Web Search Research Articles

Outpatient Medications Deimplemented by the AAP Bronchiolitis Guidelines: An Umbrella Review of Meta-Analyses.

The 2014 AAP Bronchiolitis Guidelines deimplemented or recommended against the routine therapeutic use of albuterol, hypertonic saline, and epinephrine for infants and children presenting in the outpatient setting. Our objective was to perform an umbrella review of all meta-analyses that included outpatient subanalyses or network meta-analyses with medication treatment comparisons to study the clinical benefits of these deimplemented medications in the outpatient (i.e., primary care, urgent care, and emergency department) setting. Searches were performed in the databases PubMed and Scopus and the Web search engine Google Scholar on the following three topics: albuterol and bronchiolitis, epinephrine and bronchiolitis, and hypertonic saline and bronchiolitis. Article types were limited to systematic reviews and meta-analyses with outpatient subanalyses, with English language and age restrictions. The search strategy was based on the Population, Intervention, Comparator, Outcomes, Studies (PICOS) framework. The studies were uploaded to Rayyan, a Web-based platform for managing articles of systematic reviews. Citation tracking and manual review of references were performed for the included studies. The meta-analyses and network meta-analyses were reviewed for outpatient subanalyses focused on clinical responses and risk of hospital admission. A total of 6 meta-analyses for albuterol, 4 meta-analyses for epinephrine, and 11 meta-analyses for hypertonic saline were included. Our review identified evidence from predominantly low and moderate evidence meta-analyses (assessed using GRADE and AMSTAR 2) indicating that all three deimplemented medications exhibit one or more therapeutic effects and benefits for infants with the bronchiolitis syndrome in the outpatient setting. Effect sizes ranged from medium to near medium. These clinical benefits include decreased hospital admissions and lower clinical severity scores. Given the heterogeneity of patients under the umbrella term "acute bronchiolitis" and the potential for some patients to respond clinically to albuterol, hypertonic saline, and epinephrine, current evidence supports conducting therapeutic trials in infants with acute bronchiolitis in outpatient settings. However, further well-designed and adequately powered randomized controlled trials and high-quality meta-analyses are still needed.

Integrating Large Language Models and the iDigBio Portal for Conversational Data Exploration and Retrieval

The advent of cloud-based large language model (LLM) services such as ChatGPT (Generative Pre-Trained Transformer) has given rise to a wide array of novel artificial intelligence (AI) applications. In particular, LLMs have been used to power AI assistants that serve as intermediaries between human users and online web services, namely, web-based application programming interfaces (web APIs). These AI assistants allow users to make requests in natural language to initiate complex processes, ranging from searching a database to making a reservation. We are exploring the development of AI assistants that can intelligently search for and process species occurrence data served by the Integrated Digitized Biocollections (iDigBio) Portal. Though the portal already provides a human-friendly search interface, it is tailored for a very particular use case: finding and inspecting records that match the user’s search parameters. However, the underlying iDigBio APIs that power the search interface offer direct access to biodiversity data and metadata that can support a wider range of applications. An LLM-powered AI assistant with access to such APIs has the potential to redefine how researchers discover and interact with scientific data by 1) allowing users to interact with scientific databases using natural language, 2) serving as a single unifying interface for many different use cases, and 3) enhancing the user’s experience with AI insights that are backed by citable, curated data. Fig. 1 demonstrates a prototype chatbot we have developed, which interfaces with the iDigBio Portal. The chatbot uses OpenAI’s GPT-4 to understand user requests and call the appropriate APIs as needed. It currently has access to the following iDigBio APIs: The Search API allows the chatbot to perform the same search functions as the portal search interface. The results of the search can be observed by either directly calling the API from the user's web browser, opening the existing portal search interface with generated search parameters, or visualizing the geographic distribution of matched records on an interactive map. The Download API allows the chatbot to package search results as a Darwin Core Archive to be delivered by email. The Summary API allows the chatbot to collect statistics across all of iDigBio’s data, such as a breakdown of record counts for unique species reported within a particular geographic region. The Search API allows the chatbot to perform the same search functions as the portal search interface. The results of the search can be observed by either directly calling the API from the user's web browser, opening the existing portal search interface with generated search parameters, or visualizing the geographic distribution of matched records on an interactive map. The Download API allows the chatbot to package search results as a Darwin Core Archive to be delivered by email. The Summary API allows the chatbot to collect statistics across all of iDigBio’s data, such as a breakdown of record counts for unique species reported within a particular geographic region. Because the chatbot is intended for use by researchers, transparency is critical. When responding to user requests, LLMs often include their own internalized knowledge—which may be unreliable but difficult to verify—or make up information entirely. Thus, it must be abundantly clear how the chatbot forms its responses, in particular how the LLM interprets user requests and how it queries external APIs, such that users may independently assess the correctness of the chatbot's actions and link its conclusions back to data. The approach we adopted for the design of our prototype is illustrated in Fig. 2; whenever the chatbot initiates a data retrieval or analysis process, a detailed log of that process is embedded in the conversation, including the sequence of actions taken and their inputs and outputs. The chatbot's behavior is tightly controlled by the rigid use of specialized AI agents with expert-defined validators (Fig. 3). Importantly, we do not allow the LLM to respond freely to user requests; rather, we use a fixed set of AI agents that are specialized to produce structured outputs that follow strict schema as enforced by the agent's assigned validator. An orchestrator agent is responsible for deciding which agent is most appropriate to handle each request. If no agent is chosen, the system does not attempt to answer the user's request. As a prototype, the functionality of the chatbot is currently limited to the few illustrative use cases we have outlined. However, as the system is incrementally refined and expanded, we envision the single chatbot interface to be of interest to both the general public and researchers alike. For the general public, it may be a useful tool to learn about biodiversity in their local community and around the world. Meanwhile, researchers may find the chatbot useful for quickly navigating and exploring iDigBio's hosted data and APIs. The prototype is hosted online at chat.acis.ufl.edu with source code in GitHub.

Preparing for Hospital at Home: A Review of the Current Landscape of Training Practices.

Hospital at Home (HaH) is a model of care that provides hospital-level care in the patient's home, requiring a unique set of competencies and skills from both multidisciplinary care teams and informal caregivers. These skills are often different from those required in traditional hospital settings. The aim of this paper is to consolidate the information of HaH-related education and training to support the development of standardized curricula to ensure safe hospitalization at home. We compiled relevant information from the scientific literature on HaH approaches and studies and conducted a web search. Our results indicate that healthcare professionals are trained in short training sessions, covering specific skills needed in the HaH context. These skills comprise, among others, communication, medication safety, infection control, and wound care. Patients and their families receive training in recognizing symptoms of deterioration and self-care. Concrete guidelines or standardized training programs are still missing. Future research should thus focus on developing standardized HaH training protocols and programs for both staff and patients to ensure patient safety at home.

Creating an Empirical Dermatology Dataset Through Crowdsourcing With Web Search Advertisements

Health datasets from clinical sources do not reflect the breadth and diversity of disease, impacting research, medical education, and artificial intelligence tool development. Assessments of novel crowdsourcing methods to create health datasets are needed. To evaluate if web search advertisements (ads) are effective at creating a diverse and representative dermatology image dataset. This prospective observational survey study, conducted from March to November 2023, used Google Search ads to invite internet users in the US to contribute images of dermatology conditions with demographic and symptom information to the Skin Condition Image Network (SCIN) open access dataset. Ads were displayed against dermatology-related search queries on mobile devices, inviting contributions from adults after a digital informed consent process. Contributions were filtered for image safety and measures were taken to protect privacy. Data analysis occurred January to February 2024. Dermatologist condition labels as well as estimated Fitzpatrick Skin Type (eFST) and estimated Monk Skin Tone (eMST) labels. The primary metrics of interest were the number, quality, demographic diversity, and distribution of clinical conditions in the crowdsourced contributions. Spearman rank order correlation was used for all correlation analyses, and the χ2 test was used to analyze differences between SCIN contributor demographics and the US census. In total, 5749 submissions were received, with a median of 22 (14-30) per day. Of these, 5631 (97.9%) were genuine images of dermatological conditions. Among contributors with self-reported demographic information, female contributors (1732 of 2596 contributors [66.7%]) and younger contributors (1329 of 2556 contributors [52.0%] aged <40 years) had a higher representation in the dataset compared with the US population. Of 2614 contributors who reported race and ethnicity, 852 (32.6%) reported a racial or ethnic identity other than White. Dermatologist confidence in assigning a differential diagnosis increased with the number of self-reported demographic and skin-condition-related variables (Spearman R = 0.1537; P < .001). Of 4019 contributions reporting duration since onset, 2170 (54.0%) reported onset within less than 7 days of submission. Of the 2835 contributions that could be assigned a dermatological differential diagnosis, 2523 (89.0%) were allergic, infectious, or inflammatory conditions. eFST and eMST distributions reflected the geographical origin of the dataset. The findings of this survey study suggest that search ads are effective at crowdsourcing dermatology images and could therefore be a useful method to create health datasets. The SCIN dataset bridges important gaps in the availability of images of common, short-duration skin conditions.

Memory and Identity in the Time of the Internet

There are moments that define one’s life, and while we never forget them, reflection can lead to new perspectives. In the early 1980s I was part of a long peace march for nuclear disarmament. In 2020, an Internet search for information about this peace march found some documents that provided an impetus for reflection on memory and identity. This autoethnography suggests that unexpected events can provide impetus for critical self-reflection, reflection that honours the search for threads that weave our identity from past to present. This exploration may provide support for future transitions. It also suggests that social movements can be inclusive of a range of people with different levels of knowledge. Finally, it proposes that engagement itself is a source of learning, the impact of which may be understood decades later.

Understanding patient perspectives on health-related searches on the internet: Insights from an online survey of Korean patients with systemic lupus erythematosus.

With the increasing prevalence of digital devices and internet access, digital resources have become essential for educating patients with chronic diseases. We explored the patient perspective on health-related internet searches among Korean patients with systemic lupus erythematosus (SLE). We collected data through a Google Survey from 344 SLE patients. The survey covered demographics, preferred digital devices and sources, and digital information content, and participants' views on digital resources. We analyzed patient characteristics associated with digital resource usage. Of the 344 patients, 270 reported using the internet to acquire disease-related information, including the association between nutrition and SLE, general information on SLE, and coping strategies for SLE management. SLE-related searches on the internet were more common in patients younger than 40 years (p = 0.002), those with fewer than 15 years of disease duration (p < 0.001), and those with higher education levels (p = 0.022). Disease duration was independently associated with internet use. Patients reported that internet searches for information on SLE improved their understanding of the disease in 181 cases and motivated self-management in 166 cases. In addition, 98 patients found it helpful to make a shared decision with physicians. Health-related searches on the internet are widely used by SLE patients to gather comprehensive information on the disease and to address unmet needs. The positive impact of SLE-related internet searches on disease understanding and self-management emphasizes the importance of developing high-quality digital resources to improve patient education and self-care for the disease.

Russian Immigrant Families’ Child Care Selection in the United States

AbstractResearch on immigrant populations shows that child care choices are dependent on the population’s region of origin. While the Russian immigrant population in the United States comprises the largest group of immigrants from Eastern Europe and is likely to increase in the future, there is virtually no research on Russian immigrant families’ child care search and selection criteria. This qualitative study applies the theoretical model of Pungello and Kurtz-Costes (1999), which illustrates how factors such as parental demographic characteristics, environmental context, child characteristics, and parental beliefs play into families’ child care selection. The study draws from semi-structured interviews with 11 Russian immigrant families residing in the Mid-Atlantic region of the United States to examine their child care search processes and selection criteria. Results revealed that most parents found child care through friends’ recommendations, Internet search, and online reviews. Nine themes describing child care criteria valued by Russian immigrant families emerged from the analysis. Specifically, parents mentioned culture and personal beliefs, child characteristics, convenience, cost and subsidy, education and learning, program features, facilities and environment, teacher characteristics, and rating and reputation as the major factors in selecting child care. Understanding Russian immigrant parents’ child care information sources and selection criteria will allow for early care and education (ECE) providers to accommodate the needs of Russian immigrant population and for policymakers to facilitate access to ECE programs for these families.

The Negative Transfer of Dialects on Primary School Students' English Phonetics Acquisition and Its Teaching Implications-Taking Kunming Dialect as an Example

Abstract: In the process of learning a second language, correct pronunciation is an indispensable prerequisite for having strong listening and speaking skills. Primary school students in Kunming (represented by the urban area) often experience negative phonological transfer in the process of English phonological acquisition due to their habitual use of dialects, resulting in inaccurate pronunciation and weak listening and speaking skills. Based on the information from three aspectsteaching experience, literature reading and internet search, this paper summarizes the common negative phonological transfer problems of primary school students, such as mispronouncing the vowel [a] sound as [e] sound, having difficulty in effectively distinguishing between consonant [v] sound and [w] sound, and being very prone to confusing basic long and short sounds. The above problems can be solved by strengthening teacher training, optimizing teaching strategies and introducing educational resources. Such measures can significantly aid students in improving their pronunciation accuracy, thereby laying a solid foundation for better listening and speaking skills.

Making exploratory search engines using qualitative case studies: a mixed method implementation using interviews with Detroit Artisans

Abstract Search engine algorithms are increasingly subjects of critique, with evidence indicating their role in driving polarization, exclusion, and algorithmic social harms. Many proposed solutions take a top-down approach, with experts proposing bias-corrections. A more participatory approach may be possible, with those made vulnerable by algorithmic unfairness having a voice in how they want to be “found.” By using a mixed methods approach, we sought to develop search engine criteria from the bottom-up. In this project we worked with a group of 16 African American artisanal entrepreneurs in Detroit Michigan, with a majority female and all from low-income communities. Through regular in-depth interviews with select participants, they highlighted their important services, identities and practices. We then used causal set relations with natural language processing to match queries with their qualitative narratives. We refer to this two-step process-- deliberately focusing on social groups with unaddressed needs, and carefully translating narratives to computationally accessible forms--as a “content aware” approach. The resulting content aware search outcomes place themes that participants value, in particular greater relationality, much earlier in the list of results when compared with a standard Web search. More broadly, our use of participatory design with “content awareness” adds evidence to the importance of addressing algorithmic bias by considering who gets to address it; and, that participatory search engine criteria can be modeled as robust linkages between interviews and semantic similarity using causal set relations.

Type 1 Diabetes Mellitus in Movies: The Hollywood Effect

Introduction. Type 1 diabetes mellitus (T1D) is one of the most common chronic diseases with childhood onset. Cinematic films and movies have the capability to reach populations worldwide and effect their concept of this disease. This research examined the accuracy of these movies depicting T1D from childhood into adulthood. Methods. We conducted an internet search of several databases that resulted in a list of 39 movies from 2000 to 2022 with characters that had diabetes. We ultimately assessed 13 fictional movies that dealt with management of disease, character development, access to care, and complications. Results. Movies portray severe but rare diabetes manifestations such as comas. They emphasize access to basic diabetes supplies and cost of care. Only starting in 2020, a continuous glucose monitor (CGM) and insulin pumps are shown. Female characters are presented as resilient and unaffected by the struggles of their T1D. Conclusions. This sample of fictional movies portrays extreme T1D symptoms and mostly outdated technology for monitoring and treatment. It would be beneficial if future movies reflected the advances in closed-loop CGM/insulin pumps. It is important for clinicians to be aware of how the movie industry is presenting the disease to their patients. Clinicians can use popular movies to start difficult discussions with their patients about topics pertinent to the comprehensive care of T1D.

Identifying the geographic leading edge of Lyme disease in the United States with internet searches: A spatiotemporal analysis of Google Health Trends data.

The geographic footprint of Lyme disease is expanding in the United States, which calls for novel methods to identify emerging endemic areas. The ubiquity of internet use coupled with the dominance of Google's search engine makes Google user search data a compelling data source for epidemiological research. We evaluated the potential of Google Health Trends to track spatiotemporal patterns in Lyme disease and identify the leading edge of disease risk in the United States. We analyzed internet search rates for Lyme disease-related queries at the designated market area (DMA) level (n = 206) for the 2011-2019 and 2020-2021 (COVID-19 pandemic) periods. We used maps and other exploratory methods to characterize changes in search behavior. To assess statistical correlation between searches and Lyme disease cases reported to Centers for Disease Control and Prevention (CDC) between 2011 and 2019, we performed a longitudinal ecological analysis with modified Poisson generalized estimating equation regression models. Mapping DMA-level changes in "Lyme disease" search rates revealed an expanding area of higher rates occurring along the edges of the northeastern focus of Lyme disease. Bivariate maps comparing search rates and CDC-reported incidence rates also showed a stronger than expected signal from Google Health Trends in some high-risk adjacent states such as Michigan, North Carolina, and Ohio, which may be further indication of a geographic leading edge of Lyme disease that is not fully apparent from routine surveillance. Searches for "Lyme disease" were a significant predictor of CDC-reported disease incidence. Each 100-unit increase in the search rate was significantly associated with a 10% increase in incidence rates (RR = 1.10, 95% CI: 1.07, 1.12) after adjusting for environmental covariates of Lyme disease identified in the literature. Google Health Trends data may help track the expansion of Lyme disease and inform the public and health care providers about emerging risks in their areas.

Internet image search outputs propagate climate change sentiment and impact policy support

AbstractA critical step in tackling climate change involves structural, system-level changes facilitating action. Despite their ubiquity, little is known about how internet search algorithms portray climate change, and how these portrayals impact concern and action. In a sample of 49 countries, we found that nationwide climate concern, but not nation-level climate impact, predicted the emotional arousal caused by climate change Google Image Search outputs, as rated by a naive sample (n = 383). In a follow-up experiment we randomly assigned another sample (n = 899) to receive the climate change image outputs resulting from searches conducted in countries high or low in pre-existing climate concern, and found that participants exposed to images from countries with high pre-existing concern (compared to low) became more concerned about climate change, supportive of climate policy and likely to act pro-environmentally, suggesting a cycle of climate sentiment propagation systemically facilitated by internet search algorithms. We discuss the implications of these findings for climate action interventions.

BN SO41 - A systematic search and evaluation of patient information quality on Anti-reflux surgery using modified EQIP Score

Abstract Background GORD affects 20% of western populations and internet searches are a key resource for patients considering surgery. An assessment of the quality of public intended electronic information can highlight a need for improved material to aide patient education. This study assesses the quality of internet information on anti-reflux surgery using the validated Modified Ensuring Quality Information for Patients (EQIP) tool. Method A systematic analysis of the top 200 results for the search engine query “Anti-reflux surgery” was conducted. Google (UK) was used. Search results aimed solely for healthcare professionals were excluded. Results were evaluated using the modified (EQIP) tool (score 0-36) and evaluation performed on information on surgery benefits, risks and complications. Results 107 of 200 websites were included in the main study. The majority (43%) originated from UK, while 40% were from USA. Median EQIP score for all websites was 16. Median EQIP scores for the 3 domains were 7/18, 1/6 and 8/12 for content, identification data and structure respectively. Benefits of surgery was described on 71%, surgery risks on 66%, and complications on 11%. However, only 16% of websites described the quantitative risks of surgery. Information on postoperative recovery was included on 63%, emergency advice on 22%, and mortality rates on 7% of websites. Conclusion We observed most public intended internet searches lacked information on the quantitative risks and complications of anti-reflux surgery. Moreover, the information provided was insufficient on morbidity and mortality to adequately assist patients make informed decisions. This study supports the need for improved quality of patient intended publicly available information accessible online on Anti-reflux surgery.

Counseling Gen Alpha: Pandemic's Impact on Developmental and Mental Health

The covid- 19 pandemic has had a widespread impact on global health. Many studies have discussed its effect on various populations and continue to study measures to work on them. The upcoming research has a limited focus on Generation Alpha: individuals born after 2010 who have been exposed to these adverse effects from their formative years: the digital age, social isolation, and environmental stressors. It has left a significant impression on their developmental milestones and mental health for which the healthcare professionals and parents are not fully equipped with. This review paper aims to discuss how the flexible and adaptive nature of counseling can employ strategies to help overcome these problems and assist in the ‘digital pandemic’ that is here to stay. The review method will analyze existing literature using citation tracking, conference proceedings, internet search engines, and primarily through open peer-reviewed electronic databases like PubMed, Research Gate, and ScienceDirect.. The results and implications of the study will be highlighted in the full paper.

Maximizing STEM resources for diplomats: A framework for speed, depth, and accuracy

AbstractIn the evolving landscape of science diplomacy, frontline diplomats increasingly rely on effective access to STEM knowledge to advance national interests and address global challenges. This paper presents a framework organizations can use for evaluating the approaches and tools available to their frontline diplomats, focusing on three key metrics: response time (speed), content depth and applicability, and consistency in accuracy. By evaluating tools via these metrics, the framework highlights the need for mechanisms that offer quick access to in‐depth and reliable STEM information, thus optimizing diplomatic effectiveness. The study provides examples from the US foreign affairs perspective within categories such as web searches, training programs, and consulting services, identifying opportunities for innovation and improvement. Identifying tools that maximize speed, depth, and accuracy, this framework aims to guide organizations in refining their resources, thereby equipping diplomats with the tools necessary to navigate complex, science‐driven global issues.

CONSTRUÇÃO DE UM INSTRUMENTO PARA ESTUDAR ACADEMIAS AO AR LIVRE: RESULTADOS DA APLICAÇÃO EM QUATRO CIDADES

Outdoor gyms are a democratic space to promote the health and social inclusion of their users. Thus, motor activities can be extended to people with disabilities. The question that guided this study is: do these gyms have equipment adapted for people with disabilities to perform motor activities? Therefore, the objective was to identify and classify the equipment present in outdoor gyms in four cities. It is framed as an exploratory and descriptive study, with qualitative and quantitative analysis. To begin the collection, outdoor gyms were visited in four cities, in three different states and all available equipment was photographed for later analysis. At the same time, an internet search was conducted to find the companies that sell this equipment and to identify which of them had the seal for use by people with disabilities. The results indicated the existence of 28 equipment intended for wheelchair users. The variables chosen for the collection were: 1) access to the gyms; 2) equipment present; 3) equipment intended for people with disabilities; 4) condition of the equipment; 5) written instructions for using the equipment. The data collection took place in 29 outdoor gyms and 198 pieces of equipment were analyzed, making it possible to adequately measure the variables assessed. The results of the applications indicated the need for maintenance of access points and equipment and, mainly, the installation of signs indicating how the population should use them. Received: 2024/09/03 Reformulated: 2024/09/25 Accepted: 2024/09/27

Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study.

In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score-normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with α balancing ridge and lasso effects and λ regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model's predictive accuracy and fit. An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from -1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model (α=0.1, λ=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being.

Trends in Exercise-Related Internet Search Keywords by Sex, Age, and Lifestyle: Infodemiological Study.

Exercise prescription by physicians is beneficial for initiating or intensifying physical activity. However, providing specific exercise prescriptions is challenging; therefore, few physicians prescribe exercise. This infodemiological study aimed to understand trends in exercise-related internet search keywords based on sex, age, and environmental factors to help doctors prescribe exercise more easily. Search keyword volume was collected from Yahoo! JAPAN for 2022. Ten exercise-related terms were analyzed to assess exercise interest. Total search activities were analyzed by sex and age. Characteristic scores were based on the Japanese prefecture. By performing hierarchical cluster analysis, regional features were examined, and Kruskal-Wallis tests were used to assess relationships with population and industry data. The top-searched term was "Pilates" (266,000 queries). Male individuals showed higher interest in activities such as "running" (25,400/40,700, 62.4%), "muscle training" (65,800/111,000, 59.3%), and "hiking" (23,400/40,400, 57.9%) than female individuals. Female individuals exhibited higher interest in "Pilates" (199,000/266,000, 74.8%), "yoga" (86,200/117,000, 73.7%), and "tai chi" (45,300/65,900, 68.7%) than male individuals. Based on age, search activity was highest in the 40-49 years age group for both male and female individuals across most terms. For male individuals, 7 of the 10 searched terms' volume peaked for those in their 40s; "stretch" was most popular among those in their 50s; and "tai chi" and "radio calisthenics" had the highest search volume for those in their 70s. Female individuals in their 40s led the search volume for 9 of the 10 terms, with the exception of "tai chi," which peaked for those in their 70s. Hierarchical cluster analysis using a characteristic score as a variable classified prefectures into 4 clusters. The characteristics of these clusters were as follows: cluster 1 had the largest population and a thriving tertiary industry, and individuals tended to search for Pilates and yoga. Following cluster 1, cluster 2, with its substantial population, had a thriving secondary industry, with searches for radio calisthenics and exercise bike. Cluster 4 had a small population, a thriving primary industry, and the lowest search volume for any term. Cluster 3 had a similar population to that of cluster 4 but had a larger secondary industry. Male individuals show more interest in individual activities, such as running, whereas female individuals are interested in group activities, such as Pilates. Despite the high search volume among individuals in their 40s, actual exercise habits are low among those in their 30s to 50s. Search volumes for instructor-led exercises are higher in cluster 1 than in other cluster areas, and the total number of searches decreases as the community size decreases. These results suggest that trends in search behavior depending on sex, age, and environment factors are essential when prescribing exercise for effective behavioral change.

A terrorist mindset: findings from an empirical enquiry into prosecutions, evidence, and mindset material

Abstract This paper explores the use of mindset material in terrorism cases. Mindset material is a broad category of evidence, including social media activity and internet search histories, relied on to infer or imply that the accused is affiliated or aligned with terrorists. Although mindset material plays a central role throughout the justice process in terrorism cases, no work to date has explored and discussed its use in depth. In this paper I draw on doctrinal and empirical findings, including interviews, to examine how and why it has come to play such a central role in terror cases. While mindset material is a well-intentioned tool used to selectively enforce broadly drafted and vaguely defined terrorism offences, it is also a blunt tool, and sees great emphasis placed on the accused's inferred status as terrorist (or not). In this paper I explore and problematise the use of mindset material and the inferences drawn from it, specifically in relation to to fantasists, the merely curious, and young autistic defendants. I posit that mindset material is symptomatic of a need to revisit the substantive law and to rethink the proper role of the criminal law in preventing terrorism.

Web Presence and Search Engine Optimization on Hotel Patronage in Akwa Ibom State, Nigeria

This study examined the effect of web presence and search engine optimization on hotel patronage in Akwa Ibom State, Nigeria. Web presence, search engine optimization and hotel patronage formed the specific objectives of the study. 1122 employees that had access to information of customers who made hotel reservations from forty-one hotels registered with Akwa Ibom State Hotels Management and Tourism Board formed the population. The study employed a census sampling technique. Primary data were gathered through a structured questionnaire. Simple percentages and charts were employed for the demographic analyses, leveraging the Statistical Package for Social Sciences (SPSS) version 25 to facilitate the data analyses. The univariate analyses were done using means and standard deviations to provide insights into the central tendencies and variability of the data. To test the study hypotheses, the researchers employed Partial Least Square-Structural Equation Modelling (PLS-SEM), facilitated by the SmartPLS version 3.2.9 software. Findings of the study showed that web presence (β = 0.713, t- value = 10.156, p-value = 0.011 &lt; 0.05) had positive significant effect on hotel patronage in Akwa Ibom State and search engine optimization (β = 0.613, t-value = 8.046, p-value = 0.003 &lt; 0.05, a statistically significant and positive effect on hotel patronage. Based on the empirical findings, we recommended that management of hotels in Akwa Ibom State should ensure that they have a well- designed, user-friendly, and informative website that showcase their offerings and provides a seamless booking experience for customers. Regular updates and maintenance of the website should be a priority to keep it relevant and engaging. The hotel owners and operators should prioritize the implementation of effective search engine optimization strategies to improve their visibility on search engine results pages. This should include optimizing their website content, improving website structure and navigation, and leveraging relevant keywords to attract potential customers.