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Objective:. The primary objective of this study was to develop a length of stay (LOS) prediction model. Background:. Predicting the LOS is crucial for patient care, planning, managing expectations, and optimizing hospital resources. Prolonged LOS after colorectal surgery is largely influenced by complications, and an accurate prediction model could significantly benefit patient outcomes and healthcare efficiency. Methods:. This study included patients who underwent colorectal surgery in 14 different hospitals between January 2016 and December 2020. Two distinct random forest models were developed: one solely based on preoperative variables (preoperative prediction model [PP model]) and the other incorporating both preoperative and intraoperative variables (intraoperative prediction model [IP model]). Both models underwent validation using 10-fold cross-validation. The discriminative power of the model was assessed using the area under the curve (AUC), and calibration was evaluated using a calibration curve. The 2 developed models were compared using DeLong test. Results:. A total of 2140 patients were included in the analysis. After internal validation, the PP model achieved an AUC of 0.75 (95% confidence interval [CI]: 0.73–0.77), and the IP model achieved an AUC of 0.84 (95% CI: 0.82–0.85). The difference in discrimination between the 2 models was statistically significant (DeLong test, P < 0.001). Both models exhibited good calibration. Conclusions:. Incorporating intraoperative parameters enhances the accuracy of the predictive model for LOS after colorectal surgery. Improving LOS prediction can assist in managing the increasing number of patients and optimizing the allocation of healthcare resources.

The Netherlands police are looking for measures to examine sentiment on social media related to protest demonstrations. While models exist to detect more subtle expressions of sentiment within tweets, models trained in the Dutch language are scarce. Being able to predict sentiment development during protests is relevant for parties like the Dutch government and the police to get more insight to when and where potential law enforcement is needed for public order and safety. Therefore, to analyse sentiment before, during, and after protest demonstrations, data was collected with tweets related to a Black Lives Matter protest that took place in Amsterdam during the COVID-19 pandemic. All tweets have been manually labelled by a dedicated open-source intelligence (OSINT) team within the Netherlands police following an established protocol. Both the data and the protocol are available, and interesting for researchers in natural language processing, topic detection, sentiment analysis, and protests analysis. The developed labelling tool for the labelling process is publicly available.

As global energy concerns escalate, there is a growing need for effective strategies to promote sustainable energy practices among individuals and communities. Gamification, the integration of game-design elements in non-game contexts, emerges as a promising tool to enhance user engagement and foster sustainable behaviour in energy management. In this review, we examine the theoretical aspects of gamification and its application in energy management in users’ households, highlighting its potential to transform repetitive or even monotonous tasks into engaging activities, focusing on studies that measure a long-term effect. We delve into various gamified elements adopted in long-term studies, such as feedback, social interactions, point systems, leader boards, narrative-driven challenges, etc., to understand their effect on user motivation and behavioural changes. From our set of studies, we found out that strong social game elements contribute the most to the long-term behaviour change of energy usage. One more condition of behaviour change is strong positive user satisfaction: the game should be engaging. We highlight the possible limitations of gamification in an energy management situation, a strong need for better practices of design and evaluation, and innovative approaches (such as DSM; Demand Side Management) in gamification for long-term engagement in household energy management.

In recent years, serious energy games (SEGs) garnered increasing attention as an innovative and effective approach to tackling energy-related challenges. This review delves into the multifaceted landscape of SEG, specifically focusing on their wide-ranging applications in various contexts. The study investigates potential enhancements in user engagement achieved through integrating social connections, personalization, and data integration. Among the main challenges identified, previous studies overlooked the full potential of serious games in addressing emerging needs in energy systems, opting for oversimplified approaches. Further, these studies exhibit limited scalability and constrained generalizability, which poses challenges in applying their findings to larger energy systems and diverse scenarios. By incorporating lessons learned from prior experiences, this review aims to propel the development of SEG toward more innovative and impactful directions. It is firmly believed that positive behavior changes among individuals can be effectively encouraged by using SEG.

Introduction Young people (aged 10–25 years) with chronic diseases are vulnerable to have reduced social participation and quality of life. It is important to empower young people to engage in their chronic diseases self-management. In comparison with traditional face-to-face care, interventions delivered through the internet and related technologies (eHealth) are less stigmatising and more accessible. Gamified eHealth self-management interventions may be particularly promising for young people. This systematic review aims at identifying (1) the game mechanics that have been implemented in eHealth interventions to support young people’s self-management of their chronic (somatic or psychiatric) diseases, (2) the investigators’ rationale for implementing such game mechanics and, if possible, (3) the effects of these interventions.Methods and analysis The Preferred Reporting Items for Systematic reviews and Meta-Analysis statement guidelines will be followed. A systematic search of the literature will be conducted in Embase, Psycinfo and Web of Science from inception until 30 August 2022. Studies will be eligible if focused on (1) young people (aged 10–25 years) with chronic diseases and (2) describing gamified eHealth self-management interventions. When possible, the effects of the gamified interventions will be compared with non-gamified interventions or care-as-usual. Primary quantitative, qualitative or mixed-method studies written in English will be included. Two independent reviewers will (1) select studies, (2) extract and summarise the implemented game mechanics as well as the characteristics of the intervention and study, (3) evaluate their methodological quality and (4) synthesise the evidence. The reviewers will reach a consensus through discussion, and if required, a third researcher will be consulted.Ethics and dissemination As systematic reviews use publicly available data, no formal ethical review and approval are needed. Findings will be published in peer-reviewed journals, presented at conferences and communicated to relevant stakeholders including patient organisations via the eHealth Junior Consortium.PROSPERO registration number CRD42021293037.

In teleoperations, robots are generally used because related tasks are too dangerous, uncomfortable or impossible for humans to perform. When using augmented reality to control robotic limbs in such teleoperations, it is essential to understand how these extra virtual limbs are experienced. In particular, the relationship between the embodiment experience of the user and relevant outcomes such as task performance must be examined. In this article, we study the relationship between experienced embodiment of a supernumerary virtual arm that acts alongside a user’s two real arms, and their task performance in augmented reality. Specifically, we compare how well users can trace a virtual half ring placed just outside of personal space using their virtual arm in a condition where there is expected to be low embodiment (a floating disconnected hand) and a condition where there is expected to be high embodiment (a connected arm and hand). Embodiment is measured quantitatively through skin conductance response and qualitatively through ownership, agency, and self-location questionnaires. Performance is measured in terms of tracing precision. The results show positive correlations between subjective ownership and agency, and agency and performance, but no correlation between subjective or objective ownership and performance. Also, ownership ratings were low overall, while the agency ratings were significantly higher for the disconnected hand condition than the connected arm condition, as was performance. Notably, the presence of the virtual arm evoked incorrect expectations of the movement capabilities of the arm, which may have contributed to an overall preference for the unrealistic disconnected hand over the more realistic connected arm in this particular task. Our results imply that methods to increase performance in various teleoperations can indeed be found in the experience of embodiment: not necessarily directly through ownership, but through ownership mediated by agency.

Abstract We introduce a novel spatiotemporal deformable part model for the localization of fine-grained human interactions of two persons in unsegmented videos. Our approach is the first to classify interactions and additionally provide the temporal and spatial extent of the interaction in the video. To this end, our models contain part detectors that support different scales as well as different types of feature descriptors, which are combined in a single graph. This allows us to model the detailed coordination between people in terms of body pose and motion. We demonstrate that this helps to avoid confusions between visually similar interactions. We show that robust results can be obtained when training on small numbers of training sequences (5–15) per interaction class. We achieve AuC scores of 0.82 with an IoU of 0.3 on the publicly available ShakeFive2 dataset, which contains interactions that differ slightly in their coordination. To further test the generalization of our models, we perform cross-dataset experiments where we test on two other publicly available datasets: UT-Interaction and SBU Kinect. These experiments show that our models generalize well to different environments.