An ontology of mechanisms of action in behaviour change interventions

BACKGROUND: Behaviour change interventions influence behaviour through causal processes called “mechanisms of action” (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. METHODS: To develop the MoA Ontology, we (1) defined the ontology’s scope; (2) identified, labelled and defined the ontology’s entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology’s comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology’s alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. RESULTS: An MoA was defined as “a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour”. We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 (“acceptable”) for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 21 further entities were added to the ontology resulting in 282 entities organised in seven hierarchical levels. CONCLUSIONS: The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources

Dynamic and redundant regulation of LRRK2 and LRRK1 expression

<p>Abstract</p> <p>Background</p> <p>Mutations within the <it>leucine-rich repeat kinase 2 </it>(<it>LRRK2</it>) gene account for a significant proportion of autosomal-dominant and some late-onset sporadic Parkinson's disease. Elucidation of LRRK2 protein function in health and disease provides an opportunity for deciphering molecular pathways important in neurodegeneration. In mammals, LRRK1 and LRRK2 protein comprise a unique family encoding a GTPase domain that controls intrinsic kinase activity. The expression profiles of the murine LRRK proteins have not been fully described and insufficiently characterized antibodies have produced conflicting results in the literature.</p> <p>Results</p> <p>Herein, we comprehensively evaluate twenty-one commercially available antibodies to the LRRK2 protein using mouse <it>LRRK2 </it>and human <it>LRRK2 </it>expression vectors, wild-type and <it>LRRK2</it>-null mouse brain lysates and human brain lysates. Eleven antibodies detect over-expressed human LRRK2 while four antibodies detect endogenous human LRRK2. In contrast, two antibodies recognize over-expressed mouse LRRK2 and one antibody detected endogenous mouse LRRK2. LRRK2 protein resides in both soluble and detergent soluble protein fractions. <it>LRRK2 </it>and the related <it>LRRK1 </it>genes encode low levels of expressed mRNA species corresponding to low levels of protein both during development and in adulthood with largely redundant expression profiles.</p> <p>Conclusion</p> <p>Despite previously published results, commercially available antibodies generally fail to recognize endogenous mouse LRRK2 protein; however, several antibodies retain the ability to detect over-expressed mouse LRRK2 protein. Over half of the commercially available antibodies tested detect over-expressed human LRRK2 protein and some have sufficient specificity to detect endogenous LRRK2 in human brain. The mammalian LRRK proteins are developmentally regulated in several tissues and coordinated expression suggest possible redundancy in the function between <it>LRRK1 </it>and <it>LRRK2</it>.</p

Inflammation and Oral Contraceptive Use in Female Athletes Before the Rio Olympic Games

This study investigated the association between synthetic ovarian hormone use [i.e., the oral contraceptive (OC) pill] and basal C-reactive protein (CRP), peripheral blood immune cell subsets, and circulating pro- and anti-inflammatory cytokine concentrations in elite female athletes. Elite female athletes (n = 53) selected in Rio Summer Olympic squads participated in this study; 25 were taking an OC (AthletesOC) and 28 were naturally hormonally cycling (AthletesNC). Venous blood samples were collected at rest for the determination of sex hormones, cortisol, CRP, peripheral blood mononuclear memory and naïve CD4+ T-cells, CD8+ T-cells and natural killer cells, as well as pro- and anti-inflammatory cytokine concentrations. C-reactive protein concentrations were elevated (p < 0.001) in AthletesOC (median = 2.02, IQR = 3.15) compared to AthletesNC (median = 0.57, IQR = 1.07). No differences were reported for cortisol, cytokines, or PBMC immune cell subsets, although there was a trend (p = 0.062) for higher IL-6 concentrations in AthletesNC. Female Olympians had substantially higher CRP concentrations, a marker of inflammation and tissue damage, before the Rio Olympic Games if they used an OC. Future research should examine the potential consequences for athlete performance/recovery so that, if necessary, practitioners can implement prevention programs

Upper Respiratory Symptoms, Gut Health and Mucosal Immunity in Athletes

Upper respiratory symptoms remain the most common illness in athletes. Upper respiratory symptoms during heavy training and competition may impair performance. Preventing illness is the primary reason for the use of supplements, such as probiotics and prebiotics, for maintaining or promoting gut health and immune function. While exercise-induced perturbations in the immune system may increase susceptibility to illness and infection, growing evidence indicates that upper respiratory symptoms are related to a breakdown in the homeostatic regulation of the mucosal immune system of the airways. Balancing protection of the respiratory tract with normal physiological functioning requires dynamic orchestration between a wide array of immune parameters. The intestinal microbiota regulates extra-intestinal immunity via the common mucosal immune system and new evidence implicates the microbiota of the nose, mouth and respiratory tract in upper respiratory symptoms. Omics’ approaches now facilitate comprehensive profiling at the molecular and proteomic levels to reveal new pathways and molecules of immune regulation. New targets may provide for personalised nutritional and training interventions to maintain athlete health.Griffith Health, School of Medical ScienceFull Tex

Building an army of wombat warriors : developing and sustaining a citizen science project

Citizen science websites and mobile applications are credited for their ability to engage members of the public in science and enhance scientific literacy, while operating as a cost-effective, geographically vast data-collection tool. Recruiting participants, tailoring online platforms to users’ needs and harnessing community values are key to creating a successful, sustainable citizen science project. However, few studies have conducted a detailed examination of the recruitment experience when trying to build an engaged and active citizen science audience to assess wildlife diseases in Australia. The present study aimed to determine the most effective methods to recruit and continue to engage citizens to use the tool called WomSAT (Wombat Survey and Analysis Tools). Various marketing techniques were employed to recruit participants. A survey was also disseminated to gain feedback on WomSAT and understand the driving factors behind participation. Participation in the WomSAT project was driven by a collective desire to help and learn about wombats. Preliminary distribution data collected by citizens suggest that WomSAT contains the necessary elements to enable it to be an important tool for monitoring wombats and the distribution of disease. Continuation of the WomSAT project will support scientific research while fostering conservation messages amongst the Australian community

An ontology of mechanisms of action in behaviour change interventions

Background: Behaviour change interventions influence behaviour through causal processes called “mechanisms of action” (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods: To develop the MoA Ontology, we (1) defined the ontology’s scope; (2) identified, labelled and defined the ontology’s entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology’s comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology’s alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results: An MoA was defined as “a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour”. We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 (“acceptable”) for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 21 further entities were added to the ontology resulting in 282 entities organised in seven hierarchical levels. Conclusions: The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources.</p

An ontology of mechanisms of action in behaviour change interventions [version 1; peer review: 2 approved]

Background: Behaviour change interventions influence behaviour through causal processes called “mechanisms of action” (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods: To develop the MoA Ontology, we (1) defined the ontology’s scope; (2) identified, labelled and defined the ontology’s entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology’s comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology’s alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results: An MoA was defined as “a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour”. We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 (“acceptable”) for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 21 further entities were added to the ontology resulting in 282 entities organised in seven hierarchical levels. Conclusions: The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources

An ontology of mechanisms of action in behaviour change interventions [version 2; peer review: 2 approved]

Background Behaviour change interventions influence behaviour through causal processes called “mechanisms of action” (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods To develop the MoA Ontology, we (1) defined the ontology’s scope; (2) identified, labelled and defined the ontology’s entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology’s comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology’s alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results An MoA was defined as “a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour”. We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 (“acceptable”) for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 23 further entities were added to the ontology resulting in 284 entities organised in seven hierarchical levels. Conclusions The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources