bluuebunny/crossref_metadata_2025_split huggingface dataset:
AKTIVITAS AKUATIK SEBAGAI TERAPI PSIKIS BAGI ANAK
Authors: Rithaudin Ahmad 
Published: June 2020

Teknologi dibuat untuk mempermudah masyarakat dalam melakukan aktivitas. Aktivitas hidup manusia dapat dilakukan dengan lebih efisien, apabila ditunjang oleh teknologi. Efisiensi tersebut menyangkut pula pada pemanfaatan waktu luang yang digunakan setelah menyelesaikan aktivitas utama. Pemanfaatan waktu tersebut bisa mencakup aktivitas yang tidak jauh pula dari penggunaan teknologi, seperti: game online, playstation, ataupun sekedar menonton televisi.Aktivitas ini merugikan bagi anak, apabila berlangsung tanpa kendali. Aktivitas ini akan mempengaruhi perkembangan fisik serta psikologis anak, apalagi saat anak dipisahkan dari kegiatan kesukaannya tersebut. Anak bisa saja marah, kecewa, serta stres meski dalam tingkatan yang rendah. Hal ini akan berdampak baik secara langsung terhadap kesehatan psikisnya anak tersebut. Makalah ini akan membahas tentang peran aktivitas akuatik sebagai media terapi bagi kesehatan psikis anak.Aktivitas akuatik dalam wujud permainan di air diharapkan dapat menjadikan anak lebih bisa mengungkapkan perasaan, pemikiran serta jati dirinya. Setelah anak mengikuti aktivitas akuatik tersebut anak menjadi lebih mengerti akan kondisi dirinya. Hal ini akan berakibat kesehatan psikis anak akan menjadi lebih baik.Kata Kunci: aktivitas akuatik, terapi psikis, anak
PEMASARAN OLAHRAGA MELALUI BERBAGAI EVENT OLAHRAGA
Authors: Sukarmin Yustinus 
Published: June 2020

Pada era globalisasi ini muncul terminologi sportainment yang memberikan peneguhan bahwa olahraga bukan lagi sekadar sebuah tontonan melainkan sebuah industri hiburan dan bisnis pertunjukkan yang mampu menghadirkan ribuan penonton dan jutaan pemirsa. Kini, ada tiga event olahraga yang menjadi ikon global, yaitu pertandingan sepak bola Piala Dunia, lomba balap mobil Formula 1, dan pertandingan olahraga multievent Olimpiade.Olahraga jika dikelola secara profesional dapat mendatangkan keuntungan ekonomi di samping keuntungan nonekonomi. Olimpiade Los Angeles 1984 merupakan Olimpiade Modern pertama yang menerapkan pendekatan logika ekonomi melalui sport business dengan melibatkan banyak sponsor. Keuntungan yang berhasil dikumpulkan dari kegiatan multievent empat tahunan itu sebesar US $223 juta.Keberhasilan suatu negara menyelenggarakan event olahraga ini tidak terlepas dari peran pemasaran. Untuk memasarkan sebuah event olahraga diperlukan strategi khusus yang dikenal dengan bauran pemasaran, yaitu kiat kelompok pemasaran yang digunakan untuk mencapai sasaran pemasarannya dalam pasar sasaran. Media massa punya peran penting dalam menumbuhkan brand image tentang sebuah produk tertentu kepada konsumen melalui tayangan event olahraga.Kata Kunci: pemasaran olahraga, event olahraga
Comparing a Mobile Phone Automated System With a Paper and Email Data Collection System: Substudy Within a Randomized Controlled Trial
Authors: Bond Diana M , Hammond Jeremy , Shand Antonia W , Nassar Natasha 
Published: June 2020

Background
Traditional data collection methods using paper and email are increasingly being replaced by data collection using mobile phones, although there is limited evidence evaluating the impact of mobile phone technology as part of an automated research management system on data collection and health outcomes.

Objective
The aim of this study is to compare a web-based mobile phone automated system (MPAS) with a more traditional delivery and data collection system combining paper and email data collection (PEDC) in a cohort of breastfeeding women.

Methods
We conducted a substudy of a randomized controlled trial in Sydney, Australia, which included women with uncomplicated term births who intended to breastfeed. Women were recruited within 72 hours of giving birth. A quasi-randomized number of women were recruited using the PEDC system, and the remainder were recruited using the MPAS. The outcomes assessed included the effectiveness of data collection, impact on study outcomes, response rate, acceptability, and cost analysis between the MPAS and PEDC methods.

Results
Women were recruited between April 2015 and December 2016. The analysis included 555 women: 471 using the MPAS and 84 using the PEDC. There were no differences in clinical outcomes between the 2 groups. At the end of the 8-week treatment phase, the MPAS group showed an increased response rate compared with the PEDC group (56% vs 37%; P<.001), which was also seen at the 2-, 6-, and 12-month follow-ups. At the 2-month follow-up, the MPAS participants also showed an increased rate of self-reported treatment compliance (70% vs 56%; P<.001) and a higher recommendation rate for future use (95% vs 64%; P<.001) as compared with the PEDC group. The cost analysis between the 2 groups was comparable.

Conclusions
MPAS is an effective and acceptable method for improving the overall management, treatment compliance, and methodological quality of clinical research to ensure the validity and reliability of findings.
Capturing Relevant Patient Data in Clinical Encounters Through Integration of an Electronic Patient-Reported Outcome System Into Routine Primary Care in a Boston Community Health Center: Development and Implementation Study
Authors: Loo Stephanie , Grasso Chris , Glushkina Jessica , McReynolds Justin , Lober William , Crane Heidi , Mayer Kenneth H 
Published: June 2020

Background
Electronic patient-reported outcome (ePRO) systems can improve health outcomes by detecting health issues or risk behaviors that may be missed when relying on provider elicitation.

Objective
This study aimed to implement an ePRO system that administers key health questionnaires in an urban community health center in Boston, Massachusetts.

Methods
An ePRO system that administers key health questionnaires was implemented in an urban community health center in Boston, Massachusetts. The system was integrated with the electronic health record so that medical providers could review and adjudicate patient responses in real-time during the course of the patient visit. This implementation project was accomplished through careful examination of clinical workflows and a graduated rollout process that was mindful of patient and clinical staff time and burden. Patients responded to questionnaires using a tablet at the beginning of their visit.

Results
Our program demonstrates that implementation of an ePRO system in a primary care setting is feasible, allowing for facilitation of patient-provider communication and care. Other community health centers can learn from our model in terms of applying technological innovation to streamline clinical processes and improve patient care.

Conclusions
Our program demonstrates that implementation of an ePRO system in a primary care setting is feasible, allowing for facilitation of patient-provider communication and care. Other community health centers can learn from our model for application of technological innovation to streamline clinical processes and improve patient care.
Using Machine Learning to Optimize the Quality of Survey Data: Protocol for a Use Case in India
Authors: Shah Neha , Mohan Diwakar , Bashingwa Jean Juste Harisson , Ummer Osama , Chakraborty Arpita , LeFevre Amnesty E 
Published: June 2020

Background
Data quality is vital for ensuring the accuracy, reliability, and validity of survey findings. Strategies for ensuring survey data quality have traditionally used quality assurance procedures. Data analytics is an increasingly vital part of survey quality assurance, particularly in light of the increasing use of tablets and other electronic tools, which enable rapid, if not real-time, data access. Routine data analytics are most often concerned with outlier analyses that monitor a series of data quality indicators, including response rates, missing data, and reliability of coefficients for test-retest interviews. Machine learning is emerging as a possible tool for enhancing real-time data monitoring by identifying trends in the data collection, which could compromise quality.

Objective
This study aimed to describe methods for the quality assessment of a household survey using both traditional methods as well as machine learning analytics.

Methods
In the Kilkari impact evaluation’s end-line survey amongst postpartum women (n=5095) in Madhya Pradesh, India, we plan to use both traditional and machine learning–based quality assurance procedures to improve the quality of survey data captured on maternal and child health knowledge, care-seeking, and practices. The quality assurance strategy aims to identify biases and other impediments to data quality and includes seven main components: (1) tool development, (2) enumerator recruitment and training, (3) field coordination, (4) field monitoring, (5) data analytics, (6) feedback loops for decision making, and (7) outcomes assessment. Analyses will include basic descriptive and outlier analyses using machine learning algorithms, which will involve creating features from time-stamps, “don’t know” rates, and skip rates. We will also obtain labeled data from self-filled surveys, and build models using k-folds cross-validation on a training data set using both supervised and unsupervised learning algorithms. Based on these models, results will be fed back to the field through various feedback loops.

Results
Data collection began in late October 2019 and will span through March 2020. We expect to submit quality assurance results by August 2020.

Conclusions
Machine learning is underutilized as a tool to improve survey data quality in low resource settings. Study findings are anticipated to improve the overall quality of Kilkari survey data and, in turn, enhance the robustness of the impact evaluation. More broadly, the proposed quality assurance approach has implications for data capture applications used for special surveys as well as in the routine collection of health information by health workers.

International Registered Report Identifier (IRRID)
DERR1-10.2196/17619
Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter
Authors: Viguria Iranzu , Alvarez-Mon Miguel Angel , Llavero-Valero Maria , Asunsolo del Barco Angel , Ortuño Felipe , Alvarez-Mon Melchor 
Published: June 2020

Background
Health awareness initiatives are frequent but their efficacy is a matter of controversy. We have investigated the effect of the Eating Disorder Awareness Week and Wake Up Weight Watchers campaigns on Twitter.

Objective
We aimed to examine whether the Eating Disorder Awareness Week and Wake Up Weight Watchers initiatives increased the volume and dissemination of Twitter conversations related to eating disorders and investigate what content generates the most interest on Twitter.

Methods
Over a period of 12 consecutive days in 2018, we collected tweets containing the hashtag #wakeupweightwatchers and hashtags related to Eating Disorder Awareness Week (#eatingdisorderawarenessweek, #eatingdisorderawareness, or #EDAW), with the hashtag #eatingdisorder as a control. The content of each tweet was rated as medical, testimony, help offer, awareness, pro-ana, or anti-ana. We analyzed the number of retweets and favorites generated, as well as the potential reach and impact of the hashtags and the characteristics of contributors.

Results
The number of #wakeupweightwatchers tweets was higher than that of Eating Disorder Awareness Week and #eatingdisorder tweets (3900, 2056, and 1057, respectively). The content of tweets was significantly different between the hashtags analyzed (P<.001). Medical content was lower in the awareness campaigns. Awareness and help offer content were lower in #wakeupweightwatchers tweets. Retweet and favorite ratios were highest in #wakeupweightwatchers tweets. Eating Disorder Awareness Week achieved the highest impact, and very influential contributors participated.

Conclusions
Both awareness campaigns effectively promoted tweeting about eating disorders. The majority of tweets did not promote any specific preventive or help-seeking behaviors.
Predictors of Internet Health Information–Seeking Behaviors Among Young Adults Living With HIV Across the United States: Longitudinal Observational Study
Authors: Comulada Warren Scott , Step Mary , Fletcher Jesse B , Tanner Amanda E , Dowshen Nadia L , Arayasirikul Sean , Keglovitz Baker Kristin , Zuniga James , Swendeman Dallas , Medich Melissa , Kao Uyen H , Northrup Adam , Nieto Omar , Brooks Ronald A ,   Special Projects Of National Significance Social Media Initiative Study Group
Published: June 2020

Background
Consistent with young adults’ penchant for digital communication, young adults living with HIV use digital communication media to seek out health information. Understanding the types of health information sought online and the characteristics of these information-seeking young adults is vital when designing digital health interventions for them.

Objective
This study aims to describe characteristics of young adults living with HIV who seek health information through the internet. Results will be relevant to digital health interventions and patient education.

Methods
Young adults with HIV (aged 18-34 years) self-reported internet use during an evaluation of digital HIV care interventions across 10 demonstration projects in the United States (N=716). Lasso (least absolute shrinkage and selection operator) models were used to select characteristics that predicted whether participants reported seeking general health and sexual and reproductive health (SRH) information on the internet during the past 6 months.

Results
Almost a third (211/716, 29.5%) and a fifth (155/716, 21.6%) of participants reported searching for general health and SRH information, respectively; 26.7% (36/135) of transgender young adults with HIV searched for gender-affirming care topics. Areas under the curve (>0.70) indicated success in building models to predict internet health information seeking. Consistent with prior studies, higher education and income predicted health information seeking. Higher self-reported antiretroviral therapy adherence, substance use, and not reporting transgender gender identity also predicted health information seeking. Reporting a sexual orientation other than gay, lesbian, bisexual, or straight predicted SRH information seeking.

Conclusions
Young adults living with HIV commonly seek both general health and SRH information online, particularly those exploring their sexual identity. Providers should discuss the most commonly sought SRH topics and the use of digital technology and be open to discussing information found online to better assist young adults with HIV in finding accurate information. Characteristics associated with health information–seeking behavior may also be used to develop and tailor digital health interventions for these young adults.
Mapping of Health Literacy and Social Panic Via Web Search Data During the COVID-19 Public Health Emergency: Infodemiological Study
Authors: Xu Chenjie , Zhang Xinyu , Wang Yaogang 
Published: June 2020

Background
Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19–related health information.

Objective
The aim of this paper is to analyze web search data volumes related to COVID-19 in China.

Methods
We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020).

Results
The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to “medical help,” “protective materials,” and “pandemic progress.” Web searches for “return to work” and “go back to school” have increased eight-fold compared to the previous month. Searches for content related to “closed community and remote office” have continued to rise, and searches for “remote office demand” have risen by 663% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: “return to work and prevention measures,” “return to work guarantee policy,” and “time to return to work.” Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on “online education” whereas medium-sized cities have been paying more attention to “online medical care.”

Conclusions
Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19.
How effective are population health surveys for estimating prevalence of chronic conditions compared to anonymised clinical data?
Authors: Whiffen Tony , Akbari Ashley , Paget Tony , Lowe Sarah , Lyons Ronan 
Published: June 2020

IntroductionPopulation health surveys are used to record person-reported outcome measures for chronic health conditions and provide a useful source of data when evaluating potential disease burdens. The reliability of survey-based prevalence estimates for chronic diseases is unclear nonetheless. This study applied methodological triangulation via a data linkage method to validate prevalence of selected chronic conditions (angina, myocardial infarction, heart failure, and asthma).
MethodsLinked healthcare records were used for a combined cohort of 11,323 adults from the 2013 and 2014 sweeps of the Welsh Health Survey (WHS). The approach utilised consented survey data linked to primary and secondary care electronic health record (EHR) data back to 2002 within the Secure Anonymised Information Linkage (SAIL) Databank.
ResultsThis descriptive study demonstrates validation of survey and clinical data using data linkage for selected chronic cardiovascular conditions and asthma with varied success. The results indicate that identifying cases for separate cardiovascular conditions was limited without specific medication codes for each condition, but more straightforward for asthma, where there was an extensive list of medications available. For asthma there was better agreement between prevalence estimates based on survey and clinical data as a result.
ConclusionWhilst the results provide external validity for the WHS as an instrument for estimating the burden of chronic disease, they also indicate that a data linkage appproach can be used to produce comparable prevalence estimates using clinical data if a defined condition-specific set of clinical codes are available.
Stable Algorithm Based On Lax-Friedrichs Scheme for Visual Simulation of Shallow Water
Authors: Arry Sanjoyo Bandung , Hariadi Mochamad , Purnomo Mauridhi Hery 
Published: June 2020

Many game applications require fluid flow visualization of shallow water, especially dam-break flow. A Shallow Water Equation (SWE) is a mathematical model of shallow water flow which can be used to compute the flow depth and velocity. We propose a stable algorithm for visualization of dam-break flow on flat and flat with bumps topography. We choose Lax-Friedrichs scheme as the numerical method for solving the SWE. Then, we investigate the consistency, stability, and convergence of the scheme. Finally, we transform the strategy into a visualization algorithm of SWE and analyze the complexity. The results of this paper are: 1) the Lax-Friedrichs scheme that is consistent and conditionally stable; furthermore, if the stability condition is satisfied, the scheme is convergent; 2) an algorithm to visualize flow depth and velocity which has complexity O(N) in each time iteration. We have applied the algorithm to flat and flat with bumps topography. According to visualization results, the numerical solution is very close to analytical solution in the case of flat topography. In the case of flat with bumps topography, the algorithm can visualize the dam-break flow and after a long time the numerical solution is very close to the analytical steady-state solution. Hence the proposed visualization algorithm is suitable for game applications containing flat with bumps environments.
Implementation of a V/f Controlled Variable Speed Induction Motor Drive
Authors: Duranay Zeynep Bala , Guldemir Hanifi , Tuncer Servet 
Published: June 2020

DSP implementation of speed control of three phase induction motor drive is presented in this paper. A closed loop speed control has been achieved using constant V/f technique which is a simple scalar control method used to control the magnitude of the control quantities. In this study, a fourth order polynomial drived from V/f curve is constructed instead of using look up table which takes much time for determination of voltage from frequency value. A PI controller is used in speed control. dSPACE DS-1103 controller board is used for the implementation with Matlab/Simulink, which has a simple real time interface. Steady-state speed characteristics and transient responses with various reference speed commands are presented by experimental system. The simulation and experimental results provide a smooth speed response and good performance under various dynamic operations. Real time speed control has been implemented and some results are presented.
Automatic Segmentation on Glioblastoma Brain Tumor Magnetic Resonance Imaging Using Modified U-Net
Authors: Tjahyaningtijas Hapsari Peni Agustin , Nugroho Andi Kurniawan , Angkoso Cucun Very , Purnama I Ketut Edy , Purnomo Mauridhi Hery 
Published: June 2020

Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also erroneous and takes time because of the large MRI image data. An automated segmentation approach based on fully convolutional architecture was developed to overcome the problem. One of fully convolutional network that used is U-Net framework. U-Net architecture is evaluated base on the number of epochs and drop-out values to achieve the most suitable architecture for the automatic segmentation of glioblastoma brain tumors. Through experimental findings, the most fitting architectural model is mU-Net architecture with an epoch number of 90 and a drop out layer value of 0.5. The results of the segmentation performance are shown by a dice value of 0.909 which is greater than that of the previous research.