COMPUTER IMPLEMENTED METHOD FOR PROCESSING DATA RELATED TO PRIVATE HEALTH ON A SUITABLE NUMERICAL SC

专利摘要:

公开号:BR112013007781A2
申请号:R112013007781-6
申请日:2011-09-29
公开日:2020-06-02
发明作者:Ohnemus Peter；Naef Andre；Jacobs Laurence；Leason David
申请人:Dacadoo Ag；
IPC主号:

专利说明:

METHOD IMPLEMENTED BY COMPUTER TO PROCESS DATA RELATED TO PRIVATE HEALTH IN A SUITABLE NUMERICAL SCORE FOR PUBLICATION AND HEALTH MONITORING SYSTEM
Field of invention
[001] The present invention relates to a system implemented by computer for the acquisition of medical data and its processing for diagnosis, comparative analysis, analytical purposes and / or redistribution of medical data.
Background of the invention
[002] Despite advances in many areas of technology, barriers still exist to assess a person's relative health in a fast, cost-efficient, and timely manner. With rising health care costs and the prevalence of diseases related to unhealthy lifestyles such as diabetes, and heart disease, it is important to assess the relative health of individuals, and this has not been adequately addressed. In many areas of the world, access to doctors is limited. Even in the developed world, a doctor's time is considered a precious commodity and there are often long waiting lists and doctor-to-specialist referral systems have to be navigated before they are seen. In more developed countries the ratio of doctors to the population can be in the order of 1: 1,000 people, while in less developed countries the ratio can be 1: 100,000. There are also cost barriers to accessing a doctor because consultation with a doctor can be very expensive, especially if an individual does not have any health insurance or is lacking sufficient coverage. Consequently, it can be very
2/56 difficult to get access to medical professionals to receive information about someone's health.
[003] Even if an individual has access to their health information, the mechanisms for transporting that information to others are missing or nonexistent. Privacy laws restrict the type of information that can be shared and the way in which it can be shared. Privacy laws relating to health information are particularly strict with respect to information that can be shared. This is to protect a person from disclosing sensitive information. Consequently, the sharing of health-related information is generally discouraged. It is also difficult to share health-related information with friends and family. Often, health information is only taken verbally by a doctor to a patient, or the patient only receives paper copies of laboratory test results. Systems are lacking to easily share such information with others, especially with a large group of people located in geographically remote locations.
[004] Prior art systems that provide a limited type of numerical scoreboard that is related to a person's health have been disclosed. For example, U.S. Patent Publication No. 2009/0105550 to Rothman and others discloses a system and method for providing a health score for a patient. However, this disclosure is primarily aimed at calculating a patient's health score in a hospital, post-surgery, and the health score is based on the patient's measured medical data (eg, blood pressure, temperature, respiration, etc.). This method fails
3/56 in taking into account the patient's extrinsic activities, such as the patient's daily exercise activities. U.S. Patent Publication No. 2005/0228692 to Hodgdon discloses a system that calculates a health score based on measured medical data and may include a self-assessment survey, which may include researching a participant's exercise habits. However, this only takes into account a person's intended habits, not the actual exercise activity that a person engages in each day. Consequently, the score is static and does not change in relation to the actual activity performed.
[005] Such disclosed systems are directed primarily at medical practitioners to address continuity of care problems and require nutrition from practitioners to produce and maintain scores. Clearly, although the attention of a medical practitioner is necessary in emergency and critical care situations, cost and resource factors mean that such systems are usable only in such situations and that these systems do not solve the general problems discussed.
above. Additionally, the scoreboard only is relevant to The particular moment in time in which he was updated The last time by the medical practitioner. summary of invention [006] According one aspect of present invention, is
a computer-implemented method for processing private health-related data is provided on a masked numerical scoreboard suitable for publication. The method comprises receiving data in a memory about a plurality of intrinsic medical parameters and activity parameters
4/56 extrinsic physics of a user. The received data and weighting factors are stored in memory. The received data is processed by executing code on a processor that configures the processor to apply the weighting factors to the intrinsic medical parameters and to the extrinsic physical activity parameters. Weighting factors for at least extrinsic physical activity parameters include a decline component arranged to reduce the relative weight of extrinsic physical activity parameters for physical activity depending on at least one factor associated with the user. The processed data related to the intrinsic medical parameters and the extrinsic physical activity parameters are transformed by code executed in the processor into a masked composite numeric value in which the code is operative to combine the weighted parameters according to an algorithm. The masked composite numeric value is automatically published to a designated group via a portal (such as a social website) using code executed on the processor and free from any human intervention. However, the information collected regarding intrinsic medical parameters and extrinsic physical activity parameters is kept private.
[007] According to an additional aspect of such a method as it can be implemented in a particular configuration of the same, the factor associated with the user can be an age or a user age range such that the declining component reduces the relative weight of extrinsic physical activity parameters for a first user of a first age unlike a second user of a second age
5/56 age or age range.
[008] According to yet another aspect of such a method as it can be implemented in a particular configuration thereof, the published masked composite numerical value can comprise an average of a group of users to arrive at a group composite numerical value determination using additional code running on the processor.
[009] In accordance with a further aspect of the present invention, a computer-implemented health monitoring system is provided which comprises an operable communication unit for receiving data on a plurality of intrinsic medical parameters and extrinsic physical activity parameters of a user . A memory is arranged to store the received data and to store weighting factors. Also, a processor is arranged to process the data received by executing code that configures the processor to apply the weighting factors to the intrinsic medical parameters and extrinsic physical activity parameters. Weighting factors for at least intrinsic medical parameters include a declining component arranged to reduce the relative weight of physical activity parameters for physical activity depending on at least one factor associated with the user. The processor is additionally arranged to execute code to transform the processed data related to the intrinsic medical parameters and extrinsic physical activity parameters into a masked composite numerical value using the processor combining the weighted parameters according to an algorithm. A portal is arranged to publish the masked composite numeric value to a designated group
6/56 while maintaining the information collected with respect to intrinsic medical parameters and private extrinsic physical activity parameters.
[0010] Such a system can preferably be configured such that the factor associated with the user can be an age or an age range of the user such that the declining component reduces the relative weight of intrinsic medical parameters for a user of a first age or age range unlike a second user of a second age or age range.
[0011] A configuration according to additional aspects of the invention can comprise a system that communicates the processed data or the masked composite numerical value to an exercise machine. The machine works in conjunction with the system by programming it to automatically establish an exercise program based on the reported data or the masked composite numerical value. Preferably, the system thus configured receives activity information from the exercise machine into its memory for inclusion among extrinsic physical activity parameters.
[0012] Configurations of the present invention seek to combine data from multiple medical and non-medical sources into a system and method that produce a standardized score for a person that takes into account available medical, physical activity and lifestyle data (such as diet ) in an arrangement that can be operated and updated substantially in real time and does not require frequent access to a medical practitioner. The scoreboard and trends associated with it can be used for a variety of purposes including triggering alerts to medical problems or possible repercussions,
7/56 providing feedback to the user, automated definition of motivation and / or objective, training schedule, automated indications for medical analysis. Among the alerts that can be generated are alerts that are triggered based on the monitoring of a numerical value composed of a health score that is computed, the computed value from which a feedback communication can be sent to the user (eg , within the system portal or by email, SMS, etc.) as a result of code execution on a processor and without human intervention, if monitoring detects a change in the user's scoreboard due to a decline in value per operation of the algorithm, or reduction of value due to eating habits, or the achievement of objectives fed into the system by the user or by a group with which the user has associated, or as part of a non-specific objective program for the user who system may have to motivate well-being (eg, good exercise or eating habits). The configurations of the present invention apply a weighting factor to the respective physical activity and / or lifestyle data such that recent events have a greater impact on the score than those that have occurred more in the past.
[0013] In the configurations described, a single health score computation method is released which masks underlying health statistics, and still provides comparative analysis for a variety of applications. In one configuration, a method for collecting and presenting health-related data is provided. The method includes collecting information regarding a plurality of intrinsic medical parameters and physical activity parameters
8/56 extrinsic of a user. The information collected is processed by running code on a processor that configures the processor to apply weighting factors to intrinsic medical parameters and extrinsic physical activity parameters. The information collected related to the intrinsic medical parameters and extrinsic physical activity parameters is transformed into a masked composite numerical value using the processor combining the weighted parameters according to a predetermined algorithm. The masked composite numeric value is published to a designated group via a portal while maintaining the collected information related to the intrinsic medical parameters and parameters of private extrinsic physical activity.
[0014] The preferred configurations of the present invention seek to provide a standardized classification system that can provide an assessment of the relative health of an individual that can be used as the basis for a fair comparison with other individuals having different ages, sex, medical status or life styles.
[0015] Various characteristics, aspects and advantages of the invention can be appreciated from the Description of Certain Configurations of the following Invention and the attached Drawing Figures.
Description of the drawing figures
[0016] Figure 1 is a schematic block diagram of a local health information collection and communication system according to a first implementation of the invention;
[0017] Figure IA is a network diagram according to another implementation of the invention;
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[0018] Figure 2 is a schematic flow diagram according to a configuration of the invention;
[0019] Figures 3a-3e are screenshots of a user interface according to a configuration of the invention;
[0020] Figure 3f is an illustration of progressions over time of parameters used to determine the health score in a configuration of the invention;
[0021] A figure 4th is an illustration of a format in presentation of data in according to a configuration gives invention; [0022] A figure 4b is an illustration of a format in presentation of data in according to a configuration gives invention; [0023] A figure 4c is an illustration of a format in presentation of data in according to a configuration gives invention; and [0024] A figure 4d is an illustration of a format in presentation of data in according to a configuration gives invention. Detailed Description in certain configurations of the invention
[0025] By means of overview and introduction, the present invention is described in detail in connection with a distributed system in which data acquisition, data storage, and data processing are used to produce a numerical scoreboard as a basis for evaluating the relative health of a user.
[0026] In an implementation, a system 100 includes a computer-based application for the collection of parameters related to the health of a user and an interface of
10/56 user 110 for displaying data. The computer-based application is implemented via a microcontroller 120 that includes a processor 124, memory 122 and code executed therein in order to configure the processor to perform the functionality described here. The memory is for storing data and instructions suitable for controlling the operation of the processor. A memory implementation can include, by way of example and without limitation, a random access memory (RAM), a hard disk, or a read-only memory (ROM). One of the components stored in memory is a program. The program includes instructions that make the processor perform steps that implement the methods described here. The program can be implemented as a single module or as a plurality of modules that operate in cooperation with each other. The program is considered to represent a software component that can be used in connection with a configuration of the invention.
[0027] A communication subsystem 125 is provided to communicate information from microprocessor 120 to user interface 110, such as an external device (eg, handheld unit or a computer that is connected via a network to the communication subsystem). communication 125). Information can also be communicated by the communication subsystem 125 in a variety of ways including Bluetooth, WiFi, WiMax, RF transmission, and so on. A number of different network topologies can be used in a conventional manner, such as wired, optical, 3G, 4G, and so on.
[0028] The communication subsystem can be part of an electronic communicative device including, by means of
11/56 example, a smartphone [smart phone] or cell phone, a personal digital assistant (PDA), netbook, laptop computer, and so on. For example, the communication subsystem 125 can be connected directly through a device such as a smartphone such as an iPhone, Google Android Phone, BlackBerry, Microsoft Windows Mobile enabled phone, and so on, or a device such as a monitor heart rate or blood pressure (such as those manufactured by Withings SAS), weight scales (such as those manufactured by Withings SAS), exercise equipment or the like. In each case, devices can comprise or interface with a module or unit for communication with subsystem 125 to allow information and control signals to flow between subsystem 125 and external user interface device 110. In short, the communication subsystem can cooperate with a conventional communicative device, or it may be part of a device that is dedicated for the purpose of communicating information processed by microcontroller 120.
[0029] When an electronic communicative device such as the types noted above is used as an external 110 user interface device, the display, processor, and memory of such devices can be used to process health related information to provide an assessment numeric. Otherwise, system 100 may include a display 140 and memory 150 that are associated with the external device and used to support data communication in real time or otherwise. More generally, system 100 includes a user interface that can be
12/56 implemented, in part, by software modules running on the microcontroller processor 120 or under control of the external device 130. In part, the user interface may also include a sending device such as a display (eg, display 140).
[0030] Biosensors 115 can be used to directly collect health information about a user and report that information. The biosensor can be placed in contact with the user's body to measure vital signs or other health-related information from the user. For example, the biosensor can be a pulse meter that is worn by the user in contact with the user's body such that the user's pulse can be detected, a heart rate monitor, an electrocardiogram device, a pedometer, a blood pressure monitor, blood glucose or one of many other devices or systems. The biosensor can include a communication module (eg, communication subsystem 125) such that the biosensor can communicate, via wires or wirelessly, the detected data. The biosensor can communicate the detected data to the user interface device, which in turn communicates that information to the microcontroller. Optionally, the biosensor can directly communicate the detected data to the microprocessor. The use of biosensors provides a degree of reliability in the reported data because they manually eliminate user errors associated with self-reported data.
[0031] Alternatively or in addition, the user can self-report his health-related information by manually feeding the data. So, in another
13/56 implementation, as shown in figure IA, a person's health-related data is fed directly into a computer 160 and provided over a network 170 to a server computer 180. (All computers described here have at least one processor and a memory).
[0032] Regardless of the implementation, the system provides a means to assign a numerical value that represents the relative health of an individual. The numerical value is described here as a health scoreboard and can be used to assess the individual's health based on health-related information collected from a user. The health score is calculated based on the health information collected using an algorithm. The user or the communication subsystem 125 provides the system with health-related information regarding a number of health parameters. Predetermined weighting factors are used to assign a relative value to each of the parameters that are used to calculate the health score. The user's health score is then calculated by combining the weighted parameters according to an algorithm. For example, the parameters can be a person's blood glucose level and body weight. A weighting factor a is applied to blood glucose data and a weighting factor b can be applied to body weight data. If blood glucose data is a more important factor in determining a person's health than body weight, then the weighting factor a will be greater than the weighting factor b such that blood glucose data will have a greater impact on the calculated health score (eg, Health score = Glucose * a + (Weight / 100) * b). In certain implementations, the
14/56 weighting is a non-unit value (eg, greater or less than one, but not one). Fewer or more factors can be included in the calculation of the health score, and a deviation value can be included which is added or subtracted or which modifies the entire calculation, in certain implementations such as to take age or gender into account. two possible reasons; however, the above is intended as a non-limiting example of how to calculate a health score. Other parameters that can be measured and included in the calculation include measurements of blood pressure, height, body mass index, fat mass, medical conditions such as diabetes, ventricular hypertrophy, hypertension, irregular heartbeat and fasting glucose values. Where absent, a parameter can be omitted from the calculation or it can be estimated from other parameters and / or values obtained from a group of sample individuals having similar parameters.
[0033] In addition to intrinsic medical parameters, a user's physical activity is also taken into account when calculating his health score. Physical activity can be monitored via an appropriate activity-dependent sensor. The sensors can include a GPS unit, an altimeter, a depth gauge, a pedometer, a cadence sensor, a speed sensor, a heart rate monitor or the like. In the case of gym-based activities, computerized exercise equipment can be configured to provide data directly on the user-completed program (for example, a so-called elliptical / cross trainer can provide much better data on exercise than a pedometer on a
15/56 user, etc.). Although automated capture of parameters related to a user's physical activity is preferred, a user interface for manual entry of activity is also provided. In this regard, an exercise machine such as a treadmill, elliptical, or stationary bike or weight-lifting machine with a weight rack or straps can be provided with a communications interface to communicate with the system described here to provide parameters of extrinsic physical activity for the system and to receive and additionally include a processor configured to process data from the system in order to automatically adjust an exercise program on the exercise machine to meet an objective, change, or other objective for that user. Lifestyle data such as diet, smoking, alcohol consumption and the like can also be collected and used to calculate the health score. In one configuration, a barcode or RFID scanner can be used by a user to capture data about consumed food that is then translated into a remote system, such as server 180 or a website communicating with server 180, in parameters such as daily intake of calories, fat and salt. In part, the system depends on such data being provided by the user while other data can be obtained over network data connections once permissions and connectivity rights are in place.
[0034] Physical activity and lifestyle data are tracked over time and a decline algorithm is applied when calculating its effect on the health score, as discussed in more detail below. As such, the activity
16/56 physics well in the past has a reduced positive effect on the health score. Preferably, the weighting factors used in the algorithm for computing the health scoreboard are adjusted over time according to a decline component that is arranged to reduce the relative weight of the parameters that are used in the calculation. The decline component itself may comprise a weighting value, but it may also comprise an equation that takes into account at least one factor associated specifically with the user, such as the user's weight or weight range, age or age range, any medical conditions known to the system, and any of the other parameters that may be known to the system, or a curve that is configured in view of these factors such that a value can be read from the curve as a function of the values along the geometric axes for that user. In this way, the decline component can reduce the negative weight of the parameters used in calculating the health score for a first user differently than for another user, such as when the first user is a first age or age group and the second user have a second age or age range.
[0035] A central system, preferably a database and data and website that can be hosted, for example, by server 180, maintains data about each user and their health score and associated parameters and trends over time. The data can be maintained in such a way that sensitive data is stored independently of human identities, as understood in the art.
[0036] The health score calculated for each user is then processed depending on a system, group or profile of
17/56 user in the central system. Depending on the profile settings, the health scoreboard and associated trends can trigger several automated actions. For example, it can cause: triggering an automated alert; provide user feedback such as a daily email update; trigger communication of a selected automated motivation, alerts and / or goal setting to alleviate a perceived problem; setting up a training program; or automated indication for medical analysis.
[0037] The user's health scoreboard is also provided for a designated group of recipients via a communication portal. The group of recipients can comprise selected, other users, of the system (eg, friends and family) such that the health scores of those selected, other users, can be compared against the health score of others. In alternative arrangements, all users can view other users' scores, or the group of recipients can be defined as a specific health insurance provider such that price quotes can be provided to insure the individual. Other possibilities are within the scope of the invention.
[0038] Referring now to figure 2, a schematic flow diagram according to a configuration of the invention is described in support of an assessment by a person (eg, a patient or user) to provide a health scoreboard . In step 210, the user initiates the process for collecting, processing, and publishing health-related data. For example, a person using a mobile electronic device (eg, a smartphone or a portable computing device) selects the software application, which initiates the
18/56 running the program on the device's processor, or the user can access an Internet-based web page on which code is executed on a remote processor and served to the user's local device. An identification module induces the user to identify himself and authenticate his identity. This can be accomplished by inducing the user to enter a user name and password, or by other means, such as a fingerprint reader, command, encryption or other mechanism to guarantee that user's identity. Alternatively, if the user is accessing the system via a personal electronic device, identification data can be stored in the memory of the local device and automatically accessed to automatically confirm the user's identity.
[0039] In step 220, a data collection module running on the processor can induce the user to provide health related data corresponding to a number of parameters. In an implementation, one or more of the parameters are provided automatically by the communication subsystem 125. The parameters can include body weight, height, age and user preparation activity information. Such measurable medical parameters are intrinsic parameters of the user. The user's body weight and height provide information about the user's current health status. The preparation activity information corresponds to the number of exercises that the user engages. This information is an example of a physical activity parameter that is an extrinsic parameter of the user. For example, the user can feed information about his daily preparation activities, such as the amount of time the user has spent
19/56 involved physical activity and the type of physical activity. If the user went to the gym and exercised on a bicycle for thirty minutes, for example, this information is fed into the system. The user preparation activity information provides information about the actions being taken by the user to improve his preparation.
[0040] Information about a user's body weight, height, age and preparation activity are just some of the parameters for which information can be collected. The system can collect and process a variety of other parameters that can be indicative of a user's health. For example, parameters can include blood glucose levels, blood pressure, blood chemistry data (e.g., hormone levels, essential vitamin and mineral levels, etc.), cholesterol levels, immunization data, pulse, blood oxygenation rate, information regarding the food consumed (eg, calories, fat content, fiber, sodium), body temperature, which are just some of the non-limiting, possible examples of parameters that can be collected. Several other parameters that are indicative of a person's health that can be reliably measured can be used to calculate a person's health score.
[0041] The health parameter information collected is stored in a memory in step 230. In step 240, a weighting module retrieves weighting factors from memory. Weighting factors can be multiplication coefficients that are used to increase or decrease the relative value of each of the health parameters. A weighting factor is assigned to each health parameter as shown in the formulas here. Weighting factors are
20/56 used to control the relative values of health parameters. Consequently, weighting factors are applied to health parameters, increasing or decreasing the relative effect that each factor has on the calculation of a user's health score. For example, a user's current body weight may be more important than the amount of fitness activity the user engages in. In this example, the body weight parameter would be weighted more heavily by assigning a larger weighting factor to this parameter. In step 205, the weighting module applies the weighted factors retrieved to the collected health parameter values to provide weighted health parameter values. The weighting factor can be zero in which case a particular parameter has no impact on the health score. The weighting factor can be negative for use in some algorithms.
[0042] After the parameters have been weighted, the user's health score is computed in step 260 via a scoring module operating on the processor. The scoring module combines the weighted parameters according to an algorithm. In one implementation, the health score is the average of the user's body mass index (BMI) health score and the user's preparedness health score minus twice the number of years that a person is younger than 95. The algorithm formula for this example is reproduced below:
Health score = ((BMI health score + Preparation health score) / 2) -2 * (95-age).
[0043] The user's BMI health score is a value between 0 and 1000. The BMI health score is based on the user's BMI, which is calculated based on the user's weight and height, and how much
21/56 ο ΒΜΙ of the user deviates from what is considered a healthy BMI. A graph or formula can be used to normalize the user's BMI information such that information
dissimilar can be combined. a value of Target BMI is selected to which is assigned one maximum point value (e.g., 1000). The more the BMI of user if to dodge of target value less points are awarded . 0 scoreboard of health in preparation of user is based at activity physics or
exercise of a person. In a configuration, it is the sum of the number of hours of preparation (that is, the amount of time the user has been involved in preparation activities) in the last 365 days where each hour is linearly aged in relation to that time such that less recent activity be less valued. The resulting sum is multiplied by two and is cut by 1000. This normalized the preparation information so that it can be combined to arrive at the health score. A target daily average of prep activity is selected and receives the maximum amount of points (eg, 1000). The user is awarded less points based on how much less exercise he engages compared to the target.
[0044] In another implementation, the health score is determined from a number of sub-scores that are kept in parallel in addition to the BMI health score and the preparation health score. Likewise, the health score can be determined using similar information in a combinatorial algorithm as discussed above using different or ageless settings.
[0045] The intrinsic medical parameters are processed to determine a base health score. Extrinsic parameters such as those from physical exercise
22/56 are processed to determine an amount that is allocated to a health reservoir and a bonus reservoir. The value, preferably expressed in MET hours, associated with physical activity is added to both the health reservoir and the bonus reservoir. A daily decline factor is applied to the bonus pool. Any excess decline that cannot be accommodated by the bonus reservoir is then deducted from the health reservoir. The amount of decline is determined depending on the size of the health reservoir and bonus reservoir such that greater effort is required to maintain a high health reservoir and bonus. The health reservoir value is processed in combination with the scoreboard from the intrinsic medical parameters to calculate the global health score value. This can be on a similar basis to the implementation described earlier or it can include different parameters and weighting factors. In one configuration, the health reservoir value is a logarithm or other statistical function is applied to age the respective values over time such that only the most recent activity is counted as being fully effective for the health reservoir / bonus. An exemplary user interface showing the health scoreboard, the health reservoir and other selected measured parameters (as it will be appreciated that many simply combine to form the scores) is shown in figures 3a and 3b. Several sub-scores and their trends are recorded, as shown in figure 3c.
[0046] As will be appreciated, MET hours are spent kcal divided by kilograms of body weight, that is, 100 kcal spent by a 50 kg person is 2 h MET. This is energy
Normalized 23/56 making the system fair to people of all weights. With this method, the reservoirs can be the same size for each person since the energy is normalized for the person based on their body weight.
[0047] In an implementation, each person is assigned a health reservoir having a capacity of 300 h MET and a bonus reservoir having a capacity of 60 h MET.
[0048] When someone performs activity A, the combinations are updated as follows:
H = min (H + A * alpha, 300)
B = min (B + A * (1-alpha), 60)
[0049] Where H is the health reservoir score, B is the bonus reservoir score, A is the h MET value for the activity and alpha is a wide system constant (selected between 0 and 1) that determines the proportion in which the activity contributes to the respective combinations.
[0050] The activity is divided between the health reservoir and the bonus reservoir. Any excess MET MET activity going beyond the cut of any combination is discarded. A daily decline value D is applied to the combinations as follows:
D = f (H, B)
B = B - D
If B <0:
D = D + B
B = 0
If D <):
D = 0
[0051] The decline is applied entirely to the bonus reservoir, and if the bonus reservoir is empty, the rest
24/56 is applied to the health reservoir. In this configuration, no reservoir ever goes below zero.
[0052] The system finds its balance where A equals f (H, B), that is, where the average daily activity matches the average daily decline. The function f (H, B) is highly non-linear with respect to H and B. In essence, it takes sublinearly less effort to maintain a small reservoir, and superlinearly more effort to maintain a large reservoir. This is to make sure that the average person can maintain a, say, half-full health reservoir (150, corresponding to a score of 500), while spending a massively greater effort (typically only released by a professional endurance athlete) to maintain a full health reservoir (300, corresponding to a score of 1000). Figure 3f shows a simulation of the temporary storage reservoir and health reservoir scoreboard over time, assuming activity varying between 11.5 and 16 h MET per day and 2 rest days per week. A perfect health reserve score of 1000 would require 30 h MET of activity per day, as can be seen from the curve in the upper right corner of figure 3f.
[0053] Preferably, the health score is based on a weighted reservoir of health factor (s) and the person's exercise record over time. The health factors can be updated regularly by the user. For example, the user can provide health-related information for each event that is tracked and processed by the system. The user can update after a meal, after exercising, after weighing, etc. In the case of recording an activity / event by a sensor, portable device or
25/56 similarly, the captured / calculated parameters can be automatically loaded and used to produce a revised health score. For example, feedback can be provided showing the effect of exercise while a user is running, exercising on exercise equipment, etc. In selected configurations, feedback can be provided to an administrator such as a gym assistance member where it is determined that a user is exceeding a predetermined limit (which due to the knowledge of their health can be varied in relation to their health score or other recorded data). Consequently, health-related data can be updated in an almost real way.
[0054] The user can also update the information twice a day, once a day, or at other time periods. In addition, the health score can be based on an average of the information over time. Preparation activity, for example, can be averaged over a period of time (eg, over a week, month or year). Average data over time will reduce the impact on the health scoreboard caused by data fluctuations. Periods in which the data were not characteristically high (eg, the person was involved in a large amount of preparation activity over a short period of time) or not characteristically low (eg, the person was not involved in any preparation activity for a week due to illness) do not dramatically affect the health score by averaging over time. Health-related information can be stored in memory or in a database accessible by the processor.
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[0055] The stored data can also be used to predict future health scores for a user. A prediction module can analyze past data (eg, preparation habits, eating habits, etc.) to extrapolate a predicted health score based on a hypothesis that the user will continue to act in a predictable manner. For example, if the data shows that a user has been exercising an hour a day for the past thirty days, the forecast module can predict, according to a forecasting algorithm, that the user will continue to exercise an hour for each of the next three days. Consequently, the scoring module can calculate a predicted health score at the end of the next three days based on information from the forecast module. It can also factor the forecast into other actions. For example, the system may suggest a strained level of physical activity or challenge someone who has a high health score but is predicted based on past experience to then take a few days off for recovery. Additionally, the system can provide encouragement for the user to maintain a course of activity or modify behavior. For example, the system can send a message to the user indicating that if the user increased the preparation activity for a certain amount of time, the health score would rise by a certain amount. This would allow the user to set goals to improve health.
[0056] The use of the health scoreboard allows a relative comparison of the health of a user with that of another person, although each person may have very different characteristics, which would make a direct comparison difficult. Per
27/56 example, a first user (User 1) may have a very different body composition or engage in very different preparation activities compared to a second user (User 2), which makes direct comparison of the relative health of each difficult user. The use of the health scoreboard makes comparing the two users possible with relative ease. In one example, User 1 is slightly overweight, which tends to lower User 1's health score. However, User 1 is also involved in large amounts of preparation activities, thereby raising the overall health score. of user. In contrast, User 2 has an ideal body weight, which contributes to a high health score, but engages in very little preparation activity, thereby lowering the health score. User 1 and User 2 are very different in terms of their health-related parameters. Consequently, it would be very difficult to assess and compare the relative health of User 1 and User 2. According to the invention, information related to certain health parameters is collected from User 1 and User 2, which are used to calculate a health score. global. A comparison of the health score of User 1 and User 2 allows an easy assessment and comparison of the health of these two users, although they are very different and have very different habits. Therefore, the health scoreboard has significant value such that members of a group can compare their relative health and such that other entities (eg, employers, health care insurers) can assess an individual's health. Examples are shown in figures 3d and 3e in which tabular (current) and
28/56 graphs (historical, current and predicted) of different users are shown. As can be seen in figure 3e, Katrin is expected to overtake the user (André) soon unless he improves his lifestyle and performance. In figure 3d, the impact of the decline algorithm is illustrated to show the effect on the health score of a given user (André) and on the people he identifies as friends. As noted, user André has a current health score of 669 that places this user among friends Irene (health score 670) and Helle (health score 668). The decline algorithm acted on all health scores shown in the screenshot of figure 3d, as indicated in column Δ 1 Day. More particularly, most of André's friends have their health score reduced by 1 point due to the reason of no activity. A lack of data feed to the system is a basis for the processor to run the decline algorithm to determine a no activity status for a given user. The one-day effect of this status according to the decline algorithm illustrated for most users is a reduction of 1 point in a day, and a reduction of 5 points over the course of a week. As such, the decline algorithm can have a tapered, nonlinear impact on a global health scoreboard.
[0057] As illustrated, the user André had a moderate activity recorded in a memory that is accessible by the system. As a result, the moderate activity is processed and results in a one-day change (delta) that is positive, and a change that acts against the influence of the decline algorithm. Consequently, André will be able to observe, as well as the friends who have access to his health score
Published 29/56, that he increased his score from 667 to 669 in one day, and from 662 to his present value over the past seven days as a result of moderate activity. In addition, a forecast is computed using the underlying algorithm and an extrapolation of the data based on more recent reasons (ie, data received) to increase another 5 points. On the other hand, due to low activity, but a good diet, Helle in the same period decreased by 1 point on the last day and a total of 1 point in the last 7 days and is expected to lose another point if this rate continues. As such, Helle provides feedback by executing the algorithm and the outputs provided by the system which encourage more activity. On the other hand, Irene has no activity and a poor diet that results in a more aggressive change to her current health score and to the longer history and predicted impact on her score. Again, this feedback, which can be provided to users and their friends, or to members of a group of users who have joined for a challenge, etc., serves to provide individual or team motivation to engage in preparation activities, good nutrition , and so on.
[0058] In addition, the health scoreboard provides an indication of the individual's relative health without revealing the underlying data used to calculate the health scoreboard, which may be sensitive information. For example, a user may feel uncomfortable about revealing his weight, age, or amount of time he spends exercising to other people or entities. People may be embarrassed to share their weight or the fact that they never go to the gym. However, since the health score is
30/56 derived from various factors, the underlying data used to calculate the score is kept private. This feature facilitates the sharing of the user's global health because users do not have to disclose private data about themselves. For example, a person may be slightly overweight, but they often go to the gym. Consequently, that person may receive a relatively good health score. Although the person may not wish to disclose their weight, they can still disclose their health scoreboard that conveys information about their relative health without disclosing the underlying details. The intrinsic medical parameters (eg, weight, height, etc.) and the extrinsic physical activity parameters (eg, duration, frequency, intensity, etc. of exercises) are transformed into a masked composite numerical value. The masked numerical value is published while the information collected regarding intrinsic medical parameters and extrinsic physical activity parameters is kept private. The underlying intrinsic medical parameters and extrinsic physical activity parameters are protected such that a third person is not able to determine those parameters based on the health score number. This is because the parameters can vary in many different ways and yet the number of the health score may be the same (eg, a heavier person who exercises frequently may have the same health score as a person who is not above weight, but don't exercise as often). Thus, having only the health scoreboard does not reveal the parameters related to a person's health. Consequently, the underlying health statistics are masked, and so is the health score
31/56 can be used as a comparative analysis to indicate a person's health for a variety of applications.
[0059] After the scoring module calculates the user's health score, in step 270, a publication module retrieves from memory the designated group of recipients who are authorized to receive the health score. The group of recipients can be friends or family of the user, sport companions, employers, insurers, etc. In step 280, the publication module causes the health score to be published for the designated group. In the event that the information is not published to a group of friends, the information can be published to a social networking portal based on
Internet at the which one The access to data is limited those members designated of group. [0060] The Dice in parameter of health and leaderboards of health
can be stored over time, in memory or another database, such that a user can track their progress. Graphs can be generated for a user to track progress and analyze where there can be improved behavior. In addition, trends can be identified which can lead to the diagnosis of medical problems and / or eating habits. For example, if a person's weight continues to increase despite the same amount or increased amount of prep activity, the system may trigger or suggest that they seek certain medical tests (eg, a thyroid test, pregnancy) to determine the cause of weight gain.
[0061] In certain implementations, most of the system is hosted remotely from the user and the user accesses the system via a local user interface device. Per
32/56 example, the system can be internet based and the user interacts with a local user interface device (eg personal computer or mobile electronic device) that is connected to the internet (eg via a wired / wireless communication network) to communicate data with the internet-based system. The user uses the local interface device to access the internet-based system in which the memory and software modules are operating remotely and communicating over the internet with the local device. The local device is used to communicate data to the remote processor and memory, in which the data is remotely stored, processed, transformed into a health scoreboard, and then provided to designated groups via a restricted access internet portal. Alternatively, the system can be implemented primarily via a local device on which data is stored, processed, and transformed into a health scoreboard, locally, which is then communicated to a health portal.
sharing Dice for publication remote to the designated groups. [0062] 0 system can be implemented in the shape of an network structure Social that is performed by modules in
software stored in memory and operating on processors. The system can be implemented as a social network system with a separate, independent health theme, or as an application that is integrated with an existing social network system (eg, Facebook, MySpace, etc.). The user is provided with a home page on which the user can feed information, manage what information is published to designated groups, and manage membership.
33/56 designated groups. The home page includes reminders for the user to enter health-related information for each of the various parameters. The user can enter their weight, date of birth, height, preparation activity, and other health related information. The user's health score is then calculated. The health scoreboard is shared with other users who are designated as part of a group allowed to access that information. In addition, the user can view health score information for others in the group. Consequently, the user is able to compare his overall health with the health of others in the group. Comparing health scores with others in the group can provide motivation for individuals in the group to compete to improve their health scores. Other information, such as health tips, medical news, information about medicines, local preparation events, health services, advertising and discounts for medical and / or preparation-related supplies and services, issuing preparation challenges or health-related goals , for example, can be provided via the home page.
[0063] In additional implementations, the health scoreboard can be a composite of a Metric Health Model scoreboard and a Quality of Life Model scoreboard. Combining scores from multiple models provides a more holistic assessment of a user's health. The Metric Health Model scoreboard assesses a user's health based on relatively easily quantifiable parameters (eg, age, sex, weight, etc.) and compares those numbers with acceptable population study models. The Quality of Life Model scoreboard focuses on a user's self-assessed quality of life
34/56 measure based on responses to a questionnaire (that is, the system takes into account the user's own assessment of his health and quality of life) because there are correlations between how an individual feels in his life and a realistic measure of health. A reservoir of these scores based on these two models, which will be discussed in more detail below, provides a more inclusive and holistic health assessment.
[0064] The Metric Health Model scoreboard is based on information from a user's medical parameters, such as his medical history information, attributes, physiological metrics, and lifestyle information for the system. For example, the system can provide the user with a questionnaire to induce responses (yes / no, multiple choice, numeric entry, etc.) or provide the user with form fields to complete. Medical history information may include the user's history of medical conditions and / or the prevalence of medical conditions in the user's family. Examples of medical history information may include information such as whether the user has diabetes, has direct family members with diabetes, whether the user or family members have a history of heart attacks, angina, stroke, or Transient Ischemic Attack, a history of fibrillation atrial or irregular heart rate, if the user or family members have high blood pressure requiring treatment, if the user or family members have hypothyroidism, rheumatoid arthritis, chronic kidney disease, liver failure, left ventricular hypertrophy, congestive heart failure, regular use of steroid pills, etc.
[0065] The Metric Health Model scoreboard can also be
35/56 based on user attributes. Attributes can include age, sex, ethnicity, height, weight, waist size, etc. In addition, the Health Metric Model scoreboard can be based on the user's physiological metrics. Examples of physiological metrics may include systolic blood pressure, total serum cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL), triglycerides, high sensitivity C-reactive protein, fasting blood glucose, etc. Entries can also include a user's lifestyle parameters. For example, lifestyle parameters can include entries on whether the user is a smoker (never smoked, currently smokes, level of smoke, etc.) how much exercise the user performs (frequency, intensity, type, etc.), type diet (vegetarian, high protein diet, low fat diet, high fiber diet, quick meal, restaurant, homemade food, processed and pre-packaged food, meal size, frequency of meals, etc.). These are some of the examples of parameters that can be used to compare a user's health indicators to survival probability models to calculate the Metric Health Model score.
[0066] Survival probability prediction models can be used to predict the probability that an individual will suffer one or more serious health events over a given period of time. Mathematical models can estimate this probability from observed population characteristics. Using observational data on a set of clear serious health events, such as stroke or heart attack, the models can generate
36/56 probability that an individual will suffer such an event over a given time horizon from a set of measurements of markers, or prognosticators, for the event (eg, medical history information, attributes, physiological metrics , lifestyle, etc. of a user, as described above). The distance in time between the time the predictors are measured, and the target event that is generated by such models, is referred to as a probability of survival, although it should be understood that not all of the target events considered are necessarily fatal.
[0067] These survival probability models are typically derived from the study of generally large populations that are followed for a considerable length of time, usually more than ten years, and the statistics collected in the observation of the target event (s) ( s) are summarized and generalized using mathematical methods. There are a number of such existing models that have been extensively validated and maintained and improved by periodically updating the model parameters using new data. Examples of existing models may include a subset of models developed and maintained by the Framingham Heart Study (an extensive bibliography on results obtained from the Framingham Heart study is available at www.framinghamheartstudy.org/biblio), a subset of the models developed and maintained by the University of Nottingham and the QResearch Organization (see, for example, J. Hippisley-Cox and others, Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2, [Cardiovascular risk forecast in England and Wales: derivation prospective and validation of QRISK2], BMJ 336:
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1475 doi: 10.1136 / bmj.39608.449676.25 (Published June 23, 2008)), the ASSIGN model developed by the University of Dundee (see, for example, H. Tunstall-Pedoe et al., Comparision of the prediction by 27 different factors of coronary heart disease and death in men and women of the Scottish heart health study: cohor study [Comparison of the prediction by 27 different factors of coronary heart disease and death in men and women from the Scotland heart health study: cohor study ; bmj 1998; 316: 1881), the Reynolds model (see, for example, PM Ridker et al., CReactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men [C reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds risk score for men], Circulation 2008; 118, 2243-2251, and Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women and validation of improved algorithms for the assessment of cardiovascular risk in women], JAMA, February 14, 2007 - Vol. 297, n ° 6), the PROCAM model from the Miinster Heart Study (see, for example, Simple Scoring Scheme for Calculating the Risk of Acute Coronary Events Based on the 10-Year Follow-Up of the Prospective Cardiovascular Miinster (PROCAM) Study [Simple marking scheme to calculate the risk of acute coronary events based on a 10-year follow-up of the cardiovascular study Miinster's perspective], Circulation 2002; 105: 310-315), and the SCORE model (see, for example, RM Conroy et al., Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE Project
38/56 of cardiovascular diseases in Europe; the SCORE Project], European Heart Journal (2003) 24, 987-1003). Other constituent risk models can also be included. In addition, precursor models can also be used. The precursor models predict the development of a first condition (eg, high blood pressure), where the development of the first condition is predictive of the development of a second condition (eg, heart disease). These are models that generate estimates of the likelihood of developing diabetes or high blood pressure, for example, which are two important predictors of mortality. A high probability of developing diabetes in five years, for example, will independently increase the likelihood of a serious cardiovascular event within the next ten years. Several precursor models can be included, and the inclusion of precursor models leads to more accurate metric risk models, but more importantly, it also leads to possible reduction of mortality risk through well-defined modifiable lifestyle aspects.
[0068] Traditional survival probability models have certain inherent limitations that result from the procedures used to build them. When deriving such models, the researchers compromise between precision and usability. It is difficult for an inductive model, meaning a model derived directly from data, to include all possible predictors. This is partly because not all relevant predictors of a particular event are known, but also partly because some predictors can be difficult or expensive to measure. In
39/56 In fact, several well-known risk markers, such as genetic factors, are often not included in such models. Therefore, several potential and known predictive metrics can be excluded as covariates when deriving a given survival model.
[0069] Survival probability models are constructed using data collected from a given population, and therefore summarize and generalize the characteristics of the studied population's morbidity and mortality. However, such a model may be in divergence when compared to risk estimates derived from other populations. When a given model is used in a population that differs from one where the model was built, it often underestimates or overestimates a particular risk because only a few predictors are often considered, and because other relevant predictors that may not be included in the model may well. differ between two populations.
[0070] Given the above discussion, along with basic probabilistic logic, a judicious combination of derived models for several different populations will generate a better view of the risks to which an individual chosen at random is exposed, and therefore will be more robust when estimating risks for the general population. Additionally, based on mathematical foundations, under very general assumptions, certain methods of combining models, referred to as predictor boosters, can improve the accuracy of the constituent models. In fact, the thrust of a set of models, when done correctly, will produce a model with precision that is, at worst, equal to that of the model
40/56 more accurate in the driven set.
[0071] Consequently, the Metric Health Model scoreboard can be calculated by comparing the user's medical parameter information with the survival probability models. A scoreboard, preferably in the range 0 to 1000, with the upper end signifying perfect health and the bottom signifying poor health, can be derived following a two-step process. First, an overall survival probability is obtained from a pool of survival probabilities generated by the individual survival probability models, as described above. Second, the resulting survival probability, which is a number in the range 0 to 1, is transformed using a parametric, non-linear mapping function with a high slope, in the region of typical survival probabilities, and which slopes asymptotically outward at the lower and upper ends of the survival probability distribution. The mapping function is designed to be strongly reactive to changes in the region of typical survival probability.
[0072] As discussed above, the health scoreboard can be composed of the Metric Health Model scoreboard, and also the Quality of Life Model scoreboard. The Quality of Life Model scoreboard is based on a user's responses to a set of questionnaires. The system can include several different questionnaires with some issues in common. The type of questionnaires and the type of questions presented in them to the user can be adjusted based on a user's health parameters (ie user age,
41/56 other data in the user's medical history, etc.). A specific questionnaire can be generated and presented to the user based on information about the user that is known to the system. The questions can be presented with an appropriate multiple choice answer that the user can check / tick on a form, with non-free text being fed by the user to allow easier assessment of the answers. Other types of responses are possible (eg, classifying how true a statement is for user 1-10). The following list provides several sample questions (in no particular order) on a number of health-related quality of life topics that can be used in a system questionnaire.
Sample questions • How do you rate your quality of life
• How do you rate your overall health
• How much do you enjoy life
• To what extent do you feel that your life makes sense
• How well are you able to focus
• How safe do you feel in your daily life
• How healthy is your physical environment
• Are you satisfied with your appearance
• How much opportunity do you have for laser activities
• How much do you need any medical treatment depending on your daily life
• How long have your activities been limited because of your main weakness or health problem
• Do you need help handling your personal care because of health problems
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Do you need help dealing with your daily needs because of health problems
• Are you limited in any way to any activities because of any major disability or health problem
• How true or false is each of the following statements for you :
o It seems that I get sick easier than other people o I am as healthy as anyone I know o I think my health will get worse o My health is excellent • You suffer from any of the following disability or health problems that limit your activities :
o Arthritis or rheumatism o Back or neck problem o Cancer o Depression, anxiety or any emotional problem o Vision problem o Fractures, bone / joint injury o Hearing problem o Respiratory problem o Locomotion problem o Other disability or problem • During the last 30 days, for how many days:
o your physical health was not good
o Did the pain make it difficult for you to do your usual activities, such as self-care, work, or recreation
o Did you feel sad, melancholic or depressed
o Did you feel worried, tense or anxious
o Did you feel like you didn't get enough rest or sleep
43/56 o Did you feel very healthy and full of energy
o Were you a very nervous person
o did you feel so dejected that nothing could cheer you up
o Did you feel calm and peaceful
o Did you have enough energy
o did you feel hurt and melancholy
o did you feel worn out
o Were you a happy person
o did you feel tired
• How satisfied are you with:
your sleep
o your ability to perform your daily life activities
o your ability to work
the yourself
your personal relationships
o your sex life
o the support you receive from your friends
your access to health services
your transportation
• Are you limited in any of the following activities because of your health
o Vigorous activities, such as running, lifting heavy objects, participating in strenuous sports o Moderate activities, such as moving a table, pushing a vacuum cleaner, playing bowling, or playing golf o Lifting or carrying edibles o Climbing several flights of stairs o Flexing, kneel or lean the Walk more than a mile
44/56 o Walking several blocks o Walking one block o Bathing or dressing
[0073] The list above is just a sample of questions that can be presented to a user. User responses to questions are given a value. For example, each of the multiple choice responses can be assigned a particular value, and all user responses can be scored to generate a score. In addition, different questions and different responses can be weighted differently since some questions, or the severity of the response, may have a greater predictor of the user's health. The system can also assign a value based on the user's response to a combination of questions, because certain combinations can be more predictive of health. Consequently, by evaluating the user's responses to the questionnaire, a Quality of Life Model scoreboard can be derived. Preferably, the Quality of Life Model scoreboard is a numerical value in the range 0 to 1000.
[0074] The health score is computed as a weighted average of the Metric Health Model score and the Quality of Life Model score. The health scoreboard can be presented to the user. The health scoreboard can be presented as a numerical value, as a graphic value (that is, as a meter, a bar, or a slider), or a combination of both, for example. Referring to figure 3A, the health scoreboard is presented by a combination of a numerical scoreboard 302 and a slider 304. The slider can also be color-coded to indicate the scoreboard. The position of the slider bar 306 indicates the user's score.
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[0075] An advantage of presenting the health scoreboard is that it is not necessary to present the survival probabilities and raw metrics to the user. On the contrary, users are presented with a standardized scoreboard. This is preferably true for the global metrics of the Health Metric and Quality of Life model, but it is also true for the relevant model inputs. This is done mainly to normalize all outputs, in the sense that users do not need to know whether the high values of a particular input variable are good or bad; in all cases, high scores of any fed value lead to higher health score values, and low variable input values lead to lower overall health score values.
[0076] Additionally, another advantage of standardized health scores is that users can compare health scores with other users. This allows for comparative analysis (with friends, co-workers, etc.) with other users. Such leaderboard comparisons can be a part of a game component of the system in which the user competes against other users, as will be described in more detail below. Game aspects of the system can be used to motivate the user of the health scoring system, such as a comparison of leaderboards between selected groups of users, comparison of individual leaderboards within configurable subpopulation distributions, tracking of leaderboard time, and setting goals, among others. Referring to figure 3B, the numerical scoreboard 302 and graphic scoreboard 306 of users are presented in combination with a range of leaderboards 308 from a group (eg the world) such as
46/56 that the user can see how his score compares with others in the group. In-game incentives can be extended by users to allow comparison of health scores between users who may differ substantially in one or more of the various specific input parameters, such as age, weight, and previous risk conditions. The system highlights improvements in modifiable user metrics, particularly lifestyle components, and these improvements in the scoreboard provide incentives for the user. This allows a fair comparison between users of a parent and their children, for example, via the health scoreboard. In one respect, the health scoreboard provides equalization between users of different characteristics and is therefore similar to that of a disadvantage in some sports. Referring to figure 3C, user score 306 is compared to leaderboards 310a-e and a group of friends selected by the user. Referring to the 3D figure, the user's individual medical parameters (eg, medical data provided as part of the Metric Health Model) can be compared with other users graphically without revealing the actual underlying values. The level of high density lipoprotein (HDL), level of low density lipoprotein (LDL), systolic blood pressure (sBP), diastolic blood pressure (dBP), body mass index (BMI), and blood glucose level in fasting (fBG) are shown in a graph 312. User scores are represented by a line 314, each of the user's friend scores is represented by a different point 316, and a distribution block 318 for a larger population group (eg, Switzerland) is also shown. Thus, the user can compare his individual parameters with a
47/56 group of friends and the average for a larger population group.
[0077] Users can feed data into the system at the time of an event (ie, exercise event, food consumption, blood pressure measurement, etc.), and see the resulting update of their health score in real time. The system can include detailing capabilities, allowing users to view the various scoreboard components of the health scoreboard, including tracking over time and corresponding trends across all scores; it also includes setting goals for the various leaderboards.
[0078] As an example of using the system, upon registration with the system (eg, the initial use of the system), a user is induced to provide medical history data. The user is also induced to answer a complete Quality of Life questionnaire selected by the system for the given user based on the medical history and user parameters provided by the user. After registering, at periodic intervals, users are presented with short subsets (3 to 5 questions) of their personalized Quality of Life questionnaire to keep their answers up to date and track changes. Users can feed entries to the Metric Health Model at any time, and the system induces the user to values that have not been updated in a while. Entries for the Metric Health Model can be acquired automatically by the system by accessing a series of digital measurement devices that have been integrated into the system (eg, the system can comprise a mobile electronic communication device, for example, a smartphone, that is in wireless communication with a measurement device, such as a monitor
Blood glucose, such that parameters can be measured, transmitted, and stored by the system). These can include weight, blood glucose, physical activity, and other parameters. Several multifunction digital measuring devices or devices can be included in the system. In the case of medical parameters that are more difficult to obtain with a home measuring device, such as serum lipid concentration levels, users are only prompted to provide the relevant data once per (system) configured time period (p .ex., annually and coinciding with the user's routine physical medical examination).
[0079] To avoid false scores, the system may include several algorithms to assess the validity of user inputs. Validation methods can range from those based on detecting strangers to those based on multidimensional probability estimators. When the system detects a possible bad input value, it signals and induces the user to confirm the value or to enter a new one.
[0080] The system can generate all your scores, even when one or more entries are missing. It does this by feeding the missing values or values using a variety of statistical methods ranging from ones based on global population statistics, to methods based on the use of more complicated statistical models that are built into the platform. However, whenever the entries include fed values, the system can clearly signal all affected scores, and periodically alert the user to provide the missing data. The system can also allow scoreboard simulation, in which the user can temporarily adjust its parameters such that a user
49/56 can see how changing certain parameters (eg, losing weight) affects the user's score.
[0081] The system can also provide recommendations for users to take certain actions that can improve the user's health score. These recommendations can be very specific when any variable fed is in its danger zone, and more generic when any variable fed is out of its optimal range.
[0082] As discussed above, the health scoreboard can be used as a part of a game or competition aspect of the system. The game aspect increases the fun element of the system for the user and increases the user's affinity to continue using the system. The game aspect can come in the form of obtaining higher levels based on achievements, competing against others (eg, in a league), and / or completing challenges. The level is a global indication of progress. The level can be monotonically increased and will increase by earning activity points. Activity points can be earned from performing numerous activities, such as time spent performing prep activities (eg, exercising), improving someone's health score, improving someone's BMI, taking part in discussions in the system (eg, the system can be a web-based social networking platform and discussions or classes can be offered to teach preparation skills). A user's level can be displayed in a user's profile and in discussion posts such that other users can see each other's level. A user's level status can also provide access to specific items, features and functionality of the system, or
50/56 rewards (eg, branded device).
[0083] Users can also compete within leagues in the system. Leagues are made up of user groups and users within the league can compete with each other (as part of a team or individually). Leagues can compete for a limited time (eg, monthly) and leagues can be designed based on the level of users (using the user level as discussed above), the type of activity being performed in the league, and the region of users. For example, a particular league may be the Greater Zurich Area mountain bike (sport) bronze (level) league, and the success of a user in this league is measured by the distance traveled and elevation climbed (measured amount). Therefore, bronze level users in the Greater Zurich Area who are interested in mountain biking can compete in this league. Limiting leagues to a particular region provides users with something to relate to and all users can share in common, and additionally allows users to meet face-to-face (eg, for group exercise events). A problem with a major international league is that such a league may seem anonymous, overpopulated and meaningless to some users (members competing against members residing on completely different continents with language barriers can inhibit group or team mentalities). Limiting leagues to particular level brackets equalizes the playing field for users of particular levels of experience. Quantities to be measured to determine league performance may include distance (horizontal, vertical) and duration of preparation activity performed, for example
51/56 example. Users can also form teams within the leagues. Team leagues work in the same way as the leagues outlined above, however the classification is based on the team's overall performance. Teams increase the communal aspect of participation in the activity. Teams can have a fixed size (eg, 2, 3, 5, 10, etc. users).
[0084] Users can also be presented with challenges to be completed by the system. Challenges can be registered for a period of time to complete an objective. The objectives of the challenge may be, for example, improving health scores (normalized), completing sport-related activity parameters (eg, total distance, total climb, etc.), or completing a sport-related activity within a specific period of time (eg, completing one and a half kilometers in six minutes on a specific route). The challenge can be public and any user can participate, or limited to a group (eg, friends, co-workers, social group, etc.). As an example, a particular public challenge can be a roller skating challenge in New York City for the route around the Central Park Loop measuring the time it takes to finish. Public challenges can be generated automatically by the system or system administrators. Group challenges can be issued by members of the group. The challenges provide a strong commitment dynamic, encouraging users to commit to exercising. Challenges (typically) have a lower time commitment than leagues. Route selection can be automated with the community. In a first step, the community can publish routes on the system platform (eg, a
52/56 type of social network); in a second step, the system selects popular routes (ie routes with high user activity) as weekly challenges. Route validation is done by GPS tracking. Challenges can be safely selected to prevent the promotion of unduly risky challenging activities, such as dangerous mountain bike descent routes.
[0085] The league and challenge systems provide opportunities to guarantee achievements. Achievement status indications can be collected and displayed on a user's profile. The achievements are very similar to a trophy, medal, or award provided to the user for completing challenges and / or winning in a league activity. Many different achievements are possible, such as related to the number of friends the user has in the system (participation in the community), achievements related to the time, intensity, and number of physical activities in which he was involved (level of participation in preparation) , achievements related to specific sports activities (eg, distance running), the frequency that a user measures his parameters (eg, weight) to keep the system up to date, the amount of weight lost, or the ability to maintaining someone's BMI, for example. The following list is an exemplary set of achievements and the activities required to achieve the achievements:
List of exemplary achievements • Competitor: Take part in a public challenge.
• Achieved Competitor: Take part in 10 public challenges.
• Champion: Win a challenge.
53/56 • Multi-sport champion: Win a public challenge in two different sports.
• International Competitor: Take part in a public challenge in two different countries.
• International Champion: Win a public challenge in two different countries.
• World Competitor: Take part in a public challenge on each continent.
• World Champion: Win a public challenge on each continent.
[0086] Other aspects of challenge and linking systems are that systems can be linked to marketing opportunities. For example, marketers can sponsor prizes for the winners of a challenge. The prize may be related to the challenge (eg gift certificate for health food score for the winner of the weight loss challenge). In addition, challenge routes can be selected to direct users to certain areas to increase tourism or to start / end at selected destinations (eg the bicycle challenge starts in front of the sports equipment store).
[0087] An advantage of the system is that it provides users and groups of users with comparative analysis capabilities. It allows other groups, such as insurance providers or employers, to assess the relative health of individuals to determine the risks related to the health of each individual. Consequently, users can compare themselves with others to assess their comparative level of health among a group of friends. Insurance providers can use health score information to define
54/56 awards for an individual or group of individuals (eg, employees of a company). In other implementations, health scores can be provided for a group based on the health scores of individuals in the group. For example, a health score can be calculated for a company based on its employees such that an insurance provider can set premiums based on the company's health score compared to other companies. In additional applications, the health scoreboard can be used to assess the health of professional athletes to determine the athlete's real market value. Vast amounts of money and resources are invested in athletes at all levels in professional sports. A large component of the decision to invest in an athlete is based on the athlete's past performance. Other factors may include a history of past physical injuries and the athlete undergoing a physical examination before the deal is completed. The health scoreboard can be used as an indicator of the athlete's current health and used as a predictor of the athlete's future performance. If the athlete's health score is low, this may indicate that the athlete is more likely to suffer an injury or that physical performance will decrease. Consequently, the health score can form the basis for a decision on whether to invest in an athlete. Health scores can also be used as a predictor of the outcome of a particular game played between two teams. For example, the health scores of individual team members can be aggregated to provide a team health score. A comparison of the teams' health scores can be indicative of the likely outcome of the game between two teams (eg, the team with the highest health score may be
55/56 most likely to win). Such information can be used in game contexts such as fantasy sports teams, or to set bets for sports bets. The health scoreboard can be used for club competitions (eg, group health improvement competitions, advertising based on a person's health score, game, TV / internet, etc.).
[0088] Therefore, in a broad sense, a method according to the invention can be understood as collecting health related information, processing the information on a health scoreboard, and publishing the health scoreboard is provided. A system for implementing the method may include a computer having a processor, memory, and executable code modules in the processor for the collection, processing, and publication of the information. Information related to a plurality of parameters related to the health of a user is collected, particularly, both intrinsic values related to medical parameters, measurable, of at least one natural person, and extrinsic values related to the activities of each such person as the exercise practiced, the type of work that the person has and the amount of physical work associated with the work (eg, sedentary, table work versus labor intensive labor, manual) and / or the calories / food consumed. Weighting factors are applied to the health-related parameter to control the relative effect that each parameter has on the user's calculated health score. The health score is computed using the processor combining the weighted parameters according to an algorithm. The health scoreboard is published to a designated group via a portal. In an implementation, the portal is a sharing forum
56/56 information based on the internet.
[0089] As such, the invention can be characterized by the following points in a method for collecting and presenting health-related data:
collect information related to a plurality of parameters related to a user's health;
store the information collected in a memory;
store weighting factors in memory;
processing the information collected by running code on a processor that configures the processor to apply weighting factors to related health parameters; compute a health score using the processor combining weighted parameters according to an algorithm; and provide the health score for a designated group via a portal.
[0090] The methods described here have been described in connection with flow diagrams that facilitate a description of the main processes; however, certain blocks can be invoked in an arbitrary order, such as when events trigger the flow of the program such as in an objective-oriented program implementation. Consequently, flow diagrams must be understood with exemplary flows such that the blocks can be invoked in a different order than as illustrated.
[0091] Although the invention has been described in connection with certain configurations thereof, the invention is not limited to the described configurations, but rather is more broadly defined by citations in any claims that follow and equivalents thereof.

权利要求:
Claims (15)
[1]
1. Method implemented by computer to process data related to private health in a numerical score suitable for publication, characterized by the fact of understanding the steps of:
- receiving data in a memory, the received data representing at least one intrinsic medical parameter and at least one user's extrinsic physical activity parameter;
- store the received data in memory;
- store weighting factors in memory;
- process the data received by executing code on a processor that configures the processor to:
- apply respective weighting factors to at least one intrinsic medical parameter and at least one extrinsic physical activity parameter, and
- apply a deterioration component to the processed at least one extrinsic physical activity parameter to reduce the relative weight of the processed by at least one extrinsic physical activity parameter for a physical activity depending on at least one factor associated with the user;
- transform the processed data received by executing additional code on the processor, and the processed data received are transformed into a masked composite numerical value combining the weighted parameters according to an algorithm;
- automatically publish the masked composite numeric value to a designated group via a portal, using code executed on the processor and free from human intervention, while maintaining the received data representing the
[2]
2/7 at least one intrinsic medical parameter and at least one private extrinsic physical activity parameter;
- communicate the processed received data or the masked composite numeric value to an exercise machine and automatically establish an exercise program on that basis, and
- communicate activity information from the exercise machine to memory for inclusion between at least one extrinsic physical activity parameter.
2. Method, according to claim 1, characterized by the fact that the at least one factor associated with the user is an age or age range of the user such that the deterioration component reduces the relative weight of the processed by at least one factor of extrinsic physical activity for a first user of a first age or age group unlike a second user of a second age or age group.
[3]
3. Method, according to claim 1, characterized by the fact that it additionally comprises the steps of:
- receive data in memory representing at least one extrinsic lifestyle parameter of the user, and the processing step of the received data additionally includes executing additional code in the processor that configures the processor to:
- apply the respective weighting factors to at least one extrinsic lifestyle parameter, and
- apply a deterioration component to reduce the relative weight of at least one extrinsic lifestyle parameter depending on at least one factor or at least another factor associated with the user,
3/7 and the step of transforming the processed data received includes additionally executing code in the processor that configures the processor to combine at least one processed intrinsic medical parameter, at least one processed extrinsic physical activity parameters and at least one parameter of extrinsic lifestyle processed according to the algorithm.
[4]
4. Method, according to claim 1, characterized by the fact that it additionally includes monitoring the composite numerical value and triggering a feedback communication by executing code in the processor and without human intervention.
[5]
5. Method, according to claim 4, characterized by the fact that the feedback communication is operative to provide an alert for the user to initiate a physical activity or change a programmed physical activity.
[6]
6. Method, according to claim 4, characterized by the fact that the feedback communication comprises an alert sent to a predetermined person.
[7]
7. Method, according to claim 4, characterized by the fact that the step of triggering a feedback communication comprises sending an electronic communication directed to the user including instructions on changes to the user's physical activity and / or lifestyle to improve the masked composite numeric value.
[8]
8. Method, according to claim 4, characterized in that it additionally comprises calculating, executing additional code in the processor, an assertive masked composite numerical value, which is indicative of a predicted future state based on past data, using the received data
4 / Ί of the user according to an assertive algorithm and causing the trigger of a predictable feedback communication.
[9]
9. Method, according to claim 1, characterized by the fact that the step of processing the received at least one parameter of extrinsic physical activity includes:
- obtain a measure of calories spent on physical activity within memory; and
- run additional code on the processor that configures the processor to:
- transform the measured calories into an equivalent metabolic value, MET, divided by the user's body weight;
dividing the MET value between a health reservoir and a bonus reservoir, where the bonus reservoir has a predetermined size and any divided MET value exceeding the size of the bonus reservoir is allocated to the health reservoir; and
- apply a deterioration component daily to the bonus reservoir;
the step of transforming the processed data comprises combining the processed at least one intrinsic medical parameter and a weighted health reservoir value according to the algorithm.
[10]
10. Health monitoring system, characterized by the fact that it comprises:
- a communication unit operable to receive data on at least one intrinsic medical parameter and at least one user's extrinsic physical activity parameter;
- a memory arranged to store the received data and to store weighting factors;
- a processor arranged to process the received data
5/7 running code that configures the processor to:
- apply the respective weighting factors to at least one intrinsic medical parameter and at least one extrinsic physical activity parameter,
- apply a deterioration component arranged to reduce the relative weight of the processed by at least one parameter of extrinsic physical activity for a physical activity depending on at least one factor associated with the user;
- the processor being additionally arranged to execute code to transform the processed data received into a masked composite numeric value using the processor combining the weighted parameters according to an algorithm;
- a portal arranged to publish the masked composite numeric value to a designated group while maintaining data
received representing the at least one medical parameter intrinsic and the least a parameter in physical activity extrinsic private; and - a link of communication bidirectional with a machine Exercises which is configured to:
- communicate the processed data or the masked composite numerical value to the exercise machine;
- automatically establish the exercise program based on the reported data or the masked composite numerical value, and
- receive activity information from the exercise machine within the memory for inclusion among at least one extrinsic physical activity parameter.
[11]
11. System, according to claim 10, characterized by the fact that at least one factor associated with the user
6/7 be an age or age range of the user such that the deterioration component reduces the relative weight of the sued by at least one extrinsic physical activity factor for a first user of a first age or an age range other than a second a second age user or age range
[12]
12. System according to claim 10, characterized in that the communication unit is additionally arranged to receive data about at least one user's extrinsic lifestyle parameter, the processor being additionally arranged to execute code for :
- apply the respective weighting factors to at least one extrinsic lifestyle parameter,
- apply a deterioration component to reduce the relative weight of at least one extrinsic lifestyle parameter depending on at least one factor or at least one other factor associated with the user, and
- transform the processed data by combining at least one intrinsic medical parameter, at least one extrinsic physical activity parameter and at least one extrinsic lifestyle parameter according to an algorithm.
[13]
13. System according to claim 10, characterized by the fact that it additionally comprises a monitoring unit arranged to monitor the composite numerical values and being arranged to cause a feedback communication to trigger with the detection of a predetermined event associated with the monitored composite numeric values.
[14]
14. System according to claim 12, characterized
7/7 because the monitoring unit is arranged to cause the transmission of electronic communication directed to the user including instructions on changes to the user's physical activity and / or lifestyle to improve the masked composite numerical value.
[15]
15. System, according to claim 10, characterized by the fact that the processor is arranged to process at least one extrinsic physical activity parameter by executing code that configures the processor to perform steps including:
- obtain a measure of calories spent on physical activity;
- transform the measured calories into an equivalent metabolic value, MET, divided by the user's body weight;
- divide the MET value between a health reservoir and a bonus reservoir, with the bonus reservoir having a predetermined size and any divided MET value exceeding the size of the bonus reservoir is allocated to the health reservoir;
- apply a deterioration component daily to the bonus reservoir; and
- transform the processed data by combining at least one intrinsic medical parameter and a weighted health reservoir value according to the algorithm.

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法律状态:
2020-06-23| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

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2020-07-28| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|

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2021-05-11| B06A| Patent application procedure suspended [chapter 6.1 patent gazette]|

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2021-10-19| B350| Update of information on the portal [chapter 15.35 patent gazette]|

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2022-01-11| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

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2022-03-03| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 29/09/2011, OBSERVADAS AS CONDICOES LEGAIS. PATENTE CONCEDIDA CONFORME ADI 5.529/DF, QUE DETERMINA A ALTERACAO DO PRAZO DE CONCESSAO. |

优先权:

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申请号 | 申请日 | 专利标题

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US38790610P| true| 2010-09-29|2010-09-29|

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US61/387,906|2010-09-29|

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US201161495247P| true| 2011-06-09|2011-06-09|

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US61/495,247|2011-06-09|

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PCT/US2011/053971|WO2012050969A1|2010-09-29|2011-09-29|Automated health data acquisition, processing and communication system|

---