Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Reduced mitochondrial respiratory capacity in patients with acute episodes of bipolar disorder: Could bipolar disorder be a state-dependent mitochondrial disease?

BACKGROUND: Bipolar disorder (BD) is a chronic and recurrent disease characterized by acute mood episodes and periods of euthymia. The available literature postulates that a biphasic dysregulation of mitochondrial bioenergetics might underpin the neurobiology of BD. However, most studies focused on inter-subject differences rather than intra-subject variations between different mood states. To test this hypothesis, in this preliminary proof-of-concept study, we measured in vivo mitochondrial respiration in patients with BD during a mood episode and investigated differences compared to healthy controls (HC) and to the same patients upon clinical remission. METHODS: This longitudinal study recruited 20 patients with BD admitted to our acute psychiatric ward with a manic (n = 15) or depressive (n = 5) episode, and 10 matched HC. We assessed manic and depressive symptoms using standardized psychometric scales. Different mitochondrial oxygen consumption rates (OCRs: Routine, Leak, electron transport chain [ETC], Rox) were assessed during the acute episode (T0) and after clinical remission (T1) using high-resolution respirometry at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system in one million of peripheral blood mononuclear cells (PMBC). Specific OCRs were expressed as mean ± SD in picomoles of oxygen per million cells. Significant results were adjusted for age, sex, and body mass index. RESULTS: The longitudinal analysis showed a significant increase in the maximal oxygen consumption capacity (ETC) in clinical remission (25.7 ± 16.7) compared to the acute episodes (19.1 ± 11.8, p = 0.025), and was observed separately for patients admitted with a manic episode (29.2 ± 18.9 in T1, 22.3 ± 11.9 in T0, p = 0.076), and at a trend-level for patients admitted with a depressive episode (15.4 ± 3.9 in T1 compared to 9.4 ± 3.2 in T0, p = 0.107). Compared to HC, significant differences were observed in ETC in patients with a bipolar mood episode (H = 11.7; p = 0.003). Individuals with bipolar depression showed lower ETC than those with a manic episode (t = -3.7, p = 0.001). Also, significant differences were observed in ETC rates between HC and bipolar depression (Z = 1.000, p = 0.005). CONCLUSIONS: Bioenergetic and mitochondrial dysregulation could be present in both manic and depressive phases in BD and, importantly, they may restore after clinical remission. These preliminary results suggest that mitochondrial respiratory capacity could be a biomarker of illness activity and clinical response in BD. Further studies with larger samples and similar approaches are needed to confirm these results and identify potential biomarkers in different phases of the disease.

Emotion dysregulation in bipolar disorder compared to other mental illnesses: a systematic review and meta-analysis.

People with bipolar disorder (BD) often present emotion dysregulation (ED), a pattern of emotional expression interfering with goal-directed behavior. ED is a transdiagnostic construct, and it is unclear whether it manifests itself similarly in other conditions, such as major depressive disorder (MDD) or borderline personality disorder (BPD), or has specific features in BD. The present systematic review and meta-analysis explored ED and adopted emotion regulation (ER) strategies in BD compared with other psychiatric conditions. PubMed/MEDLINE, EMBASE, Scopus, and PsycINFO databases were systematically searched from inception to April 28th, 2022. Studies implementing validated instruments assessing ED or ER strategies in BD and other psychiatric disorders were reviewed, and meta-analyses were conducted. Twenty-nine studies yielding multiple comparisons were included. BD was compared to MDD in 20 studies (n = 2451), to BPD in six studies (n = 1001), to attention deficit hyperactivity disorder in three studies (n = 232), to anxiety disorders in two studies (n = 320), to schizophrenia in one study (n = 223), and to post-traumatic stress disorder in one study (n = 31). BD patients did not differ from MDD patients in adopting most adaptive and maladaptive ER strategies. However, small-to-moderate differences in positive rumination and risk-taking behaviors were observed. In contrast, patients with BPD presented an overall higher degree of ED and more maladaptive ER strategies. There were insufficient data for a meta-analytic comparison with other psychiatric disorders. The present report further supports the idea that ED is a transdiagnostic construct spanning a continuum across different psychiatric disorders, outlining specific clinical features that could represent potential therapeutic targets.

Correlation between emotion dysregulation and mood symptoms of bipolar disorder: A systematic review and meta-analysis.

BACKGROUND: Emotion dysregulation (ED) is a transdiagnostic construct characterized by difficulties regulating intense emotions. People with bipolar disorder (BD) are more likely to show ED and use maladaptive emotion regulation strategies than adaptive ones. However, little is known about whether ED in BD is a trait or it is rather an epiphenomenon of mood symptoms. METHODS: We conducted a systematic review and meta-analysis of the evidence across major literature databases reporting correlations between measures of emotion regulation (overall ED and different emotion regulation strategies) and measures of depressive and (hypo)manic symptoms in BD from inception until April 12th, 2022. RESULTS: Fourteen studies involving 1371 individuals with BD were included in the qualitative synthesis, of which 11 reported quantitative information and were included in the meta-analysis. ED and maladaptive strategies were significantly higher during periods with more severe mood symptoms, especially depressive ones, while adaptive strategies were lower. CONCLUSION: ED significantly correlates with BD symptomatology, and it mainly occurs during mood alterations. ED may be a target for specific psychotherapeutic and pharmacological treatments, according to precision psychiatry. However, further studies are needed, including patients with mood episodes and longitudinal design, to provide more robust evidence and explore the causal direction of the associations.

Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies.

BACKGROUND: Many people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, and digital tools have been proposed as a solution. OBJECTIVE: We aimed to present the development, feasibility, and potential effectiveness of Vickybot, a chatbot aimed at screening, monitoring, and reducing anxiety-depressive symptoms and work-related burnout, and detecting suicide risk in patients from PC and health care workers. METHODS: Healthy controls (HCs) tested Vickybot for reliability. For the simulation study, HCs used Vickybot for 2 weeks to simulate different clinical situations. For feasibility and effectiveness study, people consulting PC or health care workers with mental health problems used Vickybot for 1 month. Self-assessments for anxiety (Generalized Anxiety Disorder 7-item) and depression (Patient Health Questionnaire-9) symptoms and work-related burnout (based on the Maslach Burnout Inventory) were administered at baseline and every 2 weeks. Feasibility was determined from both subjective and objective user-engagement indicators (UEIs). Potential effectiveness was measured using paired 2-tailed t tests or Wilcoxon signed-rank test for changes in self-assessment scores. RESULTS: Overall, 40 HCs tested Vickybot simultaneously, and the data were reliably transmitted and registered. For simulation, 17 HCs (n=13, 76% female; mean age 36.5, SD 9.7 years) received 98.8% of the expected modules. Suicidal alerts were received correctly. For the feasibility and potential effectiveness study, 34 patients (15 from PC and 19 health care workers; 76% [26/34] female; mean age 35.3, SD 10.1 years) completed the first self-assessments, with 100% (34/34) presenting anxiety symptoms, 94% (32/34) depressive symptoms, and 65% (22/34) work-related burnout. In addition, 27% (9/34) of patients completed the second self-assessment after 2 weeks of use. No significant differences were found between the first and second self-assessments for anxiety (t8=1.000; P=.34) or depressive (t8=0.40; P=.70) symptoms. However, work-related burnout scores were moderately reduced (z=-2.07, P=.04, r=0.32). There was a nonsignificant trend toward a greater reduction in anxiety-depressive symptoms and work-related burnout with greater use of the chatbot. Furthermore, 9% (3/34) of patients activated the suicide alert, and the research team promptly intervened with successful outcomes. Vickybot showed high subjective UEI (acceptability, usability, and satisfaction), but low objective UEI (completion, adherence, compliance, and engagement). Vickybot was moderately feasible. CONCLUSIONS: The chatbot was useful in screening for the presence and severity of anxiety and depressive symptoms, and for detecting suicidal risk. Potential effectiveness was shown to reduce work-related burnout but not anxiety or depressive symptoms. Subjective perceptions of use contrasted with low objective-use metrics. Our results are promising but suggest the need to adapt and enhance the smartphone-based solution to improve engagement. A consensus on how to report UEIs and validate digital solutions, particularly for chatbots, is required.

Cariprazine-induced mania: A case series report.

Bipolar depression is the most prevalent phase of bipolar disorder (BD). There is a risk of inducing treatment-emergent affective switches (TEAS) with antidepressants (ADs). Hence, clinical guidelines do not recommend their use in monotherapy. Cariprazine is a dopamine-serotonin partial agonist, with a recent FDA approval as a monotherapy for BD type 1 (BD-I) depression. To our knowledge, there is no significant evidence of cariprazine-induced TEAS in bipolar depression. We describe three clinical cases of patients admitted to our acute psychiatric ward who developed manic episodes after the introduction of low doses of cariprazine. Two of the patients met the DSM-5 criteria for BD-I, and one for schizoaffective disorder, bipolar type. All patients were initially treated with low doses of cariprazine (1.5 mg) during a depressive phase. All three cases were simultaneously treated with mood stabilizers, regardless of which they switched to a manic episode when cariprazine was initiated. In our review of previous studies assessing the efficacy and side effects profile of cariprazine in BD-I, TEAS have not been found to be significant. However, according to our experience, cariprazine may induce affective switches in BD-I patients. Patients and psychiatrists should receive information regarding early warning symptoms and monitor possible cariprazine-induced mood switching.

Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force.

BACKGROUND: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established. OBJECTIVES: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators. METHODS: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668). RESULTS: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators. CONCLUSION: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

Real-world Implementation of a Smartphone-Based Psychoeducation Program for Bipolar Disorder: Observational Ecological Study.

BACKGROUND: SIMPLe is an internet-delivered self-management mobile app for bipolar disorder (BD) designed to combine technology with evidence-based interventions and facilitate access to psychoeducational content. The SIMPLe app was launched to the real world to make it available worldwide within the context of BD treatment. OBJECTIVE: The main aims of this study are as follows: to describe app use, engagement, and retention rates based on server data; to identify patterns of user retention over the first 6-month follow-up of use; and to explore potential factors contributing to discontinuation of app use. METHODS: This was an observational ecological study in which we pooled available data from a real-world implementation of the SIMPLe app. Participation was open on the project website, and the data-collection sources were a web-based questionnaire on clinical data and treatment history administered at inclusion and at 6 months, subjective data gathered through continuous app use, and the use patterns captured by the app server. Characteristics and engagement of regular users, occasional users, and no users were compared using 2-tailed t tests or analysis of variance or their nonparametric equivalent. Survival analysis and risk functions were applied to regular users' data to examine and compare use and user retention. In addition, a user evaluation analysis was performed based on satisfaction, perceived usefulness, and reasons to discontinue app use. RESULTS: We included 503 participants with data collected between 2016 and 2018, of whom 77.5% (n=390) used the app. Among the app users, 44.4% (173/390) completed the follow-up assessment, and data from these participants were used in our analyses. Engagement declined gradually over the first 6 months of use. The probability of retention of the regular users after 1 month of app use was 67.4% (263/390; 95% CI 62.7%-72.4%). Age (P=.002), time passed since illness onset (P<.001), and years since diagnosis of BD (P=.048) correlate with retention duration. In addition, participants who had been diagnosed with BD for longer used the app on more days (mean 97.73, SD 69.15 days; P=.002) than those who had had a more recent onset (mean 66.49, SD 66.18 days; P=.002) or those who had been diagnosed more recently (mean 73.45, SD 66 days; P=.01). CONCLUSIONS: The user retention rate of the app decreased rapidly after each month until reaching only one-third of the users at 6 months. There exists a strong association between age and app engagement of individuals with BD. Other variables such as years lived with BD, diagnosis of an anxiety disorder, and taking antipsychotics seem relevant as well. Understanding these associations can help in the definition of the most suitable user profiles for predicting trends of engagement, optimization of app prescription, and management.

Clinical correlates of seasonality in bipolar disorder: A specifier that needs specification?

OBJECTIVE: Seasonal pattern (SP) is a bipolar disorder (BD) specifier that indicates a tendency towards affective relapses during specific moments of the year. SP affects 15%-25% of BD patients. In the past, SP was applied only to depressive relapses while, in DSM-5, SP may be applied to both depressive and (hypo)manic episodes. We examined the association between different clinical correlates of BD and SP according to its current definition in a cohort of patients with BD type I (BDI) and II (BDII). METHODS: Patients were recruited from a specialized unit and assessed according to the season of relapse and type of episode per season. SP and non-SP patients were compared looking into sociodemographic and clinical correlates. Significant variables at univariate comparisons were included in multivariate logistic regression with SP as the dependent variable. RESULTS: 708 patients were enrolled (503 BDI, 205 BDII), and 117 (16.5%) fulfilled DSM-5 criteria for SP. The mean age was 45.3 years (SD = 14.18), and 389 were female (54.9%). The logistic regression model included a significant contribution of BDII (OR = 2.23, CI 1.4-3.55), family history of mood disorder (OR = 1.97, CI 1.29-3.01), undetermined predominant polarity (OR = 0.44, CI 0.28-0.70), and aggressive behavior (OR = 0.42, CI 0.23-0.75). CONCLUSION: Our results outline a novel positive association of SP with undetermined predominant polarity, BDII, family history of mood disorder, and with fewer aggressiveness-related symptoms. Seasonality is associated with a biphasic pattern with similar dominance of (hypo)mania and depression and is more frequent in BDII as compared to BDI. Seasonal episodes may be easier to predict, but difficult to prevent.

Psychiatric Clinical Profiles and Pharmacological Interactions in COVID-19 Inpatients Referred to a Consultation Liaison Psychiatry Unit: a Cross-Sectional Study.

The Coronavirus Disease 2019 (COVID-19) can affect mental health in different ways. There is little research about psychiatric complications in hospitalized patients with COVID-19. The aim of the study was to describe the psychiatric clinical profile and pharmacological interactions in COVID-19 inpatients referred to a Consultation-Liaison Psychiatry (CLP) unit. This is a cross-sectional study, carried out at a tertiary hospital in Spain, in inpatients admitted because of COVID-19 and referred to our CLP Unit from March 17,2020 to April 28,2020. Clinical data were extracted from electronic medical records. The patients were divided in three groups depending on psychiatric diagnosis: delirium, severe mental illness (SMI) and non-severe mental illness (NSMI). Of 71 patients included (median [ICR] age 64 [54-73] years; 70.4% male), 35.2% had a delirium, 18.3% had a SMI, and 46.5% had a NSMI. Compared to patients with delirium and NSMI, patients with SMI were younger, more likely to be institutionalized and were administered less anti-COVID19 drugs. Mortality was higher among patients with delirium (21.7%) than those with SMI (0%) or NSMI (9.45%). The rate of side effects due to interactions between anti-COVID19 and psychiatric drugs was low, mainly drowsiness (4.3%) and borderline QTc prolongation (1.5%). Patients affected by SMI were more often undertreated for COVID-19. However, the rate of interactions was very low, and avoidable with a proper evaluation and drug-dose adjustment. Half of the patients with SMI were institutionalized, suggesting that living conditions in residential facilities could make them more vulnerable to infection.

Non-alcoholic fatty liver disease (NAFLD) as a neglected metabolic companion of psychiatric disorders: common pathways and future approaches.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is characterized by hepatic steatosis in over 5% of the parenchyma in the absence of excessive alcohol consumption. It is more prevalent in patients with diverse mental disorders, being part of the comorbidity driving loss of life expectancy and quality of life, yet remains a neglected entity. NAFLD can progress to non-alcoholic steatohepatitis (NASH) and increases the risk for cirrhosis and hepatic carcinoma. Both NAFLD and mental disorders share pathophysiological pathways, and also present a complex, bidirectional relationship with the metabolic syndrome (MetS) and related cardiometabolic diseases. MAIN TEXT: This review compares the demographic data on NAFLD and NASH among the global population and the psychiatric population, finding differences that suggest a higher incidence of this disease among the latter. It also analyzes the link between NAFLD and psychiatric disorders, looking into common pathophysiological pathways, such as metabolic, genetic, and lifestyle factors. Finally, possible treatments, tailored approaches, and future research directions are suggested. CONCLUSION: NAFLD is part of a complex system of mental and non-communicable somatic disorders with a common pathogenesis, based on shared lifestyle and environmental risks, mediated by dysregulation of inflammation, oxidative stress pathways, and mitochondrial function. The recognition of the prevalent comorbidity between NAFLD and mental disorders is required to inform clinical practice and develop novel interventions to prevent and treat these complex and interacting disorders.

From gut to brain: A network model of intestinal permeability, inflammation, and psychotic symptoms in schizophrenia.

Impaired intestinal permeability has recently been suggested as a possible source of chronic inflammation in schizophrenia, but its association with specific psychopathological features remains uncertain. This study aimed to explore the interaction between intestinal permeability, inflammation, and positive and negative symptoms in schizophrenia using a network analysis approach. The study sample comprised 281 adults with schizophrenia (age 40.29 ± 13.65 years, 63.0 % males), enrolled in a cross-sectional observational study assessing intestinal permeability. We estimated the network with a Gaussian graphical model, incorporating scores from 14 individual items of the Positive and Negative Syndrome Scale (PANSS), along with body mass index (BMI), and plasma C-reactive protein (CRP) and lipopolysaccharide-binding protein (LBP) levels. We calculated strength centrality and expected influence and used bridge centrality statistics to identify the bridge nodes. Distinct but highly interconnected clusters emerged for positive and negative symptoms. The biological variables were closely associated with each other. LBP was positively linked with CRP and BMI, but only indirectly connected to psychopathology. CRP exhibited direct positive relationships with various PANSS items and bridged LBP and BMI with psychopathology. Bridge nodes included Conceptual Disorganisation (P2), Active Social Avoidance (G16), Suspiciousness/Persecution (P6), and CRP. These findings support the role of gut-derived inflammation as a mechanism underlying greater symptom severity in schizophrenia and emphasise the importance of addressing dietary habits not only to enhance physical health but also to contribute to improving psychotic symptoms.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Impact of cognitive reserve in clinical, neurocognitive and lifestyle factors in chronic schizophrenia and early stages of schizophrenia.

INTRODUCTION: Although there is evidence that higher cognitive reserve (CR) is a protective factor and it has been related to better prognosis, there have been no studies to date that have explored the CR level and its impact in clinical, neurocognitive and lifestyle outcomes according to the stage of the disease: early stage of psychosis (ESP) or chronic schizophrenia (SCZ). MATERIAL AND METHODS: A total of 60 patients in the ESP and 225 patients with SCZ were enrolled in the study. To test the predictive capacity of CR for each diagnostic group, a logistic regression analysis was conducted. Hierarchical linear regression analyses were performed to explore the associations between CR and different outcomes. The mediation analyses were performed according to the principles of Baron and Kenny. RESULTS: Patients with SCZ showed lower CR than those in the ESP (p<0.001). CR correctly classified 79.6% of the cases (p<0.001; Exp(B)=1.062). In ESP group, CR was related to working memory (p=0.030) and negative symptoms (p=0.027). CR (t=3.925, p<0.001) and cannabis use (t=2.023, p=0.048) explained 26.7% of the variance on functioning (p=0.003). In patients with SCZ, CR predicted all cognitive domains, negative symptoms (R2=0.091, p=0.001) and functioning (R2=0.074, p=0.005). In both ESP and SCZ groups, higher CR was associated with lower body mass index and circumference. In ESP group, the effect of adherence to Mediterranean diet on functioning (p=0.037) was mediated by CR level (p=0.003). CONCLUSIONS: The implications of CR depend on the stage of the disease (ESP vs. SCZ), with a greater effect on neurocognition and negative symptoms in patients with chronic SCZ.

Pharmacological treatments for psychotic depression: a systematic review and network meta-analysis.

BACKGROUND: There are no recommendations based on the efficacy of specific drugs for the treatment of psychotic depression. To address this evidence gap, we did a network meta-analysis to assess and compare the efficacy and safety of pharmacological treatments for psychotic depression. METHODS: In this systematic review and network meta-analysis, we searched ClinicalTrials.gov, CENTRAL, Embase, PsycINFO, PubMed, Scopus, and Web of Science from inception to Nov 23, 2023 for randomised controlled trials published in any language that assessed pharmacological treatments for individuals of any age with a diagnosis of a major depressive episode with psychotic features, in the context of major depressive disorder or bipolar disorder in any setting. We excluded continuation or maintenance trials. We screened the study titles and abstracts identified, and we extracted data from relevant studies after full-text review. If full data were not available, we requested data from study authors twice. We analysed treatments for individual drugs (or drug combinations) and by grouping them on the basis of mechanisms of action. The primary outcomes were response rate (ie, the proportion of participants who responded to treatment) and acceptability (ie, the proportion who discontinued treatment for any reason). We calculated risk ratios and did separate frequentist network meta-analyses by using random-effects models. The risk of bias of individual studies was assessed with the Cochrane risk-of-bias tool and the confidence in the evidence with the Confidence-In-Network-Meta-Analysis (CINeMA). This study was registered with PROSPERO, CRD42023392926. FINDINGS: Of 6313 reports identified, 16 randomised controlled trials were included in the systematic review, and 14 were included in the network meta-analyses. The 16 trials included 1161 people with psychotic depression (mean age 50·5 years [SD 11·4]). 516 (44·4%) participants were female and 422 (36·3%) were male; sex data were not available for the other 223 (19·2%). 489 (42·1%) participants were White, 47 (4·0%) were African American, and 12 (1·0%) were Asian; race or ethnicity data were not available for the other 613 (52·8%). Only the combination of fluoxetine plus olanzapine was associated with a higher proportion of participants with a treatment response compared with placebo (risk ratio 1·91 [95% CI 1·27-2·85]), with no differences in terms of safety outcomes compared with placebo. When treatments were grouped by mechanism of action, the combination of a selective serotonin reuptake inhibitor with a second-generation antipsychotic was associated with a higher proportion of treatment responses than was placebo (1·89 [1·17-3·04]), with no differences in terms of safety outcomes. In head-to-head comparisons of active treatments, a significantly higher proportion of participants had a response to amitriptyline plus perphenazine (3·61 [1·23-10·56]) and amoxapine (3·14 [1·01-9·80]) than to perphenazine, and to fluoxetine plus olanzapine compared with olanzapine alone (1·60 [1·09-2·34]). Venlafaxine, venlafaxine plus quetiapine (2·25 [1·09-4·63]), and imipramine (1·95 [1·01-3·79]) were also associated with a higher proportion of treatment responses overall. In head-to-head comparisons grouped by mechanism of action, antipsychotic plus antidepressant combinations consistently outperformed monotherapies from either drug class in terms of the proportion of participants with treatment responses. Heterogeneity was low. No high-risk instances were identified in the bias assessment for our primary outcomes. INTERPRETATION: According to the available evidence, the combination of a selective serotonin reuptake inhibitor and a second-generation antipsychotic-and particularly of fluoxetine and olanzapine-could be the optimal treatment choice for psychotic depression. These findings should be taken into account in the development of clinical practice guidelines. However, these conclusions should be interpreted cautiously in view of the low number of included studies and the limitations of these studies. FUNDING: None.

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions.

BACKGROUND: Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI). OBJECTIVE: An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI. METHODS: The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group. RESULTS: Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI. CONCLUSIONS: The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.