BackgroundObesity has become a world-wide epidemic and is spreading to countries with emerging economies. Previously tested interventions are often too costly to maintain in the long term. This leaves a need for improved strategies for management of the epidemic. Nudge Theory presents a new collection of methods, deemed “nudges”, which have the potential for low-cost and broad application to guide healthier lifestyle choices without the need for restrictive regulation. There has not yet been a large-scale examination of the effectiveness of nudges, despite several policy making bodies now considering their use.MethodsTo address this gap in knowledge, an adapted systematic review methodology was used to collect and consolidate results from current Nudge papers and to determine whether Nudge strategies are successful in changing adults’ dietary choices for healthier ones.ResultsIt was found that nudges resulted in an average 15.3 % increase in healthier dietary or nutritional choices, as measured by a change in frequency of healthy choices or a change in overall caloric consumption. All of the included studies were from wealthy nations, with a particular emphasis on the United States with 31 of 42 included experiments.ConclusionsThis analysis demonstrates Nudge holds promise as a public health strategy to combat obesity. More research is needed in varied settings, however, and future studies should aim to replicate previous results in more geographically and socioeconomically diverse countries.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3272-x) contains supplementary material, which is available to authorized users.
While it is important for the evidence supporting practice guidelines to be current, that is often not the case. The advent of living systematic reviews has made the concept of "living guidelines" realistic, with the promise to provide timely, up-to-date and high-quality guidance to target users. We define living guidelines as an optimization of the guideline development process to allow updating individual recommendations as soon as new relevant evidence becomes available. A major implication of that definition is that the unit of update is the individual recommendation and not the whole guideline. We then discuss when living guidelines are appropriate, the workflows required to support them, the collaboration between living systematic reviews and living guideline teams, the thresholds for changing recommendations, and potential approaches to publication and dissemination. The success and sustainability of the concept of living guideline will depend on those of its major pillar, the living systematic review. We conclude that guideline developers should both experiment with and research the process of living guidelines.
Background The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis—systematic reviews and health guidelines—to be continually kept up to date. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and therefore, their opinions on the potential use of automation are crucial. Methods The objective of this study was to analyze the attitudes of guideline developers towards the use of automation in health evidence synthesis. The Diffusion of Innovations framework was chosen as an initial analytical framework because it encapsulates some of the core issues which are thought to affect the adoption of new innovations in practice. This well-established theory posits five dimensions which affect the adoption of novel technologies: Relative Advantage, Compatibility, Complexity, Trialability, and Observability. Eighteen interviews were conducted with individuals who were currently working, or had previously worked, in guideline development. After transcription, a multiphase mixed deductive and grounded approach was used to analyze the data. First, transcripts were coded with a deductive approach using Rogers’ Diffusion of Innovation as the top-level themes. Second, sub-themes within the framework were identified using a grounded approach. Results Participants were consistently most concerned with the extent to which an innovation is in line with current values and practices (i.e., Compatibility in the Diffusion of Innovations framework). Participants were also concerned with Relative Advantage and Observability, which were discussed in approximately equal amounts. For the latter, participants expressed a desire for transparency in the methodology of automation software. Participants were noticeably less interested in Complexity and Trialability, which were discussed infrequently. These results were reasonably consistent across all participants. Conclusions If machine learning and other automation technologies are to be used more widely and to their full potential in systematic reviews and guideline development, it is crucial to ensure new technologies are in line with current values and practice. It will also be important to maximize the transparency of the methods of these technologies to address the concerns of guideline developers.
Blood loss in the first days of life has been associated with increased morbidity and mortality in very preterm infants. In this systematic review we included randomized controlled trials comparing the effects of interventions to preserve blood volume in the infant from birth, reduce the need for sampling, or limit the blood sampled. Mortality and major neurodevelopmental disabilities were the primary outcomes. Included studies underwent risk of bias-assessment and data extraction by two review authors independently. We used risk ratio or mean difference to evaluate the treatment effect and meta-analysis for pooled results. The certainty of evidence was assessed using GRADE. We included 31 trials enrolling 3,759 infants. Twenty-five trials were pooled in the comparison delayed cord clamping or cord milking vs. immediate cord clamping or no milking. Increasing placental transfusion resulted in lower mortality during the neonatal period (RR 0.51, 95% CI 0.26 to 1.00; participants = 595; trials = 5; I2 = 0%, moderate certainty of evidence) and during first hospitalization (RR 0.70, 95% CI 0.51, 0.96; 10 RCTs, participants = 2,476, low certainty of evidence). The certainty of evidence was very low for the other primary outcomes of this review. The six remaining trials compared devices to monitor glucose levels (three trials), blood sampling from the umbilical cord or from the placenta vs. blood sampling from the infant (2 trials), and devices to reintroduce the blood after analysis vs. conventional blood sampling (1 trial); the certainty of evidence was rated as very low for all outcomes in these comparisons. Increasing placental transfusion at birth may reduce mortality in very preterm infants; However, extremely limited evidence is available to assess the effects of other interventions to reduce blood loss after birth. In future trials, infants could be randomized following placental transfusion to different blood saving approaches. Trial registration: PROSPERO CRD42020159882.
The COVID-19 pandemic has disrupted life worldwide and presented unique challenges in the health evidencesynthesis space. The urgent nature of the pandemic required extreme rapidity for keeping track of research, andthis presented a unique opportunity for long-proposed automation systems to be deployed and evaluated. Wecompared the use of novel automation technologies with conventional manual screening; and Microsoft AcademicGraph (MAG) with the MEDLINE and Embase databases locating the emerging research evidence. We foundthat a new workflow involving machine learning to identify relevant research in MAG achieved a much higherrecall with lower manual effort than using conventional approaches.
Background: Conventionally, searching for eligible articles to include in systematic reviews and maps of research has relied primarily on information specialists conducting Boolean searches of multiple databases and manually processing the results, including deduplication between these multiple sources. Searching one, comprehensive source, rather than multiple databases, could save time and resources. Microsoft Academic Graph (MAG) is potentially such a source, containing a network graph structure which provides metadata that can be exploited in machine learning processes. Research is needed to establish the relative advantage of using MAG as a single source, compared with conventional searches of multiple databases. This study sought to establish whether: (a) MAG is sufficiently comprehensive to maintain our living map of coronavirus disease 2019 (COVID-19) research; and (b) eligible records can be identified with an acceptably high level of specificity. Methods: We conducted a pragmatic, eight-arm cost-effectiveness analysis (simulation study) to assess the costs, recall and precision of our semi-automated MAG-enabled workflow versus conventional searches of MEDLINE and Embase (with and without machine learning classifiers, active learning and/or fixed screening targets) for maintaining a living map of COVID-19 research. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Results: MAG-enabled workflows dominated MEDLINE-Embase workflows in both the base case and sensitivity analyses. At one month (base case analysis) our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified n=469 more new, eligible articles for inclusion in our living map – and cost £3,179 GBP ($5,691 AUD) less – than conventional MEDLINE-Embase searches without any automation or fixed screening targets. Conclusions: MAG-enabled continuous surveillance workflows have potential to revolutionise study identification methods for living maps, specialised registers, databases of research studies and/or collections of systematic reviews, by increasing their recall and coverage, whilst reducing production costs.
Background: The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis – systematic reviews and health guidelines -- to be continually kept up to date. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and therefore their opinions on the potential use of automation are crucial. Methods: The objective of this study was to analyze the attitudes of guideline developers towards the use of automation in health evidence synthesis. The Diffusion of Innovations framework was chosen as an initial analytical framework because it encapsulates some of the core issues which are thought to affect the adoption of new innovations in practice. This well-established theory posits five dimensions which affect the adoption of novel technologies: Relative Advantage, Compatibility, Complexity, Trialability, and Observability.Eighteen interviews were conducted with individuals who were currently working, or had previously worked, in guideline development. After transcription, a multiphase mixed deductive and grounded approach was used to analyze the data. First, transcripts were coded with a deductive approach using Rogers’ Diffusion of Innovation as the top-level themes. Second, sub-themes within the framework were identified using a grounded approach.Results: Participants were consistently most concerned with the extent to which an innovation is in line with current values and practices (i.e. Compatibility in the Diffusion of Innovations framework). Participants were also concerned with Relative Advantage and Observability, which were discussed in approximately equal amounts. For the latter, participants expressed a desire for transparency in methodology of automation software. Participants were noticeably less interested in Complexity and Trialability, which were discussed infrequently. These results were reasonably consistent across all participants. Conclusions: If machine learning and other automation technologies are to be used more widely and to their full potential in systematic reviews and guideline development, it is crucial to ensure new technologies are in line with current values and practice. It will also be important to maximize the transparency of the methods of these technologies to address the concerns of guideline developers.
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