Cervical spinal manipulations (CSM) are frequently employed techniques to alleviate neck pain and headache. Minor and major complications following CSM have been described, though clear consensus on definition and the classification of the complications had not yet been achieved. As a result, incidence rates may be underestimated. The aim of this study was to develop a consensus-based classification of adverse events following cervical spinal manipulations which has good feasibility in clinical practice and research. Design: A three round Delphi-study. Medical specialists, manual therapists, and patients (n=30) participated in an online survey. In Round 1, participants were invited to select a classification system of adverse events. Potential complications were inventoried and detailed in accordance with the ICF and the ICD-10. In Round 2, panel members categorized the potential complications in their selected classification. During the third round, it was inquired of the participants whether they concurred with the answer of the majority of participants. Results: Thirty four complications were defined. Consensus was achieved for 29 complications for all durations [hours, days, weeks]. For the remaining five complications, consensus was reached for two of the three durations [hours, days, weeks]. Conclusions: A consensus-based classification system of adverse events after cervical spinal manipulation was developed which comprises patients’ and clinicians’ perspectives and has only a small number of categories. The classification system includes a precise description of potential adverse events and is based on international accepted classifications (ICD-10 and ICF). This classification system may be useful for utilization in both clinical practice and research.
Background: Adverse Childhood Experiences (ACEs) are an overlooked risk factor for behavioural, mental and physical health disparities in children with intellectual disabilities (ID) and borderline intellectual functioning (BIF). Aims: To gain insight into the presence of the 10 original Wave II ACEs and family context risk variables in a convenience sample of children with ID and BIF in Dutch residential care. Methods and procedures: 134 case-files of children with ID (n = 82) and BIF (n = 52) were analysed quantitatively. Outcomes and results: 81.7 % of the children with ID experienced at least 1 ACE, as did 92.3 % of the children with BIF. The average number of ACEs in children with ID was 2.02 (range 0???? 8) and in children with BIF 2.88 (range 0???? 7). About 20 % of the children with moderate and mild ID experienced 4 ACEs or more. Many of their families faced multiple and complex problems (ID: 69.5 %; BIF 86.5 %). Multiple regression analysis indicated an association between family context risk variables and the number of ACEs in children. Conclusions and implications: The prevalence of ACEs in children with ID and BIF appears to be considerably high. ACEs awareness in clinical practice is vital to help mitigate negative outcomes.
Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
Movebite aims to combat the issue of sedentary behavior prevalent among office workers. A recent report of the Nederlandse Sportraad reveal a concerning trend of increased sitting time among Dutch employees, leading to a myriad of musculoskeletal discomforts and significant health costs for employers due to increased sick leave. Recognizing the critical importance of addressing prolonged sitting in the workplace, Movebite has developed an innovative concept leveraging cutting-edge technology to provide a solution. The Movebite app seamlessly integrates into workplace platforms such as Microsoft Teams and Slack, offering a user-friendly interface to incorporate movement into their daily routines. Through scalable AI coaching and real-time movement feedback, Movebite assists individuals in scheduling and implementing active micro-breaks throughout the workday, thereby mitigating the adverse effects of sedentary behavior. In collaboration with the Avans research group Equal Chance on Healthy Choices, Movebite conducts user-centered testing to refine its offerings and ensure maximum efficacy. This includes testing initiatives at sports events, where the diverse crowd provides invaluable feedback to fine-tune the app's features and user experience. The testing process encompasses both quantitative and qualitative approaches based on the Health Belief Model. Through digital questionnaires, Movebite aims to gauge users' perceptions of sitting as a health threat and the potential benefits of using the app to alleviate associated risks. Additionally, semi-structured interviews delve deeper into user experiences, providing qualitative insights into the app's usability, look, and feel. By this, Movebite aims to not only understand the factors influencing adoption but also to tailor its interventions effectively. Ultimately, the goal is to create an environment encouraging individuals to embrace physical activity in small, manageable increments, thereby fostering long-term engagement promoting overall well-being.Through continuous innovation and collaboration with research partners, Movebite remains committed to empowering individuals to lead healthier, more active lifestyles, one micro-break at a time.
