Nudges Can Both Raise and Lower Physical Activity Levels : The Effects of Role Models on Stair and Escalator Use – A Pilot Study

We acknowledge support from the German Research Foundation (DFG) and the Open Access Publication Funds of Charité – Universitätsmedizin Berlin.Peer reviewedPublisher PD

What do Germans really think about health-nudges?

Peer reviewedPublisher PD

Assessing decision fatigue in general practitioners’ prescribing decisions using the Australian BEACH dataset

Peer reviewe

Adaptive evolution of hybrid bacteria by horizontal gene transfer

Horizontal gene transfer is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer, and about its effects on the recipient organism's physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ~12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative and positive selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths.Comment: The first three authors are joint first authors. Corresponding authors are Lassig and Maie

How fast is fast enough? Academic behavioural science impacting public health policy and practice

Background: COVID-19 emphasised the crucial role behaviour change plays in protecting population health. However, the interchange between academic behavioural science and Public Health (PH) policy and practice could be strengthened. We aimed to establish a sustainable method of joint working between two groups in North Scotland to enable rapid impact of behavioural science on population health.Methods: An implementation-sciences based approach tested the initial 4 steps of an 8-step collaboration process model, designed to identify a health problem (step 1), develop and test messaging interventions (step 2-4), implement the intervention (steps 5-6), and evaluate impact (steps 7-8).Results: Since October 2022, fortnightly meetings were established, implementing the process model. This project will focus on the following outcomes: perceived collaboration usefulness, collaboration-process barriers, and facilitators.Conclusions: Unless a sustainable method of joint working can be established in times where there are no urgent PH priorities, it is unlikely that the fruits of behavioural science can be aligned with PH challenges when outbreaks are happening to rapidly impact population health.<br/

How fast is fast enough? Academic behavioural science impacting public health policy and practice

Background: COVID-19 emphasised the crucial role behaviour change plays in protecting population health. However, the interchange between academic behavioural science and Public Health (PH) policy and practice could be strengthened. We aimed to establish a sustainable method of joint working between two groups in North Scotland to enable rapid impact of behavioural science on population health.Methods: An implementation-sciences based approach tested the initial 4 steps of an 8-step collaboration process model, designed to identify a health problem (step 1), develop and test messaging interventions (step 2-4), implement the intervention (steps 5-6), and evaluate impact (steps 7-8).Results: Since October 2022, fortnightly meetings were established, implementing the process model. This project will focus on the following outcomes: perceived collaboration usefulness, collaboration-process barriers, and facilitators.Conclusions: Unless a sustainable method of joint working can be established in times where there are no urgent PH priorities, it is unlikely that the fruits of behavioural science can be aligned with PH challenges when outbreaks are happening to rapidly impact population health.<br/

Behavioural Sciences Contribution to Suppressing Transmission of Covid-19 in the UK: A Systematic Literature Review

BackgroundGovernments have relied on their citizens to adhere to a variety of transmission-reducing behaviours (TRBs) to suppress the Covid-19 pandemic. Understanding the psychological and sociodemographic predictors of adherence to TRBs will be heavily influenced by the particular theories used by researchers. This review aims to identify the theories and theoretical constructs used to understand adherence to TRBs during the pandemic within the UK social and legislative context.MethodsA systematic review identified studies to understand TRBs of adults in the UK during the pandemic. Identified theoretical constructs were coded to the Theoretical Domains Framework. Data are presented as a narrative summary.ResultsThirty-five studies (n = 211,209) investigated 123 TRBs, applied 13 theoretical frameworks and reported 50 sociodemographic characteristics and 129 psychological constructs. Most studies used social cognition theories to understand TRBs and employed cross-sectional designs. Risk of sampling bias was high. Relationships between constructs and TRBs varied, but in general, beliefs about the disease (e.g. severity and risk perception) and about TRBs (e.g. behavioural norms) influenced behavioural intentions and self-reported adherence. More studies than not found that older people and females were more adherent.ConclusionsBehavioural scientists in the UK generated a significant and varied body of work to understand TRBs during the pandemic. However, more use of theories that do not rely on deliberative processes to effect behaviour change and study designs better able to support causal inferences should be used in future to inform public health policy and practice.Prospero RegistrationCRD42021282699

Long-term follow-up of beryllium sensitized workers from a single employer

<p>Abstract</p> <p>Background</p> <p>Up to 12% of beryllium-exposed American workers would test positive on beryllium lymphocyte proliferation test (BeLPT) screening, but the implications of sensitization remain uncertain.</p> <p>Methods</p> <p>Seventy two current and former employees of a beryllium manufacturer, including 22 with pathologic changes of chronic beryllium disease (CBD), and 50 without, with a confirmed positive test were followed-up for 7.4 +/-3.1 years.</p> <p>Results</p> <p>Beyond predicted effects of aging, flow rates and lung volumes changed little from baseline, while D_LCO dropped 17.4% of predicted on average. Despite this group decline, only 8 subjects (11.1%) demonstrated physiologic or radiologic abnormalities typical of CBD. Other than baseline status, no clinical or laboratory feature distinguished those who clinically manifested CBD at follow-up from those who did not.</p> <p>Conclusions</p> <p>The clinical outlook remains favorable for beryllium-sensitized individuals over the first 5-12 years. However, declines in D_LCO may presage further and more serious clinical manifestations in the future. These conclusions are tempered by the possibility of selection bias and other study limitations.</p

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i