The Effects of Graded Tasks on Physical Activity (Behaviour Change Taxonomy Version 1: 8.7): A Systematic Review and Meta-Analysis

Peer reviewe

Covid-19 public health road map: Eating behaviour

This roadmap aims to support health officials to consider changes to eating behaviour that may have occurred during the Covid-19 pandemic and to use psychologically-informed behaviour change approaches to optimise health improvement and mitigate negative eating patterns. It will focus on eating a balanced diet, as opposed to eating behaviours related to disordered eating. This guidance should be used alongside the Achieving Behaviour Change (ABC) guide {1} for local government and partners, and the Improving People’s Health behavioural and social science strategy {2} {1}https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/875385/PHEBI_Achieving_Behaviour_Change_Local_Government.pdf {2}https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/744672/Improving_Peoples_Health_Behavioural_Strategy.pd

Covid-19 public health road map: Sedentary behaviour

This roadmap aims to support health officials to consider changes to sedentary behaviour that may have occurred during the Covid-19 pandemic and to use psychologically informed behaviour change approaches to optimise health improvement and mitigate an increase in time spent sitting or lying down. This guidance should be used alongside the Achieving Behaviour Change (ABC) guide {1} for local government and partners, and the Improving People’s Health behavioural and social science strategy {2} {1}https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/875385/PHEBI_Achieving_Behaviour_Change_Local_Government.pdf {2}https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/744672/Improving_Peoples_Health_Behavioural_Strategy.pdfFinal Published versio

Covid-19 public health road map: Physical activity

This roadmap aims to support health officials to consider changes to physical activity that may have occurred during the Covid-19 pandemic and to use psychologically-informed behaviour change approaches to optimise health improvement and mitigate a reduction in activity levels. This guidance should be used alongside the Achieving Behaviour Change (ABC) guide {1} for local government and partners, and the Improving People’s Health behavioural and social science strategy {2} {1}https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/875385/PHEBI_Achieving_Behaviour_Change_Local_Government.pdf {2}https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/744672/Improving_Peoples_Health_Behavioural_Strategy.pdfFinal Published versio

What influences people’s responses to public health messages for managing risks and preventing infectious diseases? A rapid systematic review of the evidence and recommendations

Background: Individual behaviour changes, such as hand hygiene and physical distancing, are required on a population scale to reduce transmission of infectious diseases such as COVID-19. However, little is known about effective methods of communicating risk reducing information, and how populations might respond. Objective: To synthesise evidence relating to what (1) characterises effective public health messages for managing risk and preventing infectious disease and (2) influences people’s responses to messages. Design: A rapid systematic review was conducted. Protocol is published on Prospero CRD42020188704. Data sources: Electronic databases were searched: Ovid Medline, Ovid PsycINFO and Healthevidence. org, and grey literature (PsyarXiv, OSF Preprints) up to May 2020. Study selection: All study designs that (1) evaluated public health messaging interventions targeted at adults and (2) concerned a communicable disease spread via primary route of transmission of respiratory and/or touch were included. Outcomes included preventative behaviours, perceptions/awareness and intentions. Non-English language papers were excluded. Synthesis: Due to high heterogeneity studies were synthesised narratively focusing on determinants of intentions in the absence of measured adherence/ preventative behaviours. Themes were developed independently by two researchers and discussed within team to reach consensus. Recommendations were translated from narrative synthesis to provide evidence-based methods in providing effective messaging. Results: Sixty-eight eligible papers were identified. Characteristics of effective messaging include delivery by credible sources, community engagement, increasing awareness/knowledge, mapping to stage of epidemic/ pandemic. To influence intent effectively, public health messages need to be acceptable, increase understanding/perceptions of health threat and perceived susceptibility. Discussion: There are four key recommendations: (1) engage communities in development of messaging, (2) address uncertainty immediately and with transparency, (3) focus on unifying messages from sources and (4) frame messages aimed at increasing understanding, social responsibility and personal control. Embedding principles of behavioural science into public health messaging is an important step towards more effective health-risk communication during epidemics/pandemics

A rapid systematic review of public responses to health messages encouraging vaccination against infectious diseases in a pandemic or epidemic

Public health teams need to understand how the public responds to vaccination messages in a pandemic or epidemic to inform successful campaigns encouraging the uptake of new vaccines as they become available. A rapid systematic review was performed by searching PsycINFO, MED-LINE, healthevidence.org, OSF Preprints and PsyArXiv Preprints in May 2020 for studies including at least one health message promoting vaccine uptake of airborne-, droplet-and fomite-spread vi-ruses. Included studies were assessed for quality using the Mixed Methods Appraisal Tool (MMAT) or the Assessment of Multiple Systematic Reviews (AMSTAR), and for patient and public involvement (PPI) in the research. Thirty-five articles were included. Most reported messages for seasonal influenza (n = 11; 31%) or H1N1 (n = 11; 31%). Evidence from moderate to high quality studies for improving vaccine uptake included providing information about virus risks and vaccination safety, as well as addressing vaccine misunderstandings, offering vaccination reminders, including vaccination clinic details, and delivering mixed media campaigns across hospitals or communities. Behavioural influences (beliefs and intentions) were improved when: shorter, risk-reducing or relative risk framing messages were used; the benefits of vaccination to society were emphasised; and beliefs about capability and concerns among target populations (e.g., vaccine safety) were addressed. Clear, credible, messages in a language target groups can understand were associated with higher accept-ability. Two studies (6%) described PPI in the research process. Future campaigns should consider the beliefs and information needs of target populations in their design, including ensuring that vaccine eligibility and availability is clear, and messages are accessible. More high quality research is needed to demonstrate the effects of messaging interventions on actual vaccine uptake

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease