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.

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.

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.

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.

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.

The Acceptability and Initial Effectiveness of "Space From Money Worries": An Online Cognitive Behavioral Therapy Intervention to Tackle the Link Between Financial Difficulties and Poor Mental Health.

Background: Previous research has shown a strong relationship between financial difficulties and mental health problems. Psychological factors such as hope and worry about finances appear to be an important factor in this relationship. Objective: To develop an online based psychological intervention (Space from Money Worries) to tackle the psychological mechanisms underlying the relationship between poor mental health and financial difficulties, and to conduct an initial evaluation of the acceptability and preliminary efficacy of the intervention. Materials and Methods: 30 participants accessing Increasing Access to Psychological Therapies (IAPT) services completed GAD-7 to measure anxiety and PHQ-9 to measure depression upon signing up to the online intervention and again 4 to 8 weeks after this. Participants also completed a measure of perceived financial distress/wellbeing and a "Money and Mental Health Scale" constructed for the evaluation. Results: Overall, 77% (n = 23) completed the intervention and follow-up assessments. Intent to Treat Analysis showed that there were statistically significant improvements in symptoms of depression, anxiety, improved perceived financial wellbeing and reduced scores on the money and mental health scale. The vast majority of participants rated each module positively. Conclusions: Space from Money Worries appears to be acceptable and may lead to improvements in mental health, perceived financial wellbeing and a reduced relationship between financial difficulties and poor mental health. However, future research with a larger sample and a control group are needed to confirm that these changes are due to the intervention.

Durability of treatment effects following internet-delivered cognitive behavioural therapy for depression and anxiety delivered within a routine care setting.

OBJECTIVE: To investigate post-treatment relapse and remission rates 3, 6 and 9 months after completion of an acute phase of a clinician-supported internet-delivered cognitive-behavioural therapy (iCBT) for anxiety and depressive symptoms, within a routine care setting. METHOD: Secondary analysis from a 12-month pragmatic randomized-controlled trial delivered within the Improving Access to Psychological Therapies (IAPT) programme in England. Participants in the intervention arm were included if they met criteria for reliable recovery from depression (PHQ-9) and anxiety (GAD-7) at post-treatment assessment. Survival analysis was used to assess durability of treatment effects and determine predictors to relapse at 3-, 6- and 9-month follow-up. Hazard ratios predicting time-to-relapse were estimated with semi-parametric Cox proportional hazards model. RESULTS: Of the 241 participants in the intervention arm, 89 participants met the criteria for reliable recovery from depression and anxiety at the post-treatment assessment. Of these 89 eligible cases, 29.2% relapsed within the 9-month period, with 70.8% remaining in remission at 9 months post-treatment. Of those who relapsed, 53.8% experienced a relapse of depression and anxiety; 7.7% experienced a relapse of depression only; and 38.4% experienced a relapse of anxiety only. Younger age, having a long-term condition, and residual symptoms of anxiety at end-of-treatment were all significant predictors of relapse. CONCLUSIONS: This study is the first to explore the remission and relapse rates after an acute phase of iCBT treatment, within a routine, stepped-care setting. The results add to the scarce literature on the durability of the effects of iCBT treatment in routine care settings, where patients are not typically followed up after receiving a completed course of treatment.

Following up internet-delivered cognitive behaviour therapy (CBT): A longitudinal qualitative investigation of clients' usage of CBT skills.

BACKGROUND: While the acquisition and application of Cognitive Behaviour Therapy (CBT) skills is a core component and likely mechanism of effect maintenance in all CBT-based treatments, the extent of post-therapeutic CBT skills usage among internet-delivered CBT (iCBT) clients remains under-researched. METHOD: Nested within a pragmatic randomized controlled trial, 241 participants received an 8-week supported iCBT intervention for anxiety and/or depression and answered open-ended questions about their use and experience of CBT skills at 3-, 6-, 9-, and 12-month follow-up. Recurrent, cross-sectional qualitative analysis following the descriptive and interpretive approach was used to create a taxonomy, through which all qualitative data was coded. RESULTS: In total, 479 qualitative responses across 181 participants were analysed. Participants reported using a wide range of CBT skills and associated helpful and hindering experiences and impacts. The reasons for discontinued CBT skills usage were diverse, ranging from rare adverse effects to healthy adaptation. CONCLUSION: The study shows how clients receiving iCBT in routine care learn CBT skills during treatment and utilize them in productive ways post-treatment. Findings coincide with similar research in face-to-face CBT and may inform future research to drive innovation and iCBT intervention development.

Dropout From an Internet-Delivered Cognitive Behavioral Therapy Intervention for Adults With Depression and Anxiety: Qualitative Study.

BACKGROUND: Treatment dropout continues to be reported from internet-delivered cognitive behavioral therapy (iCBT) interventions, and lower completion rates are generally associated with lower treatment effect sizes. However, evidence is emerging to suggest that completion of a predefined number of modules is not always necessary for clinical benefit or consideration of the needs of each individual patient. OBJECTIVE: The aim of this study is to perform a qualitative analysis of patients' experiences with an iCBT intervention in a routine care setting to achieve a deeper insight into the phenomenon of dropout. METHODS: A total of 15 purposively sampled participants (female: 8/15, 53%) from a larger parent randomized controlled trial were interviewed via telephone using a semistructured interview schedule that was developed based on the existing literature and research on dropout in iCBT. Data were analyzed using a descriptive-interpretive approach. RESULTS: The experience of treatment leading to dropout can be understood in terms of 10 domains: relationship to technology, motivation to start, background knowledge and attitudes toward iCBT, perceived change in motivation, usage of the program, changes due to the intervention, engagement with content, experience interacting with the supporter, experience of web-based communication, and termination of the supported period. CONCLUSIONS: Patients who drop out of treatment can be distinguished in terms of their change in motivation: those who felt ready to leave treatment early and those who had negative reasons for dropping out. These 2 groups of participants have different treatment experiences, revealing the potential attributes and nonattributes of dropout. The reported between-group differences should be examined further to consider those attributes that are strongly descriptive of the experience and regarded less important than those that have become loosely affiliated.

Internet-delivered CBT intervention (Space for Sleep) for insomnia in a routine care setting: Results from an open pilot study.

Insomnia is a highly prevalent, often comorbid disorder associated with difficulties sleeping, remaining awake, and impaired quality of life. Internet-delivered Cognitive Behavioral Therapy for insomnia (ICBT-I) has the potential to help large numbers of people with sleep disorders. This study investigated the preliminary effects of an 8-week guided ICBT-I intervention within a routine stepped-care service. Fifty-six (N = 56) patients consented to participate. The primary outcome was assessed using the Insomnia Severity Index (ISI) and secondary outcome measures included the Patient Health Questionnaire 9-item (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and the Work and Social Adjustment Scale (WSAS), each administered at baseline and weekly thereafter. Intention-to-treat analyses indicated that ICBT-I produced statistically significant pre- to post- reductions in symptoms of insomnia, yielding within-group effects of d = 0.82 suggesting a potential for improved outcomes. Similar improvements were seen across secondary outcomes, with small-to-medium post-treatment within-group effects observed: depression (d = 0.63), anxiety (d = 0.39), and functional impairment (d = 0.31). These findings are supportive of the intervention's potential effectiveness and speak to the importance of several implementation factors that could enhance the effects of the intervention. The results contribute to the growing evidence base for digital interventions designed to help those with sleep difficulties and will inform the design of a future controlled evaluation of ICBT-I under routine clinical settings.

Psychometric assessment of EQ-5D-5L and ReQoL measures in patients with anxiety and depression: construct validity and responsiveness.

PURPOSE: Generic health measures have been questioned for quantifying mental-health-related outcomes. In patients with anxiety and/or depression, our aim is to assess the psychometric properties of the preference-based EQ-5D-5L (generic health) and ReQoL-UI (recovery-focussed quality of life) for economic evaluation against the PHQ-9 (depression) and GAD-7 (anxiety). EQ-5D-5L anxiety/depression item and ReQoL-10 are also assessed. METHODS: A 2:1 (intervention: control) randomised controlled trial collected measures at baseline and 8 weeks post baseline; in the intervention arm, data were also collected 3, 6, 9, and 12-months post baseline. EQ-5D-5L preference-based scores were obtained from the value set for England (VSE) and 'cross-walked' EQ-5D-3L United Kingdom (UK) value set scores. ReQoL-UI preference-based scores were obtained from its UK value set as applied to seven ReQoL-10 items. EQ-5D-5L and ReQoL measures' construct validity and responsiveness were assessed compared against PHQ-9 and GAD-7 scores and group cut-offs. RESULTS: 361 people were randomised to intervention (241) or control (120). ReQoL-UI/-10 had better construct validity with depression severity than the EQ-5D-5L (VSE/cross-walk scores), which had relatively better construct validity with anxiety severity than the ReQoL-UI/-10. Across all intervention-arm time-points relative to baseline, responsiveness was generally better for EQ-5D-5L (VSE in particular) than ReQoL-UI, but worse than ReQoL-10. CONCLUSION: There is insufficient evidence to recommend the ReQoL-UI over EQ-5D-5L for economic evaluations to capture anxiety severity. However, there may be rationale for recommending the ReQoL-UI over the EQ-5D-5L to capture depression severity given its better construct validity, albeit poorer responsiveness, and if recovery-focussed quality of life relative to condition-specific symptomology is the construct of interest.

Integration of a smartwatch within an internet-delivered intervention for depression: Protocol for a feasibility randomized controlled trial on acceptance.

BACKGROUND: Mood tracking is commonly employed within a range of mental health interventions. Physical activity and sleep are also important for contextualizing mood data but can be difficult to track manually and rely on retrospective recall. Smartwatches could enhance self-monitoring by addressing difficulties in recall of sleep and physical activity and reducing the burden on patients in terms of remembering to track and the effort of tracking. This feasibility study will explore the acceptance of a smartwatch app for self-monitoring of mood, sleep, and physical activity, in an internet-based cognitive-behavioral therapy (iCBT) for depression offered in a routine care setting. METHODS: Seventy participants will be randomly allocated to (i) iCBT intervention plus smartwatch app or (ii) iCBT intervention alone. Patient acceptance will be measured longitudinally using a theory-based acceptance questionnaire to understand and compare the evolution of acceptance of the technology-delivered self-report in the two groups. A post-treatment interview will explore participants subjective experience of using the smartwatch. Engagement with the intervention, including self-report, and clinical outcomes, will be measured across both groups to assess for any differences. IMPLICATIONS: This is the first study investigating the evolution of patient acceptance of smartwatch self-report in an iCBT delivered intervention in a clinical sample. Through an engaging and convenient means of capturing ecologically valid mood data, the study has the potential to show that smartwatches are an acceptable means for patient self-monitoring within iCBT interventions for depression and support potential use-cases for smartwatches in the context of mental health interventions in general. Prospectively registered at ClinicalTrials.gov (NCT04568317).

Implementation of a positive psychology group program in an inpatient eating disorders service: a pilot study.

CONTEXT: Eating disorders (ED) are very difficult conditions to treat. Therefore, interventions in this field are shifting their main target towards the disorder's impact in quality of life, rather than ED symptomatology in itself. In this sense, a focus in the promotion of positive emotions and well-being is emerging to ameliorate the harmful effects caused by ED. However, evidence of the potential benefits of this type of interventions is still scarce. PURPOSE: This study introduces a 4-week positive psychology group program specifically designed for ED patients' needs and to present data about feasibility and acceptability. METHOD: Seven female inpatients of an eating disorder service aged from 13 to 38 years old attended the group. Measures of affect and optimistic thinking were taken before the program and after each session. RESULTS: The program was very well rated by participants, and there was no attrition. Furthermore, possible benefits were found in terms of optimistic thinking at the end of the group program, and these benefits were noticeable, but non-significant, in the case of affect. CONCLUSIONS: This study opens the door to conduct larger and controlled studies for testing interventions aimed at promoting positive emotions and well-being in ED populations. Thus, these interventions could support the efficacy of current treatments in order to improve patients' quality of life. LEVEL OF EVIDENCE: Level IV, multiple time series analysis, with the intervention.

The effectiveness of Internet-delivered treatment for generalized anxiety disorder: An updated systematic review and meta-analysis.

BACKGROUND: Generalized anxiety disorder (GAD) is a highly prevalent, chronic disorder associated with impaired quality of life, societal burden, and poor treatment rates. Internet-delivered interventions may improve the accessibility of treatments and are increasingly being used. This study aimed to update a previous meta-analysis to determine the effectiveness of available Internet-delivered interventions in treating symptoms of GAD. METHOD: Systematic literature searches were conducted (through April 2020) using Embase, PubMed, PsychINFO, and Cochrane to find randomized controlled trials of Internet-delivered interventions for GAD. Risk of bias was evaluated, and Hedge's g was calculated at posttreatment and follow-up. RESULTS: Twenty studies met eligibility criteria and were included in the meta-analysis. Random-effect models detected large effect sizes for primary outcomes of anxiety (g = 0.79) and worry (g = 0.75), favoring treatment. Effect sizes for depression, functional impairment, and quality of life were moderate to large. Maintenance of effects at follow-up seems likely. CONCLUSIONS: Results support the effectiveness of Internet-delivered treatments for GAD. Considerable heterogeneity between studies appeared moderated by variability in the interventions themselves, highlighting the importance of further investigation into the characteristics that may optimize treatment outcomes. Overall, Internet-delivery appears to be a viable mode of treatment for GAD with potential to relieve existing gaps in the provision of treatment.

Erratum: Author Correction: A pragmatic randomized waitlist-controlled effectiveness and cost-effectiveness trial of digital interventions for depression and anxiety.

[This corrects the article DOI: 10.1038/s41746-020-0293-8.].

A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions.

Importance: The mechanisms by which engagement with internet-delivered psychological interventions are associated with depression and anxiety symptoms are unclear. Objective: To identify behavior types based on how people engage with an internet-based cognitive behavioral therapy (iCBT) intervention for symptoms of depression and anxiety. Design, Setting, and Participants: Deidentified data on 54 604 adult patients assigned to the Space From Depression and Anxiety treatment program from January 31, 2015, to March 31, 2019, were obtained for probabilistic latent variable modeling using machine learning techniques to infer distinct patient subtypes, based on longitudinal heterogeneity of engagement patterns with iCBT. Interventions: A clinician-supported iCBT-based program that follows clinical guidelines for treating depression and anxiety, delivered on a web 2.0 platform. Main Outcomes and Measures: Log data from user interactions with the iCBT program to inform engagement patterns over time. Clinical outcomes included symptoms of depression (Patient Health Questionnaire-9 [PHQ-9]) and anxiety (Generalized Anxiety Disorder-7 [GAD-7]); PHQ-9 cut point greater than or equal to 10 and GAD-7 scores greater than or equal to 8 were used to define depression and anxiety. Results: Patients spent a mean (SD) of 111.33 (118.92) minutes on the platform and completed 230.60 (241.21) tools. At baseline, mean PHQ-9 score was 12.96 (5.81) and GAD-7 score was 11.85 (5.14). Five subtypes of engagement were identified based on patient interaction with different program sections over 14 weeks: class 1 (low engagers, 19 930 [36.5%]), class 2 (late engagers, 11 674 [21.4%]), class 3 (high engagers with rapid disengagement, 13 936 [25.5%]), class 4 (high engagers with moderate decrease, 3258 [6.0%]), and class 5 (highest engagers, 5799 [10.6%]). Estimated mean decrease (SE) in PHQ-9 score was 6.65 (0.14) for class 3, 5.88 (0.14) for class 5, and 5.39 (0.14) for class 4; class 2 had the lowest rate of decrease at -4.41 (0.13). Compared with PHQ-9 score decrease in class 1, the Cohen d effect size (SE) was -0.46 (0.014) for class 2, -0.46 (0.014) for class 3, -0.61 (0.021) for class 4, and -0.73 (0.018) for class 5. Similar patterns were found across groups for GAD-7. Conclusions and Relevance: The findings of this study may facilitate tailoring interventions according to specific subtypes of engagement for individuals with depression and anxiety. Informing clinical decision needs of supporters may be a route to successful adoption of machine learning insights, thus improving clinical outcomes overall.

A pragmatic randomized waitlist-controlled effectiveness and cost-effectiveness trial of digital interventions for depression and anxiety.

Utilization of internet-delivered cognitive behavioural therapy (iCBT) for treating depression and anxiety disorders in stepped-care models, such as the UK's Improving Access to Psychological Therapies (IAPT), is a potential solution for addressing the treatment gap in mental health. We investigated the effectiveness and cost-effectiveness of iCBT when fully integrated within IAPT stepped-care settings. We conducted an 8-week pragmatic randomized controlled trial with a 2:1 (iCBT intervention: waiting-list) allocation, for participants referred to an IAPT Step 2 service with depression and anxiety symptoms (Trial registration: ISRCTN91967124). The primary outcomes measures were PHQ-9 (depressive symptoms) and GAD-7 (anxiety symptoms) and WSAS (functional impairment) as a secondary outcome. The cost-effectiveness analysis was based on EQ-5D-5L (preference-based health status) to elicit the quality-adjust life year (QALY) and a modified-Client Service Receipt Inventory (care resource-use). Diagnostic interviews were administered at baseline and 3 months. Three-hundred and sixty-one participants were randomized (iCBT, 241; waiting-list, 120). Intention-to-treat analyses showed significant interaction effects for the PHQ-9 (b = -2.75, 95% CI -4.00, -1.50) and GAD-7 (b = -2.79, 95% CI -4.00, -1.58) in favour of iCBT at 8-week and further improvements observed up to 12-months. Over 8-weeks the probability of cost-effectiveness was 46.6% if decision makers are willing to pay £30,000 per QALY, increasing to 91.2% when the control-arm's outcomes and costs were extrapolated over 12-months. Results indicate that iCBT for depression and anxiety is effective and potentially cost-effective in the long-term within IAPT. Upscaling the use of iCBT as part of stepped care could help to enhance IAPT outcomes. The pragmatic trial design supports the ecological validity of the findings.