Background Healthcare contributes substantially to global greenhouse gas emissions; however, the environmental impact of clinical research activities remains poorly understood. Quantifying emissions is a necessary first step toward reducing the carbon footprint of research. Objective To map and synthesise literature measuring carbon emissions associated with clinical research activities, with a focus on research domains assessed, tools and methods used and units of measurement reported. Methods A scoping review was conducted following the Arksey and O’Malley methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidance. PubMed, Web of Science, CINAHL, Scopus, EconBiz, GreenFile and ProQuest were searched from database inception to June 2026. Eligible studies reported measurement of carbon emissions related to clinical research activities. Data were charted and synthesised narratively. Results Twenty-five studies met the inclusion criteria, most published between 2019 and 2025 and primarily from Europe and the UK. Studies used a wide range of tools and resources, including life cycle assessment databases, online calculators, international standards, government emission factor datasets and healthcare-specific sustainability frameworks. A clear trade-off emerged between methodological rigour and accessibility: comprehensive life cycle assessment tools required expertise and licensing, while simpler calculators enabled rapid but less precise estimates. Carbon dioxide equivalent and global warming potential over a 100-year time horizon were the dominant reporting metrics, although variation in functional units limited comparability across studies. Conclusions The methodological landscape for measuring emissions in clinical research is fragmented and lacks standardisation. Development of consensus guidance and reporting standards is needed to support consistent measurement and reduction of research-related emissions.

Background Clinical research is important for advancing medical knowledge, improving patient care, evaluating interventions, and developing new therapies. However, as with many activities, clinical research generates carbon emissions. The impact of rising emissions on the climate means that we need to examine factors that contribute to emission generation, including how these emissions are measured, as well identify mitigation strategies to reduce them. Emissions occur for various reasons, including travel for staff and research participants, the generation and disposal of research equipment, the collection and processing of laboratory samples, and data management. The aim of this review is to better understand how research activity emissions are measured, including which measurement methods and tools are identified, described and categorised.. Methods The review will include searches in seven electronic databases, including health (PubMed, Web of Science, CINAHL and Scopus) and non-health databases (EconBiz, GreenFile, and ProQuest). Primary research studies, as well as peer-reviewed grey literature sources, that may include dissertations and theses, reports, guidelines, case studies, and frameworks relevant to the aim of the review will be eligible for inclusion. Secondary research sources (i.e. reviews; systematic, scoping etc.), as well as non-peer reviewed grey literature will be excluded. Articles written entirely not in English will be excluded as the team do not have the resources to facilitate translation. Screening of titles and abstracts, and full text screening of included articles will be carried out independently by two researchers. A narrative synthesis approach will be taken to analyse the extracted data. Quality appraisal of included studies will not be carried out. Discussion The findings will inform better understanding of methods for measurement and quantification of emissions from reported research activities. Research domains assessed and units of measurement described. These findings can benefit a range of stakeholders, particularly those working within the clinical research environment.

Background Clinical research is important for advancing medical knowledge, improving patient care, evaluating interventions, and developing new therapies. However, as with many activities, clinical research generates carbon emissions. The impact of rising emissions on the climate means that we need to examine factors that contribute to emission generation, including how these emissions are measured, as well identify mitigation strategies to reduce them. Emissions occur for various reasons, including travel for staff and research participants, the generation and disposal of research equipment, the collection and processing of laboratory samples, and data management. The aim of this review is to better understand how research activity emissions are measured, including which measurement methods and tools are identified, described and categorised.. Methods The review will include searches in seven electronic databases, including health (PubMed, Web of Science, CINAHL and Scopus) and non-health databases (EconBiz, GreenFile, and ProQuest). Primary research studies, as well as peer-reviewed grey literature sources, that may include dissertations and theses, reports, guidelines, case studies, and frameworks relevant to the aim of the review will be eligible for inclusion. Secondary research sources (i.e. reviews; systematic, scoping etc.), as well as non-peer reviewed grey literature will be excluded. Articles written entirely not in English will be excluded as the team do not have the resources to facilitate translation. Screening of titles and abstracts, and full text screening of included articles will be carried out independently by two researchers. A narrative synthesis approach will be taken to analyse the extracted data. Quality appraisal of included studies will not be carried out. Discussion The findings will inform better understanding of methods for measurement and quantification of emissions from reported research activities. Research domains assessed and units of measurement described. These findings can benefit a range of stakeholders, particularly those working within the clinical research environment.

Background:Digital health technology enables collection of continuous physiological and behavioral data from participants in clinical trials. This supports hybrid trial designs, potentially reducing clinic visits and participant burden for patient monitoring. Interstitial lung disease (ILD) is characterized by an unpredictable clinical course, creating a need for new treatments and more sensitive approaches to assessing treatment effectiveness, disease progression, and clinically meaningful trial end points. Objective:This study aimed to explore the experiences of individuals with ILD using digital tools in a clinical study to inform digital health-enabled clinical research. Methods:This qualitative study was conducted within the PRODIGY-ILD (Predicting Outcomes using Digital Technology in Interstitial Lung Disease) cohort, a prospective observational study using wearable devices and electronic patient-reported outcome measures for a planned 3 years of longitudinal monitoring. Participants were recruited from a specialist outpatient ILD clinic. A topic guide was developed iteratively, and individual semistructured interviews were conducted remotely via Zoom (Zoom Video Communications, Inc) and/or telephone, audio-recorded, and transcribed. Data were analyzed using reflexive thematic analysis with NVivo software (Lumivero). Results:Fifteen of the final 20 participants recruited to the PRODIGY-ILD study consented and completed interviews. Four key themes were identified, highlighting how trust, digital literacy, participant-initiated engagement with data, and illness burden shape sustained participation in digital health-enabled clinical research: (1) trust and altruism override data concerns: confidence in researchers' data handling and a desire to contribute enabled data sharing; (2) navigating digital tools: friction and flexibility: digital literacy, usability, and device compatibility varied, but participants were able to use workarounds to maintain engagement; (3) participant-initiated engagement with wearable data: participants moved from passive to active engagement, in many cases integrating devices into daily routines; and (4) life-limiting illness as a constraint on digital trial participation: managing symptoms and severe comorbidities reduces motivation and engagement with study technology. Conclusions:Despite participants' motivations to contribute data to research, engagement was shaped by usability, participant-initiated engagement, and the constraints of living with chronic illness. There is a need for patient-centered design, tailored support, and flexible trial procedures to optimize adherence in digital health-enabled clinical research.

Background Trial participants have repeatedly indicated that they would like to receive the results of the trials they were enrolled in. Limited evidence, however, on the optimal methods for sharing trial results with trial participants appears to exist. This highlights the need to expand the methodological evidence base for this important trial process. To address this, we propose to conduct a Study Within A Trial (SWAT) to evaluate the effectiveness of two different formats for sharing trial results with trial participants. Methods The SWAT will be a two-group parallel randomised trial within the PREterm birth Prevention with Oral Probiotics (PRE POP) trial (the ‘host’ trial). The SWAT intervention will be a pictorial enhanced mailed written summary complemented by an animated audiovisual of the host trial results. The SWAT control will be a standard written summary report of the host trial results. The primary outcome is participant satisfaction with dissemination formats. The secondary outcomes are participant satisfaction with the information provided, participants understanding of the summary results, opinions about receiving the results and preferences for how trial results should be shared with participants in future maternity care trials. Participants of PRE POP will be randomly allocated to the SWAT intervention or control group using computer-generated block randomisation with block sizes of four and six. Outcome data will be collected using online questionnaires, administered one-week post intervention and control distribution. Descriptive statistics will be used to present summary data. Odds Ratios with 95% Confidence Intervals will be used to compare group outcomes. Conclusion The conduct and findings of this SWAT will add to the evidence base on trial processes and may be replicated in future SWATs. The findings will contribute to future systematic reviews designed to evaluate methods for disseminating trial results to trial participants.

Background Clinical research is essential for medical advancement but contributes to carbon emissions through activities such as travel, data management, and laboratory processes. As healthcare systems move towards net-zero targets, there is a growing need to understand public awareness of and attitudes towards environmental sustainability in clinical research. Public and patient involvement (PPI) can enhance the relevance and acceptability of research, however there is limited evidence on how best to co-develop tools to assess public understanding of sustainable clinical research. Methods This study is a two-phase protocol. Phase I involves the co-development of an anonymous online survey with PPI partners through two structured workshops. Survey themes, topics, questions and format will be agreed collaboratively, and the co-development process will be evaluated using qualitative data collected during the workshops. These data will be analysed thematically, using Braun & Clarke's (2006) framework. Phase II involves distribution of the survey to clinical research, patient and public networks using an online platform. Survey data will be analysed using descriptive statistics for quantitative data and inductive content analysis for qualitative responses. Discussion This study will generate a co-developed survey instrument and provide insights into public understanding of sustainable clinical research. Reporting on both the survey findings and the PPI co-development process will inform future research and support the development of sustainable research clinical practices.

Modern medicine relies heavily on diagnostic imaging because of its beneficial role in the healthcare chain. Imaging rates are increasing globally and are expected to continue to rise in the future. In tandem with this phenomenon is the increase in inappropriate imaging that adversely affects the provision of healthcare and increases the risks to patients. To date, effective interventions to reduce inappropriate imaging have shown conflicting results due to various underlying implementation designs and strategies. The aim of this controlled before-after study is to assess the impact of a clinical decision support system (CDSS) on appropriateness rates of X-ray cervical and lumbar spine in the emergency department (ED) compared to the existing practice without one, while also exploring associated outcomes. In this study, a CDSS will be carried out using implementation science principles to enhance evidence uptake with institutional medical board-approved imaging referral guidelines embedded. Recruited ED physicians will be allocated into control and intervention groups. The control group will continue the routine practice of "do nothing" for four months, while the intervention group will have a two-month CDSS intervention after an initial two-month routine practice of "do nothing". The difference between the baseline and post-implementation appropriateness rate of imaging will be compared. This study will also evaluate the impact of CDSS on clinical effectiveness, cost avoidance, radiation doses, and sustainability. Findings from this before-after study will provide a rigorous, pragmatic test of the impact and effect of radiological CDSS as an evidence-based intervention to reduce inappropriate imaging in the ED.

Higher circulating SARS-CoV-2 IgG titers correlate with SARS-CoV-2 ex vivo viral neutralization, but how well this translates to clinical protection in the real-world setting is unclear. In a prospective cohort study, we enrolled 44 SARS-CoV-2 negative, confirmed SARS-CoV-2 close contacts. Receptor-binding domain (RBD) and full-spike IgG and SARS-CoV-2 memory B-cell frequencies were measured at exposure, and participants were serially tested for incident infection over 14 days. Those who developed SARS-CoV-2 infection had significantly lower RBD titers, but not memory B-cell frequencies. An RBD IgG titer >6321 BAU/ml was associated with a reduced SARS-CoV-2 acquisition risk (HR 0.32, 95% CI 0.13–0.81), while an RBD IgG titer >456 BAU/ml was associated with a reduced moderate or severe COVID-19 risk (HR 0.15, 95% CI 0.03–0.81), identifying this threshold as a correlate of protection.

Background Clinical research is important for advancing medical knowledge, improving patient care, evaluating interventions, and developing new therapies. However, as with many activities, clinical research generates carbon emissions. The impact of rising emissions on the climate means that we need to examine factors that contribute to emission generation, as well identify mitigation strategies to reduce them. Emissions occur for various reasons, including travel for staff and research participants, the generation and disposal of research equipment, the collection and processing of laboratory samples, and data management. The aim of this review is to better understand which clinical research activities are reported as generating carbon emissions, how these activities are described, categorised and measured. Methods The review will include searches in seven electronic databases, including health (PubMed, Web of Science, CINAHL and Scopus) and non-health databases (EconBiz, GreenFile, and ProQuest). Primary research studies, as well as peer-reviewed grey literature sources, that may include dissertations and theses, reports, guidelines, case studies, and frameworks relevant to the aim of the review will be eligible for inclusion. Secondary research sources (i.e. reviews; systematic, scoping etc.), as well as non-peer reviewed grey literature will be excluded. Articles that are written entirely not in English will be excluded as the team do not have the resources to facilitate translation. Screening of titles and abstracts, and full text screening of included articles will be carried out independently by two researchers. A narrative synthesis approach will be taken to analyse the extracted data. Quality appraisal of included studies will not be carried out. Discussion The findings will inform better understanding of reported research activities that generate emissions, which activities are more common and how emissions are measured. These findings can benefit a range of stakeholders, particularly those working within the clinical research environment.

Background The Mpox outbreak, caused by Monkeypox virus (MPXV), underscores the need for a serological assay to assess Mpox immunity. Modified Vaccinia Ankara (MVA) vaccine, an attenuated vaccinia virus (VACV), is authorised for Mpox prevention. We aimed to develop a quantitative immunoassay to differentiate infection- and vaccination- induced immunity and explore serological responses to Mpox infection and vaccination. Methods We evaluated an electrochemiluminescence assay targeting IgG to 10 MPXV and 3 VACV antigens in plasma from adults in a cohort study with previous Mpox, MVA-vaccination, or historical controls. Sensitivity and specificity to distinguish i) seropositive versus naive and ii) infection- versus vaccination-induced seropositivity were determined using ROC curves. Antibody kinetics were analysed with generalised additive models. Findings Eight of the thirteen IgG antibodies showed significant titre differences across groups identifying three key antigens: MPXVB6R, MPXVA27L, and VACVB5. A VACVB5 IgG titre of 0.082 IgG normalised units (nu) offered 74% (95% CI: 59-82%) sensitivity and 81% (73-96%) specificity for previous antigen exposure (infection or vaccine). For infection alone, an MPXVB6R IgG titre of 0.075 IgGnu provided 89% (82-98%) sensitivity and 94% (86-100%) specificity. To differentiate infection from vaccination-induced seropositivity, the sum of MPXVA27L IgG and the B6R/VACVB5 ratio provided 89% (80-96%) sensitivity and 80% (74-84%) specificity. VACVB5 IgG titres declined over time, with higher titres post-Mpox than post-vaccination (p < 0.0001). Interpretation This assay demonstrates high sensitivity and specificity in quantifying and differentiating between antibody responses to Mpox infection and vaccination. Post-Mpox antibody responses were higher than post- vaccination, though both waned over time.

INTRODUCTION:Interstitial lung disease (ILD) patients may develop a progressive phenotype usually characterised by progressive pulmonary fibrosis. While this condition is life-limiting, wide variations in its clinical course have made it difficult to predict the rate of disease progression, onset of acute exacerbations and mortality. New approaches are needed to predict the clinical course of ILD, to enable treatment planning, evaluation and clinical trial design. Advances in digital health technologies have facilitated the ability to collect 'real-time' data to monitor diseases. These data, including physiological measures, activity indices and patient-reported outcomes, may be useful as components of new outcome predictors. The objective of this study is to first deploy comprehensive data collection enabling deep profiling of patients with ILD and to use these data to develop better predictors of outcome. Finally, these predictions will be evaluated based on real observed outcomes for individual patients. METHODS AND ANALYSIS:This study is a prospective cohort study with 50 participants. INCLUSION CRITERIA:Age 18 years or older with a diagnosis of ILD and the ability to provide written informed consent. EXCLUSION CRITERIA:Age under 18 years or unwilling to wear a smartwatch for the duration of the study. Participants will be provided with a smartwatch to passively collect biometric data. These data will be combined with clinical history and course, in addition to a set of patient-reported outcome measures. Participants will be followed for 3 years to assess the rate of disease progression, occurrence of acute exacerbations and mortality. Initial data will be used to develop clinical prediction models. These models will be further evaluated for accuracy using regular follow-up data. ETHICS AND DISSEMINATION:This study was approved by the St. Vincent's University Hospital Research Ethics Committee, Dublin, Ireland (reference no: RS23-023). Results will be presented at medical conferences and disseminated via peer-reviewed journals.

Rationale Lymphangioleiomyomatosis (LAM) is a rare, low grade, metastasizing neoplasm, that occurs predominantly in women and results in diffuse cystic lung disease. Serum vascular endothelial growth factor-D (VEGF-D) concentration correlates with disease severity and can help monitor treatment response; however 30-50% of cases have normal levels. The aim of this study was to apply machine learning methods in order to identify a novel composite serum biomarker that predicts progression of disease. Methods An unbiased aptamer-based screen of more than 1000 serum proteins over a 7-log range was performed on serum samples from MILES trial participants. Analysis of placebo arm pulmonary function data identified rapid decliners by stratification of above and below the mean FEV1 decline at 6 months. Protein data at baseline was analysed to identify potential biomarkers of decline. Initial feature comparisons between groups were performed using the Wilcoxon signed-rank test, retaining those of significance (p<0.05) for further analysis. These were processed through logistic regression with cross fold validation and a random forest model for feature reduction. Sequential logistic regression models were then evaluated in 5-fold cross validation by iteratively adding the top 5 aptamers to VEGF-D based on performance. Once top performing aptamers were established the LAM Cell Atlas was utilised to identify gene expression in LAM Core cells. Results MILES placebo arm data of 32 patients was analysed, with the 25 most significantly increased proteins identified by machine learning. VEGF-D levels alone had a low precision (0.252) and low accuracy (0.371) as regards FEV1 decline at 6 months. With the addition of EFNA4 (Ephrin A4), the precision increased to 0.8, with accuracy of 0.867. The combination of VEGF-D, EFNA4, Immunoglobulin Heavy Constant Delta (IGHD), Glial Cell-Derived Neurotrophic Factor (GDNF), Transketolase (TKT) and Paraoxonase 1 (PON-1) resulted in an increase in precision to 0.95 and accuracy to 0.9. On gene query search of single cell transcriptomic analysis of the LAM lung, EFNA4 and GDNF displayed evidence of specific gene expression unique to LAM Core Cells. Conclusions An unbiased machine learning algorithm identified previously unexplored protein candidates which were further validated through analysis of LAM Cell Atlas. Potential roles in LAM pathophysiology include involvement in the mTOR pathway in EFNA-4, angiogenesis in GDNF, and endothelial cell survival in PON-1. Measurement of these markers in other cohorts of LAM is warranted to validate their potential as a novel composite biomarker.

BackgroundClinical trials are fundamental to healthcare, however, they also contribute to anthropogenic climate change. Following previous work to develop and test a method and guidance to calculate the carbon footprint of clinical trials, we have now applied the guidance to 10 further UK and international, academically sponsored clinical trials to continue the identification of hotspots and opportunities for lower carbon trial design.Methods10 collaborating clinical trial units (CTUs) self-identified and a trial was selected from their portfolio to represent a variety of designs, health areas and interventions. Trial activity data was collated by trial teams across 10 modules spanning trial setup through to closure, then multiplied by emission factors provided in the guidance to calculate the carbon footprint. Feedback was collected from trial teams on the process, experience and ease of use of the guidance.ResultsWe footprinted 10 trials: 6 investigational medicinal product trials, 1 nutritional, 1 surgical, 1 health surveillance and one complex intervention trial. Six of these were completed and four ongoing (two in follow-up and two recruiting). The carbon footprint of the 10 trials ranged from 16 to 765 tonnes CO2e. Common hotspots were identified as CTU emissions, trial-specific patient assessments and trial team meetings and travel. Hotspots for specific trial designs were also identified. The time taken to collate activity data and complete carbon calculations ranged from 5 to 60 hours. The draft guidance was updated to include new activities identified from the 10 trials and in response to user feedback.DiscussionThere are opportunities to reduce the impact of trials across all modules, particularly trial-specific meetings and travel, patient assessments and laboratory practice. A trial’s carbon footprint should be considered at the design stage, but work is required to make this common place.

Introduction A clear immune correlate of protection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been defined. We explored antibody, B-cell, and T-cell responses to the third-dose vaccine and relationship to incident SARS-CoV-2 infection. Methods Adults in a prospective cohort provided blood samples at day 0, day 14, and 10 months after the third-dose SARS-CoV-2 vaccine. Participants self-reported incident SARS-CoV-2 infection. Plasma anti-SARS-CoV-2 receptor-binding domain (RBD) and spike-subunit-1 and spike-subunit-2 antibodies were measured. A sub-study assessed SARS-CoV-2-specific plasma and memory B-cell and memory T-cell responses in peripheral blood mononuclear cells by enzyme-linked immunospot. Comparative analysis between participants who developed incident infection and uninfected participants utilised non-parametric t-tests, Kaplan-Meier survival analysis, and Cox proportional hazard ratios. Results Of the 132 participants, 47 (36%) reported incident SARS-CoV-2 infection at a median 16.5 (16.25-21) weeks after the third-dose vaccination. RBD titres and B-cell responses, but not T-cell responses, increased after the third-dose vaccine. Whereas no significant difference in day 14 antibody titres or T-cell responses was observed between participants with and without incident SARS-CoV-2 infection, RBD memory B-cell frequencies were significantly higher in those who did not develop infection [10.0% (4.5%-16.0%) versus 4.9% (1.6%-9.3%), p = 0.01]. RBD titres and memory B-cell frequencies remained significantly higher at 10 months than day 0 levels (p < 0.01). Discussion Robust antibody and B-cell responses persisted at 10 months following the third-dose vaccination. Higher memory B-cell frequencies, rather than antibody titres or T-cell responses, predicted protection from subsequent infection, identifying memory B cells as a correlate of protection.

Background Clinical trials are fundamental to healthcare, however they also contribute to anthropogenic climate change. Following previous work to develop and test a method and guidance to calculate the carbon footprint of clinical trials, we have now applied the guidance to 10 further UK and international, academically-sponsored clinical trials to begin identification of hotspots and opportunities for lower carbon trial design. Methods 10 collaborating Clinical Trial Units (CTUs) self-identified and a trial was selected from their portfolio to represent a variety of designs, health areas and interventions. Trial activity data was collated by trial teams across 10 modules spanning trial set up through to closure, then multiplied by emission factors provided in the guidance to calculate the carbon footprint. User feedback was collected on the process of applying the draft guidance. Results Six completed and four ongoing trials (two in follow up, two recruiting) were footprinted across a variety of interventions (6 IMP trials, one nutritional, one surgical, one health surveillance and one complex intervention trial). The carbon footprint of the 10 trials ranged from 15 to 765 tonnes CO2e. Common hotspots were identified as CTU emissions, trial-specific patient assessments and trial team meetings and travel. Hotspots for specific trial designs were also identified. Time taken to collate activity data and complete carbon calculations ranged from 5–60 hours. The draft guidance was updated to include new activities identified from the 10 trials, and in response to user feedback. Discussion There are opportunities to reduce the impact of trials across all modules, particularly trial specific meetings and travel, patient assessments and laboratory practice. A trial’s carbon footprint should be considered at the design stage, but work is required to make this common place.

Background Defining patterns of symptoms in long COVID is necessary to advance therapies for this heterogeneous condition. Here we aimed to describe clusters of symptoms in individuals with long COVID and explore the impact of the emergence of variants of concern (VOCs) and vaccination on these clusters. Methods In a prospective, multi centre cohort study, individuals with symptoms persisting > 4 weeks from acute COVID-19 were divided into two groups based on timing of acute infection; pre-Alpha VOC, denoted wild type (WT) group and post-Alpha VOC (incorporating alpha and delta dominant periods) denoted VOC group. We used multiple correspondence analysis (MCA) and hierarchical clustering in the WT and VOC groups to identify symptom clusters. We then used logistic regression to explore factors associated with individual symptoms. Results A total of 417 individuals were included in the analysis, 268 in WT and 149 in VOC groups respectively. In both groups MCA identified three similar clusters; a musculoskeletal (MSK) cluster characterised by joint pain and myalgia, a cardiorespiratory cluster and a less symptomatic cluster. Differences in characteristic symptoms were only seen in the cardiorespiratory cluster where a decrease in the frequency of palpitations (10% vs 34% p = 0.008) and an increase in cough (63% vs 17% p < 0.001) in the VOC compared to WT groups was observed. Analysis of the frequency of individual symptoms showed significantly lower frequency of both chest pain (25% vs 39% p = 0.004) and palpitations (12% vs 32% p < 0.001) in the VOC group compared to the WT group. In adjusted analysis being in the VOC group was significantly associated with a lower odds of both chest pain and palpitations, but vaccination was not associated with these symptoms. Conclusion This study suggests changes in long COVID phenotype in individuals infected later in the pandemic, with less palpitations and chest pain reported. Adjusted analyses suggest that these effects are mediated through introduction of variants rather than an effect from vaccination.