Previous Users Research Articles

Epidemiological Aspects of Dietary Supplement use among Saudi Medical Students: A Cross-sectional Study

Background:Dietary supplement use received wide attention and interest throughout the world, particularly in Gulf countries, because of advanced economic and industrial growth.Objective:The present study aimed to determine the prevalence and correlates of dietary supplement use among medical students at Jouf University, Saudi Arabia.Methods:For this cross-sectional study, an anonymous self-administered structured questionnaire was distributed among medical students at Jouf University, Saudi Arabia. Out of the 381 medical students, 366 answered the questionnaire with a response rate of 96%. Analysis of data was done using the Statistical Package for Social Science (SPSS) program, version 24. Statistical significance was considered at P values <0.05.Results:Regarding the prevalence of dietary supplement use, 36.3% (n=133) and 29.5% (n=108) of the respondents were previous and current users of dietary supplements, respectively. Multivitamins and vitamin D were the most prevalent dietary supplements used by the participants. The students reported that the internet was the main source of knowledge concerning dietary supplements. The significant predictors of dietary supplement use in the logistic regression analysis were: age >21 years (odds ratio (OR): 3.26; confidence interval (CI): 1.67-10.53), females sex (odds ratio (OR): 2.23; confidence interval (CI): 2.34-6.84), and being in the third academic year or more (odds ratio (OR): 2.58; confidence interval (CI): 1.82-5.37). The most reported reasons for utilizing dietary supplements were nutritional supplementation and health promotion. Nearly three-quarters of the students agreed that dietary supplementation is good for health and recommended others to use them after doctor recommendation.Conclusion:Dietary supplement use was remarkably high among medical students at Jouf University, Saudi Arabia. Consumption of dietary supplements increased significantly among older students, females, and those in the third academic year or more. This study recommends other studies involving students from non-health domain colleges to detect if there is a difference in the prevalence of dietary supplement use within this population.

Bone Mineral Density in Antiretroviral Therapy-Naïve HIV-1-Infected Young Adult -Women Using Depot Medroxyprogesterone Acetate or Nonhormonal Contraceptives in Uganda.

ABSTRACTMost studies evaluating BMD in human immunodeficiency virus (HIV)‐infected populations have focused on antiretroviral therapy (ART)‐experienced patients. In this study, the association between HIV‐1 and/or depot medroxyprogesterone acetate (DMPA) and BMD among untreated HIV‐1–infected women in a resource‐limited setting was assessed before long‐term exposure to ART. The data were then compared with that of the 2005–2008 United States National Health and Nutrition Examination Survey data for non‐Hispanic White and Black women. Women aged 18–35 years, recruited from health facilities in Kampala, Uganda, were classified based on their combination of HIV‐1 status and DMPA use: (i) HIV‐1–infected current DMPA users, (ii) HIV‐1–infected previous DMPA users, (iii) HIV‐1–infected nonhormonal‐contraceptive users, and (iv) HIV‐uninfected nonhormonal‐contraceptive users. All HIV‐1–infected women reported being ART‐naïve at baseline. BMD was measured at the lumbar spine, total hip, and femoral neck using DXA. Multivariate linear regression was used to assess the association between HIV‐1 and/or DMPA and BMD Z‐scores. Baseline data were analyzed for 452 HIV‐1–infected (220 nonhormonal users, and 177 current and 55 previous DMPA users) and 69 HIV‐1–uninfected nonhormonal‐contraceptive users. The mean age was 26.1 years (SD, 4.2) with a median duration of DMPA use among current users of 24.0 months [medians (interquartile range), 12‐48]. A higher proportion of HIV‐1–infected previous (12.7%) or current DMPA users (20.3%) and nonhormonal users (15.0%) had low BMD (Z‐score ≤−2 at any of the three sites) compared with age‐matched HIV‐1–uninfected women (2.9%). HIV‐1 infection and DMPA use were independently associated with significantly lower mean BMD Z‐scores at all sites, with the greatest difference being among HIV‐1–infected current DMPA users (5.6%–8.0%) versus uninfected nonhormonal users. Compared with non‐Hispanic White and Black women, the Ugandan local reference population had generally lower mean BMD at all sites. Newer treatment interventions are needed to mitigate BMD loss in HIV‐1–infected women in resource‐limited settings. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC. on behalf of American Society for Bone and Mineral Research.

Controlling Fake News by Collective Tagging: A Branching Process Analysis

The spread of fake news on online social networks (OSNs) has become a matter of concern. These platforms are also used for propagating important authentic information. Thus, there is a need for mitigating fake news without significantly influencing the spread of real news. We leverage users’ inherent capabilities of identifying fake news and propose a warning-based control mechanism to curb this spread. Warnings are based on previous users’ responses that indicate the authenticity of the news. We use population-size dependent continuous-time multi-type branching processes to describe the spreading under the warning mechanism. We also have new results towards these branching processes. The (time) asymptotic proportions of the individual populations are derived using stochastic approximation tools. Using these, relevant type 1, type 2 performances are derived and an appropriate optimization problem is solved. The proposed mechanism effectively controls fake news, with negligible influence on the propagation of authentic news. We validate performance measures using Monte Carlo simulations on network connections provided by Twitter data.

Search History Visualization for Collaborative Web Searching

Abstract As a trend of industrial development, autonomous driving has been renewed as a hot research topic with the rapid increase of new technologies. Every year, many researchers devote themselves to the learning and research on autonomous driving technologies. However, due to the high barriers to entry in this interdisciplinary area, beginners often feel struggling even frustrating in their early learning process. Searching on the Web has become the most important way for people to gain knowledge; studies of user habits reveal that researchers engage in many online academic searching tasks involving asynchronous collaboration with others (e.g. collect relevant literature) to advance their researches. However, current web search engines are generally designed for a single user, searching alone, which are not friendly for researchers to collaborate with each other. To address this issue, we propose LogCanvas, a graph-based user history interface for search engines, to support researchers to conduct asynchronous collaborative web search (i.e., users are in a distinct remote location, with their own computer, carry out different search processes and save efforts by consuming previous users' search results). We take researchers in autonomous driving as an example to describe the development and usage of LogCanvas. In order to investigate the efficacy of LogCanvas, we extend the user scope of LogCanvas to general users and conducted an online crowd-powered experiment inviting 387 participants to use this platform. We studied users' behaviors and collected their feedback about user experience. The results indicate that LogCanvas could benefit users' asynchronous collaborative web search and their learning.

A small number of workers with specific personality traits perform tool use in ants.

Ants use debris as tools to collect and transport liquid food to the nest. Previous studies showed that this behaviour is flexible whereby ants learn to use artificial material that is novel to them and select tools with optimal soaking properties. However, the process of tool use has not been studied at the individual level. We investigated whether workers specialise in tool use and whether there is a link between individual personality traits and tool use in the ant Aphaenogaster senilis. Only a small number of workers performed tool use and they did it repeatedly, although they also collected solid food. Personality predicted the probability to perform tool use: ants that showed higher exploratory activity and were more attracted to a prey in the personality tests became the new tool users when previous tool users were removed from the group. This suggests that, instead of extreme task specialisation, variation in personality traits within the colony may improve division of labour.

Perancangan Touchless Hand Washer Sistem Pijakan Kaki untuk Pencegahan Penyebaran COVID-19

The spread of the Coronavirus Disease (COVID-19) outbreak in all parts of the world is increasing, including in Indonesia, a virus that is easily transmitted and spreads through close contact and droplets by touching hands with other people or with objects that have been exposed to COVID-19, one of the reasons for this, reventing the spread of COVID 19 by washing hands frequently with soap. Hand washing tools have been developed, generally still using direct touch to the water tap and soap dish, the Touchless Hand Washer (THW) touchless hand washing tool is specially designed and manufactured in accordance with the COVID-19 pandemic conditions without touching with hands directly on the water tap and a soap holder with a footrest system, making it more practical, hygienic and not directly contaminated with previous Touchless Hand Washer users which could cause COVID-19 transmission from previous users. Touchless Hand Washer (THW) is designed with a pyramid-shaped frame that will support the water toren that does not harm or the water toren falls for Touchless Hand Washe users, with a THW tool height of 120 cm, top width 70 x 70 cm and bottom width of 90 x 90 cm. Keywords—Hand-washing tool, COVID-19, washing hands, touchless hand washer

Differences in firewood users’ and LPG users’ perceived relationships between cooking fuels and women’s multidimensional well-being in rural India

Clean cooking fuels are generally assumed to bring health and other benefits for women compared with solid fuels, which suggests they should be preferred. However, despite the availability of clean cooking fuels, such as liquefied petroleum gas (LPG), the scale of solid fuel use in rural India remains large. Here we examine women’s positions on fuel transition and multidimensional well-being through a qualitative analysis of data from focus group discussions with comparable groups of women who have versus those who have not transitioned to LPG. We show that women who use firewood believe their cooking fuel supports their well-being in several ways, and see no enabling relationship between LPG use and well-being. In contrast, LPG users—who were previous firewood users—claim LPG has enabled well-being. These results suggest that perspectives on the relationship between fuel and well-being shift after transition, due to the realization of new advantages. Understanding differences in the perspectives of women using different fuels is vital to unpack the dynamics of cooking fuel transition. There is consensus that using firewood negatively impacts women’s well-being, suggesting that clean alternatives will be preferred. However, this study finds that women have different views on how cooking with firewood and LPG support well-being depending on the fuel they currently use.

Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction

Electric vehicles (EVs) have been developed rapidly in recent years in China, but a further promotion of EVs is hindered by the unreasonable deployment of facilities. Therefore, a deep understanding of EV drivers’ charging behavior is viewed to be helpful to a more suitable deployment of new chargers. In this paper, charging influence factors are analyzed, and logit-based models for charging choice behaviors are proposed. First, a web-based stated preference survey is designed to obtain the data of EV drivers’ charging choice preference. Second, binary logit models are developed using the survey data, with vehicle attributes, destination activity, and subsequent travel information as influence factors. Especially, the satisfaction of charging facilities reviewed by previous users and drivers’ risk attitudes are also introduced to analyze charging choice preference. Third, latent class models are proposed to further taste the heterogeneity of EV drivers’ charging decision strategies. The results reveal two classes of decision-making patterns among our sample population: (1) service concerned, which are the majority of the respondents who value the satisfaction and queue of charging station, characterized with younger age, richer driving experience and higher income, and (2) pragmatic concerned, those who weight multiple factors, such as excess range, parking time, and charging fee. It suggests that the improvement of charging service levels, such as providing reliable and high-quality charging service, is necessary for operators to attract more EV drivers.

Relationship between Bell's Palsy and Previous Statin Use: A Case/Non-Case Study.

We intended to determine the relationship between previous statin use and Bell’s palsy in a large study population receiving statins for the past 2 years. The Korean National Health Insurance Service—Health Screening Cohort data from 2002 to 2015 were collected. Participants with Bell’s palsy (n = 3203) were matched with participants without Bell’s palsy (n = 12,812). The number of days of previous statin use for 2 years before the onset of Bell’s palsy was analyzed using conditional logistic regression. Subgroups of age, sex, obesity, smoking, alcohol consumption, total cholesterol, and blood pressure were analyzed for any association between Bell’s palsy and prior statin use. The Bell’s palsy group reported greater statin use than the non-Bell’s palsy group (84.6 (standard deviation, SD = 201.7) vs. 74.4(SD = 189.4), p = 0.009). Previous statin use was associated with Bell’s palsy in the crude model (95% confidence intervals = 1.03–1.19, p = 0.006). However, this relationship disappeared when the possible covariates were adjusted for in model 2. All subgroups showed no increased odds for Bell’s palsy in previous statin users. We did not find an association between Bell’s palsy and previous statin use in this Korean population aged ≥40 years.

Using a Chatbot to Assess Hereditary Cancer Risk.

We developed a Web-based chatbot (ItRunsInMyFamily.com) to help individuals collect their family health history (FHx) and determine their risk for hereditary cancer. The purpose of the current study was to assess the characteristics of users and identify opportunities to improve the FHx collection tool. During Family Health History Month (November 2019) we launched an FHx campaign using social media advertisements to raise awareness about hereditary cancers and encourage individuals in the general population to use ItRunsInMyFamily to collect their FHx. Through this campaign, we were able to gather information about users and identify opportunities to improve the tool. We reached 14,140 users in November 2019 through online marketing campaigns-Facebook, Google, previous ItRuns users, and Web site marketing. Of those, 3,204 completed the full FHx assessment and received risk recommendations. The campaign targeted women between age 40 and 60 years. Users came from 3,783 counties around the United States, 48 unique cancers were reported among probands, and 79 unique cancers were reported among family members, an average of two and a half cancers per family. Our results demonstrate that it is possible to gather FHx information at the population level, with high levels of engagement and interest in the topic. There is room for future enhancements and improvements to ItRunsInMyFamily to broaden its reach and encourage individuals to learn about and record their health information.

Opinion and Usage Regarding Complementary and Alternative Therapies among Patients at a Tertiary Care Hospital in Lahore, Pakistan

Background: Complementary and alternative medicine (CAM) therapies are widely used among the population in Pakistan either separately or in conjunction with convention medical therapies. CAM therapies are used for varying reasons and result in complications as conventional treatment is often delayed resulting in increased morbidity and mortality. Objective of the study was to assess the opinions and usage regarding CAM therapies among patients in a tertiary care setup. Methods: Cross sectional study was conducted at Fatima Memorial Hospital, Lahore from April 2018 to June 2018. Sample size was calculated to be 186 which were equally divided between patients in OPD, ER and IPD. A structured questionnaire was administered. SPSS 20 was used for analysis. Results: In this study 112(60.2%) patients had used CAM for an illness whereas 74(39.8%) had never used CAM therapy. Most of the patients 165(88.7%) were able to differentiate between CAM and conventional therapies. Most frequent source of knowledge cited was family and friends 139(74.7%), previous users of CAM 83(44.6%) and media/internet 69(37.1%). Spiritual Healers (Pirs) were the most used modality of CAM at 64(34.4%) followed by hakeem and homeopathy. Most common reasons for using CAM were advice from family and friends, personal will and religious/spiritual reasons. Conclusion: Use of CAM therapies is widely prevalent in our study with more than half the respondents having used a modality of CAM. Users of CAM were found to be generally satisfied with the treatment.

Emergence of polarization in a voter model with personalized information

The flourishing of fake news is favored by recommendation algorithms of online social networks which, based on previous users activity, provide content adapted to their preferences and so create filter bubbles. We introduce an analytically tractable voter model with personalized information, in which an external field tends to align the agent opinion with the one she held more frequently in the past. Our model shows a surprisingly rich dynamics despite its simplicity. An analytical mean-field approach, confirmed by numerical simulations, allows us to build a phase diagram and to predict if and how consensus is reached. Remarkably, polarization can be avoided only for weak interaction with the personalized information and if the number of agents is below a threshold. We analytically compute this critical size, which depends on the interaction probability in a strongly non linear way.

Opioid use among veterans undergoing major joint surgery managed by a multidisciplinary transitional pain service

BackgroundChronic postsurgical pain and opioid use is a problem among patients undergoing many types of surgical procedures. A multidisciplinary approach to perioperative pain management known as a transitional pain service...

Extreme unbalanced mobility network in bike sharing system

Bike sharing, such as Mobike, are thriving throughout the past three years. Mobike allows users to rent bikes left by previous users. This mobility mode generates self-organized bike distribution over the city. We analyze real data from Mobike bike sharing system in Beijing and Citibike bike sharing system in New York. In this paper, we study the spatial pattern of sharing activity from this new business model. We construct an Origin–Destination spatial network of bike sharing traffic flow based on trip data and study the characteristic of unbalance in the systems. The unbalance of nodes in Mobike bike mobility OD network is revealed to follows a power law distribution with different exponents on weekdays and weekends. We also found that there exists a scaling between maximum unbalance and network degree in both systems. These features of bike sharing can provide a better understanding of sharing mobility behaviors and help to develop more efficient management methods.

Online Knowledge Level Tracking with Data-Driven Student Models and Collaborative Filtering

Intelligent Tutoring Systems are promising tools for delivering optimal and personalized learning experiences to students. A key component for their personalization is the student model, which infers the knowledge level of the students to balance the difficulty of the exercises. While important advances have been achieved, several challenges remain. In particular, the models should be able to track in real-time the evolution of the students' knowledge levels. These evolutions are likely to follow different profiles for each student, while measuring the exact knowledge level remains difficult given the limited and noisy information provided by the interactions. This paper introduces a novel model that addresses these challenges with three contributions:. 1) the model relies on Gaussian Processes to track online the evolution of the student's knowledge level over time, 2) it uses collaborative filtering to rapidly provide long-term predictions by leveraging the information from previous users, and 3) it automatically generates abstract representations of knowledge components via automatic relevance determination of covariance matrices. The model is evaluated on three datasets, including real users. The results demonstrate that the model converges to accurate predictions in average four times faster than the compared methods.

Design and Build a Data Collection System for a Solar Power Plant Based on Wireless Fidelity

The solar cell module absorbs electrons from sunlight where the electrons are converted through a semiconductor intermediary in the solar cell module. In Kalimantan, in particular, it has the potential to install a Solar Power Plant because it is traversed by the equator. However, there are various disadvantages of the Solar Power Plant, namely the building is quite large, so it needs extra monitoring so that all the conditions of the Solar Power Plant can be known. One of the data communication methods in question is using the telemetry method or long distance communication. There are many ways for the telemetry method, one of which is to use a wireless system with a connection to a cloud storage database. In short, data in the field can be sent to users wherever they are. Data sent to previous users is sent through a cloud storage database where the data can be accessed by anyone. For data sent from the Solar Power Plant in the form of incoming and outgoing voltages as well as incoming and outgoing currents. Based on the research results, it is known that the design of the data transmission system for incoming and outgoing voltages as well as incoming and outgoing currents is made from a variety of component tools used are the solar cell module, Robotdyn Mega Wi-Fi, voltage sensor, current sensor, SCC, inverter, and battery. In the process of sending data using a Wi-Fi router, data originating from the sensor is sent to Robotdyn where inside Robotdyn there is a Wi-Fi module which will then be assisted to be forwarded via a Wi-Fi router whose internet network source uses GSM. The data sent to the cloud storage database is assisted by the PHP programming language and to display the website using the HTML programming language.

Learning Context-dependent Personal Preferences for Adaptive Recommendation

We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting context-dependent decision-making. We formulate preference modeling as online prediction algorithms over a set of learned policies, i.e., policies generated via supervised learning with interaction and context data collected from previous users. The algorithms then adapt to a target user by learning the policy that best predicts that user’s behavior and preferences. We also generalize the proposed algorithms for a more challenging learning case in which they are restricted to a limited number of trained policies at each timestep, i.e., for mobile settings with limited resources. While the proposed algorithms are kept general for use in a variety of domains, we developed an image-filter-selection application. We used this application to demonstrate how the proposed algorithms can quickly learn to match the current user’s selections. Based on these evaluations, we show that (1) the proposed algorithms exhibit better prediction accuracy compared to traditional supervised learning and bandit algorithms, (2) our algorithms are robust under challenging limited prediction settings in which a smaller number of expert policies is assumed. Finally, we conducted a user study to demonstrate how presenting users with the prediction results of our algorithms significantly improves the efficiency of the overall interaction experience.

Whistleblowing and fraud in digital era

This article discusses whistleblowing and fraud in the digital era in Indonesia using a literature study. Digital whistleblowing allows someone to reveal a number of information accompanied by photos/ videos through social media/ other online sources. This information is investigated further to find out if there is fraud after checking the truth, because there is a possibility that the information is inaccurate. The absence of a checking process and confirmation of its truth causes inaccurate information. The case that occurred in one of the SOEs in Indonesia initially emerged from a tweet on a Twitter account which was finally able to uncover the alleged fraud in terms of abuse of office. This is an example of the role of technology in digital whistleblowing and fraud. Digital technology can detect if there is a change in the softcopy file through the recorded digital footprint and then submit it to the authorities so that they can track the identification of previous users related to the possibility of document leakage. Artificial Neural Network (ANN) which is part of artificial intelligence (AI) technology can be used to detect fraud. ANN is a system that is able to process complex amounts of data through pattern recognition. There has been much discussion about the use of information technology in Indonesia, but the use of ANN has not been much discussed.

Stopping renin-angiotensin system blockers after acute kidney injury and risk of adverse outcomes: parallel population-based cohort studies in English and Swedish routine care

BackgroundThe safety of restarting angiotensin-converting enzyme inhibitors (ACEI) or angiotensin II receptor blockers (ARB) after acute kidney injury (AKI) is unclear. There is concern that previous users do not restart ACEI/ARB despite ongoing indications. We sought to determine the risk of adverse events after an episode of AKI, comparing prior ACEI/ARB users who stop treatment to those who continue.MethodsWe conducted two parallel cohort studies in English and Swedish primary and secondary care, 2006–2016. We used multivariable Cox regression to estimate hazard ratios (HR) for hospital admission with heart failure (primary analysis), AKI, stroke, or death within 2 years after hospital discharge following a first AKI episode. We compared risks of admission between people who stopped ACEI/ARB treatment to those who were prescribed ACEI/ARB within 30 days of AKI discharge. We undertook sensitivity analyses, including propensity score-matched samples, to explore the robustness of our results.ResultsIn England, we included 7303 people with AKI hospitalisation following recent ACEI/ARB therapy for the primary analysis. Four thousand three (55%) were classified as stopping ACEI/ARB based on no prescription within 30 days of discharge. In Sweden, we included 1790 people, of whom 1235 (69%) stopped treatment. In England, no differences were seen in subsequent risk of heart failure (HR 1.10; 95% confidence intervals (CI) 0.93–1.30), AKI (HR 0.90; 95% CI 0.77–1.05), or stroke (HR 0.99; 95% CI 0.71–1.38), but there was an increased risk of death (HR 1.27; 95% CI 1.15–1.41) in those who stopped ACEI/ARB compared to those who continued. Results were similar in Sweden: no differences were seen in risk of heart failure (HR 0.91; 95% CI 0.73–1.13) or AKI (HR 0.81; 95% CI 0.54–1.21). However, no increased risk of death was seen (HR 0.94; 95% CI 0.78–1.13) and stroke was less common in people who stopped ACEI/ARB (HR 0.56; 95% CI 0.34–0.93). Results were similar across all sensitivity analyses.ConclusionsPrevious ACEI/ARB users who continued treatment after an episode of AKI did not have an increased risk of heart failure or subsequent AKI compared to those who stopped the drugs.

Client perceptions of engaging with a health and social care navigation service: A qualitative study.

Health and social care navigation services provide support for people with long-term conditions. Such services are available in the New Zealand (NZ) context. However little is known nationally or internationally about clients' experience of engaging with such services. This study aimed to describe client perspectives of engaging with a health and social care navigation service in a NZ metropolitan city. The manager and navigators of the service recruited clients who were previous users of the service. We individually interviewed nine clients (F = 7; M = 2; aged between 30-80 years) in their homes. Many of the participants reported social isolation, and some were without regular income. We transcribed interviews verbatim and analysed data thematically. There was one overall theme: Restoration of my essence or being (in the Māori language, wairua), and sense of belonging (turangawaewae) through a regenerative approach developed in partnership between the navigator and the client. Thus, participants felt renewed and validated as human beings. Enabling clients to feel re-valued as human beings captures the concept of personhood whereby a person has capability and capacity for life choices. We suggest enabling a client to feel valued assists in development of self-determination and consequently improved health and well-being.