PREDICTIVE GUIDANCE SYSTEMS FOR PERSONALIZED HEALTH AND SELF-CARE, AND ASSOCIATED METHODS
Systems and methods for biomonitoring and personalized healthcare are disclosed herein. In some embodiments, a biomonitoring and healthcare guidance system is configured to generate personalized self-care recommendations (e.g., recommendations relating to sleep, exercise, diet, etc.) to guide a. patient in effectively managing and/or improving a chronic condition (e.g., diabetes, pre-diabetes, hypertension, hyperlipidemia, etc.). The system can continuously or periodically update and/or adapt the self-care recommendations (e.g., based on data from the particular patient as well as data from a plurality of other patients). The system can guide individuals toward self-care changes that are likely to improve their chronic health conditions, support them in making those changes, and adapt and/or update continuously over time.
1 . A computer-implemented method for notifying a user of a recommended self-care mode, the method comprising:
collecting health data of the user;
identifying a health metric based on the health data, wherein the health metric met a threshold health value;
identifying a self-care mode from a plurality of self-care modes by:
analyzing the plurality of self-care modes, wherein each of the plurality of self-care modes has one or more contributing health factors;
determining predictions of the health metric based on the plurality of self-care modes;
identifying contribution measures of the plurality of self-care modes for the predictions;
selecting the self-care mode from the plurality of self-care modes based on the contribution measures; and
outputting a notification for the user including the self-care mode, the prediction, and the one or more contributing health factors.
2 . The computer-implemented method of claim 1 , wherein the self-care mode is a first self-care mode, the prediction is a first prediction, and the notification is a first notification, further comprising:
collecting additional heath data of the user;
identifying a second self-care mode from the plurality of self-care modes; and
outputting a second notification for the user including a second prediction.
3 . The computer-implemented method of claim 1 , wherein:
the health data includes one or more of blood pressure data, blood glucose data, heart rate data, food data, activity data, sleep data, weight data, medication data, diagnosis data, or demographics data;
the prediction of the health metric includes a predicted future value or range for the health metric of the user; and
the one or more contributing health factors include one or more of blood pressure, blood glucose, heart rate, food, activity, sleep, weight, medication, diagnosis, or demographics.
4 . The computer-implemented method of claim 1 , wherein the health data is a first set of health data, further comprising:
determining the user has implemented the self-care mode;
collecting a second set of health data of the user;
identifying a change in the health metric based on the second set of health data; and
dynamically updating the self-care mode based on the change in the health metric.
5 . The computer-implemented method of claim 1 , wherein the prediction associated with each self-care mode of the plurality of self-care modes is determined using an optimization algorithm.
6 . The computer-implemented method of claim 1 , wherein selecting the self-care mode comprises selecting the self-care mode with a largest improvement to the health metric of the predictions.
7 . The computer-implemented method of claim 1 , wherein the notification includes a recommended action for the user related to the one or more contributing health factors.
8 . The computer-implemented method of claim 1 , wherein:
the self-care mode is a cardiovascular risk self-care mode,
the health data includes a cardiovascular risk score based on systolic blood pressure data, cholesterol levels, blood pressure variability, or combinations thereof;
the prediction is a change of the cardiovascular risk score; and
the one or more contributing health factors include diet, exercise, weight of the user, or combinations thereof.
9 . The computer implemented method of claim 1 , wherein the self-care mode provides one or more meal plans, exercise programs, and/or weight loss goals.
10 . The computer-implemented method of claim 1 , wherein:
the self-care mode is a glucose management self-care mode,
the health data includes blood glucose levels;
the prediction is a maximum blood glucose level, minimum blood glucose level, and/or blood glucose level range; and
the one or more contributing health factors includes diet, weight, exercise, or sleep of the user.
11 . The computer implemented method of claim 1 , wherein the self-care mode provides actions to be performed at specific times.
12 . The computer-implemented method of claim 1 , wherein:
the self-care mode is a weight management self-care mode,
the health data includes weight, body mass index, age, and/or sex;
the prediction a change in the weight of the user; and
the one or more contributing health factors include diet, exercise, or sleep.
13 . A computing system for notifying a user of a recommended self-care mode, the computing system comprising:
one or more processors; and
one or more memories storing instructions that, when executed by the one or more processor, cause the computing system to perform a process comprising:
collecting health data of the user;
identifying a health metric based on the health data, wherein the health metric met a threshold health value;
identifying a self-care mode from a plurality of self-care modes by:
analyzing the plurality of self-care modes, wherein each of the plurality of self-care modes has one or more contributing health factors;
determining predictions of the health metric based on the plurality of self-care modes;
selecting the self-care mode from the plurality of self-care modes based on a prediction associated with the self-care mode; and
outputting a notification for the user including the self-care mode, the prediction, and the one or more contributing health factors.
14 . The computing system of claim 13 , wherein the self-care mode is a first self-care mode, the prediction is a first prediction, and the notification is a first notification, and wherein the process further comprises:
collecting additional heath data of the user;
identifying a second self-care mode from the plurality of self-care modes; and
outputting a second notification for the user including a second prediction.
15 . The computing system of claim 13 , wherein:
the health data includes one or more of blood pressure data, blood glucose data, heart rate data, food data, activity data, sleep data, weight data, medication data, diagnosis data, or demographics data;
the prediction of the health metric includes a predicted future value or range for the health metric of the user; and
the one or more contributing health factors include one or more of blood pressure, blood glucose, heart rate, food, activity, sleep, weight, medication, diagnosis, or demographics.
16 . The computing system of claim 13 , wherein the health data is a first set of health data, and wherein the process further comprises:
determining the user has implemented the self-care mode;
collecting a second set of health data of the user; and
identifying a change in the health metric based on the second set of health data.
17 . The computing system of claim 13 , wherein the prediction associated with each self-care mode of the plurality of self-care modes is determined using an optimization algorithm.
18 . The computing system of claim 13 , wherein selecting the self-care mode comprises selecting the self-care mode with a largest improvement to the health metric of the predictions.
19 . The computing system of claim 13 , wherein the notification includes a recommended action for the user related to the one or more contributing health factors.
20 . A computer-readable storage medium having computer executable instructions stored thereon that, when executed by one or more processors, direct the one or more processors to perform a method for notifying a user of a recommended self-care mode, the method comprising:
collecting health data of the user;
identifying a health metric based on the health data, wherein the health metric met a threshold health value;
identifying a self-care mode from a plurality of self-care modes by:
analyzing the plurality of self-care modes, wherein each of the plurality of self-care modes has one or more contributing health factors;
determining predictions of the health metric based on the plurality of self-care modes;
selecting the self-care mode from the plurality of self-care modes based on a prediction associated with the self-care mode; and
outputting a notification for the user including the self-care mode, the prediction, and the one or more contributing health factors.
21 . The computer-readable storage medium of claim 20 , wherein the self-care mode is a first self-care mode, the prediction is a first prediction, and the notification is a first notification, and wherein the method further comprises:
collecting additional heath data of the user;
identifying a second self-care mode from the plurality of self-care modes; and
outputting a second notification for the user including a second prediction.
22 . The computer-readable storage medium of claim 20 , wherein:
the health data includes one or more of blood pressure data, blood glucose data, heart rate data, food data, activity data, sleep data, weight data, medication data, diagnosis data, or demographics data;
the prediction of the health metric includes a predicted future value or range for the health metric of the user; and
the one or more contributing health factors include one or more of blood pressure, blood glucose, heart rate, food, activity, sleep, weight, medication, diagnosis, or demographics.
23 . The computer-readable storage medium of claim 20 , wherein the health data is a first set of health data, and wherein the method further comprises:
determining the user has implemented the self-care mode;
collecting a second set of health data of the user; and
identifying a change in the health metric based on the second set of health data.
24 . The computer-readable storage medium of claim 20 , wherein the prediction associated with each self-care mode of the plurality of self-care modes is determined using an optimization algorithm.
25 . The computer-readable storage medium of claim 20 , wherein selecting the self-care mode comprises selecting the self-care mode with a largest improvement to the health metric of the predictions.
26 - 58 . (canceled)