Lifestyle intervention system, lifestyle intervention method, program, and program recording medium

Abstract

A lifestyle intervention system comprises: a storage unit (3) that stores multiple types of intervention details prepared in advance for the prevention of lifestyle-related diseases in correspondence with parameters; an intervention setting unit (102) that executes algorithm-based calculations using the corresponding parameters for all interventions in a chronological direction, selects the details of the intervention in correspondence with a calculated score, and sets the details as the next intervention; a display unit (11) that displays the selected intervention details to a user each time intervention details are selected; a biometric information acquisition unit (103) that notifies the user of the user's behavior record and biometric information, which serve as a result of the notified intervention, and then receives said record and information; and an evaluation unit (104) that updates the parameters of the memory unit (3) as a degree of improvement of lifestyle-related diseases from both sets of acquired information every time both sets of information are acquired. It is thereby possible to sequentially present intervention measures based on personalized causal effects on personal lifestyles.

Description

[Technical field]

[0001] The present invention relates to a technique for supporting appropriate intervention (guidance) for preventing lifestyle-related diseases. [Background technology]

[0002] Recently, lifestyle-related diseases such as hypertension, diabetes, dyslipidemia, and obesity have become widespread worldwide, and are a major cause of cancer and cardiovascular diseases, which are non-communicable diseases that account for 60% of the causes of death worldwide. Many of these lifestyle-related diseases can be prevented by improving lifestyle habits, and there are high expectations for the therapeutic effects of dietary therapy and exercise therapy in combination with drug therapy after onset. Many of the interventions for lifestyle-related diseases to prevent cardiovascular diseases have been implemented as systems, with guidelines prepared by the World Health Organization and related academic societies in each country based on the results obtained through observational studies. For example, in Japan, health guidance guidelines (Ministry of Health, Labor and Welfare "Standard Health Checkup and Health Guidance Program", Ministry of Health, Labor and Welfare "Dietary Reference Intakes for Japanese People", Ministry of Health, Labor and Welfare "Dietary Balance Guide" by the Ministry of Agriculture, Forestry and Fisheries) have been prepared, and medical professionals have studied these and put them into practice as health guidance in clinical settings.

[0003] In addition, in recent years, in the field of lifestyle-related disease prevention, a device has been proposed that scores the chronological health risks of health checkup recipients and evaluates items that need to be improved and items that need not be improved, and that supports the extraction of subjects for preventing the worsening of lifestyle-related diseases (Patent Document 1).Furthermore, a lifestyle-related disease improvement support device is known that calculates the time-series correlation between biometric information and lifestyle habits using a regression equation and uses the obtained correlation to suggest lifestyle habits for the biometric information (Patent Document 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-160012 A [Patent Document 2] JP 2014-219850 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, conventional methods of intervention in lifestyle-related diseases for preventing cardiovascular disease have the following problems. First, the level of evidence obtained from population observational studies is lower than that of randomized controlled trials, and the confounding effects of genetic factors and unobserved factors cannot be completely eliminated, limiting causal inference. Second, even if the results are obtained from randomized controlled trials, the therapeutic effect of the intervention may differ depending on individual characteristics, and this is not taken into consideration. Third, no direct knowledge is available about specific intervention methods. Fourth, even in actual clinical practice, there are time limitations in implementing health guidance in accordance with evidence. Fifth, short-term changes in health behavior have not been evaluated in the past, and the idea of ​​improving lifestyle habits in response to short-term changes has not existed.

[0006] In addition, the device described in Patent Document 1 aims to make it more efficient to prevent lifestyle-related diseases from worsening based on medical information and health check results, but does not compare specific preventive intervention methods. The device described in Patent Document 2 uses a regression equation that expresses correlation to propose the amount of change in the same type of lifestyle corresponding to the amount of change in biological information, and further reflects the delay time, but does not appropriately select and propose one of multiple interventions for biological information.

[0007] The present invention has been made in consideration of the above, and provides a lifestyle intervention system, a lifestyle intervention method, and a program that are capable of selectively presenting appropriate intervention measures for an individual's lifestyle based on machine learning and epidemiological and statistical causal inference. [Means for solving the problem]

[0008] The lifestyle intervention system of the present invention is characterized by comprising: a memory unit that stores multiple types of intervention contents prepared in advance for preventing lifestyle-related diseases in correspondence with parameters; an intervention setting means that, for all interventions over time, performs calculations based on an algorithm (machine learning and probabilistic model) using the corresponding parameters, selects an intervention content corresponding to the calculated score, and sets it as the next intervention; a notification unit that notifies a user of the selected intervention content each time an intervention content is selected; a bioinformation acquisition means that receives the user's behavioral record and bioinformation as a result of the notified intervention after notifying the user; and an evaluation means that updates parameters corresponding to the set intervention content as a degree of improvement of the lifestyle-related disease from the acquired behavioral record and bioinformation each time the behavioral record and bioinformation are acquired.

[0009] Furthermore, the lifestyle intervention method of the present invention includes a memory unit which stores multiple types of intervention content prepared in advance for preventing lifestyle-related diseases in correspondence with parameters, an intervention setting means which performs calculations based on an algorithm (machine learning and probability model) using the corresponding parameters for all interventions in a time-dependent direction, selects the intervention content corresponding to the calculated score, sets it as the next intervention, and notifies the user of the set intervention content via the notification unit, a bioinformation acquisition means which accepts the user's behavioral record and bioinformation as a result of the notified intervention after notifying the user, and an evaluation means which updates the parameters corresponding to the set intervention content as the degree of improvement of the lifestyle-related disease from the acquired behavioral record and bioinformation each time the behavioral record and bioinformation are acquired.

[0010] The program according to the present invention causes a computer to function as a lifestyle habit intervention system.

[0011] A computer-readable program recording medium according to the present invention records a program for causing a computer to function as a lifestyle habit intervention system.

[0012] According to these inventions, lifestyle intervention measures are selected and presented directly and specifically to the user, and the parameters are updated by evaluating the relationship with the recorded behavior and biological information as a result, making it possible to provide intervention based on individualized causal effects. Furthermore, it is efficient because it designs continuous and effective intervention measures according to evidence. In addition, it is possible to utilize technology and data from an information platform that allows real-time monitoring, making it possible to modify intervention methods through shorter-term feedback compared to the span of health guidance based on conventional health guidance guidelines. Effect of the Invention

[0013] According to the present invention, it is possible to sequentially present intervention strategies based on individualized causal effects for an individual's lifestyle habits. [Brief description of the drawings]

[0014] [Figure 1] 1 is an overall configuration diagram showing an embodiment of a lifestyle intervention system according to the present invention. [Diagram 2] FIG. 1 shows an embodiment of a personal terminal, in which (A) is an example of a screen in which intervention information is presented, and (B) is an example of a screen in which biological information has been input. [Diagram 3] FIG. 1 shows an example of a memory map for storing patient information, etc., where (A) shows attribute information, etc., and (B) shows the intervention strategy (type) and current parameters for the type of intervention. [Figure 4] FIG. 13 is a diagram showing an example of a memory map for storing time-series data on interventions and biological information. [Diagram 5] 13 is a flowchart illustrating an example of a new registration process. [Figure 6] 13 is a flowchart illustrating an example of an intervention process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Fig. 1 is an overall configuration diagram showing one embodiment of a lifestyle intervention system according to the present invention. The lifestyle intervention system LIS includes a personal terminal 1 owned by an individual, a server 3 connected to the personal terminal 1 via a network 2 represented by the Internet, and a medical institution terminal 4 that can be applied as necessary. The personal terminal 1 may be a desktop personal computer or a mobile terminal represented by a smartphone, and is capable of communicating with the server 3 and the medical institution terminal 4 connected to the network 2.

[0016] The biological information measuring device 5 measures the state of the living body, and is adopted according to the purpose. Typically, it is a measuring device such as a blood pressure monitor, a weight scale, a pedometer, and an activity meter. The biological information can be obtained by using various modes other than the mode obtained from the biological information measuring device 5. For example, a meal menu can be input through the operation unit 12 of the personal terminal 1, or a term expressing a degree of physical condition (for example, very good, good, normal, slightly bad, very bad) can be input, or the doctor's diagnosis can be input in text during medical treatment, or the relevant part can be obtained from the diagnosis result (electronic medical record, etc.) input through the medical institution terminal 4. In addition, for example, when calculating the amount of salt from a meal menu, the built-in nutrition calculation software can be applied from the input meal menu to obtain the amount of salt for each food using the salt content of each food set in advance, and the salt intake for each meal can be obtained. In addition, a simple test kit (for example, spot urine test paper: for measuring salt in urine) can also be applied.

[0017] In this embodiment, the server 3 mainly functions as a storage unit that stores various data. The medical institution terminal 4 is installed in a medical institution and operated by a medical staff member, and may be one or more. The medical institution terminal 4 includes a storage unit 41 that stores inputted medical information of patients (electronic medical records, etc.).

[0018] Here, an outline of the lifestyle intervention process using the lifestyle intervention system LIS will be described. Lifestyle intervention is typically performed in the following procedure. First, a person who suffers from or wishes to improve a lifestyle-related disease such as hypertension and wishes to utilize this system starts this system (application program) from his / her personal terminal 1 and registers his / her (user) information or attribute information. After registration, the lifestyle intervention system LIS selects one of multiple types of interventions prepared in advance for hypertension at a certain timing and outputs (presents) the content of the intervention to the personal terminal 1. In normal cases, the user performs a lifestyle behavior corresponding to the presented intervention content and inputs bioinformation corresponding to the intervention result within a predetermined period or at a certain point in time. The lifestyle intervention system LIS evaluates the intervention based on the content of the intervention and the bioinformation as the intervention result.

[0019] Thereafter, the lifestyle habit intervention system LIS selects one preferred intervention from the multiple types of interventions at successive times using a method described later, outputs (presents) it on the personal terminal 1, and repeats the same procedure as above. Then, if the biometric information has improved to an appropriate level, the system may terminate voluntarily, or, conversely, if no improvement is observed, may recommend a medical examination. Note that even while using this system, if hypertension, diabetes, dyslipidemia, or obesity is found during a health check, a recommendation to visit a medical institution may be made. Examples of recommendations to visit a medical institution include displaying the risk of disease on the personal terminal 1, recommending a medical examination due to abnormal test results, and recommending a medical examination by displaying a map image of the relevant medical institution.

[0020] The lifestyle intervention system LIS includes an application program that performs intervention processing for preventing lifestyle-related diseases, an information storage unit that stores various information for executing the program, and a control unit that executes the program. Note that the various information is information about all registered users and is stored for each user.

[0021] The lifestyle habit intervention system LIS may be configured in various ways. For example, as shown in Fig. 1, an application program may be downloaded from a server 3 or an external memory (not shown) to the personal terminal 1 at the time of registration and made available for use, while various information may be stored in a storage unit 32 of the server 3. In this way, the intervention process is performed by the personal terminal 1 exchanging necessary information with the server 3 and executing required calculations using that information.

[0022] In another embodiment, the application program and various information may be stored in a server 3 capable of information processing, and during intervention processing, information from the personal terminal 1 may be sent to the server 3, a predetermined calculation may be executed on the server 3 side, and the result may be received from the server 3 and presented on the personal terminal 1. In this embodiment, the personal terminal 1 functions as an input / output unit. Alternatively, the application program and the personal information may be imported into the personal terminal 1 at the time of registration, and all processing thereafter may be performed by the personal terminal 1. Also, the memory unit 41 in the medical institution terminal 4 may have the functions of the server 3.

[0023] The server 3 includes a communication processing unit 31 that controls communication with the personal terminal 1 and the medical institution terminal 4, and a storage unit 32. The storage unit 32 includes a control program storage unit 321, a personal information storage unit 322, an intervention type storage unit 323, an intervention information storage unit 324, and a biological information storage unit 325. The server 3 stores information on all users, while the communication processing unit 31 individually transmits and receives information to and from the personal terminal 1 (its telephone number, email address, or other ID by which an individual can be identified) associated with an individual user ID at the time of new registration, which will be described later.

[0024] The control program storage unit 321 stores application programs (hereinafter, referred to as control programs) for executing the intervention process and each related process.

[0025] As shown in FIG. 3A, the personal information storage unit 322 stores the user's state Li and attribute information Xi for each user ID that identifies the user. The state Li (i=1, 2, ...) includes a plurality of states for the user's daily living environment, here, the eating environment, for example, L1 "pour soy sauce", L2 "eat fruit", L3 "eat soy products", etc. The types of states are not limited to three, and it is preferable to set an appropriate number and types according to the lifestyle-related disease to be improved. The attribute information Xi includes the user's age, sex, blood pressure value, pulse rate, blood glucose value, weight, body temperature, body mass index (BMI), self-reported stress level, educational history, leisure time, participation in hobby activities, employment type, etc., which indicate the socio-economic situation. The state Li and attribute information Xi may be treated as attribute information. The user ID is set in response to the registration of a name that can identify the user input from the operation unit 12. The registration of the name may not be necessary.

[0026] As shown in FIG. 3B, the intervention type storage unit 323 updates and records indices I1, I2, I3, ..., Ii indicating combinations of contents and users prepared in advance as types of interventions (options), and parameters μ1, Σ1, μ2, Σ2, μ3, Σ3, ..., μi, Σi, which are newly calculated for each intervention as described below. The parameters μ and Σ set for each combination of intervention type and user are real values, vectors, matrices, and array variables combining these, and are parameters of a probabilistic model indicating responsiveness based on a probabilistic algorithm that learns the effect of the intervention as the user's behavior record and biological information as outcomes. For example, if necessary, the responsiveness may follow a linear model, and the mean vector μ and variance matrix Σ of the multidimensional normal distribution that follows the coefficients of the linear model may be used as parameters. In addition to applying the types of interventions prepared in advance as they are, more effective intervention contents may be individually extracted based on the user's information entered at the time of registration.

[0027] The intervention information storage unit 324 sequentially stores the contents of the selection history of the intervention I presented to the user in chronological order via the personal terminal 1, as shown in Fig. 4. In Fig. 2(A), an information display area 110 is set on the screen 11a of the display unit 11 of the personal terminal 1, and an example of the intervention contents is presented. As intervention measures for preventing lifestyle-related diseases, measures related to diet, measures related to exercise, measures related to sleep, measures related to drinking and smoking, measures related to stress management, etc. are known, and in this embodiment, any one or more of them can be adopted.

[0028] The biological information storage unit 325 sequentially stores biological information D acquired in chronological order from the biological information measuring device 5 or the operation unit 12, or input from the medical institution terminal 4 regarding medical treatment results, as shown in Fig. 4. Fig. 2(B) shows an example of biological information (numerical portion) input by a user in the information display area 110 on the screen 11a of the display unit 11.

[0029] In this way, in FIG. 4, for example, for a time series t1, t2, t3, ..., tn, interventions I are input in the order of I2(t1), I4(t2), I3(t3), ..., Ii(t4), and correspondingly acquired biometric information D is input in the order of D(t1), D(t2), D(t3), ..., D(tn).

[0030] Moreover, the personal terminal 1 typically includes a control unit 10 configured with a processor (CPU), a display unit 11, an operation unit 12, and a storage unit 13. The display unit 11 displays images, and performs the presentation of intervention contents and display of inputted bioinformation. The part of the display unit 11 that functions as an output unit of the intervention process may be audio in addition to images, and in the case of audio, it is sufficient to have a speaker. The operation unit 12 may be a transparent piezoelectric element panel that is laminated on the display unit 11 and functions as a touch panel, and inputs information via tapping, flicking, etc., to various buttons including character key buttons displayed on the display unit 11. It may also include a physical operation unit. It may also include an audio operation unit.

[0031] The control unit 10 reads out a control program into a main memory (not shown) and executes it, thereby functioning as a registration unit 101, an intervention setting unit 102, a biological information acquisition unit 103, and an evaluation unit 104. The control unit 10 also functions as a timer 105 that performs a time measurement operation.

[0032] The registration unit 101 executes new registration processing of a user that is necessary when using this system. In the new registration processing, a control program returned from the server 3 is downloaded in response to an application download request from the personal terminal 1. The registration unit 101 also starts the control program, and accepts input of registration of the type of lifestyle-related disease to be improved, information such as a name for identifying the user, status information Li, and attribute information Xi via the operation unit 12, and stores the information in the storage unit 13 as shown in Fig. 3(A) and Fig. 3(B), and also transmits the acquired information Li and Xi to the server 3 as necessary to store it in the personal information storage unit 322 or the like.

[0033] The intervention setting unit 102 selects the intervention content at a certain timing and presents it to the user via the display unit 11. More specifically, the intervention setting unit 102 selects one of the multiple interventions to be performed next, based on the previous intervention content and the intervention result, such as acquired biological information. When there are multiple interventions i=1, 2, ..., K aiming at a health outcome (here, the outcome of lifestyle habits due to the intervention: biological information, etc.), this algorithm evaluates the health outcome {Xi} of the intervention result while performing each intervention, thereby: Score y = (ΣXi) / 2Ni(t) + √(logT / 2Ni(t)) ... (Equation 1) Here, the sum of the first term in (Equation 1) is over the selected period of intervention i. In addition, this algorithm for determining the intervention content (intervention option) may be an algorithm that selects each intervention option by a probability weighted by its parameter μ(i), rather than deterministically selecting the intervention i(t) that maximizes the score y.

[0034] As shown in the above (Equation 1), the score y is obtained by adding a correction value (second term) based on the intervention time T and the number of interventions i up to that point Ni(t) to the first term, which is the average of the observed value of the intervention effect i(t). This method is an upper confidence bound (UCB) strategy as an example of a bandit algorithm in machine learning. That is, the score y is a value obtained by adding a correction term to the sample average, and the value of the correction term is set to be larger for an intervention with a smaller number of selections Ni(t). Therefore, an intervention with a small sample average but a small number of selections may be selected. In this way, when an intervention with a large sample average is simply selected, "utilization" is performed, and when an intervention with a small sample average but a small number of selections is selected, "exploration" is performed. In this way, in this embodiment, the UCB strategy is used to repeatedly select interventions while balancing exploration and utilization, aiming to maximize the target for improvement. In addition, by performing evaluation using the minimum number of tests necessary for evaluating effectiveness, interventions with a low score y, i.e., interventions that do not produce results, may be terminated in a short period of time, i.e., removed from the intervention target.

[0035] The algorithm used in the intervention setting unit 102 may use a method of Logistic Thompson Sampling, which learns a model that predicts outcomes from attributes for each intervention option by logistic regression, and updates hyperparameters that represent the mean and variance of the logistic regression coefficients, taking into account the uncertainty according to the number of samples. In this case, when selecting an intervention option, the option with the highest linear prediction value of the logistic regression using coefficients randomly selected from the distribution represented by the updated hyperparameters and the user attributes is selected. In the case of Logistic Thompson Sampling, the upper bound of regret as described above is not known, but it is empirically known that regret can be kept low. Hyperparameters μik, Σik that represent the distribution of parameters βik for each selection value i and attribute subscript k are stored. When selecting an intervention, a linear predictor βik is calculated using coefficients βik randomly selected from the multidimensional normal distribution N(μik, Σik) represented by the above hyperparameters. Score y = Σβik × Xk…(Equation 2) Select the i for which is maximum.

[0036] The algorithm used in the intervention setting unit 102 may be a model that incorporates random effects that take into account the individual level, option level, and further interactions between the individual and option levels into the aforementioned Logistic Thompson Sampling. When updating the hyperparameters after each intervention, it is assumed that the parameters of each coefficient follow a normal distribution, and the mean and variance are used as hyperparameters, and updating is performed using Laplace approximation. Hyperparameters μijk, Σijk that represent the distribution of parameter βijk for each selection value i, individual identification number j, and attribute subscript k are stored. When selecting an intervention, a linear predictor βijk is calculated for individual identification number j using coefficient βijk randomly selected from the multidimensional normal distribution N(μijk, Σijk) expressed by the above hyperparameters. Score y = Σβijk × Xk…(Equation 3) Select the i for which is maximum.

[0037] Furthermore, as an outcome, for example, for annual health checkups or consultations, it is possible to additionally apply a method of aggregating interventions for a group and their results and updating them using batch processing.

[0038] The intervention setting unit 102 may include options such as "reduce miso soup," "don't put soy sauce on the table," "reduce pickles," "eat fruits," and "eat vegetables" as examples of methods for intervening in hypertension. The types of interventions may range from a few to several hundred or more. Furthermore, the intervention setting unit 102 can notify the user of intervention information by using, for example, a "messaging service" in addition to e-mail. The "messaging service" is an extension of the well-known short message service (SMS) in the personal terminal 1, and refers to a function for sending and receiving multimedia messages including images and videos. Furthermore, when implemented in an app, it may be performed by push notification. The push notification is a function in which a message notified to an app is displayed on the personal terminal even when the user has not started the personal terminal or app.

[0039] Furthermore, the intervention setting unit 102 can set the frequency and timing of intervention appropriately or individually by applying the timer 105, and may also be configured to automatically vary the frequency and timing by observing the intervention result (for example, the span may be lengthened as the improvement is observed). The intervention timing may be, for example, by time of day, or early morning, morning, noon, afternoon, before going to bed, etc., and the frequency may be every meal, every day, once a week, every two weeks, once a month, or a longer span. The optimal timing for each of these timings may be selected as an option, and optimization may be performed by an algorithm executed by the intervention setting unit 102.

[0040] The bioinformation acquisition unit 103 imports bioinformation D(t) measured by a bioinformation measurement device 5 or the like. The imported bioinformation D(t) includes a measurement value and a measurement date and time. The evaluation unit 104 evaluates the improvement due to the lifestyle intervention based on the imported time-series bioinformation D(t). The improvement can be evaluated, for example, based on the time-series degree of improvement and the difference from a normal value. Furthermore, the evaluation unit 104 calculates new parameters based on the parameters of the intervention item and the bioinformation that is the intervention result, and updates the parameters of the corresponding intervention item in FIG. 3(B) each time an intervention is performed. By sequentially going through such parameter update processing, the type of intervention selected at the next intervention timing is optimized.

[0041] FIG. 5 is a flow chart showing an example of new registration processing. First, it is determined whether or not there is a request for new registration (step S1). If there is no request for new registration, this flow is terminated. On the other hand, if there is a request for new registration, in a mode in which a control program, which is an application program, is stored in the server 3, it is downloaded from the server 3 to the personal terminal 1 (step S3), and then the control program is started (step S5). Next, a screen for accepting input information of the name, status, and attribute information is displayed, and necessary information is accepted through input on the screen (step S7). The input information is stored in the storage unit 13 (step S9), and, if necessary, is sent to the server 3 side and stored in the storage unit 32.

[0042] Fig. 6 is a flow chart showing an example of the intervention process. Initialization at the time of start-up is performed following new registration processing, in which a lifestyle-related disease to be improved is selected, and the content of the corresponding intervention is set (step S11). The intervention content may be prepared in advance according to the type of lifestyle-related disease. Also, the state L may be input after the lifestyle-related disease to be improved is selected. Furthermore, as the initialization, a general or empirical score according to the type of intervention may be initially set for each intervention.

[0043] After the initialization is completed, whether or not the first or next intervention timing has been reached is determined by the timing process of the timer 105 (step S13). If it is determined that the intervention timing has been reached, the intervention setting unit 102 calculates the time by the above (Equation 1). ~ The calculation process of the score y shown in (Formula 3) is executed for all intervention items assuming a hypothetical case where the intervention is selected, and the intervention when the calculated value is the maximum value is determined as the current intervention content. When one intervention content is selected from a plurality of intervention contents in this manner, the intervention setting unit 102 displays the selected intervention content on the display unit 11, for example, as shown in FIG. 2(A) (step S15). Note that the first intervention may be a preset or general one, or may be determined with a random element included.

[0044] Next, it is determined whether or not biological information such as blood pressure values ​​has been accepted (step S17). The acceptance of biological information is preferably specified as the acquisition timing at the time of initial registration or initialization. When the biological information is input, an evaluation process is performed on the biological information acquired as the intervention result (step S19). Then, an evaluation result is obtained and recorded in the memory unit 13 in an updated manner (step S21). Alternatively, it may be recorded in chronological order. The evaluation result is preferably displayed on the display unit 11. Also, for this intervention, a new score is calculated based on the current score for the intervention type and the biological information, which is the intervention result, and the score for the relevant intervention type in Figure 3 (B) is updated.

[0045] Next, a suggestion is made as to whether or not to terminate the lifestyle habit intervention program (step S23). If the program is to be terminated, for example, information during the intervention period is stored on the server 3 side and made available for future use. If the symptoms do not improve or rather worsen, it is preferable to be notified of a recommendation to see a doctor. On the other hand, if the program is to be continued, the intervention setting unit 102 moves to a process of determining the next intervention timing. It is preferable that the decision as to whether or not to terminate the intervention program is made in such a way that if there is no response from the user within a predetermined period, for example, by the time of the next intervention timing, the program is automatically extended.

[0046] In addition, in order to encourage the user to follow the intervention content as much as possible while performing lifestyle activities, multiple ways of conveying the intervention content may be prepared. For example, when using voice, it is conceivable to prepare a separate item for a message using a strong and weak tone of voice as a different type of intervention.

[0047] The evaluation items after each intervention may be aimed at the onset of a disease, assuming a longer-term effect, rather than just the input value of health behavior or biological information. Below, we will explain the outline of the calculation method for each value, including this case.

[0048] <1> · State S is expressed as a set (D, L, X) where D indicates the presence or absence of a disease, L indicates lifestyle habits, and X indicates personal attributes. D: High blood pressure, diabetes, myocardial infarction, stroke, cancer, and other diseases. State L={Li} is a lifestyle, e.g., L1: "putting soy sauce on", L2: "eating fruit", L3: "eating soy products" Attribute information X={Xi}: Represents individual attributes such as age, sex, body mass index (BMI), etc.

[0049] <2> Intervention I={Ii}: intervention for lifestyle change. For example, I0: "do nothing," I1: "provide guidance for lifestyle improvement," I2: "suggest alternative methods (such as presenting a different seasoning method for those who use soy sauce)," and I3: "present options for improvement and ask the patient to choose." are possible interventions.

[0050] <3> ·reward function:g(D,L,X,I)=―P(D(t+1)|S(t)) Here, the measured biometric information of each user is combined as a reward function to evaluate the risk score of cardiovascular disease, and the sign of the risk score is inverted and used to select the intervention. The option that maximizes the reward from the intervention, i.e., the intervention option that minimizes the risk of disease, is efficiently selected. P(D(t+1) |S(t)) is defined as follows: if D(t)=1, then it takes the value 1, otherwise it takes the value h(S(t)), where h(S(t)) is the regression equation.

[0051] h(S(t)) is the risk score of the above disease, which is reflected in the reward function. D(t)=1 is the onset of the disease, for example, when a stroke actually occurs. In this case, the patient is not covered by this system and is treated at a medical institution.

[0052] <4> ·Possibility of transition:P T (L(t+1) |S(t),I(t)) ·factorization assumption:P T (L(t+1) |S(t),I(t)) =P(L(t+1) |L(t),X(t))×f(X(t),I(t))×α0 The possibility of transition is the probability of transition. It is a probability distribution function that expresses the probability of transitioning to a new lifestyle L(t+1) when an intervention I(t) is performed on a subject with a certain lifestyle and attribute S(t). This is P T This part is the formula required when verifying the effectiveness of this system through simulation.

[0053] The factorization assumption is the assumption that the above transition probability is decomposed, and the transition probability is expressed as the product of P(L(t+1)|L(t), X(t)), which is the probability that represents a natural change in lifestyle, and f(X(t),I(t)), which is the coefficient of the effect of intervention I(t) on a person with attribute information X(t). Also, α0 is the normalization constant for the probability.

[0054] P(L(t+1) | L(t), X(t)): by regression or machine learning. f(X(t),I(t))∈[0.5,2.0] In the above equation, the coefficient of responsiveness to the intervention can take values ​​between 0.5 and 2.0.

[0055] Here, the responsiveness coefficient refers to the responsiveness of certain attribute information X(t) to the intervention options required for the simulation of the transition probability above.

[0056] <5> ·target function:survival function R(T)=E[Π T t=1 (1-D(t))] is maximized.

[0057] The target function is the overall objective function, which we aim to maximize. The formula represents the probability of living from period t=1 to period t=T without contracting the disease, and the goal is to avoid contracting the disease, i.e., successful prevention.

[0058] As described above, the lifestyle intervention system of the present invention is characterized by comprising: a memory unit that stores a plurality of types of intervention contents prepared in advance for preventing lifestyle-related diseases in correspondence with parameters; an intervention setting means that, for all interventions in a time-dependent direction, executes calculations based on an algorithm (machine learning and a probability model) using the corresponding parameters, selects the intervention content corresponding to the calculated score, and sets it as the next intervention; a notification unit that notifies a user of the set intervention content each time the intervention content is set; a bioinformation acquisition means that receives the user's behavioral record and bioinformation as a result of the notified intervention after notifying the user; and an evaluation means that updates parameters corresponding to the set intervention content as a degree of improvement of lifestyle-related diseases from the acquired behavioral record and bioinformation each time the behavioral record and bioinformation are acquired.

[0059] Furthermore, the lifestyle intervention method of the present invention is characterized in that it includes a memory unit which stores multiple types of intervention content prepared in advance for preventing lifestyle-related diseases in correspondence with parameters, an intervention setting means which performs calculations based on an algorithm (machine learning and probability model) using the corresponding parameters for all interventions in a time-dependent direction, selects the intervention content in correspondence with the calculated score, sets it as the next intervention, and notifies the user of the set intervention content via the notification unit, a bioinformation acquisition means which accepts the user's behavioral record and bioinformation as a result of the notified intervention after notifying the user, and an evaluation means which updates the parameters corresponding to the set intervention content as the degree of improvement of the lifestyle-related disease from the acquired behavioral record and bioinformation each time the behavioral record and bioinformation are acquired.

[0060] The program according to the present invention causes a computer to function as a lifestyle habit intervention system.

[0061] A computer-readable program recording medium according to the present invention records a program for causing a computer to function as a lifestyle habit intervention system.

[0062] According to these inventions, lifestyle intervention measures are directly and specifically selected and presented to the user, and the parameters are updated by evaluating the relationship between the recorded behavior (e.g., diet, exercise, number of steps, sleep time, mood, perceived stress, etc.) and biological information, making it possible to provide intervention based on individualized causal effects. Furthermore, it is efficient because continuous and effective intervention measures are designed in accordance with evidence. In addition, because it is possible to utilize technology and data from an information platform capable of real-time monitoring, it is also possible to modify intervention methods through shorter-term feedback compared to the span of health guidance based on conventional health guidance guidelines.

[0063] It is also preferable that the storage unit stores the types of interventions for each user, so that appropriate interventions can be prepared and set according to the illness of the user.

[0064] The notification unit is preferably a display unit that displays an image, which makes it possible to utilize the display unit provided in the terminal as is.

[0065] It is also preferable that the intervention setting means is capable of setting at least one of the frequency and timing of intervention, which allows for manual or automatic variation based on observation of the results of the intervention (e.g., lengthening the span as improvement is observed).

[0066] Furthermore, it is preferable that the lifestyle habit intervention system according to the present invention comprises a server and a terminal operated by a user, which are communicatively connected via a network, and the terminal downloads a control program from the server and starts it when a user registers to use the system. With this configuration, the terminal is able to execute a lifestyle habit intervention process by downloading the control program from the server. [Explanation of symbols]

[0067] LIS Lifestyle Intervention System 1 Personal device 11 Display unit (notification unit) 12 Operation unit (biometric information acquisition means) 10 Control section 101 Registration Department 102 Intervention setting unit (intervention setting means) 103 Biometric information acquisition unit (biometric information acquisition means) 104 Evaluation section (evaluation means) 105 Timer 2 Network 3 Server 32 Storage section 321 Control program storage unit 5 Biometric information measuring device (biometric information acquisition unit)

Claims

1. a storage unit that stores a plurality of types of interventions prepared in advance for preventing lifestyle-related diseases in association with the parameters; An intervention setting means for performing calculations based on an algorithm using the corresponding parameters for all interventions at each intervention timing, selecting one preferred intervention from the calculated score, and setting the selected intervention as the next intervention; a notification unit that notifies a user of the set intervention; a biometric information acquiring means for accepting biometric information of the user as a result of the notified intervention after the notification to the user; and an evaluation means for updating a parameter corresponding to the set intervention as a degree of improvement of a lifestyle-related disease based on the set intervention and the biological information acquired as a result of the set intervention, each time the biological information is acquired, and storing the updated parameter in the memory unit.

2. 2. The lifestyle intervention system according to claim 1, wherein the storage unit stores types of interventions for each user.

3. The lifestyle intervention system according to claim 1 , wherein the notification unit is a display unit that displays an image.

4. 4. The lifestyle intervention system according to claim 1, wherein the intervention setting means is capable of setting at least one of a frequency and a timing of intervention.

5. A server and a terminal operated by a user are connected to each other via a network so as to be capable of communicating with each other, 3. The lifestyle intervention system according to claim 1, wherein the terminal comprises the notification unit and a registration unit that downloads a control program from the server and starts the control program when a new user registers to use the system.

6. a storage unit that stores a plurality of types of interventions prepared in advance for preventing lifestyle-related diseases in association with parameters, and an intervention setting means executes a calculation based on an algorithm using the corresponding parameters for all interventions at each intervention timing, selects one preferable intervention from the calculated score, and sets it as the next intervention; The notification unit notifies the user of the set intervention, a biometric information acquiring means for accepting the biometric information of the user as a result of the notified intervention after notifying the user; the evaluation means updates a parameter corresponding to the set intervention as a degree of improvement of a lifestyle-related disease based on the set intervention and the bioinformation acquired as a result of the set intervention each time the bioinformation is acquired, and stores the updated parameter in the memory unit.

7. A program for causing a computer to function as the lifestyle intervention system according to any one of claims 1 to 4.

8. A computer-readable program recording medium having recorded thereon a program for causing a computer to function as the lifestyle habit intervention system according to any one of claims 1 to 4.

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