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Introduction University students are one of the most vulnerable populations for anxiety disorders worldwide. In Northern Ireland, anxiety disorders appear to be more common among the university student population due to the population demographics across the region. Despite the need, these students show less inclination to access the widely available on-campus well-being services and other external professional services. Digital cognitive–behavioural therapy (CBT) aims to bridge this gap between the need for psychological help and access to it. However, challenges such as limited reach, low adoption, implementation barriers and poor long-term maintenance are mainstay issues resulting in reduced uptake of digital CBT. As a result, the potential impact of digital CBT is currently restricted. The proposed intervention ‘Cerina’ is a scalable CBT-based mobile app with an interactive user interface that can be implemented in university settings if found to be feasible and effective.Methods and analysis The study is a single-blind pilot feasibility randomised controlled trial aiming to test the feasibility and preliminary effects of Cerina in reducing Generalised Anxiety Disorder (GAD) symptoms. Participants are 90 Ulster University students aged 18 and above with self-reported GAD symptoms. They will be allocated to two conditions: treatment (ie, access to Cerina for 6 weeks) and a wait-list control group (ie, optional on-campus well-being services for 6 weeks). Participants in the wait-list will access Cerina 6 weeks after their randomisation and participants in both conditions will be assessed at baseline, at 3 (mid-assessment) and 6 weeks (postassessment). The primary outcome is the feasibility of Cerina (ie, adherence to the intervention, its usability and the potential to deliver a full trial in the future). The secondary outcomes include generalised anxiety, depression, worry and quality of life. Additionally, participants in both conditions will be invited to semistructured interviews for process evaluation.Ethics and dissemination Ethical approval for the study has been granted by the Ulster University Research Ethics Committee (ID: FCPSY-22-084). The results of the study will be disseminated through publications in scientific articles and presentations at relevant conferences and/or public events.Trial registration number NCT06146530.

Objective The aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed to identify challenges in the process of diagnosing CVD, to identify discrepancies between patient and clinician experiences of CVD diagnosis, and to identify the requirements of future health technology solutions intended to improve CVD diagnosis.Design Semistructured focus groups and one-to-one interviews to generate qualitative data that were subjected to thematic analysis.Participants UK-based individuals (N=32) with lived experience of diagnosis of CVD (n=23) and clinicians with experience in diagnosing CVD (n=9).Results We identified four key themes related to delayed or inaccurate diagnosis of CVD: symptom interpretation, patient characteristics, patient–clinician interactions and systemic challenges. Subthemes from each are discussed in depth. Challenges related to time and communication were greatest for both stakeholder groups; however, there were differences in other areas, for example, patient experiences highlighted difficulties with the psychological aspects of diagnosis and interpreting ambiguous symptoms, while clinicians emphasised the role of individual patient differences and the lack of rapport in contributing to delays or inaccurate diagnosis.Conclusions Our findings highlight key considerations when developing digital technologies that seek to improve the efficiency and accuracy of diagnosis of CVD.

The rising awareness of nutritional values has led to an increase in the popularity of meat-based diets. Hence, to ensure public health, industries and consumers are focusing more on the quality and freshness of this food. Authentic meat quality assessment methods can indeed be exorbitant and catastrophic. Furthermore, it is subjective and reliant on the knowledge of specialists. Fully automated computer-aided diagnosis systems are required to eradicate the variability among experts. However, evaluating the quality of meat automatically is challenging. Deep convolutional neural networks have shown a tremendous improvement in meat quality assessment. This research intends to utilize an ensemble framework of shallow convolutional neural networks for assessing the quality and freshness of the meat. Two compact CNN architectures (ConvNet-18 and ConvNet-24) are developed, and the efficacy of the models are evaluated using two publicly available databases. Experimental findings reveal that the ConvNet-18 outperforms other state-of-the models in classifying fresh and spoiled meat with an overall accuracy of 99.4%, whereas ConvNet-24 shows a better outcome in categorizing the meat based on its freshness. This model yields an accuracy of 96.6%, which is much better compared with standard models. Furthermore, the suggested models effectively detect the quality and freshness of the meat with less complexity than the existing state-of-the art techniques.