Social networks and future direction for obesity research: A scoping review.
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ABSTRACT:
SUBMITTER: Nam S
PROVIDER: S-EPMC4438261 | biostudies-literature | 2015 May-Jun
REPOSITORIES: biostudies-literature
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Project description:INTRODUCTION:The rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it. METHODS:A version of Arksey and O'Malley's five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date. ETHICS AND DISSEMINATION:The review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.
| S-EPMC6303712 | biostudies-other
Project description:We conducted a scoping review of social ventures in obesity and developed a taxonomy of their interventions and business models. Sources included PubMed, Business Source Premier, ABI Inform, Factiva, Google, Facebook, Twitter, social entrepreneurship networks (Ashoka, Skoll, and Schwab), and social entrepreneurship competitions. Our review identified 512 social ventures in 32 countries; 93% originated from developed countries. Their areas of intervention included diet and nutrition, urban farming, physical activity, access to healthy food, and health literacy. They addressed factors beyond health such as education, affordability, employment, and the built and natural environments. To support their programs of work, social ventures developed various business models with multiple revenue or resource streams. Social ventures designed double-duty interventions that were aligned with additional meaningful social or environmental objectives. This "bundling" of objectives allowed social ventures to appeal to a wider target audience. Most of the social ventures were initiated, supported, or sustained by local communities. Social ventures offer financially self-sufficient approaches to obesity reduction and could potentially relieve the burden on healthcare systems. Policymakers should consider social entrepreneurs as partners in obesity prevention.
| S-EPMC9285950 | biostudies-literature
Project description:IntroductionSocial emergency medicine (EM) is an emerging field that examines the intersection of emergency care and social factors that influence health outcomes. We conducted a scoping review to explore the breadth and content of existing research pertaining to social EM to identify potential areas where future social EM research efforts should be directed.MethodsWe conducted a comprehensive PubMed search using Medical Subject Heading terms and phrases pertaining to social EM topic areas (e.g., "homelessness," "housing instability") based on previously published expert consensus. For searches that yielded fewer than 100 total publications, we used the PubMed "similar publications" tool to expand the search and ensure no relevant publications were missed. Studies were independently abstracted by two investigators and classified as relevant if they were conducted in US or Canadian emergency departments (ED). We classified relevant publications by study design type (observational or interventional research, systematic review, or commentary), publication site, and year. Discrepancies in relevant publications or classification were reviewed by a third investigator.ResultsOur search strategy yielded 1,571 publications, of which 590 (38%) were relevant to social EM; among relevant publications, 58 (10%) were interventional studies, 410 (69%) were observational studies, 26 (4%) were systematic reviews, and 96 (16%) were commentaries. The majority (68%) of studies were published between 2010-2020. Firearm research and lesbian, gay, bisexual, transgender, and queer (LGBTQ) health research in particular grew rapidly over the last five years. The human trafficking topic area had the highest percentage (21%) of interventional studies. A significant portion of publications -- as high as 42% in the firearm violence topic area - included observational data or interventions related to children or the pediatric ED. Areas with more search results often included many publications describing disparities known to predispose ED patients to adverse outcomes (e.g., socioeconomic or racial disparities), or the influence of social determinants on ED utilization.ConclusionSocial emergency medicine research has been growing over the past 10 years, although areas such as firearm violence and LGBTQ health have had more research activity than other topics. The field would benefit from a consensus-driven research agenda.
| S-EPMC8597693 | biostudies-literature
Project description:INTRODUCTION:Research about autism spectrum disorder (ASD) in sub-Saharan Africa is sparse and limited. However, research studies coming from Nigeria in West Africa as a country have contributed significantly to the subject of ASD in sub-Saharan Africa. METHODS AND RESULTS:This study is a scoping review of research literature on ASD in Nigeria through June 2017 conducted by searching the key terms autism and Nigeria on PubMed and Google Scholar. Included in the review are works concerning case reports and co-morbidities associated with ASD in Nigeria; prevalence of ASD; genetic study of ASD; clinical characteristics of children with ASD; knowledge and awareness about ASD; socio-cultural factors affecting ASD. Expert opinion on social policy and public health issues affecting ASD in Nigeria are discussed. CONCLUSION:Wide research and intervention gaps are noted and we suggest directions for future research, social policy development, and intervention services for individuals with ASD in Nigeria.
| S-EPMC6815667 | biostudies-literature
Project description:Sport management scholars have called for the application of broader research approaches, including critical social science. Such approaches help uncover the less-desirable aspects of sport and, therefore, offer a basis for positive change. While there have been advancements in the use of innovative research approaches over time, there remains little understanding of how these calls have been addressed. The purpose of this scoping review is to examine trends, gaps, and the use of critical social science and associated approaches in sport management scholarship. Two hundred sixteen relevant articles were identified through a database search (i.e., five platforms), complemented with a manual search of 419 journals. Results indicate that an increase in critical research published in sport management journals was evident following 2005. Findings suggest that there remains space for increased publication of critical social science work in sport management journals and for researchers to better articulate their research approaches in scholarly outputs.
| S-EPMC8831802 | biostudies-literature
Project description:BackgroundObesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research.ObjectiveThis scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications.MethodsWe conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques.ResultsWe identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review.ConclusionsThis study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.
| S-EPMC9856437 | biostudies-literature
Project description:BackgroundAs social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research.ObjectiveThis scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods.MethodsWe followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence.ResultsAfter screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest.ConclusionsSince their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective.International registered report identifier (irrid)RR2-10.1136/bmjopen-2020-040671.
| S-EPMC8193478 | biostudies-literature
Project description:IntroductionThough the relationship between social capital and health has been widely studied, the evidence of this relationship in cardiovascular disease is limited, with varied and inconsistent measures. This scoping review seeks to address this gap by answering the following questions: (1) How has social capital been characterized and measured in the literature related to cardiovascular disease? and (2) What gaps exist in the evaluation of the relationship between social capital and cardiovascular disease?Materials and methodsA scoping review will be used to answer the research questions. The scoping review will apply established methods described by Arksey and O'Malley, Levac and colleagues, and the Joanne Briggs Institute: (1) identifying the research question(s); (2) identifying relevant studies; (3) selecting the studies; (4) charting the data; and (5) collating, summarizing, and reporting the results.ResultsOur findings will be reported in accordance with the guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement.DiscussionThe synthesis of this evidence base is intended to provide a framework for how social capital has been defined and measured in the cardiovascular literature, with additional guidance for future research and evaluation. The findings will be disseminated through peer-reviewed publication and presentations at relevant seminars.
| S-EPMC8031438 | biostudies-literature
Project description:Background: Culinary medicine (CM), an emerging discipline, is a novel approach that focuses on the art of food and cooking to prevent or improve health outcomes among chronic patients suffering from lifestyle diseases. The concept originated in the USA, gaining interest from scholars in medicine, nutrition, nursing, and the gastronomic discipline. Notably, in the last five years, there has been exponential growth in CM literature. In this regard, this study sought to examine the growth, performance and distinct research themes of CM literature over time. Methods: To achieve the study's objectives, this study employs descriptive, performance and bibliometric analysis. The descriptive analysis was applied to examine the growth of the CM literature since its emergence. The performance analysis was used to identify the most influential journals, articles, and authors in the CM domain. The bibliographic coupling analysis was adopted to discover the various research themes of the CM knowledge base. Results: This study identifies three stages of literature development: Early stage, modest growth stage, and emerging stage. Further, the results indicate that most of the studies on CM had been conducted in developed countries. Our findings reveal a clear interest in integrating the CM curriculum into medical/nutrition education programs in recent years. Additionally, the study discovers four distinct main research themes: knowledge assessment, impact measurement, acceptance and efficacy, and implementation of CM. Conclusions: These findings are helpful for scholars in medicine, nutrition, nursing, and gastronomy as they provide an overview of CM's development and research focus. Future studies could focus on expanding the geographical distribution of research on CM and further exploring the identified research themes to gain a deeper understanding of the potential of this approach for improving health outcomes among chronic disease patients.
| S-EPMC10696491 | biostudies-literature
Project description:According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.
| S-EPMC7649702 | biostudies-literature