Yang, Renfan
(2018)
SmartAsthma an iOS application development for inhaler user.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
According to the surveys from the U.S. Centers for Disease Control and Prevention (CDC), asthma continues to be a serious public health problem since an estimated 24.6 million people caught asthma and 6.1 million of them are children. Children are more easily than adults to be affected by asthma and 8.3% of children under age 18 years who currently have asthma. Therefore, it is necessary to find a way to make it easy and convenient that parents can understand their children’s asthma situation and the teenagers can be informed with their progress on asthma treatment at real time.
With the development of smartphone that users can detect location, communicate through Bluetooth and access the internet through cellular or Wi-Fi, it is a great fit and convenient to let mobile record the daily data, such as the daily usage of inhaler, most frequent locations using inhaler and the Asthma Control Test (ACT) score history, for an inhaler user.
This thesis presents a framework of an iOS application developed by Swift 4.1 that realize the data collection, data visualization and report generation. Phone will receive the information via Bluetooth each time when inhaler is pressed. A new ACT score algorithm based on QualityMetric Inc. is created so that the score can be estimated without manually answering the questions and calculation. Users can view their score in a radar chart with suggestions or complete an exacerbation score questionnaire if they which zone (Red/Yellow/Green) they are currently in. All the data including users’ accounts are backed up in the Firebase Database and the data will be back synchronized if user use a new device.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
20 September 2018 |
| Date Type: |
Publication |
| Defense Date: |
24 July 2018 |
| Approval Date: |
20 September 2018 |
| Submission Date: |
26 July 2018 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
73 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Data Collection, Data Visualization |
| Date Deposited: |
20 Sep 2018 19:41 |
| Last Modified: |
20 Sep 2018 19:41 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/35031 |
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