While online survey systems facilitate the collection on copious records on diet, exercise and other healthrelated data, scientists and other public health experts typically must download data from those systems
into external tools for conducting statistical analyses. A more convenient approach would enable
researchers to perform analyses online, without the need to coordinate additional analysis tools. This
paper presents a system illustrating such an approach, using as a testbed the WAVE project, which is a 5-
year childhood obesity prevention initiative being conducted at Oregon State University by health scientists
utilizing a web application called WavePipe. This web application has enabled health scientists to create
studies, enrol subjects, collect physical activity data, and collect nutritional data through online surveys.
This paper presents a new sub-system that enables health scientists to analyse and visualize nutritional
profiles based on large quantities of 24-hour dietary recall records for sub-groups of study subjects over
any desired period of time. In addition, the sub-system enables scientists to enter new food information
from food composition databases to build a comprehensive food profile. Interview feedback from novice
health science researchers using the new functionality indicated that it provided a usable interface and
generated high receptiveness to using the system in practice.
Data is an essential commodity and various organizations today unlock data to allow them to make business decisions that are highly informed. Data in open source has become highly available and U.K Government has a wide range of available open data to analyse. The paper of this report lies in information extraction from data sets of health for supporting development for wide range of food products that are healthy. The scope of this paper lies in analysing and extracting information from distinct data sets using a specific tool of data analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis, results for the data will be analysed for showing the requirement for a wide range of food products that are healthy.
The Concept of Precision Nutrition and Product Technology R&D Innovation ——Zh...Simba Events
The Concept of Precision Nutrition and Product Technology R&D Innovation
——Zhang Xuguang, Director Science and Technology Center, BY-HEALTH CO., LIMITED
Patient Data Collection Methods. Retrospective Insights.QUESTJOURNAL
Introduction: Multiple classic and modern data collection techniques are presented in the current paper, but only a mix of them provides the appropriate approach to address patient safety problems. The current study aims to reveal the data collection methods applied worldwide. Materials and Methods: All scientific sources of the current article were identified mainly by research on Internet. The matching words used in the search of materials are “data collection methods”, “hospital reporting systems”, “incident reporting systems”, “patient events”, “patient reported data”. Relevant articles and studies covering the 2003-2016 timeframe were selected as a reference. Results: Various data collection procedures are available worldwide. During several years of research, it was concluded that a significant number of patient studies use the following patient data collection methods: retrospective record review, record review of current inpatients, staff interview of current inpatients and nominal group technique based consensus method. Conclusion: New trends in data collection techniques are also discussed, as they reveal the potential of the electronic environment. Future insights on this topic should consider the standardization of different data collection methods in order to improve data comparability aspects.
A 10-minute presentation by Melanie Voevodin to post-graduate students in health at Dandenong Hospital, Melbourne, Australia 28th July 2011.
Voevodin, Truby, Haines, Palermo
This document was developed with inputs from many institutions and experts. Several individuals deserve special mention. Mary Arimond, Kathryn Dewey and Marie Ruel developed the analytical framework and provided technical oversight throughout the project. Eunyong Chung and Anne Swindale provided technical support. Nita Bhandari, Roberta Cohen, Hilary Creed de Kanashiro, Christine Hotz, Mourad Moursi, Helena Pachon and Cecilia C. Santos-Acuin conducted analysis of data sets. Chessa Lutter coordinated a working group to update the breastfeeding indicators. Mary Arimond and Megan Deitchler coordinated the working group that developed the Operational Guide on measurement issues which is a companion to this document. Bernadette Daelmans and José Martines coordinated the project throughout its phases. Participants in the consensus meetings held in Geneva 3–4 October 2006 and in Washington, DC 6–8 November 2007 provided invaluable inputs to formulate the recommendations put forward in this document.
This is a research paper I wrote an E-health intervention called Copacetic Diabetic (the name my group came up with). Our e-health intervention focused on newly diagnosed Diabetic patients, which we decided would be an mobile app and website. In this paper, I addressed the need for our intervention and the research literature I review. it also includes the mock-ups I created for our nutrition page. This demonstrates my research skills, I was a group member.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
More Related Content
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Data is an essential commodity and various organizations today unlock data to allow them to make business decisions that are highly informed. Data in open source has become highly available and U.K Government has a wide range of available open data to analyse. The paper of this report lies in information extraction from data sets of health for supporting development for wide range of food products that are healthy. The scope of this paper lies in analysing and extracting information from distinct data sets using a specific tool of data analytics that is either SAS JMP or SAS Enterprise guide or base SAS. After this analysis, results for the data will be analysed for showing the requirement for a wide range of food products that are healthy.
The Concept of Precision Nutrition and Product Technology R&D Innovation ——Zh...Simba Events
The Concept of Precision Nutrition and Product Technology R&D Innovation
——Zhang Xuguang, Director Science and Technology Center, BY-HEALTH CO., LIMITED
Patient Data Collection Methods. Retrospective Insights.QUESTJOURNAL
Introduction: Multiple classic and modern data collection techniques are presented in the current paper, but only a mix of them provides the appropriate approach to address patient safety problems. The current study aims to reveal the data collection methods applied worldwide. Materials and Methods: All scientific sources of the current article were identified mainly by research on Internet. The matching words used in the search of materials are “data collection methods”, “hospital reporting systems”, “incident reporting systems”, “patient events”, “patient reported data”. Relevant articles and studies covering the 2003-2016 timeframe were selected as a reference. Results: Various data collection procedures are available worldwide. During several years of research, it was concluded that a significant number of patient studies use the following patient data collection methods: retrospective record review, record review of current inpatients, staff interview of current inpatients and nominal group technique based consensus method. Conclusion: New trends in data collection techniques are also discussed, as they reveal the potential of the electronic environment. Future insights on this topic should consider the standardization of different data collection methods in order to improve data comparability aspects.
A 10-minute presentation by Melanie Voevodin to post-graduate students in health at Dandenong Hospital, Melbourne, Australia 28th July 2011.
Voevodin, Truby, Haines, Palermo
This document was developed with inputs from many institutions and experts. Several individuals deserve special mention. Mary Arimond, Kathryn Dewey and Marie Ruel developed the analytical framework and provided technical oversight throughout the project. Eunyong Chung and Anne Swindale provided technical support. Nita Bhandari, Roberta Cohen, Hilary Creed de Kanashiro, Christine Hotz, Mourad Moursi, Helena Pachon and Cecilia C. Santos-Acuin conducted analysis of data sets. Chessa Lutter coordinated a working group to update the breastfeeding indicators. Mary Arimond and Megan Deitchler coordinated the working group that developed the Operational Guide on measurement issues which is a companion to this document. Bernadette Daelmans and José Martines coordinated the project throughout its phases. Participants in the consensus meetings held in Geneva 3–4 October 2006 and in Washington, DC 6–8 November 2007 provided invaluable inputs to formulate the recommendations put forward in this document.
This is a research paper I wrote an E-health intervention called Copacetic Diabetic (the name my group came up with). Our e-health intervention focused on newly diagnosed Diabetic patients, which we decided would be an mobile app and website. In this paper, I addressed the need for our intervention and the research literature I review. it also includes the mock-ups I created for our nutrition page. This demonstrates my research skills, I was a group member.
Similar to SUPPORTING LARGE-SCALE NUTRITION ANALYSIS BASED ON DIETARY SURVEY DATA (20)
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
HEALTH DISPARITIES: DIFFERENCES IN VETERAN AND NON-VETERAN POPULATIONS USING ...hiij
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vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for
informed health system planning for veteran populations.
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an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted
by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional
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nuanced understanding of health dynamics across demographic segments and highlighting disparities
essential for veteran health system planning.
Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and
vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments.
Conclusion: Insights from this study could inform health system planning, using epidemiological data
assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of
integrating Generative AI with epidemiological analysis in shaping public health policy and health
planning.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
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intervene in the most appropriate way, bringing or keeping your blood sugar levels as close as possible to
the reference values indicated by your doctor. Currently, blood glucose meters are used to measure and
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to check their blood sugar (blood sugar) periodically throughout the day to prevent dangerous
complications. Many children newly diagnosed with diabetes and their families may face unique challenges
when dealing with the everyday management of diabetes, including treatments, adapting to dietary
changes, and the routine monitoring of blood glucose. Many questions may also arise when selecting a
blood glucose meter for paediatric patients. With current blood glucose meters, even with multiple daily
self-tests, high and low blood glucose levels may not be detected. Key factors that may be considered when
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availability of results; ease of portability to allow testing at school and during leisure time; easyto- read
numbers on display; memory options; cost of meter and supplies. In this study we will show a new
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INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
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and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
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addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
The Proposed Guidelines for Cloud Computing Migration for South African Rural...hiij
It is now overdue for the hospitals in South African rural areas to implement cloud computing technologies in order to access patient data quickly in an emergency. Sometimes medical practitioners take time to attend patients due to the unavailability of kept records, leading to either a loss of time or the reassembling of processes to recapture lost patient files. However, there are few studies that highlight challenges faced by rural hospitals but they do not recommend strategies on how they can migrate to cloud computing. The purpose of this paper was to review recent papers about the critical factors that influence South African hospitals in adopting cloud computing. The contribution of the study is to lay out the importance of cloud computing in the health sectors and to suggest guidelines that South African rural hospitals can follow in order to successfully relocate into cloud computing.The existing literature revealed that Hospitals may enhance their record-keeping procedures and conduct business more effectively with the help of the cloud computing. In conclusion, if hospitals in South African rural areas is to fully benefit from cloud-based records management systems, challenges relating to data storage, privacy, security, and the digital divide must be overcome.
AN EHEALTH ADOPTION FRAMEWORK FOR DEVELOPING COUNTRIES: A SYSTEMATIC REVIEWhiij
#Health #clinic #education #StaySafe #pharmacy #healthylifestyle
call for papers..!
-----------------------------
Health Informatics: An International Journal (HIIJ)
ISSN : 2319 - 2046 (Online); 2319 - 3190 (Print)
Here's where you can reach us : hiij@aircconline.com
visit us on : https://airccse.org/journal/hiij/index.html
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published articles..!
AN EHEALTH ADOPTION FRAMEWORK FOR
DEVELOPING COUNTRIES: A SYSTEMATIC REVIEW
https://aircconline.com/hiij/V10N3/10321hiij01.pdf
GENDER DISPARITYOF TUBERCULOSISBURDENIN LOW-AND MIDDLE-INCOME COUNTRIES: A SY...hiij
The tuberculosis burden is higher in the population from low- and middle-income countries (LMICs) and
differently affects gender. This review explored risk factors that determine gender disparity in tuberculosis
in LMICs. The research design was a systematic review. Three databases; Google Scholar, PubMed, and
HINARI provided 69 eligible papers.The synthesized data were coded, grouped and written in a descriptive
narrative style. HIV-TB co-infected women had a higher risk of mortality than TB-HIV-infected men. The
risk of Vitamin-D deficiency-induced tuberculosis was higher in women than in men. Lymph node TB,
breast TB, and cutaneous and abdominal TB occurred commonly in women whereas pleuritis, miliary TB,
meningeal TB, pleural TB and bone and joint TB were common in men. Employed men had higher contact
with tuberculosis patients and an increased chance of getting the disease. Migrant women were more likely
to develop tuberculosis than migrant men. The TB programmers and policymakers should balance the
different gaps of gender in TB-related activities and consider more appropriate approaches to be genderbased and have equal access to every TB-associated healthcare.
BRIEF COMMUNICATIONS DATA HYGIENE: IMPORTANT STEP IN DECISIONMAKING WITH IMPL...hiij
Medical and health data that have been entered into an electronic data system in real-time cannot be
assumed to be accurate and of high quality without verification. The adoption of the electronic health
record (EHR) by many countries to the support care and treatment of patients illustrates the importance of
high quality data that can be shared for efficient patient care and the operation of healthcare systems.
This brief communication provides a high-level overview of an EHR system and practices related to high
data quality and data hygiene that could contribute to the analysis and interpretation of EHR data for use
in patient care and healthcare system administration.
CHINESE PHARMACISTS LAW MODIFICATION, HOW TO PROTECT PATIENTS‘INTERESTS?hiij
The pharmacy profession is relatively new in China. Recently, the demand for pharmacists has increased
as China's hospital system has been unable to support a large patient population due to the increasing
demand for health care. This paper discusses how to improve the Chinese pharmacist law. To make
reasonable laws on pharmacists, used to regulate and manage communication between pharmacists and
patients, the ethical relationships, financial support and degree requirement, and governance of
pharmacists. Improving pharmacist laws can help improve the quality of pharmacists' work, protect patient
privacy, and enhance pharmacists' work efficiency. I will use government reports and authoritative data
collected by myself as examples to analyze what needs to be improved in pharmacist law.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
SUPPORTING LARGE-SCALE NUTRITION ANALYSIS BASED ON DIETARY SURVEY DATA
1. Health Informatics - An International Journal (HIIJ) Vol.6, No.1, February 2017
DOI: 10.5121/hiij.2017.6101 1
SUPPORTING LARGE-SCALE NUTRITION ANALYSIS
BASED ON DIETARY SURVEY DATA
Catharina Vijay and Christopher Scaffidi
School of Electrical Engineering and Computer Science, Oregon State University,
Corvallis, OR, United States
ABSTRACT
While online survey systems facilitate the collection on copious records on diet, exercise and other health-
related data, scientists and other public health experts typically must download data from those systems
into external tools for conducting statistical analyses. A more convenient approach would enable
researchers to perform analyses online, without the need to coordinate additional analysis tools. This
paper presents a system illustrating such an approach, using as a testbed the WAVE project, which is a 5-
year childhood obesity prevention initiative being conducted at Oregon State University by health scientists
utilizing a web application called WavePipe. This web application has enabled health scientists to create
studies, enrol subjects, collect physical activity data, and collect nutritional data through online surveys.
This paper presents a new sub-system that enables health scientists to analyse and visualize nutritional
profiles based on large quantities of 24-hour dietary recall records for sub-groups of study subjects over
any desired period of time. In addition, the sub-system enables scientists to enter new food information
from food composition databases to build a comprehensive food profile. Interview feedback from novice
health science researchers using the new functionality indicated that it provided a usable interface and
generated high receptiveness to using the system in practice.
KEYWORDS
Diet and nutrition, Information management and analysis
1. INTRODUCTION
To study diet, health scientists have increasingly turned to surveys and supporting software,
including government-supported and commercial systems [16][17][22]. Online collection of
survey data has grown in prominence because it enables researchers to reach a large audience
cost-effectively [6]. Having collected surveys, however, scientists need to use the data. In this
sense, health scientists studying diet face an analogous challenge to that of researchers in other
fields of health science, where the rise of “big data” demands making sense of vast quantities of
information in hopes of improving outcomes and reducing healthcare costs [15][18].
In the case of scientists studying nutrition, the problem is therefore to find a way of helping them
to process large-scale datasets from dietary surveys. A common solution in other areas of health
science and healthcare has been to provide interactive visualization methods that aid in exploring
and understanding the data [14][19]. The most effective of these visualizations reflect the specific
characteristics and nature of the phenomena that they represent. For example, Shneiderman et al
have discussed how a visualization of pharmacy data in the EventFlow system revealed crucial
insights into patterns of drug use [19]; that particular visualization accommodated the temporal
nature of the events visualized. Likewise, a tool for analysing diet datasets should take into
account the multi-dimensional aspects of the data including (but not limited to) the temporal
nature of food consumption and the relationships among food types, serving sizes and nutrient
composition. In this sense, diet is “more than just data”—it shapes the very structure of the
problem and, thus, should shape the design of the solution. Unfortunately, researchers interested
in studying diet and nutrition currently lack access to such systems for analysing and visualizing
large datasets, thus leaving the problem faced by these scientists unsolved.
The WAVE project illustrates this problem and offers a venue for exploring a solution. This is an
integrated project (Research, Education, and Extension) involving active teens ages 15-19 in
2. Health Informatics - An International Journal (HIIJ) Vol.6, No.1, February 2017
2
Oregon [24]. The goal is to teach life skills (such as gardening, food preparation, and food
preservation skills) in addition to nutrition and physical activity education to support sustainable
healthy eating and adequate physical activity among 4-H soccer team players. The project will
develop, evaluate, and compare the effectiveness of virtual- and real-world learning environments
in reducing unhealthy weight gain and sedentary lifestyle among active youth. Primary caregivers
will also receive supplemental lessons to promote learning at home. The two-year pilot study will
involve dozens of parent-child pairs, and the two-year intervention will involve hundreds of
parent-child pairs. A wide variety of outputs and outcomes are planned to be measured, including
BMI, tracking devices, smart phone Apps, food intakes, physical activity levels, and
questionnaires. Awards will be presented at the end of the interventions for the healthiest teams. It
is expected that teens will be motivated to achieve/maintain healthy lifestyles to prevent
unhealthy weight gain.
Among other objectives, this project aims to explore the nutritional profile of subjects, as well as
the food sources of nutrients. For example, research questions can include “How much caffeine
are subjects consuming and in what forms?” and “To what extent did dietary profiles change after
subjects engaged in a targeted educational intervention?” Answering questions like these has
historically required exporting the dietary recall records from the online survey system, which is
called WavePipe, into separate statistical analysis tools.
Consequently, this paper presents a new WavePipe sub-system enabling scientists to analyse the
24-hour dietary recall data, input new food composition datasheets, and visualize the nutrient
analysis in a report, without ever leaving the integrated online data management system. This new
software thus illustrates a means of providing diet and nutrition researchers with a seamless, clear,
and straightforward process for answering questions like those noted above.
2. BACKGROUND AND REQUIREMENTS
The WAVE project provides a suitable context for exploring how an online information
management and analysis sub-system could aid in the collection and analysis of dietary recall
records. It a multidisciplinary childhood obesity prevention study conducted at Oregon State
University to study whether mixed-reality experiential learning involving virtual world immersive
environments are feasible to prevent childhood obesity in high school athletes [24].
WavePipe is the online system used within this project to enable the health scientists to define
studies, to enrol subjects, to assign subjects to groups, and to gather data. Of principal interest
with respect to the current paper, these data include 24-hour dietary recall data collected via a
survey based on the Automated Multiple-Pass Method (AMPM) developed by the USDA [16].
This multi-stage survey provides memory cues and multiple prompts to aid subjects in accurately
recalling the foods that they consumed in the preceding 24-hour period [4][12][21] (Table 1).
WavePipe has provided functionality for collecting survey data via a web browser, as well as for
emailing each subject a link to the survey.
Table 1. Stages of WavePipe’s existing dietary recall survey
AMPM stage Application in WavePipe surveys
Quick List: User chooses foods from
a list
Search interface where users can find and choose food items
that they consumed the previous day
Forgotten Foods: User reviews,
chooses from oft-forgotten foods
List of beverages and other foods that users review and may
check off, selecting additional items consumed
Time and Occasion: User specifies
time and meal of each food item
Omitted due to lack of need for these data to answer the specific
research questions of this particular project
Detail Cycle: User enters quantity
of each item
User selects a unit of measure for each food item (e.g., 1 cup, or
1 ounce) and enters a count for that item
Final Probe: Final prompt to
remember forgotten foods
Prompt to reconsider if user wishes to go back and enter data on
foods eaten in transit, in meetings, while shopping, at snacks, or
other oft-forgotten occasions
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The system generates copious amounts of data. For example, a study spanning 2 years could
involve sending the survey to each of 720 subjects for 7 days at the end of each quarter in each
year (e.g., to coincide with the end of various educational interventions). Such a study would thus
yield over 40,000 dietary recall records. Each subject might consume 10 different foods across the
meals of each day, and the researcher might be interested in 10 or more distinct nutrients across
these foods. Thus, the analysis could involve 400,000 incidents of food item consumption and
over 4 million specific values indicating the quantities of nutrients consumed.
With existing software, the process that food scientists would have to go through in order to
analyse data is tedious and time consuming. The scientist would have to download the dietary
recall record and then analyse the nutrient intake with existing analysis tools (e.g., [13] and [22]).
The existing nutrient analysis software (as discussed by Section 6 in more detail) lacked the
functionality to analyse the dietary data of multiple subjects over a duration of time. Thus, the
scientist would need to export subjects’ dietary records, manually load each of the 40,000 or more
records into the nutritional analysis tool, and then collect the results into a database for statistical
analysis. This cumbersome process would not only be too time-consuming for large-scale studies
of the type envisioned in the WAVE project, but it also is fraught with opportunities for user
error. These considerations led to the requirement for streamlining this process by enabling
scientists to conduct analyses within the survey-collection system.
An integrated solution can also provide a means of coping with the diversity of foods consumed
by subjects as well as changes in the nutritional content of foods. The USDA National Food
Database can be used (as in [13] and [22]) for the analysis of AMPM 24-hour dietary recall data
[16], but this database is typically updated every two years [8]. In the interim, new foods become
available but are not reflected in food tracker databases. To compound this issue, foods are
constantly being reformulated, removed from the market, or replaced with new offerings [2].
Regionally unique foods and ethnic imports further increase the likelihood that food items may be
missing in standard databases. Consequently, online analysis of dietary recall data requires a
means whereby scientists can upload and modify food profiles indicating information about the
nutritional content of food as it becomes available.
3. SOLUTION
This section describes the new sub-system, which has two components. The first is the Diet
Analysis component, which enables scientists to access dietary recall records collected with the
WavePipe system and to visualize the nutrient profile of the foods. These analyses are
configurable, enabling scientists to narrow their analyses to selected nutrients, date ranges, and
groups of subjects, thereby directly addressing questions like those mentioned above. The new
sub-system’s second component allows scientists to extend the database with information about
new or modified food items, thereby achieving a more comprehensive food composition database.
3.1. Analysing nutrient profiles
Once the AMPM 24-hour dietary recall survey responses are collected, the scientist can go to the
study in the WavePipe web application and click on a new option called “Food Nutrition
Analysis.” Clicking the option prompts the scientist to enter the start date, end date, and the study
group that the scientist is interested in analysing (Figure 1). These dates would typically indicate
a range of time over which the scientist had conducted a particular intervention.
The tool then takes the scientist to a page for selecting a list of nutrients to be analysed. The list of
39 distinct nutrients for the scientist to select is fairly long, so they are grouped by water, energy,
macronutrients, vitamins and minerals, to avoid overwhelming the user (Figure 2). The list of
nutrients is that provided by the SR27 USDA National Database.
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On clicking the “Save Nutrients” button, the scientist is taken to the screen where the nutrient
data for all the foods, consumed by the subjects in the study group, are displayed (Figure 3). The
first column contains all the distinct foods that were consumed by the subjects in the study. The
first row in the report contains the names and amount of all the nutrients that the scientist selected
in the previous step.
The nutrient data in this table is an aggregate of the foods consumed by the subjects. If multiple
subjects in the study consumed the same food, the gram weight for each serving size is obtained,
and multiplied with the nutrient concentration for the food. These numbers are added together to
obtain a single nutrient value for the food. If the food does not contain a nutrient value in the
database (missing value), it is displayed as “null” in the table.
Finally, the sub-system generates a report providing a table for each nutrient previously selected
by the scientist (Figure 4). Each nutrient table contains three columns. The first column contains
the names of the foods consumed by the subjects in the study. The second column contains the
nutrient content of the food. The third column contains the percentage that the food contributes to
the total consumption of that nutrient by the group. All the percentages in the third column add up
to a 100% so that the scientist view the nutritional percentage breakdown of each food.
This functionality illustrates a concise, integrated approach for directly answering research
questions about nutritional profiles without imposing the need for laborious reliance on external
tools. The report directly depicts answers to questions about where subjects are getting nutrients
of interest. In addition, by comparing answers for different groups of subjects or for a given group
of subjects across different date ranges, scientists can see how these nutritional profiles vary as a
result of education or other interventions.
Figure 1. Selection of a date range and subject group within the web application
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Figure 2. Selection of nutrients to analyse
Figure 3. Selection of foods to include in the analysis
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Figure 4. Portion of a report of nutritional profile across foods consumed by subjects
3.2. Adding custom foods to the food-nutrient database
The other new component is a web form that enables the scientist to add new foods to the
database (Figure 5). The first text box requires the scientist to enter the name of the food;
the second checkbox requires a food ID called a “NDB Number,” is a unique number that
is assigned to foods in the USDA Food Composition Database. Thus, the scientist can
either overwrite the data for an existing food if desired or can select a unique
alphanumeric identifier for a new food item.
Next the scientist can add the “Measurement Description” indicating a kind of serving
size and the mass per serving (in units of 100 grams, a common unit of mass used in
nutrition research). Each food can have multiple measurement descriptions, and the
scientist has the option of adding multiple rows of measurement data. For example, a
food such as a “Lobster Bisque” might have a “1 cup” serving consisting of 2.48 units of
mass, and a “1 oz” serving consisting of 0.28 units of mass.
Lastly the scientist can enter the nutrient values per 100 grams of the food item. The unit
that the nutrient value needs to be in is indicated next to the textbox, and the scientist is
responsible for entering appropriate values for the nutrients. The scientist is allowed to
add a total of 39 nutrients and therefore most of the nutrients are hidden under the
dropdown option. By clicking the “Add More Nutrients” button the scientist can access
the complete list of nutrients. If the scientist does not wish to enter the value for a
particular nutrient, the scientist is advised to leave the textbox blank and the tool will
automatically insert a null value for the nutrient in the database. The nutrient information
entered in the form is validated before the scientist can submit the form. This prevents the
scientist from inserting invalid information into the database.
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After entering all the information in the web-form, the scientist can click the “Save
Button” to save the food and its nutrient information in the database. Once the food is
saved in the database, it will appear in the AMPM food survey for subjects. This gives
them access to the wider range of foods to choose from when creating dietary recall
records.
Although the need to add or modify many foods en masse has not manifested within the
current project, the component also provides another feature (not currently visible to
scientists) whereby a user can upload the same information shown in Figure 5 via
spreadsheets. These indicate the lists of foods, serving sizes, and nutritional content. This
spreadsheet’s format matches that of a file periodically made available with updates to
the USDA National Database. Compatibility with this file format will facilitate keeping
WavePipe’s database up to date in the future.
Figure 5. Functionality enabling scientists to augment the food-nutrient databases
5. PRELIMINARY USER FEEDBACK
The primary goal of the new sub-system is to facilitate nutrient analyses by health science
researchers, including relative novices that may lack experience with statistical analysis tools.
Consequently, two student health science researchers were invited to try the system and to
provide formative feedback. This information will aid in further refinement of the system.
5.1. Subjects and methodology
We recruited students from the College of Public Health and Human Sciences at Oregon State
University. One undergraduate student and one graduate student participated. One was from the
Public Health department and the other from the Exercise and Sport Science department.
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The study was designed to be completed in 30 minutes. Each participant was given an
information sheet containing a list of food items and quantities representative of one day’s typical
food intake by subjects in the WAVE project. Each then completed an AMPM-style survey with
the foods data from the information sheet, generated the nutrient analysis report from the
database’s accumulated dietary recall records using the new sub-system, and finally provided
feedback about the tool by answering a short questionnaire (Table 2). Likert scales offered
options of “strongly agree,” “somewhat agree,” “somewhat disagree,” and “strongly disagree.”
Table 2. Feedback questionnaire
Background questions Response
What is your experience working with dietary assessment,
particularly 24-hour dietary recalls and food frequency
questionnaire?
Multiple-choice
What is your experience working with food composition database
such as SR27?
Multiple-choice
Formative feedback
One key purpose is to speed up the nutrient analysis process when
using 24-hour dietary recall data, by automating the nutrient
analysis and providing % of nutrient contribution by food sources.
Do you think this goal is achieved?
4-point Likert
Foods from the AMPM survey and their nutrient profiles are
presented in the form of a table. How easy is it to understand this
visualization of the data?
4-point Likert
Does this report give you the information to rank food sources
based on nutrients of interest?
4-point Likert
Would you like to continue using the tool? Yes/no
Is there any other feature that you would like to see in this tool? Open-response
5.2. Results
The participants had had prior experience with conducting health science research related to diet.
One had some experience with 24-hour dietary recalls and food frequency questionnaires, but less
than one year, and no specific experience with food composition databases. The other had 2 or
more years of experience with 24-hour dietary recalls and food frequency questionnaires, as well
as 1-2 years of experience working with food composition databases such as SR27.
Both participants were able to complete all of the tasks. Neither took more than a few minutes.
Neither needed to ask questions for clarification about how to use the system, nor did either
encounter any barriers that involved stopping the process and either going back or restarting.
The participants provided generally positive feedback about the system. Both strongly agreed that
the sub-system succeeded in providing a quick means of automating the nutrient analysis of food
items. One strongly agreed that the report was easy to understand, and the other somewhat agreed.
That participant suggested that the user interface could be clearer if it more explicitly indicated
what the percentage on the final report referred to. Both participants strongly agreed that the
report did give them the information needed to rank food sources based on nutrients of interest.
Both indicated that they would want to continue using the sub-system for conducting dietary
recall analyses in the future. Finally, in addition to the suggestion about labelling of the
percentages (above), one participant suggested that an interface to view individual subjects’ data
might be a good feature to add in the future.
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6. RELATED WORK
Systems for visualizing health-related data: Visualizations play an increasing role in helping
health scientists to make sense of data—particularly in an era of “big data” involving massive
amounts of complex, diverse, and potentially erroneous data [18]. Analysing and visually
presenting these data can aid in identifying patterns and other insights to aid patient outcomes and
to reduce healthcare cost [15][18]. Specific visualizations, for example, can help to highlight
distributions of data and uncertainty in measurements [14]. These can inform researchers,
healthcare workers and the users who provide the data [19]. Systems like these illustrate the
potential value of providing visualizations of health data in general and the desirability of
supporting analysis of large-scale food intake datasets.
Systems for large-scale collection of dietary recall data: The most well-established system
similar to that presented in this paper is SuperTracker, a web-based tool developed by the United
States Department of Agriculture (USDA) and the Center for Nutrition Policy and Promotion
(CNPP), which allows the user to track diet and physical activity [22]. The core of the system is a
database of over 8000 foods, which has become a de facto standard for nutrition researchers
working in this field and a widely-used resource for consumers [1][5]. The database provides data
on the nutritional composition of foods based on various data sources including the Food and
Nutrient Database for Dietary Studies (FNDDS) and the Food Patterns Equivalents Database
(FPED). SuperTracker is the basis for initiatives and “toolkit” extensions aimed at improving
public health [5][10].
A functionally similar system, ProNutra, is a web-based tool extended with the ProNESSy
module allows dietetic professionals to track weighed food intakes [23]. The system enables
health care researchers the ability to design nutrient-intake studies and to efficiently measure,
track and manage the preparation and consumption of each user in a study.
The key limitation of systems like those above is that health scientists wishing to analyse the
nutritional content of subjects’ diets must download dietary recall records to external tools. This
requires users to learn multiple tools and to coordinate the flow and formatting of data among
those tools. Consequently, the process introduces many opportunities for error in the
management, use, and analysis of data.
Systems for individual-level tracking and analysis of dietary recall data: Other web-based
systems share the limitation noted above, and in addition also lack support for management of
data sets comprising records from groups of users. For example, the Food Works system designed
by The Nutrition Company enables individual users to track 24-hour dietary recall, multiple-day
records, recipes and menus, which has aided in research on energy intake [17]. Likewise,
SuperTracker’s consumer-oriented website is also focused on individual-level food tracking and
nutrition analysis [22]. Yet another example is the Food Processor, developed by Esha Research,
a web-based tool for nutrient analysis with a food composition database that includes foods from
USDA databases and the Canadian Nutrient File [3]. These and similar tools can analyse diets at a
single record level but, unlike the new WavePipe sub-system, lack support for analysing records
from groups of subjects.
Systems for analysis of menus and recipes: A final group of systems with related functionality
enable researchers to perform nutrition analysis of menus and recipes. For example, MenuCalc
provides restaurant operators with a “do-it-yourself” approach nutritional analysis of menu items
[7]. A similar product, NUTRIKIDS system, aids in planning of menus, with a focus on school
food service departments aiming to meet nutritional guidelines [9], and health researchers have
relied on this system for analysing the nutritional content of school cafeteria menus [20]. These
and functionally similar tools are designed to analyse menus rather than dietary recall records.
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7. CONCLUSIONS
This work has demonstrated the feasibility of creating an integrated system for collecting and
analysing nutritional data based on dietary recall surveys. The resulting functionality,
implemented as a sub-system within the WAVE project, substantially improves on the state of the
art by eliminating the cumbersome and error-prone process of downloading and analysing data
via external tools. The added functionality also enables importing new food information from
various nutritional databases as well as creating new entries for individual foods in an effort to
build a comprehensive food profile. As an output from this sub-system, a clear and concise report
is generated to show a snapshot of nutritional intake for subjects in a specified sub-group.
Formative feedback was largely positive and provided some suggestions for enhancing the user
interface. These results indicate the viability of this approach as a means for simplifying the
process of analysing dietary recall data.
This system provides a basis for further enhancement and evaluation. The WAVE project has
embarked on a two-year study applying the WavePipe, which will facilitate a future summative
evaluation of its sub-systems, including the one presented in this paper.
ACKNOWLEDGEMENTS
This work was funded by the Agriculture and Food Research Initiative (AFRI) Grant 2013-
67001-20418 from the USDA National Institute of Food and Agricultural Science Enhancement
Coordinated Agricultural Program. We thank Dr. Siew Sun Wong for guidance and feedback on
nutritional science aspects of this work.
REFERENCES
[1] Ahuja, J., Moshfegh, A., Holden, J., and Harris, E. (2013) USDA food and nutrient databases
provide the infrastructure for food and nutrition research, policy, and practice. The Journal of
Nutrition. 143(2), 241S-249S.
[2] Anderson, E., Perloff, B., Ahuja, J., and Raper, N. (2001) Tracking nutrient changes for trends
analysis in the United States. Journal of Food Composition and Analysis. 14(3), 287-294.
[3] Bazzano, L., He, J., Ogden, L., Loria, C., Vupputuri, S., Myers, L., and Whelton, P. (2002)
Agreement on nutrient intake between the databases of the first national health and nutrition
examination survey and the ESHA food processor. American Journal of Epidemiology. 156(1), 78-
85.
[4] Blanton, C., Moshfegh, A., Baer, D., and Kretsch, M. (2006) The USDA automated multiple-Pass
method accurately estimates group total energy and nutrient intake. The Journal of Nutrition.
136(10), 2594-2599.
[5] Britten, P. (2013) SuperTracker incorporates food composition data into innovative online
consumer tool. Procedia Food Science. 2, 172-179.
[6] Couper, M. (2005) Technology trends in survey data collection. Social Science Computer Review,
23(4), 486-501.
[7] FoodCalc (2016) MenuCalc Features. Retrieved January 2016 from
http://www.menucalc.com/features.aspx
[8] Haytowitz, D. (2015) Updating USDA's key foods list for what we eat in America, NHANES
2011-12. Procedia Food Science. 4, 71-78.
[9] Heartland School Solutions (2016). NUTRIKIDS suite. Retrieved January 2016 from
https://www.heartlandpaymentsystems.com/school-solutions/nutrition-technology/nutrikids
[10] Hunt, S. (2015) Body weight planner tool. Nursing for Women's Health. 19(5), 450-452.
[11] Leone, A., Gavey, E., and Holland, C. (2015) Celebrate national workplace wellness week using
the worksite wellness toolkit. Journal of the Academy of Nutrition and Dietetics. 115(4), 497-498.
11. Health Informatics - An International Journal (HIIJ) Vol.6, No.1, February 2017
11
[12] Moshfegh, A., Rhodes, D., Baer, D., Murayi, T., Clemens, J., Rumpler, W., Paul, D., and
Sebastian, R. (2008) The US Department of Agriculture Automated Multiple-Pass Method reduces
bias in the collection of energy intakes. The American Journal of Clinical Nutrition. 88(2), 324-
332.
[13] Pehrsson, P., Haytowitz, D., Holden, J., Perry, C., and Beckler, D. (2000) USDA's national food
and nutrient analysis program: Food sampling. Journal of Food Composition and Analysis. 13(4),
379-389.
[14] Potter, K. (2008) Visualization of statistical measures of uncertainty. Student Research Poster
Competition, University of Utah, 41-42. http://www.sci.utah.edu/~kpotter/talks/phdColloq.pdf
[15] Raghupathi, W., and Raghupathi, V. (2014) Big data analytics in healthcare: Promise and
potential. Health Information Science and Systems. 2(1), 3, 1-10.
[16] Raper, N., Perloff, B., Ingwersen, L., Steinfeldt, L., and Anand, J. (2004) An overview of USDA's
dietary intake data system. Journal of Food Composition and Analysis. 17(3), 545-555.
[17] Rollins, B., Dearing, K., and Epstein, L. (2010) Delay discounting moderates the effect of food
reinforcement on energy intake among non-obese women. Appetite. 55(3), 420-425.
[18] Shah, R., Echhpal, R., and Nair, S. Big data in health care analytics. International Journal on
Recent and Innovation Trends in Computing and Communication. 4(10), 134-138.
[19] Shneiderman, B., Plaisant, C., and Hesse, B. (2013) Improving healthcare with interactive
visualization. Computer. 46(5), 58-66.
[20] Smith, S., and Cunningham-Sabo, L. (2014) Food choice, plate waste and nutrient intake of
elementary-and middle-school students participating in the US national school lunch program.
Public Health Nutrition. 17(06), 1255-1263.
[21] Subar, A., Kirkpatrick, S., Mittl, B., Zimmerman, T., Thompson, F., Bingley, C., Willis, G., and
Islam, N. (2012) The Automated Self-Administered 24-hour dietary recall (ASA24): A resource
for researchers, clinicians and educators from the National Cancer Institute. Journal of the
Academy of Nutrition and Dietetics. 112(8), 1134.
[22] United States Department of Agriculture (2016) SuperTracker application and Food-A-Pedia.
Retrieved January 2016 from https://www.supertracker.USDA.gov/
[23] Weiss, R., Fong, A., and Kretsch, M. (2003) Adapting proNutra to interactively track food weights
from an electronic scale using ProNESSy. Journal of Food Composition and Analysis. 16(3), 305-
311.
[24] Wong, S., Meng, Y., Moissinac, B., Scaffidi, C., and Manore, M. (2015) WAVE Pilot Study:
Feasibility of Using Emails and Short Message Service (SMS) with High School Soccer Players to
Reinforce Compliance in an Obesity Prevention Intervention, The FASEB Journal, 29, 1
Supplement 135.6.
AUTHORS
Catharina Vijay
Catharina Vijay is a software engineer at CDK Global. She obtained M.S. in computer science from Oregon
State University with a focus on visualization and analysis of nutritional data.
Christopher Scaffidi
Christopher Scaffidi is an associate professor of computer science at Oregon State University. He obtained
a Ph.D. in software engineering from Carnegie Mellon University.