Explore the vast range of titles available.

Background Vaping among 18-24-year-old Australians has increased from 5.8% in 2019 to 21% in 2023. This protocol describes a trial to investigate the dissemination and engagement achieved by three anti-vaping messages on Facebook. Methods This research employs a 3-arm randomised experimental design. Three distinct anti-vaping messages will be disseminated via Facebook using Meta Ads Manager. Each arm has a message that focuses on either health risks, environmental impact, or anti-vape industry sentiment. The campaign will run for three months. The primary outcome is the engagement rate as a measure of the effectiveness of anti-vaping message, and the secondary outcomes include network indicators: size, density, centralisation, and centrality to evaluate the extent to which the messages are disseminated. Participants will be randomly exposed to one of the three messages. Data on reach and engagement will be compared across the groups. Discussion This study will provide insights into the dissemination of social media-based anti-vaping campaigns. By evaluating engagement rates and network indicators, the research aims to identify which message themes engage most with young Australians. The findings will contribute to the development of more effective public health strategies for vaping cessation and prevention among youth. Trial Registration The study was registered on July 19th 2024 with the Australian New Zealand Clinical Trials Registry (ACTRN12624000885594).

Environmental education is vital for promoting pro-environmental behavior, and nature-contact environmental education has progressively emerged as an important form of environmental education. Therefore, exploring the effects and mechanisms of nature-contact environmental education is crucial to enhancing pro-environmental behavior. This manuscript focuses on the Qinling ecological environmental education course at a Chinese university, which exemplifies a form of nature-contact environmental education. The research employs the randomized controlled experimental design as the research methodology. A total of 112 students who participated in the course served as the study sample, with the aim of investigating whether nature-contact environmental education can effectively improve students’ pro-environmental behavior. Additionally, the study also explores the underlying mechanisms driving this effect. The findings indicate that nature-contact environmental education significantly contributes to improving students’ pro-environmental behavior. Furthermore, environmental attitudes and environmental responsibility are identified as key mediators in the relationship between nature-contact environmental education and pro-environmental behavior. These conclusions provide valuable insights for both theoretical research and practical applications of environmental education and pro-environmental behavior.

Objectives This study examines the effectiveness of the High Risk Revocation Reduction (HRRR) program, a reentry program designed to reduce recidivism among offenders released from Minnesota state prisons. Methods Adult male release violators were randomly assigned to a treatment group that received supplemental case planning and access to community service and programs, or to a control group that received standard case management. Survival analysis was used to examine rearrest, reconviction, reincarceration for a new offense, and supervised release revocation. Results The results of Cox regression models showed that participation in HRRR significantly reduced the risk of rearrest but had no effect on the other measures of recidivism. Conclusion The results provide limited support for the program, although its effectiveness appeared to decline during the second phase of implementation. HRRR also reduced costs; however, the estimated benefits were not robust across all sensitivity analyses.

Objectives To assess whether a reentry program targeted towards high-risk offenders leaving Minnesota state prisons significantly reduced recidivism. Methods Adult male release violators serving incarceration periods of 2–6 months in two Minnesota state prisons were randomly assigned to either the control group ( n  = 77) or the High-Risk Revocation Reduction (HRRR) program ( n  = 162). The latter group was provided with supplemental case planning, housing, employment, mentoring, cognitive-behavioral programming, and transportation assistance services, while the former group was given standard case management services. After 1–2 years of post-release follow-up time, event history analysis was used to predict the following four measures of recidivism: supervised release revocation, rearrest, reconviction, and new offense reincarceration. Results The Cox regression analyses revealed that participation in HRRR significantly lowered the risk of supervised release revocations and reconvictions by 28 and 43 %, respectively. Regardless of treatment or control group membership, receiving more reentry assistance significantly reduced supervision revocation and rearrest. Analyses also revealed that employment assistance, including subsidized employment, was especially effective at reducing recidivism. Conclusions Targeting resources towards this previously under-served population may be useful for lowering overall rates of recidivism. However, a later follow-up analysis is needed to ensure that these results remain over time.

This chapter contains sections titled: Introduction Definitions of Peer Support Forms of Peer Support Initiatives Necessary Tensions in Peer Support Contexts Summary of Evidence From Peer Support Programmes Example of a Peer Support Service Recommendations Regarding Implementation of Peer Support Initiatives References

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

The use of the stepped-wedge cluster randomised trial (SW-CRT) is on the increase, and although there are still relatively few SW-CRTs currently published its use is bound to show an increase in the near future. An extension of the CONSORT reporting guideline for SW-CRTs has recently been developed. By making reporting guidelines for this innovative design available relatively early in its development, it is possible that the methodological conduct and reporting of future SW-CRTs will not be at the same risk of low-quality of reporting as is the case with many other study designs. We provide a brief overview of this reporting guideline and encourage authors to use it appropriately; and for journal editors to endorse its use.

Background The stepped wedge (SW) cluster randomized controlled trial (CRCT) design is being used with increasing frequency. However, there is limited published research on the quality of reporting of SW-CRCTs. We address this issue by conducting a literature review. Methods Medline, Ovid, Web of Knowledge, the Cochrane Library, PsycINFO, the ISRCTN registry, and ClinicalTrials.gov were searched to identify investigations employing the SW-CRCT design up to February 2015. For each included completed study, information was extracted on a selection of criteria, based on the CONSORT extension to CRCTs, to assess the quality of reporting. Results A total of 123 studies were included in our review, of which 39 were completed trial reports. The standard of reporting of SW-CRCTs varied in quality. The percentage of trials reporting each criterion varied to as low as 15.4%, with a median of 66.7%. Conclusions There is much room for improvement in the quality of reporting of SW-CRCTs. This is consistent with recent findings for CRCTs. A CONSORT extension for SW-CRCTs is warranted to standardize the reporting of SW-CRCTs.

In the cluster randomized crossover trial, a sequence of treatment conditions, rather than just one treatment condition, is assigned to each cluster. This contribution studies the optimal number of time periods in studies with a treatment switch at the end of each time period, and the optimal number of treatment switches in a trial with a fixed number of time periods. This is done for trials with a fixed number of clusters, and for trials in which the costs per cluster, subject, and treatment switch are taken into account using a budgetary constraint. The focus is on trials with a cross-sectional design where a continuous outcome variable is measured at the end of each time period. An exponential decay correlation structure is used to model dependencies among subjects within the same cluster. A linear multilevel mixed model is used to estimate the treatment effect and its associated variance. The optimal design minimizes this variance. Matrix algebra is used to identify the optimal design and other highly efficient designs. For a fixed number of clusters, a design with the maximum number of time periods is optimal and treatment switches should occur at each time period. However, when a budgetary constraint is taken into account, the optimal design may have fewer time periods and fewer treatment switches. The Shiny app was developed to facilitate the use of the methodology in this contribution.

Background Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30–40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters. Methods To remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE uses synthetic intervention (SI) to estimate individual treatment effects and thereby simulate a cross-over design, which makes it easier for a trial to reach target power within trial constraints (e.g., sample size limits). To estimate sample sizes, TAD-SIE implements a new TAD tailored to SI given that using it violates assumptions under standard TADs. In addition, our TAD overcomes the ineffectiveness of standard TADs by allowing sample sizes to be increased across iterations without any condition while controlling significance level with futility stopping. Our TAD also introduces a hyperparameter that enables trial designers to trade off between accuracy and efficiency (sample size and number of iterations) of the solution. Results On a real-world Phase-3 clinical RCT (i.e., a two-arm parallel-group superiority trial with an equal number of subjects per arm), TAD-SIE obtains operating points ranging between 63% to 84% power and 3% to 6% significance level in contrast to baseline algorithms that get at best 49% power and 6% significance level. Conclusion TAD-SIE is a superior TAD that can be used to reach typical target operating points but only for trials with rapidly measurable primary outcomes due to its sequential nature. The framework is useful to practitioners interested in leveraging the SI algorithm for their study design.