Barriers and motivators to undertaking physical activity in adults over 70-a systematic review of the quantitative literature.

BACKGROUND: Physical activity (PA) has multiple benefits for older adults (≥70 years old). Despite this many older adults do not undertake the World Health Organisation guideline recommended amount of PA. This systematic review examines barriers and motivators to PA in adults aged ≥70 years. METHODS: We analysed the quantitative literature, including observational studies and baseline data from randomised controlled trials. Studies examining specific diseases (e.g. cognitive impairment), or care home residents were excluded. Database searches of ASSIA, CINAHL, Embase, Medline, PsycINFO and Web of Science were undertaken on 7 March 2023. Quality assessment was performed using the ROBANS tool. We synthesised the results using the socioecological model. The protocol was registered on PROSPERO (CRD42021160503). RESULTS: We identified 37 papers, n = 26,961, age 70-101 years (median 78), 62% female. We undertook a narrative review; meta-analysis was not possible. Overall risk of bias was low. A total of 23 studies addressed barriers, seven motivators, seven both. The most cited barriers were: concern about physical health/fitness (14 studies), lack of motivation/interest (13 studies), fear of falls/history of falling (11 studies) and environmental barriers (10 studies). Key motivators were: support from family/friends (five studies), social interaction (five studies), personal benefits (five studies) and outside facilities (five studies). Results varied across gender, age, functional ability and geographical location. DISCUSSION: To maximise PA in older adults, important modifiable factors identified in this review should be targeted: support from healthcare professionals; reducing fear of falls; and prioritising ease of access and safety of outdoor facilities. When considering future policy, a person-centred, age group appropriate approach will have the most impact.

Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis.

OBJECTIVE: ANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting. METHODS: We studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse. RESULTS: Development and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS. CONCLUSIONS: This CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.

Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying.

OBJECTIVES: This study aims to describe the data structure and harmonisation process, explore data quality and define characteristics, treatment, and outcomes of patients across six federated antineutrophil cytoplasmic antibody-associated vasculitis (AAV) registries. METHODS: Through creation of the vasculitis-specific Findable, Accessible, Interoperable, Reusable, VASCulitis ontology, we harmonised the registries and enabled semantic interoperability. We assessed data quality across the domains of uniqueness, consistency, completeness and correctness. Aggregated data were retrieved using the semantic query language SPARQL Protocol and Resource Description Framework Query Language (SPARQL) and outcome rates were assessed through random effects meta-analysis. RESULTS: A total of 5282 cases of AAV were identified. Uniqueness and data-type consistency were 100% across all assessed variables. Completeness and correctness varied from 49%-100% to 60%-100%, respectively. There were 2754 (52.1%) cases classified as granulomatosis with polyangiitis (GPA), 1580 (29.9%) as microscopic polyangiitis and 937 (17.7%) as eosinophilic GPA. The pattern of organ involvement included: lung in 3281 (65.1%), ear-nose-throat in 2860 (56.7%) and kidney in 2534 (50.2%). Intravenous cyclophosphamide was used as remission induction therapy in 982 (50.7%), rituximab in 505 (17.7%) and pulsed intravenous glucocorticoid use was highly variable (11%-91%). Overall mortality and incidence rates of end-stage kidney disease were 28.8 (95% CI 19.7 to 42.2) and 24.8 (95% CI 19.7 to 31.1) per 1000 patient-years, respectively. CONCLUSIONS: In the largest reported AAV cohort-study, we federated patient registries using semantic web technologies and highlighted concerns about data quality. The comparison of patient characteristics, treatment and outcomes was hampered by heterogeneous recruitment settings.

Factors that influence older adults' participation in physical activity: a systematic review of qualitative studies.

BACKGROUND: Despite the advantages of physical activity (PA), older adults are often insufficiently active to maximise health. Understanding factors that influence PA engagement will support well-designed interventions for older people. Our aim was to review the qualitative evidence exploring the factors affecting older adults' engagement in PA. METHODS: We searched six electronic databases for studies of community-dwelling older adults (≥70 years) including qualitative methods. We excluded studies of a single-disease group, individuals with cognitive impairment and care home residents. Methodological rigour was assessed with the Critical Appraisal Skills Programme, and framework synthesis was applied using the Capability Opportunity Motivation-Behaviour (COM-B) model, which hypothesises that behaviour is influenced by three factors: capability, opportunity and motivation. RESULTS: Twenty-five studies were included in the review (N = 4,978; mean 79 years) and 32 themes were identified. Older adults' capability was influenced by functional capacity (e.g. strength) and perceived risk of injury from PA (e.g. falls). Opportunity was impacted by the environment 'fit' (e.g. neighbourhood safety), the availability of social interaction and socio-cultural ageing stereotypes. PA was motivated by identifying as an 'exerciser', health gains and experiencing positive emotions (e.g. enjoyment), whereas negative sensations (e.g. pain) reduced motivation. CONCLUSIONS: The qualitative synthesis showcased a complex web of interacting factors influencing PA between the sub-domains of COM-B, pinpointing directions for intervention, including a focus on whole systems approaches. There was a lack of research exploring PA influences in the oldest old and in low-income countries. Future research should seek to involve under-served groups, including a wider diversity of older people.

Evaluation of single-stage vision models for pose estimation of surgical instruments.

PURPOSE: Multiple applications in open surgical environments may benefit from adoption of markerless computer vision depending on associated speed and accuracy requirements. The current work evaluates vision models for 6-degree of freedom pose estimation of surgical instruments in RGB scenes. Potential use cases are discussed based on observed performance. METHODS: Convolutional neural nets were developed with simulated training data for 6-degree of freedom pose estimation of a representative surgical instrument in RGB scenes. Trained models were evaluated with simulated and real-world scenes. Real-world scenes were produced by using a robotic manipulator to procedurally generate a wide range of object poses. RESULTS: CNNs trained in simulation transferred to real-world evaluation scenes with a mild decrease in pose accuracy. Model performance was sensitive to input image resolution and orientation prediction format. The model with highest accuracy demonstrated mean in-plane translation error of 13 mm and mean long axis orientation error of 5[Formula: see text] in simulated evaluation scenes. Similar errors of 29 mm and 8[Formula: see text] were observed in real-world scenes. CONCLUSION: 6-DoF pose estimators can predict object pose in RGB scenes with real-time inference speed. Observed pose accuracy suggests that applications such as coarse-grained guidance, surgical skill evaluation, or instrument tracking for tray optimization may benefit from markerless pose estimation.

The Sound of Interconnectivity; The European Vasculitis Society 2022 Report.

The first European Vasculitis Society (EUVAS) meeting report was published in 2017. Herein, we report on developments in the past 5 years which were greatly influenced by the pandemic. The adaptability to engage virtually, at this critical time in society, embodies the importance of networks and underscores the role of global collaborations. We outline state-of-the-art webinar topics, updates on developments in the last 5 years, and proposals for agendas going forward. A host of newly reported clinical trials is shaping practice on steroid minimization, maintenance strategies, and the role of newer therapies. To guide longer-term strategies, a longitudinal 10-year study investigating relapse, comorbidity, malignancy, and survival rates is at an advanced stage. Disease assessment studies are refining classification criteria to differentiate forms of vasculitis more fully. A large international validation study on the histologic classification of anti-neutrophil cytoplasmic antibody (ANCA) glomerulonephritis, recruiting new multicenter sites and comparing results with the Kidney Risk Score, has been conducted. Eosinophilic granulomatosis with polyangiitis (EGPA) genomics offers potential pathogenic subset and therapeutic insights. Among biomarkers, ANCA testing is favoring immunoassay as the preferred method for diagnostic evaluation. Consolidated development of European registries is progressing with an integrated framework to analyze large clinical data sets on an unprecedented scale.

FAIRVASC: A semantic web approach to rare disease registry integration.

Rare disease data is often fragmented within multiple heterogeneous siloed regional disease registries, each containing a small number of cases. These data are particularly sensitive, as low subject counts make the identification of patients more likely, meaning registries are not inclined to share subject level data outside their registries. At the same time access to multiple rare disease datasets is important as it will lead to new research opportunities and analysis over larger cohorts. To enable this, two major challenges must therefore be overcome. The first is to integrate data at a semantic level, so that it is possible to query over registries and return results which are comparable. The second is to enable queries which do not take subject level data from the registries. To meet the first challenge, this paper presents the FAIRVASC ontology to manage data related to the rare disease anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV), which is based on the harmonisation of terms in seven European data registries. It has been built upon a set of key clinical questions developed by a team of experts in vasculitis selected from the registry sites and makes use of several standard classifications, such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Orphacode. It also presents the method for adding semantic meaning to AAV data across the registries using the declarative Relational to Resource Description Framework Mapping Language (R2RML). To meet the second challenge a federated querying approach is presented for accessing aggregated and pseudonymized data, and which supports analysis of AAV data in a manner which protects patient privacy. For additional security the federated querying approach is augmented with a method for auditing queries (and the uplift process) using the provenance ontology (PROV-O) to track when queries and changes occur and by whom. The main contribution of this work is the successful application of semantic web technologies and federated queries to provide a novel infrastructure that can readily incorporate additional registries, thus providing access to harmonised data relating to unprecedented numbers of patients with rare disease, while also meeting data privacy and security concerns.

Risk Factors for Severe Outcomes in Patients With Systemic Vasculitis and COVID-19: A Binational, Registry-Based Cohort Study.

OBJECTIVE: COVID-19 is a novel infectious disease with a broad spectrum of clinical severity. Patients with systemic vasculitis have an increased risk of serious infections and may be at risk of severe outcomes following COVID-19. We undertook this study to establish the risk factors for severe COVID-19 outcomes in these patients, including the impact of immunosuppressive therapies. METHODS: A multicenter cohort was developed through the participation of centers affiliated with national UK and Ireland vasculitis registries. Clinical characteristics and outcomes are described. Logistic regression was used to evaluate associations between potential risk factors and a severe COVID-19 outcome, defined as a requirement for advanced oxygen therapy, a requirement for invasive ventilation, or death. RESULTS: The cohort included 65 patients with systemic vasculitis who developed COVID-19 (median age 70 years, 49% women), of whom 25 patients (38%) experienced a severe outcome. Most patients (55 of 65 [85%]) had antineutrophil cytoplasmic antibody-associated vasculitis (AAV). Almost all patients required hospitalization (59 of 65 [91%]), 7 patients (11%) were admitted to intensive care, and 18 patients (28%) died. Background glucocorticoid therapy was associated with severe outcomes (adjusted odds ratio [OR] 3.7 [95% confidence interval 1.1-14.9]; P = 0.047), as was comorbid respiratory disease (adjusted OR 7.5 [95% confidence interval 1.9-38.2]; P = 0.006). Vasculitis disease activity and nonglucocorticoid immunosuppressive therapy were not associated with severe outcomes. CONCLUSION: In patients with systemic vasculitis, glucocorticoid use at presentation and comorbid respiratory disease were associated with severe outcomes in COVID-19. These data can inform clinical decision-making relating to the risk of severe COVID-19 in this vulnerable patient group.