12 research outputs found
Cognitive bias modification for social anxiety: protocol for a living systematic review of human studies and meta-analysis
No full textBackgroundSocial anxiety is a heightened fear and discomfort in social situations. Cases of elevated distress and impaired functioning can lead to a clinical diagnosis of social anxiety disorder. Altering cognitive biases associated with social anxiety has been suggested as potentially beneficial; however, little is known about the comparative effectiveness of such interventions. The aim of this living systematic review is to examine the efficacy of cognitive bias modification for reducing social anxiety.MethodsWe will search multiple electronic databases for randomised controlled trials evaluating the efficacy of cognitive bias modification for people diagnosed with social anxiety and people exposed to a social stressor. The primary outcome will be change in social anxiety related symptoms; secondary outcomes will be changes in social functioning and quality of life and adverse events. Study selection, data extraction and risk of bias assessment will be done by at least two reviewers using pre-defined tools. We will synthesise data from people with social anxiety diagnosis and those subjected to a simulated social stressor separately using random effects meta-analyses. Heterogeneity will be evaluated by investigating characteristics of included studies and we will conduct a network meta-analysis in order to compare the efficacy of subtypes of cognitive bias modification for social anxiety disorder. We will appraise the strength of the evidence for each outcome by reviewing the overall association, internal and external validity, and reporting biases. Where data allows, we will triangulate the evidence from both sources with a multidisciplinary group of experts. We will also descriptively report factors reported to mediate cognitive bias modification, The review will begin in living mode and the database search will be rerun every three months to identify potential new evidence. We will co-produce this review with members of a global lived experience advisory board. This protocol was registered on 15.10.2024 (CRD42024601380).
Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression
No full textReal-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression
No full textMost people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a timely and long-term data resource to evaluate analytical approaches for developing digital behavioral markers and understand the effectiveness of mental health care delivered continuously and remotely
Trace amine-associated receptor 1 (TAAR1) agonism for psychosis:a living systematic review and meta-analysis of human and non-human data [version 1; peer review: 3 approved]
No full textBACKGROUND: Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies.METHODS: We co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses.RESULTS: Nine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D 2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling. CONCLUSIONS: TAAR1 agonists may be less efficacious than dopamine D 2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted. REGISTRATION: PROSPERO-ID: CRD42023451628.</p
Cognitive bias modification for social anxiety: protocol for a living systematic review of human studies and meta-analysis
No full textThis living systematic review will explore the efficacy of cognitive bias modification (CBM) in individuals diagnosed with social anxiety disorder, as well as separately examining the efficacy of CBM for individuals without a mental health condition who are subjected to a social stressor. Using a multidisciplinary team with international collaborations, this living systematic review will provide valuable insights into the currently uncertain field of literature on CBM and social anxiety. This review is part of the Wellcome funded Global Alliance for Living Evidence on aNxiety, depressiOn, and pSychosis (GALENOS). This review is registered on PROSPERO (CRD42024601380
Trace amine-associated receptor 1 (TAAR1) agonism for psychosis: a living systematic review and meta-analysis of human and non-human data
No full textBackgroundTrace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies.MethodsWe co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses.ResultsNine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D 2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling.ConclusionsTAAR1 agonists may be less efficacious than dopamine D 2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted.RegistrationPROSPERO-ID: CRD42023451628
Automating the data extraction process for systematic reviews using GPT-4o and o3
No full textLarge language models have shown promise for automating data extraction (DE) in systematic reviews (SRs), but most existing approaches require manual interaction. We developed an open-source system using GPT-4o to automatically extract data with no human intervention during the extraction process. We developed the system on a dataset of 290 randomized controlled trials (RCTs) from a published SR about cognitive behavioral therapy for insomnia. We evaluated the system on two other datasets: 5 RCTs from an updated search for the same review and 10 RCTs used in a separate published study that had also evaluated automated DE. We developed the best approach across all variables in the development dataset using GPT-4o. The performance in the updated-search dataset using o3 was 74.9% sensitivity, 76.7% specificity, 75.7 precision, 93.5% variable detection comprehensiveness, and 75.3% accuracy. In both datasets, accuracy was higher for string variables (e.g., country, study design, drug names, and outcome definitions) compared with numeric variables. In the third external validation dataset, GPT-4o showed a lower performance with a mean accuracy of 84.4% compared with the previous study. However, by adjusting our DE method, while maintaining the same prompting technique, we achieved a mean accuracy of 96.3%, which was comparable to the previous manual extraction study. Our system shows potential for assisting the DE of string variables alongside a human reviewer. However, it cannot yet replace humans for numeric DE. Further evaluation across diverse review contexts is needed to establish broader applicability
The development and evaluation of prompts for a large language model to screen titles and abstracts in a living systematic review
No full textThis project aims to evaluate the effectiveness of large language models (LLM) in performing title and abstract screening compared to human reviewers. The team will work towards crafting prompts that will provide the LLM with criteria to accurately make an exclusion judgment. The team will examine how well the LLM is able to identify relevant records for inclusion, and compare the LLM decisions to the human reviewers’ screening decisions. Improving automated screening accuracy through enhanced LLM prompts will allow researchers to rely on automated screening, therefore reducing the human workload required when conducting living systematic reviews
Cognitive bias modification for social anxiety: protocol for a living systematic review of human studies and meta-analysis
No full textBackground:
Social anxiety is a heightened fear and discomfort in social situations.
Cases of elevated distress and impaired functioning can lead to a
clinical diagnosis of social anxiety disorder. Altering cognitive biases
associated with social anxiety has been suggested as potentially
beneficial; however, little is known about the comparative
effectiveness of such interventions. The aim of this living systematic
review is to examine the efficacy of cognitive bias modification for reducing social anxiety.
Methods
We will search multiple electronic databases for randomised
controlled trials evaluating the efficacy of cognitive bias modification
for people diagnosed with social anxiety and people exposed to a
social stressor. The primary outcome will be change in social anxiety
related symptoms; secondary outcomes will be changes in social
functioning and quality of life and adverse events. Study selection,
data extraction and risk of bias assessment will be done by at least
two reviewers using pre-defined tools. We will synthesise data from
people with social anxiety diagnosis and those subjected to a
simulated social stressor separately using random effects metaanalyses.
Heterogeneity will be evaluated by investigating
characteristics of included studies and we will conduct a network
meta-analysis in order to compare the efficacy of subtypes of
cognitive bias modification for social anxiety disorder. We will
appraise the strength of the evidence for each outcome by reviewing
the overall association, internal and external validity, and reporting
biases. Where data allows, we will triangulate the evidence from both
sources with a multidisciplinary group of experts. We will also
descriptively report factors reported to mediate cognitive bias
modification, The review will begin in living mode and the database
search will be rerun every three months to identify potential new
evidence. We will co-produce this review with members of a global
lived experience advisory board
Automating the data extraction process for systematic reviews using GPT-4o and o3
No full textLarge language models have shown promise for automating data extraction (DE) in systematic reviews (SRs), but most existing approaches require manual interaction. We developed an open-source system using GPT-4o to automatically extract data with no human intervention during the extraction process. We developed the system on a dataset of 290 randomized controlled trials (RCTs) from a published SR about cognitive behavioral therapy for insomnia. We evaluated the system on two other datasets: 5 RCTs from an updated search for the same review and 10 RCTs used in a separate published study that had also evaluated automated DE. We developed the best approach across all variables in the development dataset using GPT-4o. The performance in the updated-search dataset using o3 was 74.9% sensitivity, 76.7% specificity, 75.7 precision, 93.5% variable detection comprehensiveness, and 75.3% accuracy. In both datasets, accuracy was higher for string variables (e.g., country, study design, drug names, and outcome definitions) compared with numeric variables. In the third external validation dataset, GPT-4o showed a lower performance with a mean accuracy of 84.4% compared with the previous study. However, by adjusting our DE method, while maintaining the same prompting technique, we achieved a mean accuracy of 96.3%, which was comparable to the previous manual extraction study. Our system shows potential for assisting the DE of string variables alongside a human reviewer. However, it cannot yet replace humans for numeric DE. Further evaluation across diverse review contexts is needed to establish broader applicability
