Explore the vast range of titles available.

BackgroundCognitive difficulties are common in people with severe mental disorders (SMDs) and various measures of cognition are of proven validity. However, there is a lack of systematic evidence regarding the psychometric properties of these measures in low- and middle-income countries (LMICs).ObjectiveTo systematically review the psychometric properties of cognitive measures validated in people with SMDs in LMICs.MethodsWe conducted a systematic review of the literature by searching from four electronic databases. Two authors independently screened studies for their eligibility. Measurement properties of measures in all included studies were extracted. All eligible measures were assessed against criteria set for clinical and research recommendations. Results are summarized narratively and measures were grouped by measurement type and population.ResultsWe identified 23 unique measures from 28 studies. None of these was from low-income settings. Seventeen of the measures were performance-based. The majority (n = 16/23) of the measures were validated in people with schizophrenia. The most commonly reported measurement properties were: known group, convergent, and divergent validity (n = 25/28). For most psychometric property, studies of methodological qualities were found to be doubtful. Among measures evaluated in people with schizophrenia, Brief Assessment of Cognition in Schizophrenia, Cognitive Assessment Interview, MATRICS Consensus Cognitive Battery, and CogState Schizophrenia Battery were with the highest scores for clinical and research recommendation.ConclusionsStudies included in our review provide only limited quality evidence and future studies should consider adapting and validating measures using stronger designs and methods. Nonetheless, validated assessments of cognition could help in the management and allocating therapy in people with SMDs in LMICs.

Cognitive impairment is common in people with schizophrenia (PWS). To detect the presence and its consequences, cognitive measures with sound psychometric properties are needed. However, these are lacking especially in low-income countries. Hence, we developed the Ethiopian Cognitive Assessment battery in Schizophrenia (ECAS). In this study, we evaluated the psychometric properties of the ECAS in a cross-sectional study involving 350 PWS. Confirmatory factor analysis demonstrated a one-factor solution. ECAS score correlated significantly but weakly with a disability measure (r = −0.13, p = 0.02) and symptom dimensions of PANSS (r between −0.12 and −0.29, p < 0.05), except for positive symptoms (r = −0.10, p > 0.05). Years of education (β = 0.12, 95% CI (0.09, 0.14), p < 0.001), male sex (β = 0.22, 95% CI (0.05, 0.39)), age β = −0.02, 95% CI (−0.03, −0.01), and medication side effects (β = −0.03, 95% CI (−0.06, −0.01), p = 0.021) were significantly associated with the composite score of ECAS. The Item Response Theory analysis showed that the tool best functions among participants with moderate cognitive impairment (difficulty coefficient between −1.12 and 0.27). The Differential Item Functioning analyses showed that education had a positive contribution on Digit Symbol Substitution Test (MH OR = 2.64, 95% CI (1.34, 5.20)). The results showed that ECAS is valid in assessing cognition in PWS in low-resource settings.

Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals' views, but very few studies have examined the perspectives of people with psychosis on this topic. This paper aimed to provide an in-depth examination of the views of people with psychosis regarding algorithmic relapse prediction within a DRM system that incorporates active symptom monitoring and passive sensing data. People with psychosis (n=58) were recruited from 6 geographically distinct areas of the United Kingdom. They participated in semistructured qualitative interviews exploring their views about using a DRM system that predicts psychosis relapse based on a machine learning algorithm. Transcripts were analyzed using reflexive thematic analysis. People with lived experience of psychosis were involved extensively in study design, analysis, and reporting. Findings were described across 4 themes. First, accuracy was a prominent theme. Participants emphasized that transparency about algorithm sensitivity and specificity is crucial and discussed the risks of the relapse prediction algorithm producing false positives (flagging that someone was relapsing when they were not) and false negatives (missing actual relapses). In both cases, participants said that errors may be partially mitigated through a human-in-the-loop approach (theme 2), with DRM blended with human oversight, from clinicians or a dedicated digital monitoring team, and calibrated based on service user, carer, and clinician feedback. The third theme, trust, fears, and choice, noted the interplay between users' trust in the DRM system and their relationship with the clinical team. This theme described participants' fears about potential overreactions (hospitalization or excessive medication) or underreactions (no additional support) from the clinical team in response to algorithm-generated relapse predictions. It emphasized the importance of retaining choice around the use of relapse detection algorithms and the sharing of personal data. The final theme described participants' views about the benefits of using a relapse prediction algorithm, including facilitating early intervention, triaging care according to need, minimizing human bias in assessment, and efficiency in saving staff time. People with psychosis acknowledged potential benefits of algorithm-assisted relapse prediction for receiving timely or efficient care, but with several caveats. Algorithm-generated relapse alerts need to be sufficiently accurate and must be interpreted, with understanding of their limitations, by a trustworthy human who is aware of the relevant context. Algorithm-based relapse predictions should only be used with valid consent, in a way that promotes and respects the autonomy and voice of service users and avoids increasing the use of excessive restriction.

Background: Cognitive impairment is a common feature of schizophrenia spectrum disorders and has been associated with functional disruption preceding the onset of psychosis. Understanding how cognitive deficits interact with clinical symptoms and functioning in early psychosis remains challenging. In this study, we aim to investigate whether a distinct “cognitive signature” characterizes functional disruption at the onset of psychosis. Material and Methods: Clinical, cognitive, and functional data were collected from 101 first episode psychosis patients at their first hospitalization. Stepwise regression models were used to identify predictors of global functioning and symptom severity at the time of onset, as well as diagnostic outcomes at discharge. Path analysis was used to explore the relationship among symptom severity, cognition, and functional outcomes. Results: Deficits in visual memory were selectively predictive of lower functioning and higher global symptom severity at the time of psychosis onset. Reduced visual-spatial abilities were also associated with unemployment at the time preceding hospitalization and predicted a non-affective schizophrenia spectrum diagnosis at discharge. Path analysis found that visual memory fully mediated the relationship between negative symptoms and level of functioning. Conclusions: Impairment in visual cognition seems to be uniquely associated with functional impairment and global symptom severity at the onset of psychosis and to mediate the relationship between negative symptoms and functioning. The results might indicate a primary relevance of visual cognitive aspects in marking functional disruption and symptom exacerbation at psychosis onset. This might have implications for early detection and inform treatment plans.

Aims: Cue exposure therapy (CET) is a promising treatment approach for cocaine substance use disorder (SUD). CET specifically targets the psychological and physiological responses elicited by drug-related cues, aiming to reduce their motivational impact. To advance understanding of CET for cocaine treatment, this systematic review aims to categorise the range of cocaine cues used in research. Methods: A systematic review of the existing literature with searches conducted on PubMed and Web of Science bibliographic databases with no time constraints in August 2024 (PROSPERO: CRD42024554361). Three reviewers were independently involved in the screening, review and data extraction process, in line with PRISMA guidelines. Data extracted included participant demographics, study design, data on the cocaine cue task, and examples (if provided). Each study was appraised and received a quality score. The secondary outcome was to summarise examples for each category type identified. The data are presented as a narrative synthesis. Results: 3600 articles were identified and screened. 235 articles were included in the analysis. Cues identified included images, paraphernalia, drug-related words, cocaine smell, auditory stimuli presented via audiotapes, video recordings, scripts, and virtual reality environments, often combining multiple modalities. Included studies recruited cocaine-dependent individuals, recreational users, polydrug users, and non-cocaine-using controls. The sample sizes of the studies ranged from a single case study to a study including 1974 participants. Conclusions: This review found that studies employed a wide range of cue categories, but detailed examples were often lacking, limiting replication. The number and combination of cues varied: some studies used only cocaine-related images, while others included images, videos, physical items, and audiotapes. The level of immersion and personalisation also differed considerably. All studies used cocaine-specific cues, most commonly images or representations of cocaine substance, cocaine use or drug paraphernalia, drug preparation items, or conversations of cocaine use and its effects. The overall quality of the included studies was deemed good, with all adhering to standard research norms. While this review highlights the breath of cue types used in the literature, further research should focus on enhancing cue exposure techniques by incorporating more immersive and personalised stimuli, and by providing clearer documentation of cue characteristics to support replication and clinical translation.

Background: Interventions targeting cognition in people with schizophrenia have shown moderate effects on improving functioning. Recent cognitive remediation (CR) approaches have begun to target metacognition to improve functioning outcomes. This study aims to develop a novel measure of metacognition and assess whether metacognitive-based CR (mCR) can improve metacognition. Method: We use data from a single-blinded randomized controlled trial comparing mCR plus treatment as usual (TAU) to TAU alone in people with schizophrenia. Participants were assessed with measures of cognition, functioning, and a new measure of metacognition at three time points: Week 0 (baseline), Week 12, and Week 24. Results: The evaluation of the novel metacognition assessment suggests that it is a reliable and valid measure. The measure positively correlates with cognitive measures, in particular with executive function and IQ but also with a measure of functioning. We found that a course of mCR was able to increase metacognition compared to TAU by Week 24. Conclusions: Metacognition may be an important mechanism to explain how CR affects functioning outcomes in people with psychosis. The systematic assessment of metacognition as part of CR studies may help to evaluate more clearly its role and relevance to functioning.

Background A substantial proportion of people with bipolar disorder (BD) experience persistent cognitive difficulties associated with impairments in psychosocial functioning and a poorer disorder course. Emerging evidence suggests that cognitive remediation (CR), a psychological intervention with established efficacy in people with schizophrenia, can also benefit people with BD. Following a proof-of-concept trial showing that CR is feasible and potentially beneficial for people with BD, we are conducting an adequately powered trial in euthymic people with BD to 1) determine whether an individual, therapist-supported, computerised CR can reduce cognitive difficulties and improve functional outcomes; and 2) explore how CR exerts its effects. Methods CRiB2 is a two-arm, assessor-blind, multi-site, randomised controlled trial (RCT) comparing CR to treatment-as-usual (TAU). Participants are people with a diagnosis of BD, aged between 18 and 65, with no neurological or current substance use disorder, and currently euthymic. 250 participants will be recruited through primary, secondary, tertiary care, and the community. Participants will be block-randomised (1:1 ratio, stratified by site) to continue with their usual care (TAU) or receive a 12-week course of therapy and usual care (CR + TAU). The intervention comprises one-on-one CR sessions with a therapist supplemented with independent cognitive training for 30–40 h in total. Outcomes will be assessed at 13- and 25-weeks post-randomisation. Efficacy will be examined by intention-to-treat analyses estimating between-group differences in primary (i.e., psychosocial functioning at week 25 measured with the Functional Assessment Short Test) and secondary outcomes (i.e., measures of cognition, mood, patient-defined goals, and quality of life). Global cognition, metacognitive skills, affect fluctuation, and salivary cortisol levels will be evaluated as putative mechanisms of CR through mediation models. Discussion This study will provide a robust evaluation of efficacy of CR in people with BD and examine the putative mechanisms by which this therapy works. The findings will contribute to determining the clinical utility of CR and potential mechanisms of action. Trial registration Cognitive Remediation in Bipolar 2 (CRiB2): ISRCTN registry: https://www.isrctn.com/ISRCTN10362331 . Registered 04 May 2022. Overall trial status: Ongoing; Recruitment status: Recruiting.

Cognitive impairment is one of the core symptoms of schizophrenia. Quite a number of systematic reviews were published related to cognitive impairment in people with schizophrenia (PWS). This umbrella review, therefore, aimed at reviewing and synthesizing the findings of systematic reviews related to domains of cognition impaired and associated factors in PWS. We searched four electronic databases. Data related to domains, occurrence, and associated factors of cognitive impairment in PWS were extracted. The quality of all eligible systematic reviews was assessed using A MeaSurement Tool to Assess methodological quality of systematic Review (AMSTAR) tool. Results are summarized and presented in a narrative form. We identified 63 systematic reviews fulfilling the eligibility criteria. The included reviews showed that PWS had lower cognitive functioning compared to both healthy controls and people with affective disorders. Similar findings were reported among psychotropic free cases and people with first episode psychosis. Greater impairment of cognition was reported in processing speed, verbal memory, and working memory domains. Greater cognitive impairment was reported to be associated with worse functionality and poor insight. Cognitive impairment was also reported to be associated with childhood trauma and aggressive behaviour. According to our quality assessment, the majority of the reviews had moderate quality. We were able to find a good number of systematic reviews on cognitive impairment in PWS. The reviews showed that PWS had higher impairment in different cognitive domains compared to healthy controls and people with affective disorders. Impairment in domains of memory and processing speed were reported frequently.

Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning. We collected 13,313 public tweets on schizophrenia between January and May 2018. Two service user researchers manually identified stigma in 746 English tweets; 80% were used to train eight models, and 20% for testing. The two models with fewest false negatives were compared in two service user validation exercises, and the best model used to classify all extracted public English tweets. Tweets classed as stigmatising by service users were more negative in sentiment (t (744) = 12.02, p < 0.001 [95% CI: 0.196–0.273]). Our linear Support Vector Machine was the best performing model with fewest false negatives and higher service user validation. This model identified public stigma in 47% of English tweets (n5,676) which were more negative in sentiment (t (12,143) = 64.38, p < 0.001 [95% CI: 0.29–0.31]). Machine learning can identify stigmatising tweets at large scale, with service user involvement. Given the prevalence of stigma, there is an urgent need for education and online campaigns to reduce it. Machine learning can provide a real time metric on their success.