Healthy Eating Application: Information System and Hardware Analysis

This report provides an analysis of the Healthy Eating application, focusing on its features and underlying information system. The report examines the Lose It app as a prime example of digital technology in public health, highlighting its role in promoting healthy eating habits and weight loss. It details the app's information processing system, including user login, data input, and food tracking methods such as barcode scanning and image processing. The report further explores the mobile device specifications, covering input, processing, output, and storage units required for the app's functionality. It discusses the use of cameras, health tracking devices, and onscreen keyboards for input, the role of the CPU in processing data, and the output of analytical results on the mobile screen. The reflective commentary provides insights into the research process and the learning experience gained from the assignment, including areas for future exploration and improvement. The report concludes by emphasizing the app's use of information processing and mobile technology to assist in promoting healthy eating and weight loss.

Running head: THE HEALTHY EATING APPLICATION
The Healthy Eating Application
Name of the Student:
Name of the University:
Author note:

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THE HEALTHY EATING APPLICATION
Executive Summary
The report aims at studying a healthy eating app in order to uplift the need for information
system in health. The features and information system of the app is discussed with keen
details to the hardware requirements of it. This helps to conclude how a quality mobile device
and information analytic system can help to remove obesity, if taken seriously.

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Table of Contents
Introduction................................................................................................................................3
The LoseIT Application.............................................................................................................3
Information Processing System..............................................................................................3
Mobile Device Specification......................................................................................................5
Input Devices.........................................................................................................................6
Output Devices.......................................................................................................................6
Reflective Commentary.............................................................................................................6
Conclusion..................................................................................................................................7

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The Lose IT Application
People are always attracted towards tasty and easy to prepare food, in the meantime,
be inspired to at least plan to exercise and stay in shape (Zarnowiecki et al. 2012). Now that
technology has been upgraded to support every field of life, eating healthy is not much of a
problem anymore (Jones et al. 2014). These apps are developed with the intention to keep
their users fit by assisting them with proper meal choices (Schoffman et al. 2013).
The Lose IT app is considered as one of the epitome examples of the use of digital
technology to enhance public health. This app encourages healthy eating habits into its users
through fun and effective weight-loss programs. The app asks the users to set a feasible time-
frame and then helps them to track foods that they love and are healthy at the same time.
Information Processing System
The application uses some really interesting and helpful features to enhance user
experience and health. The app allows users to login using a unique user id and password to
maintain security (Agaku et al. 2013). Then they are allowed to upload their basic personal
details alongside a photograph of themselves. The app also records certain health related
information of the user at signup time. These includes initial weight at joining time, height,
health complexities and so on. The user’s food preferences are also recorded and all these
data are further used to populate the database. Once the user has signed up, they are required
to input the target weight that they wish to achieve or the number of days and many more.

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Fig 1. Weight Loss Plan
The app allows the users to login to their account each day to update their daily food
intake and exercise routines. The app offers two ways to add a food to their daily routine.
Firstly, the user can search by the name of the food. The app processes these searches by
thriving through their huge database of more than 7 million foods and presents the relevant
results. The company regularly with utter precision updates this food database. Users can also
upload their own recipes. They can add names, ingredients and images to them for other’s
reference. Another way of adding a food to a day’s routine is by clicking photograph of the
food or scanning the Bar Code on the food’s packet. The Bar codes on a food packet
generally contains all information about the food in coded graphical format (Pagoto et al.
2013). The app processes this information and presents to the user the amount of calories that
these foods contain. The user can also control the amount of the food taken to get the exact
calorie consumption rate. The app uses image processing to identify the pictures taken and
thus show appropriate information. Users can also connect their personal tracker apps or

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devices like Fibit trackers, Google Fit and so on. The daily readings from these apps and
devices are updated into the app’s database for analysis.
Fig 2. Calorie Control Section
The app uses all these daily information to show the calories burnt, the weight lost
since joining and all other necessary health charts or graphs. The app also analyses all aspects
to determine the number of days that the user would need to reach the target or goal weight.
Mobile Device Specifications
The section below will discuss the input, processing and output units that the
application requires for functioning neatly.
Input Units
The application requires a camera to scan the barcodes of the food packets and to
click pictures of foods that would be uploaded for image processing. The camera based bar
code scanner technology is used here. The image of the bar codes is recorded into the CPU,
where Image Processing is done based on the coded Artificial Intelligence technique. They

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scan the PDF417 codes and flood the output with extracted information from each of the 17
units with 4 bar groups.
Fig 3. Bar Code Scanner
The app also requires other health recording devises to import daily updates of
exercises. These devices needs to be connected to the mobile with a suitable network feature
namely Wi-Fi or Bluetooth. To update textual information into the system the user needs to
use the onscreen keyboards of the mobile device, as usual.
Processing, Output and Storage Units
The application stores all its local data into the phone’s storage memory. The app
needs almost 75 MB of storage space to install its working components. The personal
information of the user is separately stored in the internal memory. The major components of
the application are retrieved into the RAM when the application is started. This allows the
CPU to fetch required data as quickly as possible. Fig. 4 below, shows the CPU usage of the
application. It can be seen that the CPU runs many processes for the com.fitnow.loseit server

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and the system_server that hosts this application. The more the CPU speed, the faster will it
run and process instructions that it read from the server or the input units.
Fig 4. CPU Usage Statistics
The application processes all forms of inputs and outputs the data analytic results on
the mobile screen. The CPU does the calculations based on the internally written programs
and the server sends the complex analytics result. These are then stored in the Random
Access Memory (RAM) or the graphics unit of the mobile CPU, from where RGB graphic
information are sent to the display unit, which then shows the images. The better the RAM
can hold, the richer is the experience.

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Fig 3. Calorie consumption and Activity Graphs on display
In order to print the health charts, the application needs to be connected to external
printers via wireless mediums, Wi-Fi or Bluetooth preferably (Fernandes and Bamforth
2012).
Fig 4. Mobile to Printer data transfer architecture
Reflective Commentary
While preparing the report, I was extremely satisfied with the way my research
shaped up to be. Health and information is indeed a popular topic to study in this era. The

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assignment provided me with the opportunity to surf through quite a number of quality
articles. This helped me to gain a handsome quantity of knowledge on how the hardware of
mobile devices work in information processing. However, I regret not having to research
more on the topic. This is something that I will be looking forward to in my next
assignments. That is, I would use applications and devices for a longer period before
preparing any assignment report. The assignment also made me realize the effort I prefer to
put into research and perfectness.

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References
Agaku, I.T., Adisa, A.O., Ayo-Yusuf, O.A. and Connolly, G.N., 2013. Concern about
security and privacy, and perceived control over collection and use of health information are
related to withholding of health information from healthcare providers. Journal of the
American Medical Informatics Association, 21(2), pp.374-378.
Darby, A., Strum, M.W., Holmes, E. and Gatwood, J., 2016. A review of nutritional tracking
mobile applications for diabetes patient use. Diabetes technology & therapeutics, 18(3),
pp.200-212.
Fernandes, L. and Bamforth, R., 2012. The mobile print enterprise. Research Paper), Jan.
Jones, S.S., Rudin, R.S., Perry, T. and Shekelle, P.G., 2014. Health information technology:
an updated systematic review with a focus on meaningful use. Annals of internal
medicine, 160(1), pp.48-54.
Pagoto, S., Schneider, K., Jojic, M., DeBiasse, M. and Mann, D., 2013. Evidence-based
strategies in weight-loss mobile apps. American journal of preventive medicine, 45(5),
pp.576-582.
Schoffman, D.E., Turner-McGrievy, G., Jones, S.J. and Wilcox, S., 2013. Mobile apps for
pediatric obesity prevention and treatment, healthy eating, and physical activity promotion:
just fun and games?. Translational behavioral medicine, 3(3), pp.320-325.
Wharton, C.M., Johnston, C.S., Cunningham, B.K. and Sterner, D., 2014. Dietary self-
monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-
week weight loss trial. Journal of nutrition education and behavior, 46(5), pp.440-444.

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Zarnowiecki, D., Sinn, N., Petkov, J. and Dollman, J., 2012. Parental nutrition knowledge
and attitudes as predictors of 5–6-year-old children's healthy food knowledge. Public health
nutrition, 15(7), pp.1284-1290.