Understanding a constellation of eight COVID-19 disease prevention behaviours using the COM-B model and the theoretical domains framework: a qualitative study using the behaviour change wheel

Background: The use of behavioural science and behaviour change within local authorities and public health has supported healthful change; as evidenced by its importance and contribution to reducing harm during the COVID-19 pandemic. It can provide valuable information to enable the creation of evidence-based intervention strategies, co-created with the people they are aimed at, in an effective and efficient manner. Aim: This study aimed to use the COM-B model to understand the Capability, Opportunity and Motivation of performing a constellation of eight COVID-19 disease prevention behaviours related to the slogans of ‘Hands, Face, Space, Fresh Air’; ‘Find, Isolate, Test, (FIT), and Vaccinate’ in those employed in workplaces identified as high risk for transmission of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) to support intervention development. Methods: This qualitative study recruited twenty-three participants (16 female, 7 male), who were interviewed from three environments (schools, care homes, warehouses) across three local authorities. Semi-structured interviews were analysed using thematic analysis. Findings: Ten core themes were identified inductively; (1) knowledge and skills, (2) regulating the behaviour, (3) willingness to act, (4) necessity and concerns, (5) emotional impact, (6) conducive environment, (7) societal influence, (8) no longer united against COVID-19, (9) credible leadership, and (10) inconsistent adherence to COVID-19 prevention behaviours. Themes were then deductively mapped to the COM-B model of behaviour change and the theoretical domains framework and a logic model using the behaviour change wheel (BCW) was produced to inform intervention design. Conclusion: This study offers a novel approach to analysis that has included eight behaviours within a single thematic analysis and COM-B diagnosis. This will enable local authorities to direct limited resources to overarching priorities. Of key importance, was the need for supportive and credible leadership, alongside developing interventions collaboratively with the target audience. COVID-19 has had an emotional toll on those interviewed, however, promoting the value of disease prevention behaviours, over and above their costs, can facilitate behaviour. Developing knowledge and skills, through education, training, marketing and modelling can further facilitate behaviour. This supports guidance produced by the British Psychological Society COVID-19 behavioural science and disease prevention taskforce

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation:A Multi-center, Multi-vendor, and Multi-disease Study

Background: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.Purpose: Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center.Study type: Retrospective.Population: A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6–85); 42% females) and 31 healthy participants (median age (range): 34 (23–76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.Field Strength/Sequence: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI.Assessment: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.Statistical Tests: Kruskal–Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland–Altman analyses assessed agreement with manually derived lung volumes. A P value of &lt;0.05 was considered statistically significant.Results: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880–0.987), Average HD of 1.63 mm (0.65–5.45) and XOR of 0.079 (0.025–0.240) on the testing set and a DSC of 0.973 (0.866–0.987), Average HD of 1.11 mm (0.47–8.13) and XOR of 0.054 (0.026–0.255) on external validation data.Data Conclusion: The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.Evidence Level: 4.Technical Efficacy: Stage 1.</p