WO2024046045A1 - Health evaluation method and electronic device - Google Patents

Abstract

A health evaluation method and an electronic device. The method comprises: an electronic device acquiring user data, the user data comprising at least first metabolic data and first behavior data among first metabolic data, first behavior data and first environmental data of a user; determining a first health state of the user according to the user data, the first health state changing as the at least first metabolic data and first behavior data among the first metabolic data, the first behavior data and the first environmental data changes, wherein the first metabolic data changes as the first behavior data changes; and displaying the first health state on a display screen of the electronic device so as to prompt the user regarding an association influence of the first metabolic data and the first behavior data. In this way, the health state of a user can be more accurately evaluated.

Description

A health assessment method and electronic device

Cross-references to related applications

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on August 29, 2022, with application number 202211042428.3 and the application name "A health assessment method and electronic device", the entire content of which is incorporated by reference in in this application.

Technical field

The present application relates to the field of electronic technology, and in particular to a health assessment method and electronic equipment.

Background technique

Currently, with the development of technology, applications and services related to user health are becoming more and more abundant. For example, there are many applications (APPs) related to exercise, sleep, stress, etc. This kind of innovation and development in applications creates convenient conditions for promoting users' health.

However, such applications generally monitor user behavior. For example, detecting the user's movement and predicting the user's physical health status. This method cannot accurately reflect the user's health status, and it is difficult to improve the user's attention to their own health status.

Contents of the invention

The purpose of this application is to provide a health assessment method and electronic device to accurately assess the user's health status.

The first aspect is to provide a health assessment method applied to electronic devices. The method includes: obtaining user data, the user data including at least the first metabolic data and the first behavioral data among the first metabolic data, first behavioral data and first environmental data of the user; according to the User data determines the first health state of the user, and the first health state changes with at least the first metabolic data and the first behavioral data among the first metabolic data, the first behavioral data, and the first environmental data. changes with the change, wherein the first metabolic data changes with the change of the first behavioral data; the first health state is displayed on the display screen of the electronic device to prompt the user that the first metabolic data and the associated impact of said first action on the data.

That is to say, the electronic device can evaluate the user's health status by integrating the user's metabolism, behavior, environment, etc. (at least comprehensively the user's metabolism and behavior), and the evaluation results are relatively accurate; moreover, when the user's behavior changes, the user's health status, user's health status, etc. Metabolism changes accordingly, which can dynamically and real-time prompt the correlation between user behavior and metabolism, increase users' attention to bad behavior habits, and remind users to improve their behavior as much as possible.

In a possible design, the first health state includes at least one of the following:

Comprehensive health score, used to indicate the overall health level of the user;

Critical health risk factors, indicating the most serious health risk factors for said user;

Life expectancy, used to indicate the future life expectancy of the user.

That is to say, the electronic device can display information such as comprehensive health score (for example, 90 points, 80 points), key health risk factors (for example, unhealthy diet, short sleep, low amount of exercise), life expectancy (for example, expected age of 60), etc. , use this information to increase users' attention to bad behavior habits, and remind users to improve their behavior as much as possible.

In a possible design, the method further includes: obtaining second behavior data of the user, the second behavior data being based on the user's improved behavior data; updating the first behavior data according to the second behavior data. The metabolic data is second metabolic data, and the first health state is updated to a second health state, wherein the second health state is a health state redetermined based on the second behavioral data and the second metabolic data. ; Display the second health state on the display screen of the electronic device. In other words, the electronic device can update metabolic data and health status based on the user's improved behavior data. In this way, the user can see the impact of the improved behavior on the health status and remind the user to improve the behavior as much as possible.

In a possible design, before obtaining the second behavior data of the user, the method further includes: outputting an intervention plan to guide the user according to the first health state, where the intervention plan includes the user's improved behavior data. In other words, the electronic device will output an intervention plan to guide the user, so that the user can improve their behavior based on the plan and have a better experience.

In a possible design, before obtaining the second behavior data, the method further includes: receiving expected indicators input by the user, where the expected indicators include the user's improved behavior data. That is to say, the user inputs his or her expected indicators into the electronic device (for example, how much exercise is expected per day), and the electronic device can update metabolism, health status, etc. based on the expected indicators. In other words, the user can see that if his behavior reaches the prefetched indicator, What kind of metabolic level and health status will be obtained, which can encourage users to improve their behavior and increase their enthusiasm.

For example, if the user inputs expected indicators including exercising for 3 hours a day, the electronic device will output metabolic data such as BMI 19; if the user inputs expected indicators including exercising 2 hours a day, the electronic device will output metabolic data such as BMI20. This will not only encourage the user Active exercise can also estimate the amount of exercise required to reach the metabolic level in the user's mind. For example, the user's expected BMI is 20, so the user can exercise for about 2 hours a day.

In a possible design, when the first health state includes life expectancy, determining the user's first health state based on the user data includes: determining the user's first health state based on the user data. A set of health risk factors. The first set of health risk factors includes at least one dimension among exercise dimensions, sleep dimensions, diet dimensions, drinking dimensions, smoking dimensions, blood pressure dimensions, blood sugar dimensions, blood lipid dimensions, BMI dimensions, and environmental dimensions. Health risk factors; predicting the user's lifespan based on the first set of health risk factors. In other words, electronic devices can predict the user's life span by integrating exercise, sleep, diet, drinking, smoking, blood pressure, blood sugar, blood lipids, BMI, and the environment, which is relatively accurate. Moreover, electronic devices displaying users' predicted life span can increase the user's attention to bad behavior and remind users to improve their behavior.

In one possible design, predicting the life span of the user based on the first set of health risk factors includes: determining the severity level of each dimensional health risk factor in the first set of health risk factors; According to the severity level of the health risk factors in each dimension, the life span corresponding to the health risk factors in each dimension is matched; according to the life span corresponding to the health risk factors in each dimension, the life span of the user is predicted. In other words, electronic devices can determine the user's lifespan based on the severity levels of exercise, sleep, diet, drinking, smoking, blood pressure, blood sugar, blood lipids, BMI, environment, etc., and the predicted lifespan is relatively accurate. Moreover, electronic devices display users' predicted life span, which can more actively encourage users to improve their behavior.

In one possible design, predicting the life span of the user based on the first set of health risk factors includes: determining each of the M cause-of-death diseases of the target group based on the first set of health risk factors. The baseline death probability corresponding to the cause of death disease and the relative risk of death of the user relative to each cause of death disease, the target group is the group to which the user belongs, and the M is a positive integer; according to each cause of death disease The corresponding baseline death probability and the user's relative risk of death relative to each cause of death disease are used to determine the actual death probability of the user relative to each cause of death disease; based on the user's relative risk of death relative to each cause of death disease. Actual probability of death, predicting the lifespan of said user.

It can be understood that the user's first set of health risk factors, namely {x1, x2,...,xn}, reflects the user's actual metabolism, behavior, environment and other health factors. For the oth cause of death among the M types of death diseases, Taking disease as an example (o is an integer greater than 1 and less than or equal to M), the electronic device can calculate the user's relative risk of death for the o-th cause of death disease based on {x1, x2,...,xn}, for example, represented by RRo. For each cause of death disease, the corresponding relative risk of death RR can be obtained. Then, the electronic device can calculate the user's actual death probability based on the relative risk of death RR corresponding to each cause of death disease and the baseline death probability q corresponding to each cause of death disease, and then predict the user's lifespan. In this way, the accuracy of user life can be improved.

In a possible design, determining the user's relative risk of death relative to each cause of death disease based on the first set of health risk factors includes: for each cause of death disease, determining the first health risk factor The cumulative relative risk of death of all dimensional health risk factors in the risk factor set relative to the disease of that cause, and the risk factor mediation effect between the first health risk factor and the second health risk factor is removed from the cumulative relative risk of death The weight MF is used to obtain the relative risk of death of the user relative to the cause of death; wherein the first health risk factor and the second health risk factor are a group of health risks in the first health risk factor set Factor, and the MF is the correlation between the degree of influence of the first health risk factor on the cause-of-death disease and the degree of influence of the second health risk factor on the cause-of-death disease. In this way, for each cause of death disease, there is a correlation between the impact of the first health risk factor and the second health risk factor on the cause of death, so all dimensional health risk factors in the first health risk factor set are relative to The cumulative relative risk of death from a cause-related disease repeatedly calculates the impact of the first health risk factor and the second health risk factor on the cause-related disease, so the risk between the first health risk factor and the second health risk factor is removed. The factor mediation effect weight MF improves the calculation accuracy of the relative risk of death.

For example, assuming that the first health risk factor is the health risk factor of the exercise dimension, the second health risk factor that is related to the health risk factor of the exercise dimension can be the health risk factor of the blood pressure dimension, because the user's blood pressure will be affected during exercise. influence, so there is a correlation between exercise and blood pressure. Moreover, when considering a certain cause of death, exercise and blood pressure need to be considered together. However, changes in blood pressure are affected by exercise and other aspects (such as the user's congenital conditions), so when considering exercise and blood pressure, it is necessary to Filter out the impact of exercise on blood pressure, where the impact of exercise on blood pressure is the risk factor mediating effect weight MF between exercise dimension health risk factors and blood pressure dimension health risk factors. Therefore, when calculating the relative risk of death RRo for the oth cause of death disease above, the MF between the first health risk factor and the second dimension health risk factor for the oth cause of death disease was filtered out, improving the calculation The accuracy of the relative risk of death RRo improves the accuracy of predicting user life span.

In a possible design, the method further includes: determining the target group corresponding to the user based on the user's basic information; wherein the basic information includes the user's gender, age, location in the city, At least one item. That is to say, different users correspond to different target groups. For each user, the relevant data of the target group corresponding to the user (for example, the baseline death probability of the target group for each cause of death disease) can be used to perform calculations to improve life expectancy prediction. accuracy.

In a possible design, the key health risk factors include at least one of the following:

The health risk factor with the highest dimension score in the first health risk factor set; or,

The health risk factor in the first health risk factor set whose dimension score has the greatest negative impact on the comprehensive score; wherein the comprehensive score is obtained based on the dimensional scores of all dimensional health risk factors in the first health risk factor.

That is to say, after the electronic device obtains the dimension score P of each x in {x1, x2,...,xn}, it can determine that the x with the lowest dimension score P is the most serious health risk factor, or, according to the dimensions of each x The negative impact of the score on the comprehensive score is ranked by each x. The top x is the most serious health risk factor. In this way, the electronic device can find the most serious health risk factors and output the most serious health risk factors to prompt the user to improve their living habits and improve the user's health level.

In a possible design, the method further includes: the dimension score Pi of the i-th health risk factor in the first health risk factor set satisfies the following formula: Pi=(AE _fact -AE _i,worst )/( AE _i,tmrel -AE _i,worst )×a.

Wherein, AE _fact is the life span of the user predicted based on the first set of health risk factors, the AE _i,worst is the life span of the user predicted based on the second set of health risk factors, and the AE _{i , tmrel} is the life span of the user predicted based on the third set of health risk factors, and a is the highest score;

The second set of health risk factors is to replace the health risk factors of the i-th dimension in the first set of health risk factors with a first value, and the first value is the worst value corresponding to the health risk factor of the i-th dimension. Any value within the range; wherein, the worst numerical range is used to indicate that when the i-th dimension health risk factor is within the worst numerical range, the probability of causing death is the highest;

The third set of health risk factors is to replace the health risk factors of the i-th dimension in the first set of health risk factors with a second value, and the second value is the optimal value corresponding to the health risk factor of the i-th dimension. Any value within the range; wherein, the most ideal numerical range is used to indicate that when the i-th dimension health risk factor is within the most ideal numerical range, the probability of causing death is the lowest.

That is to say, the electronic device calculates the dimension score Pi of the i-th health risk factor (i.e. xi) based on the above-mentioned Pi calculation formula, and then looks for the most serious health risk factor. Among them, when calculating the dimension score Pi of the i-th health risk factor (i.e. xi) based on the above-mentioned Pi calculation formula, AE _fact -AE _i,worst and AE _i,tmrel -AE _i,worst are taken into account, and the accuracy will be higher .

In a possible design, the method further includes: determining whether the i-th dimensional health risk factor is within the worst value range or the most ideal value range; if it is determined that the i-th dimensional health risk factor is within the worst value range or the most ideal value range; If it is outside the worst numerical range and the most ideal numerical range, then the dimension score Pi of the i-th dimension health risk factor satisfies the above formula; if the i-th dimension health risk factor is within the worst numerical range Within, the dimension score Pi of the i-th health risk factor is the lowest score; if the i-th health risk factor is within the optimal numerical range, the dimension score Pi of the i-th health risk factor is the highest point.

That is to say, if xi is outside the worst value range and the most ideal value range, use the above-mentioned Pi calculation formula to calculate the dimension score Pi of the i-th health risk factor (i.e. xi). When the i-th dimensional health risk factor (ie, xi) is in the worst value range, the dimension score Pi of the i-th dimensional health risk factor is the lowest score; for example, 0 points. When the i-th health risk factor (i.e., xi) is within the optimal value range, the dimension score Pi of the i-th health risk factor is the highest score, for example, 100 points. In this way, more accurate dimensional scores can be obtained, and the most serious health risk factors for users can be accurately found, and precise lifestyle intervention can be carried out for users.

In a second aspect, an electronic device is also provided, including:

processor, memory, and, one or more programs;

Wherein, the one or more programs are stored in the memory, and the one or more programs include instructions that, when executed by the processor, cause the electronic device to perform the first aspect as described above. The method steps described.

In the third aspect, a system is also provided, including:

A terminal, configured to collect user data and provide the user data to the server, where the user data includes at least one of the user metabolic data, behavioral data and environmental data;

A server, configured to execute the method described in the first aspect above.

In a fourth aspect, a computer-readable storage medium is also provided. The computer-readable storage medium is used to store a computer program. When the computer program is run on a computer, it causes the computer to execute as described in the first aspect. Methods.

In a fifth aspect, a computer program product is also provided, including a computer program, which when the computer program is run on a computer, causes the computer to execute the method steps described in the first aspect.

In a sixth aspect, embodiments of the present application further provide a chip, which is coupled to a memory in an electronic device and used to call a computer program stored in the memory and execute the technical solution of the first aspect of the embodiment of the present application. The present application implements In this example, "coupled" means that two components are combined with each other, either directly or indirectly.

For the beneficial effects of the above second to sixth aspects, please refer to the beneficial effects of the first aspect and will not be repeated.

Description of drawings

Figure 1 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

Figure 2A is another structural schematic diagram of an electronic device provided by an embodiment of the present application;

Figure 2B is another structural schematic diagram of an electronic device provided by an embodiment of the present application;

Figure 3 is a schematic flow chart of a health assessment method provided by an embodiment of the present application;

Figure 4 is a schematic diagram of user data provided by an embodiment of the present application;

Figure 5 is a schematic diagram of multi-dimensional health risk factors and comprehensive health scores provided by an embodiment of the present application;

Figure 6 is a schematic diagram of the relationship between behavioral classes and metabolic classes provided by an embodiment of the present application;

Figure 7 is another schematic flow chart of a health assessment method provided by an embodiment of the present application;

Figure 8 is another schematic diagram of an electronic device provided by an embodiment of the present application;

FIG. 9 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In the following, some terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

The at least one involved in the embodiments of this application includes one or more; where multiple means greater than or equal to two. In addition, it should be understood that in the description of this specification, words such as "first" and "second" are only used for the purpose of distinguishing the description, and cannot be understood to express or imply relative importance, nor can they be understood to express Or suggestive order. For example, the first device and the second device do not represent the importance of the two or the order of the two, but are only used to differentiate the description. In the embodiment of this application, "and/or" only describes the association relationship, indicating that three relationships can exist, for example, A and/or B, which can mean: A exists alone, A and B exist simultaneously, and B exists alone. these three situations. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship.

Reference in this specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the specification. Therefore, the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in other embodiments", etc. appearing in different places in this specification are not necessarily References are made to the same embodiment, but rather to "one or more but not all embodiments" unless specifically stated otherwise. The terms “including,” “includes,” “having,” and variations thereof all mean “including but not limited to,” unless otherwise specifically emphasized.

The health assessment method provided by the embodiments of this application is suitable for electronic devices. For example, the electronic device can be a portable electronic device such as a mobile phone, a tablet computer, or a laptop; it can also be a wearable device such as a watch, a bracelet; or it can be a smart home device such as a television, a refrigerator; or it can be It can be a vehicle-mounted device, etc., or it can also be a virtual reality (Virtual Reality, VR) device, an augmented reality (Augmented Reality, AR) device, a mixed reality technology (Mixed Reality, MR) device, etc., in short, the embodiment of the present application The specific type of electronic equipment is not limited.

In some examples, the health assessment method provided by embodiments of the present application may be a function, service or application in an electronic device. The application may be a built-in application of the electronic device or an application downloaded from the Internet. Taking a Huawei mobile phone as an example, the Huawei mobile phone includes a health application, and the application integrates a function to perform health assessment on the user through the health assessment method provided in the embodiments of this application.

The health assessment method provided by the embodiment of the present application can also be applied to a system, which includes a first device and a second device. The first device is connected to the second device. The first device may be a device used by the user to collect user data, such as a wearable device such as a watch or bracelet; or a portable device such as a mobile phone or tablet computer (for example, collecting user data through a questionnaire). The first device may send the collected user data to the second device. The second device may use user data to execute the health assessment process provided by the embodiments of this application. The specific process will be introduced later. For example, the second device may be any device, for example, it may be a device with strong computing capabilities, such as a server. It should be understood that the following process (for example, the process shown in Figure 3) includes multiple steps, some of which can be performed by the first device, and the remaining steps can be performed by the second device. Specifically, which steps are performed by the first device? Which steps are executed by one device and which steps are executed by the second device are not limited by the embodiments of this application. In short, the health assessment method provided by the embodiments of this application can be completed by a device alone or by a system. In order to facilitate understanding, the following description mainly takes the example of a single device completing the process.

Figure 1 shows a schematic structural diagram of an electronic device. The electronic device may be a mobile phone, etc. As shown in Figure 1, the electronic device may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, and a battery 142. Antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone interface 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, Display 194, and subscriber identification module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light. Sensor 180L, bone conduction sensor 180M, etc.

The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, memory, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU) wait. Among them, different processing units can be independent devices or integrated in one or more processors. Among them, the controller can be the nerve center and command center of the electronic device. The controller can generate operation control signals based on the instruction operation code and timing signals to complete the control of fetching and executing instructions. The processor 110 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in processor 110 is cache memory. This memory may hold instructions or data that have been recently used or recycled by processor 110 . If the processor 110 needs to use the instructions or data again, it can be called directly from the memory. Repeated access is avoided and the waiting time of the processor 110 is reduced, thus improving the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces. Interfaces may include integrated circuit (inter-integrated circuit, I2C) interface, integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, pulse code modulation (pulse code modulation, PCM) interface, universal asynchronous receiver and transmitter (universal asynchronous receiver/transmitter (UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and /or universal serial bus (USB) interface, etc.

The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (derail clock line, SCL). In some embodiments, processor 110 may include multiple sets of I2C buses. The processor 110 can separately couple the touch sensor 180K, charger, flash, camera 193, etc. through different I2C bus interfaces. For example, the processor 110 can be coupled to the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100 .

The I2S interface can be used for audio communication. In some embodiments, processor 110 may include multiple sets of I2S buses. The processor 110 can be coupled with the audio module 170 through the I2S bus to implement communication between the processor 110 and the audio module 170 . In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the I2S interface to implement the function of answering calls through a Bluetooth headset.

The PCM interface can also be used for audio communications to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface to implement the function of answering calls through a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is generally used to connect the processor 110 and the wireless communication module 160 . For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to implement the Bluetooth function. In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the UART interface to implement the function of playing music through a Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 . MIPI interfaces include camera serial interface (CSI), display serial interface (DSI), etc. In some embodiments, the processor 110 and the camera 193 communicate through the CSI interface to implement the shooting function of the electronic device 100 . The processor 110 and the display screen 194 communicate through the DSI interface to implement the display function of the electronic device 100 .

The GPIO interface can be configured through software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with the camera 193, display screen 194, wireless communication module 160, audio module 170, sensor module 180, etc. The GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface that complies with the USB standard specification, and may be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones to play audio through them. This interface can also be used to connect other electronic devices, such as AR devices, etc.

It can be understood that the interface connection relationships between the modules illustrated in the embodiment of the present invention are only schematic illustrations and do not constitute a structural limitation of the electronic device 100 . In other embodiments of the present application, the electronic device 100 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

The wireless communication function of the electronic device can be realized through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor. Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in an electronic device can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be reused as a diversity antenna for a wireless LAN. In other embodiments, antennas may be used in conjunction with tuning switches.

The mobile communication module 150 can provide wireless communication solutions including 2G/3G/4G/5G applied to electronic devices. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves through the antenna 1, perform filtering, amplification and other processing on the received electromagnetic waves, and transmit them to the modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves through the antenna 1 for radiation. In some embodiments, at least part of the functional modules of the mobile communication module 150 may be disposed in the processor 110 . In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.

The wireless communication module 160 can provide wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) network), Bluetooth (BT), and global navigation satellite systems for use in electronic devices. (global navigation satellite system, GNSS), frequency modulation (FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2 , frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 . The wireless communication module 160 can also receive the signal to be sent from the processor 110, frequency modulate it, amplify it, and convert it into electromagnetic waves through the antenna 2 for radiation.

In some embodiments, the antenna 1 of the electronic device is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the electronic device can communicate with the network and other devices through wireless communication technology.

The display screen 194 is used to display the display interface of the application, etc. Display 194 includes a display panel. In some embodiments, the electronic device may include 1 or N display screens 194, where N is a positive integer greater than 1.

The electronic device 100 can implement the shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like. Among them, the ISP is used to process the data fed back by the camera 193.

Internal memory 121 may be used to store computer executable program code, which includes instructions. The processor 110 executes instructions stored in the internal memory 121 to execute various functional applications and data processing of the electronic device. The internal memory 121 may include a program storage area and a data storage area. The stored program area can store an operating system, software code of at least one application program, etc. The storage data area can store data (such as images, videos, etc.) generated during the use of the electronic device. In addition, the internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, general-purpose flash memory, etc.

The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor 110 through the external memory interface 120 to implement the data storage function. For example, save pictures, videos, etc. files on an external memory card.

The electronic device can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone interface 170D, and the application processor. Such as music playback, recording, etc.

The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. Audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110 , or some functional modules of the audio module 170 may be provided in the processor 110 .

Speaker 170A, also called "speaker", is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music through one or more speakers 170A, or listen to external playback scenarios such as hands-free calls.

The receiver 170B, also called "earpiece", may be one or more and is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a call or a voice message, the voice can be heard by bringing the receiver 170B close to the human ear.

Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals.

The headphone interface 170D is used to connect wired headphones.

The pressure sensor 180A is used to sense pressure signals and can convert the pressure signals into electrical signals. In some embodiments, pressure sensor 180A may be disposed on display screen 194 .

The gyro sensor 180B can be used to determine the motion posture of the electronic device. In some embodiments, the angular velocity of the electronic device about three axes (ie, x, y, and z axes) may be determined by gyro sensor 180B. The gyro sensor 180B can be used for image stabilization.

Air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device calculates the altitude through the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.

Magnetic sensor 180D includes a Hall sensor. The electronic device can use the magnetic sensor 180D to detect the opening and closing of the flip holster.

The acceleration sensor 180E can detect the acceleration of the electronic device in various directions (generally three axes). When the electronic device is stationary, the magnitude and direction of gravity can be detected.

Distance sensor 180F for measuring distance. Electronic devices can measure distance via infrared or laser.

Proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. Electronic devices emit infrared light through light-emitting diodes. Electronic devices use photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device. When insufficient reflected light is detected, the electronic device can determine that there is no object near the electronic device.

The ambient light sensor 180L is used to sense ambient light brightness. The electronic device can adaptively adjust the brightness of the display screen 194 based on perceived ambient light brightness.

Fingerprint sensor 180H is used to collect fingerprints.

Temperature sensor 180J is used to detect temperature.

Touch sensor 180K, also called "touch panel". The touch sensor 180K can be disposed on the display screen 194. The touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is used to detect a touch operation on or near the touch sensor 180K. The touch sensor can pass the detected touch operation to the application processor to determine the touch event type.

Bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human body's vocal part.

The buttons 190 include a power button, a volume button, etc. Key 190 may be a mechanical key. It can also be a touch button. The electronic device can receive key input and generate key signal input related to user settings and function control of the electronic device. The motor 191 can generate vibration prompts. The motor 191 can be used for vibration prompts for incoming calls and can also be used for touch vibration feedback. The indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, or may be used to indicate messages, missed calls, notifications, etc. The SIM card interface 195 is used to connect a SIM card. The SIM card can be inserted into the SIM card interface 195 or pulled out from the SIM card interface 195 to achieve contact and separation from the electronic device.

It can be understood that the components shown in Figure 1 do not constitute a specific limitation to the electronic device. The electronic device in the embodiment of the present invention may include more or fewer components than in FIG. 1 . In addition, the combination/connection relationship between the components in Figure 1 can also be adjusted and modified.

2A and 2B are software structure diagrams of electronic devices provided by embodiments of the present application.

As shown in Figure 2A, the electronic device includes four systems/modules, namely: user data collection and processing system 00, individual life prediction system 01, comprehensive health scoring system 02, and intervention plan management system 03.

User data collection and processing system 00 is used to collect user data. User data includes basic user information, metabolic data, behavioral data, environmental data, etc. Among them, the user's basic information includes the user's age, gender, city where they are located, whether they are office workers, whether they have exercise and fitness habits, nature of work, etc. Metabolic data includes user blood pressure, blood sugar, blood lipids, body mass index (BMI), etc. Behavioral data includes users’ smoking, drinking, diet, sleep, exercise, stress and other data. Environmental data includes PM2.5, humidity, temperature, etc. Among them, the data collection process of the user data collection and processing system 00 will be introduced later.

Individual life span prediction system 01 is used to predict the user's life span based on user data. The specific prediction process will be introduced later.

Comprehensive health scoring system 02 is used to comprehensively evaluate the user's health status. The specific evaluation process will be introduced later.

The intervention plan management system 03 is used to formulate a lifestyle intervention plan for the user to facilitate the user to implement the lifestyle intervention plan and improve the user's bad living habits.

Figure 2B can be understood as a refinement of Figure 2A. Specifically, it is a refinement of the four systems in Figure 2A (i.e. user data collection and processing system 00, individual life prediction system 01, comprehensive health scoring system 02, intervention plan management Respective refinement of system 03).

As shown in Figure 2B, the user data collection and processing system 00 includes three modules/units, namely:

The device data collection interface 001 can be responsible for accessing other devices to obtain user data collected by other devices. The other devices may be, for example, fitness-related devices, such as wearable devices such as sports bracelets and watches, or portable home testing devices such as body fat scales, blood pressure monitors, blood glucose meters, and blood lipid detectors. Among them, wearable devices can collect user behavior data, including exercise dimensions (daily steps, exercise type, exercise intensity, exercise time, exercise frequency, exercise heart rate, etc.), sleep dimensions (time to fall asleep, night sleep duration, deep sleep time, etc.) Sleep time, light sleep time, etc.), pressure dimensions (pressure value, pressure level) and other data. Portable home testing equipment can collect user metabolic data, including weight dimensions (BMI, body composition), blood pressure dimensions (systolic blood pressure, diastolic blood pressure), blood glucose dimensions (fasting blood glucose values) and blood lipid dimensions (low-density lipoprotein cholesterol, total cholesterol )wait.

It should be understood that some user data can be obtained through the device data collection interface 001, and some user data (such as smoking habits, eating habits, drinking habits, etc.) cannot be obtained through the device data collection interface 001. Such user data can be obtained through the health questionnaire module. 002 to obtain.

The health questionnaire module 002 mainly obtains user data in the form of a questionnaire. The questionnaire can be an electronic version sent to the user's electronic device (such as a mobile phone) or a paper version sent to the user to fill in. For example, the health questionnaire module 002 can obtain, for example, smoking dimensions (whether you smoke, years of smoking, average daily smoking volume, years of quitting smoking), drinking dimensions (whether you drink alcohol, average daily alcohol intake), diet dimensions ( Multi-dimensional user data such as daily salt intake, daily red meat intake, daily vegetable intake, and daily fruit intake).

The data processing module 003 is connected to the device data collection interface 001 and the health questionnaire module 002 respectively, and is used to obtain the user data collected by the device data collection interface 001 and the health questionnaire module 002 respectively, and preprocess the user data. The preprocessing may include: converting multi-dimensional raw data (ie, user data collected respectively by the device data collection interface 001 and the health questionnaire module 002) into multi-dimensional health risk factors.

For example, the device data collection interface 001 obtains user behavior data through wearable devices, which includes raw data of daily steps, exercise intensity, exercise time, exercise frequency, exercise heart rate and other exercise dimensions. After acquiring the original data, the data processing module 003 converts the original data into exercise-dimensional health risk factors, for example, converts the original data into a week's total physical activity (METs/week), as exercise-dimensional health risks. factor. Of course, other parameters (for example, the total amount of physical activity in a day) can also be used as health risk factors in the exercise dimension, which are not limited in the embodiments of this application.

For another example, the device data collection interface 001 obtains user behavior data through wearable devices, including original data of sleep dimensions such as sleep time, night sleep duration, deep sleep time, light sleep time, etc. After obtaining the original data, the data processing module 003 converts the original data into the average total sleep duration per day, and uses the average total sleep duration per day as the health risk factor of the sleep dimension. Of course, other parameters (for example, the average total duration of deep sleep per day) can also be used as health risk factors in the sleep dimension, which are not limited by the embodiments of this application.

For another example, the health questionnaire module 002 obtains user behavior data, including raw data such as daily salt intake. After obtaining the original data, the data processing module 003 converts the original data into grams of average daily salt intake, and uses the average daily grams of salt intake as a health risk factor in the dietary dimension. Of course, other parameters (such as average daily red meat intake) can also be used as health risk factors in the dietary dimension, which are not limited by the embodiments of this application.

It should be noted that in some examples, the health risk factors of each dimension can also be refined. Taking the health risk factors of the exercise dimension as an example, they can be refined into health risk factors of the exercise heart rate dimension (for example, average weekly exercise heart rate), and health risk factors in the dimension of exercise steps (for example, average number of exercise steps per week), etc. Taking health risk factors in the sleep dimension as an example, they can also be refined into health risk factors in the deep sleep duration dimension (for example, the average duration of deep sleep per day), and health risk factors in the light sleep duration dimension (for example, the average duration of light sleep per day). ). Taking the health risk factors of the diet dimension as an example, it can also be refined into the health risk factors of the salt intake dimension (for example, the average daily salt intake) and the health risk factors of the red meat intake dimension (for example, the average daily salt intake). red meat intake) and health risk factors along the fruit intake dimension (e.g., average daily fruit intake). In short, this article takes the health risk factors of the exercise dimension, the health risk factors of the sleep dimension, and the health risk factors of the diet dimension as examples. However, each of these dimensions can be refined into more detailed health risk factors. This article The application examples are not limiting.

Therefore, the data processing module 003 can obtain multi-dimensional health risk factors. It is assumed that the multi-dimensional health risk factors are represented by [x ₁ , x ₂ , x ₃ ,..., _xi ,..., x _n ]. For example, x1 is a health risk factor in the exercise dimension (such as METs/week), x2 is a health risk factor in the sleep dimension, x3 is a health risk factor in the diet dimension, and so on.

In summary, the user data collection and processing system 00 in Figure 2B has obtained two types of data, one is the user's basic information (gender, age, city, etc.), and the other is multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,…,x _i ,…,x _n ]. The user data collection and processing system 00 can send these two types of data to the individual life prediction system 01 for life prediction.

As shown in Figure 2B, the individual life span prediction system 01 includes an individual life span prediction model, which includes two units/modules: a model parameter database 011 and an individual life span prediction algorithm 012.

The model parameter database 011, referred to as the database, is used to store the model parameters that the individual life prediction algorithm 012 relies on when performing life prediction. For example, these parameters come from national population death cause monitoring data and/or health risk factor monitoring data, etc., and may be stored in the device in advance. The specific parameters contained in model parameter database 011 and how to use them will be introduced later.

The individual life span prediction algorithm 012 is used to determine the model parameter data of the corresponding group of people in the model parameter database 011 based on the basic user information (age, gender, city, etc.) sent by the user data collection and processing system 00, and then, based on the determined The model parameters and the multi-dimensional health risk factors sent by the user data collection and processing system 00 run the individual life span prediction algorithm 012 to predict the user's life span. The specific implementation process will be introduced later.

As shown in Figure 2B, the comprehensive health scoring system 02 includes 2 units/modules, namely: dimensional scoring module 021 and comprehensive scoring module 022.

The dimensional score module 021 is used to calculate the dimensional score of each dimensional health risk factor based on the life span predicted by the individual life span prediction system 01, for example, multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,…, x _i , _… , _{x n} ], _{the dimension score corresponding to} dimension score, and so on.

The comprehensive scoring module 022 is used to obtain a comprehensive health score based on the dimensional scores of health risk factors in each dimension, that is, [P ₁ , P ₂ , P ₃ ,..., _Pi ,..., P _n ]. The comprehensive health score can reflect the overall user health level.

As shown in Figure 2B, the intervention plan management system 03 includes two units/modules, namely: the intervention plan management module 031, and the comprehensive score prediction module 032.

The intervention plan management module 031 is used to rank the risk levels of each health risk factor according to the dimension score of each dimension and the comprehensive health score, obtain the risk factor that has the greatest negative impact on health, and use this risk factor as the need for user intervention health management goals and develop lifestyle intervention plans to improve the risk factors.

The comprehensive score prediction module 032 is used to predict the improvement effect of the lifestyle intervention plan formulated by the intervention plan management module 031 on risk factors, and predict the changing trend of the comprehensive health score.

The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

For example, please refer to Figure 3, which is a schematic flow chart of a health assessment method provided by an embodiment of the present application. This method can be applied to electronic devices or systems, as described above. The process includes:

S301, collect user data.

For example, the collection of user data can be performed by the user data collection and processing system 00 in Figure 2A or Figure 2B. For details, please refer to the relevant description of Figure 2A or Figure 2B.

For ease of understanding, please refer to Figure 4, which is a schematic diagram of user data provided by an embodiment of the present application. As shown in Figure 4, user data includes: at least one of user basic information, user metabolism data, user behavior data, and user environment data. Each type of data is introduced below.

(1) User’s basic information, including the user’s age, gender, city where they are located, whether they are office workers, whether they have exercise and fitness habits, nature of work, etc. The user's basic information may be obtained through a questionnaire, and the questionnaire may be displayed to the user in electronic form for the user to fill in.

(2) User metabolic data, including multiple dimensional data related to user metabolism, such as blood pressure dimension, blood sugar dimension, blood lipid dimension, and BMI dimension. Parameters in these dimensions can be measured through body fat scale, blood pressure monitor, blood glucose meter, and blood lipids. Detectors and other portable home testing equipment were obtained.

(3) User behavior data, including data in multiple dimensions related to user behavior, such as dietary dimensions (for example, fruit intake dimensions, salt intake dimensions, red meat intake dimensions, etc.), smoking Dimensions (smoking years, average daily smoking dimensions), drinking dimensions (for example, daily alcohol consumption), exercise dimensions (daily steps, exercise type, exercise intensity, exercise time, exercise frequency, exercise heart rate, etc.), sleep Dimensions (time to fall asleep, night sleep duration, deep sleep time, light sleep time, etc.).

(4) User environment data, including PM2.5, ambient humidity, ambient temperature and other dimensions of data.

Therefore, the electronic device obtains user data of multiple dimensions through S1, and the user data of these dimensions can be called multi-dimensional original data.

S302: Preprocess user data to obtain multi-dimensional health risk factors.

It should be noted that the user data collected in S301 includes multi-dimensional raw data, and S302 can preprocess the multi-dimensional raw data to obtain multi-dimensional health risk factors.

For example, if the user behavior data includes the original data of the exercise dimension (daily steps, exercise intensity, exercise time, exercise frequency, exercise heart rate, etc.), then S302 converts the original data of this dimension into health risk factors of the exercise dimension. . Exemplarily, the processing method includes: converting the original data (daily steps, exercise intensity, exercise time, exercise frequency, exercise heart rate, etc.) into a week's total physical activity (METs/week), as the health risk of the exercise dimension data. The specific conversion process will not be described in detail in this application.

For another example, the user behavior data includes raw data of the sleep dimension (time to fall asleep, sleep duration at night, etc.). S302 processes the original data into an average total daily sleep duration, and uses the average daily total sleep duration as a health risk factor in the sleep dimension.

For another example, user behavior data includes raw data of dietary dimensions (such as daily salt intake, etc.). S302 processes the raw data into an average daily intake, and uses the average daily intake as a health risk factor in the dietary dimension.

Therefore, electronic devices can obtain multi-dimensional health risk factors through S302. For example, [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] are used to represent multi-dimensional health risk factors. For example, x1 is a health risk factor in the exercise dimension (e.g., METs/week), x2 is a health risk factor in the sleep dimension (e.g., average total sleep time per day), and x3 is a health risk factor in the diet dimension (e.g., average daily salt intake quantity), etc.

S303: Calculate the dimension score corresponding to the health risk factor in each dimension.

For example, S303 may be executed by the comprehensive health scoring system 02 in Figure 2A or Figure 2B. In order to facilitate understanding, [x ₁ ,x ₂ ,x ₃ ,…, _xi ,…,x _n ] represents multi-dimensional health risk factors, n is the total number of dimensions; [P ₁ ,P ₂ ,P ₃ ,… ,P _i ,…,P _n ] represents the dimensional score of multi-dimensional health risk factors. Among them, P ₁ is the dimension score corresponding to the health risk factor of the x1 dimension (such as the health risk factor of the exercise dimension); P ₂ is the dimension corresponding to the health risk factor of the x2 dimension (such as the health risk factor of the sleep dimension). Rating, and so on.

The following takes xi (i is any value from 1 to n) in [x ₁ , x ₂ , x ₃ ,…, _xi ,…,x _n ] as an example to introduce the calculation process of Pi corresponding to xi. For example, the calculation method of Pi includes at least one of the following two methods:

method one

Method 1 includes the following methods 1.1 and 1.2.

Method 1.1: The electronic device (such as the dimension scoring module 021 in Figure 2B) stores the corresponding relationship between the value of each dimension and the score. The electronic device (such as the dimension scoring module 021 in Figure 2B) based on the corresponding relationship and multiple Dimension health risk factors [x ₁ ,x ₂ ,x ₃ ,…, _{xi ,} …,x _n ], get [P ₁ ,P ₂ ,P ₃ ,…,P _i ,…,P _n ].

For example, the corresponding relationship is as follows in Table 1:

Table 1: Correspondence between dimension values and scores (applicable group: female gender, age 30)

Assume that xi corresponds to the motion dimension. After the electronic device obtains the value of xi (for example, 800METs/week), it can query the dimensional range of the value of xi in the motion dimension item in Table 1 above. For example, the value of xi If the value is in the range of 600-4200METs/week, then the dimension score of xi is determined to be 60 points. It should be noted that Table 1 is an example. In actual applications, Table 1 can be more detailed. For example, the dimension range and the score corresponding to each dimension range can be more detailed. In some examples, the correspondence relationship shown in Table 1 may be stored in the electronic device in advance, or may be set by the user, which is not limited by the embodiments of this application.

Therefore, the multi-dimensional scores [P ₁ , P 2 , P ₃ , corresponding to the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] can be obtained through the above Table ₁ . …,P _i ,…,P _n ].

Method 1.2: Before the electronic device adopts the above method 1.1, you can also perform the following steps: find the target group that matches the user based on the user's basic information (age, gender, city, etc.), and based on the dimension value corresponding to the target group and Correspondence between scores, implementation method 1.1.

It should be noted that the correspondence between the dimension values and scores corresponding to various groups can be stored in the electronic device. For example, the above Table 1 is the corresponding relationship for the target group of female gender and age 30. If the target group is male and age 50, the corresponding relationship is as shown in Table 2 below:

Table 2: Correspondence between dimension values and scores (applicable group: male, age 50)

Comparing the above Table 1 and Table 2, we can see that the corresponding relationship between the dimension values and scores corresponding to different groups of people is different. Therefore, before using method 1.1, first find the corresponding dimensions based on the user's basic information (age, gender, etc.) The correspondence between values and scores, for example, first determine whether to use the above Table 1 or Table 2 based on the user's basic information, and then based on the found correspondence and multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,…, _xi ,…,x _n ], determine the multi-dimensional score [P ₁ ,P ₂ ,P ₃ ,…,P _i ,…,P _n ]. Therefore, method 1.2 is more accurate than method 1.1.

Method 2

Method 2 includes method 2.1 and method 2.2.

Method 2.1: A model parameter set (abbreviation: parameter set) is stored in the electronic device (such as the model parameter database 011 in Figure 2B). The parameter set includes: Parameter 1: Group mortality rate (Mortality Rate) of a certain cause of death disease o ,MR _o ). Parameter 2: The optimal value range and the worst value range corresponding to each dimension of health risk factors. Parameter 3: Population Attributable Fraction (PAF). Parameter 4: Risk factor mediation effect weight (Mediation Factor, MF). Parameter 5: Relative Risk (RR) of risk factors. Parameters 1 to 5 will be introduced later. Therefore, way 2.1 includes: the electronic device (such as the individual life prediction algorithm 012 in Figure 2B) can predict the user's life expectancy based on [x1, x2, x3,...,xi,...,xn] and the parameter set in the parameter model database 011 Lifespan (the prediction process will be introduced later), and then the dimension scoring module 021 uses the predicted lifespan to obtain a multi-dimensional score [P ₁ , P ₂ , P ₃ ,…, _Pi ,…, P _n ].

For example, method 2.1 includes the following steps 1 to 5:

Step 1: Input [x1, x2, x3,…,xi,…,xn] and the parameter set in the parameter model database 011 into the individual life prediction algorithm 012 to obtain the user’s actual life AE _fact . Among them, the calculation process of the individual life span prediction algorithm will be introduced later.

Step 2: Determine the xi _,tmrel and xi _,worst corresponding to xi.

In some examples, the health risk factor (ie, xi) of each dimension has an optimal value range and a worst value range. Among them, the optimal numerical range can be understood as, when the value of the health risk factor is within this numerical range, the user's body has the smallest risk of disease death attributed to the risk factor, that is, the user's body is relatively healthy and the probability of disease is low. In the same way, the worst numerical range can be understood as, when the value of a health risk factor is within this numerical range, the user's body has the greatest risk of disease death attributed to the risk factor, that is, the user is unhealthy and has a higher probability of disease.

Take xi as a health risk factor in the exercise dimension as an example. The health risk factor in the exercise dimension is, for example, the total amount of physical activity per week (unit: METs/week). It can be understood that when a person's total weekly exercise reaches a certain amount (i.e., the optimal value range), it is good for the body and the possibility of disease risk exposure is low (for example, the probability of getting sick is reduced). For example, the optimal value range of the sports dimension is 4200 METs/week or more. On the contrary, when the total amount of weekly exercise is below a certain amount (i.e., the worst value range), the possibility of disease risk exposure is higher (for example, the chance of getting sick is increased). For example, the worst value range of the sports dimension is 0-600 METs/week.

Take xi as a health risk factor in the sleep dimension as an example. The health risk factor in the sleep dimension is, for example, the average sleep duration per day. It is understandable that when a person's average daily sleep duration is within a certain range (i.e., the optimal value range), it is good for the body and the possibility of disease risk exposure is low. For example, the optimal value range for the sleep dimension is 7-9h/day. On the contrary, when the average daily sleep duration is below or above a certain amount (i.e., the worst value range), the likelihood of disease risk exposure is higher. For example, the worst value ranges of the sleep dimension are 0-5h/day and 10+h/day.

Therefore, the health risk factors (i.e., xi) of each dimension have an optimal value range and a worst value range.

Among them, x _i,tmrel can be any value within the optimal value range corresponding to the i-th dimension. Taking the i-th dimension as a sports dimension, whose health risk factors include METs/week as an example, and assuming that the optimal value range of the sports dimension is above 4200 METs/week, then x _i,tmrel can be any value within this range.

x _i,worst can be any value within the worst value range corresponding to the i-th dimension. Taking the i-th dimension as an example of the sports dimension, its health risk factors include METs/week, and assuming that the worst value range of the sports dimension is 0-600METs/week, then x _i,worst can be in the range of 0-600METs/week. any value.

As mentioned above, a parameter set is stored in the electronic device (such as the model parameter database 011 in FIG. 2B ). The parameter set includes parameter 2, that is, the optimal value range and the worst value range corresponding to each dimension of health risk factors. Therefore, step 2 can determine xi _,tmrel and _xi,worst corresponding to xi in the parameter set.

Step 3, set [x1,x2,x3,…,xi,…,xn] to [x1,x2,x3,…,xi _,tmrel ,…,xn], set [x1,x2,x3,…, x _i,tmrel ,…,xn] and the parameter set in the parameter model database 011, input the individual life span prediction model to obtain AE _i,tmrel .

Step 4, set [x1,x2,x3,…,xi,…,xn] to [x1,x2,x3,…,xi _,worst ,…,xn], set [x1,x2,x3,…, x _i,worst ,…,xn] and the parameter set in the parameter model database 011, input the individual life span prediction model to obtain AE _i,worst .

Therefore, for the health risk factors of the dimension xi, three life span values can be obtained, including: AE _fact , AE _i,tmrel , AE _i,worst , and then perform step 5.

Step 5: Calculate Pi corresponding to xi through these three life prediction values AE _fact , AE _i,tmrel and AE _i,worst .

Illustratively, Pi satisfies the following formula: Pi=(AE _fact -AE _i,worst )/(AE _i,tmrel -AE _i,worst )×100.

Method 2.2: The electronic device (such as model parameter database 011 in Figure 2B) includes parameter sets corresponding to different groups. For example, group 1 (age 30, female) corresponds to parameter set 1, which includes parameter 1 to parameter 5 (such as Group 2 (age 50, male) corresponds to parameter set 2, which includes parameter 1 to parameter 5 (as mentioned above), but the same parameters in parameter set 1 and parameter set 2 (for example, parameter 1: MRo) have different values. Therefore, before the electronic device adopts the above method 2.1, it can also perform the following steps: determine the target group matching the user based on the user's basic information, determine the parameter set corresponding to the target group in the model parameter database 011, and based on the parameters corresponding to the target group Collection, execution mode 2.1. For example, the electronic device (such as the individual life span prediction algorithm 012 in Figure 2B) can predict the user's life span based on [x1, x2, x3,...,xi,...,xn] and the parameter set corresponding to the target group in the parameter model database 011 (The prediction process will be introduced later), and then the dimension scoring module 021 uses the predicted life span to obtain the multi-dimensional score [P ₁ , P ₂ , P ₃ ,…, _Pi ,…, P _n ]. The specific implementation process is the same as the principle of method 2.1 and will not be repeated.

Method three

Different from the second method, the third method adds a step before step 1 in the second method: determine the value range of xi. If xi is within the ideal value range corresponding to the i-th dimension, the score is the highest score, such as Pi =100. If xi is in the worst value range corresponding to the i-th dimension, the score is the lowest score. For example, Pi = 0. If xi is between the best value range and the worst value range, perform step 1 in method 2. Go to step 5 to get Pi. That is to say, in the second method, when xi takes any value, Pi is obtained through steps 1 to 5. In the third method, when xi is between the optimal numerical range and the worst numerical range, the second method is used to obtain Pi. (For example, step 1 to step 5 in method 2.1) obtain Pi. When xi is within the optimal value range, Pi=100, and when xi is within the worst range, Pi=0.

Therefore, through any one of the above methods one to three, for each dimension of health risk factors, a dimension score P can be obtained, that is, the multi-dimensional score corresponding to the multi-dimensional health risk factors [P1, P2, P3 ,…,Pi,…,Pn].

S304: Calculate the comprehensive score based on the dimension scores of each dimension.

For example, the comprehensive score is recorded as HI, and HI satisfies the formula:

Among them, w _i is the weight proportion of Pi dimension. For example, please refer to Figure 5, which provides the scores of each dimension and the corresponding weight proportions provided for the embodiment of the present application. For example, the weight proportion corresponding to the dimension score P1 of the BMI dimension is w1, and the dimension score P2 of the blood pressure dimension corresponds to w1. The weight proportion of is w2, and so on. Therefore, the overall health score is equal to

In some examples, in order to more conveniently display the changes in the comprehensive health score, the above formula can also be enlarged by a certain multiple (for example, 10 times as an example). For example, the comprehensive score is recorded as HI, and HI satisfies the formula:

In some embodiments, w _i can be obtained in a variety of ways. For example, w _i is stored in the device in advance and can be used by default; or it is set by the user, or w _i can also be the health of the dimension xi. The contribution of risk factors to a certain cause of death disease (such as the oth cause of death disease) (PAF*MRo), PAF and MRo will be introduced later.

S305: Determine health management goals based on the dimension scores of each dimension and/or the comprehensive health score.

In some embodiments, the health management target may be the user's most serious health risk factor, that is, the health risk factor that most requires intervention. For example, health management goals can be determined in at least one of the following ways:

Mode A, the health management target is the risk factor with the highest dimensional score among the multi-dimensional scores [P1, P2, P3,…, Pi,…, Pn]. Assume that P1 is the lowest, and x1 corresponding to P1 is the health risk factor of the sports dimension, then the health management goal is the health risk factor of the sports dimension.

Method B, the health management target is the health risk factor corresponding to the dimensional score that has the greatest negative impact on the comprehensive score among the multi-dimensional scores [P1, P2, P3,..., Pi,..., Pn].

For example, method B may include step 1: Calculate the negative impact of each dimension score on the comprehensive score. Assuming that the negative impact on the comprehensive score is represented by D, then the negative impact of the health risk factors of the i-th dimension on the comprehensive score is represented by Di and satisfies the formula: (100-Pi)*w _i . Therefore, each dimension can get a D value. For example, suppose there are two dimensions, the BMI dimension and the blood pressure dimension. Taking the BMI dimension as an example, assuming that the weight w of _{the BMI dimension} is 0.1 and the dimension score P of the BMI dimension is 80 points, then the negative impact of the BMI dimension score on the comprehensive score is D _BMI = (100-80) × 0.1 × 10=20. For another example, take the blood pressure dimension as an example. Assume that the weight w _BP of the blood pressure dimension is 0.15 and the dimension score P of the blood pressure dimension is 90 points. Then the negative impact of the dimension score of the blood pressure dimension on the comprehensive score is D _BP = (100-90 )×0.15×10=15. Step 2: Sort according to the value of each D. Continuing to take the previous example, since D _BMI > D _BP , the BMI dimension is sorted before the blood pressure dimension, that is, the negative impact of the BMI dimension on the comprehensive score is greater than the negative impact of the blood pressure dimension on the comprehensive score. Step 3: Determine the dimension corresponding to the top-ranked D as the health management goal. Continuing to take the previous example, since D _BMI ranks first, the health risk factors in the BMI dimension are health management targets.

S306, formulate a lifestyle intervention plan corresponding to health management goals.

In some embodiments, the electronic device determines a lifestyle intervention plan related to the health management goal based on the goal. Taking the BMI dimension as the health management goal as an example, lifestyle intervention plans related to BMI include: exercise and fitness plans, healthy eating plans such as limiting daily caloric intake and nutritional balance, alcohol restriction plans, sleep improvement plans, etc.

In other embodiments, the electronic device can also combine the user's basic information and health management goals to formulate a corresponding lifestyle intervention plan. For example, the user's basic information includes: gender, age, whether he is an office worker, whether he has exercise and fitness habits, nature of work, etc. Therefore, the lifestyle intervention plan formulated by combining the user's basic information and health management goals is more accurate. For example, for a user who is 35 years old, male, office worker, has no exercise habits, works in IT, and has a drinking habit, and the health management goal is BMI, the lifestyle intervention plan can include: 30 minutes of moderate-intensity jogging and aerobic exercise every day. Low-calorie and nutritionally balanced work meals, and the alcohol content of daily drinking should not exceed 25 grams. For another example, for a 65-year-old, female, retired user with exercise habits and sleep disorders, when the health management goal is blood sugar control, the lifestyle intervention plan can include: healthy recipes and consumption for blood sugar management, sleep improvement plan (meditation) , deep sleep exercises, etc.), low-intensity strength training courses.

S307, predict the comprehensive score change trend of the lifestyle intervention plan.

In the embodiment of this application, after the electronic device specifies a lifestyle intervention plan, in order to facilitate the user to follow the plan, the electronic device can provide a comprehensive score change trend. The comprehensive score change trend can be understood as, if the user follows the plan in the future When the lifestyle intervention plan is implemented, what will happen to the comprehensive score to improve the user's execution of the intervention plan?

For example, the electronic device can predict the change trend of the user's comprehensive score if the user implements the lifestyle intervention plan within a period of time in the future. The future period can be one week, two weeks, one month or two months. If it is one week or two weeks, etc., it is called the comprehensive score change trend prediction in the weekly dimension. If it is one month or two months, etc., then It is called the comprehensive score change trend prediction in the monthly dimension.

The following takes the weekly dimension as an example to illustrate the prediction process of the change trend of the comprehensive score.

Taking lifestyle intervention plans including: behavioral data such as exercise dimensions (such as 30 minutes of moderate-intensity jogging and aerobic exercise every day), diet dimensions (such as low-calorie nutritionally balanced work meals) as an example, lifestyle intervention formulated by electronic devices The behavioral data included in the plan can replace the behavioral data in the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] with the behavioral data in the lifestyle intervention plan data, other data remains unchanged. For example, in multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ], x ₁ , x ₂ , x ₃ ,..., x _i , are behavioral data, x _{i+ 1} ...,x _n is metabolic data, then replace x ₁ ,x ₂ ,x ₃ ,..., _xi with behavioral data x ₁ ^, ,x ₂ ^, ,x ₃ ^, ,..., in the lifestyle intervention plan x _i ^, therefore, the changes in multidimensional health risk factors are [x ₁ ^, ,x ₂ ^, ,x ₃ ^, ,…, _xi ^, ,…,x _n ]. This is because, considering that the user's metabolic data will not change much in the next week, the metabolic data in [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] will remain unchanged. Change, only change the behavioral data. Therefore, the electronic device obtains new multi-dimensional health risk factors [x ₁ ^, ,x ₂ ^, ,x ₃ ^, ,..., _xi ^, ,...,x _n ]. Therefore, S303 to S304 in the previous article can be executed to obtain the future Comprehensive health score for the week.

The following uses the monthly dimension as an example to illustrate the prediction process of the change trend of the comprehensive score.

Taking lifestyle intervention plans including: behavioral data such as exercise dimensions (such as 30 minutes of moderate-intensity jogging and aerobic exercise every day), diet dimensions (such as low-calorie nutritionally balanced work meals) as an example, lifestyle intervention formulated by electronic devices The behavioral data included in the plan can replace the behavioral data in the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] with the behavioral data in the lifestyle intervention plan data, and replace the metabolic data in the multidimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., _xi ,..., x _n ] with new metabolic data. In other words, the difference from the previous weekly dimension calculation process is that the weekly dimension calculation process only needs to replace behavioral data, while the monthly dimension calculation process not only needs to replace behavioral data, but also needs to replace metabolic data. This is because considering that the user's metabolism will change in the next month or more, the metabolic data needs to be updated, so the multi-dimensional health risk factors need to be replaced with new metabolic data [x ₁ , x ₂ , x ₃ ,…, _xi ,…,x _n ]. For example, please refer to Figure 6. The impact on the comprehensive score has two parts, one part is behavioral categories such as exercise, and the other part is metabolic categories such as BMI, blood pressure, blood sugar, etc., but over a longer period of time (such as one month or More than a month), metabolic data will be affected by behavioral changes. For example, if the user improves his behavior (such as exercising, sleeping, and dietary recommendations), it will have an impact on metabolism after a period of time. Therefore, the new metabolic data can be predicted based on the behavioral data included in the lifestyle intervention plan (the prediction process will be introduced later). For example, in the multidimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ], x ₁ , x ₂ , x ₃ ,..., x _i , are behavioral data, x _j ... ,x _n is metabolic data, then replace x ₁ ,x ₂ ,x ₃ ,…, _xi ,with behavioral data x ₁ ^, ,x ₂ ^, ,x ₃ ^, ,…,x in the lifestyle intervention plan _i ^, replace x _j ...,x _n with new metabolic data x _j ^, ...,x _n ^, . Therefore, the multidimensional health risk factors [x ₁ ,x ₂ ,x ₃ ,…, _xi ,…,x _n ] change to [x ₁ ^, ,x ₂ ^, ,x ₃ ^, ,…, _xi ^, ,x _j ^, …,x _n ^, ]. After the electronic device obtains the new multi-dimensional health risk factors [x ₁ ^, ,x ₂ ^, ,x ₃ ^, ,..., _xi ^, ,...,x _n ^, ], S303 to S304 in the previous article can be executed to obtain the next month comprehensive health score.

The new metabolic data mentioned above can be predicted based on the behavioral data included in the lifestyle intervention plan. Specifically, the electronic device can obtain the effect of behavioral intervention on metabolic indicators in stages (for example, 1 to 3 months) (for example, it can be obtained through a large number of randomized controlled trials), such as the effect of exercise on BMI, in the target population (age : 40 to 50 years old, BMI range: 27 to 33kg/m2, no exercise habit), 1-month, 2-month and 3-month exercise intervention (exercise duration 1.5 hours/day, exercise frequency 6 days/week, exercise The impact of intensity (60% to 70% of maximum heart rate range) on BMI was a decrease of 5%, 8% and 10% respectively. Therefore, the electronic device can predict the user's metabolic data based on the behavioral data included in the intervention plan and the phased impact of behavioral intervention on metabolic indicators.

Embodiment 2

This second embodiment provides an individual lifespan prediction method. This method can be used to implement the electronic device in the first embodiment mentioned above. The electronic device inputs [x1, x2, x3,...,xi,...,xn] into the individual lifespan prediction algorithm to obtain the user's lifespan. process. For example, please refer to FIG. 6 , which is a schematic flow chart of the life prediction method provided in this embodiment. As shown in Figure 7, the process includes:

S701, obtain user data.

S702: Preprocess user data to obtain multi-dimensional health risk factors.

S703, predict the user's lifespan based on multi-dimensional health risk factors and individual lifespan prediction models; the individual lifespan prediction model includes a model parameter database and algorithm formula, and the model parameter database includes national population death cause monitoring data and/or health risk factor monitoring data , the algorithm formula is used to predict the life span of the user based on the user data and the model parameter database.

For example, S703 can be implemented in a variety of ways, including but not limited to at least one of the following ways:

method one

For example, method one includes at least one of method 1.1 and method 1.2.

Method 1.1: The model parameter database includes: the correspondence between the values/severity levels of health risk factors in each dimension and life span. Therefore, after the electronic device obtains the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., _xi ,..., x _n ], the life span can be predicted based on the corresponding relationship. As an example, please see Table 3 below for the correspondence between health risk factors in various dimensions and life span:

Table 3: Correspondence between health risk factors and life span (applicable group: male, age 50)

After the electronic device obtains the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., _xi ,..., x _n ], for each value of x, find the dimension where the value of x is located in the above Table 1 range, and then determine the corresponding life span. Taking xi as a health risk factor in the exercise dimension as an example, assuming xi=800METs/week, that is, xi is in the range of 600-3000METs/week, then according to the above Table 3, the corresponding life span can be determined to be 80. Therefore, for each value of x, a lifespan can be obtained, that is, a total of n lifespan values can be obtained. The electronic device can obtain the final life based on n life values. For example, the final life is equal to the average or weighted average of the n lifespans.

Method 2.2: Before the electronic device adopts the above method 1.1, you can also perform the following steps: find the target group that matches the user based on the user's basic information (age, gender, city, etc.), based on the health risk factors and lifespan corresponding to the target group The correspondence between execution methods 1.1.

It should be noted that the correspondence between health risk factors and life spans corresponding to different groups can be stored in electronic devices. For example, the above Table 3 shows the corresponding relationship between health risk factors and life span for a group of male gender and age 50. If the group is female and age 30, another health risk factor is corresponding to life span. Correspondence, such as the correspondence shown in Table 4 below:

Table 4: Correspondence between health risk factors and life span (applicable group: female, age 30)

Comparing Table 3 and Table 4 above, we can see that the corresponding relationship between health risk factors and life span is different for different groups of people. Therefore, before using method 1.1, first find the corresponding health risk factors based on the user’s basic information (age, gender, etc.) Correspondence between risk factors and life span. For example, first determine whether to use the above Table 3 or Table 4 based on the user's basic information, and then based on the found correspondence and multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ], determine the life span. Therefore, method 1.2 is more accurate than method 1.1.

Method 2

The model parameter database includes a model parameter set (referred to as: parameter set). The parameter set includes the following parameters:

Parameter 1: The group mortality rate (MR _o ) of a certain cause of death disease o, used to indicate that a certain population/group (such as a group of people aged 50 and female) within a certain time period (such as 1 year), The proportion of people who died due to a certain cause of death o to the total number of deaths during that time period.

Parameter 2: The optimal numerical range and the worst numerical range corresponding to each dimension of health risk factors. Please refer to the previous introduction for the optimal numerical range and the worst numerical range. Among them, the optimal numerical range is also called the theoretical minimum (optimal) risk exposure level (Theoretical minimum risk exposure level, TMREL). The worst value range is also called the theoretical worst risk exposure level (TWREL).

Parameter 3: Population Attributable Fraction (PAF), used to indicate the proportion of the incidence of a certain disease in the total population that is attributed to a certain risk factor in a certain population/group (such as a group of people aged 50 and female) The proportion of the total incidence; or, it can also be understood as the proportion of the population that can reduce the incidence of the disease after eliminating a certain risk factor. Taking hypertensive disease as an example, the proportion of the disease that is attributed to dietary health risk factors (such as average daily salt intake) accounts for the total number of people in a certain group or the total number of people with the disease in a certain group. For the convenience of description, in this article, PAF _jo represents the PAF between health risk factor j and cause-of-death disease o, which is used to indicate that the number of cases of cause-of-death disease o attributed to health risk factor j accounts for the total number of people in a certain group or in a certain group. proportion of the total number of patients.

Parameter 4: Risk factor mediation effect weight (Mediation Factor, MF), used to indicate the role and size of the first risk factor in the causal path of the second risk factor to a certain disease. Take heart disease as an example. There are many health risk factors that cause the disease, such as exercise dimensions, BMI dimensions, etc. The role of BMI in the path of the impact of exercise on heart disease can be understood as the impact of exercise on heart disease will also have an impact on BMI. Therefore, the impact of exercise on heart disease has been included in the path. Part of the impact of BMI on heart disease. If we want to consider both the impact of the exercise dimension on heart disease and the impact of the BMI dimension on heart disease, then we need to remove the impact of the exercise dimension on the BMI dimension, that is, we need to remove the impact of the exercise dimension on the BMI dimension. Remove the correlation factor between the exercise dimension and the BMI dimension, that is, the MF of the exercise dimension and the BMI dimension. For ease of understanding, MF _ijo is expressed in this article as the MF between risk factor i and risk factor j for the cause of death disease o (such as heart disease).

Parameter 5: Relative Risk (RR) of risk factors, which is used to indicate the difference between the number of deaths among the population exposed to the risk factors and the optimal value range for a certain dimension of risk factors that cause a certain disease in a group. The proportion of deaths in the population. Take heart disease as an example. The risk factors that cause heart disease include risk factors in the sports dimension. Then among the total number of deaths in this group, the number of deaths among people with different exposure levels to the risk factors in the sports dimension is the same as the number of deaths among people within the optimal value range. The ratio of deaths, that is, for heart disease, the relative risk RR of risk factors in the exercise dimension. In order to facilitate understanding, this article expresses RR _ko as the RR of risk factor k to disease o.

The above parameters 1 to 5 may be stored in advance in the database of the electronic device (for example, the model parameter database 011 in FIG. 2B).

Specifically, method two includes method 2.1 and method 2.2.

Method 2.1: The electronic device can input the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] and the parameter set in the model parameter database into the individual life span prediction algorithm, and calculate User life. Examples include the following steps 1 to 1:

Step 1: Determine the baseline mortality rate BD _o for a certain fatal disease o in a certain group without exposure to risk factors.

Among them, no risk factor exposure can be understood as a situation where everyone in the group has no health risk factors, that is, each person's risk factors of each dimension are within the optimal value range. For example, the baseline mortality rate BD _o satisfies the following formula: BD _o =MR _o ×(1-PAF _Jo )···Formula (2)

Among them, J is the set of risk factors exposed in the group. PAF _jo represents the PAF of the i-th dimension risk factor in the risk factor set to the fatal disease o. I represents the set of risk factors associated with the risk factor j and the presence of associated factors for the fatal disease o. MF _ijo represents the MF between risk factor i and risk factor j for fatal disease o. MR _o represents the population mortality rate for fatal disease o.

Step 2: Based on the baseline mortality BD _o , determine the baseline death probability q _o of the group due to the fatal disease o in each subsequent year without risk exposure. For example, the baseline death probability q _o can satisfy the following formula:

Among them, g is the weight, for example, it can be 0.4, 0.5, 0, 6, etc. The above steps 1 and 2 calculate the baseline death probability q _o of a group due to a certain death disease o without risk exposure. It can be understood that it is assumed that each dimensional health risk factor of each person in the group is within the optimal value range (that is, it is assumed that each person in [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] x is within the optimal value range), that is, when everyone is relatively healthy, the baseline death probability q _o due to fatal disease o.

In other embodiments, the baseline death probability q _o of the fatal disease o may be stored in the model parameter database in advance and does not need to be calculated in steps 1 and 2 above.

Step 3: Predict the user’s life span based on the baseline death probability q _o and the user’s multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,…, _xi ,…, x _n ].

It can be understood that in the previous steps 1 and 2, it is assumed that each x in [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] is within the optimal numerical range. In fact, Not every value in [x ₁ ,x ₂ ,x ₃ ,…, _xi ,…,x _n ] is necessarily within the optimal value range, so step 3 is based on the actual [x ₁ ,x ₂ ,x ₃ ,…, _xi ,…,x _n ] for calculation.

Specifically, step 3 includes the following steps 3.1 to 3.4.

Step 3.1: Determine the relative risk of death RR _Ko of the multidimensional risk factors [x ₁ , x ₂ , x ₃ ,…, _xi ,…, x _n ] and the fatal disease o. For example, the relative risk of death RR _Ko can satisfy the following formula:

Among them, K is the user's health risk factor set, that is, [x ₁ , x ₂ , x ₃ ,..., _xi ,..., x _n ]; L is the set of risk factors related to the cause of death disease o and risk factor k; MF _lko is used to indicate the MF between the risk factors in the lth dimension and the risk factors in the kth dimension. It should be noted that, from the above formula (4), it can be seen that for the o-th cause of death disease, determine all dimensions in the health risk factor set [x ₁ , x ₂ , x ₃ ,..., _xi ,..., x _n ] The cumulative relative risk of death of health risk factors relative to the oth cause of death disease, and remove the MF between the risk factors of the lth dimension and the risk factors of the kth dimension from the cumulative relative risk of death, to obtain the relative risk of the user relative to the kth dimension. Relative risk of death RR _Ko for o causes of death.

Step 3.2: Based on the relative risk of death RR _Ko , calculate the user's death probability q' ₀ for each subsequent year due to the user's death disease o. For example, the death probability q′ _o can satisfy the following formula: q′ _o ＝RR _Ko ×q _o ···Formula (5)

Step 3.3: Comprehensive user's death probability q' _o for each cause of death disease, and obtain the user's all-cause death probability Q' in each subsequent year. For example, the all-cause death probability Q′ can satisfy the following formula:

Among them, M represents the death disease set, and death disease o is one of the sets.

Step 3.4: Predict the user’s life span based on the user’s all-cause death probability Q′ in the following years.

For example, assuming that the user life is represented by AE, the user life AE can satisfy the following formula: t′ _i = (1-Q′ _a )×(1-Q′ _a+1 )×(1-Q _{a +2} )×…×(1-Q′ _i-1 )×Q′ _i ×i···Formula (7)AE＝t′ _a +t′ _a+1 +t′ _a+2 +…+t′ ₁₂₀ ···Formula (8)

Among them, a is the actual age of the user, i represents the user's life span (range is a ~ 120), t′ _i represents the probability of the user living to age i multiplied by age i. Another expression of living to age i is a year ~ There is no death at age i-1 and death at age i. In formula (7), 1-Q′ _i-1 represents the probability that the user does not die at age i-1, and Q′ _i represents the probability that the user dies at age i. As shown in the following formula (8), the user life expectancy AE is obtained by accumulating t′ _a to t′ ₁₂₀ .

Method 2.2: The model parameter database includes parameter sets corresponding to different groups. Therefore, before executing method 2.1, you can also perform steps: find the target group matching the user based on the user's basic information (age, gender, city, etc.), and determine the parameter set corresponding to the target group in the model parameter database. Execution method 2.1 based on the parameter set corresponding to the target group and the individual life span prediction algorithm. For example, the electronic device can input the multi-dimensional health risk factors [x ₁ , x ₂ , x ₃ ,..., x _i ,..., x _n ] and the parameter set corresponding to the target group in the model parameter database into the individual life span prediction algorithm, and calculate Get the user lifespan.

For example, the parameter sets corresponding to each group are shown in Table 5 below:

Table 5: Parameter sets corresponding to each group

For example, the user's basic information includes the user's age of 22, male gender, and the province Anhui where he is located. Based on the above table 3, it can be determined that the group matched by the user is group 3, and the corresponding parameter set is parameter set 3, and then based on the multi-dimensional health risk factors Input to the individual life span prediction algorithm, which uses the above parameter set 3 to predict the user's life span.

Figure 8 is a schematic flow chart of a health assessment method provided by this application. As shown in Figure 8, the process includes:

S801. Obtain user data. The user data includes at least the first metabolic data and the first behavioral data among the user's first metabolic data, first behavioral data and first environment data.

Among them, regarding the first metabolic data, the first behavioral data, the first environmental data, etc., please refer to the metabolic data, behavioral data, and environmental data mentioned above, and will not be repeated here.

S802: Determine the first health state of the user based on the user data. The first health state changes with changes in at least the first metabolic data and the first behavioral data among the first metabolic data, the first behavioral data, and the first environmental data. , wherein the first metabolic data changes with changes in the first behavioral data.

The process by which the electronic device determines the first health state based on at least one of the first metabolic data, the first behavioral data, and the first environmental data has been described above and will not be repeated. When at least one of the first metabolic data, the first behavioral data, and the first environmental data changes, the first health state changes accordingly. For example, the first health state is a comprehensive health score. When at least one of the first metabolic data, the first behavioral data, and the first environmental data changes, the comprehensive health score changes as the user can see their behavior, The impact on health status when metabolism and environment are improved, and users are encouraged to actively improve their behavior, metabolism, environment, etc.

In addition, the first metabolic data changes as the first behavioral data changes. For example, when the user inputs expected indicators (including improved behavioral data), the electronic device outputs corresponding metabolic data, which can prompt the user to improve behavior to improve Metabolism (for example, blood sugar, blood lipids, blood pressure, etc.) will be introduced in detail later.

S803: Display the first health state on the display screen of the electronic device to prompt the user of the associated impact of the first metabolic data and the first behavioral data.

In other words, the electronic device can evaluate the user's health status by integrating the user's metabolism, behavior, environment, etc. (at least comprehensively the user's metabolism and behavior), and the evaluation results are relatively accurate; moreover, when the user's behavior changes, the user's health status, user's health status, etc. Metabolism changes accordingly, which can dynamically and real-time prompt the correlation between user behavior and metabolism, increase users' attention to bad behavior habits, and remind users to improve their behavior as much as possible.

For example, the first health state includes: at least one of comprehensive health score, key health risk factors, and life expectancy. Among them, the comprehensive health score is used to indicate the user's comprehensive health level. For example, taking the 100-point mechanism as an example, the comprehensive score is 80 points, 90 points, etc. Users can determine their own physical health level through the comprehensive health score. Among them, the key health risk factors are used to indicate the most serious health risk factors of the user; for example, the key health risk factors are health risk factors in the diet dimension (for example, excessive salt intake) or health risk factors in the exercise dimension ( For example, less exercise), etc., so that users can know how they need to improve their behavior. Among them, life expectancy is used to indicate the user's future life expectancy. For example, the life expectancy is 60 years, 70 years, etc. The life expectancy can increase the user's attention to bad behavior habits and remind users to improve their behavior as much as possible. Among them, the comprehensive health score, key health risk factors, life expectancy, etc. have been described previously and will not be repeated.

In order to conveniently remind the user to improve their behavior, the electronic device can output an intervention plan to guide the user based on the first health state. The intervention plan includes the user's improved behavior data. Intervention plans include, for example, exercise planning: daily exercise for 2 hours or 3 hours, diet planning: daily vegetable intake, daily salt intake, etc.

In some examples, after the electronic device outputs the intervention plan, the user can input expected indicators that include the user's improved behavior data. It can be understood that the user-improved behavioral data in the expected indicators input by the user are not necessarily the same as the behavioral data in the intervention plan. For example, the intervention plan is 3 hours of daily exercise, and the expected indicator is 2 hours of daily exercise. In this way, the electronic device can update the metabolic data (for example, the first metabolic data is updated to the second metabolic data) and the health status (for example, the first health status is updated to the second health status) based on the user improvement behavior data included in the expected indicators input by the user. ), and displays updated metabolic data and health status. For example, if the user inputs expected indicators including exercising for 3 hours a day, the electronic device will output metabolic data such as BMI 19; if the user inputs expected indicators including exercising 2 hours a day, the electronic device will output metabolic data such as BMI20. This will not only encourage the user Active exercise can also estimate the amount of exercise required to reach the metabolic level in the user's mind. For example, the user's expected BMI is 20, so the user can exercise for about 2 hours a day.

[Correction 13.10.2023 under Rule 91]
FIG. 9 is a schematic structural diagram of an electronic device 900 provided by an embodiment of the present application. The electronic device 900 may be the aforementioned electronic device such as a bracelet, a watch, etc. As shown in Figure 9, the electronic device 900 may include: one or more processors 901; one or more memories 902; a communication interface 903, and one or more computer programs 904. Each of the above devices can communicate through one or more Bus 905 connection. Where the one or more computer programs 904 are stored in the memory 902 and configured to be executed by the one or more processors 901 , the one or more computer programs 904 include instructions. For example, when the electronic device 900 is the electronic device mentioned above, the instruction can be used to perform the relevant steps of the electronic device in the above corresponding embodiments, for example, the implementation shown in any of Figure 3, Figure 7 or Figure 8 The steps in the example. The communication interface 903 is used to implement communication between the electronic device 900 and other devices. For example, the communication interface may be a transceiver.

In the above-mentioned embodiments provided by the present application, the methods provided by the embodiments of the present application are introduced from the perspective of electronic devices (such as bracelets, watches, mobile phones, and tablet computers) as execution subjects. In order to implement each function in the method provided by the above embodiments of the present application, the electronic device may include a hardware structure and/or a software module to implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether one of the above functions is performed as a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application and design constraints of the technical solution.

As used in the above embodiments, depending on the context, the terms "when" or "after" may be interpreted to mean "if..." or "after" or "in response to determining..." or "in response to detecting …”. Similarly, depending on the context, the phrase "when determining..." or "if (stated condition or event) is detected" may be interpreted to mean "if it is determined..." or "in response to determining..." or "on detecting (stated condition or event)” or “in response to detecting (stated condition or event)”. In addition, in the above embodiments, relational terms such as first and second are used to distinguish one entity from another entity, without limiting any actual relationship and order between these entities.

Reference in this specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Therefore, the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in other embodiments", etc. appearing in different places in this specification are not necessarily References are made to the same embodiment, but rather to "one or more but not all embodiments" unless specifically stated otherwise. The terms “including,” “includes,” “having,” and variations thereof all mean “including but not limited to,” unless otherwise specifically emphasized.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, e.g., the computer instructions may be transferred from a website, computer, server, or data center Transmission to another website, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), etc. As long as there is no conflict, the solutions of the above embodiments can be used in combination.

It should be noted that part of this patent application document contains content protected by copyright. The copyright owner retains copyright except in making copies of the contents of the patent document or records in the Patent Office.

Claims (15)

A health assessment method, characterized in that it is applied to electronic equipment, and the method includes: Obtaining user data, where the user data includes at least the first metabolic data and the first behavioral data among the user's first metabolic data, first behavioral data and first environmental data; According to the user data, a first health state of the user is determined, and the first health state changes with at least the first metabolic data, the first metabolic data, the first environmental data, and the first metabolic data, the first behavioral data, and the first environmental data. Changes with changes in behavioral data, wherein the first metabolic data changes with changes in the first behavioral data; The first health state is displayed on the display screen of the electronic device to prompt the user of the associated impact of the first metabolic data and the first behavioral data. The method of claim 1, wherein the first health state includes at least one of the following: Comprehensive health score, used to indicate the overall health level of the user; Critical health risk factors, indicating the most serious health risk factors for said user; Life expectancy, used to indicate the future life expectancy of the user. The method according to claim 1 or 2, characterized in that, the method further includes: Obtaining second behavior data of the user, the second behavior data is based on the user's improved behavior data; According to the second behavior data, the first metabolic data is updated to be the second metabolic data, and the first health state is updated to a second health state, wherein the second health state is based on the second behavior data and said secondary metabolic data to re-determine health status; The second health state is displayed on the display screen of the electronic device. The method according to claim 3, characterized in that before obtaining the user's second behavior data, it further includes: Based on the first health state, an intervention plan is output to guide the user, and the intervention plan includes improved behavior data of the user. The method according to claim 3, characterized in that, before obtaining the second behavior data, it further includes: Expected metrics input by the user are received, the expected metrics including improved behavior data of the user. The method of claim 2, wherein when the first health state includes life expectancy, determining the user's first health state based on the user data includes: According to the user data, the first set of health risk factors of the user is determined. The first set of health risk factors includes exercise dimensions, sleep dimensions, diet dimensions, drinking dimensions, smoking dimensions, blood pressure dimensions, blood sugar dimensions, and blood lipids. Health risk factors in at least one dimension among dimensions, BMI dimension, and environmental dimension; Predicting the user's lifespan based on the first set of health risk factors. The method of claim 6, wherein predicting the user's life span based on the first set of health risk factors includes: Determine the severity level of each dimensional health risk factor in the first health risk factor set; According to the severity level of the health risk factors in each dimension, match the life span corresponding to the health risk factors in each dimension; The user's lifespan is predicted based on the lifespan corresponding to the health risk factors in each dimension. The method of claim 6, wherein predicting the user's life span based on the first set of health risk factors includes: According to the first set of health risk factors, the baseline death probability corresponding to each of the M cause-of-death diseases of the target group and the relative risk of death of the user relative to each cause-of-death disease are determined, and the target group is the group to which the user belongs, and M is a positive integer; Determine the actual death probability of the user relative to each cause of death disease based on the baseline death probability corresponding to each cause of death disease and the user's relative risk of death relative to each cause of death disease; The user's lifespan is predicted based on the user's actual probability of death relative to each cause of death disease. The method of claim 8, wherein determining the user's relative risk of death relative to each cause of death disease based on the first set of health risk factors includes: For each cause of death disease, determine the cumulative relative risk of death of all dimensional health risk factors in the first health risk factor set relative to the disease of that cause, and remove the first health risk factor from the cumulative relative risk of death and the second health risk factor The mediating effect weight MF of the risk factors between them is used to obtain the relative risk of death of the user relative to the cause of death; Wherein, the first health risk factor and the second health risk factor are a group of health risk factors in the first health risk factor set, and the MF is the ratio of the first health risk factor to the cause of death disease. The correlation between the degree of influence of the second health risk factor and the degree of influence of the second health risk factor on the cause of death disease. The method of claim 8, further comprising: Determine the target group corresponding to the user based on the user's basic information; The basic information includes at least one of the user's gender, age, and city. The method according to claim 2, characterized in that the key health risk factors include at least one of the following: The health risk factor with the highest dimension score in the first health risk factor set; or, The health risk factor in the first health risk factor set whose dimension score has the greatest negative impact on the comprehensive score; wherein the comprehensive score is obtained based on the dimensional scores of all dimensional health risk factors in the first health risk factor. The method according to claim 11, characterized in that, the method further includes: The dimension score Pi of the i-th health risk factor in the first health risk factor set satisfies the following formula:
Pi＝(AE _fact -AE _i,worst )/(AE _i,tmrel -AE _i,worst )×a Wherein, AE _fact is the life span of the user predicted based on the first set of health risk factors, the AE _i,worst is the life span of the user predicted based on the second set of health risk factors, and the AE _{i , tmrel} is the life span of the user predicted based on the third set of health risk factors, and a is the highest score; The second set of health risk factors is to replace the health risk factors of the i-th dimension in the first set of health risk factors with a first value, and the first value is the worst value corresponding to the health risk factor of the i-th dimension. Any value within the range; wherein, the worst numerical range is used to indicate that when the i-th dimension health risk factor is within the worst numerical range, the probability of causing death is the highest; The third set of health risk factors is to replace the health risk factors of the i-th dimension in the first set of health risk factors with a second value, and the second value is the optimal value corresponding to the health risk factor of the i-th dimension. Any value within the range; wherein, the most ideal numerical range is used to indicate that when the i-th dimension health risk factor is within the most ideal numerical range, the probability of causing death is the lowest. The method of claim 12, further comprising: Determine whether the i-th dimension health risk factor is within the worst value range or the best value range; If it is determined that the i-th dimensional health risk factor is outside the worst value range and the most ideal value range, then the dimension score Pi of the i-th dimensional health risk factor satisfies the formula; If the i-th dimensional health risk factor is within the worst value range, the dimension score Pi of the i-th dimensional health risk factor is the lowest score; If the i-th health risk factor is within the optimal numerical range, the dimension score Pi of the i-th health risk factor is the highest score. An electronic device, characterized by including: processor, memory, and, one or more programs; Wherein, the one or more programs are stored in the memory, and the one or more programs include instructions that, when executed by the processor, cause the electronic device to perform the steps of claim 1- The method steps described in any one of 13. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program. When the computer program is run on a computer, it causes the computer to execute any one of claims 1 to 13. method described in the item.