychological therapy is a standard treatment for depression and anxiety. Online therapy is an acceptable setting for smoking cessation support; however, integrated smoking and mental health support is not available. This novel study aims to assess the acceptability and feasibility of an online smoking cessation intervention, and trial procedures, offered alongside online mental health treatment as it offers increased reach to people with common mental health difficulties who smoke. METHODS: A two-armed; Intervention (Integrated SilverCloud smoking cessation support) and control group (SilverCloud usual care), pragmatic, randomised controlled feasibility trial. We aim to recruit 500 adult smokers eligible for online mental health treatment. Follow-up will be conducted at 3-months and 6-months. We will assess the acceptability and feasibility of the trial procedures (i.e., recruitment, data completeness, self-reported acceptability and satisfaction) and the intervention (i.e., self-reported quit attempt, engagement with the smoking cessation and mental health programs, smoking cessation medicine and e-cigarette use, self-reported acceptability and satisfaction) and pilot clinical outcomes (i.e., biologically validated smoking abstinence, anxiety, depression, quality of health). CONCLUSION: If the Trial is successful, a randomised controlled effectiveness trial will follow to examine whether integrated smoking cessation and mental health treatment increases smoking abstinence and improves depression and anxiety compared to usual care. TRIAL REGISTRATION: ISRCTN10612149 (https://doi.org/10.1186/ISRCTN10612149), 02/02/2023.

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression.

BACKGROUND: Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance. METHODS: N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample). RESULTS: The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (ß = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (ß = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (ß = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (ß = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620). CONCLUSIONS: The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Citizen science to improve patient and public involvement in GUideline Implementation in oral health and DEntistry (the GUIDE platform).

BACKGROUND: Citizen science is a way to democratise science by involving groups of citizens in the research process. Clinical guidelines are used to improve practice, but their implementation can be limited. Involving patients and the public can enhance guideline implementation, but there is uncertainty about the best approaches to achieve this. Citizen science is a potential way to involve patients and the public in improving clinical guideline implementation. We aimed to explore the application of citizen science methods to involve patients and the public in the dissemination and implementation of clinical guidelines in oral health and dentistry. METHODS: We developed GUIDE (GUideline Implementation in oral health and DEntistry), a citizen science online platform, using a participatory approach with researchers, oral health professionals, guideline developers and citizens. Recruitment was conducted exclusively online. The platform focused on prespecified challenges related to oral health assessment guidelines, and asked citizens to generate ideas, as well as vote and comment on other citizens' ideas to improve those challenges. Citizens also shared their views via surveys and two online synchronous group meetings. Data were collected on participant's demographics, platform engagement and experience of taking part. The most promising idea category was identified by an advisory group based on engagement, feasibility and relevance. We presented quantitative data using descriptive statistics and analysed qualitative data using inductive and deductive thematic analysis. RESULTS: The platform was open for 6 months and we recruited 189 citizens, from which over 90 citizens actively engaged with the platform. Most citizens were over 34 years (64%), female (58%) and had a university degree (50%). They generated 128 ideas, 146 comments and 248 votes. The challenge that led to most engagement was related to prevention and oral health self-care. To take this challenge forward, citizens generated a further 36 ideas to improve a pre-existing National Health Service oral care prevention leaflet. Citizens discussed motivations to take part in the platform (understanding, values, self-care), reasons to stay engaged (communication and feedback, outputs and impact, and relevance of topics discussed) and suggestions to improve future platforms. CONCLUSION: Citizen science is an effective approach to generate and prioritise ideas from a group of citizens to improve oral health and dental services. Prevention and oral health self-care were of particular interest to citizens. More research is needed to ensure recruitment of a diverse group of citizens and to improve retention in citizen science projects. PATIENT OR PUBLIC CONTRIBUTION: This project was inherently conducted with the input of public partners (citizen scientists) in all key aspects of its conduct and interpretation. In addition, two public partners were part of the research team and contributed to the design of the project, as well as key decisions related to its conduct, analysis, interpretation and dissemination and are co-authors of this manuscript.

Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety.

In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapted treatment delivery and improve outcomes. This study seeks to develop a state-of-the-art deep-learning framework for predicting clinical outcomes in internet-delivered Cognitive Behavioural Therapy (iCBT) by leveraging large-scale, high-dimensional time-series data of client-reported mental health symptoms and platform interaction data. We use de-identified data from 45,876 clients on SilverCloud Health, a digital platform for the psychological treatment of depression and anxiety. We train deep recurrent neural network (RNN) models to predict whether a client will show reliable improvement by the end of treatment using clinical measures, interaction data with the iCBT program, or both. Outcomes are based on total improvement in symptoms of depression (Patient Health Questionnaire-9, PHQ-9) and anxiety (Generalized Anxiety Disorder-7, GAD-7), as reported within the iCBT program. Using internal and external datasets, we compare the proposed models against several benchmarks and rigorously evaluate them according to their predictive accuracy, sensitivity, specificity and AUROC over treatment. Our proposed RNN models consistently predict reliable improvement in PHQ-9 and GAD-7, using past clinical measures alone, with above 87% accuracy and 0.89 AUROC after three or more review periods, outperforming all benchmark models. Additional evaluations demonstrate the robustness of the achieved models across (i) different health services; (ii) geographic locations; (iii) iCBT programs, and (iv) client severity subgroups. Results demonstrate the robust performance of dynamic prediction models that can yield clinically helpful prognostic information ready for implementation within iCBT systems to support timely decision-making and treatment adjustments by iCBT clinical supporters towards improved client outcomes.

Implementing digital mental health interventions at scale: one-year evaluation of a national digital CBT service in Ireland.

BACKGROUND: In recent years, exponential growth in digital innovations and internet access has provided opportunities to deliver health services at a much greater scale than previously possible. Evidence-based technology-enabled interventions can provide cost-effective, accessible, and resource-efficient solutions for addressing mental health issues. This study evaluated the first year of a supported digital cognitive behavioral therapy (CBT) service provided by the national health service in Ireland, which has been accessible to individuals who receive a referral from one of five referring groups: General Practitioners, Primary Care Psychology, Counselling Primary Care, Community Mental Health, and Jigsaw (a nationwide youth mental health service). METHODS: A retrospective, observational study examining data from the service between April 2021 to April 2022 was conducted. Descriptive statistics on referrals, account activations, user demographics, program usage, and user satisfaction were extracted, and pre-to-post clinical outcomes for depression measured by the Patient Health Questionnaire-9 and for anxiety measured by the Generalised Anxiety Disorder-7 were analysed using linear mixed effect models. RESULTS: There were 5,298 referrals and 3,236 (61%) account activations within the year. Most users were female (72.9%) and aged between 18 and 44 years (75.4%). The CBT programs were associated with significant reductions in both depression (ß = 3.34, 95% CI [3.03, 3.65], p < 0.001) and anxiety (ß = 3.64, 95% CI [3.36, 3.93], p < 0.001), with large effect sizes (Cohen's d > 0.8). Time spent using the programs was also found to be a predictor of the variability in these clinical outcomes (p < 0.001), and accounting for this resulted in significantly better model fits (p < 0.001). User satisfaction ratings were also very high, exceeding 94%. CONCLUSIONS: Efforts to improve the representation of male and older adult users are warranted. However, overall, the results demonstrate how digital CBT can be provided at scale and lead to symptom reductions with large effect sizes for patients seeking help for depression and anxiety. The findings substantiate the continued use and expansion of this service in Ireland and the more widespread implementation of similar services in other international public healthcare settings.

An observational treatment study of metacognition in anxious-depression.

Prior studies have found metacognitive biases are linked to a transdiagnostic dimension of anxious-depression, manifesting as reduced confidence in performance. However, previous work has been cross-sectional and so it is unclear if under-confidence is a trait-like marker of anxious-depression vulnerability, or if it resolves when anxious-depression improves. Data were collected as part of a large-scale transdiagnostic, four-week observational study of individuals initiating internet-based cognitive behavioural therapy (iCBT) or antidepressant medication. Self-reported clinical questionnaires and perceptual task performance were gathered to assess anxious-depression and metacognitive bias at baseline and 4-week follow-up. Primary analyses were conducted for individuals who received iCBT (n=649), with comparisons between smaller samples that received antidepressant medication (n=82) and a control group receiving no intervention (n=88). Prior to receiving treatment, anxious-depression severity was associated with under-confidence in performance in the iCBT arm, replicating previous work. From baseline to follow-up, levels of anxious-depression were significantly reduced, and this was accompanied by a significant increase in metacognitive confidence in the iCBT arm (ß=0.17, SE=0.02, p<0.001). These changes were correlated (r(647)=-0.12, p=0.002); those with the greatest reductions in anxious-depression levels had the largest increase in confidence. While the three-way interaction effect of group and time on confidence was not significant (F(2, 1632)=0.60, p=0.550), confidence increased in the antidepressant group (ß=0.31, SE = 0.08, p<0.001), but not among controls (ß=0.11, SE = 0.07, p=0.103). Metacognitive biases in anxious-depression are state-dependent; when symptoms improve with treatment, so does confidence in performance. Our results suggest this is not specific to the type of intervention.

Implementing internet-delivered cognitive behavioral therapy in healthcare services: a qualitative exploration of stakeholder experience.

Background: This study consisted of a qualitative exploration of stakeholder experience regarding the implementation of internet-delivered cognitive behavioral therapy (iCBT) as part of routine service provision within the UK's Improving Access to Psychological Therapies program. Methods: Stakeholder groups included service providers (n = 6), commercial iCBT representatives (n = 6) and patients who received a course of iCBT as part of treatment at the service (n = 7). Participants took part in a semi-structured interview over the telephone, and subsequent data were qualitatively analyzed using the descriptive-interpretive approach. Results: Service providers highlighted the importance of effective leadership and management, training initiatives, the provision of feedback to commercial iCBT representatives and creating work structures around iCBT to facilitate iCBT supporting staff in their use of it. Commercial iCBT representatives similarly reported the importance of training clinicians in iCBT use, identifying the appropriate individuals at all levels of the service to drive iCBT implementation, and the importance of being responsive to any problems or needs that arise from the service. Patients reported an overall positive experience of receiving iCBT but highlighted a need for more information from their supporter and the intervention to better structure their usage. Contextual factors, in terms of barriers and facilitators, were also highlighted by service provider and commercial participants; citing negative clinician attitudes and technological issues/bugs as barriers to implementation, and the exposure to iCBT created by COVID-19 and perseverance in using iCBT over time as facilitators. Discussion: The findings contribute to a growing field of literature that seeks to understand the experience of relevant stakeholders who are involved with and contribute to iCBT implementation, including commercial iCBT representatives who, to our knowledge, have not been accounted for as part of published research to date.

The Effectiveness of Low-Intensity Psychological Interventions for Comorbid Depression and Anxiety in Patients with Long-Term Conditions: A Real-World Naturalistic Observational Study in IAPT Integrated Care.

BACKGROUND: Low-intensity psychological interventions may be a cost-effective, accessible solution for treating depression and anxiety in patients with long-term conditions, but evidence from real-world service settings is lacking. This study examined the effectiveness of low-intensity psychological interventions provided in the Improving Access to Psychological Therapies programme in England for patients with and without long-term conditions. METHODS: A retrospective analysis was conducted on patients (total N = 21,051, long-term conditions n = 4024) enrolled in three low-intensity psychological interventions, i.e. Internet-delivered cognitive behavioural therapy (iCBT), guided self-help (GSH), and psychoeducational group therapy (PGT) within a Talking Therapies service from 2016 to 2020. Primary outcomes included pre-post-treatment changes in depression (Patient Health Questionnaire-9) and anxiety (Generalised Anxiety Disorder-7). RESULTS: Overall, both cohorts significantly improved on all outcomes post-treatment, with large effect sizes. Patients with long-term conditions experienced a greater reduction in depression while those without experienced a greater reduction in anxiety, but these differences were marginal (< 1 score difference on both measures). No difference between the cohorts was shown when comparing the differential effectiveness across interventions, but those engaging in iCBT showed greater reduction in depression and anxiety than those in GSH and PGT, while those in GSH improved more than PGT. CONCLUSIONS: Low-intensity psychological interventions, particularly iCBT, were effective in treating depression and anxiety in patients with long-term conditions in a real-world service setting. Our large-scale study supports the continued and increased implementation of low-intensity psychological interventions for this subpopulation via integrated care.

Trajectories of Symptoms in Digital Interventions for Depression and Anxiety Using Routine Outcome Monitoring Data: Secondary Analysis Study.

BACKGROUND: Research suggests there is heterogeneity in treatment response for internet-delivered cognitive behavioral therapy (iCBT) users, but few studies have investigated the trajectory of individual symptom change across iCBT treatment. Large patient data sets using routine outcome measures allows the investigation of treatment effects over time as well as the relationship between outcomes and platform use. Understanding trajectories of symptom change, as well as associated characteristics, may prove important for tailoring interventions or identifying patients who may not benefit from the intervention. OBJECTIVE: We aimed to identify latent trajectories of symptom change during the iCBT treatment course for depression and anxiety and to investigate the patients' characteristics and platform use for each of these classes. METHODS: This is a secondary analysis of data from a randomized controlled trial designed to examine the effectiveness of guided iCBT for anxiety and depression in the UK Improving Access to Psychological Therapies (IAPT) program. This study included patients from the intervention group (N=256) and followed a longitudinal retrospective design. As part of the IAPT's routine outcome monitoring system, patients were prompted to complete the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) after each supporter review during the treatment period. Latent class growth analysis was used to identify the underlying trajectories of symptom change across the treatment period for both depression and anxiety. Differences in patient characteristics were then evaluated between these trajectory classes, and the presence of a time-varying relationship between platform use and trajectory classes was investigated. RESULTS: Five-class models were identified as optimal for both PHQ-9 and GAD-7. Around two-thirds (PHQ-9: 155/221, 70.1%; GAD-7: 156/221, 70.6%) of the sample formed various trajectories of improvement classes that differed in baseline score, the pace of symptom change, and final clinical outcome score. The remaining patients were in 2 smaller groups: one that saw minimal to no gains and another with consistently high scores across the treatment journey. Baseline severity, medication status, and program assigned were significantly associated (P<.001) with different trajectories. Although we did not find a time-varying relationship between use and trajectory classes, we found an overall effect of time on platform use, suggesting that all participants used the intervention significantly more in the first 4 weeks (P<.001). CONCLUSIONS: Most patients benefit from treatment, and the various patterns of improvement have implications for how the iCBT intervention is delivered. Identifying predictors of nonresponse or early response might inform the level of support and monitoring required for different types of patients. Further work is necessary to explore the differences between these trajectories to understand what works best for whom and to identify early on those patients who are less likely to benefit from treatment.

An online tiered screening procedure to identify mental health problems among refugees.

BACKGROUND: Many refugees suffer from mental health problems due to stressful and traumatic events before, during, and after migration. However, refugees are facing a wide variety of barriers, limiting their access to mental health care. Internet-based tools, available in several languages, could be one way to increase the availability of mental health services for refugees. The present study aimed to develop and test a screening tool to screen for clinically relevant symptoms of psychiatric disorders common among refugees (i.e. Depression, Anxiety, Post-traumatic stress disorder, and Insomnia). We, designed, translated, and adapted an internet-based tiered screening procedure suitable for use with the largest refugee populations residing in Sweden. The tool aims to accurately identify symptoms of mental distress (Tier 1), differentiate between symptoms of specific psychiatric disorders (Tier 2), and assess symptom severity (Tier 3). We tested the overall efficiency of using a tiered screening procedure. METHODS: Seven hundred fifty-seven refugees residing in Sweden, speaking any of the languages Arabic, Dari, Farsi, English, or Swedish, completed an online questionnaire following a three-tiered procedure with screening instruments for each tier. In this study, the Tier 3 scales were used as reference standards for clinically relevant symptoms, to evaluate screening efficiency in terms of accuracy and reduction of item burden in previous tiers. RESULTS: The results show that the tiered procedure could reduce the item burden while maintaining high accuracy, with up to 86% correctly assessed symptoms and few false negatives with moderate symptoms and above (at most 9%), and very few with severe symptoms (at most 1.3%). DISCUSSION: This study generated an accurate screening tool that efficiently identifies clinically relevant symptoms of common psychiatric disorders among refugees. Using an adapted online tiered procedure to screen for multiple mental health issues among refugees has the potential to facilitate screening and increase access to mental health services for refugees. We discuss the utility of the screening tool and the necessity of further evaluation.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation.

BACKGROUND: Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field. METHODS: A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation. RESULTS: Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97). CONCLUSIONS: An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Comparison of outcomes across low-intensity psychological interventions for depression and anxiety within a stepped-care setting: A naturalistic cohort study using propensity score modelling.

Low-intensity interventions for common mental disorders (CMD) address issues such as clinician shortages and barriers to accessing care. However, there is a lack of research into their comparative effectiveness in routine care. We aimed to compare treatment effects of three such interventions, utilizing four years' worth of routine clinical data. Users completing a course of guided self-help bibliotherapy (GSH), internet-delivered cognitive behavioural therapy (iCBT) or psychoeducational group therapy (PGT) from a stepped-care service within the NHS in England were included. Propensity score models (stratification and weighting) were used to control for allocation bias and determine average treatment effect (ATE) between the interventions. 21,215 users comprised the study sample (GSH = 12,896, iCBT = 6862, PGT = 1457). Adherence-to-treatment rates were higher in iCBT. All interventions showed significant improvements in depression (PHQ-9), anxiety (GAD-7) and functioning (WSAS) scores, with largest effect sizes for iCBT. Both propensity score models showed a significant ATE in favour of iCBT versus GSH and PGT, and in favour of GSH versus PGT. Discernible differences in effectiveness were seen for iCBT in comparison with GSH and PGT. Given variance in delivery mode and human resources between different low-intensity interventions, building on these findings would be valuable for future service provision and policy decision making.

The relationship between posttherapeutic Cognitive Behavior Therapy skills usage and follow-up outcomes of internet-delivered Cognitive Behavior Therapy.

BACKGROUND: Clients independently applying Cognitive Behavior Therapy (CBT) skills is an important outcome of CBT-based treatments. The relationship between posttherapeutic CBT skills usage and clinical outcomes remains under-researched-especially after internet-delivered CBT (iCBT). OBJECTIVE: Explore contemporaneous and lagged effects of posttherapeutic CBT skills usage frequency on iCBT follow-up outcomes. METHOD: Nested within a randomized controlled trial, 241 participants received 8-week supported iCBT for anxiety and/or depression, completing measures of anxiety, depression, functional impairment, and CBT skills usage frequency at 3-, 6-, 9-, and 12-month follow-up. Cross-lagged panel models evaluated primary aims. RESULTS: While analyses support a contemporaneous relationship between anxiety, depression, functional impairment, and CBT skills usage frequency, no consistent lagged effects were observed. CONCLUSION: Findings align with qualitative research but the role of CBT skills usage in the maintenance of iCBT effects remains unclear. Innovative research modeling temporal and possibly circular relationships between CBT skill usage and clinical outcomes is needed to inform iCBT optimization.

Water motion and pH jointly impact the availability of dissolved inorganic carbon to macroalgae.

The supply of dissolved inorganic carbon to seaweeds is a key factor regulating photosynthesis. Thinner diffusive boundary layers at the seaweed surface or greater seawater carbon dioxide (CO2) concentrations increase CO2 supply to the seaweed surface. This may benefit seaweeds by alleviating carbon limitation either via an increased supply of CO2 that is taken up by passive diffusion, or via the down-regulation of active carbon concentrating mechanisms (CCMs) that enable the utilization of the abundant ion bicarbonate (HCO3-). Laboratory experiments showed that a 5 times increase in water motion increases DIC uptake efficiency in both a non-CCM (Hymenena palmata, Rhodophyta) and CCM (Xiphophora gladiata, Phaeophyceae) seaweed. In a field survey, brown and green seaweeds with active-CCMs maintained their CCM activity under diverse conditions of water motion. Whereas red seaweeds exhibited flexible photosynthetic rates depending on CO2 availability, and species switched from a non-CCM strategy in wave-exposed sites to an active-CCM strategy in sheltered sites where mass transfer of CO2 would be reduced. 97-99% of the seaweed assemblages at both wave-sheltered and exposed sites consisted of active-CCM species. Variable sensitivities to external CO2 would drive different responses to increasing CO2 availability, although dominance of the CCM-strategy suggests this will have minimal impact within shallow seaweed assemblages.

Factors Associated With Dental Caries: Differential Experiences Among Incarcerated Individuals in Scotland.

Incarcerated individuals experience poorer health, including dental health, when compared with the general population. Although interventions that target multiple determinants of health are more effective, there are limited investigations of the determinants of dental health, and how these differ, among incarcerated individuals. This article describes a secondary data analysis to investigate caries (dental decay) experience and associated risk indicators from a survey of females, adult males, and young males incarcerated in Scotland. Substance misuse significantly explained caries risk for adults, and tailored interventions are warranted to address smoking and dental attitudes among females, and for adult males housing support alongside oral health education are indicated.

Effectiveness of the internet-based Unified Protocol transdiagnostic intervention for the treatment of depression, anxiety and related disorders in a primary care setting: a randomized controlled trial.

BACKGROUND: Research has shown that internet-based cognitive behavioural therapy (iCBT) can be a very promising solution to increase access to and the dissemination of evidence-based treatments to all of the population in need. However, iCBT is still underutilized in clinical contexts, such as primary care. In order to achieve the effective implementation of these protocols, more studies in ecological settings are needed. The Unified Protocol (UP) is a transdiagnostic CBT protocol for the treatment of emotional disorders, which includes depression, anxiety and related disorders, that has shown its efficacy across different contexts and populations. An internet-based UP (iUP) programme has recently been developed as an emerging internet-based treatment for emotional disorders. However, the internet-delivered version of the UP (iUP) has not yet been examined empirically. The current project seeks to analyse the effectiveness of the iUP as a treatment for depression, anxiety and related emotional disorders in a primary care public health setting. METHODS: The current study will employ a parallel-group, randomized controlled trial design. Participants will be randomly assigned to (a) the internet-based Unified Protocol (iUP), or (b) enhanced waiting list control (eWLC). Randomization will follow a 2:1 allocation ratio, with sample size calculations suggesting a required sample of 120 (iUP=80; eWLC=40). The Mini-International Neuropsychiatric Interview (M.I.N.I.) will be used for assessing potential participants. The Overall Anxiety Severity and Impairment Scale (OASIS) and the Overall Depression Severity and Impairment Scale (ODSIS) as well as other standardized questionnaires will be used for assessments at baseline, 4 weeks, 8 weeks and 12 weeks from baseline and for the iUP condition during the follow-up. DISCUSSION: Combining the advantages of a transdiagnostic treatment with an online delivery format may have the potential to significantly lower the burden of emotional disorders in public health primary care setting. Anxiety and depression, often comorbid, are the most prevalent psychological disorders in primary care. Because the iUP allows for the treatment of different disorders and comorbidity, this treatment could represent an adequate choice for patients that demand mental health care in a primary care setting. TRIAL REGISTRATION: ISRCTN18056450 https://doi.org/10.1186/ISRCTN18056450 .

The Appalachia Mind Health Initiative (AMHI): a pragmatic randomized clinical trial of adjunctive internet-based cognitive behavior therapy for treating major depressive disorder among primary care patients.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems. METHODS: Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE. DISCUSSION: The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT. TRIAL REGISTRATION: ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019.

Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study.

BACKGROUND: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective. METHODS: We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms. DISCUSSION: By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation. TRIAL STATUS: IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024. TRIAL REGISTRATION: ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021.

Estimating Cost-Effectiveness Using Alternative Preference-Based Scores and Within-Trial Methods: Exploring the Dynamics of the Quality-Adjusted Life-Year Using the EQ-5D 5-Level Version and Recovering Quality of Life Utility Index.

OBJECTIVES: This study aimed to explore quality-adjusted life-year (QALY) and subsequent cost-effectiveness estimates based on the more physical health-focused EQ-5D 5-level version (EQ-5D-5L) value set for England or cross-walked EQ-5D 3-level version UK value set scores or more mental health recovery-focused Recovering Quality of Life Utility Index (ReQoL-UI), when using alternative within-trial statistical methods. We describe possible reasons for the different QALY estimates based on the interaction between item scores, health state profiles, preference-based scores, and mathematical and statistical methods chosen. METHODS: QALYs are calculated over 8 weeks from a case study 2:1 (intervention:control) randomized controlled trial in patients with anxiety or depression. Complete case and with missing cases imputed using multiple-imputation analyses are conducted, using unadjusted and regression baseline-adjusted QALYs. Cost-effectiveness is judged using incremental cost-effectiveness ratios and acceptability curves. We use previously established psychometric results to reflect on estimated QALYs. RESULTS: A total of 361 people (241:120) were randomized. EQ-5D-5L crosswalk produced higher incremental QALYs than the value set for England or ReQoL-UI, which produced similar unadjusted QALYs, but contrasting baseline-adjusted QALYs. Probability of cost-effectiveness <£30 000 per QALY ranged from 6% (complete case ReQoL-UI baseline-adjusted QALYs) to 64.3% (multiple-imputation EQ-5D-5L crosswalk unadjusted QALYs). The control arm improved more on average than the intervention arm on the ReQoL-UI, a result not mirrored on the EQ-5D-5L nor condition-specific (Patient-Health Questionnaire-9, depression; Generalized Anxiety Disorder-7, anxiety) measures. CONCLUSIONS: ReQoL-UI produced contradictory cost-effectiveness results relative to the EQ-5D-5L. The EQ-5D-5L's better responsiveness and "anxiety/depression" and "usual activities" items drove the incremental QALY results. The ReQoL-UI's single physical health item and "personal recovery" construct may have influenced its lower 8-week incremental QALY estimates in this patient sample.