Patent Application
20230282364
Embodiments of the present invention relate to the fields of physiological-signal processing, computational biology, health assessment and lifestyle quantification through wearable, nearable, ingestible and implantable sensors. In particular, embodiments of a platform for computing morbidity and mortality risk from digital health data to monitor risk and life style choices are presented.
With recent advances in the quality of medication and healthcare, longevity of humans has increased significantly. However, this acquired longevity is not necessarily accompanied by acceptable health levels, but rather by multiple, and often complex, terminal health conditions. This co-occurrence of at least two chronic illnesses in an individual, termed multi-morbidity, with the prevalence amongst the elderly of any given nation usually exceeding 60%, does not only greatly diminish the quality of life for those affected, but has become one of the main challenges in, and burdens on, health-care and -insurance worldwide.
The monitoring of population health is essential not only in terms of global economy, but also as a measure of quality of life of a nation. Various measures of health have been developed and improved upon, which lies beyond the scope of this discussion.
In recent years, a growing body of research on how variation in physiological measurements may relate to long term outcomes, such as the risk of disease development and all-cause mortality, has emerged. In addition, a significant body of research exists that demonstrates the expected changes in physiological parameters in response to lifestyle choices such as performing aerobic exercise.
In survival analysis, several techniques are demonstrated in the scientific literature to quantify the health risk associated with various physiological variations. One of the more commonly used techniques, Cox regression, is often used to express the logarithm of all-cause mortality risk as a function of age (or time) plus a linear combination of demographic and physiological risk factors. By using the specific values of physiological parameters for an individual in such population based models, it is possible to compute mortality or morbidity risks and to compute an expected life expectancy in years.
Many studies, spanning across groups of up to millions of individuals, have been conducted to improve our understanding of the all-cause mortality and disease risk implications associated with variations in physiological parameter values. An example of such a parameter is the resting heart rate of a person, for which a dose dependent increase in the risk of all-cause mortality has been observed. The risk associated with such a physiological parameter is often expressed as the ratio of the risk that a person with said resting heart rate is exposed to, compared to the risk of an individual or population that holds a normative value for the parameter. This ratio of hazards (hazard for subject/hazard of individual or group with normative parameter value) is known as the hazard ratio and is available for many physiological parameters.
A unit, such as all-cause mortality risk, cause-specific risk, morbidity risk, and the hazard ratio or life expectancy, mediates the expression of the consequences of lifestyle choices as a single number with a common understandable unit, for example, years of life expectancy gained or lost associated with a specific choice, such as commencing an exercise program, or, taking up smoking. This enables the direct comparison of these choices and optimization of various lifestyle choices in a numerical fashion. Similarly, methods exist for converting the survival analysis associated with life expectancy figures to a so called biological age by calculating the equivalent age of a reference population for which the all-cause mortality risk is equal to the all-cause mortality risk of a specific individual in question, i.e., the risk equivalent age.
Research has also been performed to elucidate the positive correlation between changes in physiological parameters to lifestyle interventions exemplified by, but not limited to, diet and exercise regimes. An example of such a study is the response of overall aerobic fitness (as measured during a VO2max test) to exercise intervention. Depending on the frequency, duration, intensity and initial fitness levels of a participant, specific changes in VO2max have been observed. It is possible to capture such data in a model to extrapolate from past studies directly to the individual, whereby expected changes in VO2max value in response to a planned duration, intensity and frequency of exercise, may be provided. Similarly, it is possible to make predictions of planned weight loss values, based on estimates of an individual's metabolic rate and diet and how weight loss would affect VO2max value, which is expressed in ml/min/kg of body mass. Changes in blood pressure values in response to changes in dietary sodium levels is another area that has been well studied and where a projection of expected changes at the hand of a low sodium diet may be provided, given an online diary and/or blood pressure measurements.
Using body monitoring technology, it has also become possible to track the lifestyle changes implemented by users, in a passive way. By using a wrist worn wearable device equipped with one or more optical sensors as well as an accelerometer, the measuring of real-time heart rate and exercise activity is facilitated. Moreover, the passive tracking of the frequency, intensity and duration of exercise can be enabled which, in turn, could be used to compare measured behavior to planned behavior.
Physiological parameters such as body mass, blood pressure and VO2max may be tracked either now or in the foreseeable future, by using body monitoring technology. For example, various connected Wi-Fi scale models exist that automatically upload the weight of a user to a cloud server. Regarding wearable technology, some devices include a sub-maximal exertion protocol which may be employed in conjunction with an exercise treadmill to obtain frequent estimation of the VO2max value of said user. Similarly, a connected sphygmomanometer and/or less intrusive body monitoring technologies may be used to measure blood pressure values in a more continuous fashion, after which said values may be communicated to a cloud server. The platform proposed herein has the necessary architecture for considering data from such external services via API (Application Programming Interface) calls or other relevant methods for sharing and accessing data and calculations in an anonymized, Health Insurance Portability and Accountability Act (HIPAA) compliant manner.
Certain physiological parameters, exemplified by, but not limited to VO2max value, RHR, maximum heart rate and BMI are indicative of morbidity- and mortality risk. Embodiments of the claimed invention comprise methods by which data gathered from, for example, wearable devices, are used to track the value of these parameters to predict morbidity and mortality risk and derivatives thereof, such as life expectancy and biological age. In addition, lifestyle choices such as exercise can also be tracked to project how current lifestyle will affect said physiological parameters and also how that will affect morbidity-and mortality risk and derivatives thereof. Moreover, in the case of predicted life expectancy, the disclosure can produce a value in years, a single unit wherein the impact of different lifestyle choices can be expressed and compared against each other to make an optimal choice.
The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the disclosure and to enable a person skilled in the pertinent art to make and use the embodiments.
Embodiments of the present disclosure will be described with reference to the accompanying drawings. In the drawings, like reference numbers indicate identical or functionally similar modules.
The exemplary embodiments described herein are provided for illustrative purposes, and are not limiting. Other exemplary embodiments are possible, and modifications may be made to the exemplary embodiments within the spirit and scope of the disclosure. Therefore, the Detailed Description is not meant to limit the disclosure. Rather, the scope of the disclosure is defined only in accordance with the following claims and their equivalents.
Embodiments may be implemented in hardware (e.g., circuits), firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. Further, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be appreciated that such descriptions are merely for convenience and that such actions in fact result from computing devices, processors, controllers, or other devices executing the firmware, software, routines, instructions, etc. Further, any of the implementation variations may be carried out by a general purpose computer, as described below.
For purposes of this discussion, any reference to the term “module” shall be understood to include at least one of software, firmware, and hardware (such as one or more circuit, microchip, or device, or any combination thereof), and any combination thereof. In addition, it will be understood that each module may include one, or more than one, component within an actual device, and each component that forms a part of the described module may function either cooperatively or independently of any other component forming a part of the module. Conversely, multiple modules described herein may represent a single component within an actual device. Further, components within a module may be in a single device or distributed among multiple devices in a wired or wireless manner.
The following detailed description of the exemplary embodiments will so fully reveal the general nature of the disclosure that others can, by applying knowledge of those skilled in relevant art(s), readily modify and/or adapt for various applications such exemplary embodiments, without undue experimentation, without departing from the spirit and scope of the disclosure. Therefore, such adaptations and modifications are intended to be within the meaning and plurality of equivalents of the exemplary embodiments based upon the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by those skilled in relevant art(s) in light of the teachings herein.
Embodiments of the claimed invention improve upon the existing stratification process used by life insurers by offering a lower friction, more scalable, as well as a more physiologically sound and potentially dynamic stratification process underwritten by the translation from interventions pertaining to lifestyle (such as commencing an exercise program) to physiological parameter changes coupled to risk (such as VO2max value), to life- and/or health expectancy. Embodiments of the proposed platform implement not only planned life style interventions, but also past life style interventions, enabling the user to quantify how previous behavior translated to health gains.
For example, in an embodiment, the proposed platform is capable of translating past and planned lifestyle intervention such as exercise into physiological parameter changes, specifically in those physiological parameters coupled to morbidity and mortality risk, into dynamic life expectancy in the form of years of life gained or lost. Not only do changes in the physiological parameters mentioned translate into changes in life expectancy, but also into changes in health expectancy, as athletes that have a lifelong increase in VO2max compared to the general population, also experience an increased health-span that allows them to have youthful performance levels, even in advanced age. The physiological parameters targeted in embodiments and which have been tracked in large studies are typically those associated with both increased health and life expectancy such as increased fitness levels, optimal body mass index (BMI), lower resting heart rate (RHR) and optimal blood pressure.
Having several connected users, it also becomes possible to provide additional utility to users by allowing them to connect to each other and to share lifestyle interventions for which the success can be quantified based on accrued effects measured on the platform that connects these users. In this way, new diets, exercise programs, health supplements and many other lifestyle interventions can be tested and promoted, based on objectively measured evidence collected by the wearables/nearables/ingestible technologies connected to the proposed platform.
The disclosure is organized as follows. First, an example embodiment of an overall system with example inputs, outputs and different devices connected to it, is described. Second, example methods by which physiological measurements are translated into predictions of physiological risk are described. Third, an embodiment of the integration of risk to provide a life expectancy value is described. Finally, an example process of monitoring lifestyle behavior using the inputs to the platform, as well as prediction and presentation of physiological parameter changes and thereby health risk, via for example life expectancy changes, is described with respect to an embodiment.
An embodiment of the invention comprises a platform that gathers physiological data derived from connected sensors from individuals. Such a process will be referred to as body monitoring. Said data is then interpreted and/or transformed at the hand of models based on published research studies and/or privately funded studies, for example pilot projects with third parties having a vested interest in quantifying and improving health. In an embodiment, such an interpretation yields, for example and without limitation, one or more of the following: (i) risks of mortality, morbidity or disease development associated with the monitored person's physiology; (ii) life expectancy, biological age estimation and other forms of health quantification based on the monitored person's physiology; (iii) projected changes to physiological parameters based on lifestyle choices such as diet and exercise plans; (iv) projected modification to physiological parameters based on the measured behavior of the user; (v) actual modification to physiological parameters, by measuring said parameters via body monitoring data streams; (vi) modification of disease and all-cause mortality risk associated with life style plans, either as projected for a certain lifestyle plan, measured lifestyle intervention or measured changes to physiological parameters.
In an example survival analysis, an individual is considered to exist under a relatively low and relatively constant hazard of dying or developing disease at any given instant in time. Under such an assumption, there is no single deterministic life duration, but rather, a distribution of possible life expectancies with different probabilities implied. When the term life expectancy is used for an individual, it generally refers to the most likely duration of a lifetime. However, the assumption that the hazard is not completely constant is necessary, especially due to the known increasing risk of disease development and mortality with advanced age. In the early 1800s, Benjamin Gompertz demonstrated that human mortality risk increases exponentially with advancing age. A simple rule of thumb is that the risk of mortality doubles for every eight years of adult human life span.
The basic equation used to model survival is essentially an ordinary differential equation of the form:
where S(t) represents the survival fraction (between 0 and 1) and μ is the mortality risk expressed as the fraction of survivors dying per time unit. In this way, even when considering a survival analysis for an individual, we are looking at the individual as if consisting of the average outcome of a population of clones, a fraction of which dies at every time step from birth to advanced age.
The mortality risk μ, is then a function of age, or t in the model, and typically follows an exponential doubling such that μ=μ0et/λ, where lambda is proportional to the doubling time for risk, as mentioned before, being close to 8 years in human populations.
Studies have shown that the risk of mortality in a population also varies with physiological parameters, such as resting heart rate value. This implies that μ is not only a function of age, but also of the various physiological parameters that could be measured on an individual. Considering a parameter such as resting heart rate, it has been shown that there is a dose-dependent increase in mortality risk. In this case, the reported value is an increase in mortality risk of 16% for each 10 bpm increment. An individual with a 70 bpm resting heart rate would thereby display two 10 bmp increments over 50 bpm, resulting in a 1.16×1.16=1.3456-fold risk increase relative to a reference individual with a 50 bpm resting heart rate and where other factors are equal. Therefore, it is important to express the risk of mortality, μ, as a function not only of time, but also as a function of the physiological parameters of an individual, ρ, which would include factors that affect mortality risk, exemplified by, but not limited to resting heart rate, fitness level (VO2max), BMI, total sleep time, sleep quality, sleep time variability and blood pressure values. Symbolically this can be expressed by:
μ=μ0et/λek(ρ−ρ
where ρ represents said physiological parameter and ρ0 represents a reference value for a parameter. We do not limit ourselves to the exponential form used here for illustrative purposes. For example, it is well known that the risk associated with BMI follows a bathtub like distribution, where very low or very high values are associated with increased risk.
In the preceding paragraph the hazard ratio associated with deviation in a physiological parameter, specifically heart rate, was treated as a constant value of 1.16 (16% risk increase) for every 10 bpm increase. Resting heart rate is an example of a physiological parameter that does not appear to change with age in an adult. However, this is not the case for many other physiological parameters, such as fitness level expressed as VO2max. To use a hazard ratio reported for VO2max, the age of the participant must be brought into account.
Referring to
In the alternative case where the model used (14) does have regression coefficients for both age and VO2max, the age (13) and VO2max (11) of the participant in this case, 50 years, can simply be substituted into the risk model directly (14). Numerous studies providing demographic data that can be used to build such multivariate demographic models (17) exist. In
Factors such as exercise behavior and weight vary to create the different VO2max values over the course of a lifetime of an individual, as seen in
This personalized hazard rate (26) is the value required for as described above. Considering the individual discussed in
Less data exists to monitor the effect on mortality of focused lifestyle interventions such as exercise and diet programs, as most of the studies on risk associated with physiological parameters (14) did not involve coordinated interventions. However, for various reasons, the individuals enrolled in such studies do vary in the values of their physiology, lifestyle intervention being only one of many causative factors. Therefore, the understanding of how risk is affected when an individual changes physiological parameters through lifestyle intervention, is not fully understood. However, given the limited knowledge that we do have from population based risk studies, the most likely mortality risk that we would predict for the individual, should they partake in an all-cause mortality study, would be the risk associated with their current measurable physiological state. Considerable evidence has been presented on a narrow range of lifestyle interventions, notably smoking, where states such as abstaining, commencing or completely ceasing the behavior at different ages and in many individuals in a cohort, is prevalent. The literature on this hazard, that is understood to cause advanced aging and increased disease risk, shows that ceasing a habit such as smoking before age 40 leaves only 10% of the residual risk in later life, whereas ceasing at age 50 leaves around one third of the risk remaining throughout life. This substantiates to some degree the basic working assumption that the risk estimate associated with an individual's current physiological parameter values should shift in the direction of the physiological changes arrived at by lifestyle intervention. Further, at young ages, this assumption appears to hold more strongly, while there is some physiological damage that accrues when living in unhealthy states for extended periods of time, which permanently elevates risk compared to the general population.
The assumption that changes in physiological parameters are associated with changes in all-cause mortality risk or disease risk in a similar qualitative pattern as found in large cohorts, implies that the life expectancy of an individual, continuously changing in parameters such as fitness level (VO2max), resting heart rate, BMI or blood pressure, also changes continuously. Consequently, some estimate thereof may be calculated by using body monitoring data. Such information, when integrated over all tracked physiological parameter values and demographic parameters such as age, sex and nationality could then provide a time varying life expectancy that is subject to the variation in physiological parameters. Such an output may also be combined with another aspect of the technology—a planned lifestyle intervention such as weight loss, exercise programs or a low sodium diet may be used to, at the hand of the appropriate studies, predict changes to physiological parameters such as VO2max, BMI and blood pressure, and convert these projected changes to days of life expectancy gained or lost due to each proposed intervention (19). In this manner, it could also be possible to compare the benefits of not just alternate interventions, but also of multiple interventions in a single one-dimensional unit of years of life expectancy gained or lost. The advantage of such an approach to the subject using such a system (or consented third party) is that, based on an informed projection of their benefits, specific interventions may be selected. In addition, the lifestyle interventions (19) may be tracked at a behavioral level through body monitoring technology (2 and 7) and signal processing algorithms, as well as at a physiological parameter level by directly measuring weight, blood pressure, resting heart rate or VO2max values (2 and 3). Finally, factors such as genetics and their impact can be considered in the lifestyle impact model (9) to modify the predicted changes in for example VO2max in response to exercise, as some genetic variations influencing trainability are already known. Similarly, the subjective experience of exercise at a fixed intensity has also been shown to have a genetic component, and such information can be factored in to further personalize the lifestyle impact module (23).
In an embodiment used as part of a digital health platform (5), biological signals may be continuously detected and digitized from a range of supported devices into data streams and recorded by, for example, a wrist based device fitted with sensors (2). Consequently, data streams may be transformed into and represented as one or more physiological parameters (8) by analytics services (6), which may be internal or external to the digital health platform (5). These physiological parameters (8) can include, but are not limited to, VO2max value, heart rate, respiratory rate, genetics (12), blood pressure and sleep parameters such as total sleep time. Additionally, information requiring the execution of manual measurements (for example body mass, height and skinfold measurements) and/or verbal or written input from the individual (for example gender, exercise events and alcohol consumption), may be manually entered either onto the device itself and/or one or more external devices including, but not limited to smartphones, tablets or personal computers, and/or a cloud-based platform. Said data and/or information may be stored and/or processed and/or displayed on the device itself, and/or relayed wirelessly between the device and/or one or more of said external devices and/or a cloud-based platform.
Using the sensor data streams (2) that have entered the digital health platform (5), analytics services on or external to the digital health platform can be used to measure aspects of user behavior such as exercise and sleeping patterns (7). In an embodiment, a Lifestyle impact module (23) then considers the current physiology (8) of the subscriber (1) and optionally other context such as the subscriber's genomic data (12), to predict how physiological parameters will change over time. An example is a prediction of how a subscriber's (1) VO2max would change in response to the current frequency, intensity and duration of exercise given his/her current VO2max values and genetic propensity (12) to respond to exercise. This allows a projection of user physiology into the future which can optionally be included as input stream to the Health risk module (24).
In
By following a subject's behavior and physiological parameters in this way, it becomes possible to give feedback to the user in terms of a) the degree of planned adherence (expressed as intensity, frequency and duration of exercise in the specific example), b) the progress in measured physiological parameters (8) compared to projected changes in these parameters (10) and c) for example, life expectancy gains of a planned and executed lifestyle intervention (19).
Returning to
Once the set of risk and risk adjustment factors compatible with the cohort (18) has been calculated in 24, a biological hazard ratio (11) for the subscriber (1) can be calculated in combination with the health risk models (14). This hazard ratio reflects the risk for the individual due to biological factors described (including physiological and genomic) and to perform further analysis, hazard rates can be obtained from a larger population based model (25) that provides hazard rates at different ages or birth dates for a subscriber based on demographic information that is exemplified, but not limited to nationality. The product of this latter hazard rate and the biological hazard ratio (11) can be used to calculate a biological hazard rate (26) which can be used to calculate other derivatives for the subscriber (1) such as current and projected health risk, morbidity, mortality, biological age and life expectancy. Such information can also be delivered based on current physiology, projected physiology according to diet and or exercise or other life style intervention plans as indicated by the A, B and C in 19. In some of such cases, a single number such as life expectancy can then be used to determine and optimize a lifestyle intervention plan by choosing the intervention or combination of interventions that will yield the highest decrease in risk, lowest biological age or highest life expectancy value.
One consideration involves how hazards on different physiological parameters, reported in different studies and cohorts, may be combined to arrive at a holistic assessment of risk for an individual. A common practice in the literature on all-cause mortality or disease hazard is to correct for known, confounding variables to provide what is known as adjusted hazard ratios. In such cases, multiple physiological parameters can be fed into a Cox-regression model to get a hazard ratio for a subscriber. In cases where individual risk factors have separate studies outlining the risk for each, the interaction of these factors relating to risk is unknown and therefore one can reduce all covariation between two or more such parameters by statistical processes such as whitening of the multivariate distribution which guarantees that one only considers the non-correlated or orthogonal variation in each of the parameters. In such a case, the two or more hazard ratios derived from two or more studies can be combined by multiplying them due to their engineered independence.
One or more processors 604 may each be a graphics processing unit (GPU). In an embodiment, a GPU is a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common to computer graphics applications, images, videos, etc.
Computer system 600 also includes user input/output device(s) 603, such as monitors, keyboards, pointing devices, etc., that communicate with communication infrastructure 606 through user input/output interface(s) 602.
Computer system 600 also includes a main or primary memory 608, such as random access memory (RAM). Main memory 608 may include one or more levels of cache. Main memory 608 has stored therein control logic (i.e., computer software) and/or data.
Computer system 600 may also include one or more secondary storage devices or memory 610. Secondary memory 610 may include, for example, a hard disk drive 612 and/or a removable storage device or drive 614. Removable storage drive 614 may be a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup device, and/or any other storage device/drive.
Removable storage drive 614 may interact with a removable storage unit 618. Removable storage unit 618 includes a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. Removable storage unit 618 may be a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, and/ any other computer data storage device. Removable storage drive 614 reads from and/or writes to removable storage unit 618 in a well-known manner.
According to an example embodiment, secondary memory 610 may include other means, instrumentalities or other approaches for allowing computer programs and/or other instructions and/or data to be accessed by computer system 600. Such means, instrumentalities or other approaches may include, for example, a removable storage unit 622 and an interface 620. Examples of the removable storage unit 622 and the interface 620 may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and/or any other removable storage unit and associated interface.
Computer system 600 may further include a communication or network interface 624. Communication interface 624 enables computer system 600 to communicate and interact with any combination of remote devices, remote networks, remote entities, etc. (individually and collectively referenced by reference number 628). For example, communication interface 624 may allow computer system 600 to communicate with remote devices 628 over communications path 626, which may be wired and/or wireless, and which may include any combination of LANs, WANs, the Internet, etc. Control logic and/or data may be transmitted to and from computer system 600 via communication path 626.
In an embodiment, a tangible apparatus or article of manufacture comprising a tangible computer useable or readable medium having control logic (software) stored thereon is also referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 600, main memory 608, secondary memory 610, and removable storage units 618 and 622, as well as tangible articles of manufacture embodying any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 600), causes such data processing devices to operate as described herein.
Example embodiments of the present invention may include the following features, alone or in combination where applicable:
A1. A system, comprising a first plurality of sensors configured to produce their respective plurality of signals related to a physiology of a first individual; and a first device configured to receive the first plurality of signals from the sensor and to generate a first plurality of data streams based on the first plurality of signals; and a second device different from the first device and configured to receive at least one of the data stream and a derivative of the data steam from the first device and to transmit the at least one of the data steam and the derivative of the data stream to a first cloud computing platform (also referred to herein as a cloud computing device), wherein at least one of the first device and the second device and the first cloud computing platform is configured to derive a first plurality of physiological parameters from the data stream, wherein the first cloud based platform is configured to receive the first plurality of physiological parameters from the second device; derive one or more morbidity or mortality associated parameters from the first plurality of physiological parameters; and transmit the mortality or morbidity related parameters to computing devices of any of the first individual and a first permitted third party via the cloud based platform.
A2. The system of embodiment A1, wherein the morbidity or mortality associated parameters are replaced by the values of the first physiological parameters in the context of respective ranges associated with increasing/decreasing levels of mortality/morbidity.
A3. The system of embodiment A1, wherein the permitted third party is an insurance provider and the individual is a subscriber to the insurance provider, and wherein the first mortality or morbidity associated parameter is used to determine at least one of a premium, a benefit, and a reward for the individual.
A4. The system of embodiment A1, where at least one of the transmission of the plurality of data streams or plurality of derivatives of the data streams to the first cloud computing platform is handled by at least one of the second device or the first device.
A5. The system of embodiment A4, wherein the at least one of the first device and the second device and the first cloud platform is further configured to derive a plurality of behavioral parameters from the data stream, wherein at least one of the morbidity related parameters and the mortality related parameters of the first individual is expressed as at least one of:
A6. The system of embodiment A5, wherein the at least one of the first device and the second device and the first cloud platform is further configured to use the at least one of a plurality of first physiological parameters and first behavioral parameters to estimate a combined mortality associated parameter or combined morbidity associated parameter by using a model, exemplified by, but not limited to Cox regression, wherein at least one of the following holds:
A7. The system of embodiment A6, wherein the first individual and a second individual are each part of a social network having a lifestyle plan, and wherein at least one of the combined morbidity and combined mortality associated parameter and plurality of physiological parameters and behavioral parameters is used to evaluate progress on the lifestyle plan or to project the benefit of a change to the lifestyle plan or to compute an alternative lifestyle plan.
A8. The system of embodiment A6, wherein the at least one of the first device and the second device and the cloud platform is further configured to adjust a parameter of an algorithm for computing life expectancy of the first individual by adjusting the overall risk modification value of the first individual and to determine a life expectancy value for the individual based on the adjusted parameter, wherein the parameter determines the estimated chance of the first individual surviving to the life expectancy value, and wherein the algorithm for computing the estimated survival of the first individual uses the morbidity risk to quantify a chance of disability of the first individual.
A9. The system of embodiment A6, wherein the at least one of the first device and the second device and the first cloud platform is further configured to determine a second risk modification factor for a particular age of a second individual different than the first individual, wherein the age of the second individual is different than the age of the first individual, wherein the second risk modification factor is based on an aging algorithm to predict one or more age related changes for the plurality of physiological parameters.
A10. The system of embodiment A6, wherein the at least one of the first device and the second device and the first cloud platform is further configured to determine a second risk modification factor based on genetic data of the individual.
A11. The system of embodiment A6 wherein the at least one of the first device and the second device and the first cloud platform is further configured to calculate an estimate of the benefit of behavioral choices as recorded and calculated from the plurality of sensor data-streams for the individual based on the combined mortality or morbidity associated parameter.
A12. The system of embodiment A6, wherein the at least one of the first device and the second device and the first cloud platform is further configured to calculate a projection of at least one of a first risk and a first benefit associated with a range of different planned lifestyle choices for the individual based on at least one of the combined morbidity and mortality parameter.
A13. The system of embodiment A5, wherein at least one of the mortality and or morbidity associated parameters are scores established in the scientific literature, exemplified by, but not limited to the Framingham Risk score associated with cardiovascular risk morbidity.
A14. The system of embodiment A5, wherein at least one of the morbidity or mortality associated parameters are expressed as a biological age, indicating the age of a reference group for which the morbidity or mortality associated parameter would attain approximately the same value.
A15. The system of embodiment A5, where the system receives from a consented third party, data from which behavioral or physiological or medical parameters can be derived and uses these as additional sources for determining at least one of a plurality of mortality and morbidity associated parameters and combined mortality and morbidity associated parameters.
Based on the teachings contained in this disclosure, it will be apparent to persons skilled in the relevant art(s) how to make and use embodiments of the present disclosure using data processing devices, computer systems and/or computer architectures other than that shown in
The present application is a continuation of U.S. patent application Ser. No. 16/190,080 filed Nov. 13, 2018, which claims the benefit of and priority to U.S. Provisional application Ser. No. 62/585,373, the entire contents of which are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62585373 | Nov 2017 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16190080 | Nov 2018 | US |
| Child | 18181319 | US |
