Neurotechnology is a rapidly emerging field with vast potential within healthcare, but also has inherent concerns. There is, therefore, a need to ensure the responsible and ethical development and regulation of these technologies. This horizon scan aimed to provide an overview of neurotechnologies in development and those approved by the FDA as of June 2024 for a range of conditions relating to mental health, healthy ageing, and physical disability. Searches of clinical trials, conferences, journals, and news were performed in March 2024. Relevant technologies were identified through a process of screening, data extraction and synthesis. A total of 81 unique neurotechnologies were identified, with 23 relating to mental health, 31 to healthy ageing, and 42 to physical disability. A total of 79% percent did not yet have FDA approval and 77.4% were at earlier stages of development (pilot/feasibility studies), with 22.6% at pivotal or post-market stages. Digital elements were common features of the technologies, including software, apps, and connectivity to other devices; however, there were only three technologies with an identifiable AI component. A complex regulatory landscape and unique ethical and safety concerns associated with neurotechnology could pose challenges to innovators, though the emerging nature of the field also provides an opportunity to pre-emptively address potential issues.

Background: Adaptive designs are increasingly being used in clinical trials within diverse clinical areas. They can offer advantages over traditional non-adaptive approaches, including improved efficiency and patient benefit. The level of improvement observed in practice depends to a large degree on conducting interim analyses (at which adaptations can be made to the trial based on collected data) rapidly and to a high standard. Methods: The ROBust INterims for adaptive designs (ROBIN) project aimed to identify best practice for conducting high-quality and rapid interim analyses. This was done through evidence synthesis of published work, qualitative research with trial stakeholders working at public sector clinical trials units, engagement with patients and the public, and a meeting of trial stakeholders to discuss findings and agree recommendations. Results: This paper provides recommendations for teams that conduct adaptive trials about how to ensure interim analyses are done rapidly and to a high standard. We break down recommendations by stage of the trial. We also identify a lack of methodology on how best to involve patients in adaptive trials and related decision-making. A limitation of our recommendations is that the research was mostly focused on UK academic settings, although we believe much of the recommendations are relevant in other countries and to industry-sponsored trials. Conclusions: When following the recommendations outlined in this paper, the process of planning and executing interim analyses will be smoother; in turn, this will lead to more benefits from using adaptive designs.

Clinical trials often enrol populations that differ from those who ultimately receive the interventions, raising concerns about external validity and health equity. Trial registries could provide an early opportunity to assess representativeness, but it is unclear whether registry data contain sufficient information to enable such assessments. This study evaluated the feasibility of using registry data to assess representativeness in Phase II and III pharmacological randomised controlled trials. A search of ClinicalTrials.gov (December 2024 to January 2025) identified trials with results posted after 1 January 2023 across cardiovascular disease (CVD) excluding stroke, diabetes mellitus, and selected mental health disorders. Of 1,328 records screened, 98 trials met inclusion criteria (51 Phase III, 47 Phase II). Reporting completeness was variable, particularly in Phase II studies. CVD and diabetes trials predominantly included middle-aged to older adults, while mental health trials recruited mainly individuals aged 36 to 50 years. Across CVD and mental health trials, participants were largely male. Reporting of BMI, contraception, and comorbidity criteria was inconsistent, though available data suggested these factors influenced sample composition. Fewer than 10% of trials reported equity-relevant characteristics beyond age and sex, and none addressed intersectionality. Assessing equity using registry data is feasible but constrained by incomplete and inconsistent reporting.

Background: Adaptive designs (ADs) are increasingly used, whereby outcome data accumulated during the trial may inform a trial’s course in accordance with pre-specified rules at interim analyses. The benefits however will only be realised if the approaches to conducting interim analyses are optimised. This study aims to identify existing literature highlighting best practice (and existing evidence gaps) with regard to conducting high-quality, timely interim analyses. Methods: Medline and Embase databases were searched for studies published between 2005 and July 2025 to identify best practice models and lessons learned for interim analyses in the delivery of adaptive trials. Specifically, we considered papers that discussed adaptive trial methodological approaches (Phases II–IV) in any clinical population and condition. A narrative synthesis focused on the following outcomes was conducted: design considerations, project/trial management, data management, statistical processes, trial committee processes, the implementation of interim decisions and patient and public involvement and engagement (PPIE). Results: We screened 6720 articles from databases and citation chaining and assessed 329 articles at full text. One hundred and one articles representing 92 unique studies were deemed eligible for inclusion. Key issues included the following: (a) Detailed planning of interim analyses set out in the protocol, and all patient information sheets for possible changes prepared in advance; (b) Effective communication and collaborative decision-making processes across stakeholders, effective staff training, and prompt site query resolutions; (c) Use of electronic data capture with automated data flow processes with integrated query processes to improve data quality; and (d) Use of secure databases with data transfer procedures to maintain data integrity. Conclusion: The currently available data suggest that although there is a considerable volume of evidence with regards to the conduct of adaptive trials and good trial management, the existing literature base offers limited specific guidance for interim analyses. There are significant gaps in the literature regarding best practice guidance related to PPIE and statistical considerations in adaptive trials. While there are examples of innovative methods speeding up the collation and analysis of data used for interim analyses, we advocate more robust research exploring the operational opportunities and challenges with regards to undertaking adaptive trials.