The use of androgenic anabolic steroids (AASs) among recreational athletes is steadily increasing. However, knowledge regarding the potentially harmful effects of AAS primarily originates from case reports and small observational studies. This large-scale study aims to investigate the impact of AAS use on vascular plaque formation, preclinical coronary disease, cardiac function, circulating cardiovascular risk markers, quality of life (QoL) and mental health in a broad population of illicit AAS users.

A nationwide cross-sectional cohort study including a diverse population of men and women aged ≥18 years, with current or previous illicit AAS use for at least 3 months. Conducted at Odense University Hospital, Denmark, the study comprises two parts. In part A (the pilot study), 120 recreational athletes with an AAS history will be compared with a sex-matched and age-matched control population of 60 recreational athletes with no previous AAS use. Cardiovascular outcomes include examination of non-calcified coronary plaque volume and calcium score using coronary CT angiography, myocardial structure and function via echocardiography, and assessing carotid and femoral artery plaques using ultrasonography. Retinal microvascular status is evaluated through fundus photography. Cardiovascular risk markers are measured in blood. Mental health outcomes include health-related QoL, interpersonal difficulties, body image concerns, aggression dimensions, anxiety symptoms, depressive severity and cognitive function assessed through validated questionnaires. The findings of our comprehensive study will be used to compose a less intensive investigatory cohort study of cardiovascular and mental health (part B) involving a larger group of recreational athletes with a history of illicit AAS use.

The study received approval from the Regional Committee on Health Research Ethics for Southern Denmark (S-20210078) and the Danish Data Protection Agency (21/28259). All participants will provide signed informed consent. Research outcomes will be disseminated through peer-reviewed journals and scientific conferences.

NCT05178537.

To (1) pilot a study of behavioural characterisation based on risk and time preferences in clinically well-characterised individuals, (2) assess the distribution of preferences in this population and (3) explore differences in preferences between individuals with ‘lifestyle-related’ (LS) and ‘non-lifestyle-related’ (NLS) cardiovascular diseases.

Cross-sectional study with an economic online experiment to collect risk and time preferences, a detailed clinical characterisation and a sociodemographic and lifestyle survey. A definition of LS and NLS groups was developed.

Specialist outpatient clinics of the clinic for cardiology and pneumology of the University Hospital Düsseldorf and patients from a cardiology practice in Düsseldorf.

A total of 74 individuals with cardiovascular diseases.

Risk and time preferences.

The implementation of the study process, including participant recruitment and data collection, ran smoothly. The medical checklist, the survey and the time preference instrument were well received. However, the conceptual understanding of the risk preference instrument resulted in inconsistent choices for many participants (47%). The remaining individuals were more risk averse (27%) than risk seeking (16%) and risk neutral (10%). Individuals in our sample were also more impatient (49%) than patient (42%). The participant classification showed that 65% belonged to the LS group, 19% to the NLS group and 16% could not be assigned (unclear allocation to lifestyle (ULS) group). Excluding the ULS group, we show that individuals in the LS group were more risk seeking, and unexpectedly, more patient than those in the NLS group.

The process of the pilot study and its results can be used as a basis for the design of the main study. The differences in risk and time preferences between the LS and NLS groups provide us with a novel hypothesis for unhealthy behaviours: individuals never give up a bad habit, they simply postpone the latter, which can be tested alongside other additional research questions.

The need for public research funding to be more accountable and demonstrate impact beyond typical academic outputs is increasing. This is particularly challenging and the science behind this form of research is in its infancy when applied to collaborative research funding such as that provided by the Australian National Health and Medical Research Council to the Centre for Research Excellence in Digestive Health (CRE-DH).

In this paper, we describe the protocol for applying the Framework to Assess the Impact from Translational health research to the CRE-DH. The study design involves a five-stage sequential mixed-method approach. In phase I, we developed an impact programme logic model to map the pathway to impact and establish key domains of benefit such as knowledge advancement, capacity building, clinical implementation, policy and legislation, community and economic impacts. In phase 2, we have identified and selected appropriate, measurable and timely impact indicators for each of these domains and established a data plan to capture the necessary data. Phase 3 will develop a model for cost–consequence analysis and identification of relevant data for microcosting and valuation of consequences. In phase 4, we will determine selected case studies to include in the narrative whereas phase 5 involves collation, data analysis and completion of the reporting of impact.

We expect this impact evaluation to comprehensively describe the contribution of the CRE-DH for intentional activity over the CRE-DH lifespan and beyond to improve outcomes for people suffering with chronic and debilitating digestive disorders.

This impact evaluation study has been registered with the Hunter New England Human Research Ethics Committee as project 2024/PID00336 and ethics application 2024/ETH00290. Results of this study will be disseminated via medical conferences, peer-reviewed publications, policy submissions, direct communication with relevant stakeholders, media and social media channels such as X (formely Twitter).

Subjects with schizophrenia have a 2–3 fold higher mortality rate than the general population and a reduced life expectancy of 10–20 years. Approximately one-third of this excess mortality has been attributed to obesity-related type 2 diabetes (T2D) and to cardiovascular disease. Glucagon-like peptide-1 (GLP-1) analogues increase satiety and delay gastric emptying, thereby reducing food intake and weight. GLP-1 analogues also exert beneficial effects on cardiovascular outcomes in high-risk patients with T2D.

Our aim is to investigate whether 30 weeks add-on treatment with the GLP-1 analogue semaglutide can reduce HbA1c sufficiently to reverse pre-diabetes and the metabolic syndrome in overweight schizophrenic patients.

We will perform a 30 week, two-armed, multicentre, superiority, double-blinded, randomised trial investigating the effect of weekly injections of semaglutide versus placebo in mental health facilities in Region of Southern Denmark and Region of Zealand, Denmark. In total, 154 adults with schizophrenia spectrum disease, aged 18–60 years treated with second generation antipsychotic treatment, HbA1c 39–47 mmol/mol and body mass index >27 kg/m² will be randomised to injections of 1.0 mg semaglutide or placebo. The primary outcome is changes in HbA1c. Secondary outcomes encompass metabolic measures, psychotic symptoms and quality of life. Exploratory outcomes encompass insulin sensitivity, cardiovascular risk profile, medication adherence, general well-being and physical activity.

This study will be carried out in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. This research has obtained approval from both the Danish Medicines Agency and The Regional Committees on Health Research Ethics for Southern Denmark.

NCT05193578 European Clinical Trials Database Number (EudraCT) 2020-004374-22, Regional Ethical Committee number S-20200182.

Purrble, a socially assistive robot, was codesigned with children to support in situ emotion regulation. Preliminary evidence has found that LGBTQ+ youth are receptive to Purrble and find it to be an acceptable intervention to assist with emotion dysregulation and their experiences of self-harm. The present study is designed to evaluate the impact of access to Purrble among LGBTQ+ youth who have self-harmful thoughts, when compared with waitlist controls.

The study is a single-blind, randomised control trial comparing access to the Purrble robot with waitlist control. A total of 168 LGBTQ+ youth aged 16–25 years with current self-harmful ideation will be recruited, all based within the UK. The primary outcome is emotion dysregulation (Difficulties with Emotion Regulation Scale-8) measured weekly across a 13-week period, including three pre-deployment timepoints. Secondary outcomes include self-harm (Self-Harm Questionnaire), anxiety (Generalised Anxiety Disorder-7) and depression (Patient Health Questionnaire-9). We will conduct analyses using linear mixed models to assess primary and secondary hypotheses. Intervention participants will have unlimited access to Purrble over the deployment period, which can be used as much or as little as they like. After all assessments, control participants will receive their Purrble, with all participants keeping the robot after the end of the study. After the study has ended, a subset of participants will be invited to participate in semistructured interviews to explore engagement and appropriation of Purrble, considering the young people’s own views of Purrble as an intervention device.

Ethical approval was received from King’s College London (RESCM-22/23-34570). Findings will be disseminated in peer review open access journals and at academic conferences.

NCT06025942.

Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs.

This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs.

5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer).

Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases.

The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.

Digital health is now routinely being applied in clinical care, and with a variety of clinician-facing systems available, healthcare organisations are increasingly required to make decisions about technology implementation and evaluation. However, few studies have examined how digital health research is prioritised, particularly research focused on clinician-facing decision support systems. This study aimed to identify criteria for prioritising digital health research, examine how these differ from criteria for prioritising traditional health research and determine priority decision support use cases for a collaborative implementation research programme.

Drawing on an interpretive listening model for priority setting and a stakeholder-driven approach, our prioritisation process involved stakeholder identification, eliciting decision support use case priorities from stakeholders, generating initial use case priorities and finalising preferred use cases based on consultations. In this qualitative study, online focus group session(s) were held with stakeholders, audiorecorded, transcribed and analysed thematically.

Fifteen participants attended the online priority setting sessions. Criteria for prioritising digital health research fell into three themes, namely: public health benefit, health system-level factors and research process and feasibility. We identified criteria unique to digital health research as the availability of suitable governance frameworks, candidate technology’s alignment with other technologies in use,and the possibility of data-driven insights from health technology data. The final selected use cases were remote monitoring of patients with pulmonary conditions, sepsis detection and automated breast screening.

The criteria for determining digital health research priority areas are more nuanced than that of traditional health condition focused research and can neither be viewed solely through a clinical lens nor technological lens. As digital health research relies heavily on health technology implementation, digital health prioritisation criteria comprised enablers of successful technology implementation. Our prioritisation process could be applied to other settings and collaborative projects where research institutions partner with healthcare delivery organisations.