Tom Wilkinson

Background: Adherence to chronic obstructive pulmonary disease (COPD) self-management plans can improve health outcomes, yet access to pulmonary rehabilitation remains limited. Digital self-management interventions, such as myCOPD , offer a potential means to extend support across care pathways. However, little is known about how these tools are delivered in routine practice or how delivery influences patient engagement across different clinical settings. Objective: This study explored perceived barriers and facilitators to the delivery of and engagement with myCOPD by examining the perspectives of both patients and health care professionals (HCPs) across 2 National Health Service settings. Methods: This qualitative study was conducted between November 2023 and April 2024 across 2 contrasting clinical pathways: community pulmonary rehabilitation and a hospital discharge service following acute exacerbation of COPD. Semistructured interviews were conducted with patients using myCOPD and HCPs involved in its delivery. Topic guides covered health background, digital technology use, and experiences of engaging with the intervention. A convenience sampling approach, with elements of purposive sampling, ensured representation across settings. Clinical data were obtained from health records with consent, and usage data were provided by my mhealth Ltd. Data were analyzed using abductive thematic analysis informed by the Medical Research Council’s process evaluation framework. Results: Thirty interviews were completed (16 patients and 14 HCPs). Patients described how multimorbidity, perceived digital ability, and socioeconomic context shaped their engagement with myCOPD , operating as “layers of vulnerability” that influenced whether COPD self-management could be prioritized. HCPs highlighted organizational and workflow constraints that shaped how the intervention was introduced and supported. Delivery approaches differed markedly between settings: community teams adopted proactive, relationship-based support that enabled iterative discussion, tailored recommendations, and continuity, whereas hospital teams described passive, onboarding-focused delivery shaped by time pressures, staffing constraints, and discharge priorities. These contrasting delivery models influenced engagement patterns, with proactive support associated with more multifaceted engagement (characterized as routine and sustained) and passive onboarding associated with single-aspect engagement (characterized as short-term and transactional). Participants also identified perceived barriers (eg, usability concerns, limited integration with in-person care) and perceived benefits (eg, improved inhaler technique, increased confidence, lifestyle adjustments). Conclusions: Delivery context and local workflows play a central role in shaping how patients engage with digital self-management tools. Proactive, relational support appears to facilitate deeper and more sustained engagement, whereas passive onboarding may limit the intervention’s potential. Implementation strategies should align digital tools with local capacities, staffing structures, and patient characteristics, attending to layered vulnerabilities such as multimorbidity and digital confidence. Further work is needed to understand how proactive delivery models can be resourced and integrated within routine COPD care.

COVID-19 continues to present ongoing global health challenges driven by diverse immune responses and heterogeneous clinical outcomes. The ACCORD trial evaluated 3 investigational treatments—bemcentinib, tozorakimab, and zilucoplan—in patients hospitalized with COVID-19, each of which has demonstrated clinical efficacy. To better understand their molecular mechanisms, we conducted a mechanistic follow-up study, integrating transcriptomic and clinical data from 65 patients and applying cellular deconvolution, differential expression, coexpression, and pathway enrichment analyses to uncover treatment-specific immune responses. Each therapy induced transcriptional shifts and modulated distinct immune pathways implicated in severe disease. Bemcentinib primarily modulated myeloid cell populations and inflammatory signalling; zilucoplan enhanced B-cell signalling and lymphocyte-associated pathways; and tozorakimab exerted broad immune and cellular responses across immune cell types. Co-expression analysis revealed gene networks associated with clinical improvement, each driven by distinct treatment-specific hub genes, indicating diverse regulatory mechanisms across treatments. Improved outcomes correlated with gene expression shifts in 4 key immunological pathways: B-cell signalling, antiviral defense, innate inflammation, and platelet/coagulation activity. In contrast, nonresponders had persistent dysregulation of 1 or more of these gene signatures. Our findings define molecular signatures of treatment response and failure in COVID-19, providing mechanistic insight into how distinct therapies modulate the immune system. These insights support the need for adaptive precision medicine approaches tailored to individual, evolving immune trajectories. Moreover, the immunological mechanisms targeted by these repurposed immunomodulatory therapies may inform treatment strategies across a broader spectrum of immune-mediated diseases beyond COVID-19.

Over 400 million people have chronic obstructive pulmonary disease (COPD), with exacerbations representing a major health burden. Although the overall incidence of invasive pulmonary aspergillosis (IPA) in patients with COPD with hospitalised exacerbation is only 1 to 4%, certain factors substantially increase this frequency. Risk factors compromising defences against Aspergillus spp in COPD patients include systemic or high-dose inhaled corticosteroids, comorbidities including bronchiectasis, diabetes, and cardiovascular disease, and prolonged courses of antibiotics. An international group of experts met to develop criteria for diagnosing IPA based on existing literature and consensus in non-ventilated COPD patients. The preliminary diagnostic recommendations were further evaluated by additional experts using the Delphi methodology. A hospitalized exacerbation of COPD with two or more of the above clinical risk factors should prompt 1) a CT scan of the chest, 2) sending a respiratory sample (sputum, induced sputum or bronchoscopy sample) for direct microscopy for fungi, high volume fungal culture and preferably Aspergillus PCR, and if a bronchoscopy sample is obtained then also Aspergillus antigen (galactomannan), 3) a serum sample for galactomannan and Aspergillus IgG. The combination of a high-risk COPD patient, with compatible imaging abnormalities and any two positive tests (two samples or different tests on the same respiratory sample) for Aspergillus is sufficient to establish the diagnosis of IPA with enough confidence to initiate antifungal therapy and/or enroll the patient in a clinical or epidemiological study of IPA in COPD. Additional studies are required to augment performance data for most assays in COPD and validate the proposed diagnostic criteria.

Aims.: Long-term adherence to cardiac rehabilitation (CR) remains low despite its proven benefits for secondary prevention. We explore the usability and engagement patterns of CUOREMA, a personalized mobile-health system integrating self-monitoring, wearable data, virtual coaching, and reinforcement learning-enhanced adaptive interventions to support lifestyle change during and after outpatient CR. Methods.: In a six-month two-center observational study (N = 53, Switzerland and France), patients in outpatient CR used the CUOREMA app. Usability was assessed via validated and solution-specific questionnaires. Engagement patterns were identified using k-means clustering on weekly app usage. Clusters were compared by demographics and psychological themes of personal goals. Health-related questionnaires and the 6-minute walk test (6MWT) were collected at baseline and follow-up visits. Results.: Three engagement clusters (dropout, sporadic, consistent) were identified and sup-ported by matching patterns across app components and smartwatch wearing time. Clusters were not associated with demographic factors. Psychological themes of personal goals sug-gested that intrinsic motivation was more common among sustained users, while extrinsic motivation was more frequent among early dropouts. User experience was rated positively. One center showed significant 6MWT improvement, though the study was not powered for clinical outcomes. However, attrition was high: only 19% of participants used the app on more than 100 days, and questionnaire response rates declined from 55% at baseline to 13% at six months, and health-related drop-out can not be ruled out. Conclusion.: Engagement variability driven by motivational rather than demographic fac-tors is a central challenge in digital CR. Tailoring interventions to individual motivational profiles may improve adherence. Graphical

BACKGROUND Adherence to self-management plans leads to improved health outcomes among COPD patients. This study investigated an existing digital health intervention, myCOPD, available via the NHS to support self-management. This is the first study that combines the analysis of patient and professional perspectives regarding the delivery and engagement of a digital self-management intervention for COPD. OBJECTIVE The aim was to develop an understanding of perceived barriers to and facilitators of delivery and engagement of myCOPD. METHODS This study was conducted across two clinical settings: community pulmonary rehabilitation and hospital discharge. Participants completed qualitative semi-structured interviews. A reflexive approach to abductive thematic analysis was adopted using NVivo 14 for initial coding and the development of themes. RESULTS This study suggests that patient characteristics (multimorbidity, perceived digital ability, and socioeconomic status) potentially influence engagement. The study associated healthcare settings with passive and proactive approaches to delivering myCOPD. Healthcare settings and styles of delivery, in turn, have the potential to influence types of engagement among patients via single-aspect and multifaceted engagement. Descriptions of these two types of engagement were associated with two types of benefits (targeted behaviour change and wider lifestyle adjustments). These factors seemingly affect perceptions of barriers to and facilitators of engagement with myCOPD. CONCLUSIONS This study suggests that myCOPD can provide a range of benefits that meet recommendations of the care pathway for COPD patients. The consideration of patient characteristics may improve engagement with, and evaluation of, myCOPD, particularly for those negotiating multimorbidity. The intervention content was consistent at both sites, and the demographic characteristics were ostensibly similar; consequently, the healthcare setting and delivery site are highlighted as important factors for facilitating engagement. Further integration and sustained engagement may develop from considering how and when specific frontline teams are most able to support delivery and engagement and if any additional resources are needed. CLINICALTRIAL N/A

Background: Asthma and COPD are long-term respiratory conditions that require active self-management to improve quality of life and reduce health care burdens. Digital health interventions (DHIs) are increasingly used to support behavior change, symptom monitoring, and medication adherence, offering new opportunities for personalized care and real-time feedback. Understanding patient engagement with digital tools is essential for optimizing intervention design, improving clinical outcomes, and addressing potential inequalities in access and effectiveness. This review is informed by a novel conceptual foundation combining the Analyzing and Measuring Usage and Engagement Data (AMUsED) framework (for the analysis of digital engagement) and layered vulnerabilities (an intersectional approach). Together, these frameworks enable a more nuanced examination of how engagement is shaped by user behavior and structural factors. Objective: This study aims to evaluate how diverse patient groups engage with digital self-management interventions for asthma and COPD by examining the reporting of demographic characteristics, outcome measures, and usage data. The review also explores how these data types are combined in analysis, how authors interpret results, and the extent to which current reporting practices support equitable and meaningful evaluation of digital interventions. Methods: A 2-phase study selection process was applied. First, empirical studies were systematically identified if they reported demographic characteristics, clinical outcome measures, and usage data. Second, reported usage measures were reviewed to identify measures that were meaningful across interventions, defined as numerically comparable measures without subjective user input. Descriptive thematic analysis was conducted to map key concepts across studies, and patient and public involvement sessions were used to contextualize findings and inform interpretation of the results. Results: Twenty-seven studies met the inclusion criteria. Four comparable usage measures were identified, with studies reporting a mean of 2.15 (SD 0.74) usage measures. Thirteen (48.1%) studies reported 2 or more types of outcome measures (disease-specific self-reported, physiological, or other self-reported). Age, sex, and disease severity were reported in all studies, but characteristics linked to health inequalities were underreported; for example, 8 (29.6%) studies reported ethnicity and 2 (7.4%) reported socioeconomic status. Seventeen (62.9%) studies did not combine demographic, outcome, and usage data in analysis. Thematic analysis identified three cross-cutting issues: (1) limited characterization of engagement patterns, (2) dominance of single-trait demographic analysis, and (3) inconsistent conceptualization of health care support. Conclusions: This review is the first to integrate the AMUsED framework with layered vulnerabilities and to map how demographic, outcome, and usage data are reported and combined in digital self-management research. By identifying structural gaps in reporting and analysis, the review provides recommendations for more equitable and analytically rigorous digital health research. Strengthening reporting practices, particularly through richer usage data and intersectional analyses, will support clinicians, developers, and policymakers in tailoring digital self-management tools to diverse patient populations and improving real-world effectiveness.

COPD remains a leading cause of morbidity and mortality, with outcomes stagnating relative to other long-term conditions. Current diagnostic pathways rely on spirometry, which detects airflow obstruction only after irreversible small airway and parenchymal damage has accrued, whereas pathogenic processes begin decades earlier. This review examines how understanding early pathogenic processes could inform alternative approaches to diagnosis and treatment. We highlight the contribution of developmental and environmental exposures, genetic susceptibility and epigenetic modification to disease initiation. We outline how these convergent mechanisms drive structural and functional abnormalities undetectable by conventional diagnostics but measurable with novel techniques. Advanced imaging–parametric response mapping, hyperpolarised gas magnetic resonance imaging and computed tomography-based vascular metrics–can detect emphysema, small airways disease and vascular pruning before spirometric thresholds are reached. Physiological tools including forced oscillation techniques and capnography show promise for early detection in primary care and may be scalable, affordable alternatives to spirometry. Biofluid-based platforms, including exhaled breath analysis, extracellular matrix neo-epitopes and blood-based inflammatory signatures, offer noninvasive phenotyping and risk stratification, though require validation and pathway integration. We argue for a shift from a spirometry-centric model to a multidimensional diagnostic framework integrating imaging, molecular, physiological and biomarker data. Recent longitudinal evidence, including diagnostic schemas combining imaging with symptom burden, indicates that such approaches identify high-risk individuals missed by spirometry alone. Proactive COPD detection in its earliest stages is therefore an essential step to altering disease trajectory and improving patient outcomes, and it is time our community looks beyond spirometry to deliver this.

Asthma is a heterogeneous disease. It is usually characterised by chronic airway inflammation and is defined by a history of respiratory symptoms and variable expiratory airflow limitation [1, 2]. Despite the significant advances in asthma treatment over recent years, a proportion of patients remain uncontrolled [3]. To achieve the best possible long-term outcomes for patients with asthma, new approaches are needed to improve treatment access [2]. In clinical trials, robust endpoints or outcome measures are essential to assess the efficacy and safety of the study intervention; however, asthma represents a challenge in study design because of disease control existing across multiple domains, including exacerbations, lung function as measured by spirometry (or peak flow), reliever inhaler use and symptoms. Specifically, to achieve clinical asthma remission, treatments are required that result in patients having no symptoms, no oral corticosteroid use, stable or improving lung function, and an absence of exacerbations [2]. The recent influx of approved biologics in severe asthma have mostly relied on severe exacerbations as an endpoint in pivotal studies.

Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems. Objective: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits. Methods: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention. Conclusions: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.