Risk factor coaching engine that determines a user health score
Risk factor data can be processed by a risk factor coaching engine to determine health risk for a user. The risk factor coaching engine may be executed within a health coaching protocol to perform actions that provide a user with information, recommendations and alerts via other coaching engines, and appointments with health care professionals. The risk factor coaching engine may also predict attribute values for a user based on a time period and goals for user health data upon which the predicted attribute value is based.
1. A method for processing risk factors for a user, comprising:
receiving protocol data for creating a risk factor coaching engine by an application stored and executed at a computing device, the risk factor coaching engine stored in memory and executable by a processor to process a first set of stored user health data for a user, wherein the protocol data includes a rule and one or more health attribute values, the rule and the one or more health attribute values each including a computer programming expression editable by a user through a graphical interface, the graphical interface including a first menu for editing the programmable expressions and a second menu for entering the user health data;
determining a user health score through execution of the risk factor coaching engine by the processor and based on the user health data, wherein determining the user health score includes evaluating the expressions of the one or more health attribute values by inputting the user health data to calculate one or more calculated health attribute values, and then evaluating the expression of the rule by inputting the one or more calculated health attribute values to calculate the user health score;
performing a first action through execution of the risk factor coaching engine by the processor and based on the user health score, the first action including reporting information to the user about the presence of the health risk; and
storing updated user health data in memory based on the performed first action, the updated user health data including the user health score.
2. The method of claim 1 , further comprising performing a second action through execution of the risk factor coaching engine by the processor and based on the user health score, wherein the health data includes a food consumption trend generated from food consumption data received from the user, the first action includes determining a predicted user weight based on the food consumption trend, and the second action includes reporting to the user a health action required to achieve or avoid the predicted user weight.
3. The method of claim 2 , further comprising reporting the predicted user weight through a graphical interface as an image of a human figure, the size and the shape of the human figure determined by the value of the predicted user weight.
4. The method of claim 1 , wherein the expression of the rule returns a predicted user life span based on the one or more calculated health attribute values.
5. The method claim 4 , wherein the first action includes reporting the predicted user life span.
6. The method of claim 1 , wherein the first action includes determining a predicted user weight based on the one or more calculated health attribute values.
7. The method of claim 1 , wherein the first action includes determining a predicted user attribute based on a trend calculated from a portion of the user health data.
8. The method of claim 1 , wherein the user health score is associated with a user expected life span.
9. The method of claim 1 , wherein the first action includes reporting to the user a user action required to achieve a user goal, the user health data including the user goal.
10. The method of claim 1 , further comprising:
executing the risk factor coaching engine based on the updated user health data; and
performing a second action through execution of the risk factor coaching engine by the processor and based on the updated user health data.
11. A method for processing risk factors for a user, comprising:
receiving protocol data for creating a risk factor coaching engine by an application stored in at a computing device, the risk factor coaching engine stored in memory and executable by a processor to process a first set of stored user health data for a user by a protocol rule, wherein the protocol data includes the protocol rule and one or more health attribute values, the protocol rule and the one or more health attribute values each including a computer programming expression editable by a user through a graphical interface, the graphical interface including a first menu for editing the programmable expressions and a second menu for entering the user health data;
executing the risk factor coaching engine to apply the protocol rule to the user health data to determine that the user is subject to a health risk based on the one or more health attribute values, wherein applying the protocol rule to the user health data includes evaluating the expressions of the one or more health attribute values by inputting the user health data to calculate one or more calculated health attribute values, and then evaluating the expression of the protocol rule by inputting the one or more calculated health attribute values to calculate a user health score, the user health score indicating the presence of the health risk;
performing a first action by the risk factor coaching engine at the computing device based on the presence of the health risk, the first action including reporting information to the user about the presence of the health risk; and
storing updated user health data in memory based on the performed first action, the updated user health data including the user health score.
12. The method of claim 11 , further comprising performing a second action through execution of the risk factor coaching engine by the processor and based on the user health score, wherein the health data includes a food consumption trend generated from food consumption data received from the user, the first action includes determining a predicted user weight based on the food consumption trend, and the second action includes reporting to the user a health action required to achieve or avoid the predicted user weight.
13. The method of claim 11 , wherein the first action includes setting a goal for a user health risk attribute value.
14. The method of claim 11 , further comprising calculating a trend based on the user health data and determining that the trend indicates a health risk applicable to the user.
15. The method of claim 12 , further comprising reporting the predicted user weight through the graphical interface as an image of a human figure, the size and the shape of the human figure determined by the value of the predicted user weight.
16. The method of claim 11 , wherein the first action includes determining a predicted user attribute based on a trend calculated from a portion of the user health data.
17. A computer readable non-transitory storage medium having embodied thereon a program, the program being executable by a processor to perform a method for processing risk factors for a user, the method comprising:
receiving protocol data for creating a risk factor coaching engine whereby the risk factor coaching engine processes a first set of stored user health data for a user, wherein the protocol data includes a rule and one or more health attribute values, the rule and the one or more health attribute values each including a computer programming expression editable by a user through a graphical interface, the graphical interface including a first menu for editing the programmable expressions and a second menu for entering the user health data;
determining a user health score based on the user health data, wherein determining the user health score includes evaluating the expressions of the one or more health attribute values by inputting the user health data to calculate one or more calculated health attribute values, and then evaluating the expression of the rule by inputting the one or more calculated health attribute values to calculate the user health score;
performing a first action based on the user health score, the first action including reporting information to the user about the presence of the health risk; and
storing updated user health data based on the performed first action, the updated user health data including the user health score.
18. The computer readable storage medium of claim 17 , further comprising determining a user health score when user health data has changed.
19. The computer readable storage medium of claim 17 , further comprising performing a second action through execution of the risk factor coaching engine by the processor and based on the user health score, wherein the health data includes a food consumption trend generated from food consumption data received from the user, the first action includes determining a predicted user weight based on the food consumption trend, and the second action includes reporting to the user a health action required to achieve or avoid the predicted user weight.
20. The computer readable storage medium of claim 17 , wherein the expression returns a predicted user life span based on one or more user risk factor attributes.
21. The computer readable storage medium of claim 17 , wherein the first action includes reporting a predicted user life span.
22. The computer readable storage medium of claim 17 , wherein the first action includes determining a predicted user weight.
23. The computer readable storage medium of claim 17 , wherein the first action includes determining a predicted user attribute based on a trend calculated from a portion of the user health data.
24. The computer readable storage medium of claim 17 , wherein the user health score is associated with a user expected life span.
25. The computer readable storage medium of claim 17 , wherein the first action includes reporting to the user a user action required to achieve a user goal, the user health data including the user goal.