Are community-based nurse-led self-management support interventions effective in chronic patients? Results of a systematic review and meta-analysis

The expansion of primary care and community-based service delivery systems is intended to meet emerging needs, reduce the costs of hospital-based ambulatory care and prevent avoidable hospital use by the provision of more appropriate care. Great emphasis has been placed on the role of self-management in the complex process of care of patient with long-term conditions. Several studies have determined that nurses, among the health professionals, are more recommended to promote health and deliver preventive programs within the primary care context. The aim of this systematic review and meta-analysis is to assess the efficacy of the nurse-led self-management support versus usual care evaluating patient outcomes in chronic care community programs. Systematic review was carried out in MEDLINE, CINAHL, Scopus and Web of Science including RCTs of nurse-led self-management support interventions performed to improve observer reported outcomes (OROs) and patients reported outcomes (PROs), with any method of communication exchange or education in a community setting on patients >18 years of age with a diagnosis of chronic diseases or multi-morbidity. Of the 7,279 papers initially retrieved, 29 met the inclusion criteria. Meta-analyses on systolic (SBP) and diastolic (DBP) blood pressure reduction (10 studies-3,881 patients) and HbA1c reduction (7 studies-2,669 patients) were carried-out. The pooled MD were: SBP -3.04 (95% CI -5.01--1.06), DBP -1.42 (95% CI -1.42--0.49) and HbA1c -0.15 (95% CI -0.32-0.01) in favor of the experimental groups. Meta-analyses of subgroups showed, among others, a statistically significant effect if the interventions were delivered to patients with diabetes (SBP) or CVD (DBP), if the nurses were specifically trained, if the studies had a sample size higher than 200 patients and if the allocation concealment was not clearly defined. Effects on other OROs and PROs as well as quality of life remain inconclusive

Investigating White Matter Lesion Load, Intrinsic Functional Connectivity, and Cognitive Abilities in Older Adults

Changes to the while matter of the brain disrupt neural communication between spatially distributed brain regions and are associated with cognitive changes in later life. While approximately 95% of older adults experience these brain changes, not everyone who has significant white matter damage displays cognitive impairment. Few studies have investigated the association between white matter changes and cognition in the context of functional brain network integrity. This study used a data-driven, multivariate analytical model to investigate intrinsic functional connectivity patterns associated with individual variability in white matter lesion load as related to fluid and crystallized intelligence in a sample of healthy older adults (n = 84). Several primary findings were noted. First, a reliable pattern emerged associating whole-brain resting-state functional connectivity with individual variability in measures of white matter lesion load, as indexed by total white matter lesion volume and number of lesions. Secondly, white matter lesion load was associated with increased network disintegration and dedifferentiation. Specifically, lower white matter lesion load was associated with greater within- versus between-network connectivity. Higher white matter lesion load was associated with greater between-network connectivity compared to within. These associations between intrinsic functional connectivity and white matter lesion load were not reliably associated with crystallized and fluid intelligence performance. These results suggest that changes to the white matter of the brain in typically aging older adults are characterized by increased functional brain network dedifferentiation. The findings highlight the role of white matter lesion load in altering the functional network architecture of the brain

The Design of Interactive Visualizations and Analytics for Public Health Data

Public health data plays a critical role in ensuring the health of the populace. Professionals use data as they engage in efforts to improve and protect the health of communities. For the public, data influences their ability to make health-related decisions. Health literacy, which is the ability of an individual to access, understand, and apply health data, is a key determinant of health. At present, people seeking to use public health data are confronted with a myriad of challenges some of which relate to the nature and structure of the data. Interactive visualizations are a category of computational tools that can support individuals as they seek to use public health data. With interactive visualizations, individuals can access underlying data, change how data is represented, manipulate various visual elements, and in certain tools control and perform analytic tasks. That being said, currently, in public health, simple visualizations, which fail to effectively support the exploration of large sets of data, are predominantly used. The goal of this dissertation is to demonstrate the benefit of sophisticated interactive visualizations and analytics. As improperly designed visualizations can negatively impact users’ discourse with data, there is a need for frameworks to help designers think systematically about design issues. Furthermore, there is a need to demonstrate how such frameworks can be utilized. This dissertation includes a process by which designers can create health visualizations. Using this process, five novel visualizations were designed to facilitate making sense of public health data. Three studies were conducted with the visualizations. The first study explores how computational models can be used to make sense of the discourse of health on a social media platform. The second study investigates the use of instructional materials to improve visualization literacy. Visualization literacy is important because even when visualizations are designed properly, there still exists a gap between how a tool works and users’ perceptions of how the tool should work. The last study examines the efficacy of visualizations to improve health literacy. Overall then, this dissertation provides designers with a deeper understanding of how to systematically design health visualizations

Information Bias Condemning Radical Food Innovators? The Case of Insect-Based Products in the Netherlands

In this paper we analyze whether information bias is affecting consumers’ WTP for radical food innovations. We collect data in the Netherlands on consumers’ WTP for insect-based products. We used product attributes directly affected by information and EU legislation such as the visualization of insects on the products, the use of logo and health claims, different information treatments on positive environmental and social effects of eating insects as meat-substitutes. Results indicate that visualization negatively influenced consumers’ WTP while information treatments do not mitigate this effect. We derive that EU legislators need to move fast in clarifying the status of insect-based foodsradical innovationinsect-based food productsconsumer acceptancethe NetherlandsPublishe

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 341)

This bibliography lists 133 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

무작위 배정을 이용한 미세먼지 건강영향 정보 지도시각화 효과 평가

학위논문(석사)--서울대학교 대학원 :보건대학원 보건학과(보건학전공),2019. 8. 황승식.2013년부터 대기오염 위험에 관한 기사가 쏟아지기 시작했으며 대기 오염의 건강 위험에 대한 우려 또한 증가하였다. 시민들의 우려가 증가하면서 정부의 위험 커뮤니케이션의 중요성이 부각되었다. 본 연구에서는 효과적인 위험 소통을 위한 효율적인 의사 소통 수단으로 잘 알려진 시각화에 초점을 맞추어 위험 소통 측면에서 대기 오염으로 인한 건강 정보의 시각화 효과를 확인하고자 한다. 179명이 참여한 2×3형태의 무작위 배정 연구이다. 건강 정보 제공 여부와 텍스트, 인포그래픽, 맞춤형 3가지 모델에 따른 결과(정보의 유용성, 쉬운이해정도, 정보탐색 동기 정도, 대처 실천 정도, 위험 심각성 인지 정도, 주관적 위험 인지 정도)를 확인할 것이다. 순위형 자료를 이용한 2요인 분석방법(two-way ordinal analysis)으로 분석하였다. 정보의 유용성, 쉬운이해정도, 정보탐색 동기 정도, 대처 실천 정도, 주관적 위험 인지 정도에서 모델별로 유의한 차이가 나타났다. 유용성, 심각성, 위험 심각성인지, 예방 동기 측면에서, 건강 정보 제공에 따라 모델별로 유의한 차이가 있었다. 하지만, 모델과 건강 정보 제공 유무에 따른 인터렉션 효과는 나타나지 않았다. 본 연구는 정보의 유용성, 쉬운이해정도, 정보탐색 동기정도, 대처실천정도, 위험심각성 인지, 주관적 위험인지 정도에 대한 응답을 통해 대기오염으로인한 건강정보를 지도시각화한 효과를 확인하였다. 또한 건강 정보 제공 유무에 따른 효과를 확인하였다. 본 연구는 무작위배정의 강점을 가지고 있지만, 실험적 연구이기 대문에 내적 타당성의 한계가 있다. 또한 주관적 측정방법인 설문 연구를 수행하여 객관적 측정 지표가 아니라는 한계가 있으므로 시선추적(eye-tracking)과 같은 객관적인 측정방법을 사용한 연구가 필요하다. 하지만, 본 연구를 통해 시각화 또는 맞춤형 자료일수록, 건강정보를 포함할 수록 위험소통에 효과적임을 알 수 있었다. 위험소통시 정보전달에 시각화와 맞춤화를 사용한다면 유용성, 정보탐색 동기정도, 위험 심각성 인지, 대처실천 동기 정도를 효과적으로 높일 수 있을 것이다. 또한 위험소통에 건강 정보를 포함한다면, 위험에 대한 심각성, 위험 인지, 대처 실천 정도가 증가할 것이다. 하지만, 더욱 효과적인 건강정보의 리터러시 향상 측면에서, 상세한 시각화, 맞춤화 방법과 건강 정보 표현, 제공 방법에 대한 논의가 필요하다.From 2013, as the articles about the risk of air pollution have been flooded, the concerns on the health risk of air pollution have been increasing. As rises the worrying, the importance of the governments risk communication arises, too. In this study, we identified the effectiveness of visualized health information on air pollution, which is well-known as an effective way of communication to efficient communication. It is Randomized study with 179 participants. It is 2 × 3 study design with two steps: first is the availability of the information about health information, second is model: text, infographic, tailored. We analyzed the outcome with two-way ordinal analysis with CLM. In usefulness, ease, motivation to search for information, the severity of the risk, and motivation for taking action of prevention, there is a significant difference by models. In the severity of the risk, perception of the risk, and the motivation for taking action of prevention, there is a significant difference by the availability of health information. However, there is no interaction effect by model and availability of health information in all questions. Through the scores of the usefulness of information, ease of information, motivation to search for the information, the severity of the risk, perception of the risk, and motivation for taking action of prevention, this study explored to find out the effectiveness of the geo-visualized health information of air pollution, also, the effectiveness of the availability of health information. This explorative study has a strength of randomized comparison and tried to minimize the biases (e.g., allocation, confounding), but there is a limitation of internal validity. Also, this study has a limitation in objectives because of using the questionnaire. The questionnaire is a subjective measuring tool, so there is a need to study in an objective approach like eye-tracking. Through this study, we could identify that it is the more visualized or tailored (personalized) or including the health information, it is more effective in risk communication. If using the visualized or the tailored (personalized) method to risk communication, then communicate more efficient in usefulness, motivating to search for information, risk severity, motivating for taking action of prevention. If using the health information in risk communication, then the severity of the risk, the perception of the risk, and the motivation for taking action of prevention are increasing. However, for improving the literacy of health information, details of visualization or personalization methods, and the way of providing the health information (for example, using the plain words) are needs more discussion to apply in practice.Introduction 1 1. Background 1 2. Geo-Visualization of Health Information of Air Pollution 5 3. Literature Review 21 4. Objectives and Hypothesis 28 Method 33 1. Participants 33 2. Study Design 35 3. Outcomes 53 4. Statistical methods 57 Results 59 1. Characteristics of Participants 59 2. Distributions (%) of the score in each question by model and availability of health information 68 3. Results of Randomized comparision 71 4. Sensitivity analysis 77 Discussion 84 Conclusion 90 Reference 92 Appendix 99 1. Used data 99 2. Full Questionnaire sheets 100 3. Histogram by model and health information in each Question 115 4. Summary of Likert score by Health and Model in each Question 118 5. CONSORT checklist 120 Abstract (Korean) 125Maste

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review