Effect evaluation method of nutritional health-care product

Abstract

The invention provides an efficacy evaluation method of a nutritional health care product, which comprises the following steps: step 1, continuously acquiring physical sign data of a user, and dividing the physical sign data into pre-node physical sign data and post-node physical sign data by taking the time of eating the health care product as a node; and step 2, inputting a to-be-improved physical sign index, extracting physical sign data needing to be evaluated according to the physical sign index, comparing extracted data before and after nodes, and judging whether the body characteristics are improved or not. The method has the beneficial effects that whether the physical sign indexes are improved or not can be judged according to the change of the physical signs, so that whether the efficacy of the nutritional health-care product reaches the expectation or not is evaluated, a user can more intuitively obtain a conclusion, the waste of precious recuperation time is avoided, and the waste of money is also avoided.

Description

Technical Field

[0001] The invention relates to the field of health information technology, in particular to an efficacy evaluation method for nutritional health products. Background Art

[0002] As the number of people suffering from chronic diseases grows, people have adopted a variety of methods to combat chronic diseases, including exercise, diet therapy, and comprehensive therapy. Depending on individual cognition, there are also a variety of methods to control or delay chronic diseases.

[0003] In addition to more exercise and healthy eating, there are many kinds of health supplements on the market, which claim to improve certain physical signs after taking them for a period of time. However, after taking them for a period of time, users do not feel obvious improvements in certain characteristics and cannot feel the effects, so they cannot decide whether to continue taking the nutritional supplements. Summary of the invention

[0004] The present invention proposes a method for evaluating the efficacy of nutritional health products, which can determine whether the user has improved in certain physical signs by continuously detecting physical signs, and display the data to the user more intuitively, and is achieved through the following technical solutions.

[0005] A method for evaluating the efficacy of a nutritional health product comprises the following steps: Step 1: Continuously obtain the user's vital sign data, taking the consumption of health products as a node, and divide it into pre-node data and post-node data; Step 2: Input the physical sign to be improved, extract the physical sign data that needs to be evaluated based on the physical sign, compare the extracted pre-node data and post-node data, and determine whether the physical characteristics are improved.

[0006] The beneficial effect of the present invention is that it is possible to judge whether the physical sign indicators have improved through the changes in physical signs, thereby evaluating whether the efficacy of nutritional health products has reached expectations, and users can obtain conclusions more intuitively, avoiding the waste of precious recuperation time and money. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 4 is a flow chart of a method for evaluating the efficacy of a nutritional health product according to an embodiment of the present invention.

[0008] Figure 2 This is a flowchart for determining whether immunity has improved according to the first embodiment of the present invention.

[0009] Figure 3 4 is a flowchart for determining whether sleep is improved according to the second embodiment of the present invention.

[0010] Figure 4 This is a flowchart for determining whether anemia has improved according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0011] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0012] like Figure 1 As shown, a method for evaluating the efficacy of a nutritional health product comprises the following steps: S1. Continuously obtain the user's physical sign data, taking the time of taking health products as a node, and divide it into physical sign data before the node and physical sign data after the node; S2. Input the physical sign index to be improved, extract the physical sign data to be evaluated according to the physical sign index, compare the data before and after the extracted node, and determine whether the physical characteristics are improved.

[0013] The user's physical sign data of the present invention is collected and stored on a daily basis, that is, on a natural day, and is divided into physical sign data before consumption and data after consumption, taking the day of consumption of health products as a node.

[0014] Node refers to the date on which health products are consumed.

[0015] Physical sign indicators are the claimed effects of nutritional supplements, such as improving immunity, supplementing trace elements, vitamins, preventing anemia, supplementing calcium, improving sleep quality, etc.

[0016] Vital sign data refers to raw data such as PPG data, ECG data, and secondary processed data such as heart rate, heart rate variability, blood pressure, respiratory rate, etc. obtained based on these raw data.

[0017] By non-invasively obtaining blood sugar and blood lipid values, the changing trends of blood sugar and blood lipids can be determined. The method for determining blood sugar and blood lipids can use radio frequency signals to determine the blood sugar and blood lipid content.

[0018] The specific vital sign data include: exercise information (including number of steps, exercise time, exercise intensity, exercise type, etc.), sleep (including time to fall asleep at noon / night, sleep quality, etc.), mood, stress and fatigue, heart health (including heart rate variability and incidence of arrhythmia, etc.), blood pressure, blood sugar (including blood sugar value, blood sugar variability, changes in blood sugar extreme values, etc.), blood lipids, body temperature and other vital signs.

[0019] However, not all vital sign data can be used in the same model. They need to be used specifically according to the vital sign indicators. Different vital sign indicators require different vital sign data.

[0020] According to the physical sign indicators, physical sign data corresponding to the physical sign indicators are proposed, input into the corresponding physical sign indicator model, and the results of the improvement of the physical sign indicators are output.

[0021] like Figure 2As shown, in the first embodiment of the present invention, taking immunity as an example, there are many health products on the market that can enhance immunity, such as Ganoderma lucidum spore powder, etc. The effects of such health products are unknown to many users, and the effects can only be understood through trial and error.

[0022] Immunity is mainly reflected in: the number of times one gets sick, mental state, probability of allergic reactions, etc. Nutritional supplements improve immunity by reducing the number of times one gets sick.

[0023] Through continuous vital sign monitoring, the user's vital sign values, such as PPG data, are obtained and divided into data before and after the node according to the node. For example, when the immunity is low, the heart rate is often abnormal due to frequent illness, and the number of times the heart rate is too high is high.

[0024] Mental state and allergic reactions lead to frequent abnormal blood pressure events, each lasting a long time.

[0025] A model for judging immunity is established, and heart rate and blood pressure values ​​are input into the model. The model judges whether immunity has improved based on the changing trends of heart rate and blood pressure before and after the node.

[0026] like Figure 3 As shown, in the second embodiment of the present invention, the goal is to improve the sleep quality, the method is: Continuously monitor the user's sleep, compare the physical sign data before the node and the physical sign data after the node, and evaluate the quality of sleep mainly by: the number of awakenings in the middle of the night, the ratio of deep sleep to light sleep, and the average sleep duration.

[0027] Determine whether you are asleep by obtaining movement posture and PPG data.

[0028] Comparing the data before and after the node, if the number of awakenings decreases or disappears, the sleep quality has improved; Compare the data before and after the node, the ratio of deep sleep to light sleep, that is: deep sleep / light sleep*100%, take the interval of 25%~50% as the standard, according to the data and the standard value, the farther below the standard value, the worse the sleep condition, if it is below 25%, there is more light sleep and less deep sleep, that is, poor sleep quality. If the ratio is lower than 15% for a long time, the sleep quality is very poor.

[0029] The length of sleep is a relatively intuitive standard. If the sleep time is less than 7 hours for a long time, combined with age, it is judged that the sleep quality of young and middle-aged people is poor. The standard for the elderly is 6 hours. The standard for teenagers is 8-10 hours. If the sleep time is less than the standard, it is judged as poor sleep.

[0030] Establish a model for sleep judgment, input the number of awakenings during sleep before and after the node, the ratio of deep sleep to light sleep, and the length of sleep, and output the conclusion on whether sleep has improved.

[0031] like Figure 4 As shown, in the third embodiment of the present invention, improvement of anemia can also be judged by physical signs, and the data before and after the node are also compared. The judgment method is: Obtain vital sign data before and after the node in the motion stage, such as running, walking, etc.

[0032] Then obtain the PPG value and convert it into heart rate value and heart rate variability value according to the PPG value.

[0033] Compare the heart rate changes, that is, the heart rate change and average value before the node with the heart rate change and average value after the node. If the amplitude of the heart rate change is significantly lower, it is judged that the anemia has improved, otherwise there is no improvement; compare the average value, if the average heart rate under exercise is lower, it is judged that the anemia has improved, otherwise there is no improvement.

[0034] By comparing the heart rate variability and the data before and after the node, if the heart rate variability is high, it is judged that the anemia has not improved, and if the heart rate variability is reduced, it is judged that the anemia has improved.

[0035] Wearable data continuously acquires human body’s vital signs and motion data, and stores them as pre-node data by time; Input nodes and vital signs indicators, and store the data after the nodes as data after the nodes; Taking the node as the center, obtain the data of the same time period before and after the node, and compare the data according to the vital signs indicators.

[0036] Establish an anemia judgment model, input the heart rate change value and heart rate variability value during exercise before and after the node, and output the conclusion whether the anemia has improved.

[0037] In the embodiment of the present invention, the vital sign data is collected by using wearable devices, such as smart watches and wristbands. The data is mainly processed by wearable devices and mobile phones.

[0038] After the mobile phone collects the wearable device data, or the wearable device, it is pre-processed first.

[0039] The data analysis before and after the node described in the present invention is based on the data preprocessing module. The preprocessed data is then analyzed to make the analysis result more accurate.

[0040] Data analysis is based on the acquired wearable information data, basic physical data, clinical examination data, living environment data, etc., and the above data are trained using a deep learning algorithm based on time series analysis. The output result is the pre- and post-node data analysis of various vital signs indicators.

[0041] The result display module is used to display the comparison results of the vital sign data before and after the node.

[0042] The effect of the present invention is that it is possible to judge whether the physical sign index has improved through the change of physical signs, thereby evaluating whether the efficacy of the nutritional health product has reached the expected level, and the conclusion is intuitively displayed to the user through the model, and the user decides whether to continue to consume the nutritional health product. If a nutritional health product has no effect after consumption, it is not necessary to continue to consume it, which avoids wasting precious recuperation time and money.

[0043] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the efficacy of a nutritional health product, characterized in that: The steps include: Step 1: Continuously obtain the user's physical sign data, taking the time of taking the health care products as the node, and divide it into physical sign data before taking and physical sign data after taking; Step 2: Input the physical sign index to be improved, extract the physical sign data to be evaluated based on the physical sign index, compare the extracted pre-consumption data with the post-consumption data, and determine whether the physical characteristics have been improved.

2. The efficacy evaluation method according to claim 1, characterized in that: The vital sign data include PPG, ECG, movement posture, and body temperature data.

3. The efficacy evaluation method according to claim 2, characterized in that: The physical sign data also includes secondary processed physical sign data: sleep, breathing, heart rate, and heart rate variability.

4. The efficacy evaluation method according to claim 1, characterized in that: According to the physical sign indicators, physical sign data corresponding to the physical sign indicators are proposed, input into the corresponding physical sign indicator model, and the results of the improvement of the physical sign indicators are output.

5. The efficacy evaluation method according to claim 4, characterized in that: The physical signs and indicators shown include a method for judging the improvement of immunity, the steps are: Through continuous vital sign monitoring, the user's PPG data is obtained and divided into pre- and post-node data according to the nodes; Secondary analysis of PPG data, converted into heart rate and blood pressure values; Build a model and input heart rate and blood pressure values; Analyze heart rate and blood pressure levels, and output results on whether immunity has improved.

6. The efficacy evaluation method according to claim 1, characterized in that: The physical sign index includes a method for judging sleep improvement, the steps are: Through continuous vital sign monitoring, the user's sleep data is obtained and divided into data before and after the node; Build a model, input the number of awakenings, the ratio of deep sleep to light sleep, and the average sleep duration in the sleep data; Output the result of whether sleep is improved.

7. The efficacy evaluation method according to claim 1, characterized in that: The physical sign index includes a method for judging the improvement of anemia, the steps are: Obtain the vital sign data of the movement stage, which is divided into the vital sign data before and after the node; Establish a model and input the heart rate variation range, heart rate average value and heart rate variability value; Whether the output anemia improves.

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