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In most European countries, balneotherapy and spa therapy are widely prescribed by physicians and preferred by European citizens for the treatment of musculoskeletal problems including chronic low back pain (LBP). We aimed to review and evaluate the recent evidence on the effectiveness of balneotherapy and spa therapy for patients with LBP. We comprehensively searched data bases for randomized controlled trials (RCTs) published in English between July 2005 and December 2013. We identified all trials testing balneotherapy or spa therapy for LBP that reported that the sequence of allocation was randomized. We finally included total of eight RCTs: two on balneotherapy and six on spa therapy. All reviewed trials reported that balneotherapy was superior in long term to tap water therapy in relieving pain and improving function and that spa therapy combining balneotherapy with mud pack therapy and/or exercise therapy, physiotherapy, and/or education was effective in the management of low back pain and superior or equally effective to the control treatments in short and long terms. We used Jadad scale to grade the methodological quality. Only three out of total eight had a score of above 3 indicating the good quality. The data from the RCTs indicates that overall evidence on effectiveness of balneotherapy and spa therapy in LBP is encouraging and reflects the consistency of previous evidence. However, the overall quality of trials is generally low. Better quality RCTs (well designed, conducted, and reported) are needed testing short- and long-term effects for relieving chronic back pain and proving broader beneficial effects.

In the era of expanded access to effective antiretroviral therapy (ART), the life expectancy of the estimated 1.2 million people with HIV (PWH) in the United States has significantly increased. There is a timely need to develop and evaluate interventions for older PWH to improve their health and functioning. The primary objective of the present work was to describe the pilot trial methodology that aimed to evaluate the feasibility and acceptability of a transdiagnostic cognitive behavioral therapy (CBT) intervention for HIV and Symptom Management - \"CHAMP\" designed to promote healthy aging by way of decreasing psychological distress, health risk behaviors, and inflammation among older PWH. Ultimately, these data will be used to refine the intervention and study methods, and inform a future efficacy trial.

Praise for the Second Edition: \"...a grand feast for biostatisticians. It stands ready to satisfy the appetite of any pharmaceutical scientist with a respectable statistical appetite.\" —Journal of Clinical Research Best Practices The Third Edition of Design and Analysis of Clinical Trials provides complete, comprehensive, and expanded coverage of recent health treatments and interventions. Featuring a unified presentation, the book provides a well-balanced summary of current regulatory requirements and recently developed statistical methods as well as an overview of the various designs and analyses that are utilized at different stages of clinical research and development. Additional features of this Third Edition include: • New chapters on biomarker development and target clinical trials, adaptive design, trials for evaluating diagnostic devices, statistical methods for translational medicine, and traditional Chinese medicine • A balanced overview of current and emerging clinical issues as well as newly developed statistical methodologies • Practical examples of clinical trials that demonstrate everyday applicability, with illustrations and examples to explain key concepts • New sections on bridging studies and global trials, QT studies, multinational trials, comparative effectiveness trials, and the analysis of QT/QTc prolongation • A complete and balanced presentation of clinical and scientific issues, statistical concepts, and methodologies for bridging clinical and statistical disciplines • An update of each chapter that reflects changes in regulatory requirements for the drug review and approval process and recent developments in statistical design and methodology for clinical research and development Design and Analysis of Clinical Trials, Third Edition continues to be an ideal clinical research reference for academic, pharmaceutical, medical, and regulatory scientists/researchers, statisticians, and graduate-level students.

To propose a tool to assist trialists in making design decisions that are consistent with their trial's stated purpose. Randomized trials have been broadly categorized as either having a pragmatic or explanatory attitude. Pragmatic trials seek to answer the question, “Does this intervention work under usual conditions?,” whereas explanatory trials are focused on the question, “Can this intervention work under ideal conditions?” Design decisions make a trial more (or less) pragmatic or explanatory, but no tool currently exists to help researchers make the best decisions possible in accordance with their trial's primary goal. During the course of two international meetings, participants with experience in clinical care, research commissioning, health care financing, trial methodology, and reporting defined and refined aspects of trial design that distinguish pragmatic attitudes from explanatory. We have developed a tool (called PRECIS) with 10 key domains and which identifies criteria to help researchers determine how pragmatic or explanatory their trial is. The assessment is summarized graphically. We believe that PRECIS is a useful first step toward a tool that can help trialists to ensure that their design decisions are consistent with the stated purpose of the trial.

Background: Interventions to change behaviour have substantial potential to impact positively on individual and overall public health. Despite an increasing focus on health behaviour change intervention research, interventions do not always have the desired effect on outcomes, while others have diluted effects once implemented into real-life settings. There is little investment into understanding how or why such interventions work or do not work. Methodological inadequacies of trials of behavioural interventions have been previously suggested as a barrier to the quality and advancement of behavioural research, with intervention fidelity acknowledged as a key area for improvement. However, there is much ambiguity regarding the terminology and conceptualisation of intervention fidelity and a lack of practical guidance regarding how to address it sufficiently, particularly within trials of complex behavioural interventions. Objectives: This article outlines specific issues concerning intervention fidelity within trials of health behaviour change interventions and suggests practical considerations and specific recommendations for researchers, with examples from the literature presented. Conclusions: Recommendations pertain to (1) clarifying how fidelity is defined and conceptualised, (2) considering fidelity beyond intervention delivery, (3) considering strategies to both enhance and assess fidelity, (4) making use of existing frameworks and guidance, (5) considering the quality and comprehensiveness of fidelity assessment strategies, (6) considering the balance between fidelity and adaptation and (7) reporting the use of fidelity enhancement and assessment strategies and their results. Suggestions for future research to improve our understanding of, and ability to, address fidelity in behaviour change interventions are also provided.

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Clinical research in neurodevelopmental disorders remains reliant upon clinician and caregiver measures. Limitations of these approaches indicate a need for objective, quantitative, and reliable biomarkers to advance clinical research. Extant research suggests the potential utility of multiple candidate biomarkers; however, effective application of these markers in trials requires additional understanding of replicability, individual differences, and intra-individual stability over time. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a multi-site study designed to investigate a battery of electrophysiological (EEG) and eye-tracking (ET) indices as candidate biomarkers for autism spectrum disorder (ASD). The study complements published biomarker research through: inclusion of large, deeply phenotyped cohorts of children with ASD and typical development; a longitudinal design; a focus on well-evidenced candidate biomarkers harmonized with an independent sample; high levels of clinical, regulatory, technical, and statistical rigor; adoption of a governance structure incorporating diverse expertise in the ASD biomarker discovery and qualification process; prioritization of open science, including creation of a repository containing biomarker, clinical, and genetic data; and use of economical and scalable technologies that are applicable in developmental populations and those with special needs. The ABC-CT approach has yielded encouraging results, with one measure accepted into the FDA's Biomarker Qualification Program to date. Through these advances, the ABC-CT and other biomarker studies in progress hold promise to deliver novel tools to improve clinical trials research in ASD.

People from ethnic minority groups are underserved by randomized trials, and poor representation of these groups reduces generalizability of results. There is no guidance on which ethnicity categories are appropriate for use in trials and thus inconsistency exists. The purpose of this study is to establish, in a large sample of trials, if participant ethnicity is recorded, how it is obtained (categories used), and if its reporting varies from its recording. We reviewed trial documentation for 407 randomized controlled trials published in the UK National Institute of Health Research library from 2016 to 2021. We extracted data on the recording (if it was recorded and the categories used) and reporting (if the categories remained the same as those obtained, or not) of ethnicity for each trial along with demographics. In the analysis we categorized the manner of recording and reporting of ethnicity in the trials according to UK Census ethnicity categories. Ethnicity was recorded in 67.3% (n = 274) of trials. The location in the trial report where ethnicity was recorded was available for 42% (n = 116) of trials. The details on how ethnicity was collected (predefined categories or self-defined) was available for 54/274 (20%) of trials and details on the specifics of the categories recorded was available for 44 (16%) trials. Of the 44, 6 of those did not go on to report on ethnicity in the trial report. Of the remaining 38, only 13 reported ethnicity exactly as it had been recorded. Taken as a whole from the 407 trial reports examined 9.3% (38/407) of trials demonstrated exactly how they both recorded, and reported, ethnicity. Authors made reference to whom results were relevant in terms of ethnicity in 80/407 (19.7%). Ethnicity is underrecorded and underreported in clinical trials. This is a threat to the generalizability of the findings and needs to be improved.

Randomised trials, especially those intended to directly inform clinical practice and policy, should be designed to reflect all those who could benefit from the intervention under test should it prove effective. This does not always happen. The UK National Institute for Health and Care Research (NIHR) INCLUDE project identified many groups in the UK that are under-served by trials, including ethnic minorities. This guidance document presents four key recommendations for designing and running trials that include the ethnic groups needed by the trial. These are (1) ensure eligibility criteria and recruitment pathway do not limit participation in ways you do not intend, (2) ensure your trial materials are developed with inclusion in mind, (3) ensure staff are culturally competent and (4) build trusting partnerships with community organisations that work with ethnic minority groups. Each recommendation comes with best practice advice, public contributor testimonials, examples of the inclusion problem tackled by the recommendation, or strategies to mitigate the problem, as well as a collection of resources to support implementation of the recommendations. We encourage trial teams to follow the recommendations and, where possible, evaluate the strategies they use to implement them. Finally, while our primary audience is those designing, running and reporting trials, we hope funders, grant reviewers and approvals agencies may also find our guidance useful.

Background Despite the problem of inadequate recruitment to randomised trials, there is little evidence to guide researchers on decisions about how people are effectively recruited to take part in trials. The PRioRiTy study aimed to identify and prioritise important unanswered trial recruitment questions for research. The PRioRiTy study - Priority Setting Partnership (PSP) included members of the public approached to take part in a randomised trial or who have represented participants on randomised trial steering committees, health professionals and research staff with experience of recruiting to randomised trials, people who have designed, conducted, analysed or reported on randomised trials and people with experience of randomised trials methodology. Methods This partnership was aided by the James Lind Alliance and involved eight stages: (i) identifying a unique, relevant prioritisation area within trial methodology; (ii) establishing a steering group (iii) identifying and engaging with partners and stakeholders; (iv) formulating an initial list of uncertainties; (v) collating the uncertainties into research questions; (vi) confirming that the questions for research are a current recruitment challenge; (vii) shortlisting questions and (viii) final prioritisation through a face-to-face workshop. Results A total of 790 survey respondents yielded 1693 open-text answers to 6 questions, from which 1880 potential questions for research were identified. After merging duplicates, the number of questions was reduced to 496. Questions were combined further, and those that were submitted by fewer than 15 people and/or fewer than 6 of the 7 stakeholder groups were excluded from the next round of prioritisation resulting in 31 unique questions for research. All 31 questions were confirmed as being unanswered after checking relevant, up-to-date research evidence. The 10 highest priority questions were ranked at a face-to-face workshop. The number 1 ranked question was “How can randomised trials become part of routine care and best utilise current clinical care pathways?” The top 10 research questions can be viewed at www.priorityresearch.ie . Conclusion The prioritised questions call for a collective focus on normalising trials as part of clinical care, enhancing communication, addressing barriers, enablers and motivators around participation and exploring greater public involvement in the research process.