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Background: An idiopathic outflow tract premature ventricular complex (OT-PVC) is a common arrhythmia, and the accuracy of site of origin prediction using the 12-lead electrocardiogram (ECG) algorithm is not high. There are no studies about a systematic strategy that can provide practical help to electrophysiologists in OT-PVC mapping and ablation. This study aims to evaluate the efficacy and safety of the proposed ablation protocol and establish an optimal catheter ablation strategy by simultaneously investigating and synthesizing various indicators observed during the mapping procedure. Methods and design: This study (ABOUT-PVC) was designed as a prospective multicenter study to enroll 210 patients from 11 tertiary university hospitals over an estimated 27 months. Patients with idiopathic OT-PVC requiring catheter ablation will receive the procedure through a proposed ablation strategy and will be followed up for at least 12 months. The primary outcome is the acute procedural success rate. The secondary outcomes are clinical success rate, procedure time, complication rate, symptom relief, and changes in echocardiographic parameters. Conclusions: The ABOUT-PVC study was designed to investigate the efficacy and safety of the proposed ablation strategy and establish an optimal catheter ablation strategy. We expect this study to overcome the limitations of the ECG prediction algorithms and provide a practical guide to electrophysiologists, increasing the procedure's success rate and reducing complications and procedure time. [ABSTRACT FROM AUTHOR]

Background: There is limited research on the intra‐individual efficacy of ventricular pacing minimization algorithms developed by Biotronik—the Ventricular Pace Suppression algorithm (VpS) and the Intrinsic Rhythm Support plus algorithm (IRSplus) (BIOTRONIK SE & Co. KG, Berlin, Germany). We performed a randomized pilot trial that evaluated the efficacy of two algorithms in patients with symptomatic sinus node dysfunction (SND) who received a dual‐chamber pacemaker. Methods: The trial was conducted in 11 tertiary hospitals in South Korea. The patients were randomized to either the VpS or IRSplus algorithm group after a 3‐month period of fixed atrioventricular (AV) delay. The primary outcome was the ventricular pacing percentage (Vp%) at each follow‐up visit. The secondary outcomes were the occurrence of heart failure (HF) and atrial fibrillation (AF) during the study period. Results: Data from 131 patients were analyzed. Initially, their average Vp% over 3 months with a fixed AV interval was 14.1 ± 19.4%. Patients were randomly assigned to VpS and IRSplus groups, with 66 and 65 in each. Algorithms reduced average Vp% to 4.0 ± 11.3% at 9 months and 6.7 ± 14.9% at 15 months. These algorithms were more effective for patients with paced AV delay (PAVD) ≤300 ms compared to those with PAVD >300 ms. Both algorithms were equally effective in reducing Vp%. Clinical AF or HF hospitalization was not observed during the study period. Conclusion: The VpS and IRSplus algorithms are effective and safe in minimizing unnecessary ventricular pacing in patients with SND. [ABSTRACT FROM AUTHOR]

The growing interest in intelligent automation adoption and development in tourism presents many potential solutions for sustainable tourism issues. To map the current landscape of research on intelligent automation in sustainable tourism, a systematic review was conducted of 213 scholarly articles. Five major themes were identified: intelligent automation to enhance tourist experience, preserve heritage, promote quality of life, measure tourist experience, and conserve the environment. Scholarly work on this topic has paid more attention to the economic and sociocultural aspects of sustainability but less to addressing environmental issues. To uncover further research gaps, this study outlines sustainability transition pathways using two dimensions: sustainability inclusion and tourist involvement. Twenty-three distinct AI-based innovations were mapped onto the pathways to reveal future research directions. The findings explain the lack of AI-based solutions that offer high levels of sustainability inclusion and tourist involvement. Thus, this study proposes the "AI4GoodTourism" framework, inferring that intelligent automation with high sustainability inclusion levels can scale up the marginal contributions that tourists provide collectively. This framework serves to guide future research and development for various stakeholders who are progressing sustainable tourism agendas through intelligent automation. [ABSTRACT FROM AUTHOR]

Originality is an important goal of research. However, relatively little is known about the characteristics and motivations of individual researchers or about the facilitating or hindering factors that, in combination, can lead to original research outputs. This is a gap this study aims to fill. Interviews with 20 highly original academics (identified by their peers) active in the field of tourism identify four shared main traits among such researchers—nonconformism, commitment, self-confidence, and interdisciplinarity—and the importance of situational factors. The findings also show that there is no single optimum way of "becoming original" and, therefore, efforts to "replicate" originality may constrain rather than enable originality. From a managerial perspective, this suggests that it is easier to remove barriers than to positively facilitate original research. [ABSTRACT FROM AUTHOR]

Competitiveness is a well-discussed research topic in various disciplines and fields, amongst which competitiveness in the visitor economy is a prominent research stream. With rapid transformations in the visitor economy, destinations, regions, sectors and businesses have had to adapt – with varying degrees of success – to internal and external changes, significantly affecting their competitiveness. Existing studies are dominantly based on a few pioneering models and indicators and relatively few empirically challenge the assumed causality of competitiveness factors at different scales. This article, therefore, conducts a systematic literature review of competitiveness in the visitor economy post-2005 and examines the intellectual and conceptual structures of the extant literature as a platform to identify knowledge gaps and emerging trends and perspectives for future research. [ABSTRACT FROM AUTHOR]

Background: The use of non-vitamin K antagonist oral anticoagulants (NOACs) in patients with non-valvular atrial fibrillation (AF) has been increasing. Accordingly, the combined use of antiplatelet agents (APT) and NOAC therapy is commonly encountered in clinical practice. The purpose of this study was to compare the clinical outcomes between combination therapy (NOAC and APT) vs. monotherapy (NOAC only) in patients with AF.Methods: We retrospectively analyzed patients who were prescribed NOACs between January 2012 and December 2016. The primary outcome was major bleeding and any bleeding events, and the secondary outcomes were stroke/systemic embolic (SE) events and major adverse cardiac events (MACE).Results: Of the 1068 participants, there were 264 (24.7%) patients in the combination therapy group. The prevalence of diabetes (p = 0.017) and history of stroke and transient ischemic attacks (p < 0.001) was higher in the combination group than in the monotherapy group. During the mean 14.6 ± 9.8 months of follow-up, the incidence of any bleeding was significantly higher in the combination therapy group than in the monotherapy group (p < 0.001). The rate of major bleeding, stroke/SE, and MACE between the two groups was similar. The rate of under-dosage NOAC prescriptions was higher in the combination therapy group than in the monotherapy group (p = 0.024).Conclusions: The combination therapy group had higher incidences of any bleeding events compared to the monotherapy in patients with appropriate dosing. However, there was no difference in stroke/SE, and MACE. The bleeding risk in AF patients taking the combination of NOACs and APT should be carefully evaluated. [ABSTRACT FROM AUTHOR]

The authors developed and validated a diagnostic algorithm using the optimal upper and lower cut‐off values of office and home BP at which ambulatory BP measurements need to be applied. Patients presenting with high BP (≥140/90 mm Hg) at the outpatient clinic were referred to measure office, home, and ambulatory BP. Office and home BP were divided into hypertension, intermediate (requiring diagnosis using ambulatory BP), and normotension zones. The upper and lower BP cut‐off levels of intermediate zone were determined corresponding to a level of 95% specificity and 95% sensitivity for detecting daytime ambulatory hypertension by using the receiver operator characteristic curve. A diagnostic algorithm using three methods, OBP‐ABP: office BP measurement and subsequent ambulatory BP measurements if office BP is intermediate zone; OBP‐HBP‐ABP: office BP, subsequent home BP measurement if office BP is within intermediate zone and subsequent ambulatory BP measurement if home BP is within intermediate zone; and HBP‐ABP: home BP measurement and subsequent ambulatory BP measurements if home BP is within intermediate zone, were developed and validated. In the development population (n = 256), the developed algorithm yielded better diagnostic accuracies than 75.8% (95%CI 70.1–80.9) for office BP alone and 76.2% (95%CI 70.5–81.3) for home BP alone as follows: 96.5% (95%CI: 93.4–98.4) for OBP‐ABP, 93.4% (95%CI: 89.6–96.1) for OBP‐HBP‐ABP, and 94.9% (95%CI: 91.5–97.3%) for HBP‐ABP. In the validation population (n = 399), the developed algorithm showed similarly improved diagnostic accuracy. The developed algorithm applying ambulatory BP measurement to the intermediate zone of office and home BP improves the diagnostic accuracy for hypertension. [ABSTRACT FROM AUTHOR]

AbstractGiven proper facilitation, pro-environmental behaviour in tourist destinations may spill over to the daily lives of tourists. Recent advancements in conversational artificial intelligence (AI) may lead to the emergence of more effective technology that will encourage people to become more environmentally friendly. However, limited research has been devoted to understanding the complexity of using such technologies for pro-social nudging. This study applies rigorous scale development procedures to test how advanced conversational AI, like chatbots, can be effective in encouraging pro-environmental behaviour spillover. The conceptual model introduces seven factors that predict people’s intention to use the chatbot and three factors that predict their intention to embrace the target behaviour. Performance expectancy and timing were the most significant in predicting the intention of individuals to adopt the technology. Efficiency and government support were the chief drivers of the intention to adopt pro-environmental behaviour. The findings suggest that although introducing a technology-mediated nudging mechanism can trigger people’s intention to behave pro-environmentally, other factors must be fulfilled in order to ensure sustained behaviour change in society. [ABSTRACT FROM AUTHOR]