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CSE5810: Intro to Biomedical Informatics

Mobile Computing to Impact Patient Health and Data Availability for Diseases Monitoring

Xian Shao

xian.shao@engr.uconn.edu

Advisor: Prof. Steven A. Demurjian, Sr.steve@engr.uconn.edu

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What is Mobile Computing Mobile computing is:

Human-computer interaction Computing enabled by presence of wireless enabled

portable devices (PDAs, cell phones, tablet etc.) Involves mobile communication, mobile hardware, and

mobile software Many other names/overlapping computing paradigms:

Pervasive Computing Ubiquitous Computing Wireless Computing Embedded Computing Nomadic Computing Wireless Sensor Networks Ad-Hoc Networks Mesh Networks Vehicular Networks ...

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Mobile Computing

Mobil computing

Wireless communication

Applications Location-awareness Mobility Support Security Resource Management

Network Protocol Broadcast Technologies Standards Wireless Medium

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Mobile Computing Wireless communication:

Cellular data service: GSM, CDMA, GPRS, 3G, 4G, etc.

Wi-Fi connection Satellite Internet access

Mobile devices: Personal digital assistant/enterprise digital assistant Smartphone Tablet computer Ultra-Mobile PC Wearable computer

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Mobile Computing Revolution:

Mobile is global Cost effectively, Convenient Anytime and anywhere Contextual

Limitation: Range & Bandwidth Security standards Power consumption Transmission interferences Potential health hazards Human interface with device

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Mobile Applications for Diseases Monitoring Background:

Mobile devices are becoming more and more ubiquitous in our daily life.

Chronic patients must carry out a rigorous control of diverse factors in their lives.

As the technologies rapidly evolving, more and more mobile device applications related to healthcare are being developed .

Why use Mobile devices: Mobile devices are much cheaper than the desktop

nowadays People carry mobile device with them everywhere People stored their information in their mobile devices

include the health condition The technical capabilities of mobile devices increased

significant

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Mobile Applications for Diseases Monitoring Mobile Technology Capabilities for Monitoring

Patients: Text message (SMS) Camera Native applications Automated sensing （ pedometer, blood pressure

monitors, glucose meters, and fitness equipment ） Internet Access

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Healthcare Applications Application types:

Data collection Information entered manually

– gIUCModel– Personal Health Assistant (PHA)

Collect information automatically– A mobile monitoring application for chronic diseases (Vladimir

Villarreal 2013)– Mobile access to health information (Lena Mamykina 2006)– etc.

Data collection & analysis One application (Maarten van der Heijden 2013 ) Another application (Mark Beattie 2014) Etc…

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Healthcare Applications gIUCModel

gIUCModl is an application that use mobile technology to help to monitor chronic diseases, especially for diabetes. Using this application, patients can collect their daily information and upload through mobile phones or other platforms. And physicians can review each patient’s information and return suggestions to patients.

General Structure:

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Healthcare Applications Five modules of the structure:

Data Interface– Automatically: importing data from a XML file generated by

glucometers.– Manually

Database– A cloud database (Microsoft Health Vault)– A local database

gIUCModel recommender system– system automatically evaluate patient data and provide

suggestions, according to the information which patients provided.

E-learning module – it offers a fully virtualized education space with

recommendations from recommendation system.

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Healthcare Applications Glucose model module

– It obtains a customized model each patient’s glucose blood levels with the information provided by patients through evolutionary computation.

gIUCModel recommender system, E-Learning module and Glucose model module are three new models provided by this application.

User profiles: Administrator

– Create accounts within the application.

Physician – Introduce new patients and communicate with them, observe

their data and their evolution.

Patient– Upload their data and receive the recommendations related to

his condition.

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Healthcare Applications A mobile monitoring application for chronic diseases

(Vladimir Villarreal 2013) This application contains three components that enable the

semiautomatic development of software, independent of the target disease and adaptable to the particular needs. The first component is ontologies that classify medical elements

such as disease, recommendations, preventions, food, mobile

devices and diet suggestions. The second component is the distribution of the devices in layers,

allowing the generation of final applications distributed in a medical context. These layers are defined to develop and maintain the set of applications.

The third and most important element is developing patterns known as MobiPatterns. – A MobiPattern defines the schema of each control module that is a part

of the final application.

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Healthcare Applications Relationship between the distributed application and

multiple devices:

Elements: biometric devices, mobile devices, medical server (use MySQL as local database).

Developed application (Android): One corresponding to the needs of physician One to the needs of patient

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Healthcare Applications MobiPatterns:

This application defined and developed a set of patterns known as MoiPatterns to integrate the modules into the mobile device. These MobiPatterns define the schema of each screen of the final application and the functional structure. And one MobiPattern always depends on another MobiPattern.

Distribution and relationship:

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Healthcare Applications Structure of the ontology for the application:

Ontology classification:– Patient’s Profile: defines each patient’s data (Common Profile and

Individual Profile).

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Healthcare Applications– Common profile: this profile stores the information about the patient’s

diseases. – Diseases: defines a classification of diseases. – ModuleDefinition: elements generated according to each patient’s

profile. – Food: defines a classification of the different types of food to be

consumed by the patient.

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Mobile access to health information (Lena Mamykna 2006) It presents three approached to investigating health

management on diabetes. Face-to-face interview Observation of Diabetes Support Group Cognitive Probe

Cognitive probe It has dual nature:

– it was meant to heighten behavior and engage them in reflective analysis.

– it served as an early prototype of a health monitoring solution. The application, Continuous Health Awareness Program (CHAP).

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Healthcare Applications The CHAP application

It utilized sensing and self-report techniques to capture individuals’ actions and daily blood sugar trends.

Components:– GlucoWatch G2 Biographer: a commercially available glucose

monitoring device worn as a wrist-watch that non-invasively samples blood sugar every 10 minutes.

– X10 motion detection sensors: which positioned in places of usual activity, it’s unique for each household.

– A computer-based diary application: it allowing individuals to report on their activities, composition of meals or medications as well as their emotional state. The diary application was available from a laptop screen augmented with touch-sensitive MagicTouch cover to simplify user interaction.

– A webcam: it used for free-form comments or notes for the research team. The main purpose of the motion detection sensors was to provide an additional reference for the research team and help assess accuracy of self-reports.

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Healthcare Applications A system for chronic obstructive pulmonary disease

(COPD) exacerbation management. (Maarten 2013) It has the novel feature of including automatic data

interpretation by a probabilistic risk model, enabling autonomous operation to support patient self-management.

General architecture:

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Healthcare Applications The systems components:

A smartphone: communication and computation Sensor: obtain objective information on the patient’s health status,

transmitted wirelessly to the smartphone A web-based systems: scheduling tasks and collecting patient data

Hardware components: Smartphone (Android OS) Sensor interface: the phone communicates with the sensors via a

Mobi, a Bluetooth capable multichannel sensor-interface Pulse-oximeter Spirometer

The Aerial application This application is based on Android system It provides following functionality:

– A time alarm o signal the registration– A touch-screen interface for the questionnaire

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Healthcare Applications– Processing of the spirometer data to compute the forced

expiratory volume in 1s. – Computation of the probability of an exacerbation based on the

observed data – Asynchronously transmission of the observed data to the server

over a secured data connection Risk model

The main component of this system Based on the data that is gathered, the model can compute the

probability of an exacerbation. The model that this system use is a Bayesian network.

For COPD models, the main outcome variable is exacerbation, focusing on a symptom based definition. But the nature of a Bayesian network allows us to easily inspect probabilities for any variable.

TBD

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Healthcare Applications Web-centre

It is the administration web-application that was built using the workflow management system iTask.

The workflow system implements advanced feature to generate and coordinate tasks and provides a generic (web) interface.

The other data collection and analysis applications (TBD)

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Conclusion In this review, I evaluated five eHealth applications,

they are gIUCModel, PHA, A mobile monitoring application for chronic diseases (Vladimir Villarreal 2013), Mobile access to health information (Lena Mamykna 2006) and A system for chronic obstructive pulmonary disease (COPD) exacerbation management. (Maarten 2013).

In this applications, gIUCModel and PHA only support manually enter, Vladimir Villarreal 2013 and Lena Mamykna 2006, these two application support automated data collection, all these four applications only can collect data. Maarten 2013, this application can collect data, but analysis data as well.

More applications will be determined.

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Reference Hidalgo JI, Maqueda E, Risco – Martin JL, Cuesta- Infante A, Colmenar JM, Nobel J,

“glUCModel - A monitoring and modeling system for chronic diseases,”, J Biomed Inform. 2014 Jan 7. pii: S1532-0464(13)00206-2. doi: 10.1016/j.jbi.2013.12.015J Biomed Inform. 2014 Jan 7. pii: S1532-0464(13)00206-2. doi: 10.1016/j.jbi.2013.12.015: http://www.ncbi.nlm.nih.gov/pubmed/24407050

PHA-course project Vladimir Villarreal, Jesus Fontecha, Ramon Hervas, Jose Bravo, “Mobile and ubiquitous

architecture for the medical control of chronic diseases through the use of intelligent devices: Using the architecture for patients with diabetes”, Future Generation Computer Systems, Volume 34, May 2014, Pages 161–175: http://www.sciencedirect.com/science/article/pii/S0167739X1300277X

L. Mamykina, E.D. Mynatt, D.R. Kaufman, Investigating health management practices of individuals with diabetes, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, Montreal, Quebec, Canada, 2006.

Van der Heijden, Maarten, et al. "An autonomous mobile system for the management of COPD." Journal of biomedical informatics 46.3 (2013): 458-469.