To evaluate the effectiveness of Culturally Adapted Cognitive-Behavioral Therapy (CA-CBT) compared to standard CBT in alleviating depression in college students.

A randomized trial was conducted on college students with mild to moderate depression. The CA-CBT group (21 students) received a group CA-CBT intervention incorporating idioms, while the standard CBT group (20 students) received a standard group-based CBT intervention. Both group interventions were conducted once a week, with each session lasting one and a half hours, for a total duration of eight weeks. Eighteen students from the standard CBT group and 12 from the CA-CBT group completed the trial. Depressive symptoms, cognitive distortions, emotional regulation, and CBT understanding were assessed at baseline, week five, post-treatment, and three months after post-treatment using validated scales. Data were analyzed using repeated measures ANOVA, t-tests, and chi-square tests via SPSS 26.0.

The repeated measures ANOVA results for the pre-treatment and post-treatment showed a significant interaction effect of time and intervention and a significant main effect of time for the sleep disturbance factor of the Hamilton Depression Rating Scale. However, only the main effect of time was significant for all other variables. T-tests indicated that the CA-CBT group had significantly lower Hamilton scores at follow-up compared to the standard CBT group. Meanwhile, the standard CBT group had significantly better CBT understanding post-treatment. Except for the above two items, no other significant difference were found by T-test.

Compared with standard CBT, the CA-CBT group that incorporates idioms is beneficial for college students with depressive symptoms to continue to achieve relief for a certain period of time after the end of treatment. Specifically, this relief may be reflected in the improvement of sleep problems. However, CA-CBT did not better improve participants' emotion regulation, understanding of cognitive behavioral therapy, or reduction of cognitive distortions.At the same time, the CA-CBT group did not perform better than the standard CBT group in terms of dropout rate, treatment satisfaction, recommendation, and re-use rate.

The trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2300074752|| http://www.chictr.org.cn/ ) on August 15, 2023.

Progress in the treatment of psychopathology has slowed and much remains unknown about how treatments achieve their beneficial effects. We propose that computational models can be used to provide new insights into how treatments may work and how they may be improved. We argue that treatments can be understood as interventions on systems of interacting components, and that computational models are needed if we are to accurately and precisely determine the effect an intervention will have on this system. We demonstrate this approach by using a computational model of panic disorder to conduct an in silico dismantling study of cognitive behavioral therapy. This simulated trial allows us to: identify a common source of treatment failure; propose a revised treatment protocol that mitigates this source of failure; and demonstrate that, if the model is accurate, this revised protocol will lead to improved treatment outcomes for 10% of patients. We conclude with a discussion of the promise and challenges of using computational models for treatment research.

Digital therapeutics (DTx) are a new treatment class for major depressive disorder (MDD). This study evaluated the effectiveness and safety of a novel DTx, CT-152 (Rejoyn™), for MDD adjunctive to antidepressant medication monotherapy.

Adults aged 22-64 years with MDD having an inadequate response to current antidepressant medication monotherapy were enrolled in a phase 3 multicenter, randomized, blinded, sham-controlled, remote trial with a 6-week intervention and 4-week extension. Delivered via smartphone apps, the CT-152 group received a cognitive-emotional and behavioral therapeutic intervention; the control group received a sham app with a working memory task. Both groups received supportive text messages and continued current antidepressant medication. The primary outcome was Montgomery-Åsberg Depression Rating Scale (MADRS) score change from baseline to week 6. Treatment-emergent adverse events (TEAEs) were assessed.

Overall, 386 participants were randomly assigned (CT-152, n = 194; sham, n = 192). In the primary efficacy analysis of participants with ≥1 treatment session and ≥ 1 MADRS assessment post-baseline (n = 354), MADRS score changed -9.03 in the CT-152 group and - 7.25 in the sham (difference - 1.78, P = 0.0568). These results were consistent with data from additional patient and clinician scales. In a supportive analysis of the intent-to-treat sample (n = 386), the between-group difference in 6-week MADRS change from baseline was -2.12 (P = 0.0211), favoring CT-152. No TEAEs or discontinuations were considered related to CT-152, and no deaths occurred.

CT-152 resulted in depression symptom improvement and a favorable safety profile. Based on these data, CT-152 became the first FDA-authorized prescription DTx for the adjunctive treatment of MDD.

Major depressive disorder (MDD) represents a serious public health problem that affects a quarter billion individuals worldwide. Consequently, there is a need to identify modifiable factors of services that support treatment success. The relationship between circadian preferences (i.e., chronotype), treatment time of day, and outcomes is an understudied research area. Executing optimal treatment timing based on these factors could lead to substantial returns on a modifiable variable.

The present study evaluated the associations between chronotype and treatment time of day on post-treatment depression symptom severity. In a tertiary setting, outpatients with MDD (n = 227) received 14 sessions of cognitive behavioural therapy in a group format, at one of three time of days: morning, afternoon, or evening. Participants completed measures of depression and chronotype at baseline and post-treatment.

Statistically significant increases in morningness were found for the afternoon and evening groups, but not the morning group. There was no significant interaction effect between pre-treatment morningness-eveningness scores and treatment time of day on post-treatment depression scores nor treatment response. However, there was a significant interaction effect of change in morningness-eveningness scores and post-treatment depression severity in the afternoon group.

The lack of a control group limits conclusions drawn.

The results suggest that individual circadian phase may impact treatment outcomes in relation to time of day. Further intentionally designed research is warranted to improve understanding of predictors, moderators, and mediators of patient outcomes based on treatment time of day and circadian phase and amplitude.

Depression and anxiety are common mental disorders in later life. Digital intelligence interventions overcome the limitations of conventional psychotherapy and offer new treatments for depression and anxiety. However, the effectiveness among older adults remains unclear.

Databases including Pubmed, Web of Science, the Cochrane Library, Medline, CINAHL, PsycINFO, and Embase were searched for Randomized Controlled Trials (RCTs) from inception to November 22, 2023. Statistical analyses were conducted using Stata 18.0 and Review Manager 5.4.

The initial search found 9369 papers, with 21 meeting the inclusion criteria (e.g., RCTs involving older adults aged 50 and older that assessed digital intelligence interventions on depression and anxiety symptoms). Meta-analyses revealed that, compared to control groups, digital intelligence interventions significantly reduced depression symptoms (SMD: -0.58; 95 % CI: -0.80, -0.35) and anxiety symptoms (SMD: -0.39; 95 % CI: -0.58, -0.19). Subgroup analysis revealed that internet-based Cognitive Behavioral Therapy (iCBT), interventions lasting 7 to 10 weeks, and the use of the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder-7 (GAD-7) scales, especially in other regions, had the most pronounced effects.

Digital intelligence interventions reduce depressive and anxious symptoms in older adults, supporting the development of evidence-based treatment guidelines in the digital era.

Research suggests that individuals who commit crimes often exhibit various early maladaptive schemas (EMSs). EMSs are a broad and pervasive theme or pattern consisting of memories, emotions, cognitions, and bodily sensations concerning oneself and one's relationships with others. Furthermore, EMSs play a crucial role in the onset and maintenance of different types of offending behaviors, highlighting the need to implement schema therapy (ST) for perpetrators. Therefore, the present systematic review assesses the effectiveness of ST for individuals who committed crimes. Four databases (PubMed, Scopus, Web of Science, and Scielo) were searched for studies examining the effectiveness of ST for individuals who committed crimes. Seventeen studies were identified, but only 15 met the criteria for inclusion. Results showed that ST can lead to beneficial effects in EMSs, schema modes, personality symptoms, and risk factors to commit crimes (e.g., cognitive distortions). However, the studies, besides being scarce, revealed some methodological limitations. ST is a promising therapy for individuals who committed crimes, despite the studies' methodological shortcomings, which prevent us from drawing more firm conclusions. Although promising, more research is needed to enhance our understanding of the impact of ST therapies in forensic settings.

Despite the high prevalence and severity of intimate partner violence (IPV) perpetration among men with mental health (MH) problems and substance use (SU), there is limited evidence on the most effective ways to reduce IPV within these groups. Hence, the present systematic review aims to evaluate the effectiveness of psychological interventions for male IPV perpetrators with MH issues and SU problems. Five databases (B-On, Pubmed PsycInfo, Science Direct, and Scopus) were searched for studies examining the effectiveness of IPV interventions. Twenty-three studies met the inclusion criteria, with 13 interventions described. Interventions were grouped into (1) specific interventions for SU among IPV perpetrators (k = 8), (2) nonspecific interventions for SU among IPV perpetrators (K = 3), and (3) specific interventions targeting MH among IPV perpetrators (k = 2). Cognitive behavioral therapy and motivational interviewing techniques were the most common approaches. Both specific and nonspecific programs addressing SU problems showed some positive effects on perpetrators' behavior and attitudes. However, data from the two intervention programs focusing on MH showed reduced symptoms and re-assaults but without significant differences between the conditions. Despite methodological shortcomings in the studies, the specific and nonspecific interventions targeting SU and IPV show promise, which hinders drawing firmer conclusions. Nonetheless, further research is necessary to deepen our understanding of the MH impact interventions on IPV perpetrators.

Mental health is an increasing topic of focus since more than 500 million people in the world suffer from depression and anxiety. In this multifactorial disorder, parameters such as inflammation, the state of the microbiota and, therefore, the patient's nutrition are receiving more attention. In addition, food products are the source of many essential ingredients involved in the regulation of mental processes, including amino acids, neurotransmitters, vitamins, and others. For this reason, this narrative review was carried out with the aim of analyzing the role of nutrition in depression and anxiety disorders. To reach the review aim, a critical review was conducted utilizing both primary sources, such as scientific publications and secondary sources, such as bibliographic indexes, web pages, and databases. The search was conducted in PsychINFO, MedLine (Pubmed), Cochrane (Wiley), Embase, and CinAhl. The results show a direct relationship between what we eat and the state of our nervous system. The gut-brain axis is a complex system in which the intestinal microbiota communicates directly with our nervous system and provides it with neurotransmitters for its proper functioning. An imbalance in our microbiota due to poor nutrition will cause an inflammatory response that, if sustained over time and together with other factors, can lead to disorders such as anxiety and depression. Changes in the functions of the microbiota-gut-brain axis have been linked to several mental disorders. It is believed that the modulation of the microbiome composition may be an effective strategy for a new treatment of these disorders. Modifications in nutritional behaviors and the use of ergogenic components are presented as important non-pharmacological interventions in anxiety and depression prevention and treatment. It is desirable that the choice of nutritional and probiotic treatment in individual patients be based on the results of appropriate biochemical and microbiological tests.

With increasing research attention on game-based digital interventions for mental disorders, a number of studies have been conducted to explore the effectiveness of digital game-based interventions on mental disorders. However, findings from previous research were inconsistent. Thus, we conducted a comprehensive meta-analytic review of the effectiveness of game-based digital interventions for mental disorders. By searching the articles in databases, we identified 53 studies in which 2433 participants were involved, and 282 effect sizes were extracted. Among the 53 studies, 14 employed within-group (pre/post) designs, and the remaining 39 utilized controlled trial designs. Using a three-level random-effects meta-analytic model, a medium effect size of game-based digital interventions (g = 0.47, 95 % CI: 0.33, 0.61) was revealed in the controlled trial designs and a close-to-medium effect size (g = 0.45, 95 % CI: 0.32, 0.58) was found in the within-group (pre/post) designs, indicating close-to-medium-sized efficacy of game-based digital interventions for mental disorders. Moderator analyses showed that age in the controlled trial designs had contributed to the heterogeneity in previous studies, suggesting that interventions might be more effective for the elderly. However, given that only a limited number of studies were focused on the elderly, more studies with older participants should be conducted in the future to provide more robust evidence and explore the mechanisms of how digital gaming interventions can be more effective in improving mental disorders symptoms.

In-person cognitive behavioral therapy (CBT) can reduce self-reported anxiety in older adults. However, studies are limited for remote CBT. We assessed the effectiveness of remote CBT in mitigating self-reported anxiety in older adults.

We conducted a systematic review and meta-analysis based on a literature search of PubMed, Embase, PsycInfo, and Cochrane databases up to March 31, 2021, for randomized controlled clinical trials comparing the effectiveness of remote CBT versus non-CBT controls on mitigating self-reported anxiety in older adults. We calculated within-group pre-to-post-treatment standardized mean difference using Cohen's d, obtained the difference between a remote CBT group and a non-CBT control group as our effect size for cross-study comparison, and conducted a random-effects meta-analysis. Changes in scores on self-reported anxiety symptoms (Generalized Anxiety Disorder-7 item Scale, Penn State Worry Questionnaire, or Penn State Worry Questionnaire - Abbreviated), and self-reported depressive symptoms (Patient Health Questionnaire-9 item Scale or Beck Depression Inventory) were primary and secondary outcomes, respectively.

Six eligible studies, containing 633 participants with a pooled mean age of 66.6 years, were included in the systematic review and meta-analysis. There was a significant mitigating effect of intervention on self-reported anxiety, favoring remote CBT over non-CBT controls (between-group effect size: -0.63; 95% CI: -0.99 to -0.28). We also found a significant mitigating effect of intervention on self-reported depressive symptoms (between-group effect size: -0.74; 95% CI: -1.24 to -0.25).

Remote CBT is more effective in reducing self-reported anxiety and depressive symptoms than non-CBT control in older adults.

This review compared the efficacy of personalized psychological interventions to standardized interventions for adolescents. We conducted a scoping review and meta-analysis of randomized controlled trials that compared personalized interventions with standardized interventions in adolescents. Data was analyzed using Bayesian multilevel random effects meta-analysis. Eligible studies were identified through five databases: Scopus, PsycINFO, MEDLINE, Web of Science, and EMBASE. Moderation analysis was conducted to explain potential sources of effect size heterogeneity. Eight studies across 13 articles (participant N = 2,490) met inclusion criteria for the review with seven studies across 10 articles (N = 1,347) providing sufficient data for inclusion in the meta-analysis. A small but significant effect size favoring personalized interventions was found (d = 0.21, 95% CrI [0.02, 0.39]), indicating that personalized interventions are associated with superior treatment outcomes compared to standardized interventions. Moderate between-study heterogeneity was found (I2 = 53.3%). There was no evidence of publication bias. The review also found significant variation in methods of treatment personalization. This review provides evidence that personalization of adolescent psychological interventions is an effective way to improve treatment outcomes. Given the large number of adolescents worldwide who will experience some sort of mental health problem, personalization could have a significantly large impact on global mental health outcomes.

https://doi.org/10.17605/OSF.IO/XRNCG.

Established trauma-focused cognitive-behavioral therapies for posttraumatic stress disorder (PTSD) have remission rates of approximately 30%-40%. Alternatively, interpersonal psychotherapy (IPT) and psychodynamic psychotherapy (PDT) focus on disrupted attachment, mentalization, and social connection in PTSD and may help some patients. The authors conducted a meta-analysis on these interpersonal and affect-oriented approaches to treating PTSD.

Building on a prior meta-analysis, the authors searched for randomized controlled trials (RCTs) comparing IPT or PDT with other established PTSD treatments or control conditions for adults diagnosed as having PTSD. Random-effects meta-analyses were conducted to assess outcome effect sizes and dropout rates. RCTs were rated via the Randomized Controlled Trial Psychotherapy Quality Rating Scale.

Ten RCTs (eight of IPT) comparing IPT or PDT with control (k=7) or active treatment (k=4) conditions were identified, nine of which were of adequate quality. IPT (k=5) and PDT (k=2), when analyzed together, were superior to control conditions overall (g=-1.14, p=0.011 [as was IPT alone: g=-0.88, p=0.034]) and to waitlist (g=-1.49) and treatment-as-usual (g=-0.70) groups. Effect sizes, however, may have been inflated by outliers or publication bias. IPT (k=3) and PDT (k=1), when analyzed together, were equally efficacious compared with other active PTSD treatments (primarily exposure-based psychotherapies), as was IPT alone, and had lower dropout rates (relative risk=0.63, p=0.049 for IPT and PDT analyzed together; relative risk=0.61, p=0.098 for IPT alone).

Affect-focused therapies hold promise in the treatment of PTSD. IPT has demonstrated efficacy in multiple trials, whereas the evidence base for PDT is sparse.

This is the protocol for a Campbell systematic review. The objectives are as follows. The proposed systematic review is an update to, and extension of, Lipsey et al. (2007). As such we build on their previous aims to: (i) Assess and synthesise the overall impact of cognitive behavioural therapy (CBT) on offender recidivism; (ii) Examine possible sources of variability in the effectiveness of CBT. Data permitting, we will examine if the effectiveness of CBT varies by: (a) Characteristics of the CBT intervention (e.g., cognitive restructuring vs. cognitive skills training, group v. individual implementation; and/or custodial v. community setting, and/or), (b) Characteristics of the population (e.g., juveniles vs. adult offenders), (c) Implementation factors (e.g., implementing practitioner, use of structured/manualised approaches, delivery mode, and/or programme duration or intensity), (d) Evaluation methods (e.g., randomised vs. non-randomised research designs); (iv) Determine whether there is a decline in the effect of CBT on recidivism over time; and (v) Investigate whether there is an interaction between implementation factors and time in terms of the effect on recidivism.

Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences.

This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers.

A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers.

As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit.

This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents.

DERR1-10.2196/43931.

Currently no comprehensive meta-analysis of MBI efficacy on global and unique cognitive subdomains exist.

Examined the effects of MBIs on global cognition and 15 cognitive subdomains. Inclusion criteria: meditation naïve participants; randomized controlled trial; outcome included one objective or subjective cognitive functioning measure; primary focus was teaching mindfulness skills. Exclusion criteria: inadequate data; one-session ; control condition contained any MBI component. Robust variance estimation and moderator analyses controlling for presence of treatment fidelity were conducted.

One-hundred-and-eleven RCTs (n = 9,538) met eligibility criteria. MBIs had small-to-moderate significant effects on global cognition, executive attention, working memory accuracy, inhibition accuracy, shifting accuracy, sustained attention, and subjective cognitive functioning (vs. waitlist/no-treatment, g = 0.257-0.643; vs. active controls, g = 0.192-0.394). MBIs did not impact executive functioning (EF) latency indices, verbal fluency, processing speed, episodic memory, and cognitive error. Treatment effects were stronger for those with elevated psychiatric symptoms vs. healthy controls, and medical samples, studies with complete-case (vs. intention-to-treat) analysis, face-to-face (vs. self-guided) delivery, and non-standard (vs. standard MBI).

MBIs consistently yielded small-to-moderate yet practically meaningful effect sizes on global cognition and six cognitive subdomains that captured accuracy vs. latency-based indices of EF and sustained accuracy.

Social and Therapeutic Horticulture (STH) is a process where trained practitioners work with plants and people to improve an individual's physical and psychological health, communication and thinking skills. Evidence suggests that STH can support individuals with mental ill-health, however, current commissioning of STH within mental health care is limited. This study aimed to understand the barriers to commissioning STH in mental health care and to identify potential solutions to barriers, to support more widespread availability of services.  METHODS: Individuals with a role in mental health care commissioning from across the UK were invited to take part in semi-structured interviews via zoom. Interviews explored factors influencing the mental health services they commission or refer to, their perception of the role of STH in mental health care and the barriers to commissioning STH, together with potential solutions to any barriers identified.

Commissioners identified a lack of knowledge of STH and evidence of its effectiveness, and a culture which prioritises traditional medical models, as barriers to commissioning. Challenges for STH providers in responding to large-scale commissioning requirements were also highlighted as a barrier.

To upscale commissioning of STH in mental health care, STH interventions need to be embedded within NHS priorities and information on STH services and their effectiveness needs to be easily accessible to practitioners. The sector should also be supported in working collaboratively to enable commissioning of services at scale.

Anxiety, a common symptom of Parkinson's disease (PD), results in reduced life quality, reduced participation in meaningful roles and daily activities, and increased health burden. There are no evidence-based interventions to reduce the impact of anxiety in PD on participation.

This study aimed to identify the key components required for the co-production of an occupation- and community-based intervention for people with PD-related anxiety.

A participatory mixed-methods research study was conducted using online Group Concept Mapping methodology that included five stages: brainstorming, idea synthesis, sorting activity, rating activity, and analysis. A cluster map, pattern match, and 'go-zone' charts were created through multivariate statistical analysis based on participants' responses. The stages were guided by questions generated by the research team working with stakeholders.

Eighty-three people participated, with 64 taking part in more than one activity. Participants included people with PD (n = 72), care partners (n = 6), and occupational therapists (n = 5). The final map contained 119 statements with eight clusters (stress value 0.252): exercise, lifestyle changes, self-help, coping, access to information, professional help, peers and groups, support from others. Significant agreement existed between the importance and feasibility rating activities (r = -0.07). 'Go-zone' charts highlighted the priority statements for intervention development.

This novel participatory study highlighted priority components that provide starting points for future development of an occupation- and community-based intervention for people with PD-related anxiety.

Providers treating adults with advanced cancer increasingly seek to engage patients and surrogates in advance care planning (ACP) and end-of-life (EOL) decision making; however, anxiety and depression may interfere with engagement. The intersection of these two key phenomena is examined among patients with metastatic cancer and their surrogates: the need to prepare for and engage in ACP and EOL decision making and the high prevalence of anxiety and depression.

Using a critical review framework, we examine the specific ways that anxiety and depression are likely to affect both ACP and EOL decision making.

The review indicates that depression is associated with reduced compliance with treatment recommendations, and high anxiety may result in avoidance of difficult discussions involved in ACP and EOL decision making. Depression and anxiety are associated with increased decisional regret in the context of cancer treatment decision making, as well as a preference for passive (not active) decision making in an intensive care unit setting. Anxiety about death in patients with advanced cancer is associated with lower rates of completion of an advance directive or discussion of EOL wishes with the oncologist. Patients with advanced cancer and elevated anxiety report higher discordance between wanted versus received life-sustaining treatments, less trust in their physicians, and less comprehension of the information communicated by their physicians.

Anxiety and depression are commonly elevated among adults with advanced cancer and health care surrogates, and can result in less engagement and satisfaction with ACP, cancer treatment, and EOL decisions. We offer practical strategies and sample scripts for oncology care providers to use to reduce the effects of anxiety and depression in these contexts.

To date, the effectiveness of acceptance and commitment therapy (ACT) for acute stroke patients has not been well recognized. The study aimed to discover the effectiveness of group-based ACT in treating depression for acute stroke patients.

We conducted a randomized controlled trial with 140 acute stroke patients with depression. The ACT intervention comprised seven sessions, of 45-60 min over 4 weeks. Data were collected pre- and post-intervention and at 3-month follow-up, assessing depression, health-related quality of life (HRQoL), psychological flexibility, cognitive fusion, sleep quality, and confidence.

Overall, 99.3% of the included patients were assessed as having mild depression. The ACT intervention significantly reduced depression in acute stroke patients in comparison with the control group post-intervention and at 3 months (partialη 2 = . 306 $\eta^{2}=.306$ ). Additionally ACT significantly improved HRQoL-mental component summary, sleep quality, psychological flexibility, cognitive fusion, and confidence compared with control group.

ACT is effective in treating acute stroke patients with depression, and the efficacy was maintained at 3-month follow-up.

The use of virtual treatment services increased dramatically during the COVID-19 pandemic. Unfortunately, large-scale research on virtual treatment for substance use disorder (SUD), including factors that may influence outcomes, has not advanced with the rapidly changing landscape.

This study aims to evaluate the link between clinician-level factors and patient outcomes in populations receiving virtual and in-person intensive outpatient services.

Data came from patients (n=1410) treated in a virtual intensive outpatient program (VIOP) and an in-person intensive outpatient program (IOP), who were discharged between January 2020 and March 2021 from a national treatment organization. Patient data were nested by treatment providers (n=58) examining associations with no-shows and discharge with staff approval. Empathy, comfort with technology, perceived stress, resistance to change, and demographic covariates were examined at the clinician level.

The VIOP (β=-5.71; P=.03) and the personal distress subscale measure (β=-6.31; P=.003) were negatively associated with the percentage of no-shows. The VIOP was positively associated with discharges with staff approval (odds ratio [OR] 2.38, 95% CI 1.50-3.76). Clinician scores on perspective taking (β=-9.22; P=.02), personal distress (β=-9.44; P=.02), and male clinician gender (β=-6.43; P=.04) were negatively associated with in-person no-shows. Patient load was positively associated with discharge with staff approval (OR 1.04, 95% CI 1.02-1.06).

Overall, patients in the VIOP had fewer no-shows and a higher rate of successful discharge. Few clinician-level characteristics were significantly associated with patient outcomes. Further research is necessary to understand the relationships among factors such as clinician gender, patient load, personal distress, and patient retention.

The present special section critical of Acceptance and Commitment Therapy or Training (ACT in either case) and its basis in psychological flexibility, relational frame theory, functional contextualism, and contextual behavioral science (CBS) contains both worthwhile criticisms and fundamental misunderstandings. Noting the important historical role that behavior analysis has played in the cognitive behavioral therapy (CBT) tradition, we argue that CBS as a modern face of behavior analytic thinking has a potentially important positive role to play in CBT going forward. We clarify functional contextualism and its link to ethical behavior, attempting to clear up misunderstandings that could seriously undermine genuine scientific conversations. We then examine the limits of using syndromes and protocols as a basis for further developing models and methods; the role of measurement and processes of change in driving progress toward more personalized interventions; how pragmatically useful concepts can help basic science inform practice; how both small- and large-scale studies can contribute to scientific progress; and how all these strands can be pulled together to benefit humanity. In each area, we argue that further progress will require major modifications in our traditional approaches to such areas as psychometrics, the conduct of randomized trials, the analysis of findings using traditional normative statistics, and the use of data from diverse cultures and marginalized populations. There have been multiple generational shifts in our field's history, and a similar shift appears to be taking place once again.

The purpose of this manuscript is to provide an overview of, and rationale for, the increasing adoption of a wide range of cutting-edge technological methods in assessment and intervention which are relevant for treatment. First, we review traditional approaches to measuring and monitoring affect, behavior, and cognition in behavior and cognitive-behavioral therapy. Second, we describe evolving active and passive technology-enabled approaches to behavior assessment including emerging applications of digital phenotyping facilitated through fitness trackers, smartwatches, and social media. Third, we describe ways that these emerging technologies may be used for intervention, focusing on novel applications for the use of technology in intervention efforts. Importantly, though some of the methods and approaches we describe here warrant future testing, many aspects of technology can already be easily incorporated within an established treatment framework.

Existing treatments for young people with severe depression have limited effectiveness. The aim of the Study of Ketamine for Youth Depression (SKY-D) trial is to determine whether a 4-week course of low-dose subcutaneous ketamine is an effective adjunct to treatment-as-usual in young people with major depressive disorder (MDD).

SKY-D is a double-masked, randomised controlled trial funded by the Australian Government's National Health and Medical Research Council (NHMRC). Participants aged between 16 and 25 years (inclusive) with moderate-to-severe MDD will be randomised to receive either low-dose ketamine (intervention) or midazolam (active control) via subcutaneous injection once per week for 4 weeks. The primary outcome is change in depressive symptoms on the Montgomery-Åsberg Depression Rating Scale (MADRS) after 4 weeks of treatment. Further follow-up assessment will occur at 8 and 26 weeks from treatment commencement to determine whether treatment effects are sustained and to investigate safety outcomes.

Results from this trial will be important in determining whether low-dose subcutaneous ketamine is an effective treatment for young people with moderate-to-severe MDD. This will be the largest randomised trial to investigate the effects of ketamine to treat depression in young people.

Australian and New Zealand Clinical Trials Registry ID: ACTRN12619000683134. Registered on May 7, 2019. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377513 .

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

Research in the last decade has expressed considerable optimism about the clinical potential of psychedelics for the treatment of mental disorders. This optimism is reflected in an increase in research papers, investments by pharmaceutical companies, patents, media coverage, as well as political and legislative changes. However, psychedelic science is facing serious challenges that threaten the validity of core findings and raise doubt regarding clinical efficacy and safety. In this paper, we introduce the 10 most pressing challenges, grouped into easy, moderate, and hard problems. We show how these problems threaten internal validity (treatment effects are due to factors unrelated to the treatment), external validity (lack of generalizability), construct validity (unclear working mechanism), or statistical conclusion validity (conclusions do not follow from the data and methods). These problems tend to co-occur in psychedelic studies, limiting conclusions that can be drawn about the safety and efficacy of psychedelic therapy. We provide a roadmap for tackling these challenges and share a checklist that researchers, journalists, funders, policymakers, and other stakeholders can use to assess the quality of psychedelic science. Addressing today's problems is necessary to find out whether the optimism regarding the therapeutic potential of psychedelics has been warranted and to avoid history repeating itself.

This meta-analysis was to assess the efficacy of digital psychological interventions to improve physical symptoms (i.e., fatigue, pain, disturbed sleep, and physical well-being) among cancer patients, as well as to evaluate the variables that possibly moderate intervention effects.

Nine databases were searched for the literature up to February 2023. Two reviewers independently conducted a quality assessment. Effect sizes were reported as the standardized mean difference (Hedge's g) and estimated using a random-effects model.

The meta-analysis included 44 randomized clinical trials comprising 7200 adults with cancer. Digital psychological interventions were associated with significant improvements in short-term fatigue (g = -0.33; 95% CI, -0.58 to -0.07) and disturbed sleep (g = -0.36; 95% CI, -0.57 to -0.15), but with non-significant changes in pain (g = -0.23; 95% CI, -0.68 to 0.21) and physical well-being (g = 0.31; 95% CI, -0.18 to 0.80). Additionally, no alleviation in long-term physical symptoms was observed. In subgroup analysis, results suggest that the country significantly moderated the effectiveness of digital psychological interventions in alleviating fatigue.

Digital psychological interventions can be effective for improving short-term fatigue and disturbed sleep in patients with cancer. Clinicians could consider digital psychological interventions as a possible and efficient addition to better manage some of the physical symptoms during and after cancer treatment.

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

Problem-solving (PS) has been identified as a therapeutic technique found in multiple evidence-based treatments for depression. To further understand for whom and how this intervention works, we undertook a systematic review of the evidence for PS's effectiveness in preventing and treating depression among adolescents and young adults. We searched electronic databases (PsycINFO, Medline, and Cochrane Library) for studies published between 2000 and 2022. Studies meeting the following criteria were included: (a) the intervention was described by authors as a PS intervention or including PS; (b) the intervention was used to treat or prevent depression; (c) mean or median age between 13-25 years; (d) at least one depression outcome was reported. Risk of bias of included studies was assessed using the Cochrane Risk of Bias 2.0 tool. A narrative synthesis was undertaken given the high level of heterogeneity in study variables. Twenty-five out of 874 studies met inclusion criteria. The interventions studied were heterogeneous in population, intervention, modality, comparison condition, study design, and outcome. Twelve studies focused purely on PS; 13 used PS as part of a more comprehensive intervention. Eleven studies found positive effects in reducing depressive symptoms and two in reducing suicidality. There was little evidence that the intervention impacted PS skills or that PS skills acted as a mediator or moderator of effects on depression. There is mixed evidence about the effectiveness of PS as a prevention and treatment of depression among AYA. Our findings indicate that pure PS interventions to treat clinical depression have the strongest evidence, while pure PS interventions used to prevent or treat sub-clinical depression and PS as part of a more comprehensive intervention show mixed results. Possible explanations for limited effectiveness are discussed, including missing outcome bias, variability in quality, dosage, and fidelity monitoring; small sample sizes and short follow-up periods.

Migraine affects 1.1 billion people globally and is the second leading cause of disability worldwide. In clinical trials, treatment efficacy is evaluated by comparing the differential responses in the treatment and placebo arms. Although placebo response in preventive migraine trials has been studied, there is limited research examining temporal trends. This study evaluates the trend of placebo response over thirty years in migraine prevention trials and investigates the association of potential confounders, such as patient, treatment, and study characteristics on placebo response using meta-analysis with regression.

We conducted literature searches from January 1990 to August 2021 in bibliographical databases (PubMed, Cochrane Library, and EMBASE). Studies were selected according to PICOS criteria and included randomized, double-blind, placebo-controlled trials evaluating preventive migraine treatments in adult patients diagnosed with episodic or chronic migraine, with or without aura. The protocol was registered with PROSPERO (CRD42021271732). Migraine efficacy outcomes included were either continuous (e.g., monthly migraine days) or dichotomous (e.g., ≥ 50% responder rate (yes/no)). We assessed the correlation of the change in outcome from baseline in the placebo arm, with the year of publication. The relationship between placebo response and year of publication was also assessed after accounting to confounders.

A total of 907 studies were identified, and 83 were found eligible. For the continuous outcomes, the change from baseline in mean placebo response showed an increase over the years (rho = 0.32, p = 0.006). The multivariable regression analysis also showed an overall increase in placebo response over the years. The correlation analysis of dichotomous responses showed no significant linear trend between publication year and mean placebo response (rho = 0.08, p = 0.596). Placebo response also varied by route of administration.

Placebo response increased over the past 30 years in migraine preventive trials. This phenomenon should be considered when designing clinical trials and conducting meta-analyses.

Antisocial behavior during adolescence can have long-lasting negative effects and leads to high societal costs. Forensic Outpatient Systemic Therapy (Forensische Ambulante Systeem Therapie; FAST) is a promising treatment for juveniles aged 12-21 showing severe antisocial behavior. The intensity, content and duration of FAST can be adjusted to the needs of the juvenile and their caregiver(s), which is considered crucial for effective treatment. Next to the regular version of FAST (FASTr), a blended version (FASTb) in which face-to-face contacts are replaced by minimally 50% online contacts over the duration of intervention was developed during the Covid-19 pandemic. The current study will investigate whether FASTb is equally effective as FASTr, and through which mechanisms of change, for whom, and under which conditions FASTr and FASTb work.

A randomized controlled trial (RCT) will be carried out. Participants (N = 200) will be randomly assigned to FASTb (n = 100) or FASTr (n = 100). Data collection will consist of self-report questionnaires and case file analysis, and include a pre-test at the start of the intervention, a post-test immediately after the intervention, and a six month follow-up. Mechanisms of change will be investigated using monthly questionnaires of key variables during treatment. Official recidivism data will be collected at two-year follow-up.

This study aims to improve the effectiveness and quality of forensic mental health care for juveniles with antisocial behavior by studying the effectiveness of blended care, which has not been studied before in treatment of externalizing behavior. If found to be at least as effective as face-to-face treatment, blended treatment can help meet the urgent need for more flexible and efficient interventions in this field. In addition, the proposed study aims to unravel what works for whom, knowledge urgently needed in mental health care for juveniles with severe antisocial behavior.

This trial was registered at ClinicalTrials.gov on 07/11/2022, registration number NCT05606978.

Different cognitive behavioral therapies (CBT) have strong research support for treatment of adult depressive disorders (DD). Given the scarcity of knowledge about the performance of CBT in routine clinical care, a systematic review and meta-analysis of CBT for adults with DD treated in this context was conducted.

Published studies until the end of September 2022, were systematically searched in Ovid MEDLINE, Embase OVID, and PsycINFO. The effectiveness of CBT, methodological quality, and moderators of treatment outcome were examined, and benchmarked by meta-analytically comparing with efficacy studies for DD.

Twenty-eight studies, comprising 3734 participants, were included. Large within-group effect sizes (ES) were obtained for DD-severity at post-treatment, and follow-up, on average 8 months post-treatment. Benchmarking analysis showed that effectiveness studies had very similar ESs as efficacy studies at post-treatment (1.51 vs. 1.71) and follow-up (1.71 vs. 1.85). Remission rates were also very similar; effectiveness studies 44 % and 46 %, efficacy studies 45 % and 46 %, at post-treatment and follow-up, respectively.

Only studies published in English-language peer-reviewed journals were included and the use of pre-post ES in the meta-analyses could contribute to biased outcomes.

CBT for DD is an effective treatment when delivered in routine clinical care and the outcomes of effectiveness studies for DD are comparable to the effects obtained in efficacy studies.

MASKED.

Hundreds of randomised controlled trials and dozens of meta-analyses have examined psychotherapies for depression-yet not all points in the same direction. Are these discrepancies a result of specific meta-analytical decisions or do most analytical strategies reaching the same conclusion?

We aim to solve these discrepancies by conducting a multiverse meta-analysis containing all possible meta-analyses, using all statistical methods.

We searched four bibliographical databases (PubMed, EMBASE, PsycINFO and Cochrane Register of Controlled Trials), including studies published until 1 January 2022. We included all randomised controlled trials comparing psychotherapies with control conditions without restricting the type of psychotherapy, target group, intervention format, control condition and diagnosis. We defined all possible meta-analyses emerging from combinations of these inclusion criteria and estimated the resulting pooled effect sizes with fixed-effect, random-effects, 3-level, robust variance estimation, p-uniform and PET-PEESE (precision-effect test and precision-effect estimate with SE) meta-analysis models. This study was preregistered (https://doi.org/10.1136/bmjopen-2021-050197).

A total of 21 563 records were screened, and 3584 full texts were retrieved; 415 studies met our inclusion criteria containing 1206 effect sizes and 71 454 participants. Based on all possible combinations between inclusion criteria and meta-analytical methods, we calculated 4281 meta-analyses. The average summary effect size for these meta-analyses was Hedges' g mean=0.56, a medium effect size, and ranged from g=-0.66 to 2.51. In total, 90% of these meta-analyses reached a clinically relevant magnitude.

The multiverse meta-analysis revealed the overall robustness of the effectiveness of psychotherapies for depression. Notably, meta-analyses that included studies with a high risk of bias, compared the intervention with wait-list control groups, and not correcting for publication bias produced larger effect sizes.

Depression is the leading cause of disability worldwide and its effects can be fatal, with over 800,000 people dying by suicide each year. Neuromodulatory treatments such as transcranial magnetic stimulation (TMS) are being used to treat depression. Despite its endorsement by two regulatory bodies: NICE (2016) and the FDA (2008), there are major questions about the treatment efficacy and biological mechanisms of TMS. Ahn et al.'s (2013) justified the use of TMS in a clinical context in an important study indicating that excitatory TMS increases reward responsiveness. A pseudo-replication of this study by Duprat et al., (2016) also found a similar effect of active TMS, but only with the addition of an exploratory covariate to the analyses-trait reward responsiveness. Here we replicate Ahn et al.'s (2013) key study, and to test the reliability of the effects, and their dependency on trait reward responsiveness as described by Duprat et al., (2016). Using excitatory and sham TMS, we tested volunteers using the probabilistic learning task to measure their reward responsiveness both before and after stimulation. We also examined affect (positive, negative) following stimulation. Irrespective of TMS, the task was shown to be sensitive to reward responsiveness. However, we did not show TMS to be effective in increasing reward responsiveness and we did not replicate Ahn et al., (2013) or Duprat et al., (2016)'s key findings for TMS efficacy, where we provide evidence favouring the null. Moreover, exploratory analyses suggested following active stimulation, positive affect was reduced. Given our findings, we question the basic effects, which support the use of TMS for depression, particularly considering potential deleterious effects of reduced positive affect in patients with depression.

Cognitive behavior therapy (CBT) is by far the most examined type of psychological treatment for depression and is recommended in most treatment guide-lines. However, no recent meta-analysis has integrated the results of randomized trials examining its effects, and its efficacy in comparison with other psychotherapies, pharmacotherapies and combined treatment for depression remains uncertain. We searched PubMed, PsycINFO, Embase and the Cochrane Library to identify studies on CBT, and separated included trials into several subsets to conduct random-effects meta-analyses. We included 409 trials (518 comparisons) with 52,702 patients, thus conducting the largest meta-analysis ever of a specific type of psychotherapy for a mental disorder. The quality of the trials was found to have increased significantly over time (with increasing numbers of trials with low risk of bias, less waitlist control groups, and larger sample sizes). CBT had moderate to large effects compared to control conditions such as care as usual and waitlist (g=0.79; 95% CI: 0.70-0.89), which remained similar in sensitivity analyses and were still significant at 6-12 month follow-up. There was no reduction of the effect size of CBT according to the publication year (<2001 vs. 2001-2010 vs. >2011). CBT was significantly more effective than other psychotherapies, but the difference was small (g=0.06; 95% CI: 0-0.12) and became non-significant in most sensitivity analyses. The effects of CBT did not differ significantly from those of pharmacotherapies at the short term, but were significantly larger at 6-12 month follow-up (g=0.34; 95% CI: 0.09-0.58), although the number of trials was small, and the difference was not significant in all sensitivity analyses. Combined treatment was more effective than pharmacotherapies alone at the short (g=0.51; 95% CI: 0.19-0.84) and long term (g=0.32; 95% CI: 0.09-0.55), but it was not more effective than CBT alone at either time point. CBT was also effective as unguided self-help intervention (g=0.45; 95% CI: 0.31-0.60), in institutional settings (g=0.65; 95% CI: 0.21-1.08), and in children and adolescents (g=0.41; 95% CI: 0.25-0.57). We can conclude that the efficacy of CBT in depression is documented across different formats, ages, target groups, and settings. However, the superiority of CBT over other psychotherapies for depression does not emerge clearly from this meta-analysis. CBT appears to be as effective as pharmacotherapies at the short term, but more effective at the longer term.

Many patients with major depressive disorder (MDD) remain untreated or do not respond to cognitive behavioral therapy (CBT). Physical exercise shows antidepressive effects and may serve as an effective augmentation treatment. However, research on combining exercise with CBT is sparse in MDD and underlying mechanisms of exercise are not well understood to date.

120 outpatients with MDD were randomized to either a high intensity exercise group (HEX), a low intensity exercise group (LEX), or a waiting list control group (WL). After 12 weeks of exercise training or waiting period, all patients received a manualized CBT.

Seventy-five patients with MDD completed both the exercise program/ waiting period and the CBT. While physical fitness improved in HEX after the exercise program, it did not change in LEX and WL. Depressive symptoms improved in all three groups from baseline to post-CBT and the group by time interaction was not significant. Regression analyses revealed that the amount of fitness improvement during exercise predicted the subsequent CBT response.

The dropout rate was relatively high, preparatory CBT sessions during exercise / waiting period may have influenced depressive symptoms, and no patients with severe MDD were included.

High intense physical exercise did not lead to a general enhancement of CBT outcome, but higher increases in physical fitness seem to improve symptom change during CBT. Our results suggest that the implementation of more individually tailored exercise programs could be a promising approach for future research and clinical practice.

An initial dose of alcohol can motivate-or prime-further drinking and may precipitate (re)lapse and bingeing. Lab-based studies have investigated the alcohol priming effect; however, heterogeneity in designs has resulted in some inconsistent findings. The aims of this meta-analysis were to (i) determine the pooled effect size for motivation to drink following priming, measured by alcohol consumption and craving, and (ii) examine whether design characteristics influenced any priming effect.

Literature searches of PsycINFO, PubMed and Scopus in October 2020 (updated October 2021) identified lab-based alcohol priming studies that assessed effect of priming on motivation to drink. A tailored risk-of-bias tool assessed quality of lab-based studies. Random effects meta-analyses were computed on outcome data from 38 studies comparing the effect of a priming dose of alcohol against control on subsequent alcohol consumption/self-reported craving. Study characteristics that might have affected outcomes were design type (within/between-participant), dose of prime, time of motivation assessment, type of control drink (placebo alcohol/soft drink).

Relative to control, alcohol had a small-to-moderate priming effect on subsequent alcohol consumption (standardised mean difference [SMD] = 0.336 [95% CI, 0.171, 0.500]) and craving (SMD = 0.431 [95% CI, 0.306, 0.555]). Aspects of study design differentially affected consumption and craving. The size of the priming dose had no effect on consumption, but larger doses were sometimes associated with greater craving (with craving generally following the blood alcohol curve). Alcohol priming effects for consumption, but not craving, were smaller when compared with placebo, relative to soft drink, control.

Lab-based alcohol priming studies are a valid paradigm from which to investigate the impact of acute intoxication on alcohol motivation. Designs are needed that assess the impact of acute consumption on motivation to drink in more varied and realistic ways.

Anxiety and depression are important risk factors for chronic obstructive pulmonary disease (COPD). The aim of this study was to develop a prediction model to predict anxiety or depression in COPD patients. The retrospective study was conducted in COPD patients receiving stable treatment between 2018 and 2020 to develop prediction model. The variables, were readily available in clinical practice, were analysed. After data preprocessing, model training and performance evaluation were performed. Validity of the prediction model was verified in 3 comparative model training. Between 2018 and 2020, 375 eligible patients were analysed. Thirteen variables were included into the final model: gender, age, marital status, education level, long-term residence, per capita annual household income, payment method of medical expenses, direct economic costs of treating COPD in the past year, smoking, COPD progression, number of acute exacerbation of COPD in the last year, regular treatment with inhalants and family oxygen therapy. Risk score threshold in each sample in the training set was 1.414. The area under the curve value was respectively 0.763 and 0.702 in the training set and test set, which were higher than three comparative models. The simple prediction model to predict anxiety or depression in patients with COPD has been developed. Based on 13 available data in clinical indicators, the model may serve as an instrument for clinical decision-making for COPD patients who may have anxiety or depression.Key messagesThirteen variables were included into the prediction model.The AUC value was, respectively, 0.763 and 0.702 in the training set and test set, which were higher than three comparative models.The simple prediction model to predict anxiety or depression in patients with COPD has been developed.

Psychotherapy theorists can often become fervent advocates of the schools they follow and place the doctrines of the theories they adopt above all else. This situation can sometimes turn into a war of theories between researchers as well. However, therapists should not aim to shape therapy sessions according to their methods but to use them in line with clients' needs. Although it is emphasised that the integration of both psychoanalytic and cognitive behavioural therapy techniques, which is going to be named dynamic cognitive behavioural therapy (DCBT) in this case report, will provide more effective and permanent treatment, a discernible gap exists regarding the integration of these theories and their use in psychotherapy. Taking into account this gap, it is considered important to use this approach with a client who has a social anxiety disorder (SAD). Therefore, this study aims to describe the almost forgotten DCBT approach step by step through a case report and reveal the effectiveness of this approach. As a result, DCBT seems to be effective in the treatment of SAD.

Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.

Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.

A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.

The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

The UK government has invested £5.77 million in green social prescribing to prevent and tackle mental ill-health. Therapeutic community gardening, one type of green social prescription, provides a range of health outcomes. However, for increased accessibility, a greater understanding of how it impacts mental health and the facilitators and barriers to referral, uptake, and attendance by individuals with mental health problems is required. We conducted and thematically analysed interviews with thirteen stakeholders including social prescribing link workers and garden staff; and focus groups with twenty garden members. The mechanisms by which therapeutic community gardening were suggested to impact mental health were by engaging members with nature and the outdoors, providing hope for the future and facilitating social support and relationships. Factors facilitating referral, uptake, and attendance included a holistic and person-centred approach, which is flexible around health needs. Barriers included awareness of the full offering of therapeutic community gardens and accessibility, in terms of physical location and waiting lists. Given that nature-based interventions have the potential to protect and enhance population health and offer cost savings through reduced reliance on other health services; overcoming these barriers is key to ensuring that therapeutic community gardening is more widely available as an additional mental health treatment.

At-home Ketamine-assisted therapy (KAT) with psychosocial support and remote monitoring through telehealth platforms addresses access barriers, including the COVID-19 pandemic. Large-scale evaluation of this approach is needed for questions regarding safety and effectiveness for depression and anxiety.

In this prospective study, a large outpatient sample received KAT over four weeks through a telehealth provider. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression, and the Generalized Anxiety Disorder scale (GAD-7) for anxiety. Demographics, adverse events, and patient-reported dissociation were also analyzed. Symptom trajectories were identified using Growth Mixture Modeling, along with outcome predictors.

A sample of 1247 completed treatment with sufficient data, 62.8 % reported a 50 % or greater improvement on the PHQ-9, d = 1.61, and 62.9 % on the GAD-7, d = 1.56. Remission rates were 32.6 % for PHQ-9 and 31.3 % for GAD-7, with 0.9 % deteriorating on the PHQ-9, and 0.6 % on the GAD-7. Four patients left treatment early due to side effects or clinician disqualification, and two more due to adverse events. Three patient subpopulations emerged, characterized by Improvement (79.3 %), Chronic (11.4 %), and Delayed Improvement (9.3 %) for PHQ-9 and GAD-7. Endorsing side effects at Session 2 was associated with delayed symptom improvement, and Chronic patients were more likely than the other two groups to report dissociation at Session 4.

At-home KAT response and remission rates indicated rapid and significant antidepressant and anxiolytic effects. Rates were consistent with laboratory- and clinic-administered ketamine treatment. Patient screening and remote monitoring maintained low levels of adverse events. Future research should assess durability of effects.