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Issue 13.3 ('XDC 2015')
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FEATURE

Metrics for Personal Health

Adding a health metrics feature to the PHR solution

Issue: 13.3 (May/June 2015)
Author: JC Cruz
Author Bio: JC is a freelance writer based in British Columbia. He is a regular contributor to MacTech Magazine and Dr Dobb's Journal. Away from the writing pile, JC spends quality time with his nephew, as a proper uncle should.
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Article Length (in bytes): 47,989
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Article Number: 13305
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Download Icon 13305.zip Updated: 2015-05-18 09:57:50

Related Web Link(s):

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http://en.wikipedia.org/wiki/Bradycardia
http://en.wikipedia.org/wiki/Human_body_temperature
http://en.wikipedia.org/wiki/Hypertension
http://en.wikipedia.org/wiki/Hyperthermia
http://en.wikipedia.org/wiki/Hypotension
http://en.wikipedia.org/wiki/Hypothermia
http://en.wikipedia.org/wiki/Tachycardia
http://en.wikipedia.org/wiki/Waist-to-height_ratio

Excerpt of article text...

Personal health is not only about collecting a user-patient's health data. It is also about analyzing that data, and about presenting the results in clear, readable terms.

This article shows us how we could add such a feature to our PHR project. It begins by explaining what health metrics are and what they reveal about the user-patient's state of health. It shows how to calculate those metrics and how to present them to the user. A copy of the demo project, FooHealth, is available from the magazine's website.

Concept of Health Metrics

If there is one thing that annoys user-patients, it is trying to make sense of their health entries. After all, reading lines and lines of raw numbers is not something your average user-patient likes to do. Much better to have the PHR digest the numbers and relay its findings to the user-patient.

This is where health metrics come into play. Health metrics works by "boiling down" the health readings into the essential facts. The user-patient then learns if he is overweight or not, if his heart is in trouble, if he has the chills, or if all is well.

Health metrics, however, are accurate only when the underlying data is also accurate. When user-patients enter false or incorrect readings, those same metrics will give out the wrong results. Health metrics are also sensitive to age and gender. Results that are normal for a young, teen male may not be so for a middle-aged woman.

...End of Excerpt. Please purchase the magazine to read the full article.