¿Influye la comunicación con el paciente sobre la kinesiofobia? Una revisión sistemática / Does patient communication influence kinesiophobia? A systematic review

El objetivo de la presente revisión sistemática consistió en determinar el efecto de la comunicación en el ámbito sanitario sobre la kinesiofobia. Para ello, se realizó una búsqueda bibliográfica en siete bases de datos entre noviembre de 2022 y febrero de 2023. La revisión se efectuó acorde a la declaración Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) y para el análisis de la calidad metodológica se utilizaron: la escala Physiotherapy Evidence Database (PEDro), los criterios de Van Tulder y el análisis del riesgo de sesgo de la Colaboración Cochrane. Se incluyeron un total de 13 artículos que presentaron una calidad metodológica media de 7,1 sobre 10. Se obtuvieron resultados significativos para al menos una variable (kinesiofobia, discapacidad o nivel de actividad física) en 12 trabajos. Existe evidencia sólida de que la comunicación puede influir sobre la kinesiofobia del sujeto. Es más probable que esta influencia ocurra en un sentido negativo o discapacitante, pero también puede actuar en sentido positivo disminuyendo la misma. (AU)

The aim of the present systematic review was to determine the effect of communication in the health care setting on kinesiophobia. To this end, a literature search was conducted in seven databases between November 2022 and February 2023. The review was carried out following the PRISMA statement and for the analysis of methodological quality we used: PEDro Scale, Van Tulder criteria and risk of bias analysis of the Cochrane Collaboration. A total of 13 articles were included with a mean methodological quality of 7.1 out of 10. Significant results were obtained for at least one variable (kinesiophobia, disability or level of physical activity) in 12 articles. There is strong evidence that communication can influence a subject's kinesiophobia. This influence is most likely to be in a negative or disabling sense, but it can also act in a positive sense by decreasing it. (AU)

Social network analysis for medical narcotics in South Korea: focusing on patients and healthcare organizations.

BACKGROUND: Medical narcotics must be administered under medical supervision because of their potential for misuse and abuse, leading to more dangerous and addictive substances. The control of medical narcotics requires close monitoring to ensure that they remain safe and effective. This study proposes a methodology that can effectively identify the overprescription of medical narcotics in hospitals and patients. METHODS: Social network analysis (SNA) was applied to prescription networks for medical narcotics. Prescription data were obtained from the Narcotics Information Management System in South Korea, which contains all data on narcotic usage nationwide. Two-mode networks comprising hospitals and patients were constructed based on prescription data from 2019 to 2021 for the three most significant narcotics: appetite suppressants, zolpidem, and propofol. Two-mode networks were then converted into one-mode networks for hospitals. Network structures and characteristics were analyzed to identify hospitals suspected of overprescribing. RESULTS: The SNA identified hospitals that overprescribed medical narcotics. Patients suspected of experiencing narcotic addiction seek treatment in such hospitals. The structure of the network was different for the three narcotics. While appetite suppressants and propofol networks had a more centralized structure, zolpidem networks showed a less centralized but more fragmented structure. During the analysis, two types of hospitals caught our attention: one with a high degree, meaning that potential abusers have frequently visited the hospital, and the other with a high weighted degree, meaning that the hospital may overprescribe. For appetite suppressants, these two types of hospitals matched 84.6%, compared with 30.0% for propofol. In all three narcotics, clinics accounted for the largest share of the network. Patients using appetite suppressants were most likely to visit multiple locations, whereas those using zolpidem and propofol tended to form communities around their neighborhoods. CONCLUSIONS: The significance of this study lies in its analysis of nationwide narcotic use reports and the differences observed across different types of narcotics. The social network structure between hospitals and patients varies depending on the composition of the medical narcotics. Therefore, these characteristics should be considered when controlling medication with narcotics. The results of this study provide guidelines for controlling narcotic use in other countries.

Individual-centralized seeding strategy for influence maximization in information-limited networks.

Peer effects can directly or indirectly rely on interaction networks to drive people to follow ideas or behaviours triggered by a few individuals, and such effects can be largely improved by targeting the so-called influential individuals. In this article, we study the current most promising seeding strategy used in field experiments, the one-hop strategy, where the underlying interaction networks are generally too impractical or prohibitively expensive to be obtained, and propose an individual-centralized seeding approach to target influential seeds in information-limited networks. The presented strategy works by reasonable follow-up questions to respondents, such as Who do you think has more connections/friends?, and constructs the seeding set by those nodes with the most nominations. In this manner, the proposed method could acquire more information about the studied interaction network from the inference of respondents without surveying additional individuals. We evaluate our strategy on networks from various experimental datasets. Results show that the obtained seeds are much more influential compared to the one-hop strategy and other methods. We also show how the proposed approach could be implemented in field studies and potentially provide better interventions in real scenarios.

Variation in Trust in Cancer Information Sources by Perceptions of Social Media Health Mis- and Disinformation and by Race and Ethnicity Among Adults in the United States: Cross-Sectional Study.

BACKGROUND: Mis- and disinformation on social media have become widespread, which can lead to a lack of trust in health information sources and, in turn, lead to negative health outcomes. Moreover, the effect of mis- and disinformation on trust in information sources may vary by racial and ethnic minoritized populations. OBJECTIVE: We evaluated how trust in multiple sources of cancer information varied by perceptions of health mis- and disinformation on social media and by race and ethnicity. METHODS: Cross-sectional, nationally representative survey data from noninstitutionalized adults in the United States from the 2022 Health Information National Trends Survey 6 (HINTS 6) were analyzed (N=4137). The dependent variable measured the level of trust in cancer information sources. The independent variables were perceptions about health mis- and disinformation on social media and race and ethnicity. Multivariable logistic regression models were adjusted for survey weight and design, age, birth gender, race and ethnicity, marital status, urban/rural designation, education, employment status, feelings about household income, frequency of social media visits, and personal and family history of cancer. We also tested the interaction effect between perceptions of social media health mis- and disinformation and participants' self-reported race and ethnicity. RESULTS: Perception of "a lot of" health mis- and disinformation on social media, relative to perception of "less than a lot," was associated with a lower likelihood of high levels of trusting cancer information from government health agencies (odds ratio [OR] 0.60, 95% CI 0.47-0.77), family or friends (OR 0.56, 95% CI 0.44-0.71), charitable organizations (OR 0.78, 95% CI 0.63-0.96), and religious organizations and leaders (OR 0.64, 95% CI 0.52-0.79). Among White participants, those who perceived a lot of health mis- and disinformation on social media were less likely to have high trust in cancer information from government health agencies (margin=61%, 95% CI 57%-66%) and family or friends (margin=49%, 95% CI 43%-55%) compared to those who perceived less than a lot of health mis- and disinformation on social media. Among Black participants, those who perceived a lot of health mis- and disinformation on social media were less likely to have high trust in cancer information from religious organizations and leaders (margin=20%, 95% CI 10%-30%) compared to participants who perceived no or a little health mis- and disinformation on social media. CONCLUSIONS: Certain sources of cancer information may need enhanced support against the threat of mis- and disinformation, such as government health agencies, charitable organizations, religious organizations and leaders, and family or friends. Moreover, interventions should partner with racial and ethnically minoritized populations that are more likely to have low trust in certain cancer information sources associated with mis- and disinformation on social media.

Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Quantifying synergy and redundancy between networks.

Understanding how different networks relate to each other is key for understanding complex systems. We introduce an intuitive yet powerful framework to disentangle different ways in which networks can be (dis)similar and complementary to each other. We decompose the shortest paths between nodes as uniquely contributed by one source network, or redundantly by either, or synergistically by both together. Our approach considers the networks' full topology, providing insights at multiple levels of resolution: from global statistics to individual paths. Our framework is widely applicable across scientific domains, from public transport to brain networks. In humans and 124 other species, we demonstrate the prevalence of unique contributions by long-range white-matter fibers in structural brain networks. Across species, efficient communication also relies on significantly greater synergy between long-range and short-range fibers than expected by chance. Our framework could find applications for designing network systems or evaluating existing ones.

Identification of potential biomarkers for bone metastasis using human cancer metastasis database.

Objective: Information theory has been successfully employed to identify optimal pathway networks, mutual information (MI), and entropy as a dynamic response in statistical methods and estimate input and output information in systems biology. This research aims to investigate potentially integrated gene signatures for bone metastasis using graph-based information theory from the dynamic interaction interphase. Methods: The expression dataset with the series ID GSE26964 for bone metastasis from prostate cancer was retrieved. The dataset was segregated for differentially expressed genes (DEGs) using the Human Cancer Metastasis Database. MI was considered to capture non-linear connections to classify the key DEGs from the collected dataset using gene-gene statistical analysis and then a protein-protein interaction network (PPIN). The PPIN was used to calculate centrality metrics, bottlenecks, and functional annotations. Results: A total of 531 DEGs were identified. Thirteen genes were classified as highly correlated based on their gene expression data matrix. The extended PPIN of the 13 genes comprised 53 nodes and 372 edges. A total of four DEGs were identified as hubs. One novel gene was identified with strong network connectivity. Conclusion: The novel biomarkers for metastasis may provide information on cancer metastasis to the bone by implying MI and information theory.

Tracing the Untraceables: A Joint Outbreak Investigation With Law Enforcement Using a Geographic Information System.

The application of geospatial data often allows the tracing of people who are involved in activities of an illegal nature. In June 2021, we estimated the true magnitude of the spread of COVID-19 within the networks of escort-karaoke bars in Seoul, Republic of Korea, using geographic information system (GIS)-based contact tracing that was applied to our epidemiological investigation. Our joint rapid response team, composed of epidemic investigation officers and police personnel, identified 19 paper-traced cases and 158 GIS-traced cases from 5,692 confirmed cases in Seoul during the study period (June to July 2021). Our findings suggest that collaboration with law enforcement agencies and the use of overlaid satellite imagery in outbreak investigations enhances high vigilance and reduces the risk of potential breaches of human rights in the process.

Associations between information and communication technology use and frailty in community-dwelling old-old adults: results from the ILSA-J.

OBJECTIVES: Frailty is a significant cause of adverse health events including long-term care and hospitalization. Although information and communication technology (ICT) has become an integral part of modern life, it remains unclear whether ICT use is associated with frailty. DESIGN: A cross-sectional study (Integrated Longitudinal Studies on Aging in Japan, ILSA-J). SETTING AND PARTICIPANTS: Aged 75 and older data from the ILSA-J in 2017 (n = 2893). METHODS: ICT use was measured using the technology usage sub-items of the Japan Science and Technology Agency Index of Competence. Specifically, the use of mobile phones, ATMs, DVD players, and sending e-mails were rated as "yes" (able to do) or "no" (unable to do), with the first quintile (≤1 point) defined as ICT non-users. Frailty was assessed using the Japanese version of the Cardiovascular Health Study criteria based on the phenotype model (e.g., weight loss, slowness, weakness, exhaustion, and low activity). Further, multivariate logistic regression analysis analyzed its association with ICT use. Subgroup analyses were stratified according to gender, years of education, and living arrangements. RESULTS: Higher ICT use was not associated with frailty after adjusting for covariates (odds ratio [OR]: 0.53; 95%CI 0.39-0.73). Similar associations were found in the sub-groups of women (OR 0.45, 95%CI 0.30-0.66), <13 years of education (OR 0.48, 95%CI 0.34-0.67), living alone (OR 0.46, 95%CI 0.27-0.79), and living together (OR 0.57, 95%CI 0.38-0.85). No association existed between using ICT and frailty in the sub-groups of men and ≥13 years of education. CONCLUSIONS AND IMPLICATIONS: Higher ICT use is associated with the absence of frailty in individuals 75 years and older. Such benefits may be particularly pronounced in women, those with lower levels of education, and older adults living alone or with others.

Clinical Physician Assistant Students' Perceptions of Evidence-Based Medicine Resources: A Cross-Sectional Examination.

Evidence-based medicine (EBM) instruction is required for physician assistant (PA) students. As a follow-up to an initial didactic year survey, this study seeks to understand which attributes of EBM resources clinical PA students find most and least useful, their self-efficacy utilizing medical literature, and their usage of EBM tools in the clinic. Results indicate that students preferred UpToDate and PubMed. PA students valued ease of use, which can inform instructors and librarians. Respondents utilized EBM tools daily or a few days a week, underscoring the importance of EBM tools in real-world scenarios. After their clinical year, students felt moderately confident utilizing the medical literature, emphasizing EBM training.

Getting to Know Named Entity Recognition: Better Information Retrieval.

Named entity recognition (NER) is a powerful computer system that utilizes various computing strategies to extract information from raw text input, since the early 1990s. With rapid advancement in AI and computing, NER models have gained significant attention and been serving as foundational tools across numerus professional domains to organize unstructured data for research and practical applications. This is particularly evident in the medical and healthcare fields, where NER models are essential in efficiently extract critical information from complex documents that are challenging for manual review. Despite its successes, NER present limitations in fully comprehending natural language nuances. However, the development of more advanced and user-friendly models promises to improve work experiences of professional users significantly.

The use of fixed study main effects in arm-based network meta-analysis.

Methods of network meta-analysis (NMA) can be classified as arm-based and contrast-based approaches. There are several arm-based approaches, and some of these have been criticized because they recover inter-study information and hence do not obey the principle of concurrent control. Here, we point out that recovery of inter-study information in arm-based NMA can be prevented by fitting a fixed main effect for studies. Advantages of arm-based NMA are discussed.

Layer 1 NDNF interneurons are specialized top-down master regulators of cortical circuits.

Diverse types of inhibitory interneurons (INs) impart computational power and flexibility to neocortical circuits. Whereas markers for different IN types in cortical layers 2-6 (L2-L6) have been instrumental for generating a wealth of functional insights, only the recent identification of a selective marker (neuron-derived neurotrophic factor [NDNF]) has opened comparable opportunities for INs in L1 (L1INs). However, at present we know very little about the connectivity of NDNF L1INs with other IN types, their input-output conversion, and the existence of potential NDNF L1IN subtypes. Here, we report pervasive inhibition of L2/3 INs (including parvalbumin INs and vasoactive intestinal peptide INs) by NDNF L1INs. Intersectional genetics revealed similar physiology and connectivity in the NDNF L1IN subpopulation co-expressing neuropeptide Y. Finally, NDNF L1INs prominently and selectively engage in persistent firing, a physiological hallmark disconnecting their output from the current input. Collectively, our work therefore identifies NDNF L1INs as specialized master regulators of superficial neocortex according to their pervasive top-down afferents.

GNN-based structural information to improve DNN-based basal ganglia segmentation in children following early brain lesion.

Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by the presence of a lesion. Although current deep neural networks (DNN) perform well in segmenting subcortical brain structures in healthy brains, they lack robustness when faced with lesion variability, leading to structural inconsistencies. Given the established spatial organization of the basal ganglia, we propose enhancing the DNN-based segmentation through post-processing with a graph neural network (GNN). The GNN conducts node classification on graphs encoding both class probabilities and spatial information regarding the regions segmented by the DNN. In this study, we focus on neonatal arterial ischemic stroke (NAIS) in children. The approach is evaluated on both healthy children and children after NAIS using three DNN backbones: U-Net, UNETr, and MSGSE-Net. The results show an improvement in segmentation performance, with an increase in the median Dice score by up to 4% and a reduction in the median Hausdorff distance (HD) by up to 93% for healthy children (from 36.45 to 2.57) and up to 91% for children suffering from NAIS (from 40.64 to 3.50). The performance of the method is compared with atlas-based methods. Severe cases of neonatal stroke result in a decline in performance in the injured hemisphere, without negatively affecting the segmentation of the contra-injured hemisphere. Furthermore, the approach demonstrates resilience to small training datasets, a widespread challenge in the medical field, particularly in pediatrics and for rare pathologies.

Dual-mode Hydrogels with Structural and Fluorescent Colors Towards Multistage Secure Information Encryption.

Constructing an anti-counterfeiting material with non-interference dual optical modes is an effective way to improve information security. However, it remains challenging to achieve multistage secure information encryption due to the limited stimulus responsiveness and color tunability of the current dual-mode materials. Herein, a dual-mode hydrogel with both independently tunable structural and fluorescent colors towards multistage information encryption is reported. In this hydrogel system, the rigid lamellar structure of poly(dodecylglyceryl itaconate) (pDGI) formed by shear flow-induced self-assembly provides the restricted domains wherein monomers undergo polymerization to form a hydrogel network, producing structural color. The introduction of fluorescent monomer 6-acrylamidopicolinate (6APA) as a complexation site provides the possibility of fluorescent color formation. The hydrogel's angle-dependent structural color can be controlled by adjusting the crosslinking density and water content. Additionally, the fluorescence color can be modulated by adjusting the ratio of lanthanide ions. Information of dual-mode can be displayed separately in different channels and synergistically overlayed to read the ultimate message. Thus, a multistage information encryption system based on this hydrogel is devised through the programmed decryption process. This strategy holds tremendous potential as a platform for encrypting and safeguarding valuable and authentic information in the field of anti-counterfeiting. This article is protected by copyright. All rights reserved.

[Advancing standards for the development of evidence-based health information for consumers: Needs and priorities among members of the Network for Evidence-based Medicine]. / Inhaltliche Weiterentwicklung von Standards zur Erstellung evidenzbasierter Gesundheitsinformationen: Eine Bedarfserhebung und Priorisierung im EbM-Netzwerk.

BACKGROUND: The Good Practice Guidelines for health information, Good Health Information Austria and Guideline Evidence-based Health Information are established resources for developing evidence-based health information in the German-speaking regions. The aim of this project was to capture challenges in applying these standards in practice, identify gaps and development needs and gain insights for their further development. METHODS: In December 2020, members of the working group for patient information and involvement of the Network for Evidence-based Medicine were invited to share their experiences and needs in applying German standards for evidence-based health information through an online survey focussing on open questions (part 1: needs assessment). The feedback was analysed using qualitative content analysis and presented in a workshop at the EbM Congress 2021 with the goal of specifying the feedback from the needs assessment and discussing ideas for the further development of the standards (part 2: specification). In the final step, a second survey was conducted in February 2023 to prioritize the identified topics by the working group members (part 3: prioritization). The results were analysed descriptively. RESULTS: Among the 41 participants, only 23% considered the standards to be sufficient, and only 55% found their application in the development of information to be easy or rather easy. The needs assessment and workshop (n=46) helped to identify various areas of action. With regard to the application of the standards, the following challenges were identified: lack of user orientation, content gaps, methods and risk communication. Gaps in the standards were identified regarding formats, content, and the connection to healthcare provision. For the advancement of the standards ideas for additional content, stakeholder involvement, and improvement of the usability of the standards were identified. In the prioritization survey, the topic areas, "presenting benefits and harms" and "content beyond treatments (in particular, diagnostics and prognosis)" were considered to be the most important (n=36). DISCUSSION: Among members of the working group for patient information and involvement of the German Network for Evidence-based Medicine, a high demand has been identified for the further development of standards for creating evidence-based health information. In addition to content development, the integration of existing documents and tools should also be considered, including products issued by other institutions. The success of advancing the standards also depends on improving their applicability - for example through an attractive online platform. The results are limited by the sample which only included members of the EbM Network's patient information and participation working group and a limited response rate. CONCLUSIONS: The needs assessment showed that the currently established standards and recommendations for the development of evidence-based health information in the German-speaking regions represent important cornerstones but need to be expanded to answer more practice-oriented questions. The challenges and proposed solutions stated by the participants can help further develop the standards. The prioritization can be used to set priorities for the development of the standards, guide the order of possible work packages and allocate resources.